From 220ad3acf0f19c20bf7123874dd51c4147fbabc3 Mon Sep 17 00:00:00 2001 From: Brandon Wilk Date: Tue, 18 Apr 2023 10:27:53 -0500 Subject: [PATCH 01/13] clean up part 1 --- .gitmodules | 3 - .../testing_variants_hg38_vep-annotated.tsv | 1360 ----------------- configs/cluster_config.json | 16 - configs/col_config.yaml | 997 ------------ configs/columns_config.yaml | 295 ---- configs/dbnsfp_column_config.yaml | 640 -------- configs/envs/testing.yaml | 24 - configs/testing.yaml | 159 -- dag.png | Bin 36929 -> 0 bytes predict_variant_score.sh | 21 - ...testing_variants_hg38_vep-annotated.vcf.gz | Bin 118179 -> 0 bytes .../.test/data/raw/testing_variants_hg38.vcf | 679 -------- variant_annotation/README.md | 73 - .../configs/cluster_config.json | 10 - variant_annotation/configs/env/vep.yaml | 6 - .../configs/snakemake_slurm_profile | 1 - variant_annotation/src/Snakefile | 137 -- variant_annotation/src/run_pipeline.sh | 80 - 18 files changed, 4501 deletions(-) delete mode 100644 annotation_parsing/.test/testing_variants_hg38_vep-annotated.tsv delete mode 100644 configs/cluster_config.json delete mode 100644 configs/col_config.yaml delete mode 100644 configs/columns_config.yaml delete mode 100644 configs/dbnsfp_column_config.yaml delete mode 100644 configs/envs/testing.yaml delete mode 100644 configs/testing.yaml delete mode 100644 dag.png delete mode 100755 predict_variant_score.sh delete mode 100644 variant_annotation/.test/data/processed/vep/testing_variants_hg38_vep-annotated.vcf.gz delete mode 100644 variant_annotation/.test/data/raw/testing_variants_hg38.vcf delete mode 100644 variant_annotation/README.md delete mode 100644 variant_annotation/configs/cluster_config.json delete mode 100644 variant_annotation/configs/env/vep.yaml delete mode 160000 variant_annotation/configs/snakemake_slurm_profile delete mode 100644 variant_annotation/src/Snakefile delete mode 100755 variant_annotation/src/run_pipeline.sh diff --git a/.gitmodules b/.gitmodules index 86d84df..380238a 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,6 +1,3 @@ -[submodule "variant_annotation/configs/snakemake_profile"] - path = variant_annotation/configs/snakemake_profile - url = git@gitlab.rc.uab.edu:center-for-computational-genomics-and-data-science/sciops/pipelines/small_variant_caller_pipeline.git [submodule "variant_annotation/configs/snakemake_slurm_profile"] path = variant_annotation/configs/snakemake_slurm_profile url = git@gitlab.rc.uab.edu:center-for-computational-genomics-and-data-science/sciops/external-projects/snakemake_slurm_profile.git diff --git a/annotation_parsing/.test/testing_variants_hg38_vep-annotated.tsv b/annotation_parsing/.test/testing_variants_hg38_vep-annotated.tsv deleted file mode 100644 index 9ba3c7f..0000000 --- a/annotation_parsing/.test/testing_variants_hg38_vep-annotated.tsv +++ /dev/null @@ -1,1360 +0,0 @@ -Chromosome Position Reference Allele Alternate Allele VEP_Allele_Identifier Consequence IMPACT SYMBOL Gene Feature_type Feature BIOTYPE EXON INTRON HGVSc HGVSp cDNA_position CDS_position Protein_position Amino_acids Codons Existing_variation DISTANCE STRAND FLAGS SYMBOL_SOURCE HGNC_ID REFSEQ_MATCH REFSEQ_OFFSET GIVEN_REF USED_REF BAM_EDIT SOURCE SIFT PolyPhen CADD_PHRED CADD_RAW CADD_phred DANN_score Eigen-PC-phred_coding Eigen-PC-raw_coding Eigen-PC-raw_coding_rankscore Eigen-phred_coding Eigen-raw_coding Eigen-raw_coding_rankscore FATHMM_score GERP++_RS GenoCanyon_score LRT_score M-CAP_score MetaLR_score MetaSVM_score MutationAssessor_score MutationTaster_score PROVEAN_score SiPhy_29way_logOdds VEST4_score fathmm-MKL_coding_score integrated_fitCons_score phastCons100way_vertebrate phastCons30way_mammalian phyloP100way_vertebrate phyloP30way_mammalian GERP gnomADv3 gnomADv3_AC gnomADv3_AN gnomADv3_AF gnomADv3_AF_afr gnomADv3_AF_afr_female gnomADv3_AF_afr_male gnomADv3_AF_ami gnomADv3_AF_ami_female gnomADv3_AF_ami_male gnomADv3_AF_amr gnomADv3_AF_amr_female gnomADv3_AF_amr_male gnomADv3_AF_asj gnomADv3_AF_asj_female gnomADv3_AF_asj_male gnomADv3_AF_eas gnomADv3_AF_eas_female gnomADv3_AF_eas_male gnomADv3_AF_female gnomADv3_AF_fin gnomADv3_AF_fin_female gnomADv3_AF_fin_male gnomADv3_AF_male gnomADv3_AF_nfe gnomADv3_AF_nfe_female gnomADv3_AF_nfe_male gnomADv3_AF_oth gnomADv3_AF_oth_female gnomADv3_AF_oth_male gnomADv3_AF_raw gnomADv3_AF_sas gnomADv3_AF_sas_female gnomADv3_AF_sas_male clinvar clinvar_AF_ESP clinvar_AF_EXAC clinvar_AF_TGP clinvar_ALLELEID clinvar_CLNDN clinvar_CLNDNINCL clinvar_CLNDISDB clinvar_CLNDISDBINCL clinvar_CLNREVSTAT clinvar_CLNSIG clinvar_CLNSIGCONF clinvar_CLNSIGINCL clinvar_CLNVC clinvar_GENEINFO clinvar_MC clinvar_ORIGIN clinvar_RS clinvar_SSR testing_variants allele depth testing_variants total depth testing_variants allele percent reads -1 7977659 TG T - frameshift_variant HIGH PARK7 11315 Transcript NM_001123377.1 protein_coding 6/7 436 331 111 A/X Gct/ct 1 EntrezGene G G OK 6.54 15 30 50.0 -1 7977659 TG T - frameshift_variant HIGH PARK7 11315 Transcript NM_007262.5 protein_coding 6/7 437 331 111 A/X Gct/ct 1 EntrezGene G G 6.54 15 30 50.0 -1 7977659 TG T - frameshift_variant HIGH PARK7 11315 Transcript XM_005263424.3 protein_coding 6/7 514 331 111 A/X Gct/ct 1 EntrezGene G G 6.54 15 30 50.0 -1 26438228 A G G missense_variant MODERATE DHDDS 79947 Transcript NM_001243564.1 protein_coding 3/8 274 124 42 K/E Aag/Gag 1 EntrezGene A A OK 0 0.983 24.9 3.558226 24.9 0.99849544252413858 8.267994 0.725985331198606 0.84350 7.018498 0.692725091918815 0.79154 -1.08&-1.08&-1.08&-1.08&-1.08&-1.08&-1.08&-1.08&-1.08&2.23&2.23&.&2.23&-1.08 5.72 0.999999983602192 0.000000 0.128543 0.5849 0.1476 .&1.97&1.97&1.97&.&1.97&.&.&.&.&.&.&.&. 1&1&1&1&1&1 -3.72&-3.52&-3.65&-3.78&-3.72&-3.65&-3.72&-3.72&-3.72&-4.0&-3.99&-3.66&-4.0&-3.98 15.1835 0.633&0.68&0.624&0.616&0.651&0.62&.&.&.&.&.&.&.&. 0.99256 0.706548 1.000000 1.000000 9.290000 1.288000 6.54 rs147394623 21 143366 1.46478e-04 2.37676e-05 0.00000e+00 5.16742e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.81638e-03 6.24291e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.89486e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.00746e-04 6.19368e-05 8.02225e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.46456e-04 0.00000e+00 0.00000e+00 0.00000e+00 30709 0.00015 0.00012 39666 Retinitis_pigmentosa_type_59&Retinitis_pigmentosa&Retinitis_pigmentosa_59¬_provided .&Human_Phenotype_Ontology:HP:0000547&MONDO:MONDO:0019200&MeSH:D012174&MedGen:C0035334&OMIM:268000&OMIM:PS268000&Orphanet:ORPHA791&SNOMED_CT:28835009&MONDO:MONDO:0013468&MedGen:C3151227&OMIM:613861&MONDO:MONDO:0044326&MedGen:C4693376&OMIM:617836 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant DHDDS:79947 SO:0001583&missense_variant&SO:0001623&5_prime_UTR_variant 5 147394623 15 30 50.0 -1 26438228 A G G missense_variant MODERATE DHDDS 79947 Transcript NM_001243565.1 protein_coding 3/8 274 124 42 K/E Aag/Gag 1 EntrezGene A A OK 0 0.999 24.9 3.558226 24.9 0.99849544252413858 8.267994 0.725985331198606 0.84350 7.018498 0.692725091918815 0.79154 -1.08&-1.08&-1.08&-1.08&-1.08&-1.08&-1.08&-1.08&-1.08&2.23&2.23&.&2.23&-1.08 5.72 0.999999983602192 0.000000 0.128543 0.5849 0.1476 .&1.97&1.97&1.97&.&1.97&.&.&.&.&.&.&.&. 1&1&1&1&1&1 -3.72&-3.52&-3.65&-3.78&-3.72&-3.65&-3.72&-3.72&-3.72&-4.0&-3.99&-3.66&-4.0&-3.98 15.1835 0.633&0.68&0.624&0.616&0.651&0.62&.&.&.&.&.&.&.&. 0.99256 0.706548 1.000000 1.000000 9.290000 1.288000 6.54 rs147394623 21 143366 1.46478e-04 2.37676e-05 0.00000e+00 5.16742e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.81638e-03 6.24291e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.89486e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.00746e-04 6.19368e-05 8.02225e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.46456e-04 0.00000e+00 0.00000e+00 0.00000e+00 30709 0.00015 0.00012 39666 Retinitis_pigmentosa_type_59&Retinitis_pigmentosa&Retinitis_pigmentosa_59¬_provided .&Human_Phenotype_Ontology:HP:0000547&MONDO:MONDO:0019200&MeSH:D012174&MedGen:C0035334&OMIM:268000&OMIM:PS268000&Orphanet:ORPHA791&SNOMED_CT:28835009&MONDO:MONDO:0013468&MedGen:C3151227&OMIM:613861&MONDO:MONDO:0044326&MedGen:C4693376&OMIM:617836 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant DHDDS:79947 SO:0001583&missense_variant&SO:0001623&5_prime_UTR_variant 5 147394623 15 30 50.0 -1 26438228 A G G 5_prime_UTR_variant MODIFIER DHDDS 79947 Transcript NM_001319959.1 protein_coding 3/9 274 1 EntrezGene A A OK 24.9 3.558226 6.54 rs147394623 21 143366 1.46478e-04 2.37676e-05 0.00000e+00 5.16742e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.81638e-03 6.24291e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.89486e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.00746e-04 6.19368e-05 8.02225e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.46456e-04 0.00000e+00 0.00000e+00 0.00000e+00 30709 0.00015 0.00012 39666 Retinitis_pigmentosa_type_59&Retinitis_pigmentosa&Retinitis_pigmentosa_59¬_provided .&Human_Phenotype_Ontology:HP:0000547&MONDO:MONDO:0019200&MeSH:D012174&MedGen:C0035334&OMIM:268000&OMIM:PS268000&Orphanet:ORPHA791&SNOMED_CT:28835009&MONDO:MONDO:0013468&MedGen:C3151227&OMIM:613861&MONDO:MONDO:0044326&MedGen:C4693376&OMIM:617836 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant DHDDS:79947 SO:0001583&missense_variant&SO:0001623&5_prime_UTR_variant 5 147394623 15 30 50.0 -1 26438228 A G G missense_variant MODERATE DHDDS 79947 Transcript NM_024887.3 protein_coding 3/9 274 124 42 K/E Aag/Gag 1 EntrezGene A A OK 0 0.43 24.9 3.558226 24.9 0.99849544252413858 8.267994 0.725985331198606 0.84350 7.018498 0.692725091918815 0.79154 -1.08&-1.08&-1.08&-1.08&-1.08&-1.08&-1.08&-1.08&-1.08&2.23&2.23&.&2.23&-1.08 5.72 0.999999983602192 0.000000 0.128543 0.5849 0.1476 .&1.97&1.97&1.97&.&1.97&.&.&.&.&.&.&.&. 1&1&1&1&1&1 -3.72&-3.52&-3.65&-3.78&-3.72&-3.65&-3.72&-3.72&-3.72&-4.0&-3.99&-3.66&-4.0&-3.98 15.1835 0.633&0.68&0.624&0.616&0.651&0.62&.&.&.&.&.&.&.&. 0.99256 0.706548 1.000000 1.000000 9.290000 1.288000 6.54 rs147394623 21 143366 1.46478e-04 2.37676e-05 0.00000e+00 5.16742e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.81638e-03 6.24291e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.89486e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.00746e-04 6.19368e-05 8.02225e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.46456e-04 0.00000e+00 0.00000e+00 0.00000e+00 30709 0.00015 0.00012 39666 Retinitis_pigmentosa_type_59&Retinitis_pigmentosa&Retinitis_pigmentosa_59¬_provided .&Human_Phenotype_Ontology:HP:0000547&MONDO:MONDO:0019200&MeSH:D012174&MedGen:C0035334&OMIM:268000&OMIM:PS268000&Orphanet:ORPHA791&SNOMED_CT:28835009&MONDO:MONDO:0013468&MedGen:C3151227&OMIM:613861&MONDO:MONDO:0044326&MedGen:C4693376&OMIM:617836 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant DHDDS:79947 SO:0001583&missense_variant&SO:0001623&5_prime_UTR_variant 5 147394623 15 30 50.0 -1 26438228 A G G missense_variant MODERATE DHDDS 79947 Transcript NM_205861.3 protein_coding 3/9 235 124 42 K/E Aag/Gag 1 EntrezGene A A 0 0.566 24.9 3.558226 24.9 0.99849544252413858 8.267994 0.725985331198606 0.84350 7.018498 0.692725091918815 0.79154 -1.08&-1.08&-1.08&-1.08&-1.08&-1.08&-1.08&-1.08&-1.08&2.23&2.23&.&2.23&-1.08 5.72 0.999999983602192 0.000000 0.128543 0.5849 0.1476 .&1.97&1.97&1.97&.&1.97&.&.&.&.&.&.&.&. 1&1&1&1&1&1 -3.72&-3.52&-3.65&-3.78&-3.72&-3.65&-3.72&-3.72&-3.72&-4.0&-3.99&-3.66&-4.0&-3.98 15.1835 0.633&0.68&0.624&0.616&0.651&0.62&.&.&.&.&.&.&.&. 0.99256 0.706548 1.000000 1.000000 9.290000 1.288000 6.54 rs147394623 21 143366 1.46478e-04 2.37676e-05 0.00000e+00 5.16742e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.81638e-03 6.24291e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.89486e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.00746e-04 6.19368e-05 8.02225e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.46456e-04 0.00000e+00 0.00000e+00 0.00000e+00 30709 0.00015 0.00012 39666 Retinitis_pigmentosa_type_59&Retinitis_pigmentosa&Retinitis_pigmentosa_59¬_provided .&Human_Phenotype_Ontology:HP:0000547&MONDO:MONDO:0019200&MeSH:D012174&MedGen:C0035334&OMIM:268000&OMIM:PS268000&Orphanet:ORPHA791&SNOMED_CT:28835009&MONDO:MONDO:0013468&MedGen:C3151227&OMIM:613861&MONDO:MONDO:0044326&MedGen:C4693376&OMIM:617836 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant DHDDS:79947 SO:0001583&missense_variant&SO:0001623&5_prime_UTR_variant 5 147394623 15 30 50.0 -1 33010831 A AGGATGT GGATGT inframe_insertion&splice_region_variant MODERATE AK2 204 Transcript NM_001199199.2 protein_coding 7/7 727-728 672-673 224-225 -/TS -/ACATCC -1 EntrezGene 20.9 2.205337 6.54&5.61 rs752889879 736 120728 6.09635e-03 6.76014e-03 7.43418e-03 5.97249e-03 4.15512e-03 7.93651e-03 0.00000e+00 8.91327e-03 8.27749e-03 9.40249e-03 5.21221e-03 5.58659e-03 4.78469e-03 4.57666e-03 5.01672e-03 4.20757e-03 5.74546e-03 7.60582e-03 9.43396e-03 7.04833e-03 6.46846e-03 4.84692e-03 4.01800e-03 6.00197e-03 5.83864e-03 4.17537e-03 7.55940e-03 7.86315e-02 7.80089e-03 1.00402e-02 7.29262e-03 15 30 50.0 -1 33010831 A AGGATGT GGATGT inframe_insertion&splice_region_variant MODERATE AK2 204 Transcript NM_001319139.2 protein_coding 8/8 869-870 552-553 184-185 -/TS -/ACATCC -1 EntrezGene 20.9 2.205337 6.54&5.61 rs752889879 736 120728 6.09635e-03 6.76014e-03 7.43418e-03 5.97249e-03 4.15512e-03 7.93651e-03 0.00000e+00 8.91327e-03 8.27749e-03 9.40249e-03 5.21221e-03 5.58659e-03 4.78469e-03 4.57666e-03 5.01672e-03 4.20757e-03 5.74546e-03 7.60582e-03 9.43396e-03 7.04833e-03 6.46846e-03 4.84692e-03 4.01800e-03 6.00197e-03 5.83864e-03 4.17537e-03 7.55940e-03 7.86315e-02 7.80089e-03 1.00402e-02 7.29262e-03 15 30 50.0 -1 33010831 A AGGATGT GGATGT downstream_gene_variant MODIFIER AK2 204 Transcript NM_001319140.1 protein_coding 393 -1 EntrezGene 20.9 2.205337 6.54&5.61 rs752889879 736 120728 6.09635e-03 6.76014e-03 7.43418e-03 5.97249e-03 4.15512e-03 7.93651e-03 0.00000e+00 8.91327e-03 8.27749e-03 9.40249e-03 5.21221e-03 5.58659e-03 4.78469e-03 4.57666e-03 5.01672e-03 4.20757e-03 5.74546e-03 7.60582e-03 9.43396e-03 7.04833e-03 6.46846e-03 4.84692e-03 4.01800e-03 6.00197e-03 5.83864e-03 4.17537e-03 7.55940e-03 7.86315e-02 7.80089e-03 1.00402e-02 7.29262e-03 15 30 50.0 -1 33010831 A AGGATGT GGATGT splice_region_variant&3_prime_UTR_variant LOW AK2 204 Transcript NM_001319141.2 protein_coding 8/8 800-801 -1 EntrezGene 20.9 2.205337 6.54&5.61 rs752889879 736 120728 6.09635e-03 6.76014e-03 7.43418e-03 5.97249e-03 4.15512e-03 7.93651e-03 0.00000e+00 8.91327e-03 8.27749e-03 9.40249e-03 5.21221e-03 5.58659e-03 4.78469e-03 4.57666e-03 5.01672e-03 4.20757e-03 5.74546e-03 7.60582e-03 9.43396e-03 7.04833e-03 6.46846e-03 4.84692e-03 4.01800e-03 6.00197e-03 5.83864e-03 4.17537e-03 7.55940e-03 7.86315e-02 7.80089e-03 1.00402e-02 7.29262e-03 15 30 50.0 -1 33010831 A AGGATGT GGATGT inframe_insertion&splice_region_variant MODERATE AK2 204 Transcript NM_001319142.2 protein_coding 6/6 625-626 570-571 190-191 -/TS -/ACATCC -1 EntrezGene 20.9 2.205337 6.54&5.61 rs752889879 736 120728 6.09635e-03 6.76014e-03 7.43418e-03 5.97249e-03 4.15512e-03 7.93651e-03 0.00000e+00 8.91327e-03 8.27749e-03 9.40249e-03 5.21221e-03 5.58659e-03 4.78469e-03 4.57666e-03 5.01672e-03 4.20757e-03 5.74546e-03 7.60582e-03 9.43396e-03 7.04833e-03 6.46846e-03 4.84692e-03 4.01800e-03 6.00197e-03 5.83864e-03 4.17537e-03 7.55940e-03 7.86315e-02 7.80089e-03 1.00402e-02 7.29262e-03 15 30 50.0 -1 33010831 A AGGATGT GGATGT downstream_gene_variant MODIFIER AK2 204 Transcript NM_001319143.1 protein_coding 2212 -1 EntrezGene OK 20.9 2.205337 6.54&5.61 rs752889879 736 120728 6.09635e-03 6.76014e-03 7.43418e-03 5.97249e-03 4.15512e-03 7.93651e-03 0.00000e+00 8.91327e-03 8.27749e-03 9.40249e-03 5.21221e-03 5.58659e-03 4.78469e-03 4.57666e-03 5.01672e-03 4.20757e-03 5.74546e-03 7.60582e-03 9.43396e-03 7.04833e-03 6.46846e-03 4.84692e-03 4.01800e-03 6.00197e-03 5.83864e-03 4.17537e-03 7.55940e-03 7.86315e-02 7.80089e-03 1.00402e-02 7.29262e-03 15 30 50.0 -1 33010831 A AGGATGT GGATGT 3_prime_UTR_variant MODIFIER AK2 204 Transcript NM_001625.4 protein_coding 6/6 3124-3125 -1 EntrezGene 20.9 2.205337 6.54&5.61 rs752889879 736 120728 6.09635e-03 6.76014e-03 7.43418e-03 5.97249e-03 4.15512e-03 7.93651e-03 0.00000e+00 8.91327e-03 8.27749e-03 9.40249e-03 5.21221e-03 5.58659e-03 4.78469e-03 4.57666e-03 5.01672e-03 4.20757e-03 5.74546e-03 7.60582e-03 9.43396e-03 7.04833e-03 6.46846e-03 4.84692e-03 4.01800e-03 6.00197e-03 5.83864e-03 4.17537e-03 7.55940e-03 7.86315e-02 7.80089e-03 1.00402e-02 7.29262e-03 15 30 50.0 -1 33010831 A AGGATGT GGATGT inframe_insertion&splice_region_variant MODERATE AK2 204 Transcript NM_013411.5 protein_coding 7/7 751-752 696-697 232-233 -/TS -/ACATCC -1 EntrezGene 20.9 2.205337 6.54&5.61 rs752889879 736 120728 6.09635e-03 6.76014e-03 7.43418e-03 5.97249e-03 4.15512e-03 7.93651e-03 0.00000e+00 8.91327e-03 8.27749e-03 9.40249e-03 5.21221e-03 5.58659e-03 4.78469e-03 4.57666e-03 5.01672e-03 4.20757e-03 5.74546e-03 7.60582e-03 9.43396e-03 7.04833e-03 6.46846e-03 4.84692e-03 4.01800e-03 6.00197e-03 5.83864e-03 4.17537e-03 7.55940e-03 7.86315e-02 7.80089e-03 1.00402e-02 7.29262e-03 15 30 50.0 -1 33010831 A AGGATGT GGATGT splice_region_variant&non_coding_transcript_exon_variant LOW AK2 204 Transcript NR_134976.2 misc_RNA 6/6 656-657 -1 EntrezGene 20.9 2.205337 6.54&5.61 rs752889879 736 120728 6.09635e-03 6.76014e-03 7.43418e-03 5.97249e-03 4.15512e-03 7.93651e-03 0.00000e+00 8.91327e-03 8.27749e-03 9.40249e-03 5.21221e-03 5.58659e-03 4.78469e-03 4.57666e-03 5.01672e-03 4.20757e-03 5.74546e-03 7.60582e-03 9.43396e-03 7.04833e-03 6.46846e-03 4.84692e-03 4.01800e-03 6.00197e-03 5.83864e-03 4.17537e-03 7.55940e-03 7.86315e-02 7.80089e-03 1.00402e-02 7.29262e-03 15 30 50.0 -1 33010831 A AGGATGT GGATGT splice_region_variant&non_coding_transcript_exon_variant LOW AK2 204 Transcript XR_001737036.1 misc_RNA 6/6 1774-1775 -1 EntrezGene 20.9 2.205337 6.54&5.61 rs752889879 736 120728 6.09635e-03 6.76014e-03 7.43418e-03 5.97249e-03 4.15512e-03 7.93651e-03 0.00000e+00 8.91327e-03 8.27749e-03 9.40249e-03 5.21221e-03 5.58659e-03 4.78469e-03 4.57666e-03 5.01672e-03 4.20757e-03 5.74546e-03 7.60582e-03 9.43396e-03 7.04833e-03 6.46846e-03 4.84692e-03 4.01800e-03 6.00197e-03 5.83864e-03 4.17537e-03 7.55940e-03 7.86315e-02 7.80089e-03 1.00402e-02 7.29262e-03 15 30 50.0 -1 33010831 A AGGATGT GGATGT splice_region_variant&non_coding_transcript_exon_variant LOW AK2 204 Transcript XR_246248.2 misc_RNA 7/7 1869-1870 -1 EntrezGene 20.9 2.205337 6.54&5.61 rs752889879 736 120728 6.09635e-03 6.76014e-03 7.43418e-03 5.97249e-03 4.15512e-03 7.93651e-03 0.00000e+00 8.91327e-03 8.27749e-03 9.40249e-03 5.21221e-03 5.58659e-03 4.78469e-03 4.57666e-03 5.01672e-03 4.20757e-03 5.74546e-03 7.60582e-03 9.43396e-03 7.04833e-03 6.46846e-03 4.84692e-03 4.01800e-03 6.00197e-03 5.83864e-03 4.17537e-03 7.55940e-03 7.86315e-02 7.80089e-03 1.00402e-02 7.29262e-03 15 30 50.0 -1 37540757 T TGCTTCACTTTGACTGTTGAGTGGTGAGGACTTCGGTTTCTCTTACTGCGAGG GCTTCACTTTGACTGTTGAGTGGTGAGGACTTCGGTTTCTCTTACTGCGAGG splice_region_variant&intron_variant LOW SNIP1 79753 Transcript NM_024700.4 protein_coding 2/3 -1 EntrezGene 6.45&1.38 15 30 50.0 -1 37540757 T TGCTTCACTTTGACTGTTGAGTGGTGAGGACTTCGGTTTCTCTTACTGCGAGG GCTTCACTTTGACTGTTGAGTGGTGAGGACTTCGGTTTCTCTTACTGCGAGG downstream_gene_variant MODIFIER LOC105378649 105378649 Transcript XR_001737980.1 lncRNA 544 1 EntrezGene 6.45&1.38 15 30 50.0 -1 37540757 T TGCTTCACTTTGACTGTTGAGTGGTGAGGACTTCGGTTTCTCTTACTGCGAGG GCTTCACTTTGACTGTTGAGTGGTGAGGACTTCGGTTTCTCTTACTGCGAGG downstream_gene_variant MODIFIER LOC105378649 105378649 Transcript XR_947190.2 lncRNA 1796 1 EntrezGene 6.45&1.38 15 30 50.0 -1 70415987 C T T missense_variant MODERATE CTH 1491 Transcript NM_001190463.1 protein_coding 2/11 398 200 67 T/I aCt/aTt 1 EntrezGene C C 0.01 0.903 25.5 3.732937 25.5 0.99906710229661511 14.7398 0.922308322611668 0.96473 13.42158 0.983801676259925 0.95225 -1.73&-2.32&-1.73 5.55 0.999999999999946 0.000000 0.8474 0.8938 3.455&3.455&3.455 1&1&1 -5.49&-5.47&-5.52 18.6468 0.874&0.885&0.876 0.95340 0.75658 1.000000 0.974000 6.098000 1.026000 6.51 chr1:70415987-70415987 975 143254 6.80609e-03 1.76040e-03 2.02661e-03 1.44793e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.57555e-02 1.10095e-02 1.93748e-02 1.05295e-02 8.51305e-03 1.28041e-02 0.00000e+00 0.00000e+00 0.00000e+00 6.81276e-03 2.58176e-03 3.19489e-03 2.38874e-03 6.79900e-03 9.38604e-03 9.68329e-03 8.97719e-03 6.04651e-03 5.46448e-03 6.65399e-03 6.80125e-03 1.31406e-03 0.00000e+00 1.61421e-03 2939 0.00260 17978 Cystathioninuria¬_provided Human_Phenotype_Ontology:HP:0003153&MONDO:MONDO:0009058&MedGen:C0220993&OMIM:219500&Orphanet:ORPHA212&SNOMED_CT:13003007&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Pathogenic(1)&Uncertain_significance(2) single_nucleotide_variant CTH:1491 SO:0001583&missense_variant 1 28941785 15 30 50.0 -1 70415987 C T T missense_variant MODERATE CTH 1491 Transcript NM_001902.6 protein_coding 2/12 348 200 67 T/I aCt/aTt 1 EntrezGene C C 0.01 0.993 25.5 3.732937 25.5 0.99906710229661511 14.7398 0.922308322611668 0.96473 13.42158 0.983801676259925 0.95225 -1.73&-2.32&-1.73 5.55 0.999999999999946 0.000000 0.8474 0.8938 3.455&3.455&3.455 1&1&1 -5.49&-5.47&-5.52 18.6468 0.874&0.885&0.876 0.95340 0.75658 1.000000 0.974000 6.098000 1.026000 6.51 chr1:70415987-70415987 975 143254 6.80609e-03 1.76040e-03 2.02661e-03 1.44793e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.57555e-02 1.10095e-02 1.93748e-02 1.05295e-02 8.51305e-03 1.28041e-02 0.00000e+00 0.00000e+00 0.00000e+00 6.81276e-03 2.58176e-03 3.19489e-03 2.38874e-03 6.79900e-03 9.38604e-03 9.68329e-03 8.97719e-03 6.04651e-03 5.46448e-03 6.65399e-03 6.80125e-03 1.31406e-03 0.00000e+00 1.61421e-03 2939 0.00260 17978 Cystathioninuria¬_provided Human_Phenotype_Ontology:HP:0003153&MONDO:MONDO:0009058&MedGen:C0220993&OMIM:219500&Orphanet:ORPHA212&SNOMED_CT:13003007&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Pathogenic(1)&Uncertain_significance(2) single_nucleotide_variant CTH:1491 SO:0001583&missense_variant 1 28941785 15 30 50.0 -1 70415987 C T T missense_variant MODERATE CTH 1491 Transcript NM_153742.4 protein_coding 2/11 398 200 67 T/I aCt/aTt 1 EntrezGene C C 0.01 0.843 25.5 3.732937 25.5 0.99906710229661511 14.7398 0.922308322611668 0.96473 13.42158 0.983801676259925 0.95225 -1.73&-2.32&-1.73 5.55 0.999999999999946 0.000000 0.8474 0.8938 3.455&3.455&3.455 1&1&1 -5.49&-5.47&-5.52 18.6468 0.874&0.885&0.876 0.95340 0.75658 1.000000 0.974000 6.098000 1.026000 6.51 chr1:70415987-70415987 975 143254 6.80609e-03 1.76040e-03 2.02661e-03 1.44793e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.57555e-02 1.10095e-02 1.93748e-02 1.05295e-02 8.51305e-03 1.28041e-02 0.00000e+00 0.00000e+00 0.00000e+00 6.81276e-03 2.58176e-03 3.19489e-03 2.38874e-03 6.79900e-03 9.38604e-03 9.68329e-03 8.97719e-03 6.04651e-03 5.46448e-03 6.65399e-03 6.80125e-03 1.31406e-03 0.00000e+00 1.61421e-03 2939 0.00260 17978 Cystathioninuria¬_provided Human_Phenotype_Ontology:HP:0003153&MONDO:MONDO:0009058&MedGen:C0220993&OMIM:219500&Orphanet:ORPHA212&SNOMED_CT:13003007&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Pathogenic(1)&Uncertain_significance(2) single_nucleotide_variant CTH:1491 SO:0001583&missense_variant 1 28941785 15 30 50.0 -1 70415987 C T T 5_prime_UTR_variant MODIFIER CTH 1491 Transcript XM_005270509.3 protein_coding 2/12 418 1 EntrezGene C C 25.5 3.732937 6.51 chr1:70415987-70415987 975 143254 6.80609e-03 1.76040e-03 2.02661e-03 1.44793e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.57555e-02 1.10095e-02 1.93748e-02 1.05295e-02 8.51305e-03 1.28041e-02 0.00000e+00 0.00000e+00 0.00000e+00 6.81276e-03 2.58176e-03 3.19489e-03 2.38874e-03 6.79900e-03 9.38604e-03 9.68329e-03 8.97719e-03 6.04651e-03 5.46448e-03 6.65399e-03 6.80125e-03 1.31406e-03 0.00000e+00 1.61421e-03 2939 0.00260 17978 Cystathioninuria¬_provided Human_Phenotype_Ontology:HP:0003153&MONDO:MONDO:0009058&MedGen:C0220993&OMIM:219500&Orphanet:ORPHA212&SNOMED_CT:13003007&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Pathogenic(1)&Uncertain_significance(2) single_nucleotide_variant CTH:1491 SO:0001583&missense_variant 1 28941785 15 30 50.0 -1 74342872 C CACTCCATGGG ACTCCATGGG frameshift_variant HIGH FPGT-TNNI3K 100526835 Transcript NM_001112808.2 protein_coding 10/27 1083-1084 1055-1056 352 P/PLHGX cca/ccACTCCATGGGa 1 EntrezGene 31 4.373349 6.15&-1.22 rs556027408 26 143178 1.81592e-04 2.14255e-04 1.32298e-04 3.10398e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.24743e-03 1.35455e-03 1.16550e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.49056e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.16201e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.81382e-04 0.00000e+00 0.00000e+00 0.00000e+00 782683 707507 not_provided MedGen:CN517202 criteria_provided&_single_submitter Benign Insertion TNNI3K:51086&FPGT-TNNI3K:100526835 SO:0001589&frameshift_variant 1 556027408 15 30 50.0 -1 74342872 C CACTCCATGGG ACTCCATGGG frameshift_variant HIGH FPGT-TNNI3K 100526835 Transcript NM_001199327.1 protein_coding 10/24 1083-1084 1055-1056 352 P/PLHGX cca/ccACTCCATGGGa 1 EntrezGene OK 31 4.373349 6.15&-1.22 rs556027408 26 143178 1.81592e-04 2.14255e-04 1.32298e-04 3.10398e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.24743e-03 1.35455e-03 1.16550e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.49056e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.16201e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.81382e-04 0.00000e+00 0.00000e+00 0.00000e+00 782683 707507 not_provided MedGen:CN517202 criteria_provided&_single_submitter Benign Insertion TNNI3K:51086&FPGT-TNNI3K:100526835 SO:0001589&frameshift_variant 1 556027408 15 30 50.0 -1 74342872 C CACTCCATGGG ACTCCATGGG frameshift_variant HIGH TNNI3K 51086 Transcript NM_015978.3 protein_coding 8/25 778-779 713-714 238 P/PLHGX cca/ccACTCCATGGGa 1 EntrezGene 31 4.373349 6.15&-1.22 rs556027408 26 143178 1.81592e-04 2.14255e-04 1.32298e-04 3.10398e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.24743e-03 1.35455e-03 1.16550e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.49056e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.16201e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.81382e-04 0.00000e+00 0.00000e+00 0.00000e+00 782683 707507 not_provided MedGen:CN517202 criteria_provided&_single_submitter Benign Insertion TNNI3K:51086&FPGT-TNNI3K:100526835 SO:0001589&frameshift_variant 1 556027408 15 30 50.0 -1 75761161 A G G missense_variant MODERATE ACADM 34 Transcript NM_000016.6 protein_coding 11/12 1064 985 329 K/E Aaa/Gaa 1 EntrezGene A A 0.19 0.036 22.9 2.740200 22.9 0.99622351986593394 3.220074 0.207475271092013 0.50260 2.480803 -0.00856808828046626 0.41465 -3.7&-3.7&-3.7&-3.7 5.21 0.990716028394288 0.000000 0.105499 0.5182 0.1612 .&0.36&.&. 1&1&1&1&1 -0.68&-0.68&-0.68&-0.68 14.2135 0.927&0.932&0.948&0.929 0.97993 0.732398 1.000000 1.000000 8.582000 1.312000 6.4 chr1:75761161-75761161 510 143344 3.55787e-03 1.21238e-03 1.10055e-03 1.34367e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.48829e-03 2.02908e-03 2.83871e-03 1.20337e-03 2.27015e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.77670e-03 7.63650e-04 1.19808e-03 6.27195e-04 3.32518e-03 6.22464e-03 6.09658e-03 6.40082e-03 4.64684e-03 6.36364e-03 2.85171e-03 3.55683e-03 3.27654e-04 0.00000e+00 4.02253e-04 3586 0.00332 0.00100 18625 Medium-chain_acyl-coenzyme_A_dehydrogenase_deficiency¬_provided MONDO:MONDO:0008721&MedGen:C0220710&OMIM:201450&Orphanet:ORPHA42&SNOMED_CT:128596003&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(18)&Uncertain_significance(1) single_nucleotide_variant ACADM:34 SO:0001583&missense_variant 9 77931234 15 30 50.0 -1 75761161 A G G missense_variant MODERATE ACADM 34 Transcript NM_001127328.2 protein_coding 11/12 1438 997 333 K/E Aaa/Gaa 1 EntrezGene A A 0.22 0.056 22.9 2.740200 22.9 0.99622351986593394 3.220074 0.207475271092013 0.50260 2.480803 -0.00856808828046626 0.41465 -3.7&-3.7&-3.7&-3.7 5.21 0.990716028394288 0.000000 0.105499 0.5182 0.1612 .&0.36&.&. 1&1&1&1&1 -0.68&-0.68&-0.68&-0.68 14.2135 0.927&0.932&0.948&0.929 0.97993 0.732398 1.000000 1.000000 8.582000 1.312000 6.4 chr1:75761161-75761161 510 143344 3.55787e-03 1.21238e-03 1.10055e-03 1.34367e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.48829e-03 2.02908e-03 2.83871e-03 1.20337e-03 2.27015e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.77670e-03 7.63650e-04 1.19808e-03 6.27195e-04 3.32518e-03 6.22464e-03 6.09658e-03 6.40082e-03 4.64684e-03 6.36364e-03 2.85171e-03 3.55683e-03 3.27654e-04 0.00000e+00 4.02253e-04 3586 0.00332 0.00100 18625 Medium-chain_acyl-coenzyme_A_dehydrogenase_deficiency¬_provided MONDO:MONDO:0008721&MedGen:C0220710&OMIM:201450&Orphanet:ORPHA42&SNOMED_CT:128596003&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(18)&Uncertain_significance(1) single_nucleotide_variant ACADM:34 SO:0001583&missense_variant 9 77931234 15 30 50.0 -1 75761161 A G G missense_variant MODERATE ACADM 34 Transcript NM_001286042.1 protein_coding 10/11 1338 877 293 K/E Aaa/Gaa 1 EntrezGene A A 0.23 0.27 22.9 2.740200 22.9 0.99622351986593394 3.220074 0.207475271092013 0.50260 2.480803 -0.00856808828046626 0.41465 -3.7&-3.7&-3.7&-3.7 5.21 0.990716028394288 0.000000 0.105499 0.5182 0.1612 .&0.36&.&. 1&1&1&1&1 -0.68&-0.68&-0.68&-0.68 14.2135 0.927&0.932&0.948&0.929 0.97993 0.732398 1.000000 1.000000 8.582000 1.312000 6.4 chr1:75761161-75761161 510 143344 3.55787e-03 1.21238e-03 1.10055e-03 1.34367e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.48829e-03 2.02908e-03 2.83871e-03 1.20337e-03 2.27015e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.77670e-03 7.63650e-04 1.19808e-03 6.27195e-04 3.32518e-03 6.22464e-03 6.09658e-03 6.40082e-03 4.64684e-03 6.36364e-03 2.85171e-03 3.55683e-03 3.27654e-04 0.00000e+00 4.02253e-04 3586 0.00332 0.00100 18625 Medium-chain_acyl-coenzyme_A_dehydrogenase_deficiency¬_provided MONDO:MONDO:0008721&MedGen:C0220710&OMIM:201450&Orphanet:ORPHA42&SNOMED_CT:128596003&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(18)&Uncertain_significance(1) single_nucleotide_variant ACADM:34 SO:0001583&missense_variant 9 77931234 15 30 50.0 -1 75761161 A G G missense_variant MODERATE ACADM 34 Transcript NM_001286043.1 protein_coding 12/13 1525 1084 362 K/E Aaa/Gaa 1 EntrezGene A A 0.18 0.101 22.9 2.740200 22.9 0.99622351986593394 3.220074 0.207475271092013 0.50260 2.480803 -0.00856808828046626 0.41465 -3.7&-3.7&-3.7&-3.7 5.21 0.990716028394288 0.000000 0.105499 0.5182 0.1612 .&0.36&.&. 1&1&1&1&1 -0.68&-0.68&-0.68&-0.68 14.2135 0.927&0.932&0.948&0.929 0.97993 0.732398 1.000000 1.000000 8.582000 1.312000 6.4 chr1:75761161-75761161 510 143344 3.55787e-03 1.21238e-03 1.10055e-03 1.34367e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.48829e-03 2.02908e-03 2.83871e-03 1.20337e-03 2.27015e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.77670e-03 7.63650e-04 1.19808e-03 6.27195e-04 3.32518e-03 6.22464e-03 6.09658e-03 6.40082e-03 4.64684e-03 6.36364e-03 2.85171e-03 3.55683e-03 3.27654e-04 0.00000e+00 4.02253e-04 3586 0.00332 0.00100 18625 Medium-chain_acyl-coenzyme_A_dehydrogenase_deficiency¬_provided MONDO:MONDO:0008721&MedGen:C0220710&OMIM:201450&Orphanet:ORPHA42&SNOMED_CT:128596003&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(18)&Uncertain_significance(1) single_nucleotide_variant ACADM:34 SO:0001583&missense_variant 9 77931234 15 30 50.0 -1 75761161 A G G missense_variant MODERATE ACADM 34 Transcript NM_001286044.1 protein_coding 8/9 1156 418 140 K/E Aaa/Gaa 1 EntrezGene A A 0.41 0.036 22.9 2.740200 22.9 0.99622351986593394 3.220074 0.207475271092013 0.50260 2.480803 -0.00856808828046626 0.41465 -3.7&-3.7&-3.7&-3.7 5.21 0.990716028394288 0.000000 0.105499 0.5182 0.1612 .&0.36&.&. 1&1&1&1&1 -0.68&-0.68&-0.68&-0.68 14.2135 0.927&0.932&0.948&0.929 0.97993 0.732398 1.000000 1.000000 8.582000 1.312000 6.4 chr1:75761161-75761161 510 143344 3.55787e-03 1.21238e-03 1.10055e-03 1.34367e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.48829e-03 2.02908e-03 2.83871e-03 1.20337e-03 2.27015e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.77670e-03 7.63650e-04 1.19808e-03 6.27195e-04 3.32518e-03 6.22464e-03 6.09658e-03 6.40082e-03 4.64684e-03 6.36364e-03 2.85171e-03 3.55683e-03 3.27654e-04 0.00000e+00 4.02253e-04 3586 0.00332 0.00100 18625 Medium-chain_acyl-coenzyme_A_dehydrogenase_deficiency¬_provided MONDO:MONDO:0008721&MedGen:C0220710&OMIM:201450&Orphanet:ORPHA42&SNOMED_CT:128596003&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(18)&Uncertain_significance(1) single_nucleotide_variant ACADM:34 SO:0001583&missense_variant 9 77931234 15 30 50.0 -1 93028124 C T T intergenic_variant MODIFIER 2.001 0.089750 0.241 chr1:93028124-93028124 142362 143318 9.93330e-01 9.78693e-01 9.78468e-01 9.78958e-01 1.00000e+00 1.00000e+00 1.00000e+00 9.97217e-01 9.97123e-01 9.97290e-01 1.00000e+00 1.00000e+00 1.00000e+00 1.00000e+00 1.00000e+00 1.00000e+00 9.93015e-01 1.00000e+00 1.00000e+00 1.00000e+00 9.93665e-01 9.99923e-01 9.99947e-01 9.99890e-01 9.92558e-01 9.92727e-01 9.92381e-01 9.93313e-01 9.99672e-01 1.00000e+00 9.99597e-01 15 30 50.0 -1 94031015 G A A stop_gained HIGH ABCA4 24 Transcript NM_000350.3 protein_coding 28/50 4337 4234 1412 Q/* Cag/Tag -1 EntrezGene G G 50 9.527626 50 0.99787988251647985 7.865683 0.705960214427774 0.82831 10.70982 0.886605708261828 0.91087 .&. 4.07 0.999936633744562 0.001228 .&. 1 .&. 14.7007 0.788&0.838 0.87193 0.554377 0.986000 0.893000 2.805000 1.176000 6.54 rs61750137 1 143278 6.97944e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44022e-05 1.54856e-05 0.00000e+00 3.67863e-05 0.00000e+00 0.00000e+00 0.00000e+00 6.97457e-06 0.00000e+00 0.00000e+00 0.00000e+00 99263 105152 Retinal_dystrophy&Stargardt_disease_1¬_provided Human_Phenotype_Ontology:HP:0000556&Human_Phenotype_Ontology:HP:0007736&Human_Phenotype_Ontology:HP:0007910&Human_Phenotype_Ontology:HP:0007974&Human_Phenotype_Ontology:HP:0007982&MONDO:MONDO:0019118&MeSH:D058499&MedGen:C0854723&Orphanet:ORPHA71862&SNOMED_CT:314407005&MONDO:MONDO:0009549&MeSH:C535804&MedGen:C1855465&OMIM:248200&Orphanet:ORPHA364055&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ABCA4:24 SO:0001587&nonsense 1 61750137 15 30 50.0 -1 97082391 T A A missense_variant MODERATE DPYD 1806 Transcript NM_000110.4 protein_coding 22/23 2958 2846 949 D/V gAt/gTt -1 EntrezGene T T 0 0.929 25.5 3.728162 25.5 0.99484402844520115 12.36455 0.865839117879941 0.93907 11.78872 0.927664345811922 0.93048 -3.15 5.82 0.999999999999308 0.000000 0.352187 0.9075 1.0432 2.12 1 -7.16 16.1779 0.954 0.94804 0.693126 1.000000 0.963000 7.819000 1.135000 6.53 rs67376798 470 143226 3.28153e-03 1.16556e-03 1.05718e-03 1.29279e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.61575e-03 1.52698e-03 1.68350e-03 6.01685e-04 5.67537e-04 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.64489e-03 9.54016e-05 0.00000e+00 1.25345e-04 2.89525e-03 6.01215e-03 6.18308e-03 5.77716e-03 2.78810e-03 3.64299e-03 1.89753e-03 3.27841e-03 6.56599e-04 0.00000e+00 8.06452e-04 88974 0.00392 0.00263 0.00220 94529 Dihydropyrimidine_dehydrogenase_deficiency&Inborn_genetic_diseases&Fluorouracil_response&Pyrimidine_analogues_response_-_Toxicity/ADR&_Metabolism/PK&capecitabine_response_-_Toxicity/ADR&_Metabolism/PK&fluorouracil_response_-_Toxicity/ADR&_Metabolism/PK&tegafur_response_-_Toxicity/ADR&_Metabolism/PK¬_provided MONDO:MONDO:0010130&MedGen:C1959620&OMIM:274270&Orphanet:ORPHA1675&MeSH:D030342&MedGen:C0950123&MedGen:CN077983&MedGen:CN240586&MedGen:CN240593&MedGen:CN240604&MedGen:CN240607&MedGen:CN517202 reviewed_by_expert_panel drug_response single_nucleotide_variant DPYD:1806 SO:0001583&missense_variant 1 67376798 15 30 50.0 -1 97082391 T A A missense_variant MODERATE DPYD 1806 Transcript XM_005270562.3 protein_coding 21/22 2767 2630 877 D/V gAt/gTt -1 EntrezGene T T 25.5 3.728162 25.5 0.99484402844520115 12.36455 0.865839117879941 0.93907 11.78872 0.927664345811922 0.93048 -3.15 5.82 0.999999999999308 0.000000 0.352187 0.9075 1.0432 2.12 1 -7.16 16.1779 0.954 0.94804 0.693126 1.000000 0.963000 7.819000 1.135000 6.53 rs67376798 470 143226 3.28153e-03 1.16556e-03 1.05718e-03 1.29279e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.61575e-03 1.52698e-03 1.68350e-03 6.01685e-04 5.67537e-04 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.64489e-03 9.54016e-05 0.00000e+00 1.25345e-04 2.89525e-03 6.01215e-03 6.18308e-03 5.77716e-03 2.78810e-03 3.64299e-03 1.89753e-03 3.27841e-03 6.56599e-04 0.00000e+00 8.06452e-04 88974 0.00392 0.00263 0.00220 94529 Dihydropyrimidine_dehydrogenase_deficiency&Inborn_genetic_diseases&Fluorouracil_response&Pyrimidine_analogues_response_-_Toxicity/ADR&_Metabolism/PK&capecitabine_response_-_Toxicity/ADR&_Metabolism/PK&fluorouracil_response_-_Toxicity/ADR&_Metabolism/PK&tegafur_response_-_Toxicity/ADR&_Metabolism/PK¬_provided MONDO:MONDO:0010130&MedGen:C1959620&OMIM:274270&Orphanet:ORPHA1675&MeSH:D030342&MedGen:C0950123&MedGen:CN077983&MedGen:CN240586&MedGen:CN240593&MedGen:CN240604&MedGen:CN240607&MedGen:CN517202 reviewed_by_expert_panel drug_response single_nucleotide_variant DPYD:1806 SO:0001583&missense_variant 1 67376798 15 30 50.0 -1 97082391 T A A missense_variant MODERATE DPYD 1806 Transcript XM_017000507.1 protein_coding 21/22 2866 2735 912 D/V gAt/gTt -1 EntrezGene T T 25.5 3.728162 25.5 0.99484402844520115 12.36455 0.865839117879941 0.93907 11.78872 0.927664345811922 0.93048 -3.15 5.82 0.999999999999308 0.000000 0.352187 0.9075 1.0432 2.12 1 -7.16 16.1779 0.954 0.94804 0.693126 1.000000 0.963000 7.819000 1.135000 6.53 rs67376798 470 143226 3.28153e-03 1.16556e-03 1.05718e-03 1.29279e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.61575e-03 1.52698e-03 1.68350e-03 6.01685e-04 5.67537e-04 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.64489e-03 9.54016e-05 0.00000e+00 1.25345e-04 2.89525e-03 6.01215e-03 6.18308e-03 5.77716e-03 2.78810e-03 3.64299e-03 1.89753e-03 3.27841e-03 6.56599e-04 0.00000e+00 8.06452e-04 88974 0.00392 0.00263 0.00220 94529 Dihydropyrimidine_dehydrogenase_deficiency&Inborn_genetic_diseases&Fluorouracil_response&Pyrimidine_analogues_response_-_Toxicity/ADR&_Metabolism/PK&capecitabine_response_-_Toxicity/ADR&_Metabolism/PK&fluorouracil_response_-_Toxicity/ADR&_Metabolism/PK&tegafur_response_-_Toxicity/ADR&_Metabolism/PK¬_provided MONDO:MONDO:0010130&MedGen:C1959620&OMIM:274270&Orphanet:ORPHA1675&MeSH:D030342&MedGen:C0950123&MedGen:CN077983&MedGen:CN240586&MedGen:CN240593&MedGen:CN240604&MedGen:CN240607&MedGen:CN517202 reviewed_by_expert_panel drug_response single_nucleotide_variant DPYD:1806 SO:0001583&missense_variant 1 67376798 15 30 50.0 -1 97082391 T A A missense_variant MODERATE DPYD 1806 Transcript XM_017000508.2 protein_coding 24/25 3136 2351 784 D/V gAt/gTt -1 EntrezGene T T 25.5 3.728162 25.5 0.99484402844520115 12.36455 0.865839117879941 0.93907 11.78872 0.927664345811922 0.93048 -3.15 5.82 0.999999999999308 0.000000 0.352187 0.9075 1.0432 2.12 1 -7.16 16.1779 0.954 0.94804 0.693126 1.000000 0.963000 7.819000 1.135000 6.53 rs67376798 470 143226 3.28153e-03 1.16556e-03 1.05718e-03 1.29279e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.61575e-03 1.52698e-03 1.68350e-03 6.01685e-04 5.67537e-04 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.64489e-03 9.54016e-05 0.00000e+00 1.25345e-04 2.89525e-03 6.01215e-03 6.18308e-03 5.77716e-03 2.78810e-03 3.64299e-03 1.89753e-03 3.27841e-03 6.56599e-04 0.00000e+00 8.06452e-04 88974 0.00392 0.00263 0.00220 94529 Dihydropyrimidine_dehydrogenase_deficiency&Inborn_genetic_diseases&Fluorouracil_response&Pyrimidine_analogues_response_-_Toxicity/ADR&_Metabolism/PK&capecitabine_response_-_Toxicity/ADR&_Metabolism/PK&fluorouracil_response_-_Toxicity/ADR&_Metabolism/PK&tegafur_response_-_Toxicity/ADR&_Metabolism/PK¬_provided MONDO:MONDO:0010130&MedGen:C1959620&OMIM:274270&Orphanet:ORPHA1675&MeSH:D030342&MedGen:C0950123&MedGen:CN077983&MedGen:CN240586&MedGen:CN240593&MedGen:CN240604&MedGen:CN240607&MedGen:CN517202 reviewed_by_expert_panel drug_response single_nucleotide_variant DPYD:1806 SO:0001583&missense_variant 1 67376798 15 30 50.0 -1 97082391 T A A missense_variant MODERATE DPYD 1806 Transcript XM_017000509.2 protein_coding 23/24 3035 2351 784 D/V gAt/gTt -1 EntrezGene T T 25.5 3.728162 25.5 0.99484402844520115 12.36455 0.865839117879941 0.93907 11.78872 0.927664345811922 0.93048 -3.15 5.82 0.999999999999308 0.000000 0.352187 0.9075 1.0432 2.12 1 -7.16 16.1779 0.954 0.94804 0.693126 1.000000 0.963000 7.819000 1.135000 6.53 rs67376798 470 143226 3.28153e-03 1.16556e-03 1.05718e-03 1.29279e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.61575e-03 1.52698e-03 1.68350e-03 6.01685e-04 5.67537e-04 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.64489e-03 9.54016e-05 0.00000e+00 1.25345e-04 2.89525e-03 6.01215e-03 6.18308e-03 5.77716e-03 2.78810e-03 3.64299e-03 1.89753e-03 3.27841e-03 6.56599e-04 0.00000e+00 8.06452e-04 88974 0.00392 0.00263 0.00220 94529 Dihydropyrimidine_dehydrogenase_deficiency&Inborn_genetic_diseases&Fluorouracil_response&Pyrimidine_analogues_response_-_Toxicity/ADR&_Metabolism/PK&capecitabine_response_-_Toxicity/ADR&_Metabolism/PK&fluorouracil_response_-_Toxicity/ADR&_Metabolism/PK&tegafur_response_-_Toxicity/ADR&_Metabolism/PK¬_provided MONDO:MONDO:0010130&MedGen:C1959620&OMIM:274270&Orphanet:ORPHA1675&MeSH:D030342&MedGen:C0950123&MedGen:CN077983&MedGen:CN240586&MedGen:CN240593&MedGen:CN240604&MedGen:CN240607&MedGen:CN517202 reviewed_by_expert_panel drug_response single_nucleotide_variant DPYD:1806 SO:0001583&missense_variant 1 67376798 15 30 50.0 -1 97082391 T A A missense_variant MODERATE DPYD 1806 Transcript XM_017000510.1 protein_coding 22/23 2980 2351 784 D/V gAt/gTt -1 EntrezGene T T 25.5 3.728162 25.5 0.99484402844520115 12.36455 0.865839117879941 0.93907 11.78872 0.927664345811922 0.93048 -3.15 5.82 0.999999999999308 0.000000 0.352187 0.9075 1.0432 2.12 1 -7.16 16.1779 0.954 0.94804 0.693126 1.000000 0.963000 7.819000 1.135000 6.53 rs67376798 470 143226 3.28153e-03 1.16556e-03 1.05718e-03 1.29279e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.61575e-03 1.52698e-03 1.68350e-03 6.01685e-04 5.67537e-04 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.64489e-03 9.54016e-05 0.00000e+00 1.25345e-04 2.89525e-03 6.01215e-03 6.18308e-03 5.77716e-03 2.78810e-03 3.64299e-03 1.89753e-03 3.27841e-03 6.56599e-04 0.00000e+00 8.06452e-04 88974 0.00392 0.00263 0.00220 94529 Dihydropyrimidine_dehydrogenase_deficiency&Inborn_genetic_diseases&Fluorouracil_response&Pyrimidine_analogues_response_-_Toxicity/ADR&_Metabolism/PK&capecitabine_response_-_Toxicity/ADR&_Metabolism/PK&fluorouracil_response_-_Toxicity/ADR&_Metabolism/PK&tegafur_response_-_Toxicity/ADR&_Metabolism/PK¬_provided MONDO:MONDO:0010130&MedGen:C1959620&OMIM:274270&Orphanet:ORPHA1675&MeSH:D030342&MedGen:C0950123&MedGen:CN077983&MedGen:CN240586&MedGen:CN240593&MedGen:CN240604&MedGen:CN240607&MedGen:CN517202 reviewed_by_expert_panel drug_response single_nucleotide_variant DPYD:1806 SO:0001583&missense_variant 1 67376798 15 30 50.0 -1 114679616 T A A missense_variant MODERATE AMPD1 270 Transcript NM_000036.3 protein_coding 7/16 935 860 287 K/I aAa/aTa -1 EntrezGene T T 0 1 29.9 4.335282 29.9 0.99566596249392691 13.3077 0.889445809054819 0.95096 12.54345 0.954341640209264 0.94156 -1.99&-1.99 5.63 0.99997604940268 0.000000 0.7984 0.8307 3.81&. 1&1&1 -6.86&-6.86 16.1485 0.351&0.392 0.98422 0.487112 1.000000 1.000000 8.017000 1.138000 6.54 rs34526199 3837 142144 2.69938e-02 5.63557e-03 5.88550e-03 5.34199e-03 1.24444e-01 1.31915e-01 1.16279e-01 1.67182e-02 1.65191e-02 1.68700e-02 2.39394e-02 2.68265e-02 2.06718e-02 0.00000e+00 0.00000e+00 0.00000e+00 2.50785e-02 8.38778e-02 7.63389e-02 8.62636e-02 2.90301e-02 3.48200e-02 3.44605e-02 3.53132e-02 1.91768e-02 1.83150e-02 2.00765e-02 2.69170e-02 1.17687e-02 1.44404e-02 1.11570e-02 92338 0.02414 0.02822 0.01098 98249 Muscle_AMP_deaminase_deficiency¬_provided MONDO:MONDO:0014220&MedGen:C3714933&OMIM:615511&SNOMED_CT:9105005&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(2)&Uncertain_significance(1) single_nucleotide_variant AMPD1:270 SO:0001583&missense_variant 1 34526199 15 30 50.0 -1 114679616 T A A missense_variant MODERATE AMPD1 270 Transcript NM_001172626.1 protein_coding 6/15 995 947 316 K/I aAa/aTa -1 EntrezGene T T OK 0 0.998 29.9 4.335282 29.9 0.99566596249392691 13.3077 0.889445809054819 0.95096 12.54345 0.954341640209264 0.94156 -1.99&-1.99 5.63 0.99997604940268 0.000000 0.7984 0.8307 3.81&. 1&1&1 -6.86&-6.86 16.1485 0.351&0.392 0.98422 0.487112 1.000000 1.000000 8.017000 1.138000 6.54 rs34526199 3837 142144 2.69938e-02 5.63557e-03 5.88550e-03 5.34199e-03 1.24444e-01 1.31915e-01 1.16279e-01 1.67182e-02 1.65191e-02 1.68700e-02 2.39394e-02 2.68265e-02 2.06718e-02 0.00000e+00 0.00000e+00 0.00000e+00 2.50785e-02 8.38778e-02 7.63389e-02 8.62636e-02 2.90301e-02 3.48200e-02 3.44605e-02 3.53132e-02 1.91768e-02 1.83150e-02 2.00765e-02 2.69170e-02 1.17687e-02 1.44404e-02 1.11570e-02 92338 0.02414 0.02822 0.01098 98249 Muscle_AMP_deaminase_deficiency¬_provided MONDO:MONDO:0014220&MedGen:C3714933&OMIM:615511&SNOMED_CT:9105005&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(2)&Uncertain_significance(1) single_nucleotide_variant AMPD1:270 SO:0001583&missense_variant 1 34526199 15 30 50.0 -1 155235002 C T T missense_variant MODERATE GBA 2629 Transcript NM_000157.4 protein_coding 11/11 1741 1604 535 R/H cGc/cAc -1 EntrezGene C C 0.02 0.022 21.5 2.283146 21.5 0.93717618169297356 0.7054094 -0.83773678903354 0.13582 0.4943085 -0.920953785037989 0.10352 -3.81&-3.81&-3.81&-3.81 1.17 0.0924306402363633 0.134164 0.143353 0.3813 -0.5069 0.46&0.46&.&. 0.0772729&0.0772729&0.274986&0.274986&0.0772729 -1.76&-1.76&-1.85&-1.85 5.5218 0.682&0.688&0.386&0.299 0.76740 0.732398 1.000000 0.920000 0.830000 -0.332000 5.2 rs75822236 5 82348 6.07179e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.28315e-03 2.36593e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.82418e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.70139e-05 2.39590e-05 0.00000e+00 6.07460e-05 8.74126e-04 1.52905e-03 0.00000e+00 1.15191e-04 0.00000e+00 0.00000e+00 0.00000e+00 4311 0.00031 19350 Lewy_body_dementia&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease¬_provided MONDO:MONDO:0007488&MedGen:C0752347&OMIM:127750&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 1 75822236 15 30 50.0 -1 155235002 C T T missense_variant MODERATE GBA 2629 Transcript NM_001005741.3 protein_coding 12/12 1794 1604 535 R/H cGc/cAc -1 EntrezGene C C 0.02 0.022 21.5 2.283146 21.5 0.93717618169297356 0.7054094 -0.83773678903354 0.13582 0.4943085 -0.920953785037989 0.10352 -3.81&-3.81&-3.81&-3.81 1.17 0.0924306402363633 0.134164 0.143353 0.3813 -0.5069 0.46&0.46&.&. 0.0772729&0.0772729&0.274986&0.274986&0.0772729 -1.76&-1.76&-1.85&-1.85 5.5218 0.682&0.688&0.386&0.299 0.76740 0.732398 1.000000 0.920000 0.830000 -0.332000 5.2 rs75822236 5 82348 6.07179e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.28315e-03 2.36593e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.82418e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.70139e-05 2.39590e-05 0.00000e+00 6.07460e-05 8.74126e-04 1.52905e-03 0.00000e+00 1.15191e-04 0.00000e+00 0.00000e+00 0.00000e+00 4311 0.00031 19350 Lewy_body_dementia&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease¬_provided MONDO:MONDO:0007488&MedGen:C0752347&OMIM:127750&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 1 75822236 15 30 50.0 -1 155235002 C T T missense_variant MODERATE GBA 2629 Transcript NM_001005742.3 protein_coding 12/12 1775 1604 535 R/H cGc/cAc -1 EntrezGene C C 0.02 0.022 21.5 2.283146 21.5 0.93717618169297356 0.7054094 -0.83773678903354 0.13582 0.4943085 -0.920953785037989 0.10352 -3.81&-3.81&-3.81&-3.81 1.17 0.0924306402363633 0.134164 0.143353 0.3813 -0.5069 0.46&0.46&.&. 0.0772729&0.0772729&0.274986&0.274986&0.0772729 -1.76&-1.76&-1.85&-1.85 5.5218 0.682&0.688&0.386&0.299 0.76740 0.732398 1.000000 0.920000 0.830000 -0.332000 5.2 rs75822236 5 82348 6.07179e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.28315e-03 2.36593e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.82418e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.70139e-05 2.39590e-05 0.00000e+00 6.07460e-05 8.74126e-04 1.52905e-03 0.00000e+00 1.15191e-04 0.00000e+00 0.00000e+00 0.00000e+00 4311 0.00031 19350 Lewy_body_dementia&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease¬_provided MONDO:MONDO:0007488&MedGen:C0752347&OMIM:127750&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 1 75822236 15 30 50.0 -1 155235002 C T T missense_variant MODERATE GBA 2629 Transcript NM_001171811.2 protein_coding 10/10 1611 1343 448 R/H cGc/cAc -1 EntrezGene C C 0.02 0.022 21.5 2.283146 21.5 0.93717618169297356 0.7054094 -0.83773678903354 0.13582 0.4943085 -0.920953785037989 0.10352 -3.81&-3.81&-3.81&-3.81 1.17 0.0924306402363633 0.134164 0.143353 0.3813 -0.5069 0.46&0.46&.&. 0.0772729&0.0772729&0.274986&0.274986&0.0772729 -1.76&-1.76&-1.85&-1.85 5.5218 0.682&0.688&0.386&0.299 0.76740 0.732398 1.000000 0.920000 0.830000 -0.332000 5.2 rs75822236 5 82348 6.07179e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.28315e-03 2.36593e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.82418e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.70139e-05 2.39590e-05 0.00000e+00 6.07460e-05 8.74126e-04 1.52905e-03 0.00000e+00 1.15191e-04 0.00000e+00 0.00000e+00 0.00000e+00 4311 0.00031 19350 Lewy_body_dementia&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease¬_provided MONDO:MONDO:0007488&MedGen:C0752347&OMIM:127750&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 1 75822236 15 30 50.0 -1 155235002 C T T missense_variant MODERATE GBA 2629 Transcript NM_001171812.2 protein_coding 10/10 1594 1457 486 R/H cGc/cAc -1 EntrezGene C C 0.02 0.062 21.5 2.283146 21.5 0.93717618169297356 0.7054094 -0.83773678903354 0.13582 0.4943085 -0.920953785037989 0.10352 -3.81&-3.81&-3.81&-3.81 1.17 0.0924306402363633 0.134164 0.143353 0.3813 -0.5069 0.46&0.46&.&. 0.0772729&0.0772729&0.274986&0.274986&0.0772729 -1.76&-1.76&-1.85&-1.85 5.5218 0.682&0.688&0.386&0.299 0.76740 0.732398 1.000000 0.920000 0.830000 -0.332000 5.2 rs75822236 5 82348 6.07179e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.28315e-03 2.36593e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.82418e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.70139e-05 2.39590e-05 0.00000e+00 6.07460e-05 8.74126e-04 1.52905e-03 0.00000e+00 1.15191e-04 0.00000e+00 0.00000e+00 0.00000e+00 4311 0.00031 19350 Lewy_body_dementia&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease¬_provided MONDO:MONDO:0007488&MedGen:C0752347&OMIM:127750&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 1 75822236 15 30 50.0 -1 155235252 A G G missense_variant MODERATE GBA 2629 Transcript NM_000157.4 protein_coding 10/11 1585 1448 483 L/P cTg/cCg -1 EntrezGene A A 0.02 0.821 24.7 3.490794 24.7 0.99633310815343268 2.572971 0.0591650995951062 0.42515 3.313518 0.202346945348632 0.51314 -5.92&-5.92&-5.92&-5.92 3.16 0.999416608577853 0.005056 0.9738 1.1096 3.19&3.19&.&. 1&1&1&1&1 -5.0&-5.0&-5.19&-5.19 9.6811 0.962&0.962&0.957&0.961 0.91810 0.706548 1.000000 0.205000 7.949000 1.180000 6.11 chr1:155235252-155235252 34 143120 2.37563e-04 4.28898e-04 2.64714e-04 6.21697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16855e-04 1.91131e-04 0.00000e+00 2.51193e-04 2.59598e-04 1.85937e-04 2.40822e-04 1.10432e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.11620e-04 6.59196e-04 1.77305e-03 4.04858e-04 4288 0.00310 0.00339 19327 Hypomimic_face&Movement_disorder&Parkinsonism&Resting_tremor&Thoracolumbar_scoliosis&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease&Dementia&_Lewy_body&_susceptibility_to¬_provided Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease&_perinatal_lethal Human_Phenotype_Ontology:HP:0000338&Human_Phenotype_Ontology:HP:0008769&MedGen:C0813217&Human_Phenotype_Ontology:HP:0001294&Human_Phenotype_Ontology:HP:0100022&MONDO:MONDO:0005395&MedGen:C0026650&Human_Phenotype_Ontology:HP:0001300&MedGen:C0242422&Human_Phenotype_Ontology:HP:0002322&MedGen:C0234379&Human_Phenotype_Ontology:HP:0002944&Human_Phenotype_Ontology:HP:0004567&Human_Phenotype_Ontology:HP:0004585&MedGen:C0749379&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:C2676021&MedGen:CN517202 MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic&_risk_factor 4297:Pathogenic&424818:Likely_pathogenic&424819:Likely_pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 1 421016 15 30 50.0 -1 155235252 A G G missense_variant MODERATE GBA 2629 Transcript NM_001005741.3 protein_coding 11/12 1638 1448 483 L/P cTg/cCg -1 EntrezGene A A 0.02 0.821 24.7 3.490794 24.7 0.99633310815343268 2.572971 0.0591650995951062 0.42515 3.313518 0.202346945348632 0.51314 -5.92&-5.92&-5.92&-5.92 3.16 0.999416608577853 0.005056 0.9738 1.1096 3.19&3.19&.&. 1&1&1&1&1 -5.0&-5.0&-5.19&-5.19 9.6811 0.962&0.962&0.957&0.961 0.91810 0.706548 1.000000 0.205000 7.949000 1.180000 6.11 chr1:155235252-155235252 34 143120 2.37563e-04 4.28898e-04 2.64714e-04 6.21697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16855e-04 1.91131e-04 0.00000e+00 2.51193e-04 2.59598e-04 1.85937e-04 2.40822e-04 1.10432e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.11620e-04 6.59196e-04 1.77305e-03 4.04858e-04 4288 0.00310 0.00339 19327 Hypomimic_face&Movement_disorder&Parkinsonism&Resting_tremor&Thoracolumbar_scoliosis&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease&Dementia&_Lewy_body&_susceptibility_to¬_provided Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease&_perinatal_lethal Human_Phenotype_Ontology:HP:0000338&Human_Phenotype_Ontology:HP:0008769&MedGen:C0813217&Human_Phenotype_Ontology:HP:0001294&Human_Phenotype_Ontology:HP:0100022&MONDO:MONDO:0005395&MedGen:C0026650&Human_Phenotype_Ontology:HP:0001300&MedGen:C0242422&Human_Phenotype_Ontology:HP:0002322&MedGen:C0234379&Human_Phenotype_Ontology:HP:0002944&Human_Phenotype_Ontology:HP:0004567&Human_Phenotype_Ontology:HP:0004585&MedGen:C0749379&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:C2676021&MedGen:CN517202 MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic&_risk_factor 4297:Pathogenic&424818:Likely_pathogenic&424819:Likely_pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 1 421016 15 30 50.0 -1 155235252 A G G missense_variant MODERATE GBA 2629 Transcript NM_001005742.3 protein_coding 11/12 1619 1448 483 L/P cTg/cCg -1 EntrezGene A A 0.02 0.821 24.7 3.490794 24.7 0.99633310815343268 2.572971 0.0591650995951062 0.42515 3.313518 0.202346945348632 0.51314 -5.92&-5.92&-5.92&-5.92 3.16 0.999416608577853 0.005056 0.9738 1.1096 3.19&3.19&.&. 1&1&1&1&1 -5.0&-5.0&-5.19&-5.19 9.6811 0.962&0.962&0.957&0.961 0.91810 0.706548 1.000000 0.205000 7.949000 1.180000 6.11 chr1:155235252-155235252 34 143120 2.37563e-04 4.28898e-04 2.64714e-04 6.21697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16855e-04 1.91131e-04 0.00000e+00 2.51193e-04 2.59598e-04 1.85937e-04 2.40822e-04 1.10432e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.11620e-04 6.59196e-04 1.77305e-03 4.04858e-04 4288 0.00310 0.00339 19327 Hypomimic_face&Movement_disorder&Parkinsonism&Resting_tremor&Thoracolumbar_scoliosis&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease&Dementia&_Lewy_body&_susceptibility_to¬_provided Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease&_perinatal_lethal Human_Phenotype_Ontology:HP:0000338&Human_Phenotype_Ontology:HP:0008769&MedGen:C0813217&Human_Phenotype_Ontology:HP:0001294&Human_Phenotype_Ontology:HP:0100022&MONDO:MONDO:0005395&MedGen:C0026650&Human_Phenotype_Ontology:HP:0001300&MedGen:C0242422&Human_Phenotype_Ontology:HP:0002322&MedGen:C0234379&Human_Phenotype_Ontology:HP:0002944&Human_Phenotype_Ontology:HP:0004567&Human_Phenotype_Ontology:HP:0004585&MedGen:C0749379&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:C2676021&MedGen:CN517202 MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic&_risk_factor 4297:Pathogenic&424818:Likely_pathogenic&424819:Likely_pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 1 421016 15 30 50.0 -1 155235252 A G G missense_variant MODERATE GBA 2629 Transcript NM_001171811.2 protein_coding 9/10 1455 1187 396 L/P cTg/cCg -1 EntrezGene A A 0.01 0.821 24.7 3.490794 24.7 0.99633310815343268 2.572971 0.0591650995951062 0.42515 3.313518 0.202346945348632 0.51314 -5.92&-5.92&-5.92&-5.92 3.16 0.999416608577853 0.005056 0.9738 1.1096 3.19&3.19&.&. 1&1&1&1&1 -5.0&-5.0&-5.19&-5.19 9.6811 0.962&0.962&0.957&0.961 0.91810 0.706548 1.000000 0.205000 7.949000 1.180000 6.11 chr1:155235252-155235252 34 143120 2.37563e-04 4.28898e-04 2.64714e-04 6.21697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16855e-04 1.91131e-04 0.00000e+00 2.51193e-04 2.59598e-04 1.85937e-04 2.40822e-04 1.10432e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.11620e-04 6.59196e-04 1.77305e-03 4.04858e-04 4288 0.00310 0.00339 19327 Hypomimic_face&Movement_disorder&Parkinsonism&Resting_tremor&Thoracolumbar_scoliosis&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease&Dementia&_Lewy_body&_susceptibility_to¬_provided Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease&_perinatal_lethal Human_Phenotype_Ontology:HP:0000338&Human_Phenotype_Ontology:HP:0008769&MedGen:C0813217&Human_Phenotype_Ontology:HP:0001294&Human_Phenotype_Ontology:HP:0100022&MONDO:MONDO:0005395&MedGen:C0026650&Human_Phenotype_Ontology:HP:0001300&MedGen:C0242422&Human_Phenotype_Ontology:HP:0002322&MedGen:C0234379&Human_Phenotype_Ontology:HP:0002944&Human_Phenotype_Ontology:HP:0004567&Human_Phenotype_Ontology:HP:0004585&MedGen:C0749379&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:C2676021&MedGen:CN517202 MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic&_risk_factor 4297:Pathogenic&424818:Likely_pathogenic&424819:Likely_pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 1 421016 15 30 50.0 -1 155235252 A G G missense_variant MODERATE GBA 2629 Transcript NM_001171812.2 protein_coding 9/10 1438 1301 434 L/P cTg/cCg -1 EntrezGene A A 0.03 0.207 24.7 3.490794 24.7 0.99633310815343268 2.572971 0.0591650995951062 0.42515 3.313518 0.202346945348632 0.51314 -5.92&-5.92&-5.92&-5.92 3.16 0.999416608577853 0.005056 0.9738 1.1096 3.19&3.19&.&. 1&1&1&1&1 -5.0&-5.0&-5.19&-5.19 9.6811 0.962&0.962&0.957&0.961 0.91810 0.706548 1.000000 0.205000 7.949000 1.180000 6.11 chr1:155235252-155235252 34 143120 2.37563e-04 4.28898e-04 2.64714e-04 6.21697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16855e-04 1.91131e-04 0.00000e+00 2.51193e-04 2.59598e-04 1.85937e-04 2.40822e-04 1.10432e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.11620e-04 6.59196e-04 1.77305e-03 4.04858e-04 4288 0.00310 0.00339 19327 Hypomimic_face&Movement_disorder&Parkinsonism&Resting_tremor&Thoracolumbar_scoliosis&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease&Dementia&_Lewy_body&_susceptibility_to¬_provided Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease&_perinatal_lethal Human_Phenotype_Ontology:HP:0000338&Human_Phenotype_Ontology:HP:0008769&MedGen:C0813217&Human_Phenotype_Ontology:HP:0001294&Human_Phenotype_Ontology:HP:0100022&MONDO:MONDO:0005395&MedGen:C0026650&Human_Phenotype_Ontology:HP:0001300&MedGen:C0242422&Human_Phenotype_Ontology:HP:0002322&MedGen:C0234379&Human_Phenotype_Ontology:HP:0002944&Human_Phenotype_Ontology:HP:0004567&Human_Phenotype_Ontology:HP:0004585&MedGen:C0749379&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:C2676021&MedGen:CN517202 MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic&_risk_factor 4297:Pathogenic&424818:Likely_pathogenic&424819:Likely_pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 1 421016 15 30 50.0 -1 155235727 C G G missense_variant MODERATE GBA 2629 Transcript NM_000157.4 protein_coding 9/11 1479 1342 448 D/H Gac/Cac -1 EntrezGene C C 0.05 0.093 23.3 2.922991 23.3 0.97299900248961679 2.782747 0.11163616023788 0.45143 2.684961 0.0476591086150871 0.44034 -5.68&-5.68&-5.68&-5.68 3.53 0.999999094068054 0.000001 0.438444 0.9198 1.1708 2.755&2.755&.&. 0.999997&0.999997&0.999997&0.999982&0.999982 -3.57&-3.57&-3.71&-3.74 12.9565 0.65&0.649&0.639&0.63 0.98141 0.706548 1.000000 0.742000 5.374000 0.936000 6.11 rs1064651 28 143104 1.95662e-04 2.15023e-04 2.21082e-04 2.07900e-04 0.00000e+00 0.00000e+00 0.00000e+00 5.12821e-04 6.76819e-04 3.87597e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.19081e-04 0.00000e+00 5.93120e-04 2.03340e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87493e-04 1.70342e-04 1.60445e-04 1.83959e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.93071e-04 0.00000e+00 0.00000e+00 0.00000e+00 4293 0.00011 19332 Lewy_body_dementia&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease¬_provided Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease MONDO:MONDO:0007488&MedGen:C0752347&OMIM:127750&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 4334:Pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 1 1064651 15 30 50.0 -1 155235727 C G G missense_variant MODERATE GBA 2629 Transcript NM_001005741.3 protein_coding 10/12 1532 1342 448 D/H Gac/Cac -1 EntrezGene C C 0.05 0.093 23.3 2.922991 23.3 0.97299900248961679 2.782747 0.11163616023788 0.45143 2.684961 0.0476591086150871 0.44034 -5.68&-5.68&-5.68&-5.68 3.53 0.999999094068054 0.000001 0.438444 0.9198 1.1708 2.755&2.755&.&. 0.999997&0.999997&0.999997&0.999982&0.999982 -3.57&-3.57&-3.71&-3.74 12.9565 0.65&0.649&0.639&0.63 0.98141 0.706548 1.000000 0.742000 5.374000 0.936000 6.11 rs1064651 28 143104 1.95662e-04 2.15023e-04 2.21082e-04 2.07900e-04 0.00000e+00 0.00000e+00 0.00000e+00 5.12821e-04 6.76819e-04 3.87597e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.19081e-04 0.00000e+00 5.93120e-04 2.03340e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87493e-04 1.70342e-04 1.60445e-04 1.83959e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.93071e-04 0.00000e+00 0.00000e+00 0.00000e+00 4293 0.00011 19332 Lewy_body_dementia&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease¬_provided Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease MONDO:MONDO:0007488&MedGen:C0752347&OMIM:127750&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 4334:Pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 1 1064651 15 30 50.0 -1 155235727 C G G missense_variant MODERATE GBA 2629 Transcript NM_001005742.3 protein_coding 10/12 1513 1342 448 D/H Gac/Cac -1 EntrezGene C C 0.05 0.093 23.3 2.922991 23.3 0.97299900248961679 2.782747 0.11163616023788 0.45143 2.684961 0.0476591086150871 0.44034 -5.68&-5.68&-5.68&-5.68 3.53 0.999999094068054 0.000001 0.438444 0.9198 1.1708 2.755&2.755&.&. 0.999997&0.999997&0.999997&0.999982&0.999982 -3.57&-3.57&-3.71&-3.74 12.9565 0.65&0.649&0.639&0.63 0.98141 0.706548 1.000000 0.742000 5.374000 0.936000 6.11 rs1064651 28 143104 1.95662e-04 2.15023e-04 2.21082e-04 2.07900e-04 0.00000e+00 0.00000e+00 0.00000e+00 5.12821e-04 6.76819e-04 3.87597e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.19081e-04 0.00000e+00 5.93120e-04 2.03340e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87493e-04 1.70342e-04 1.60445e-04 1.83959e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.93071e-04 0.00000e+00 0.00000e+00 0.00000e+00 4293 0.00011 19332 Lewy_body_dementia&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease¬_provided Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease MONDO:MONDO:0007488&MedGen:C0752347&OMIM:127750&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 4334:Pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 1 1064651 15 30 50.0 -1 155235727 C G G missense_variant MODERATE GBA 2629 Transcript NM_001171811.2 protein_coding 8/10 1349 1081 361 D/H Gac/Cac -1 EntrezGene C C 0.04 0.093 23.3 2.922991 23.3 0.97299900248961679 2.782747 0.11163616023788 0.45143 2.684961 0.0476591086150871 0.44034 -5.68&-5.68&-5.68&-5.68 3.53 0.999999094068054 0.000001 0.438444 0.9198 1.1708 2.755&2.755&.&. 0.999997&0.999997&0.999997&0.999982&0.999982 -3.57&-3.57&-3.71&-3.74 12.9565 0.65&0.649&0.639&0.63 0.98141 0.706548 1.000000 0.742000 5.374000 0.936000 6.11 rs1064651 28 143104 1.95662e-04 2.15023e-04 2.21082e-04 2.07900e-04 0.00000e+00 0.00000e+00 0.00000e+00 5.12821e-04 6.76819e-04 3.87597e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.19081e-04 0.00000e+00 5.93120e-04 2.03340e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87493e-04 1.70342e-04 1.60445e-04 1.83959e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.93071e-04 0.00000e+00 0.00000e+00 0.00000e+00 4293 0.00011 19332 Lewy_body_dementia&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease¬_provided Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease MONDO:MONDO:0007488&MedGen:C0752347&OMIM:127750&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 4334:Pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 1 1064651 15 30 50.0 -1 155235727 C G G missense_variant MODERATE GBA 2629 Transcript NM_001171812.2 protein_coding 8/10 1332 1195 399 D/H Gac/Cac -1 EntrezGene C C 0.04 0.206 23.3 2.922991 23.3 0.97299900248961679 2.782747 0.11163616023788 0.45143 2.684961 0.0476591086150871 0.44034 -5.68&-5.68&-5.68&-5.68 3.53 0.999999094068054 0.000001 0.438444 0.9198 1.1708 2.755&2.755&.&. 0.999997&0.999997&0.999997&0.999982&0.999982 -3.57&-3.57&-3.71&-3.74 12.9565 0.65&0.649&0.639&0.63 0.98141 0.706548 1.000000 0.742000 5.374000 0.936000 6.11 rs1064651 28 143104 1.95662e-04 2.15023e-04 2.21082e-04 2.07900e-04 0.00000e+00 0.00000e+00 0.00000e+00 5.12821e-04 6.76819e-04 3.87597e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.19081e-04 0.00000e+00 5.93120e-04 2.03340e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87493e-04 1.70342e-04 1.60445e-04 1.83959e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.93071e-04 0.00000e+00 0.00000e+00 0.00000e+00 4293 0.00011 19332 Lewy_body_dementia&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease¬_provided Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease MONDO:MONDO:0007488&MedGen:C0752347&OMIM:127750&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 4334:Pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 1 1064651 15 30 50.0 -1 155235749 GGGACTGTCGACAAAGTTACGCACCCAATTGGGTCCTCCTTCGGGGTTCAGGGCAA G - frameshift_variant HIGH GBA 2629 Transcript NM_000157.4 protein_coding 9/11 1402-1456 1265-1319 422-440 LALNPEGGPNWVRNFVDSP/X cTTGCCCTGAACCCCGAAGGAGGACCCAATTGGGTGCGTAACTTTGTCGACAGTCCc/cc -1 EntrezGene GGACTGTCGACAAAGTTACGCACCCAATTGGGTCCTCCTTCGGGGTTCAGGGCAA GGACTGTCGACAAAGTTACGCACCCAATTGGGTCCTCCTTCGGGGTTCAGGGCAA 6.26&-2.74&6.53&0.581&6.53&-13.1&6.53&-1.97&6.53&3.75&6.53&1.57&6.53&-1.8&6.53&0.579&6.53&4.66&6.53&-13.1&6.53&1.47&6.53&1.95&6.53&-13.1&1.57&6.53&3.24&6.53&3.33&5.42&4.69&2.72&6.53&5.63&-13.1&6.53 193611 190774 Gaucher's_disease&_type_1&Gaucher_disease&_perinatal_lethal&Gaucher_disease¬_provided MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion GBA:2629&LOC106627981:106627981 SO:0001589&frameshift_variant 1 80356768 15 30 50.0 -1 155235749 GGGACTGTCGACAAAGTTACGCACCCAATTGGGTCCTCCTTCGGGGTTCAGGGCAA G - frameshift_variant HIGH GBA 2629 Transcript NM_001005741.3 protein_coding 10/12 1455-1509 1265-1319 422-440 LALNPEGGPNWVRNFVDSP/X cTTGCCCTGAACCCCGAAGGAGGACCCAATTGGGTGCGTAACTTTGTCGACAGTCCc/cc -1 EntrezGene GGACTGTCGACAAAGTTACGCACCCAATTGGGTCCTCCTTCGGGGTTCAGGGCAA GGACTGTCGACAAAGTTACGCACCCAATTGGGTCCTCCTTCGGGGTTCAGGGCAA 6.26&-2.74&6.53&0.581&6.53&-13.1&6.53&-1.97&6.53&3.75&6.53&1.57&6.53&-1.8&6.53&0.579&6.53&4.66&6.53&-13.1&6.53&1.47&6.53&1.95&6.53&-13.1&1.57&6.53&3.24&6.53&3.33&5.42&4.69&2.72&6.53&5.63&-13.1&6.53 193611 190774 Gaucher's_disease&_type_1&Gaucher_disease&_perinatal_lethal&Gaucher_disease¬_provided MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion GBA:2629&LOC106627981:106627981 SO:0001589&frameshift_variant 1 80356768 15 30 50.0 -1 155235749 GGGACTGTCGACAAAGTTACGCACCCAATTGGGTCCTCCTTCGGGGTTCAGGGCAA G - frameshift_variant HIGH GBA 2629 Transcript NM_001005742.3 protein_coding 10/12 1436-1490 1265-1319 422-440 LALNPEGGPNWVRNFVDSP/X cTTGCCCTGAACCCCGAAGGAGGACCCAATTGGGTGCGTAACTTTGTCGACAGTCCc/cc -1 EntrezGene GGACTGTCGACAAAGTTACGCACCCAATTGGGTCCTCCTTCGGGGTTCAGGGCAA GGACTGTCGACAAAGTTACGCACCCAATTGGGTCCTCCTTCGGGGTTCAGGGCAA 6.26&-2.74&6.53&0.581&6.53&-13.1&6.53&-1.97&6.53&3.75&6.53&1.57&6.53&-1.8&6.53&0.579&6.53&4.66&6.53&-13.1&6.53&1.47&6.53&1.95&6.53&-13.1&1.57&6.53&3.24&6.53&3.33&5.42&4.69&2.72&6.53&5.63&-13.1&6.53 193611 190774 Gaucher's_disease&_type_1&Gaucher_disease&_perinatal_lethal&Gaucher_disease¬_provided MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion GBA:2629&LOC106627981:106627981 SO:0001589&frameshift_variant 1 80356768 15 30 50.0 -1 155235749 GGGACTGTCGACAAAGTTACGCACCCAATTGGGTCCTCCTTCGGGGTTCAGGGCAA G - frameshift_variant HIGH GBA 2629 Transcript NM_001171811.2 protein_coding 8/10 1272-1326 1004-1058 335-353 LALNPEGGPNWVRNFVDSP/X cTTGCCCTGAACCCCGAAGGAGGACCCAATTGGGTGCGTAACTTTGTCGACAGTCCc/cc -1 EntrezGene GGACTGTCGACAAAGTTACGCACCCAATTGGGTCCTCCTTCGGGGTTCAGGGCAA GGACTGTCGACAAAGTTACGCACCCAATTGGGTCCTCCTTCGGGGTTCAGGGCAA 6.26&-2.74&6.53&0.581&6.53&-13.1&6.53&-1.97&6.53&3.75&6.53&1.57&6.53&-1.8&6.53&0.579&6.53&4.66&6.53&-13.1&6.53&1.47&6.53&1.95&6.53&-13.1&1.57&6.53&3.24&6.53&3.33&5.42&4.69&2.72&6.53&5.63&-13.1&6.53 193611 190774 Gaucher's_disease&_type_1&Gaucher_disease&_perinatal_lethal&Gaucher_disease¬_provided MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion GBA:2629&LOC106627981:106627981 SO:0001589&frameshift_variant 1 80356768 15 30 50.0 -1 155235749 GGGACTGTCGACAAAGTTACGCACCCAATTGGGTCCTCCTTCGGGGTTCAGGGCAA G - frameshift_variant HIGH GBA 2629 Transcript NM_001171812.2 protein_coding 8/10 1255-1309 1118-1172 373-391 LALNPEGGPNWVRNFVDSP/X cTTGCCCTGAACCCCGAAGGAGGACCCAATTGGGTGCGTAACTTTGTCGACAGTCCc/cc -1 EntrezGene GGACTGTCGACAAAGTTACGCACCCAATTGGGTCCTCCTTCGGGGTTCAGGGCAA GGACTGTCGACAAAGTTACGCACCCAATTGGGTCCTCCTTCGGGGTTCAGGGCAA 6.26&-2.74&6.53&0.581&6.53&-13.1&6.53&-1.97&6.53&3.75&6.53&1.57&6.53&-1.8&6.53&0.579&6.53&4.66&6.53&-13.1&6.53&1.47&6.53&1.95&6.53&-13.1&1.57&6.53&3.24&6.53&3.33&5.42&4.69&2.72&6.53&5.63&-13.1&6.53 193611 190774 Gaucher's_disease&_type_1&Gaucher_disease&_perinatal_lethal&Gaucher_disease¬_provided MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion GBA:2629&LOC106627981:106627981 SO:0001589&frameshift_variant 1 80356768 15 30 50.0 -1 155235772 C A A missense_variant MODERATE GBA 2629 Transcript NM_000157.4 protein_coding 9/11 1434 1297 433 V/L Gtg/Ttg -1 EntrezGene C C 0.01 0.492 24.5 3.421560 24.5 0.99690645991617677 5.525425 0.538119837831143 0.70575 5.287546 0.531196165947597 0.68945 -6.09&-6.09&-6.09&-6.09 4.95 0.999999998337437 0.000017 0.576637 0.9770 1.0767 2.305&2.305&.&. 1&1&1&1&1 -2.03&-2.03&-2.03&-2.0 16.0504 0.904&0.905&0.795&0.856 0.98976 0.706548 1.000000 0.963000 5.474000 1.026000 6.53 rs80356769 1 143334 6.97671e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.00842e-04 5.67537e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35366e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97408e-06 0.00000e+00 0.00000e+00 0.00000e+00 4292 0.00003 19331 Lewy_body_dementia&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease MONDO:MONDO:0007488&MedGen:C0752347&OMIM:127750&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 1 80356769 15 30 50.0 -1 155235772 C A A missense_variant MODERATE GBA 2629 Transcript NM_001005741.3 protein_coding 10/12 1487 1297 433 V/L Gtg/Ttg -1 EntrezGene C C 0.01 0.492 24.5 3.421560 24.5 0.99690645991617677 5.525425 0.538119837831143 0.70575 5.287546 0.531196165947597 0.68945 -6.09&-6.09&-6.09&-6.09 4.95 0.999999998337437 0.000017 0.576637 0.9770 1.0767 2.305&2.305&.&. 1&1&1&1&1 -2.03&-2.03&-2.03&-2.0 16.0504 0.904&0.905&0.795&0.856 0.98976 0.706548 1.000000 0.963000 5.474000 1.026000 6.53 rs80356769 1 143334 6.97671e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.00842e-04 5.67537e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35366e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97408e-06 0.00000e+00 0.00000e+00 0.00000e+00 4292 0.00003 19331 Lewy_body_dementia&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease MONDO:MONDO:0007488&MedGen:C0752347&OMIM:127750&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 1 80356769 15 30 50.0 -1 155235772 C A A missense_variant MODERATE GBA 2629 Transcript NM_001005742.3 protein_coding 10/12 1468 1297 433 V/L Gtg/Ttg -1 EntrezGene C C 0.01 0.492 24.5 3.421560 24.5 0.99690645991617677 5.525425 0.538119837831143 0.70575 5.287546 0.531196165947597 0.68945 -6.09&-6.09&-6.09&-6.09 4.95 0.999999998337437 0.000017 0.576637 0.9770 1.0767 2.305&2.305&.&. 1&1&1&1&1 -2.03&-2.03&-2.03&-2.0 16.0504 0.904&0.905&0.795&0.856 0.98976 0.706548 1.000000 0.963000 5.474000 1.026000 6.53 rs80356769 1 143334 6.97671e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.00842e-04 5.67537e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35366e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97408e-06 0.00000e+00 0.00000e+00 0.00000e+00 4292 0.00003 19331 Lewy_body_dementia&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease MONDO:MONDO:0007488&MedGen:C0752347&OMIM:127750&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 1 80356769 15 30 50.0 -1 155235772 C A A missense_variant MODERATE GBA 2629 Transcript NM_001171811.2 protein_coding 8/10 1304 1036 346 V/L Gtg/Ttg -1 EntrezGene C C 0.01 0.492 24.5 3.421560 24.5 0.99690645991617677 5.525425 0.538119837831143 0.70575 5.287546 0.531196165947597 0.68945 -6.09&-6.09&-6.09&-6.09 4.95 0.999999998337437 0.000017 0.576637 0.9770 1.0767 2.305&2.305&.&. 1&1&1&1&1 -2.03&-2.03&-2.03&-2.0 16.0504 0.904&0.905&0.795&0.856 0.98976 0.706548 1.000000 0.963000 5.474000 1.026000 6.53 rs80356769 1 143334 6.97671e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.00842e-04 5.67537e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35366e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97408e-06 0.00000e+00 0.00000e+00 0.00000e+00 4292 0.00003 19331 Lewy_body_dementia&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease MONDO:MONDO:0007488&MedGen:C0752347&OMIM:127750&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 1 80356769 15 30 50.0 -1 155235772 C A A missense_variant MODERATE GBA 2629 Transcript NM_001171812.2 protein_coding 8/10 1287 1150 384 V/L Gtg/Ttg -1 EntrezGene C C 0.01 0.998 24.5 3.421560 24.5 0.99690645991617677 5.525425 0.538119837831143 0.70575 5.287546 0.531196165947597 0.68945 -6.09&-6.09&-6.09&-6.09 4.95 0.999999998337437 0.000017 0.576637 0.9770 1.0767 2.305&2.305&.&. 1&1&1&1&1 -2.03&-2.03&-2.03&-2.0 16.0504 0.904&0.905&0.795&0.856 0.98976 0.706548 1.000000 0.963000 5.474000 1.026000 6.53 rs80356769 1 143334 6.97671e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.00842e-04 5.67537e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35366e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97408e-06 0.00000e+00 0.00000e+00 0.00000e+00 4292 0.00003 19331 Lewy_body_dementia&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease MONDO:MONDO:0007488&MedGen:C0752347&OMIM:127750&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 1 80356769 15 30 50.0 -1 155235843 T G G missense_variant&splice_region_variant MODERATE GBA 2629 Transcript NM_000157.4 protein_coding 9/11 1363 1226 409 N/T aAc/aCc -1 EntrezGene T T 0.01 0.393 24.4 3.413341 24.4 0.99513110469390098 3.200089 0.203436220411773 0.50037 3.254215 0.188836850341292 0.50664 -5.8&-5.8&-5.8&-5.8 3.53 0.999878789656551 0.000100 0.834629 0.9389 1.0405 2.015&2.015&.&. 1&1&1&1&1 -3.06&-3.06&-3.2&-3.2 10.3278 0.686&0.686&0.673&0.682 0.98223 0.732398 1.000000 0.994000 4.359000 1.038000 6.11 38301 47036 Gaucher_disease MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355 no_assertion_criteria_provided Pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 76763715 15 30 50.0 -1 155235843 T G G missense_variant&splice_region_variant MODERATE GBA 2629 Transcript NM_001005741.3 protein_coding 10/12 1416 1226 409 N/T aAc/aCc -1 EntrezGene T T 0.01 0.393 24.4 3.413341 24.4 0.99513110469390098 3.200089 0.203436220411773 0.50037 3.254215 0.188836850341292 0.50664 -5.8&-5.8&-5.8&-5.8 3.53 0.999878789656551 0.000100 0.834629 0.9389 1.0405 2.015&2.015&.&. 1&1&1&1&1 -3.06&-3.06&-3.2&-3.2 10.3278 0.686&0.686&0.673&0.682 0.98223 0.732398 1.000000 0.994000 4.359000 1.038000 6.11 38301 47036 Gaucher_disease MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355 no_assertion_criteria_provided Pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 76763715 15 30 50.0 -1 155235843 T G G missense_variant&splice_region_variant MODERATE GBA 2629 Transcript NM_001005742.3 protein_coding 10/12 1397 1226 409 N/T aAc/aCc -1 EntrezGene T T 0.01 0.393 24.4 3.413341 24.4 0.99513110469390098 3.200089 0.203436220411773 0.50037 3.254215 0.188836850341292 0.50664 -5.8&-5.8&-5.8&-5.8 3.53 0.999878789656551 0.000100 0.834629 0.9389 1.0405 2.015&2.015&.&. 1&1&1&1&1 -3.06&-3.06&-3.2&-3.2 10.3278 0.686&0.686&0.673&0.682 0.98223 0.732398 1.000000 0.994000 4.359000 1.038000 6.11 38301 47036 Gaucher_disease MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355 no_assertion_criteria_provided Pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 76763715 15 30 50.0 -1 155235843 T G G missense_variant&splice_region_variant MODERATE GBA 2629 Transcript NM_001171811.2 protein_coding 8/10 1233 965 322 N/T aAc/aCc -1 EntrezGene T T 0.01 0.393 24.4 3.413341 24.4 0.99513110469390098 3.200089 0.203436220411773 0.50037 3.254215 0.188836850341292 0.50664 -5.8&-5.8&-5.8&-5.8 3.53 0.999878789656551 0.000100 0.834629 0.9389 1.0405 2.015&2.015&.&. 1&1&1&1&1 -3.06&-3.06&-3.2&-3.2 10.3278 0.686&0.686&0.673&0.682 0.98223 0.732398 1.000000 0.994000 4.359000 1.038000 6.11 38301 47036 Gaucher_disease MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355 no_assertion_criteria_provided Pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 76763715 15 30 50.0 -1 155235843 T G G missense_variant&splice_region_variant MODERATE GBA 2629 Transcript NM_001171812.2 protein_coding 8/10 1216 1079 360 N/T aAc/aCc -1 EntrezGene T T 0.01 0.864 24.4 3.413341 24.4 0.99513110469390098 3.200089 0.203436220411773 0.50037 3.254215 0.188836850341292 0.50664 -5.8&-5.8&-5.8&-5.8 3.53 0.999878789656551 0.000100 0.834629 0.9389 1.0405 2.015&2.015&.&. 1&1&1&1&1 -3.06&-3.06&-3.2&-3.2 10.3278 0.686&0.686&0.673&0.682 0.98223 0.732398 1.000000 0.994000 4.359000 1.038000 6.11 38301 47036 Gaucher_disease MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355 no_assertion_criteria_provided Pathogenic single_nucleotide_variant GBA:2629&LOC106627981:106627981 SO:0001583&missense_variant 76763715 15 30 50.0 -1 155240629 C T T splice_donor_variant HIGH GBA 2629 Transcript NM_000157.4 protein_coding 2/10 -1 EntrezGene C C 33 4.764473 6.11 chr1:155240629-155240629 9 143282 6.28132e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.00842e-04 5.67537e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.77195e-05 0.00000e+00 0.00000e+00 0.00000e+00 5.75971e-05 9.29167e-05 1.06980e-04 7.35727e-05 4.64684e-04 0.00000e+00 9.50570e-04 8.36913e-05 3.28084e-04 0.00000e+00 4.02901e-04 93445 0.00012 0.00040 99352 Lewy_body_dementia&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease¬_provided MONDO:MONDO:0007488&MedGen:C0752347&OMIM:127750&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant GBA:2629 SO:0001575&splice_donor_variant&SO:0001627&intron_variant 1 104886460 15 30 50.0 -1 155240629 C T T splice_donor_variant HIGH GBA 2629 Transcript NM_001005741.3 protein_coding 3/11 -1 EntrezGene C C 33 4.764473 6.11 chr1:155240629-155240629 9 143282 6.28132e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.00842e-04 5.67537e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.77195e-05 0.00000e+00 0.00000e+00 0.00000e+00 5.75971e-05 9.29167e-05 1.06980e-04 7.35727e-05 4.64684e-04 0.00000e+00 9.50570e-04 8.36913e-05 3.28084e-04 0.00000e+00 4.02901e-04 93445 0.00012 0.00040 99352 Lewy_body_dementia&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease¬_provided MONDO:MONDO:0007488&MedGen:C0752347&OMIM:127750&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant GBA:2629 SO:0001575&splice_donor_variant&SO:0001627&intron_variant 1 104886460 15 30 50.0 -1 155240629 C T T splice_donor_variant HIGH GBA 2629 Transcript NM_001005742.3 protein_coding 3/11 -1 EntrezGene C C 33 4.764473 6.11 chr1:155240629-155240629 9 143282 6.28132e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.00842e-04 5.67537e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.77195e-05 0.00000e+00 0.00000e+00 0.00000e+00 5.75971e-05 9.29167e-05 1.06980e-04 7.35727e-05 4.64684e-04 0.00000e+00 9.50570e-04 8.36913e-05 3.28084e-04 0.00000e+00 4.02901e-04 93445 0.00012 0.00040 99352 Lewy_body_dementia&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease¬_provided MONDO:MONDO:0007488&MedGen:C0752347&OMIM:127750&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant GBA:2629 SO:0001575&splice_donor_variant&SO:0001627&intron_variant 1 104886460 15 30 50.0 -1 155240629 C T T intron_variant MODIFIER GBA 2629 Transcript NM_001171811.2 protein_coding 1/9 -1 EntrezGene C C 33 4.764473 6.11 chr1:155240629-155240629 9 143282 6.28132e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.00842e-04 5.67537e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.77195e-05 0.00000e+00 0.00000e+00 0.00000e+00 5.75971e-05 9.29167e-05 1.06980e-04 7.35727e-05 4.64684e-04 0.00000e+00 9.50570e-04 8.36913e-05 3.28084e-04 0.00000e+00 4.02901e-04 93445 0.00012 0.00040 99352 Lewy_body_dementia&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease¬_provided MONDO:MONDO:0007488&MedGen:C0752347&OMIM:127750&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant GBA:2629 SO:0001575&splice_donor_variant&SO:0001627&intron_variant 1 104886460 15 30 50.0 -1 155240629 C T T splice_donor_variant HIGH GBA 2629 Transcript NM_001171812.2 protein_coding 2/9 -1 EntrezGene C C 33 4.764473 6.11 chr1:155240629-155240629 9 143282 6.28132e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.00842e-04 5.67537e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.77195e-05 0.00000e+00 0.00000e+00 0.00000e+00 5.75971e-05 9.29167e-05 1.06980e-04 7.35727e-05 4.64684e-04 0.00000e+00 9.50570e-04 8.36913e-05 3.28084e-04 0.00000e+00 4.02901e-04 93445 0.00012 0.00040 99352 Lewy_body_dementia&Parkinson_disease&_late-onset&Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease&_perinatal_lethal&Gaucher_disease¬_provided MONDO:MONDO:0007488&MedGen:C0752347&OMIM:127750&MONDO:MONDO:0008199&MedGen:C3160718&OMIM:168600&SNOMED_CT:49049000&MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0011945&MedGen:C1842704&OMIM:608013&Orphanet:ORPHA85212&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant GBA:2629 SO:0001575&splice_donor_variant&SO:0001627&intron_variant 1 104886460 15 30 50.0 -1 155240660 G GC C frameshift_variant HIGH GBA 2629 Transcript NM_000157.4 protein_coding 2/11 221-222 84-85 28-29 -/X -/G -1 EntrezGene 24.0 3.253025 3.02&2.24 rs387906315 8 143236 5.58519e-05 2.38050e-05 0.00000e+00 5.17652e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.46542e-04 1.69147e-04 1.29266e-04 1.50512e-03 2.27273e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 6.77231e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32239e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 5.57989e-05 0.00000e+00 0.00000e+00 0.00000e+00 4302 0.00005 19341 Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease¬_provided MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Duplication GBA:2629 SO:0001589&frameshift_variant&SO:0001627&intron_variant 1 387906315 15 30 50.0 -1 155240660 G GC C frameshift_variant HIGH GBA 2629 Transcript NM_001005741.3 protein_coding 3/12 274-275 84-85 28-29 -/X -/G -1 EntrezGene 24.0 3.253025 3.02&2.24 rs387906315 8 143236 5.58519e-05 2.38050e-05 0.00000e+00 5.17652e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.46542e-04 1.69147e-04 1.29266e-04 1.50512e-03 2.27273e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 6.77231e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32239e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 5.57989e-05 0.00000e+00 0.00000e+00 0.00000e+00 4302 0.00005 19341 Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease¬_provided MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Duplication GBA:2629 SO:0001589&frameshift_variant&SO:0001627&intron_variant 1 387906315 15 30 50.0 -1 155240660 G GC C frameshift_variant HIGH GBA 2629 Transcript NM_001005742.3 protein_coding 3/12 255-256 84-85 28-29 -/X -/G -1 EntrezGene 24.0 3.253025 3.02&2.24 rs387906315 8 143236 5.58519e-05 2.38050e-05 0.00000e+00 5.17652e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.46542e-04 1.69147e-04 1.29266e-04 1.50512e-03 2.27273e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 6.77231e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32239e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 5.57989e-05 0.00000e+00 0.00000e+00 0.00000e+00 4302 0.00005 19341 Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease¬_provided MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Duplication GBA:2629 SO:0001589&frameshift_variant&SO:0001627&intron_variant 1 387906315 15 30 50.0 -1 155240660 G GC C intron_variant MODIFIER GBA 2629 Transcript NM_001171811.2 protein_coding 1/9 -1 EntrezGene 24.0 3.253025 3.02&2.24 rs387906315 8 143236 5.58519e-05 2.38050e-05 0.00000e+00 5.17652e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.46542e-04 1.69147e-04 1.29266e-04 1.50512e-03 2.27273e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 6.77231e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32239e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 5.57989e-05 0.00000e+00 0.00000e+00 0.00000e+00 4302 0.00005 19341 Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease¬_provided MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Duplication GBA:2629 SO:0001589&frameshift_variant&SO:0001627&intron_variant 1 387906315 15 30 50.0 -1 155240660 G GC C frameshift_variant HIGH GBA 2629 Transcript NM_001171812.2 protein_coding 2/10 221-222 84-85 28-29 -/X -/G -1 EntrezGene 24.0 3.253025 3.02&2.24 rs387906315 8 143236 5.58519e-05 2.38050e-05 0.00000e+00 5.17652e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.46542e-04 1.69147e-04 1.29266e-04 1.50512e-03 2.27273e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 6.77231e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32239e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 5.57989e-05 0.00000e+00 0.00000e+00 0.00000e+00 4302 0.00005 19341 Gaucher's_disease&_type_1&Acute_neuronopathic_Gaucher's_disease&Subacute_neuronopathic_Gaucher's_disease&Gaucher_disease_type_3C&Gaucher_disease¬_provided MONDO:MONDO:0009265&MedGen:C1961835&OMIM:230800&Orphanet:ORPHA77259&SNOMED_CT:62201009&MONDO:MONDO:0009266&MedGen:C0268250&OMIM:230900&Orphanet:ORPHA77260&SNOMED_CT:12246008&MONDO:MONDO:0009267&MedGen:C0268251&OMIM:231000&Orphanet:ORPHA77261&SNOMED_CT:5963005&MONDO:MONDO:0009268&MedGen:C1856476&OMIM:231005&Orphanet:ORPHA2072&MONDO:MONDO:0018150&MedGen:C0017205&Orphanet:ORPHA355&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Duplication GBA:2629 SO:0001589&frameshift_variant&SO:0001627&intron_variant 1 387906315 15 30 50.0 -1 193424454 C CTA TA intergenic_variant MODIFIER 5.643 0.419609 -0.629&2.94 chr1:193424455-193424455 7582 113784 6.66350e-02 2.19504e-02 2.24856e-02 2.13117e-02 1.80929e-01 1.87204e-01 1.74242e-01 6.12675e-02 6.08315e-02 6.16065e-02 1.21886e-01 1.24842e-01 1.18497e-01 4.83991e-03 5.53797e-03 4.21941e-03 6.96023e-02 2.84238e-02 4.25764e-02 2.49464e-02 6.32418e-02 9.83340e-02 9.79882e-02 9.88330e-02 6.95971e-02 6.22120e-02 7.79221e-02 8.43819e-02 5.14113e-02 5.68182e-02 5.02451e-02 15 30 50.0 -1 236861122 TAGGGCA T - splice_acceptor_variant&coding_sequence_variant HIGH MTR 4548 Transcript NM_000254.3 protein_coding 20/33 19/32 ?-2459 ?-2047 ?-683 1 EntrezGene AGGGCA AGGGCA 32 4.547235 6.54&2.71&6.54 rs769309528 601 140720 4.27089e-03 2.38761e-03 2.18672e-03 2.62385e-03 1.25858e-02 1.53509e-02 9.56938e-03 2.37565e-03 3.26460e-03 1.69935e-03 8.57843e-03 9.25926e-03 7.81250e-03 3.55297e-03 4.21941e-03 2.98686e-03 4.72856e-03 1.52905e-03 4.10959e-03 7.87402e-04 3.78399e-03 6.06422e-03 6.20899e-03 5.86477e-03 3.80590e-03 6.46950e-03 9.80392e-04 8.40142e-03 3.68139e-03 1.81818e-03 4.10172e-03 15 30 50.0 -1 236861122 TAGGGCA T - intron_variant MODIFIER MTR 4548 Transcript NM_001291939.1 protein_coding 19/31 1 EntrezGene AGGGCA AGGGCA OK 32 4.547235 6.54&2.71&6.54 rs769309528 601 140720 4.27089e-03 2.38761e-03 2.18672e-03 2.62385e-03 1.25858e-02 1.53509e-02 9.56938e-03 2.37565e-03 3.26460e-03 1.69935e-03 8.57843e-03 9.25926e-03 7.81250e-03 3.55297e-03 4.21941e-03 2.98686e-03 4.72856e-03 1.52905e-03 4.10959e-03 7.87402e-04 3.78399e-03 6.06422e-03 6.20899e-03 5.86477e-03 3.80590e-03 6.46950e-03 9.80392e-04 8.40142e-03 3.68139e-03 1.81818e-03 4.10172e-03 15 30 50.0 -1 236861122 TAGGGCA T - splice_acceptor_variant&coding_sequence_variant HIGH MTR 4548 Transcript NM_001291940.2 protein_coding 19/32 18/31 ?-2346 ?-826 ?-276 1 EntrezGene AGGGCA AGGGCA 32 4.547235 6.54&2.71&6.54 rs769309528 601 140720 4.27089e-03 2.38761e-03 2.18672e-03 2.62385e-03 1.25858e-02 1.53509e-02 9.56938e-03 2.37565e-03 3.26460e-03 1.69935e-03 8.57843e-03 9.25926e-03 7.81250e-03 3.55297e-03 4.21941e-03 2.98686e-03 4.72856e-03 1.52905e-03 4.10959e-03 7.87402e-04 3.78399e-03 6.06422e-03 6.20899e-03 5.86477e-03 3.80590e-03 6.46950e-03 9.80392e-04 8.40142e-03 3.68139e-03 1.81818e-03 4.10172e-03 15 30 50.0 -1 236861122 TAGGGCA T - splice_acceptor_variant&coding_sequence_variant HIGH MTR 4548 Transcript XM_005273141.5 protein_coding 20/33 19/32 ?-2439 ?-2044 ?-682 1 EntrezGene AGGGCA AGGGCA 32 4.547235 6.54&2.71&6.54 rs769309528 601 140720 4.27089e-03 2.38761e-03 2.18672e-03 2.62385e-03 1.25858e-02 1.53509e-02 9.56938e-03 2.37565e-03 3.26460e-03 1.69935e-03 8.57843e-03 9.25926e-03 7.81250e-03 3.55297e-03 4.21941e-03 2.98686e-03 4.72856e-03 1.52905e-03 4.10959e-03 7.87402e-04 3.78399e-03 6.06422e-03 6.20899e-03 5.86477e-03 3.80590e-03 6.46950e-03 9.80392e-04 8.40142e-03 3.68139e-03 1.81818e-03 4.10172e-03 15 30 50.0 -1 236861122 TAGGGCA T - splice_acceptor_variant&coding_sequence_variant HIGH MTR 4548 Transcript XM_006711770.3 protein_coding 14/27 13/26 ?-1418 ?-1111 ?-371 1 EntrezGene AGGGCA AGGGCA 32 4.547235 6.54&2.71&6.54 rs769309528 601 140720 4.27089e-03 2.38761e-03 2.18672e-03 2.62385e-03 1.25858e-02 1.53509e-02 9.56938e-03 2.37565e-03 3.26460e-03 1.69935e-03 8.57843e-03 9.25926e-03 7.81250e-03 3.55297e-03 4.21941e-03 2.98686e-03 4.72856e-03 1.52905e-03 4.10959e-03 7.87402e-04 3.78399e-03 6.06422e-03 6.20899e-03 5.86477e-03 3.80590e-03 6.46950e-03 9.80392e-04 8.40142e-03 3.68139e-03 1.81818e-03 4.10172e-03 15 30 50.0 -1 236861122 TAGGGCA T - splice_acceptor_variant&coding_sequence_variant HIGH MTR 4548 Transcript XM_011544194.3 protein_coding 19/32 18/31 ?-3232 ?-2215 ?-739 1 EntrezGene AGGGCA AGGGCA 32 4.547235 6.54&2.71&6.54 rs769309528 601 140720 4.27089e-03 2.38761e-03 2.18672e-03 2.62385e-03 1.25858e-02 1.53509e-02 9.56938e-03 2.37565e-03 3.26460e-03 1.69935e-03 8.57843e-03 9.25926e-03 7.81250e-03 3.55297e-03 4.21941e-03 2.98686e-03 4.72856e-03 1.52905e-03 4.10959e-03 7.87402e-04 3.78399e-03 6.06422e-03 6.20899e-03 5.86477e-03 3.80590e-03 6.46950e-03 9.80392e-04 8.40142e-03 3.68139e-03 1.81818e-03 4.10172e-03 15 30 50.0 -1 236861122 TAGGGCA T - intron_variant MODIFIER MTR 4548 Transcript XM_017001329.2 protein_coding 18/30 1 EntrezGene AGGGCA AGGGCA 32 4.547235 6.54&2.71&6.54 rs769309528 601 140720 4.27089e-03 2.38761e-03 2.18672e-03 2.62385e-03 1.25858e-02 1.53509e-02 9.56938e-03 2.37565e-03 3.26460e-03 1.69935e-03 8.57843e-03 9.25926e-03 7.81250e-03 3.55297e-03 4.21941e-03 2.98686e-03 4.72856e-03 1.52905e-03 4.10959e-03 7.87402e-04 3.78399e-03 6.06422e-03 6.20899e-03 5.86477e-03 3.80590e-03 6.46950e-03 9.80392e-04 8.40142e-03 3.68139e-03 1.81818e-03 4.10172e-03 15 30 50.0 -1 236861122 TAGGGCA T - splice_acceptor_variant&coding_sequence_variant HIGH MTR 4548 Transcript XM_017001330.2 protein_coding 19/30 18/29 ?-3232 ?-2215 ?-739 1 EntrezGene AGGGCA AGGGCA 32 4.547235 6.54&2.71&6.54 rs769309528 601 140720 4.27089e-03 2.38761e-03 2.18672e-03 2.62385e-03 1.25858e-02 1.53509e-02 9.56938e-03 2.37565e-03 3.26460e-03 1.69935e-03 8.57843e-03 9.25926e-03 7.81250e-03 3.55297e-03 4.21941e-03 2.98686e-03 4.72856e-03 1.52905e-03 4.10959e-03 7.87402e-04 3.78399e-03 6.06422e-03 6.20899e-03 5.86477e-03 3.80590e-03 6.46950e-03 9.80392e-04 8.40142e-03 3.68139e-03 1.81818e-03 4.10172e-03 15 30 50.0 -1 239642204 T A A intron_variant MODIFIER CHRM3 1131 Transcript NM_000740.3 protein_coding 2/4 1 EntrezGene T T OK 2.606 0.152892 0 rs1156387600 1168 138482 8.43431e-03 2.50463e-03 2.77264e-03 2.18747e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.04721e-03 9.03935e-03 7.29139e-03 2.43376e-02 2.43619e-02 2.43101e-02 0.00000e+00 0.00000e+00 0.00000e+00 8.92658e-03 6.74859e-03 7.51880e-03 6.50385e-03 7.90585e-03 1.24945e-02 1.24416e-02 1.25675e-02 9.71817e-03 9.46970e-03 9.98004e-03 1.08331e-02 1.42857e-03 1.92308e-03 1.31579e-03 15 30 50.0 -1 239642204 T A A intron_variant MODIFIER CHRM3 1131 Transcript NM_001347716.2 protein_coding 4/7 1 EntrezGene T T 2.606 0.152892 0 rs1156387600 1168 138482 8.43431e-03 2.50463e-03 2.77264e-03 2.18747e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.04721e-03 9.03935e-03 7.29139e-03 2.43376e-02 2.43619e-02 2.43101e-02 0.00000e+00 0.00000e+00 0.00000e+00 8.92658e-03 6.74859e-03 7.51880e-03 6.50385e-03 7.90585e-03 1.24945e-02 1.24416e-02 1.25675e-02 9.71817e-03 9.46970e-03 9.98004e-03 1.08331e-02 1.42857e-03 1.92308e-03 1.31579e-03 15 30 50.0 -1 239642204 T A A intron_variant MODIFIER CHRM3 1131 Transcript NM_001375978.1 protein_coding 4/6 1 EntrezGene T T 2.606 0.152892 0 rs1156387600 1168 138482 8.43431e-03 2.50463e-03 2.77264e-03 2.18747e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.04721e-03 9.03935e-03 7.29139e-03 2.43376e-02 2.43619e-02 2.43101e-02 0.00000e+00 0.00000e+00 0.00000e+00 8.92658e-03 6.74859e-03 7.51880e-03 6.50385e-03 7.90585e-03 1.24945e-02 1.24416e-02 1.25675e-02 9.71817e-03 9.46970e-03 9.98004e-03 1.08331e-02 1.42857e-03 1.92308e-03 1.31579e-03 15 30 50.0 -1 239642204 T A A intron_variant MODIFIER CHRM3 1131 Transcript NM_001375979.1 protein_coding 3/5 1 EntrezGene T T 2.606 0.152892 0 rs1156387600 1168 138482 8.43431e-03 2.50463e-03 2.77264e-03 2.18747e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.04721e-03 9.03935e-03 7.29139e-03 2.43376e-02 2.43619e-02 2.43101e-02 0.00000e+00 0.00000e+00 0.00000e+00 8.92658e-03 6.74859e-03 7.51880e-03 6.50385e-03 7.90585e-03 1.24945e-02 1.24416e-02 1.25675e-02 9.71817e-03 9.46970e-03 9.98004e-03 1.08331e-02 1.42857e-03 1.92308e-03 1.31579e-03 15 30 50.0 -1 239642204 T A A intron_variant MODIFIER CHRM3 1131 Transcript NM_001375980.1 protein_coding 2/4 1 EntrezGene T T 2.606 0.152892 0 rs1156387600 1168 138482 8.43431e-03 2.50463e-03 2.77264e-03 2.18747e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.04721e-03 9.03935e-03 7.29139e-03 2.43376e-02 2.43619e-02 2.43101e-02 0.00000e+00 0.00000e+00 0.00000e+00 8.92658e-03 6.74859e-03 7.51880e-03 6.50385e-03 7.90585e-03 1.24945e-02 1.24416e-02 1.25675e-02 9.71817e-03 9.46970e-03 9.98004e-03 1.08331e-02 1.42857e-03 1.92308e-03 1.31579e-03 15 30 50.0 -1 239642204 T A A intron_variant MODIFIER CHRM3 1131 Transcript NM_001375981.1 protein_coding 3/5 1 EntrezGene T T 2.606 0.152892 0 rs1156387600 1168 138482 8.43431e-03 2.50463e-03 2.77264e-03 2.18747e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.04721e-03 9.03935e-03 7.29139e-03 2.43376e-02 2.43619e-02 2.43101e-02 0.00000e+00 0.00000e+00 0.00000e+00 8.92658e-03 6.74859e-03 7.51880e-03 6.50385e-03 7.90585e-03 1.24945e-02 1.24416e-02 1.25675e-02 9.71817e-03 9.46970e-03 9.98004e-03 1.08331e-02 1.42857e-03 1.92308e-03 1.31579e-03 15 30 50.0 -1 239642204 T A A intron_variant MODIFIER CHRM3 1131 Transcript NM_001375982.1 protein_coding 4/6 1 EntrezGene T T 2.606 0.152892 0 rs1156387600 1168 138482 8.43431e-03 2.50463e-03 2.77264e-03 2.18747e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.04721e-03 9.03935e-03 7.29139e-03 2.43376e-02 2.43619e-02 2.43101e-02 0.00000e+00 0.00000e+00 0.00000e+00 8.92658e-03 6.74859e-03 7.51880e-03 6.50385e-03 7.90585e-03 1.24945e-02 1.24416e-02 1.25675e-02 9.71817e-03 9.46970e-03 9.98004e-03 1.08331e-02 1.42857e-03 1.92308e-03 1.31579e-03 15 30 50.0 -1 239642204 T A A intron_variant MODIFIER CHRM3 1131 Transcript NM_001375983.1 protein_coding 3/6 1 EntrezGene T T 2.606 0.152892 0 rs1156387600 1168 138482 8.43431e-03 2.50463e-03 2.77264e-03 2.18747e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.04721e-03 9.03935e-03 7.29139e-03 2.43376e-02 2.43619e-02 2.43101e-02 0.00000e+00 0.00000e+00 0.00000e+00 8.92658e-03 6.74859e-03 7.51880e-03 6.50385e-03 7.90585e-03 1.24945e-02 1.24416e-02 1.25675e-02 9.71817e-03 9.46970e-03 9.98004e-03 1.08331e-02 1.42857e-03 1.92308e-03 1.31579e-03 15 30 50.0 -1 239642204 T A A intron_variant MODIFIER CHRM3 1131 Transcript NM_001375984.1 protein_coding 3/6 1 EntrezGene T T 2.606 0.152892 0 rs1156387600 1168 138482 8.43431e-03 2.50463e-03 2.77264e-03 2.18747e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.04721e-03 9.03935e-03 7.29139e-03 2.43376e-02 2.43619e-02 2.43101e-02 0.00000e+00 0.00000e+00 0.00000e+00 8.92658e-03 6.74859e-03 7.51880e-03 6.50385e-03 7.90585e-03 1.24945e-02 1.24416e-02 1.25675e-02 9.71817e-03 9.46970e-03 9.98004e-03 1.08331e-02 1.42857e-03 1.92308e-03 1.31579e-03 15 30 50.0 -1 239642204 T A A intron_variant MODIFIER CHRM3 1131 Transcript NM_001375985.1 protein_coding 2/5 1 EntrezGene T T 2.606 0.152892 0 rs1156387600 1168 138482 8.43431e-03 2.50463e-03 2.77264e-03 2.18747e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.04721e-03 9.03935e-03 7.29139e-03 2.43376e-02 2.43619e-02 2.43101e-02 0.00000e+00 0.00000e+00 0.00000e+00 8.92658e-03 6.74859e-03 7.51880e-03 6.50385e-03 7.90585e-03 1.24945e-02 1.24416e-02 1.25675e-02 9.71817e-03 9.46970e-03 9.98004e-03 1.08331e-02 1.42857e-03 1.92308e-03 1.31579e-03 15 30 50.0 -1 239642204 T A A intron_variant&non_coding_transcript_variant MODIFIER CHRM3 1131 Transcript NR_164748.1 misc_RNA 4/8 1 EntrezGene T T 2.606 0.152892 0 rs1156387600 1168 138482 8.43431e-03 2.50463e-03 2.77264e-03 2.18747e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.04721e-03 9.03935e-03 7.29139e-03 2.43376e-02 2.43619e-02 2.43101e-02 0.00000e+00 0.00000e+00 0.00000e+00 8.92658e-03 6.74859e-03 7.51880e-03 6.50385e-03 7.90585e-03 1.24945e-02 1.24416e-02 1.25675e-02 9.71817e-03 9.46970e-03 9.98004e-03 1.08331e-02 1.42857e-03 1.92308e-03 1.31579e-03 15 30 50.0 -1 239642204 T A A intron_variant MODIFIER CHRM3 1131 Transcript XM_011544043.2 protein_coding 4/8 1 EntrezGene T T 2.606 0.152892 0 rs1156387600 1168 138482 8.43431e-03 2.50463e-03 2.77264e-03 2.18747e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.04721e-03 9.03935e-03 7.29139e-03 2.43376e-02 2.43619e-02 2.43101e-02 0.00000e+00 0.00000e+00 0.00000e+00 8.92658e-03 6.74859e-03 7.51880e-03 6.50385e-03 7.90585e-03 1.24945e-02 1.24416e-02 1.25675e-02 9.71817e-03 9.46970e-03 9.98004e-03 1.08331e-02 1.42857e-03 1.92308e-03 1.31579e-03 15 30 50.0 -1 239642204 T A A intron_variant MODIFIER CHRM3 1131 Transcript XM_011544044.2 protein_coding 4/7 1 EntrezGene T T 2.606 0.152892 0 rs1156387600 1168 138482 8.43431e-03 2.50463e-03 2.77264e-03 2.18747e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.04721e-03 9.03935e-03 7.29139e-03 2.43376e-02 2.43619e-02 2.43101e-02 0.00000e+00 0.00000e+00 0.00000e+00 8.92658e-03 6.74859e-03 7.51880e-03 6.50385e-03 7.90585e-03 1.24945e-02 1.24416e-02 1.25675e-02 9.71817e-03 9.46970e-03 9.98004e-03 1.08331e-02 1.42857e-03 1.92308e-03 1.31579e-03 15 30 50.0 -1 239642204 T A A intron_variant MODIFIER CHRM3 1131 Transcript XM_011544047.2 protein_coding 4/5 1 EntrezGene T T 2.606 0.152892 0 rs1156387600 1168 138482 8.43431e-03 2.50463e-03 2.77264e-03 2.18747e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.04721e-03 9.03935e-03 7.29139e-03 2.43376e-02 2.43619e-02 2.43101e-02 0.00000e+00 0.00000e+00 0.00000e+00 8.92658e-03 6.74859e-03 7.51880e-03 6.50385e-03 7.90585e-03 1.24945e-02 1.24416e-02 1.25675e-02 9.71817e-03 9.46970e-03 9.98004e-03 1.08331e-02 1.42857e-03 1.92308e-03 1.31579e-03 15 30 50.0 -1 239642204 T A A intron_variant MODIFIER CHRM3 1131 Transcript XM_017000152.2 protein_coding 5/7 1 EntrezGene T T 2.606 0.152892 0 rs1156387600 1168 138482 8.43431e-03 2.50463e-03 2.77264e-03 2.18747e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.04721e-03 9.03935e-03 7.29139e-03 2.43376e-02 2.43619e-02 2.43101e-02 0.00000e+00 0.00000e+00 0.00000e+00 8.92658e-03 6.74859e-03 7.51880e-03 6.50385e-03 7.90585e-03 1.24945e-02 1.24416e-02 1.25675e-02 9.71817e-03 9.46970e-03 9.98004e-03 1.08331e-02 1.42857e-03 1.92308e-03 1.31579e-03 15 30 50.0 -1 239642204 T A A intron_variant MODIFIER CHRM3 1131 Transcript XM_017000154.1 protein_coding 3/5 1 EntrezGene T T 2.606 0.152892 0 rs1156387600 1168 138482 8.43431e-03 2.50463e-03 2.77264e-03 2.18747e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.04721e-03 9.03935e-03 7.29139e-03 2.43376e-02 2.43619e-02 2.43101e-02 0.00000e+00 0.00000e+00 0.00000e+00 8.92658e-03 6.74859e-03 7.51880e-03 6.50385e-03 7.90585e-03 1.24945e-02 1.24416e-02 1.25675e-02 9.71817e-03 9.46970e-03 9.98004e-03 1.08331e-02 1.42857e-03 1.92308e-03 1.31579e-03 15 30 50.0 -1 239642204 T A A intron_variant MODIFIER CHRM3 1131 Transcript XM_017000157.2 protein_coding 2/4 1 EntrezGene T T 2.606 0.152892 0 rs1156387600 1168 138482 8.43431e-03 2.50463e-03 2.77264e-03 2.18747e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.04721e-03 9.03935e-03 7.29139e-03 2.43376e-02 2.43619e-02 2.43101e-02 0.00000e+00 0.00000e+00 0.00000e+00 8.92658e-03 6.74859e-03 7.51880e-03 6.50385e-03 7.90585e-03 1.24945e-02 1.24416e-02 1.25675e-02 9.71817e-03 9.46970e-03 9.98004e-03 1.08331e-02 1.42857e-03 1.92308e-03 1.31579e-03 15 30 50.0 -1 239642204 T A A intron_variant MODIFIER CHRM3 1131 Transcript XM_017000159.1 protein_coding 3/4 1 EntrezGene T T 2.606 0.152892 0 rs1156387600 1168 138482 8.43431e-03 2.50463e-03 2.77264e-03 2.18747e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.04721e-03 9.03935e-03 7.29139e-03 2.43376e-02 2.43619e-02 2.43101e-02 0.00000e+00 0.00000e+00 0.00000e+00 8.92658e-03 6.74859e-03 7.51880e-03 6.50385e-03 7.90585e-03 1.24945e-02 1.24416e-02 1.25675e-02 9.71817e-03 9.46970e-03 9.98004e-03 1.08331e-02 1.42857e-03 1.92308e-03 1.31579e-03 15 30 50.0 -1 239642204 T A A intron_variant MODIFIER CHRM3 1131 Transcript XM_017000160.2 protein_coding 1/3 1 EntrezGene T T 2.606 0.152892 0 rs1156387600 1168 138482 8.43431e-03 2.50463e-03 2.77264e-03 2.18747e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.04721e-03 9.03935e-03 7.29139e-03 2.43376e-02 2.43619e-02 2.43101e-02 0.00000e+00 0.00000e+00 0.00000e+00 8.92658e-03 6.74859e-03 7.51880e-03 6.50385e-03 7.90585e-03 1.24945e-02 1.24416e-02 1.25675e-02 9.71817e-03 9.46970e-03 9.98004e-03 1.08331e-02 1.42857e-03 1.92308e-03 1.31579e-03 15 30 50.0 -1 239642204 T A A intron_variant MODIFIER CHRM3 1131 Transcript XM_017000162.1 protein_coding 3/4 1 EntrezGene T T 2.606 0.152892 0 rs1156387600 1168 138482 8.43431e-03 2.50463e-03 2.77264e-03 2.18747e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.04721e-03 9.03935e-03 7.29139e-03 2.43376e-02 2.43619e-02 2.43101e-02 0.00000e+00 0.00000e+00 0.00000e+00 8.92658e-03 6.74859e-03 7.51880e-03 6.50385e-03 7.90585e-03 1.24945e-02 1.24416e-02 1.25675e-02 9.71817e-03 9.46970e-03 9.98004e-03 1.08331e-02 1.42857e-03 1.92308e-03 1.31579e-03 15 30 50.0 -1 240207719 TCTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA T - inframe_deletion MODERATE FMN2 56776 Transcript NM_001305424.2 protein_coding 6/19 3147-3245 2920-3018 974-1006 LPGAGIPPPPPLPGAGIPPPPPLPGAGIPPPPP/- CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA/- 1 EntrezGene CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA 2.48&4.19&-10.6&4.38&4.36&-10.6&3.47&3.4&-10.6&-8.05&-7.23&5.32&1.44&-5.07&1.79&5.32&-2.21&5.43&3.59&-6.47&4.52&5.43&-9.82&-3.93&4.52&-0.233&5.18&4.26&-10.3&5.42&-0.641&1.55&2.89&-9.64&-3.65&5.18&-10.4&1.26&3.28&-10.3&-4&1.2&5.16&-10.3&4.01&5.16&0.172&5.16&5.4&-7.91&5.31&0.316&-0.118&3.26&-9.92&5.16&4.28&-10.4&5.19&4.37&-3.92&3.54&4.28&-10.6&5.31&-10.6&5.31&4.4&-10.6&-3.6&-5.52&-1.28&4.03&2.08&-8.46&3.83&5.08&-3.33&4.14&5.08&-1.98&2.18&5.01&-2.99&1.03&3.15&-3.68&5.01&-10&3.98&4.89&-6.21 chr1:240207720-240207819 112 65478 1.71050e-03 4.21452e-04 4.89045e-04 3.42544e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.09361e-03 1.37994e-03 8.34028e-04 1.64114e-03 2.12314e-03 1.12867e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.74625e-03 3.31295e-03 3.87597e-03 3.03721e-03 1.66961e-03 2.57404e-03 2.49973e-03 2.67871e-03 1.04167e-03 0.00000e+00 2.12766e-03 2.51228e-03 7.56430e-04 0.00000e+00 9.31099e-04 15 30 50.0 -1 240207719 TCTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA T - intron_variant MODIFIER FMN2 56776 Transcript NM_001348094.2 protein_coding 4/14 1 EntrezGene CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA 2.48&4.19&-10.6&4.38&4.36&-10.6&3.47&3.4&-10.6&-8.05&-7.23&5.32&1.44&-5.07&1.79&5.32&-2.21&5.43&3.59&-6.47&4.52&5.43&-9.82&-3.93&4.52&-0.233&5.18&4.26&-10.3&5.42&-0.641&1.55&2.89&-9.64&-3.65&5.18&-10.4&1.26&3.28&-10.3&-4&1.2&5.16&-10.3&4.01&5.16&0.172&5.16&5.4&-7.91&5.31&0.316&-0.118&3.26&-9.92&5.16&4.28&-10.4&5.19&4.37&-3.92&3.54&4.28&-10.6&5.31&-10.6&5.31&4.4&-10.6&-3.6&-5.52&-1.28&4.03&2.08&-8.46&3.83&5.08&-3.33&4.14&5.08&-1.98&2.18&5.01&-2.99&1.03&3.15&-3.68&5.01&-10&3.98&4.89&-6.21 chr1:240207720-240207819 112 65478 1.71050e-03 4.21452e-04 4.89045e-04 3.42544e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.09361e-03 1.37994e-03 8.34028e-04 1.64114e-03 2.12314e-03 1.12867e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.74625e-03 3.31295e-03 3.87597e-03 3.03721e-03 1.66961e-03 2.57404e-03 2.49973e-03 2.67871e-03 1.04167e-03 0.00000e+00 2.12766e-03 2.51228e-03 7.56430e-04 0.00000e+00 9.31099e-04 15 30 50.0 -1 240207719 TCTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA T - inframe_deletion MODERATE FMN2 56776 Transcript NM_020066.5 protein_coding 5/18 3135-3233 2908-3006 970-1002 LPGAGIPPPPPLPGAGIPPPPPLPGAGIPPPPP/- CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA/- 1 EntrezGene CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA 2.48&4.19&-10.6&4.38&4.36&-10.6&3.47&3.4&-10.6&-8.05&-7.23&5.32&1.44&-5.07&1.79&5.32&-2.21&5.43&3.59&-6.47&4.52&5.43&-9.82&-3.93&4.52&-0.233&5.18&4.26&-10.3&5.42&-0.641&1.55&2.89&-9.64&-3.65&5.18&-10.4&1.26&3.28&-10.3&-4&1.2&5.16&-10.3&4.01&5.16&0.172&5.16&5.4&-7.91&5.31&0.316&-0.118&3.26&-9.92&5.16&4.28&-10.4&5.19&4.37&-3.92&3.54&4.28&-10.6&5.31&-10.6&5.31&4.4&-10.6&-3.6&-5.52&-1.28&4.03&2.08&-8.46&3.83&5.08&-3.33&4.14&5.08&-1.98&2.18&5.01&-2.99&1.03&3.15&-3.68&5.01&-10&3.98&4.89&-6.21 chr1:240207720-240207819 112 65478 1.71050e-03 4.21452e-04 4.89045e-04 3.42544e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.09361e-03 1.37994e-03 8.34028e-04 1.64114e-03 2.12314e-03 1.12867e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.74625e-03 3.31295e-03 3.87597e-03 3.03721e-03 1.66961e-03 2.57404e-03 2.49973e-03 2.67871e-03 1.04167e-03 0.00000e+00 2.12766e-03 2.51228e-03 7.56430e-04 0.00000e+00 9.31099e-04 15 30 50.0 -1 240207719 TCTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA T - inframe_deletion MODERATE FMN2 56776 Transcript XM_011544237.3 protein_coding 6/8 3143-3241 2920-3018 974-1006 LPGAGIPPPPPLPGAGIPPPPPLPGAGIPPPPP/- CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA/- 1 EntrezGene CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA 2.48&4.19&-10.6&4.38&4.36&-10.6&3.47&3.4&-10.6&-8.05&-7.23&5.32&1.44&-5.07&1.79&5.32&-2.21&5.43&3.59&-6.47&4.52&5.43&-9.82&-3.93&4.52&-0.233&5.18&4.26&-10.3&5.42&-0.641&1.55&2.89&-9.64&-3.65&5.18&-10.4&1.26&3.28&-10.3&-4&1.2&5.16&-10.3&4.01&5.16&0.172&5.16&5.4&-7.91&5.31&0.316&-0.118&3.26&-9.92&5.16&4.28&-10.4&5.19&4.37&-3.92&3.54&4.28&-10.6&5.31&-10.6&5.31&4.4&-10.6&-3.6&-5.52&-1.28&4.03&2.08&-8.46&3.83&5.08&-3.33&4.14&5.08&-1.98&2.18&5.01&-2.99&1.03&3.15&-3.68&5.01&-10&3.98&4.89&-6.21 chr1:240207720-240207819 112 65478 1.71050e-03 4.21452e-04 4.89045e-04 3.42544e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.09361e-03 1.37994e-03 8.34028e-04 1.64114e-03 2.12314e-03 1.12867e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.74625e-03 3.31295e-03 3.87597e-03 3.03721e-03 1.66961e-03 2.57404e-03 2.49973e-03 2.67871e-03 1.04167e-03 0.00000e+00 2.12766e-03 2.51228e-03 7.56430e-04 0.00000e+00 9.31099e-04 15 30 50.0 -1 240207719 TCTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA T - inframe_deletion MODERATE FMN2 56776 Transcript XM_017001837.1 protein_coding 6/10 3143-3241 2920-3018 974-1006 LPGAGIPPPPPLPGAGIPPPPPLPGAGIPPPPP/- CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA/- 1 EntrezGene CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA 2.48&4.19&-10.6&4.38&4.36&-10.6&3.47&3.4&-10.6&-8.05&-7.23&5.32&1.44&-5.07&1.79&5.32&-2.21&5.43&3.59&-6.47&4.52&5.43&-9.82&-3.93&4.52&-0.233&5.18&4.26&-10.3&5.42&-0.641&1.55&2.89&-9.64&-3.65&5.18&-10.4&1.26&3.28&-10.3&-4&1.2&5.16&-10.3&4.01&5.16&0.172&5.16&5.4&-7.91&5.31&0.316&-0.118&3.26&-9.92&5.16&4.28&-10.4&5.19&4.37&-3.92&3.54&4.28&-10.6&5.31&-10.6&5.31&4.4&-10.6&-3.6&-5.52&-1.28&4.03&2.08&-8.46&3.83&5.08&-3.33&4.14&5.08&-1.98&2.18&5.01&-2.99&1.03&3.15&-3.68&5.01&-10&3.98&4.89&-6.21 chr1:240207720-240207819 112 65478 1.71050e-03 4.21452e-04 4.89045e-04 3.42544e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.09361e-03 1.37994e-03 8.34028e-04 1.64114e-03 2.12314e-03 1.12867e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.74625e-03 3.31295e-03 3.87597e-03 3.03721e-03 1.66961e-03 2.57404e-03 2.49973e-03 2.67871e-03 1.04167e-03 0.00000e+00 2.12766e-03 2.51228e-03 7.56430e-04 0.00000e+00 9.31099e-04 15 30 50.0 -1 240207719 TCTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA T - inframe_deletion MODERATE FMN2 56776 Transcript XM_017001838.1 protein_coding 6/8 3143-3241 2920-3018 974-1006 LPGAGIPPPPPLPGAGIPPPPPLPGAGIPPPPP/- CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA/- 1 EntrezGene CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA 2.48&4.19&-10.6&4.38&4.36&-10.6&3.47&3.4&-10.6&-8.05&-7.23&5.32&1.44&-5.07&1.79&5.32&-2.21&5.43&3.59&-6.47&4.52&5.43&-9.82&-3.93&4.52&-0.233&5.18&4.26&-10.3&5.42&-0.641&1.55&2.89&-9.64&-3.65&5.18&-10.4&1.26&3.28&-10.3&-4&1.2&5.16&-10.3&4.01&5.16&0.172&5.16&5.4&-7.91&5.31&0.316&-0.118&3.26&-9.92&5.16&4.28&-10.4&5.19&4.37&-3.92&3.54&4.28&-10.6&5.31&-10.6&5.31&4.4&-10.6&-3.6&-5.52&-1.28&4.03&2.08&-8.46&3.83&5.08&-3.33&4.14&5.08&-1.98&2.18&5.01&-2.99&1.03&3.15&-3.68&5.01&-10&3.98&4.89&-6.21 chr1:240207720-240207819 112 65478 1.71050e-03 4.21452e-04 4.89045e-04 3.42544e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.09361e-03 1.37994e-03 8.34028e-04 1.64114e-03 2.12314e-03 1.12867e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.74625e-03 3.31295e-03 3.87597e-03 3.03721e-03 1.66961e-03 2.57404e-03 2.49973e-03 2.67871e-03 1.04167e-03 0.00000e+00 2.12766e-03 2.51228e-03 7.56430e-04 0.00000e+00 9.31099e-04 15 30 50.0 -1 240207719 TCTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA T - inframe_deletion MODERATE FMN2 56776 Transcript XM_017001840.2 protein_coding 5/18 1386-1484 1048-1146 350-382 LPGAGIPPPPPLPGAGIPPPPPLPGAGIPPPPP/- CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA/- 1 EntrezGene CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA 2.48&4.19&-10.6&4.38&4.36&-10.6&3.47&3.4&-10.6&-8.05&-7.23&5.32&1.44&-5.07&1.79&5.32&-2.21&5.43&3.59&-6.47&4.52&5.43&-9.82&-3.93&4.52&-0.233&5.18&4.26&-10.3&5.42&-0.641&1.55&2.89&-9.64&-3.65&5.18&-10.4&1.26&3.28&-10.3&-4&1.2&5.16&-10.3&4.01&5.16&0.172&5.16&5.4&-7.91&5.31&0.316&-0.118&3.26&-9.92&5.16&4.28&-10.4&5.19&4.37&-3.92&3.54&4.28&-10.6&5.31&-10.6&5.31&4.4&-10.6&-3.6&-5.52&-1.28&4.03&2.08&-8.46&3.83&5.08&-3.33&4.14&5.08&-1.98&2.18&5.01&-2.99&1.03&3.15&-3.68&5.01&-10&3.98&4.89&-6.21 chr1:240207720-240207819 112 65478 1.71050e-03 4.21452e-04 4.89045e-04 3.42544e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.09361e-03 1.37994e-03 8.34028e-04 1.64114e-03 2.12314e-03 1.12867e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.74625e-03 3.31295e-03 3.87597e-03 3.03721e-03 1.66961e-03 2.57404e-03 2.49973e-03 2.67871e-03 1.04167e-03 0.00000e+00 2.12766e-03 2.51228e-03 7.56430e-04 0.00000e+00 9.31099e-04 15 30 50.0 -1 240207719 TCTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA T - inframe_deletion MODERATE FMN2 56776 Transcript XM_017001841.2 protein_coding 5/18 1844-1942 1048-1146 350-382 LPGAGIPPPPPLPGAGIPPPPPLPGAGIPPPPP/- CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA/- 1 EntrezGene CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA CTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA 2.48&4.19&-10.6&4.38&4.36&-10.6&3.47&3.4&-10.6&-8.05&-7.23&5.32&1.44&-5.07&1.79&5.32&-2.21&5.43&3.59&-6.47&4.52&5.43&-9.82&-3.93&4.52&-0.233&5.18&4.26&-10.3&5.42&-0.641&1.55&2.89&-9.64&-3.65&5.18&-10.4&1.26&3.28&-10.3&-4&1.2&5.16&-10.3&4.01&5.16&0.172&5.16&5.4&-7.91&5.31&0.316&-0.118&3.26&-9.92&5.16&4.28&-10.4&5.19&4.37&-3.92&3.54&4.28&-10.6&5.31&-10.6&5.31&4.4&-10.6&-3.6&-5.52&-1.28&4.03&2.08&-8.46&3.83&5.08&-3.33&4.14&5.08&-1.98&2.18&5.01&-2.99&1.03&3.15&-3.68&5.01&-10&3.98&4.89&-6.21 chr1:240207720-240207819 112 65478 1.71050e-03 4.21452e-04 4.89045e-04 3.42544e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.09361e-03 1.37994e-03 8.34028e-04 1.64114e-03 2.12314e-03 1.12867e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.74625e-03 3.31295e-03 3.87597e-03 3.03721e-03 1.66961e-03 2.57404e-03 2.49973e-03 2.67871e-03 1.04167e-03 0.00000e+00 2.12766e-03 2.51228e-03 7.56430e-04 0.00000e+00 9.31099e-04 15 30 50.0 -1 241497927 A ATTT TTT inframe_insertion MODERATE FH 2271 Transcript NM_000143.4 protein_coding 10/10 1466-1467 1433-1434 478 N/KN aat/aaAAAt -1 EntrezGene 16.37 1.598937 -3.26&4.06 rs367543046 148 143280 1.03294e-03 3.33080e-04 4.40606e-04 2.06868e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.39432e-04 3.38524e-04 5.16396e-04 5.11432e-03 2.83768e-03 7.68246e-03 3.19081e-04 6.90608e-04 0.00000e+00 1.20541e-03 1.90985e-04 3.99361e-04 1.25502e-04 8.49581e-04 1.62624e-03 1.84571e-03 1.32441e-03 1.39535e-03 9.10747e-04 1.90114e-03 1.03224e-03 0.00000e+00 0.00000e+00 0.00000e+00 42095 51261 Fumarase_deficiency&Hereditary_cancer-predisposing_syndrome¬_specified¬_provided MONDO:MONDO:0011730&MedGen:C0342770&OMIM:606812&Orphanet:ORPHA24&SNOMED_CT:237983002&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(3)&Pathogenic(2)&Uncertain_significance(4) Duplication FH:2271 SO:0001821&inframe_insertion 1 367543046 15 30 50.0 -1 241497927 A ATTT TTT inframe_insertion MODERATE FH 2271 Transcript XM_011544132.2 protein_coding 10/10 1936-1937 1205-1206 402 N/KN aat/aaAAAt -1 EntrezGene 16.37 1.598937 -3.26&4.06 rs367543046 148 143280 1.03294e-03 3.33080e-04 4.40606e-04 2.06868e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.39432e-04 3.38524e-04 5.16396e-04 5.11432e-03 2.83768e-03 7.68246e-03 3.19081e-04 6.90608e-04 0.00000e+00 1.20541e-03 1.90985e-04 3.99361e-04 1.25502e-04 8.49581e-04 1.62624e-03 1.84571e-03 1.32441e-03 1.39535e-03 9.10747e-04 1.90114e-03 1.03224e-03 0.00000e+00 0.00000e+00 0.00000e+00 42095 51261 Fumarase_deficiency&Hereditary_cancer-predisposing_syndrome¬_specified¬_provided MONDO:MONDO:0011730&MedGen:C0342770&OMIM:606812&Orphanet:ORPHA24&SNOMED_CT:237983002&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(3)&Pathogenic(2)&Uncertain_significance(4) Duplication FH:2271 SO:0001821&inframe_insertion 1 367543046 15 30 50.0 -1 247424041 G A A missense_variant MODERATE NLRP3 114548 Transcript NM_001079821.3 protein_coding 5/11 719 592 198 V/M Gtg/Atg 1 EntrezGene G G 0.37 0.007 0.125 -0.489652 0.125 0.72672109334390289 0.04064689 -1.75656881570094 0.00909 0.03637804 -1.70017292574289 0.00841 -2.33&-2.33&-2.33&-2.33&-2.33&.&.&-2.33 -7.37 0.999945305624792 0.234251 0.2122 -0.7300 .&.&.&.&.&.&.&. 2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11 -0.22&-0.15&-0.15&-0.32&-0.22&.&.&-0.11 9.2956 0.07&0.049&0.049&0.065&0.061&.&.&0.072 0.05110 0.554377 0.000000 0.046000 -0.254000 -0.825000 -8.79 chr1:247424041-247424041 1039 143160 7.25761e-03 1.33391e-03 1.27866e-03 1.39882e-03 0.00000e+00 0.00000e+00 0.00000e+00 6.59534e-03 6.09756e-03 6.97494e-03 1.23345e-02 1.36209e-02 1.08835e-02 3.19693e-04 0.00000e+00 5.94530e-04 6.55649e-03 2.56410e-02 2.07502e-02 2.71835e-02 8.00404e-03 8.61294e-03 8.93239e-03 8.17318e-03 6.51769e-03 5.47445e-03 7.60456e-03 7.26134e-03 4.29610e-03 5.31915e-03 4.06174e-03 4371 0.00399 19410 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_provided&Familial_cold_autoinflammatory_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MONDO:MONDO:0033261&MedGen:C4521680&OMIM:617772&MedGen:C0343068&OMIM:PS120100&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(7)&Uncertain_significance(4) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 17 121908147 15 30 50.0 -1 247424041 G A A missense_variant MODERATE NLRP3 114548 Transcript NM_001127461.3 protein_coding 4/9 1361 592 198 V/M Gtg/Atg 1 EntrezGene G G 0.35 0.015 0.125 -0.489652 0.125 0.72672109334390289 0.04064689 -1.75656881570094 0.00909 0.03637804 -1.70017292574289 0.00841 -2.33&-2.33&-2.33&-2.33&-2.33&.&.&-2.33 -7.37 0.999945305624792 0.234251 0.2122 -0.7300 .&.&.&.&.&.&.&. 2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11 -0.22&-0.15&-0.15&-0.32&-0.22&.&.&-0.11 9.2956 0.07&0.049&0.049&0.065&0.061&.&.&0.072 0.05110 0.554377 0.000000 0.046000 -0.254000 -0.825000 -8.79 chr1:247424041-247424041 1039 143160 7.25761e-03 1.33391e-03 1.27866e-03 1.39882e-03 0.00000e+00 0.00000e+00 0.00000e+00 6.59534e-03 6.09756e-03 6.97494e-03 1.23345e-02 1.36209e-02 1.08835e-02 3.19693e-04 0.00000e+00 5.94530e-04 6.55649e-03 2.56410e-02 2.07502e-02 2.71835e-02 8.00404e-03 8.61294e-03 8.93239e-03 8.17318e-03 6.51769e-03 5.47445e-03 7.60456e-03 7.26134e-03 4.29610e-03 5.31915e-03 4.06174e-03 4371 0.00399 19410 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_provided&Familial_cold_autoinflammatory_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MONDO:MONDO:0033261&MedGen:C4521680&OMIM:617772&MedGen:C0343068&OMIM:PS120100&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(7)&Uncertain_significance(4) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 17 121908147 15 30 50.0 -1 247424041 G A A missense_variant MODERATE NLRP3 114548 Transcript NM_001127462.3 protein_coding 4/9 1361 592 198 V/M Gtg/Atg 1 EntrezGene G G 0.35 0.015 0.125 -0.489652 0.125 0.72672109334390289 0.04064689 -1.75656881570094 0.00909 0.03637804 -1.70017292574289 0.00841 -2.33&-2.33&-2.33&-2.33&-2.33&.&.&-2.33 -7.37 0.999945305624792 0.234251 0.2122 -0.7300 .&.&.&.&.&.&.&. 2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11 -0.22&-0.15&-0.15&-0.32&-0.22&.&.&-0.11 9.2956 0.07&0.049&0.049&0.065&0.061&.&.&0.072 0.05110 0.554377 0.000000 0.046000 -0.254000 -0.825000 -8.79 chr1:247424041-247424041 1039 143160 7.25761e-03 1.33391e-03 1.27866e-03 1.39882e-03 0.00000e+00 0.00000e+00 0.00000e+00 6.59534e-03 6.09756e-03 6.97494e-03 1.23345e-02 1.36209e-02 1.08835e-02 3.19693e-04 0.00000e+00 5.94530e-04 6.55649e-03 2.56410e-02 2.07502e-02 2.71835e-02 8.00404e-03 8.61294e-03 8.93239e-03 8.17318e-03 6.51769e-03 5.47445e-03 7.60456e-03 7.26134e-03 4.29610e-03 5.31915e-03 4.06174e-03 4371 0.00399 19410 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_provided&Familial_cold_autoinflammatory_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MONDO:MONDO:0033261&MedGen:C4521680&OMIM:617772&MedGen:C0343068&OMIM:PS120100&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(7)&Uncertain_significance(4) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 17 121908147 15 30 50.0 -1 247424041 G A A missense_variant MODERATE NLRP3 114548 Transcript NM_001243133.2 protein_coding 4/10 1361 592 198 V/M Gtg/Atg 1 EntrezGene G G 0.37 0.007 0.125 -0.489652 0.125 0.72672109334390289 0.04064689 -1.75656881570094 0.00909 0.03637804 -1.70017292574289 0.00841 -2.33&-2.33&-2.33&-2.33&-2.33&.&.&-2.33 -7.37 0.999945305624792 0.234251 0.2122 -0.7300 .&.&.&.&.&.&.&. 2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11 -0.22&-0.15&-0.15&-0.32&-0.22&.&.&-0.11 9.2956 0.07&0.049&0.049&0.065&0.061&.&.&0.072 0.05110 0.554377 0.000000 0.046000 -0.254000 -0.825000 -8.79 chr1:247424041-247424041 1039 143160 7.25761e-03 1.33391e-03 1.27866e-03 1.39882e-03 0.00000e+00 0.00000e+00 0.00000e+00 6.59534e-03 6.09756e-03 6.97494e-03 1.23345e-02 1.36209e-02 1.08835e-02 3.19693e-04 0.00000e+00 5.94530e-04 6.55649e-03 2.56410e-02 2.07502e-02 2.71835e-02 8.00404e-03 8.61294e-03 8.93239e-03 8.17318e-03 6.51769e-03 5.47445e-03 7.60456e-03 7.26134e-03 4.29610e-03 5.31915e-03 4.06174e-03 4371 0.00399 19410 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_provided&Familial_cold_autoinflammatory_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MONDO:MONDO:0033261&MedGen:C4521680&OMIM:617772&MedGen:C0343068&OMIM:PS120100&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(7)&Uncertain_significance(4) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 17 121908147 15 30 50.0 -1 247424041 G A A missense_variant MODERATE NLRP3 114548 Transcript NM_004895.5 protein_coding 4/10 1361 598 200 V/M Gtg/Atg 1 EntrezGene G G 0.37 0.007 0.125 -0.489652 0.125 0.72672109334390289 0.04064689 -1.75656881570094 0.00909 0.03637804 -1.70017292574289 0.00841 -2.33&-2.33&-2.33&-2.33&-2.33&.&.&-2.33 -7.37 0.999945305624792 0.234251 0.2122 -0.7300 .&.&.&.&.&.&.&. 2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11 -0.22&-0.15&-0.15&-0.32&-0.22&.&.&-0.11 9.2956 0.07&0.049&0.049&0.065&0.061&.&.&0.072 0.05110 0.554377 0.000000 0.046000 -0.254000 -0.825000 -8.79 chr1:247424041-247424041 1039 143160 7.25761e-03 1.33391e-03 1.27866e-03 1.39882e-03 0.00000e+00 0.00000e+00 0.00000e+00 6.59534e-03 6.09756e-03 6.97494e-03 1.23345e-02 1.36209e-02 1.08835e-02 3.19693e-04 0.00000e+00 5.94530e-04 6.55649e-03 2.56410e-02 2.07502e-02 2.71835e-02 8.00404e-03 8.61294e-03 8.93239e-03 8.17318e-03 6.51769e-03 5.47445e-03 7.60456e-03 7.26134e-03 4.29610e-03 5.31915e-03 4.06174e-03 4371 0.00399 19410 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_provided&Familial_cold_autoinflammatory_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MONDO:MONDO:0033261&MedGen:C4521680&OMIM:617772&MedGen:C0343068&OMIM:PS120100&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(7)&Uncertain_significance(4) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 17 121908147 15 30 50.0 -1 247424041 G A A missense_variant MODERATE NLRP3 114548 Transcript NM_183395.3 protein_coding 4/8 1361 592 198 V/M Gtg/Atg 1 EntrezGene G G 0.38 0.015 0.125 -0.489652 0.125 0.72672109334390289 0.04064689 -1.75656881570094 0.00909 0.03637804 -1.70017292574289 0.00841 -2.33&-2.33&-2.33&-2.33&-2.33&.&.&-2.33 -7.37 0.999945305624792 0.234251 0.2122 -0.7300 .&.&.&.&.&.&.&. 2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11 -0.22&-0.15&-0.15&-0.32&-0.22&.&.&-0.11 9.2956 0.07&0.049&0.049&0.065&0.061&.&.&0.072 0.05110 0.554377 0.000000 0.046000 -0.254000 -0.825000 -8.79 chr1:247424041-247424041 1039 143160 7.25761e-03 1.33391e-03 1.27866e-03 1.39882e-03 0.00000e+00 0.00000e+00 0.00000e+00 6.59534e-03 6.09756e-03 6.97494e-03 1.23345e-02 1.36209e-02 1.08835e-02 3.19693e-04 0.00000e+00 5.94530e-04 6.55649e-03 2.56410e-02 2.07502e-02 2.71835e-02 8.00404e-03 8.61294e-03 8.93239e-03 8.17318e-03 6.51769e-03 5.47445e-03 7.60456e-03 7.26134e-03 4.29610e-03 5.31915e-03 4.06174e-03 4371 0.00399 19410 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_provided&Familial_cold_autoinflammatory_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MONDO:MONDO:0033261&MedGen:C4521680&OMIM:617772&MedGen:C0343068&OMIM:PS120100&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(7)&Uncertain_significance(4) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 17 121908147 15 30 50.0 -1 247424041 G A A missense_variant MODERATE NLRP3 114548 Transcript XM_011544048.2 protein_coding 4/10 1483 598 200 V/M Gtg/Atg 1 EntrezGene G G 0.37 0.007 0.125 -0.489652 0.125 0.72672109334390289 0.04064689 -1.75656881570094 0.00909 0.03637804 -1.70017292574289 0.00841 -2.33&-2.33&-2.33&-2.33&-2.33&.&.&-2.33 -7.37 0.999945305624792 0.234251 0.2122 -0.7300 .&.&.&.&.&.&.&. 2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11 -0.22&-0.15&-0.15&-0.32&-0.22&.&.&-0.11 9.2956 0.07&0.049&0.049&0.065&0.061&.&.&0.072 0.05110 0.554377 0.000000 0.046000 -0.254000 -0.825000 -8.79 chr1:247424041-247424041 1039 143160 7.25761e-03 1.33391e-03 1.27866e-03 1.39882e-03 0.00000e+00 0.00000e+00 0.00000e+00 6.59534e-03 6.09756e-03 6.97494e-03 1.23345e-02 1.36209e-02 1.08835e-02 3.19693e-04 0.00000e+00 5.94530e-04 6.55649e-03 2.56410e-02 2.07502e-02 2.71835e-02 8.00404e-03 8.61294e-03 8.93239e-03 8.17318e-03 6.51769e-03 5.47445e-03 7.60456e-03 7.26134e-03 4.29610e-03 5.31915e-03 4.06174e-03 4371 0.00399 19410 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_provided&Familial_cold_autoinflammatory_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MONDO:MONDO:0033261&MedGen:C4521680&OMIM:617772&MedGen:C0343068&OMIM:PS120100&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(7)&Uncertain_significance(4) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 17 121908147 15 30 50.0 -1 247424041 G A A missense_variant MODERATE NLRP3 114548 Transcript XM_017000181.1 protein_coding 5/11 960 598 200 V/M Gtg/Atg 1 EntrezGene G G 0.37 0.007 0.125 -0.489652 0.125 0.72672109334390289 0.04064689 -1.75656881570094 0.00909 0.03637804 -1.70017292574289 0.00841 -2.33&-2.33&-2.33&-2.33&-2.33&.&.&-2.33 -7.37 0.999945305624792 0.234251 0.2122 -0.7300 .&.&.&.&.&.&.&. 2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11 -0.22&-0.15&-0.15&-0.32&-0.22&.&.&-0.11 9.2956 0.07&0.049&0.049&0.065&0.061&.&.&0.072 0.05110 0.554377 0.000000 0.046000 -0.254000 -0.825000 -8.79 chr1:247424041-247424041 1039 143160 7.25761e-03 1.33391e-03 1.27866e-03 1.39882e-03 0.00000e+00 0.00000e+00 0.00000e+00 6.59534e-03 6.09756e-03 6.97494e-03 1.23345e-02 1.36209e-02 1.08835e-02 3.19693e-04 0.00000e+00 5.94530e-04 6.55649e-03 2.56410e-02 2.07502e-02 2.71835e-02 8.00404e-03 8.61294e-03 8.93239e-03 8.17318e-03 6.51769e-03 5.47445e-03 7.60456e-03 7.26134e-03 4.29610e-03 5.31915e-03 4.06174e-03 4371 0.00399 19410 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_provided&Familial_cold_autoinflammatory_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MONDO:MONDO:0033261&MedGen:C4521680&OMIM:617772&MedGen:C0343068&OMIM:PS120100&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(7)&Uncertain_significance(4) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 17 121908147 15 30 50.0 -1 247424041 G A A missense_variant MODERATE NLRP3 114548 Transcript XM_017000182.1 protein_coding 5/11 905 598 200 V/M Gtg/Atg 1 EntrezGene G G 0.37 0.007 0.125 -0.489652 0.125 0.72672109334390289 0.04064689 -1.75656881570094 0.00909 0.03637804 -1.70017292574289 0.00841 -2.33&-2.33&-2.33&-2.33&-2.33&.&.&-2.33 -7.37 0.999945305624792 0.234251 0.2122 -0.7300 .&.&.&.&.&.&.&. 2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11 -0.22&-0.15&-0.15&-0.32&-0.22&.&.&-0.11 9.2956 0.07&0.049&0.049&0.065&0.061&.&.&0.072 0.05110 0.554377 0.000000 0.046000 -0.254000 -0.825000 -8.79 chr1:247424041-247424041 1039 143160 7.25761e-03 1.33391e-03 1.27866e-03 1.39882e-03 0.00000e+00 0.00000e+00 0.00000e+00 6.59534e-03 6.09756e-03 6.97494e-03 1.23345e-02 1.36209e-02 1.08835e-02 3.19693e-04 0.00000e+00 5.94530e-04 6.55649e-03 2.56410e-02 2.07502e-02 2.71835e-02 8.00404e-03 8.61294e-03 8.93239e-03 8.17318e-03 6.51769e-03 5.47445e-03 7.60456e-03 7.26134e-03 4.29610e-03 5.31915e-03 4.06174e-03 4371 0.00399 19410 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_provided&Familial_cold_autoinflammatory_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MONDO:MONDO:0033261&MedGen:C4521680&OMIM:617772&MedGen:C0343068&OMIM:PS120100&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(7)&Uncertain_significance(4) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 17 121908147 15 30 50.0 -1 247424041 G A A missense_variant MODERATE NLRP3 114548 Transcript XM_024452862.1 protein_coding 4/8 1483 598 200 V/M Gtg/Atg 1 EntrezGene G G 0.125 -0.489652 0.125 0.72672109334390289 0.04064689 -1.75656881570094 0.00909 0.03637804 -1.70017292574289 0.00841 -2.33&-2.33&-2.33&-2.33&-2.33&.&.&-2.33 -7.37 0.999945305624792 0.234251 0.2122 -0.7300 .&.&.&.&.&.&.&. 2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11 -0.22&-0.15&-0.15&-0.32&-0.22&.&.&-0.11 9.2956 0.07&0.049&0.049&0.065&0.061&.&.&0.072 0.05110 0.554377 0.000000 0.046000 -0.254000 -0.825000 -8.79 chr1:247424041-247424041 1039 143160 7.25761e-03 1.33391e-03 1.27866e-03 1.39882e-03 0.00000e+00 0.00000e+00 0.00000e+00 6.59534e-03 6.09756e-03 6.97494e-03 1.23345e-02 1.36209e-02 1.08835e-02 3.19693e-04 0.00000e+00 5.94530e-04 6.55649e-03 2.56410e-02 2.07502e-02 2.71835e-02 8.00404e-03 8.61294e-03 8.93239e-03 8.17318e-03 6.51769e-03 5.47445e-03 7.60456e-03 7.26134e-03 4.29610e-03 5.31915e-03 4.06174e-03 4371 0.00399 19410 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_provided&Familial_cold_autoinflammatory_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MONDO:MONDO:0033261&MedGen:C4521680&OMIM:617772&MedGen:C0343068&OMIM:PS120100&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(7)&Uncertain_significance(4) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 17 121908147 15 30 50.0 -1 247424041 G A A missense_variant MODERATE NLRP3 114548 Transcript XM_024452874.1 protein_coding 4/7 1483 598 200 V/M Gtg/Atg 1 EntrezGene G G 0.125 -0.489652 0.125 0.72672109334390289 0.04064689 -1.75656881570094 0.00909 0.03637804 -1.70017292574289 0.00841 -2.33&-2.33&-2.33&-2.33&-2.33&.&.&-2.33 -7.37 0.999945305624792 0.234251 0.2122 -0.7300 .&.&.&.&.&.&.&. 2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11&2.97861e-11 -0.22&-0.15&-0.15&-0.32&-0.22&.&.&-0.11 9.2956 0.07&0.049&0.049&0.065&0.061&.&.&0.072 0.05110 0.554377 0.000000 0.046000 -0.254000 -0.825000 -8.79 chr1:247424041-247424041 1039 143160 7.25761e-03 1.33391e-03 1.27866e-03 1.39882e-03 0.00000e+00 0.00000e+00 0.00000e+00 6.59534e-03 6.09756e-03 6.97494e-03 1.23345e-02 1.36209e-02 1.08835e-02 3.19693e-04 0.00000e+00 5.94530e-04 6.55649e-03 2.56410e-02 2.07502e-02 2.71835e-02 8.00404e-03 8.61294e-03 8.93239e-03 8.17318e-03 6.51769e-03 5.47445e-03 7.60456e-03 7.26134e-03 4.29610e-03 5.31915e-03 4.06174e-03 4371 0.00399 19410 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_provided&Familial_cold_autoinflammatory_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MONDO:MONDO:0033261&MedGen:C4521680&OMIM:617772&MedGen:C0343068&OMIM:PS120100&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(7)&Uncertain_significance(4) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 17 121908147 15 30 50.0 -1 247425556 C A A missense_variant MODERATE NLRP3 114548 Transcript NM_001079821.3 protein_coding 5/11 2234 2107 703 Q/K Cag/Aag 1 EntrezGene C C 0.63 0.01 0.438 -0.230891 0.438 0.26450865337110768 0.2444453 -1.24906822791398 0.05144 0.2389972 -1.18329923690612 0.05252 -2.37&-2.37&-2.37&0.66&-2.37&.&.&0.66 -0.977 0.99871367089653 0.001058 0.1192 -0.7308 .&.&.&.&.&.&.&. 1&1&1&1&1&1 0.2&-0.02&-0.02&-0.14&0.2&.&.&0.52 2.2553 0.105&0.066&0.063&0.142&0.04&.&.&0.083 0.06425 0.549168 0.000000 0.000000 -0.197000 0.078000 -1.58 rs35829419 4687 143236 3.27222e-02 9.94622e-03 1.04837e-02 9.31484e-03 4.89978e-02 4.25532e-02 5.60748e-02 2.41026e-02 2.55586e-02 2.29915e-02 6.86747e-02 5.85893e-02 8.00256e-02 6.38978e-04 0.00000e+00 1.18906e-03 3.30462e-02 4.76190e-02 4.94812e-02 4.70322e-02 3.23775e-02 4.63895e-02 4.69477e-02 4.56218e-02 3.20930e-02 2.64117e-02 3.80228e-02 3.27582e-02 3.41880e-02 3.54610e-02 3.38983e-02 259561 0.03483 0.04095 0.02236 249858 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(8)&Uncertain_significance(1) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 1 35829419 15 30 50.0 -1 247425556 C A A missense_variant MODERATE NLRP3 114548 Transcript NM_001127461.3 protein_coding 4/9 2876 2107 703 Q/K Cag/Aag 1 EntrezGene C C 0.54 0.022 0.438 -0.230891 0.438 0.26450865337110768 0.2444453 -1.24906822791398 0.05144 0.2389972 -1.18329923690612 0.05252 -2.37&-2.37&-2.37&0.66&-2.37&.&.&0.66 -0.977 0.99871367089653 0.001058 0.1192 -0.7308 .&.&.&.&.&.&.&. 1&1&1&1&1&1 0.2&-0.02&-0.02&-0.14&0.2&.&.&0.52 2.2553 0.105&0.066&0.063&0.142&0.04&.&.&0.083 0.06425 0.549168 0.000000 0.000000 -0.197000 0.078000 -1.58 rs35829419 4687 143236 3.27222e-02 9.94622e-03 1.04837e-02 9.31484e-03 4.89978e-02 4.25532e-02 5.60748e-02 2.41026e-02 2.55586e-02 2.29915e-02 6.86747e-02 5.85893e-02 8.00256e-02 6.38978e-04 0.00000e+00 1.18906e-03 3.30462e-02 4.76190e-02 4.94812e-02 4.70322e-02 3.23775e-02 4.63895e-02 4.69477e-02 4.56218e-02 3.20930e-02 2.64117e-02 3.80228e-02 3.27582e-02 3.41880e-02 3.54610e-02 3.38983e-02 259561 0.03483 0.04095 0.02236 249858 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(8)&Uncertain_significance(1) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 1 35829419 15 30 50.0 -1 247425556 C A A missense_variant MODERATE NLRP3 114548 Transcript NM_001127462.3 protein_coding 4/9 2876 2107 703 Q/K Cag/Aag 1 EntrezGene C C 0.48 0 0.438 -0.230891 0.438 0.26450865337110768 0.2444453 -1.24906822791398 0.05144 0.2389972 -1.18329923690612 0.05252 -2.37&-2.37&-2.37&0.66&-2.37&.&.&0.66 -0.977 0.99871367089653 0.001058 0.1192 -0.7308 .&.&.&.&.&.&.&. 1&1&1&1&1&1 0.2&-0.02&-0.02&-0.14&0.2&.&.&0.52 2.2553 0.105&0.066&0.063&0.142&0.04&.&.&0.083 0.06425 0.549168 0.000000 0.000000 -0.197000 0.078000 -1.58 rs35829419 4687 143236 3.27222e-02 9.94622e-03 1.04837e-02 9.31484e-03 4.89978e-02 4.25532e-02 5.60748e-02 2.41026e-02 2.55586e-02 2.29915e-02 6.86747e-02 5.85893e-02 8.00256e-02 6.38978e-04 0.00000e+00 1.18906e-03 3.30462e-02 4.76190e-02 4.94812e-02 4.70322e-02 3.23775e-02 4.63895e-02 4.69477e-02 4.56218e-02 3.20930e-02 2.64117e-02 3.80228e-02 3.27582e-02 3.41880e-02 3.54610e-02 3.38983e-02 259561 0.03483 0.04095 0.02236 249858 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(8)&Uncertain_significance(1) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 1 35829419 15 30 50.0 -1 247425556 C A A missense_variant MODERATE NLRP3 114548 Transcript NM_001243133.2 protein_coding 4/10 2876 2107 703 Q/K Cag/Aag 1 EntrezGene C C 0.63 0.01 0.438 -0.230891 0.438 0.26450865337110768 0.2444453 -1.24906822791398 0.05144 0.2389972 -1.18329923690612 0.05252 -2.37&-2.37&-2.37&0.66&-2.37&.&.&0.66 -0.977 0.99871367089653 0.001058 0.1192 -0.7308 .&.&.&.&.&.&.&. 1&1&1&1&1&1 0.2&-0.02&-0.02&-0.14&0.2&.&.&0.52 2.2553 0.105&0.066&0.063&0.142&0.04&.&.&0.083 0.06425 0.549168 0.000000 0.000000 -0.197000 0.078000 -1.58 rs35829419 4687 143236 3.27222e-02 9.94622e-03 1.04837e-02 9.31484e-03 4.89978e-02 4.25532e-02 5.60748e-02 2.41026e-02 2.55586e-02 2.29915e-02 6.86747e-02 5.85893e-02 8.00256e-02 6.38978e-04 0.00000e+00 1.18906e-03 3.30462e-02 4.76190e-02 4.94812e-02 4.70322e-02 3.23775e-02 4.63895e-02 4.69477e-02 4.56218e-02 3.20930e-02 2.64117e-02 3.80228e-02 3.27582e-02 3.41880e-02 3.54610e-02 3.38983e-02 259561 0.03483 0.04095 0.02236 249858 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(8)&Uncertain_significance(1) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 1 35829419 15 30 50.0 -1 247425556 C A A missense_variant MODERATE NLRP3 114548 Transcript NM_004895.5 protein_coding 4/10 2876 2113 705 Q/K Cag/Aag 1 EntrezGene C C 0.64 0.01 0.438 -0.230891 0.438 0.26450865337110768 0.2444453 -1.24906822791398 0.05144 0.2389972 -1.18329923690612 0.05252 -2.37&-2.37&-2.37&0.66&-2.37&.&.&0.66 -0.977 0.99871367089653 0.001058 0.1192 -0.7308 .&.&.&.&.&.&.&. 1&1&1&1&1&1 0.2&-0.02&-0.02&-0.14&0.2&.&.&0.52 2.2553 0.105&0.066&0.063&0.142&0.04&.&.&0.083 0.06425 0.549168 0.000000 0.000000 -0.197000 0.078000 -1.58 rs35829419 4687 143236 3.27222e-02 9.94622e-03 1.04837e-02 9.31484e-03 4.89978e-02 4.25532e-02 5.60748e-02 2.41026e-02 2.55586e-02 2.29915e-02 6.86747e-02 5.85893e-02 8.00256e-02 6.38978e-04 0.00000e+00 1.18906e-03 3.30462e-02 4.76190e-02 4.94812e-02 4.70322e-02 3.23775e-02 4.63895e-02 4.69477e-02 4.56218e-02 3.20930e-02 2.64117e-02 3.80228e-02 3.27582e-02 3.41880e-02 3.54610e-02 3.38983e-02 259561 0.03483 0.04095 0.02236 249858 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(8)&Uncertain_significance(1) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 1 35829419 15 30 50.0 -1 247425556 C A A missense_variant MODERATE NLRP3 114548 Transcript NM_183395.3 protein_coding 4/8 2876 2107 703 Q/K Cag/Aag 1 EntrezGene C C 0.58 0.014 0.438 -0.230891 0.438 0.26450865337110768 0.2444453 -1.24906822791398 0.05144 0.2389972 -1.18329923690612 0.05252 -2.37&-2.37&-2.37&0.66&-2.37&.&.&0.66 -0.977 0.99871367089653 0.001058 0.1192 -0.7308 .&.&.&.&.&.&.&. 1&1&1&1&1&1 0.2&-0.02&-0.02&-0.14&0.2&.&.&0.52 2.2553 0.105&0.066&0.063&0.142&0.04&.&.&0.083 0.06425 0.549168 0.000000 0.000000 -0.197000 0.078000 -1.58 rs35829419 4687 143236 3.27222e-02 9.94622e-03 1.04837e-02 9.31484e-03 4.89978e-02 4.25532e-02 5.60748e-02 2.41026e-02 2.55586e-02 2.29915e-02 6.86747e-02 5.85893e-02 8.00256e-02 6.38978e-04 0.00000e+00 1.18906e-03 3.30462e-02 4.76190e-02 4.94812e-02 4.70322e-02 3.23775e-02 4.63895e-02 4.69477e-02 4.56218e-02 3.20930e-02 2.64117e-02 3.80228e-02 3.27582e-02 3.41880e-02 3.54610e-02 3.38983e-02 259561 0.03483 0.04095 0.02236 249858 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(8)&Uncertain_significance(1) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 1 35829419 15 30 50.0 -1 247425556 C A A missense_variant MODERATE NLRP3 114548 Transcript XM_011544048.2 protein_coding 4/10 2998 2113 705 Q/K Cag/Aag 1 EntrezGene C C 0.64 0.01 0.438 -0.230891 0.438 0.26450865337110768 0.2444453 -1.24906822791398 0.05144 0.2389972 -1.18329923690612 0.05252 -2.37&-2.37&-2.37&0.66&-2.37&.&.&0.66 -0.977 0.99871367089653 0.001058 0.1192 -0.7308 .&.&.&.&.&.&.&. 1&1&1&1&1&1 0.2&-0.02&-0.02&-0.14&0.2&.&.&0.52 2.2553 0.105&0.066&0.063&0.142&0.04&.&.&0.083 0.06425 0.549168 0.000000 0.000000 -0.197000 0.078000 -1.58 rs35829419 4687 143236 3.27222e-02 9.94622e-03 1.04837e-02 9.31484e-03 4.89978e-02 4.25532e-02 5.60748e-02 2.41026e-02 2.55586e-02 2.29915e-02 6.86747e-02 5.85893e-02 8.00256e-02 6.38978e-04 0.00000e+00 1.18906e-03 3.30462e-02 4.76190e-02 4.94812e-02 4.70322e-02 3.23775e-02 4.63895e-02 4.69477e-02 4.56218e-02 3.20930e-02 2.64117e-02 3.80228e-02 3.27582e-02 3.41880e-02 3.54610e-02 3.38983e-02 259561 0.03483 0.04095 0.02236 249858 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(8)&Uncertain_significance(1) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 1 35829419 15 30 50.0 -1 247425556 C A A missense_variant MODERATE NLRP3 114548 Transcript XM_017000181.1 protein_coding 5/11 2475 2113 705 Q/K Cag/Aag 1 EntrezGene C C 0.64 0.01 0.438 -0.230891 0.438 0.26450865337110768 0.2444453 -1.24906822791398 0.05144 0.2389972 -1.18329923690612 0.05252 -2.37&-2.37&-2.37&0.66&-2.37&.&.&0.66 -0.977 0.99871367089653 0.001058 0.1192 -0.7308 .&.&.&.&.&.&.&. 1&1&1&1&1&1 0.2&-0.02&-0.02&-0.14&0.2&.&.&0.52 2.2553 0.105&0.066&0.063&0.142&0.04&.&.&0.083 0.06425 0.549168 0.000000 0.000000 -0.197000 0.078000 -1.58 rs35829419 4687 143236 3.27222e-02 9.94622e-03 1.04837e-02 9.31484e-03 4.89978e-02 4.25532e-02 5.60748e-02 2.41026e-02 2.55586e-02 2.29915e-02 6.86747e-02 5.85893e-02 8.00256e-02 6.38978e-04 0.00000e+00 1.18906e-03 3.30462e-02 4.76190e-02 4.94812e-02 4.70322e-02 3.23775e-02 4.63895e-02 4.69477e-02 4.56218e-02 3.20930e-02 2.64117e-02 3.80228e-02 3.27582e-02 3.41880e-02 3.54610e-02 3.38983e-02 259561 0.03483 0.04095 0.02236 249858 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(8)&Uncertain_significance(1) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 1 35829419 15 30 50.0 -1 247425556 C A A missense_variant MODERATE NLRP3 114548 Transcript XM_017000182.1 protein_coding 5/11 2420 2113 705 Q/K Cag/Aag 1 EntrezGene C C 0.64 0.01 0.438 -0.230891 0.438 0.26450865337110768 0.2444453 -1.24906822791398 0.05144 0.2389972 -1.18329923690612 0.05252 -2.37&-2.37&-2.37&0.66&-2.37&.&.&0.66 -0.977 0.99871367089653 0.001058 0.1192 -0.7308 .&.&.&.&.&.&.&. 1&1&1&1&1&1 0.2&-0.02&-0.02&-0.14&0.2&.&.&0.52 2.2553 0.105&0.066&0.063&0.142&0.04&.&.&0.083 0.06425 0.549168 0.000000 0.000000 -0.197000 0.078000 -1.58 rs35829419 4687 143236 3.27222e-02 9.94622e-03 1.04837e-02 9.31484e-03 4.89978e-02 4.25532e-02 5.60748e-02 2.41026e-02 2.55586e-02 2.29915e-02 6.86747e-02 5.85893e-02 8.00256e-02 6.38978e-04 0.00000e+00 1.18906e-03 3.30462e-02 4.76190e-02 4.94812e-02 4.70322e-02 3.23775e-02 4.63895e-02 4.69477e-02 4.56218e-02 3.20930e-02 2.64117e-02 3.80228e-02 3.27582e-02 3.41880e-02 3.54610e-02 3.38983e-02 259561 0.03483 0.04095 0.02236 249858 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(8)&Uncertain_significance(1) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 1 35829419 15 30 50.0 -1 247425556 C A A missense_variant MODERATE NLRP3 114548 Transcript XM_024452862.1 protein_coding 4/8 2998 2113 705 Q/K Cag/Aag 1 EntrezGene C C 0.438 -0.230891 0.438 0.26450865337110768 0.2444453 -1.24906822791398 0.05144 0.2389972 -1.18329923690612 0.05252 -2.37&-2.37&-2.37&0.66&-2.37&.&.&0.66 -0.977 0.99871367089653 0.001058 0.1192 -0.7308 .&.&.&.&.&.&.&. 1&1&1&1&1&1 0.2&-0.02&-0.02&-0.14&0.2&.&.&0.52 2.2553 0.105&0.066&0.063&0.142&0.04&.&.&0.083 0.06425 0.549168 0.000000 0.000000 -0.197000 0.078000 -1.58 rs35829419 4687 143236 3.27222e-02 9.94622e-03 1.04837e-02 9.31484e-03 4.89978e-02 4.25532e-02 5.60748e-02 2.41026e-02 2.55586e-02 2.29915e-02 6.86747e-02 5.85893e-02 8.00256e-02 6.38978e-04 0.00000e+00 1.18906e-03 3.30462e-02 4.76190e-02 4.94812e-02 4.70322e-02 3.23775e-02 4.63895e-02 4.69477e-02 4.56218e-02 3.20930e-02 2.64117e-02 3.80228e-02 3.27582e-02 3.41880e-02 3.54610e-02 3.38983e-02 259561 0.03483 0.04095 0.02236 249858 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(8)&Uncertain_significance(1) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 1 35829419 15 30 50.0 -1 247425556 C A A missense_variant MODERATE NLRP3 114548 Transcript XM_024452874.1 protein_coding 4/7 2998 2113 705 Q/K Cag/Aag 1 EntrezGene C C 0.438 -0.230891 0.438 0.26450865337110768 0.2444453 -1.24906822791398 0.05144 0.2389972 -1.18329923690612 0.05252 -2.37&-2.37&-2.37&0.66&-2.37&.&.&0.66 -0.977 0.99871367089653 0.001058 0.1192 -0.7308 .&.&.&.&.&.&.&. 1&1&1&1&1&1 0.2&-0.02&-0.02&-0.14&0.2&.&.&0.52 2.2553 0.105&0.066&0.063&0.142&0.04&.&.&0.083 0.06425 0.549168 0.000000 0.000000 -0.197000 0.078000 -1.58 rs35829419 4687 143236 3.27222e-02 9.94622e-03 1.04837e-02 9.31484e-03 4.89978e-02 4.25532e-02 5.60748e-02 2.41026e-02 2.55586e-02 2.29915e-02 6.86747e-02 5.85893e-02 8.00256e-02 6.38978e-04 0.00000e+00 1.18906e-03 3.30462e-02 4.76190e-02 4.94812e-02 4.70322e-02 3.23775e-02 4.63895e-02 4.69477e-02 4.56218e-02 3.20930e-02 2.64117e-02 3.80228e-02 3.27582e-02 3.41880e-02 3.54610e-02 3.38983e-02 259561 0.03483 0.04095 0.02236 249858 Familial_cold_urticaria&Familial_amyloid_nephropathy_with_urticaria_AND_deafness&Chronic_infantile_neurological&_cutaneous_and_articular_syndrome&Cryopyrin_associated_periodic_syndrome¬_specified¬_provided MONDO:MONDO:0007349&MedGen:C4551895&OMIM:120100&Orphanet:ORPHA47045&SNOMED_CT:238687000&MONDO:MONDO:0008633&MedGen:C0268390&OMIM:191900&Orphanet:ORPHA575&SNOMED_CT:15123008&MONDO:MONDO:0011776&MedGen:C0409818&OMIM:607115&Orphanet:ORPHA1451&SNOMED_CT:239826001&MONDO:MONDO:0016168&MedGen:C2316212&Orphanet:ORPHA208650&SNOMED_CT:430079001&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(8)&Uncertain_significance(1) single_nucleotide_variant NLRP3:114548 SO:0001583&missense_variant 1 35829419 15 30 50.0 -2 4619138 T C C intergenic_variant MODIFIER 1.222 -0.018763 -4.37 rs826009 67641 142990 4.73047e-01 6.86993e-01 6.83048e-01 6.91625e-01 5.42316e-01 5.17094e-01 5.69767e-01 3.15743e-01 3.24975e-01 3.08706e-01 5.18362e-01 5.17026e-01 5.19872e-01 6.38053e-01 6.23269e-01 6.50775e-01 4.73728e-01 3.67749e-01 3.72400e-01 3.66279e-01 4.72322e-01 3.70884e-01 3.69393e-01 3.72937e-01 4.44548e-01 4.36248e-01 4.53244e-01 4.73357e-01 5.39500e-01 5.12456e-01 5.45638e-01 15 30 50.0 -2 21006160 G A A missense_variant MODERATE APOB 338 Transcript NM_000384.3 protein_coding 26/29 10836 10708 3570 H/Y Cac/Tac -1 EntrezGene G G 1 0.005 0.891 -0.084985 0.891 0.9811992347550077 0.5529585 -0.950256694190647 0.10917 0.4886634 -0.925633909591818 0.10245 -0.5 0.757 0.995003517643111 0.125205 0.016788 0.2208 -1.0546 1 -1.76 1.3833 0.1 0.18988 0.553676 0.000000 0.002000 -0.109000 0.222000 -0.000322 rs201736972 29 143240 2.02457e-04 2.37982e-05 0.00000e+00 5.17545e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.97958e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.00859e-04 4.33678e-04 5.88456e-04 2.20767e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.02279e-04 0.00000e+00 0.00000e+00 0.00000e+00 431988 0.00015 0.00015 425464 Familial_hypercholesterolemia&Familial_hypercholesterolemia_1&Familial_hypercholesterolemia_2&Hypobetalipoproteinemia&_familial&_1¬_specified¬_provided MONDO:MONDO:0005439&MedGen:C0020445&OMIM:PS143890&SNOMED_CT:398036000&MONDO:MONDO:0007750&MedGen:C0745103&OMIM:143890&SNOMED_CT:397915002&MONDO:MONDO:0007751&MedGen:C1704417&OMIM:144010&MONDO:MONDO:0014252&MedGen:C4551990&OMIM:615558&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Uncertain_significance single_nucleotide_variant APOB:338 SO:0001583&missense_variant 1 201736972 15 30 50.0 -2 26195184 C G G missense_variant MODERATE HADHA 3030 Transcript NM_000182.5 protein_coding 15/20 1564 1528 510 E/Q Gag/Cag -1 EntrezGene C C 0 1 26.2 3.865062 26.2 0.99854474735248155 16.80406 0.964191365232841 0.97807 16.70255 1.08045046419057 0.97755 -2.51&.&. 5.29 0.999999999999997 0.000000 0.532387 0.9076 1.0817 4.765&.&. 1 -2.85&.&. 17.5319 0.955&.&. 0.97206 0.706548 1.000000 0.990000 7.758000 0.966000 6.38 rs137852769 144 143116 1.00618e-03 1.19161e-04 4.41228e-05 2.07297e-04 0.00000e+00 0.00000e+00 0.00000e+00 5.14177e-04 8.48033e-04 2.59134e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13251e-04 2.67686e-03 1.19904e-03 3.14149e-03 1.21146e-03 1.59581e-03 1.36451e-03 1.91402e-03 4.64684e-04 0.00000e+00 9.50570e-04 1.00452e-03 0.00000e+00 0.00000e+00 0.00000e+00 100085 0.00120 0.00020 23767 Deficiency_of_long-chain_3-hydroxyacyl-coenzyme_A_dehydrogenase&HADHA-Related_Disorders&Mitochondrial_trifunctional_protein_deficiency&Long-chain_3-hydroxyacyl-CoA_dehydrogenase_deficiency&Inborn_genetic_diseases&Lchad_deficiency_with_maternal_acute_fatty_liver_of_pregnancy¬_specified&LCHAD_Deficiency¬_provided .&.&MONDO:MONDO:0012172&MedGen:C1969443&OMIM:609015&Orphanet:ORPHA746&SNOMED_CT:237999008&MONDO:MONDO:0012173&MedGen:C3711645&OMIM:609016&Orphanet:ORPHA5&MeSH:D030342&MedGen:C0950123&MedGen:C1833202&MedGen:CN169374&MedGen:CN239369&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant HADHA:3030&GAREM2:150946 SO:0001583&missense_variant 1 137852769 15 30 50.0 -2 26195184 C G G intron_variant MODIFIER GAREM2 150946 Transcript XM_011532567.3 protein_coding 6/6 1 EntrezGene C C 26.2 3.865062 6.38 rs137852769 144 143116 1.00618e-03 1.19161e-04 4.41228e-05 2.07297e-04 0.00000e+00 0.00000e+00 0.00000e+00 5.14177e-04 8.48033e-04 2.59134e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13251e-04 2.67686e-03 1.19904e-03 3.14149e-03 1.21146e-03 1.59581e-03 1.36451e-03 1.91402e-03 4.64684e-04 0.00000e+00 9.50570e-04 1.00452e-03 0.00000e+00 0.00000e+00 0.00000e+00 100085 0.00120 0.00020 23767 Deficiency_of_long-chain_3-hydroxyacyl-coenzyme_A_dehydrogenase&HADHA-Related_Disorders&Mitochondrial_trifunctional_protein_deficiency&Long-chain_3-hydroxyacyl-CoA_dehydrogenase_deficiency&Inborn_genetic_diseases&Lchad_deficiency_with_maternal_acute_fatty_liver_of_pregnancy¬_specified&LCHAD_Deficiency¬_provided .&.&MONDO:MONDO:0012172&MedGen:C1969443&OMIM:609015&Orphanet:ORPHA746&SNOMED_CT:237999008&MONDO:MONDO:0012173&MedGen:C3711645&OMIM:609016&Orphanet:ORPHA5&MeSH:D030342&MedGen:C0950123&MedGen:C1833202&MedGen:CN169374&MedGen:CN239369&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant HADHA:3030&GAREM2:150946 SO:0001583&missense_variant 1 137852769 15 30 50.0 -2 31370367 A AAGGT AGGT frameshift_variant HIGH XDH 7498 Transcript NM_000379.4 protein_coding 18/36 2043-2044 1967-1968 656 F/LPX ttt/ttACCTt -1 EntrezGene 5.23&6.37 15 30 50.0 -2 31370367 A AAGGT AGGT frameshift_variant HIGH XDH 7498 Transcript XM_011533095.2 protein_coding 18/36 2075-2076 1964-1965 655 F/LPX ttt/ttACCTt -1 EntrezGene 5.23&6.37 15 30 50.0 -2 31370367 A AAGGT AGGT frameshift_variant HIGH XDH 7498 Transcript XM_011533096.2 protein_coding 18/29 2077-2078 1967-1968 656 F/LPX ttt/ttACCTt -1 EntrezGene 5.23&6.37 15 30 50.0 -2 73926914 G T T missense_variant MODERATE DGUOK 1716 Transcript NM_001318859.2 protein_coding 1/5 35 4 2 A/S Gcc/Tcc 1 EntrezGene G G 0.02 0.287 14.67 1.339969 14.67 0.99292705326507225 0.8084026 -0.764086255037038 0.15377 0.7374184 -0.745711592529267 0.14750 .&-5.37&-4.83 1.19 1.0 0.686943 0.211792 0.8694 0.5177 2.015&2.015&2.015 0.999897&0.999897&0.999907 .&-0.12&-0.27 10.8387 0.738&0.376&0.376 0.49627 0.441713 0.018000 0.668000 -0.201000 0.222000 -1.61 rs147551003 428 143328 2.98616e-03 9.98621e-04 8.36489e-04 1.18900e-03 2.22222e-03 2.12766e-03 2.32558e-03 2.41546e-03 2.53635e-03 2.32318e-03 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.53352e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.40412e-03 5.14282e-03 5.64413e-03 4.45311e-03 7.42804e-03 1.18182e-02 2.84630e-03 2.99222e-03 0.00000e+00 0.00000e+00 0.00000e+00 193482 0.00338 0.00235 0.00120 190646 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(2)&Uncertain_significance(6) single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 9 147551003 15 30 50.0 -2 73926914 G T T 5_prime_UTR_variant MODIFIER DGUOK 1716 Transcript NM_001318860.2 protein_coding 1/6 35 1 EntrezGene G G 14.67 1.339969 -1.61 rs147551003 428 143328 2.98616e-03 9.98621e-04 8.36489e-04 1.18900e-03 2.22222e-03 2.12766e-03 2.32558e-03 2.41546e-03 2.53635e-03 2.32318e-03 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.53352e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.40412e-03 5.14282e-03 5.64413e-03 4.45311e-03 7.42804e-03 1.18182e-02 2.84630e-03 2.99222e-03 0.00000e+00 0.00000e+00 0.00000e+00 193482 0.00338 0.00235 0.00120 190646 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(2)&Uncertain_significance(6) single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 9 147551003 15 30 50.0 -2 73926914 G T T 5_prime_UTR_variant MODIFIER DGUOK 1716 Transcript NM_001318861.2 protein_coding 1/7 35 1 EntrezGene G G 14.67 1.339969 -1.61 rs147551003 428 143328 2.98616e-03 9.98621e-04 8.36489e-04 1.18900e-03 2.22222e-03 2.12766e-03 2.32558e-03 2.41546e-03 2.53635e-03 2.32318e-03 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.53352e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.40412e-03 5.14282e-03 5.64413e-03 4.45311e-03 7.42804e-03 1.18182e-02 2.84630e-03 2.99222e-03 0.00000e+00 0.00000e+00 0.00000e+00 193482 0.00338 0.00235 0.00120 190646 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(2)&Uncertain_significance(6) single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 9 147551003 15 30 50.0 -2 73926914 G T T 5_prime_UTR_variant MODIFIER DGUOK 1716 Transcript NM_001318862.2 protein_coding 1/7 35 1 EntrezGene G G 14.67 1.339969 -1.61 rs147551003 428 143328 2.98616e-03 9.98621e-04 8.36489e-04 1.18900e-03 2.22222e-03 2.12766e-03 2.32558e-03 2.41546e-03 2.53635e-03 2.32318e-03 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.53352e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.40412e-03 5.14282e-03 5.64413e-03 4.45311e-03 7.42804e-03 1.18182e-02 2.84630e-03 2.99222e-03 0.00000e+00 0.00000e+00 0.00000e+00 193482 0.00338 0.00235 0.00120 190646 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(2)&Uncertain_significance(6) single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 9 147551003 15 30 50.0 -2 73926914 G T T 5_prime_UTR_variant MODIFIER DGUOK 1716 Transcript NM_001318863.2 protein_coding 1/6 35 1 EntrezGene G G 14.67 1.339969 -1.61 rs147551003 428 143328 2.98616e-03 9.98621e-04 8.36489e-04 1.18900e-03 2.22222e-03 2.12766e-03 2.32558e-03 2.41546e-03 2.53635e-03 2.32318e-03 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.53352e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.40412e-03 5.14282e-03 5.64413e-03 4.45311e-03 7.42804e-03 1.18182e-02 2.84630e-03 2.99222e-03 0.00000e+00 0.00000e+00 0.00000e+00 193482 0.00338 0.00235 0.00120 190646 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(2)&Uncertain_significance(6) single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 9 147551003 15 30 50.0 -2 73926914 G T T missense_variant MODERATE DGUOK 1716 Transcript NM_080916.3 protein_coding 1/7 35 4 2 A/S Gcc/Tcc 1 EntrezGene G G 0.04 0.027 14.67 1.339969 14.67 0.99292705326507225 0.8084026 -0.764086255037038 0.15377 0.7374184 -0.745711592529267 0.14750 .&-5.37&-4.83 1.19 1.0 0.686943 0.211792 0.8694 0.5177 2.015&2.015&2.015 0.999897&0.999897&0.999907 .&-0.12&-0.27 10.8387 0.738&0.376&0.376 0.49627 0.441713 0.018000 0.668000 -0.201000 0.222000 -1.61 rs147551003 428 143328 2.98616e-03 9.98621e-04 8.36489e-04 1.18900e-03 2.22222e-03 2.12766e-03 2.32558e-03 2.41546e-03 2.53635e-03 2.32318e-03 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.53352e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.40412e-03 5.14282e-03 5.64413e-03 4.45311e-03 7.42804e-03 1.18182e-02 2.84630e-03 2.99222e-03 0.00000e+00 0.00000e+00 0.00000e+00 193482 0.00338 0.00235 0.00120 190646 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(2)&Uncertain_significance(6) single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 9 147551003 15 30 50.0 -2 73926914 G T T missense_variant MODERATE DGUOK 1716 Transcript NM_080918.3 protein_coding 1/5 35 4 2 A/S Gcc/Tcc 1 EntrezGene G G 0.02 0.081 14.67 1.339969 14.67 0.99292705326507225 0.8084026 -0.764086255037038 0.15377 0.7374184 -0.745711592529267 0.14750 .&-5.37&-4.83 1.19 1.0 0.686943 0.211792 0.8694 0.5177 2.015&2.015&2.015 0.999897&0.999897&0.999907 .&-0.12&-0.27 10.8387 0.738&0.376&0.376 0.49627 0.441713 0.018000 0.668000 -0.201000 0.222000 -1.61 rs147551003 428 143328 2.98616e-03 9.98621e-04 8.36489e-04 1.18900e-03 2.22222e-03 2.12766e-03 2.32558e-03 2.41546e-03 2.53635e-03 2.32318e-03 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.53352e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.40412e-03 5.14282e-03 5.64413e-03 4.45311e-03 7.42804e-03 1.18182e-02 2.84630e-03 2.99222e-03 0.00000e+00 0.00000e+00 0.00000e+00 193482 0.00338 0.00235 0.00120 190646 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(2)&Uncertain_significance(6) single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 9 147551003 15 30 50.0 -2 73926914 G T T non_coding_transcript_exon_variant MODIFIER DGUOK 1716 Transcript NR_134893.2 misc_RNA 1/4 35 1 EntrezGene G G 14.67 1.339969 -1.61 rs147551003 428 143328 2.98616e-03 9.98621e-04 8.36489e-04 1.18900e-03 2.22222e-03 2.12766e-03 2.32558e-03 2.41546e-03 2.53635e-03 2.32318e-03 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.53352e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.40412e-03 5.14282e-03 5.64413e-03 4.45311e-03 7.42804e-03 1.18182e-02 2.84630e-03 2.99222e-03 0.00000e+00 0.00000e+00 0.00000e+00 193482 0.00338 0.00235 0.00120 190646 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(2)&Uncertain_significance(6) single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 9 147551003 15 30 50.0 -2 73926914 G T T non_coding_transcript_exon_variant MODIFIER DGUOK 1716 Transcript NR_134894.2 misc_RNA 1/5 35 1 EntrezGene G G 14.67 1.339969 -1.61 rs147551003 428 143328 2.98616e-03 9.98621e-04 8.36489e-04 1.18900e-03 2.22222e-03 2.12766e-03 2.32558e-03 2.41546e-03 2.53635e-03 2.32318e-03 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.53352e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.40412e-03 5.14282e-03 5.64413e-03 4.45311e-03 7.42804e-03 1.18182e-02 2.84630e-03 2.99222e-03 0.00000e+00 0.00000e+00 0.00000e+00 193482 0.00338 0.00235 0.00120 190646 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(2)&Uncertain_significance(6) single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 9 147551003 15 30 50.0 -2 73926914 G T T non_coding_transcript_exon_variant MODIFIER DGUOK 1716 Transcript NR_134895.2 misc_RNA 1/3 35 1 EntrezGene G G 14.67 1.339969 -1.61 rs147551003 428 143328 2.98616e-03 9.98621e-04 8.36489e-04 1.18900e-03 2.22222e-03 2.12766e-03 2.32558e-03 2.41546e-03 2.53635e-03 2.32318e-03 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.53352e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.40412e-03 5.14282e-03 5.64413e-03 4.45311e-03 7.42804e-03 1.18182e-02 2.84630e-03 2.99222e-03 0.00000e+00 0.00000e+00 0.00000e+00 193482 0.00338 0.00235 0.00120 190646 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(2)&Uncertain_significance(6) single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 9 147551003 15 30 50.0 -2 73926914 G T T non_coding_transcript_exon_variant MODIFIER DGUOK 1716 Transcript NR_134896.2 misc_RNA 1/4 35 1 EntrezGene G G 14.67 1.339969 -1.61 rs147551003 428 143328 2.98616e-03 9.98621e-04 8.36489e-04 1.18900e-03 2.22222e-03 2.12766e-03 2.32558e-03 2.41546e-03 2.53635e-03 2.32318e-03 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.53352e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.40412e-03 5.14282e-03 5.64413e-03 4.45311e-03 7.42804e-03 1.18182e-02 2.84630e-03 2.99222e-03 0.00000e+00 0.00000e+00 0.00000e+00 193482 0.00338 0.00235 0.00120 190646 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(2)&Uncertain_significance(6) single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 9 147551003 15 30 50.0 -2 73926914 G T T non_coding_transcript_exon_variant MODIFIER DGUOK 1716 Transcript NR_134897.2 misc_RNA 1/6 35 1 EntrezGene G G 14.67 1.339969 -1.61 rs147551003 428 143328 2.98616e-03 9.98621e-04 8.36489e-04 1.18900e-03 2.22222e-03 2.12766e-03 2.32558e-03 2.41546e-03 2.53635e-03 2.32318e-03 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.53352e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.40412e-03 5.14282e-03 5.64413e-03 4.45311e-03 7.42804e-03 1.18182e-02 2.84630e-03 2.99222e-03 0.00000e+00 0.00000e+00 0.00000e+00 193482 0.00338 0.00235 0.00120 190646 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(2)&Uncertain_significance(6) single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 9 147551003 15 30 50.0 -2 73926914 G T T non_coding_transcript_exon_variant MODIFIER DGUOK 1716 Transcript NR_134898.2 misc_RNA 1/5 35 1 EntrezGene G G 14.67 1.339969 -1.61 rs147551003 428 143328 2.98616e-03 9.98621e-04 8.36489e-04 1.18900e-03 2.22222e-03 2.12766e-03 2.32558e-03 2.41546e-03 2.53635e-03 2.32318e-03 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.53352e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.40412e-03 5.14282e-03 5.64413e-03 4.45311e-03 7.42804e-03 1.18182e-02 2.84630e-03 2.99222e-03 0.00000e+00 0.00000e+00 0.00000e+00 193482 0.00338 0.00235 0.00120 190646 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(2)&Uncertain_significance(6) single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 9 147551003 15 30 50.0 -2 73926914 G T T missense_variant MODERATE DGUOK 1716 Transcript XM_011532647.2 protein_coding 1/7 85 4 2 A/S Gcc/Tcc 1 EntrezGene G G 14.67 1.339969 14.67 0.99292705326507225 0.8084026 -0.764086255037038 0.15377 0.7374184 -0.745711592529267 0.14750 .&-5.37&-4.83 1.19 1.0 0.686943 0.211792 0.8694 0.5177 2.015&2.015&2.015 0.999897&0.999897&0.999907 .&-0.12&-0.27 10.8387 0.738&0.376&0.376 0.49627 0.441713 0.018000 0.668000 -0.201000 0.222000 -1.61 rs147551003 428 143328 2.98616e-03 9.98621e-04 8.36489e-04 1.18900e-03 2.22222e-03 2.12766e-03 2.32558e-03 2.41546e-03 2.53635e-03 2.32318e-03 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.53352e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.40412e-03 5.14282e-03 5.64413e-03 4.45311e-03 7.42804e-03 1.18182e-02 2.84630e-03 2.99222e-03 0.00000e+00 0.00000e+00 0.00000e+00 193482 0.00338 0.00235 0.00120 190646 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(2)&Uncertain_significance(6) single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 9 147551003 15 30 50.0 -2 73926914 G T T 5_prime_UTR_variant MODIFIER DGUOK 1716 Transcript XM_024452739.1 protein_coding 1/8 37 1 EntrezGene G G 14.67 1.339969 -1.61 rs147551003 428 143328 2.98616e-03 9.98621e-04 8.36489e-04 1.18900e-03 2.22222e-03 2.12766e-03 2.32558e-03 2.41546e-03 2.53635e-03 2.32318e-03 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.53352e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.40412e-03 5.14282e-03 5.64413e-03 4.45311e-03 7.42804e-03 1.18182e-02 2.84630e-03 2.99222e-03 0.00000e+00 0.00000e+00 0.00000e+00 193482 0.00338 0.00235 0.00120 190646 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(2)&Uncertain_significance(6) single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 9 147551003 15 30 50.0 -2 73926914 G T T non_coding_transcript_exon_variant MODIFIER DGUOK 1716 Transcript XR_001738656.1 misc_RNA 1/6 88 1 EntrezGene G G 14.67 1.339969 -1.61 rs147551003 428 143328 2.98616e-03 9.98621e-04 8.36489e-04 1.18900e-03 2.22222e-03 2.12766e-03 2.32558e-03 2.41546e-03 2.53635e-03 2.32318e-03 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.53352e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.40412e-03 5.14282e-03 5.64413e-03 4.45311e-03 7.42804e-03 1.18182e-02 2.84630e-03 2.99222e-03 0.00000e+00 0.00000e+00 0.00000e+00 193482 0.00338 0.00235 0.00120 190646 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(2)&Uncertain_significance(6) single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 9 147551003 15 30 50.0 -2 73926914 G T T non_coding_transcript_exon_variant MODIFIER DGUOK 1716 Transcript XR_244926.3 misc_RNA 1/6 87 1 EntrezGene G G 14.67 1.339969 -1.61 rs147551003 428 143328 2.98616e-03 9.98621e-04 8.36489e-04 1.18900e-03 2.22222e-03 2.12766e-03 2.32558e-03 2.41546e-03 2.53635e-03 2.32318e-03 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.53352e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.40412e-03 5.14282e-03 5.64413e-03 4.45311e-03 7.42804e-03 1.18182e-02 2.84630e-03 2.99222e-03 0.00000e+00 0.00000e+00 0.00000e+00 193482 0.00338 0.00235 0.00120 190646 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(2)&Uncertain_significance(6) single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 9 147551003 15 30 50.0 -2 73950650 A G G intron_variant MODIFIER DGUOK 1716 Transcript NM_001318859.2 protein_coding 3/4 1 EntrezGene A A 26.9 3.988512 6.54 rs74874677 2082 143308 1.45281e-02 4.28225e-03 4.18428e-03 4.39731e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.43443e-02 1.55563e-02 1.34194e-02 8.42359e-03 7.94552e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.45259e-02 2.19633e-02 2.03512e-02 2.24705e-02 1.45305e-02 2.16785e-02 2.14450e-02 2.19999e-02 1.34758e-02 1.27273e-02 1.42586e-02 1.45418e-02 5.90551e-03 7.06714e-03 5.64061e-03 137082 0.01668 0.01530 0.00919 140785 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral&Progressive_external_ophthalmoplegia_with_mitochondrial_DNA_deletions&_autosomal_recessive_4¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MONDO:MONDO:0014899&MedGen:C4310733&OMIM:617070&Orphanet:ORPHA329314&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Benign/Likely_benign single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 74874677 15 30 50.0 -2 73950650 A G G missense_variant MODERATE DGUOK 1716 Transcript NM_001318860.2 protein_coding 3/6 427 218 73 Q/R cAg/cGg 1 EntrezGene A A 0.02 0.999 26.9 3.988512 26.9 0.99783997658218382 8.514832 0.737441587945016 0.85217 8.022645 0.758503282566834 0.83457 -4.66 5.67 0.999999999971409 0.000000 0.8635 0.5703 2.505 1&1&1 -2.61 14.9038 0.317 0.99201 0.706548 1.000000 1.000000 9.064000 1.312000 6.54 rs74874677 2082 143308 1.45281e-02 4.28225e-03 4.18428e-03 4.39731e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.43443e-02 1.55563e-02 1.34194e-02 8.42359e-03 7.94552e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.45259e-02 2.19633e-02 2.03512e-02 2.24705e-02 1.45305e-02 2.16785e-02 2.14450e-02 2.19999e-02 1.34758e-02 1.27273e-02 1.42586e-02 1.45418e-02 5.90551e-03 7.06714e-03 5.64061e-03 137082 0.01668 0.01530 0.00919 140785 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral&Progressive_external_ophthalmoplegia_with_mitochondrial_DNA_deletions&_autosomal_recessive_4¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MONDO:MONDO:0014899&MedGen:C4310733&OMIM:617070&Orphanet:ORPHA329314&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Benign/Likely_benign single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 74874677 15 30 50.0 -2 73950650 A G G missense_variant MODERATE DGUOK 1716 Transcript NM_001318861.2 protein_coding 4/7 513 218 73 Q/R cAg/cGg 1 EntrezGene A A 0.02 0.999 26.9 3.988512 26.9 0.99783997658218382 8.514832 0.737441587945016 0.85217 8.022645 0.758503282566834 0.83457 -4.66 5.67 0.999999999971409 0.000000 0.8635 0.5703 2.505 1&1&1 -2.61 14.9038 0.317 0.99201 0.706548 1.000000 1.000000 9.064000 1.312000 6.54 rs74874677 2082 143308 1.45281e-02 4.28225e-03 4.18428e-03 4.39731e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.43443e-02 1.55563e-02 1.34194e-02 8.42359e-03 7.94552e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.45259e-02 2.19633e-02 2.03512e-02 2.24705e-02 1.45305e-02 2.16785e-02 2.14450e-02 2.19999e-02 1.34758e-02 1.27273e-02 1.42586e-02 1.45418e-02 5.90551e-03 7.06714e-03 5.64061e-03 137082 0.01668 0.01530 0.00919 140785 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral&Progressive_external_ophthalmoplegia_with_mitochondrial_DNA_deletions&_autosomal_recessive_4¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MONDO:MONDO:0014899&MedGen:C4310733&OMIM:617070&Orphanet:ORPHA329314&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Benign/Likely_benign single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 74874677 15 30 50.0 -2 73950650 A G G missense_variant MODERATE DGUOK 1716 Transcript NM_001318862.2 protein_coding 4/7 495 200 67 Q/R cAg/cGg 1 EntrezGene A A 0.03 0.999 26.9 3.988512 26.9 0.99783997658218382 8.514832 0.737441587945016 0.85217 8.022645 0.758503282566834 0.83457 -4.66 5.67 0.999999999971409 0.000000 0.8635 0.5703 2.505 1&1&1 -2.61 14.9038 0.317 0.99201 0.706548 1.000000 1.000000 9.064000 1.312000 6.54 rs74874677 2082 143308 1.45281e-02 4.28225e-03 4.18428e-03 4.39731e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.43443e-02 1.55563e-02 1.34194e-02 8.42359e-03 7.94552e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.45259e-02 2.19633e-02 2.03512e-02 2.24705e-02 1.45305e-02 2.16785e-02 2.14450e-02 2.19999e-02 1.34758e-02 1.27273e-02 1.42586e-02 1.45418e-02 5.90551e-03 7.06714e-03 5.64061e-03 137082 0.01668 0.01530 0.00919 140785 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral&Progressive_external_ophthalmoplegia_with_mitochondrial_DNA_deletions&_autosomal_recessive_4¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MONDO:MONDO:0014899&MedGen:C4310733&OMIM:617070&Orphanet:ORPHA329314&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Benign/Likely_benign single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 74874677 15 30 50.0 -2 73950650 A G G missense_variant MODERATE DGUOK 1716 Transcript NM_001318863.2 protein_coding 3/6 409 200 67 Q/R cAg/cGg 1 EntrezGene A A 0.03 0.999 26.9 3.988512 26.9 0.99783997658218382 8.514832 0.737441587945016 0.85217 8.022645 0.758503282566834 0.83457 -4.66 5.67 0.999999999971409 0.000000 0.8635 0.5703 2.505 1&1&1 -2.61 14.9038 0.317 0.99201 0.706548 1.000000 1.000000 9.064000 1.312000 6.54 rs74874677 2082 143308 1.45281e-02 4.28225e-03 4.18428e-03 4.39731e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.43443e-02 1.55563e-02 1.34194e-02 8.42359e-03 7.94552e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.45259e-02 2.19633e-02 2.03512e-02 2.24705e-02 1.45305e-02 2.16785e-02 2.14450e-02 2.19999e-02 1.34758e-02 1.27273e-02 1.42586e-02 1.45418e-02 5.90551e-03 7.06714e-03 5.64061e-03 137082 0.01668 0.01530 0.00919 140785 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral&Progressive_external_ophthalmoplegia_with_mitochondrial_DNA_deletions&_autosomal_recessive_4¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MONDO:MONDO:0014899&MedGen:C4310733&OMIM:617070&Orphanet:ORPHA329314&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Benign/Likely_benign single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 74874677 15 30 50.0 -2 73950650 A G G missense_variant MODERATE DGUOK 1716 Transcript NM_080916.3 protein_coding 4/7 540 509 170 Q/R cAg/cGg 1 EntrezGene A A 0.02 0.999 26.9 3.988512 26.9 0.99783997658218382 8.514832 0.737441587945016 0.85217 8.022645 0.758503282566834 0.83457 -4.66 5.67 0.999999999971409 0.000000 0.8635 0.5703 2.505 1&1&1 -2.61 14.9038 0.317 0.99201 0.706548 1.000000 1.000000 9.064000 1.312000 6.54 rs74874677 2082 143308 1.45281e-02 4.28225e-03 4.18428e-03 4.39731e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.43443e-02 1.55563e-02 1.34194e-02 8.42359e-03 7.94552e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.45259e-02 2.19633e-02 2.03512e-02 2.24705e-02 1.45305e-02 2.16785e-02 2.14450e-02 2.19999e-02 1.34758e-02 1.27273e-02 1.42586e-02 1.45418e-02 5.90551e-03 7.06714e-03 5.64061e-03 137082 0.01668 0.01530 0.00919 140785 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral&Progressive_external_ophthalmoplegia_with_mitochondrial_DNA_deletions&_autosomal_recessive_4¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MONDO:MONDO:0014899&MedGen:C4310733&OMIM:617070&Orphanet:ORPHA329314&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Benign/Likely_benign single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 74874677 15 30 50.0 -2 73950650 A G G intron_variant MODIFIER DGUOK 1716 Transcript NM_080918.3 protein_coding 3/4 1 EntrezGene A A 26.9 3.988512 6.54 rs74874677 2082 143308 1.45281e-02 4.28225e-03 4.18428e-03 4.39731e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.43443e-02 1.55563e-02 1.34194e-02 8.42359e-03 7.94552e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.45259e-02 2.19633e-02 2.03512e-02 2.24705e-02 1.45305e-02 2.16785e-02 2.14450e-02 2.19999e-02 1.34758e-02 1.27273e-02 1.42586e-02 1.45418e-02 5.90551e-03 7.06714e-03 5.64061e-03 137082 0.01668 0.01530 0.00919 140785 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral&Progressive_external_ophthalmoplegia_with_mitochondrial_DNA_deletions&_autosomal_recessive_4¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MONDO:MONDO:0014899&MedGen:C4310733&OMIM:617070&Orphanet:ORPHA329314&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Benign/Likely_benign single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 74874677 15 30 50.0 -2 73950650 A G G intron_variant&non_coding_transcript_variant MODIFIER DGUOK 1716 Transcript NR_134893.2 misc_RNA 2/3 1 EntrezGene A A 26.9 3.988512 6.54 rs74874677 2082 143308 1.45281e-02 4.28225e-03 4.18428e-03 4.39731e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.43443e-02 1.55563e-02 1.34194e-02 8.42359e-03 7.94552e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.45259e-02 2.19633e-02 2.03512e-02 2.24705e-02 1.45305e-02 2.16785e-02 2.14450e-02 2.19999e-02 1.34758e-02 1.27273e-02 1.42586e-02 1.45418e-02 5.90551e-03 7.06714e-03 5.64061e-03 137082 0.01668 0.01530 0.00919 140785 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral&Progressive_external_ophthalmoplegia_with_mitochondrial_DNA_deletions&_autosomal_recessive_4¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MONDO:MONDO:0014899&MedGen:C4310733&OMIM:617070&Orphanet:ORPHA329314&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Benign/Likely_benign single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 74874677 15 30 50.0 -2 73950650 A G G non_coding_transcript_exon_variant MODIFIER DGUOK 1716 Transcript NR_134894.2 misc_RNA 3/5 427 1 EntrezGene A A 26.9 3.988512 6.54 rs74874677 2082 143308 1.45281e-02 4.28225e-03 4.18428e-03 4.39731e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.43443e-02 1.55563e-02 1.34194e-02 8.42359e-03 7.94552e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.45259e-02 2.19633e-02 2.03512e-02 2.24705e-02 1.45305e-02 2.16785e-02 2.14450e-02 2.19999e-02 1.34758e-02 1.27273e-02 1.42586e-02 1.45418e-02 5.90551e-03 7.06714e-03 5.64061e-03 137082 0.01668 0.01530 0.00919 140785 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral&Progressive_external_ophthalmoplegia_with_mitochondrial_DNA_deletions&_autosomal_recessive_4¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MONDO:MONDO:0014899&MedGen:C4310733&OMIM:617070&Orphanet:ORPHA329314&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Benign/Likely_benign single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 74874677 15 30 50.0 -2 73950650 A G G intron_variant&non_coding_transcript_variant MODIFIER DGUOK 1716 Transcript NR_134895.2 misc_RNA 1/2 1 EntrezGene A A 26.9 3.988512 6.54 rs74874677 2082 143308 1.45281e-02 4.28225e-03 4.18428e-03 4.39731e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.43443e-02 1.55563e-02 1.34194e-02 8.42359e-03 7.94552e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.45259e-02 2.19633e-02 2.03512e-02 2.24705e-02 1.45305e-02 2.16785e-02 2.14450e-02 2.19999e-02 1.34758e-02 1.27273e-02 1.42586e-02 1.45418e-02 5.90551e-03 7.06714e-03 5.64061e-03 137082 0.01668 0.01530 0.00919 140785 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral&Progressive_external_ophthalmoplegia_with_mitochondrial_DNA_deletions&_autosomal_recessive_4¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MONDO:MONDO:0014899&MedGen:C4310733&OMIM:617070&Orphanet:ORPHA329314&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Benign/Likely_benign single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 74874677 15 30 50.0 -2 73950650 A G G intron_variant&non_coding_transcript_variant MODIFIER DGUOK 1716 Transcript NR_134896.2 misc_RNA 2/3 1 EntrezGene A A 26.9 3.988512 6.54 rs74874677 2082 143308 1.45281e-02 4.28225e-03 4.18428e-03 4.39731e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.43443e-02 1.55563e-02 1.34194e-02 8.42359e-03 7.94552e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.45259e-02 2.19633e-02 2.03512e-02 2.24705e-02 1.45305e-02 2.16785e-02 2.14450e-02 2.19999e-02 1.34758e-02 1.27273e-02 1.42586e-02 1.45418e-02 5.90551e-03 7.06714e-03 5.64061e-03 137082 0.01668 0.01530 0.00919 140785 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral&Progressive_external_ophthalmoplegia_with_mitochondrial_DNA_deletions&_autosomal_recessive_4¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MONDO:MONDO:0014899&MedGen:C4310733&OMIM:617070&Orphanet:ORPHA329314&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Benign/Likely_benign single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 74874677 15 30 50.0 -2 73950650 A G G non_coding_transcript_exon_variant MODIFIER DGUOK 1716 Transcript NR_134897.2 misc_RNA 4/6 471 1 EntrezGene A A 26.9 3.988512 6.54 rs74874677 2082 143308 1.45281e-02 4.28225e-03 4.18428e-03 4.39731e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.43443e-02 1.55563e-02 1.34194e-02 8.42359e-03 7.94552e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.45259e-02 2.19633e-02 2.03512e-02 2.24705e-02 1.45305e-02 2.16785e-02 2.14450e-02 2.19999e-02 1.34758e-02 1.27273e-02 1.42586e-02 1.45418e-02 5.90551e-03 7.06714e-03 5.64061e-03 137082 0.01668 0.01530 0.00919 140785 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral&Progressive_external_ophthalmoplegia_with_mitochondrial_DNA_deletions&_autosomal_recessive_4¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MONDO:MONDO:0014899&MedGen:C4310733&OMIM:617070&Orphanet:ORPHA329314&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Benign/Likely_benign single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 74874677 15 30 50.0 -2 73950650 A G G intron_variant&non_coding_transcript_variant MODIFIER DGUOK 1716 Transcript NR_134898.2 misc_RNA 2/4 1 EntrezGene A A 26.9 3.988512 6.54 rs74874677 2082 143308 1.45281e-02 4.28225e-03 4.18428e-03 4.39731e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.43443e-02 1.55563e-02 1.34194e-02 8.42359e-03 7.94552e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.45259e-02 2.19633e-02 2.03512e-02 2.24705e-02 1.45305e-02 2.16785e-02 2.14450e-02 2.19999e-02 1.34758e-02 1.27273e-02 1.42586e-02 1.45418e-02 5.90551e-03 7.06714e-03 5.64061e-03 137082 0.01668 0.01530 0.00919 140785 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral&Progressive_external_ophthalmoplegia_with_mitochondrial_DNA_deletions&_autosomal_recessive_4¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MONDO:MONDO:0014899&MedGen:C4310733&OMIM:617070&Orphanet:ORPHA329314&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Benign/Likely_benign single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 74874677 15 30 50.0 -2 73950650 A G G missense_variant MODERATE DGUOK 1716 Transcript XM_011532647.2 protein_coding 4/7 572 491 164 Q/R cAg/cGg 1 EntrezGene A A 26.9 3.988512 26.9 0.99783997658218382 8.514832 0.737441587945016 0.85217 8.022645 0.758503282566834 0.83457 -4.66 5.67 0.999999999971409 0.000000 0.8635 0.5703 2.505 1&1&1 -2.61 14.9038 0.317 0.99201 0.706548 1.000000 1.000000 9.064000 1.312000 6.54 rs74874677 2082 143308 1.45281e-02 4.28225e-03 4.18428e-03 4.39731e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.43443e-02 1.55563e-02 1.34194e-02 8.42359e-03 7.94552e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.45259e-02 2.19633e-02 2.03512e-02 2.24705e-02 1.45305e-02 2.16785e-02 2.14450e-02 2.19999e-02 1.34758e-02 1.27273e-02 1.42586e-02 1.45418e-02 5.90551e-03 7.06714e-03 5.64061e-03 137082 0.01668 0.01530 0.00919 140785 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral&Progressive_external_ophthalmoplegia_with_mitochondrial_DNA_deletions&_autosomal_recessive_4¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MONDO:MONDO:0014899&MedGen:C4310733&OMIM:617070&Orphanet:ORPHA329314&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Benign/Likely_benign single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 74874677 15 30 50.0 -2 73950650 A G G missense_variant MODERATE DGUOK 1716 Transcript XM_024452739.1 protein_coding 5/8 628 218 73 Q/R cAg/cGg 1 EntrezGene A A 0.02 0.999 26.9 3.988512 26.9 0.99783997658218382 8.514832 0.737441587945016 0.85217 8.022645 0.758503282566834 0.83457 -4.66 5.67 0.999999999971409 0.000000 0.8635 0.5703 2.505 1&1&1 -2.61 14.9038 0.317 0.99201 0.706548 1.000000 1.000000 9.064000 1.312000 6.54 rs74874677 2082 143308 1.45281e-02 4.28225e-03 4.18428e-03 4.39731e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.43443e-02 1.55563e-02 1.34194e-02 8.42359e-03 7.94552e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.45259e-02 2.19633e-02 2.03512e-02 2.24705e-02 1.45305e-02 2.16785e-02 2.14450e-02 2.19999e-02 1.34758e-02 1.27273e-02 1.42586e-02 1.45418e-02 5.90551e-03 7.06714e-03 5.64061e-03 137082 0.01668 0.01530 0.00919 140785 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral&Progressive_external_ophthalmoplegia_with_mitochondrial_DNA_deletions&_autosomal_recessive_4¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MONDO:MONDO:0014899&MedGen:C4310733&OMIM:617070&Orphanet:ORPHA329314&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Benign/Likely_benign single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 74874677 15 30 50.0 -2 73950650 A G G intron_variant&non_coding_transcript_variant MODIFIER DGUOK 1716 Transcript XR_001738656.1 misc_RNA 3/5 1 EntrezGene A A 26.9 3.988512 6.54 rs74874677 2082 143308 1.45281e-02 4.28225e-03 4.18428e-03 4.39731e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.43443e-02 1.55563e-02 1.34194e-02 8.42359e-03 7.94552e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.45259e-02 2.19633e-02 2.03512e-02 2.24705e-02 1.45305e-02 2.16785e-02 2.14450e-02 2.19999e-02 1.34758e-02 1.27273e-02 1.42586e-02 1.45418e-02 5.90551e-03 7.06714e-03 5.64061e-03 137082 0.01668 0.01530 0.00919 140785 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral&Progressive_external_ophthalmoplegia_with_mitochondrial_DNA_deletions&_autosomal_recessive_4¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MONDO:MONDO:0014899&MedGen:C4310733&OMIM:617070&Orphanet:ORPHA329314&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Benign/Likely_benign single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 74874677 15 30 50.0 -2 73950650 A G G non_coding_transcript_exon_variant MODIFIER DGUOK 1716 Transcript XR_244926.3 misc_RNA 4/6 592 1 EntrezGene A A 26.9 3.988512 6.54 rs74874677 2082 143308 1.45281e-02 4.28225e-03 4.18428e-03 4.39731e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.43443e-02 1.55563e-02 1.34194e-02 8.42359e-03 7.94552e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.45259e-02 2.19633e-02 2.03512e-02 2.24705e-02 1.45305e-02 2.16785e-02 2.14450e-02 2.19999e-02 1.34758e-02 1.27273e-02 1.42586e-02 1.45418e-02 5.90551e-03 7.06714e-03 5.64061e-03 137082 0.01668 0.01530 0.00919 140785 Mitochondrial_DNA-depletion_syndrome_3&_hepatocerebral&Progressive_external_ophthalmoplegia_with_mitochondrial_DNA_deletions&_autosomal_recessive_4¬_specified¬_provided MONDO:MONDO:0009636&MedGen:C3151513&OMIM:251880&Orphanet:ORPHA279934&MONDO:MONDO:0014899&MedGen:C4310733&OMIM:617070&Orphanet:ORPHA329314&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Benign/Likely_benign single_nucleotide_variant DGUOK:1716 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 74874677 15 30 50.0 -2 98396811 C A A missense_variant MODERATE CNGA3 1261 Transcript NM_001079878.2 protein_coding 7/7 1703 1587 529 F/L ttC/ttA 1 EntrezGene C C 0 1 20.3 2.119432 20.3 0.99568173454122266 3.022521 0.166189082938351 0.48008 3.822413 0.308329973254842 0.56576 -4.84&-4.84&-4.84&-4.84 1.85 0.402344719204604 0.000000 0.658157 0.9680 1.0634 3.125&.&3.125&. 1&1&1&1 -5.88&-5.88&-5.88&-5.88 8.3342 0.956&0.969&0.958&0.972 0.81586 0.497415 0.954000 0.970000 0.030000 -1.194000 -0.734 chr2:98396811-98396811 17 143252 1.18672e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35424e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.00850e-04 2.01357e-04 2.40719e-04 1.47200e-04 9.28505e-04 9.09091e-04 9.48767e-04 1.18579e-04 6.56599e-04 0.00000e+00 8.06452e-04 9478 24517 Retinal_dystrophy&Monochromacy&Achromatopsia&Achromatopsia_2¬_provided Human_Phenotype_Ontology:HP:0000556&Human_Phenotype_Ontology:HP:0007736&Human_Phenotype_Ontology:HP:0007910&Human_Phenotype_Ontology:HP:0007974&Human_Phenotype_Ontology:HP:0007982&MONDO:MONDO:0019118&MeSH:D058499&MedGen:C0854723&Orphanet:ORPHA71862&SNOMED_CT:314407005&Human_Phenotype_Ontology:HP:0007803&Human_Phenotype_Ontology:HP:0007954&MedGen:C5201048&Human_Phenotype_Ontology:HP:0011516&MONDO:MONDO:0018852&MedGen:C0152200&Orphanet:ORPHA49382&SNOMED_CT:56852002&MONDO:MONDO:0009003&MedGen:C1857618&OMIM:216900&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant CNGA3:1261 SO:0001583&missense_variant 5 104893617 15 30 50.0 -2 98396811 C A A missense_variant MODERATE CNGA3 1261 Transcript NM_001298.3 protein_coding 8/8 1757 1641 547 F/L ttC/ttA 1 EntrezGene C C 0 0.999 20.3 2.119432 20.3 0.99568173454122266 3.022521 0.166189082938351 0.48008 3.822413 0.308329973254842 0.56576 -4.84&-4.84&-4.84&-4.84 1.85 0.402344719204604 0.000000 0.658157 0.9680 1.0634 3.125&.&3.125&. 1&1&1&1 -5.88&-5.88&-5.88&-5.88 8.3342 0.956&0.969&0.958&0.972 0.81586 0.497415 0.954000 0.970000 0.030000 -1.194000 -0.734 chr2:98396811-98396811 17 143252 1.18672e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35424e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.00850e-04 2.01357e-04 2.40719e-04 1.47200e-04 9.28505e-04 9.09091e-04 9.48767e-04 1.18579e-04 6.56599e-04 0.00000e+00 8.06452e-04 9478 24517 Retinal_dystrophy&Monochromacy&Achromatopsia&Achromatopsia_2¬_provided Human_Phenotype_Ontology:HP:0000556&Human_Phenotype_Ontology:HP:0007736&Human_Phenotype_Ontology:HP:0007910&Human_Phenotype_Ontology:HP:0007974&Human_Phenotype_Ontology:HP:0007982&MONDO:MONDO:0019118&MeSH:D058499&MedGen:C0854723&Orphanet:ORPHA71862&SNOMED_CT:314407005&Human_Phenotype_Ontology:HP:0007803&Human_Phenotype_Ontology:HP:0007954&MedGen:C5201048&Human_Phenotype_Ontology:HP:0011516&MONDO:MONDO:0018852&MedGen:C0152200&Orphanet:ORPHA49382&SNOMED_CT:56852002&MONDO:MONDO:0009003&MedGen:C1857618&OMIM:216900&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant CNGA3:1261 SO:0001583&missense_variant 5 104893617 15 30 50.0 -2 98396811 C A A missense_variant MODERATE CNGA3 1261 Transcript XM_006712243.2 protein_coding 8/8 2136 1752 584 F/L ttC/ttA 1 EntrezGene C C 20.3 2.119432 20.3 0.99568173454122266 3.022521 0.166189082938351 0.48008 3.822413 0.308329973254842 0.56576 -4.84&-4.84&-4.84&-4.84 1.85 0.402344719204604 0.000000 0.658157 0.9680 1.0634 3.125&.&3.125&. 1&1&1&1 -5.88&-5.88&-5.88&-5.88 8.3342 0.956&0.969&0.958&0.972 0.81586 0.497415 0.954000 0.970000 0.030000 -1.194000 -0.734 chr2:98396811-98396811 17 143252 1.18672e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35424e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.00850e-04 2.01357e-04 2.40719e-04 1.47200e-04 9.28505e-04 9.09091e-04 9.48767e-04 1.18579e-04 6.56599e-04 0.00000e+00 8.06452e-04 9478 24517 Retinal_dystrophy&Monochromacy&Achromatopsia&Achromatopsia_2¬_provided Human_Phenotype_Ontology:HP:0000556&Human_Phenotype_Ontology:HP:0007736&Human_Phenotype_Ontology:HP:0007910&Human_Phenotype_Ontology:HP:0007974&Human_Phenotype_Ontology:HP:0007982&MONDO:MONDO:0019118&MeSH:D058499&MedGen:C0854723&Orphanet:ORPHA71862&SNOMED_CT:314407005&Human_Phenotype_Ontology:HP:0007803&Human_Phenotype_Ontology:HP:0007954&MedGen:C5201048&Human_Phenotype_Ontology:HP:0011516&MONDO:MONDO:0018852&MedGen:C0152200&Orphanet:ORPHA49382&SNOMED_CT:56852002&MONDO:MONDO:0009003&MedGen:C1857618&OMIM:216900&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant CNGA3:1261 SO:0001583&missense_variant 5 104893617 15 30 50.0 -2 98396811 C A A missense_variant MODERATE CNGA3 1261 Transcript XM_011510554.2 protein_coding 9/9 2280 1806 602 F/L ttC/ttA 1 EntrezGene C C 20.3 2.119432 20.3 0.99568173454122266 3.022521 0.166189082938351 0.48008 3.822413 0.308329973254842 0.56576 -4.84&-4.84&-4.84&-4.84 1.85 0.402344719204604 0.000000 0.658157 0.9680 1.0634 3.125&.&3.125&. 1&1&1&1 -5.88&-5.88&-5.88&-5.88 8.3342 0.956&0.969&0.958&0.972 0.81586 0.497415 0.954000 0.970000 0.030000 -1.194000 -0.734 chr2:98396811-98396811 17 143252 1.18672e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35424e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.00850e-04 2.01357e-04 2.40719e-04 1.47200e-04 9.28505e-04 9.09091e-04 9.48767e-04 1.18579e-04 6.56599e-04 0.00000e+00 8.06452e-04 9478 24517 Retinal_dystrophy&Monochromacy&Achromatopsia&Achromatopsia_2¬_provided Human_Phenotype_Ontology:HP:0000556&Human_Phenotype_Ontology:HP:0007736&Human_Phenotype_Ontology:HP:0007910&Human_Phenotype_Ontology:HP:0007974&Human_Phenotype_Ontology:HP:0007982&MONDO:MONDO:0019118&MeSH:D058499&MedGen:C0854723&Orphanet:ORPHA71862&SNOMED_CT:314407005&Human_Phenotype_Ontology:HP:0007803&Human_Phenotype_Ontology:HP:0007954&MedGen:C5201048&Human_Phenotype_Ontology:HP:0011516&MONDO:MONDO:0018852&MedGen:C0152200&Orphanet:ORPHA49382&SNOMED_CT:56852002&MONDO:MONDO:0009003&MedGen:C1857618&OMIM:216900&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant CNGA3:1261 SO:0001583&missense_variant 5 104893617 15 30 50.0 -2 205776516 CGCA C - intron_variant MODIFIER NRP2 8828 Transcript NM_003872.3 protein_coding 15/16 1 EntrezGene GCA GCA -1.26&6.54&1.84 15 30 50.0 -2 205776516 CGCA C - inframe_deletion MODERATE NRP2 8828 Transcript NM_018534.4 protein_coding 16/16 3502-3504 2712-2714 904-905 SH/S tcGCAc/tcc 1 EntrezGene GCA GCA -1.26&6.54&1.84 15 30 50.0 -2 205776516 CGCA C - intron_variant MODIFIER NRP2 8828 Transcript NM_201266.2 protein_coding 15/16 1 EntrezGene GCA GCA -1.26&6.54&1.84 15 30 50.0 -2 205776516 CGCA C - inframe_deletion MODERATE NRP2 8828 Transcript NM_201267.2 protein_coding 16/16 3487-3489 2697-2699 899-900 SH/S tcGCAc/tcc 1 EntrezGene GCA GCA -1.26&6.54&1.84 15 30 50.0 -2 205776516 CGCA C - intron_variant MODIFIER NRP2 8828 Transcript NM_201279.2 protein_coding 15/15 1 EntrezGene GCA GCA -1.26&6.54&1.84 15 30 50.0 -2 205776516 CGCA C - intron_variant MODIFIER NRP2 8828 Transcript XM_017005187.2 protein_coding 16/16 1 EntrezGene GCA GCA -1.26&6.54&1.84 15 30 50.0 -2 205776516 CGCA C - intron_variant MODIFIER NRP2 8828 Transcript XM_017005188.2 protein_coding 16/16 1 EntrezGene GCA GCA -1.26&6.54&1.84 15 30 50.0 -2 205776516 CGCA C - intron_variant&non_coding_transcript_variant MODIFIER NRP2 8828 Transcript XR_923055.3 misc_RNA 14/15 1 EntrezGene GCA GCA -1.26&6.54&1.84 15 30 50.0 -2 205776516 CGCA C - downstream_gene_variant MODIFIER NRP2 8828 Transcript XR_923056.3 misc_RNA 182 1 EntrezGene GCA GCA -1.26&6.54&1.84 15 30 50.0 -2 205776516 CGCA C - intron_variant&non_coding_transcript_variant MODIFIER NRP2 8828 Transcript XR_923057.3 misc_RNA 15/15 1 EntrezGene GCA GCA -1.26&6.54&1.84 15 30 50.0 -2 218890289 T A A missense_variant MODERATE WNT10A 80326 Transcript NM_025216.3 protein_coding 3/4 826 682 228 F/I Ttt/Att 1 EntrezGene T T 0 0.989 28.4 4.169916 28.4 0.99184124780659755 8.303652 0.727675118763491 0.84478 8.941385 0.808121596999172 0.86611 -1.54 4.46 0.999999999159291 0.000007 0.5283 0.4056 3.37 1 -4.95 13.3675 0.347 0.98268 0.706548 1.000000 1.000000 7.937000 1.138000 6.33 rs121908120 2039 143208 1.42380e-02 4.26170e-03 4.71948e-03 3.72478e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.63298e-02 1.50694e-02 1.72903e-02 3.34136e-02 2.55682e-02 4.22535e-02 0.00000e+00 0.00000e+00 0.00000e+00 1.55149e-02 4.20489e-03 7.18276e-03 3.26715e-03 1.28804e-02 2.24054e-02 2.33305e-02 2.11325e-02 1.34758e-02 1.18397e-02 1.51803e-02 1.42498e-02 1.97239e-03 0.00000e+00 2.42131e-03 4462 0.01273 0.00599 19501 Hypohidrotic_ectodermal_dysplasia&Reduced_number_of_teeth&Tooth_agenesis&_selective&_4&Schopf-Schulz-Passarge_syndrome&Odonto-onycho-dermal_dysplasia&Inborn_genetic_diseases¬_specified¬_provided Human_Phenotype_Ontology:HP:0007607&MONDO:MONDO:0016535&MedGen:C1706004&Orphanet:ORPHA238468&Human_Phenotype_Ontology:HP:0009804&MedGen:C4024202&MONDO:MONDO:0007881&MedGen:C1835492&OMIM:150400&MONDO:MONDO:0009145&MedGen:C1857069&OMIM:224750&Orphanet:ORPHA50944&MONDO:MONDO:0009773&MedGen:C0796093&OMIM:257980&Orphanet:ORPHA2721&MeSH:D030342&MedGen:C0950123&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(4)&Pathogenic(4)&Uncertain_significance(1) single_nucleotide_variant WNT10A:80326 SO:0001583&missense_variant 1 121908120 15 30 50.0 -2 218890289 T A A missense_variant MODERATE WNT10A 80326 Transcript XM_011511929.2 protein_coding 4/5 753 586 196 F/I Ttt/Att 1 EntrezGene T T 28.4 4.169916 28.4 0.99184124780659755 8.303652 0.727675118763491 0.84478 8.941385 0.808121596999172 0.86611 -1.54 4.46 0.999999999159291 0.000007 0.5283 0.4056 3.37 1 -4.95 13.3675 0.347 0.98268 0.706548 1.000000 1.000000 7.937000 1.138000 6.33 rs121908120 2039 143208 1.42380e-02 4.26170e-03 4.71948e-03 3.72478e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.63298e-02 1.50694e-02 1.72903e-02 3.34136e-02 2.55682e-02 4.22535e-02 0.00000e+00 0.00000e+00 0.00000e+00 1.55149e-02 4.20489e-03 7.18276e-03 3.26715e-03 1.28804e-02 2.24054e-02 2.33305e-02 2.11325e-02 1.34758e-02 1.18397e-02 1.51803e-02 1.42498e-02 1.97239e-03 0.00000e+00 2.42131e-03 4462 0.01273 0.00599 19501 Hypohidrotic_ectodermal_dysplasia&Reduced_number_of_teeth&Tooth_agenesis&_selective&_4&Schopf-Schulz-Passarge_syndrome&Odonto-onycho-dermal_dysplasia&Inborn_genetic_diseases¬_specified¬_provided Human_Phenotype_Ontology:HP:0007607&MONDO:MONDO:0016535&MedGen:C1706004&Orphanet:ORPHA238468&Human_Phenotype_Ontology:HP:0009804&MedGen:C4024202&MONDO:MONDO:0007881&MedGen:C1835492&OMIM:150400&MONDO:MONDO:0009145&MedGen:C1857069&OMIM:224750&Orphanet:ORPHA50944&MONDO:MONDO:0009773&MedGen:C0796093&OMIM:257980&Orphanet:ORPHA2721&MeSH:D030342&MedGen:C0950123&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(4)&Pathogenic(4)&Uncertain_significance(1) single_nucleotide_variant WNT10A:80326 SO:0001583&missense_variant 1 121908120 15 30 50.0 -2 218890289 T A A intron_variant MODIFIER WNT10A 80326 Transcript XM_011511930.1 protein_coding 2/2 1 EntrezGene T T 28.4 4.169916 6.33 rs121908120 2039 143208 1.42380e-02 4.26170e-03 4.71948e-03 3.72478e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.63298e-02 1.50694e-02 1.72903e-02 3.34136e-02 2.55682e-02 4.22535e-02 0.00000e+00 0.00000e+00 0.00000e+00 1.55149e-02 4.20489e-03 7.18276e-03 3.26715e-03 1.28804e-02 2.24054e-02 2.33305e-02 2.11325e-02 1.34758e-02 1.18397e-02 1.51803e-02 1.42498e-02 1.97239e-03 0.00000e+00 2.42131e-03 4462 0.01273 0.00599 19501 Hypohidrotic_ectodermal_dysplasia&Reduced_number_of_teeth&Tooth_agenesis&_selective&_4&Schopf-Schulz-Passarge_syndrome&Odonto-onycho-dermal_dysplasia&Inborn_genetic_diseases¬_specified¬_provided Human_Phenotype_Ontology:HP:0007607&MONDO:MONDO:0016535&MedGen:C1706004&Orphanet:ORPHA238468&Human_Phenotype_Ontology:HP:0009804&MedGen:C4024202&MONDO:MONDO:0007881&MedGen:C1835492&OMIM:150400&MONDO:MONDO:0009145&MedGen:C1857069&OMIM:224750&Orphanet:ORPHA50944&MONDO:MONDO:0009773&MedGen:C0796093&OMIM:257980&Orphanet:ORPHA2721&MeSH:D030342&MedGen:C0950123&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(4)&Pathogenic(4)&Uncertain_significance(1) single_nucleotide_variant WNT10A:80326 SO:0001583&missense_variant 1 121908120 15 30 50.0 -2 218890289 T A A downstream_gene_variant MODIFIER LOC107984111 107984111 Transcript XR_001739886.1 lncRNA 1355 1 EntrezGene T T 28.4 4.169916 6.33 rs121908120 2039 143208 1.42380e-02 4.26170e-03 4.71948e-03 3.72478e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.63298e-02 1.50694e-02 1.72903e-02 3.34136e-02 2.55682e-02 4.22535e-02 0.00000e+00 0.00000e+00 0.00000e+00 1.55149e-02 4.20489e-03 7.18276e-03 3.26715e-03 1.28804e-02 2.24054e-02 2.33305e-02 2.11325e-02 1.34758e-02 1.18397e-02 1.51803e-02 1.42498e-02 1.97239e-03 0.00000e+00 2.42131e-03 4462 0.01273 0.00599 19501 Hypohidrotic_ectodermal_dysplasia&Reduced_number_of_teeth&Tooth_agenesis&_selective&_4&Schopf-Schulz-Passarge_syndrome&Odonto-onycho-dermal_dysplasia&Inborn_genetic_diseases¬_specified¬_provided Human_Phenotype_Ontology:HP:0007607&MONDO:MONDO:0016535&MedGen:C1706004&Orphanet:ORPHA238468&Human_Phenotype_Ontology:HP:0009804&MedGen:C4024202&MONDO:MONDO:0007881&MedGen:C1835492&OMIM:150400&MONDO:MONDO:0009145&MedGen:C1857069&OMIM:224750&Orphanet:ORPHA50944&MONDO:MONDO:0009773&MedGen:C0796093&OMIM:257980&Orphanet:ORPHA2721&MeSH:D030342&MedGen:C0950123&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(4)&Pathogenic(4)&Uncertain_significance(1) single_nucleotide_variant WNT10A:80326 SO:0001583&missense_variant 1 121908120 15 30 50.0 -2 218890289 T A A downstream_gene_variant MODIFIER LOC105373882 105373882 Transcript XR_923916.2 lncRNA 3721 -1 EntrezGene T T 28.4 4.169916 6.33 rs121908120 2039 143208 1.42380e-02 4.26170e-03 4.71948e-03 3.72478e-03 1.11111e-03 2.12766e-03 0.00000e+00 1.63298e-02 1.50694e-02 1.72903e-02 3.34136e-02 2.55682e-02 4.22535e-02 0.00000e+00 0.00000e+00 0.00000e+00 1.55149e-02 4.20489e-03 7.18276e-03 3.26715e-03 1.28804e-02 2.24054e-02 2.33305e-02 2.11325e-02 1.34758e-02 1.18397e-02 1.51803e-02 1.42498e-02 1.97239e-03 0.00000e+00 2.42131e-03 4462 0.01273 0.00599 19501 Hypohidrotic_ectodermal_dysplasia&Reduced_number_of_teeth&Tooth_agenesis&_selective&_4&Schopf-Schulz-Passarge_syndrome&Odonto-onycho-dermal_dysplasia&Inborn_genetic_diseases¬_specified¬_provided Human_Phenotype_Ontology:HP:0007607&MONDO:MONDO:0016535&MedGen:C1706004&Orphanet:ORPHA238468&Human_Phenotype_Ontology:HP:0009804&MedGen:C4024202&MONDO:MONDO:0007881&MedGen:C1835492&OMIM:150400&MONDO:MONDO:0009145&MedGen:C1857069&OMIM:224750&Orphanet:ORPHA50944&MONDO:MONDO:0009773&MedGen:C0796093&OMIM:257980&Orphanet:ORPHA2721&MeSH:D030342&MedGen:C0950123&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(4)&Pathogenic(4)&Uncertain_significance(1) single_nucleotide_variant WNT10A:80326 SO:0001583&missense_variant 1 121908120 15 30 50.0 -3 10046722 TAGTA T - splice_donor_variant&coding_sequence_variant&intron_variant HIGH FANCD2 2177 Transcript NM_001018115.3 protein_coding 15/44 15/43 1348-? 1278-? 426-? 1 EntrezGene AGTA AGTA 1.81&5.56&5.28&1.56 218823 215264 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance Deletion FANCD2:2177&LOC107303338:107303338 SO:0001575&splice_donor_variant 0 369823368 15 30 50.0 -3 10046722 TAGTA T - splice_donor_variant&coding_sequence_variant&intron_variant HIGH FANCD2 2177 Transcript NM_001319984.2 protein_coding 15/44 15/43 1501-? 1278-? 426-? 1 EntrezGene AGTA AGTA 1.81&5.56&5.28&1.56 218823 215264 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance Deletion FANCD2:2177&LOC107303338:107303338 SO:0001575&splice_donor_variant 0 369823368 15 30 50.0 -3 10046722 TAGTA T - splice_donor_variant&coding_sequence_variant&intron_variant HIGH FANCD2 2177 Transcript NM_001374253.1 protein_coding 15/43 15/42 1348-? 1278-? 426-? 1 EntrezGene AGTA AGTA 1.81&5.56&5.28&1.56 218823 215264 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance Deletion FANCD2:2177&LOC107303338:107303338 SO:0001575&splice_donor_variant 0 369823368 15 30 50.0 -3 10046722 TAGTA T - splice_donor_variant&coding_sequence_variant&intron_variant HIGH FANCD2 2177 Transcript NM_001374254.1 protein_coding 15/42 15/41 1348-? 1278-? 426-? 1 EntrezGene AGTA AGTA 1.81&5.56&5.28&1.56 218823 215264 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance Deletion FANCD2:2177&LOC107303338:107303338 SO:0001575&splice_donor_variant 0 369823368 15 30 50.0 -3 10046722 TAGTA T - splice_donor_variant&coding_sequence_variant&intron_variant HIGH FANCD2 2177 Transcript NM_033084.6 protein_coding 15/43 15/42 1348-? 1278-? 426-? 1 EntrezGene AGTA AGTA 1.81&5.56&5.28&1.56 218823 215264 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance Deletion FANCD2:2177&LOC107303338:107303338 SO:0001575&splice_donor_variant 0 369823368 15 30 50.0 -3 49101380 A ATAT TAT inframe_insertion MODERATE QARS 5859 Transcript NM_001272073.2 protein_coding 10/24 841-842 817-818 273 I/NI atc/aATAtc -1 EntrezGene 6.22 15 30 50.0 -3 49101380 A ATAT TAT inframe_insertion MODERATE QARS 5859 Transcript NM_005051.3 protein_coding 10/24 874-875 850-851 284 I/NI atc/aATAtc -1 EntrezGene 6.22 15 30 50.0 -3 49101380 A ATAT TAT non_coding_transcript_exon_variant MODIFIER QARS 5859 Transcript NR_073590.2 misc_RNA 10/24 825-826 -1 EntrezGene 6.22 15 30 50.0 -3 49101380 A ATAT TAT upstream_gene_variant MODIFIER MIR6890 102465536 Transcript NR_106950.1 miRNA 1466 -1 EntrezGene 6.22 15 30 50.0 -3 49101380 A ATAT TAT inframe_insertion MODERATE QARS 5859 Transcript XM_017006965.2 protein_coding 10/23 875-876 850-851 284 I/NI atc/aATAtc -1 EntrezGene 6.22 15 30 50.0 -3 90449078 A C C intergenic_variant MODIFIER 1.429 0.014514 0 15 30 50.0 -3 95240551 G C C intergenic_variant MODIFIER 0.785 -0.111504 -0.541 chr3:95240551-95240551 41079 143028 2.87209e-01 1.12159e-01 1.13175e-01 1.10967e-01 2.58352e-01 2.73504e-01 2.41860e-01 4.09351e-01 4.09739e-01 4.09056e-01 3.59855e-01 3.51136e-01 3.69705e-01 4.06671e-01 3.97222e-01 4.14779e-01 2.84440e-01 3.37879e-01 3.29473e-01 3.40541e-01 2.90157e-01 3.57592e-01 3.57422e-01 3.57827e-01 3.11567e-01 3.20255e-01 3.02481e-01 2.87428e-01 2.79605e-01 2.67730e-01 2.82310e-01 15 30 50.0 -3 136283929 CT T T frameshift_variant HIGH PCCB 5096 Transcript NM_000532.5 protein_coding 6/15 672-673 636-637 212-213 DF/DX gaCTtc/gaTtc 1 EntrezGene CT CT 1.62&6.35 15 30 50.0 -3 136283929 CT T T frameshift_variant HIGH PCCB 5096 Transcript NM_001178014.1 protein_coding 7/16 747-748 696-697 232-233 DF/DX gaCTtc/gaTtc 1 EntrezGene CT CT OK 1.62&6.35 15 30 50.0 -3 136283929 CT T T frameshift_variant HIGH PCCB 5096 Transcript XM_011512873.2 protein_coding 6/11 672-673 636-637 212-213 DF/DX gaCTtc/gaTtc 1 EntrezGene CT CT 1.62&6.35 15 30 50.0 -3 150972565 A C C missense_variant MODERATE CLRN1 7401 Transcript NM_001195794.1 protein_coding 1/4 435 144 48 N/K aaT/aaG -1 EntrezGene A A 0 0.998 18.09 1.848163 18.09 0.99662953499182161 1.876284 -0.163341695483794 0.32897 2.532894 0.00611496932349204 0.42134 -0.55&-0.55&. -5.15 0.58844688520007 0.000000 0.137726 0.4261 -0.1002 2.36&2.36&. 0.999998&0.999998 -2.98&-3.13&. 14.2519 0.924&0.928&. 0.66611 0.516011 0.497000 0.972000 -0.181000 -0.407000 -8.53 rs111033258 22 143338 1.53483e-04 2.37778e-05 0.00000e+00 5.16903e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.31957e-05 1.69147e-04 0.00000e+00 5.41842e-03 3.97727e-03 7.04225e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21846e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87120e-04 3.09655e-05 2.67408e-05 3.67755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.53438e-04 0.00000e+00 0.00000e+00 0.00000e+00 4395 0.00008 0.00021 19434 Retinitis_pigmentosa&Retinal_dystrophy&Usher_syndrome&_type_3A&Usher_Syndrome&_Type_III¬_specified¬_provided&Rare_genetic_deafness Human_Phenotype_Ontology:HP:0000547&MONDO:MONDO:0019200&MeSH:D012174&MedGen:C0035334&OMIM:268000&OMIM:PS268000&Orphanet:ORPHA791&SNOMED_CT:28835009&Human_Phenotype_Ontology:HP:0000556&Human_Phenotype_Ontology:HP:0007736&Human_Phenotype_Ontology:HP:0007910&Human_Phenotype_Ontology:HP:0007974&Human_Phenotype_Ontology:HP:0007982&MONDO:MONDO:0019118&MeSH:D058499&MedGen:C0854723&Orphanet:ORPHA71862&SNOMED_CT:314407005&MONDO:MONDO:0010170&MedGen:C3510450&OMIM:276902&MONDO:MONDO:0016485&MedGen:C1568248&Orphanet:ORPHA231183&MedGen:CN169374&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant CLRN1:7401&CLRN1-AS1:116933 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 5 111033258 15 30 50.0 -3 150972565 A C C missense_variant MODERATE CLRN1 7401 Transcript NM_001256819.2 protein_coding 1/4 163 144 48 N/K aaT/aaG -1 EntrezGene A A 0 0.999 18.09 1.848163 18.09 0.99662953499182161 1.876284 -0.163341695483794 0.32897 2.532894 0.00611496932349204 0.42134 -0.55&-0.55&. -5.15 0.58844688520007 0.000000 0.137726 0.4261 -0.1002 2.36&2.36&. 0.999998&0.999998 -2.98&-3.13&. 14.2519 0.924&0.928&. 0.66611 0.516011 0.497000 0.972000 -0.181000 -0.407000 -8.53 rs111033258 22 143338 1.53483e-04 2.37778e-05 0.00000e+00 5.16903e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.31957e-05 1.69147e-04 0.00000e+00 5.41842e-03 3.97727e-03 7.04225e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21846e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87120e-04 3.09655e-05 2.67408e-05 3.67755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.53438e-04 0.00000e+00 0.00000e+00 0.00000e+00 4395 0.00008 0.00021 19434 Retinitis_pigmentosa&Retinal_dystrophy&Usher_syndrome&_type_3A&Usher_Syndrome&_Type_III¬_specified¬_provided&Rare_genetic_deafness Human_Phenotype_Ontology:HP:0000547&MONDO:MONDO:0019200&MeSH:D012174&MedGen:C0035334&OMIM:268000&OMIM:PS268000&Orphanet:ORPHA791&SNOMED_CT:28835009&Human_Phenotype_Ontology:HP:0000556&Human_Phenotype_Ontology:HP:0007736&Human_Phenotype_Ontology:HP:0007910&Human_Phenotype_Ontology:HP:0007974&Human_Phenotype_Ontology:HP:0007982&MONDO:MONDO:0019118&MeSH:D058499&MedGen:C0854723&Orphanet:ORPHA71862&SNOMED_CT:314407005&MONDO:MONDO:0010170&MedGen:C3510450&OMIM:276902&MONDO:MONDO:0016485&MedGen:C1568248&Orphanet:ORPHA231183&MedGen:CN169374&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant CLRN1:7401&CLRN1-AS1:116933 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 5 111033258 15 30 50.0 -3 150972565 A C C missense_variant MODERATE CLRN1 7401 Transcript NM_174878.3 protein_coding 1/3 163 144 48 N/K aaT/aaG -1 EntrezGene A A 0 0.998 18.09 1.848163 18.09 0.99662953499182161 1.876284 -0.163341695483794 0.32897 2.532894 0.00611496932349204 0.42134 -0.55&-0.55&. -5.15 0.58844688520007 0.000000 0.137726 0.4261 -0.1002 2.36&2.36&. 0.999998&0.999998 -2.98&-3.13&. 14.2519 0.924&0.928&. 0.66611 0.516011 0.497000 0.972000 -0.181000 -0.407000 -8.53 rs111033258 22 143338 1.53483e-04 2.37778e-05 0.00000e+00 5.16903e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.31957e-05 1.69147e-04 0.00000e+00 5.41842e-03 3.97727e-03 7.04225e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21846e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87120e-04 3.09655e-05 2.67408e-05 3.67755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.53438e-04 0.00000e+00 0.00000e+00 0.00000e+00 4395 0.00008 0.00021 19434 Retinitis_pigmentosa&Retinal_dystrophy&Usher_syndrome&_type_3A&Usher_Syndrome&_Type_III¬_specified¬_provided&Rare_genetic_deafness Human_Phenotype_Ontology:HP:0000547&MONDO:MONDO:0019200&MeSH:D012174&MedGen:C0035334&OMIM:268000&OMIM:PS268000&Orphanet:ORPHA791&SNOMED_CT:28835009&Human_Phenotype_Ontology:HP:0000556&Human_Phenotype_Ontology:HP:0007736&Human_Phenotype_Ontology:HP:0007910&Human_Phenotype_Ontology:HP:0007974&Human_Phenotype_Ontology:HP:0007982&MONDO:MONDO:0019118&MeSH:D058499&MedGen:C0854723&Orphanet:ORPHA71862&SNOMED_CT:314407005&MONDO:MONDO:0010170&MedGen:C3510450&OMIM:276902&MONDO:MONDO:0016485&MedGen:C1568248&Orphanet:ORPHA231183&MedGen:CN169374&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant CLRN1:7401&CLRN1-AS1:116933 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 5 111033258 15 30 50.0 -3 150972565 A C C upstream_gene_variant MODIFIER CLRN1-AS1 116933 Transcript NR_024066.2 lncRNA 113 1 EntrezGene A A 18.09 1.848163 -8.53 rs111033258 22 143338 1.53483e-04 2.37778e-05 0.00000e+00 5.16903e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.31957e-05 1.69147e-04 0.00000e+00 5.41842e-03 3.97727e-03 7.04225e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21846e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87120e-04 3.09655e-05 2.67408e-05 3.67755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.53438e-04 0.00000e+00 0.00000e+00 0.00000e+00 4395 0.00008 0.00021 19434 Retinitis_pigmentosa&Retinal_dystrophy&Usher_syndrome&_type_3A&Usher_Syndrome&_Type_III¬_specified¬_provided&Rare_genetic_deafness Human_Phenotype_Ontology:HP:0000547&MONDO:MONDO:0019200&MeSH:D012174&MedGen:C0035334&OMIM:268000&OMIM:PS268000&Orphanet:ORPHA791&SNOMED_CT:28835009&Human_Phenotype_Ontology:HP:0000556&Human_Phenotype_Ontology:HP:0007736&Human_Phenotype_Ontology:HP:0007910&Human_Phenotype_Ontology:HP:0007974&Human_Phenotype_Ontology:HP:0007982&MONDO:MONDO:0019118&MeSH:D058499&MedGen:C0854723&Orphanet:ORPHA71862&SNOMED_CT:314407005&MONDO:MONDO:0010170&MedGen:C3510450&OMIM:276902&MONDO:MONDO:0016485&MedGen:C1568248&Orphanet:ORPHA231183&MedGen:CN169374&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant CLRN1:7401&CLRN1-AS1:116933 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 5 111033258 15 30 50.0 -3 150972565 A C C non_coding_transcript_exon_variant MODIFIER CLRN1 7401 Transcript NR_046380.3 misc_RNA 1/5 163 -1 EntrezGene A A 18.09 1.848163 -8.53 rs111033258 22 143338 1.53483e-04 2.37778e-05 0.00000e+00 5.16903e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.31957e-05 1.69147e-04 0.00000e+00 5.41842e-03 3.97727e-03 7.04225e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21846e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87120e-04 3.09655e-05 2.67408e-05 3.67755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.53438e-04 0.00000e+00 0.00000e+00 0.00000e+00 4395 0.00008 0.00021 19434 Retinitis_pigmentosa&Retinal_dystrophy&Usher_syndrome&_type_3A&Usher_Syndrome&_Type_III¬_specified¬_provided&Rare_genetic_deafness Human_Phenotype_Ontology:HP:0000547&MONDO:MONDO:0019200&MeSH:D012174&MedGen:C0035334&OMIM:268000&OMIM:PS268000&Orphanet:ORPHA791&SNOMED_CT:28835009&Human_Phenotype_Ontology:HP:0000556&Human_Phenotype_Ontology:HP:0007736&Human_Phenotype_Ontology:HP:0007910&Human_Phenotype_Ontology:HP:0007974&Human_Phenotype_Ontology:HP:0007982&MONDO:MONDO:0019118&MeSH:D058499&MedGen:C0854723&Orphanet:ORPHA71862&SNOMED_CT:314407005&MONDO:MONDO:0010170&MedGen:C3510450&OMIM:276902&MONDO:MONDO:0016485&MedGen:C1568248&Orphanet:ORPHA231183&MedGen:CN169374&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant CLRN1:7401&CLRN1-AS1:116933 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 5 111033258 15 30 50.0 -4 33388675 A AAT AT intergenic_variant MODIFIER 2.889 0.179113 1.52&-0.863 chr4:33388676-33388676 7829 73792 1.06096e-01 1.09586e-01 1.08648e-01 1.10768e-01 3.61842e-02 4.16667e-02 3.04054e-02 9.82143e-02 1.08414e-01 8.88060e-02 8.81447e-02 9.43953e-02 8.13205e-02 1.16983e-01 1.23782e-01 1.11111e-01 1.09482e-01 3.24074e-02 5.62914e-02 2.73492e-02 1.01880e-01 1.11728e-01 1.11822e-01 1.11588e-01 9.98134e-02 1.19134e-01 7.91506e-02 1.40423e-01 6.99638e-02 6.16883e-02 7.18519e-02 15 30 50.0 -4 70642685 T TAG AG frameshift_variant HIGH ENAM 10117 Transcript NM_001368133.1 protein_coding 2/2 804-805 605-606 202 L/LX ctg/ctAGg 1 EntrezGene 23.3 2.949542 5.38&3.94 rs587776588 28 143220 1.95503e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35472e-04 2.86533e-04 3.99361e-04 2.51067e-04 2.59351e-04 3.40768e-04 2.40770e-04 4.78293e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.95321e-04 9.92063e-04 0.00000e+00 1.22050e-03 4238 0.00016 0.00033 19277 Amelogenesis_imperfecta_-_hypoplastic_autosomal_dominant_-_local&Amelogenesis_imperfecta&_type_IC¬_provided MONDO:MONDO:0007092&MedGen:C0399368&OMIM:104500&SNOMED_CT:234961008&MONDO:MONDO:0008770&MedGen:C2673923&OMIM:204650&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic Insertion ENAM:10117 SO:0001589&frameshift_variant 1 587776588 15 30 50.0 -4 70642685 T TAG AG frameshift_variant HIGH ENAM 10117 Transcript NM_031889.3 protein_coding 9/9 1540-1541 1259-1260 420 L/LX ctg/ctAGg 1 EntrezGene 23.3 2.949542 5.38&3.94 rs587776588 28 143220 1.95503e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35472e-04 2.86533e-04 3.99361e-04 2.51067e-04 2.59351e-04 3.40768e-04 2.40770e-04 4.78293e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.95321e-04 9.92063e-04 0.00000e+00 1.22050e-03 4238 0.00016 0.00033 19277 Amelogenesis_imperfecta_-_hypoplastic_autosomal_dominant_-_local&Amelogenesis_imperfecta&_type_IC¬_provided MONDO:MONDO:0007092&MedGen:C0399368&OMIM:104500&SNOMED_CT:234961008&MONDO:MONDO:0008770&MedGen:C2673923&OMIM:204650&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic Insertion ENAM:10117 SO:0001589&frameshift_variant 1 587776588 15 30 50.0 -4 70642685 T TAG AG frameshift_variant HIGH ENAM 10117 Transcript XM_006714056.4 protein_coding 8/8 3289-3290 1259-1260 420 L/LX ctg/ctAGg 1 EntrezGene 23.3 2.949542 5.38&3.94 rs587776588 28 143220 1.95503e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35472e-04 2.86533e-04 3.99361e-04 2.51067e-04 2.59351e-04 3.40768e-04 2.40770e-04 4.78293e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.95321e-04 9.92063e-04 0.00000e+00 1.22050e-03 4238 0.00016 0.00033 19277 Amelogenesis_imperfecta_-_hypoplastic_autosomal_dominant_-_local&Amelogenesis_imperfecta&_type_IC¬_provided MONDO:MONDO:0007092&MedGen:C0399368&OMIM:104500&SNOMED_CT:234961008&MONDO:MONDO:0008770&MedGen:C2673923&OMIM:204650&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic Insertion ENAM:10117 SO:0001589&frameshift_variant 1 587776588 15 30 50.0 -4 110684608 G C C intergenic_variant MODIFIER 0.748 -0.121700 -0.304 rs2595112 89793 143014 6.27862e-01 5.96275e-01 5.95965e-01 5.96639e-01 8.03333e-01 7.97872e-01 8.09302e-01 5.96961e-01 6.10169e-01 5.86872e-01 4.89765e-01 4.91487e-01 4.87821e-01 4.27611e-01 4.28769e-01 4.26611e-01 6.38664e-01 5.90595e-01 5.88658e-01 5.91208e-01 6.16359e-01 6.79156e-01 6.86948e-01 6.68435e-01 6.19646e-01 6.24545e-01 6.14504e-01 6.27820e-01 5.51679e-01 5.67857e-01 5.48023e-01 15 30 50.0 -4 141883074 T C C intergenic_variant MODIFIER 5.664 0.421505 0 rs200382796 20430 136238 1.49958e-01 1.38708e-01 1.37850e-01 1.39722e-01 9.54545e-02 7.82609e-02 1.14286e-01 2.31078e-01 2.41120e-01 2.23510e-01 1.05166e-01 1.08061e-01 1.01948e-01 4.33921e-01 4.46212e-01 4.22365e-01 1.50564e-01 1.11313e-01 1.32525e-01 1.04555e-01 1.49304e-01 1.36358e-01 1.37113e-01 1.35306e-01 1.55819e-01 1.73004e-01 1.37295e-01 1.62913e-01 1.71741e-01 1.56904e-01 1.75144e-01 15 30 50.0 -5 92105771 C T T intergenic_variant MODIFIER 2.347 0.127390 1.7 rs4352630 91484 142882 6.40277e-01 6.99513e-01 7.00177e-01 6.98733e-01 4.46548e-01 4.59402e-01 4.32558e-01 6.34132e-01 6.39606e-01 6.29956e-01 6.36747e-01 6.35641e-01 6.37997e-01 7.03325e-01 7.10526e-01 6.97150e-01 6.39590e-01 6.37257e-01 6.34137e-01 6.38241e-01 6.41007e-01 6.03701e-01 6.03593e-01 6.03849e-01 6.27323e-01 6.18182e-01 6.36882e-01 6.40217e-01 6.43092e-01 6.39286e-01 6.43952e-01 15 30 50.0 -5 95516411 TAAAAA T - splice_region_variant&intron_variant LOW TTC37 9652 Transcript NM_014639.4 protein_coding 24/42 -1 EntrezGene AAAAA AAAAA 9.106 0.770743 2.88&4.14&4.09&1.44&-2.48 rs746874042 14 143250 9.77312e-05 2.37835e-05 0.00000e+00 5.16956e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.08357e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.64304e-05 2.01419e-04 2.14030e-04 1.84067e-04 0.00000e+00 0.00000e+00 0.00000e+00 9.76467e-05 0.00000e+00 0.00000e+00 0.00000e+00 31236 40193 Trichohepatoenteric_syndrome_1¬_provided MONDO:MONDO:0024541&MedGen:C4551982&OMIM:222470&MedGen:CN517202 no_assertion_criteria_provided Pathogenic Deletion TTC37:9652 SO:0001627&intron_variant 1 746874042 15 30 50.0 -5 95516411 TAAAAA T - splice_region_variant&intron_variant&non_coding_transcript_variant LOW TTC37 9652 Transcript XR_001742370.2 misc_RNA 24/30 -1 EntrezGene AAAAA AAAAA 9.106 0.770743 2.88&4.14&4.09&1.44&-2.48 rs746874042 14 143250 9.77312e-05 2.37835e-05 0.00000e+00 5.16956e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.08357e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.64304e-05 2.01419e-04 2.14030e-04 1.84067e-04 0.00000e+00 0.00000e+00 0.00000e+00 9.76467e-05 0.00000e+00 0.00000e+00 0.00000e+00 31236 40193 Trichohepatoenteric_syndrome_1¬_provided MONDO:MONDO:0024541&MedGen:C4551982&OMIM:222470&MedGen:CN517202 no_assertion_criteria_provided Pathogenic Deletion TTC37:9652 SO:0001627&intron_variant 1 746874042 15 30 50.0 -5 135778811 T C C intron_variant MODIFIER SLC25A48 153328 Transcript NM_001349335.1 protein_coding 3/10 1 EntrezGene T T OK 1.944 0.083075 0 rs112557342 2165 5552 3.89950e-01 4.19738e-01 4.11765e-01 4.29319e-01 4.56522e-01 5.00000e-01 4.23077e-01 3.30000e-01 3.43750e-01 3.19767e-01 3.80000e-01 3.84615e-01 3.75000e-01 4.04348e-01 3.97959e-01 4.09091e-01 3.88207e-01 3.71901e-01 4.56522e-01 3.20000e-01 3.91902e-01 3.77729e-01 3.71372e-01 3.85552e-01 3.85714e-01 3.80952e-01 3.92857e-01 4.62290e-01 4.41176e-01 4.37500e-01 4.44444e-01 15 30 50.0 -5 135778811 T C C intron_variant MODIFIER SLC25A48 153328 Transcript NM_001349345.2 protein_coding 3/9 1 EntrezGene T T 1.944 0.083075 0 rs112557342 2165 5552 3.89950e-01 4.19738e-01 4.11765e-01 4.29319e-01 4.56522e-01 5.00000e-01 4.23077e-01 3.30000e-01 3.43750e-01 3.19767e-01 3.80000e-01 3.84615e-01 3.75000e-01 4.04348e-01 3.97959e-01 4.09091e-01 3.88207e-01 3.71901e-01 4.56522e-01 3.20000e-01 3.91902e-01 3.77729e-01 3.71372e-01 3.85552e-01 3.85714e-01 3.80952e-01 3.92857e-01 4.62290e-01 4.41176e-01 4.37500e-01 4.44444e-01 15 30 50.0 -5 177994145 ACT A - frameshift_variant HIGH PROP1 5626 Transcript NM_006261.5 protein_coding 2/3 610-611 301-302 101 S/X AGt/t -1 EntrezGene CT CT 23.8 3.187652 1.39&-0.674 rs193922688 21 142932 1.46923e-04 2.38846e-05 0.00000e+00 5.19696e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.34107e-05 0.00000e+00 1.29299e-04 3.00842e-04 5.67537e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.76443e-04 9.59141e-05 0.00000e+00 1.26167e-04 1.15517e-04 2.63599e-04 3.21337e-04 1.84176e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.46578e-04 0.00000e+00 0.00000e+00 0.00000e+00 8098 23137 Combined_pituitary_hormone_deficiency_type_2&Pituitary_hormone_deficiency&_combined_2&Pituitary_hormone_deficiency&_combined¬_provided .&MONDO:MONDO:0009878&MedGen:C0878683&OMIM:262600&MONDO:MONDO:0013099&MedGen:CN078207&OMIM:PS613038&Orphanet:ORPHA95494&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite PROP1:5626 SO:0001589&frameshift_variant 1 193922688 15 30 50.0 -6 6167589 C T T missense_variant MODERATE F13A1 2162 Transcript NM_000129.4 protein_coding 13/15 1871 1777 593 G/S Ggc/Agc -1 EntrezGene C C 0.73 0.001 2.709 0.162601 2.709 0.6810928584826722 0.277068 -1.2071708680959 0.05794 0.2598873 -1.15570099498232 0.05686 -2.12 -0.331 0.00259890581354301 0.021210 0.0639 0.2678 -0.8042 0.99999 0.97 7.2104 0.657 0.02222 0.638212 0.000000 0.159000 -0.174000 0.128000 0.462 rs138754417 332 143132 2.31954e-03 7.38693e-04 9.70702e-04 4.66273e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.79507e-04 6.77507e-04 1.03359e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.73780e-03 4.79294e-04 8.01282e-04 3.78024e-04 1.87455e-03 4.30634e-03 4.57415e-03 3.93788e-03 2.79070e-03 2.73723e-03 2.84630e-03 2.31604e-03 0.00000e+00 0.00000e+00 0.00000e+00 627022 0.00269 0.00175 0.00100 615416 Factor_XIII_subunit_A_deficiency¬_provided Human_Phenotype_Ontology:HP:0040233&MedGen:C2750514&OMIM:613225&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Uncertain_significance(2) single_nucleotide_variant F13A1:2162 SO:0001583&missense_variant 1 138754417 15 30 50.0 -6 6167589 C T T upstream_gene_variant MODIFIER MIR5683 100847034 Transcript NR_049863.1 miRNA 1745 1 EntrezGene C C 2.709 0.162601 0.462 rs138754417 332 143132 2.31954e-03 7.38693e-04 9.70702e-04 4.66273e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.79507e-04 6.77507e-04 1.03359e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.73780e-03 4.79294e-04 8.01282e-04 3.78024e-04 1.87455e-03 4.30634e-03 4.57415e-03 3.93788e-03 2.79070e-03 2.73723e-03 2.84630e-03 2.31604e-03 0.00000e+00 0.00000e+00 0.00000e+00 627022 0.00269 0.00175 0.00100 615416 Factor_XIII_subunit_A_deficiency¬_provided Human_Phenotype_Ontology:HP:0040233&MedGen:C2750514&OMIM:613225&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Uncertain_significance(2) single_nucleotide_variant F13A1:2162 SO:0001583&missense_variant 1 138754417 15 30 50.0 -6 6167589 C T T downstream_gene_variant MODIFIER MIR7853 102466866 Transcript NR_107007.1 miRNA 1715 -1 EntrezGene C C 2.709 0.162601 0.462 rs138754417 332 143132 2.31954e-03 7.38693e-04 9.70702e-04 4.66273e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.79507e-04 6.77507e-04 1.03359e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.73780e-03 4.79294e-04 8.01282e-04 3.78024e-04 1.87455e-03 4.30634e-03 4.57415e-03 3.93788e-03 2.79070e-03 2.73723e-03 2.84630e-03 2.31604e-03 0.00000e+00 0.00000e+00 0.00000e+00 627022 0.00269 0.00175 0.00100 615416 Factor_XIII_subunit_A_deficiency¬_provided Human_Phenotype_Ontology:HP:0040233&MedGen:C2750514&OMIM:613225&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Uncertain_significance(2) single_nucleotide_variant F13A1:2162 SO:0001583&missense_variant 1 138754417 15 30 50.0 -6 13407909 CCCAA C - intron_variant MODIFIER GFOD1 54438 Transcript NM_001242628.1 protein_coding 1/1 -1 EntrezGene CCAA CCAA OK 7.446 0.593154 3.53&5.37&3.69&-1.51 rs10573330 80935 142566 5.67702e-01 2.31887e-01 2.32329e-01 2.31368e-01 7.08989e-01 6.92641e-01 7.26636e-01 6.98395e-01 6.87925e-01 7.06386e-01 5.74577e-01 5.77841e-01 5.70876e-01 5.85917e-01 5.70531e-01 5.99159e-01 5.60993e-01 7.20745e-01 7.15372e-01 7.22464e-01 5.74858e-01 7.24299e-01 7.24181e-01 7.24460e-01 5.83022e-01 5.93066e-01 5.72519e-01 5.68634e-01 7.03716e-01 7.18638e-01 7.00326e-01 15 30 50.0 -6 13407909 CCCAA C - splice_region_variant&5_prime_UTR_variant LOW GFOD1 54438 Transcript NM_001242630.1 protein_coding 1/2 225-228 -1 EntrezGene CCAA CCAA OK 7.446 0.593154 3.53&5.37&3.69&-1.51 rs10573330 80935 142566 5.67702e-01 2.31887e-01 2.32329e-01 2.31368e-01 7.08989e-01 6.92641e-01 7.26636e-01 6.98395e-01 6.87925e-01 7.06386e-01 5.74577e-01 5.77841e-01 5.70876e-01 5.85917e-01 5.70531e-01 5.99159e-01 5.60993e-01 7.20745e-01 7.15372e-01 7.22464e-01 5.74858e-01 7.24299e-01 7.24181e-01 7.24460e-01 5.83022e-01 5.93066e-01 5.72519e-01 5.68634e-01 7.03716e-01 7.18638e-01 7.00326e-01 15 30 50.0 -6 13407909 CCCAA C - intron_variant MODIFIER GFOD1 54438 Transcript NM_018988.4 protein_coding 1/1 -1 EntrezGene CCAA CCAA 7.446 0.593154 3.53&5.37&3.69&-1.51 rs10573330 80935 142566 5.67702e-01 2.31887e-01 2.32329e-01 2.31368e-01 7.08989e-01 6.92641e-01 7.26636e-01 6.98395e-01 6.87925e-01 7.06386e-01 5.74577e-01 5.77841e-01 5.70876e-01 5.85917e-01 5.70531e-01 5.99159e-01 5.60993e-01 7.20745e-01 7.15372e-01 7.22464e-01 5.74858e-01 7.24299e-01 7.24181e-01 7.24460e-01 5.83022e-01 5.93066e-01 5.72519e-01 5.68634e-01 7.03716e-01 7.18638e-01 7.00326e-01 15 30 50.0 -6 18130687 T C C missense_variant MODERATE TPMT 7172 Transcript NM_000367.5 protein_coding 9/9 808 719 240 Y/C tAt/tGt -1 EntrezGene T T 0 0.998 26.2 3.867206 26.2 0.99807344176072321 9.525838 0.778852102787149 0.88287 9.488735 0.83430422051236 0.88195 -0.21 5.92 0.99622652781227 0.000036 0.5132 0.2149 3.59 2.05255e-07 -5.15 15.1814 0.783 0.94757 0.732398 1.000000 0.877000 4.694000 1.138000 6.2 chr6:18130687-18130687 6310 143292 4.40360e-02 5.46224e-02 5.57220e-02 5.33313e-02 1.55902e-02 1.28205e-02 1.86047e-02 5.09442e-02 5.39581e-02 4.86452e-02 1.83624e-02 2.09989e-02 1.53846e-02 1.08626e-02 8.99032e-03 1.24703e-02 4.50877e-02 2.94174e-02 2.55387e-02 3.06379e-02 4.29172e-02 4.27222e-02 4.20388e-02 4.36622e-02 3.95349e-02 3.81818e-02 4.09524e-02 4.40532e-02 1.87131e-02 2.12014e-02 1.81452e-02 12725 0.03669 0.03914 27764 Thiopurine_methyltransferase_deficiency¬_provided Thiopurine_methyltransferase_deficiency MONDO:MONDO:0012503&MedGen:C0342801&OMIM:610460&SNOMED_CT:238012003&MedGen:CN517202 MONDO:MONDO:0012503&MedGen:C0342801&OMIM:610460&SNOMED_CT:238012003 criteria_provided&_multiple_submitters&_no_conflicts Likely_benign&_other 12722:drug_response single_nucleotide_variant TPMT:7172 SO:0001583&missense_variant 1 1142345 15 30 50.0 -6 18130687 T C C missense_variant MODERATE TPMT 7172 Transcript NM_001346817.1 protein_coding 10/10 1279 719 240 Y/C tAt/tGt -1 EntrezGene T T OK 0 0.998 26.2 3.867206 26.2 0.99807344176072321 9.525838 0.778852102787149 0.88287 9.488735 0.83430422051236 0.88195 -0.21 5.92 0.99622652781227 0.000036 0.5132 0.2149 3.59 2.05255e-07 -5.15 15.1814 0.783 0.94757 0.732398 1.000000 0.877000 4.694000 1.138000 6.2 chr6:18130687-18130687 6310 143292 4.40360e-02 5.46224e-02 5.57220e-02 5.33313e-02 1.55902e-02 1.28205e-02 1.86047e-02 5.09442e-02 5.39581e-02 4.86452e-02 1.83624e-02 2.09989e-02 1.53846e-02 1.08626e-02 8.99032e-03 1.24703e-02 4.50877e-02 2.94174e-02 2.55387e-02 3.06379e-02 4.29172e-02 4.27222e-02 4.20388e-02 4.36622e-02 3.95349e-02 3.81818e-02 4.09524e-02 4.40532e-02 1.87131e-02 2.12014e-02 1.81452e-02 12725 0.03669 0.03914 27764 Thiopurine_methyltransferase_deficiency¬_provided Thiopurine_methyltransferase_deficiency MONDO:MONDO:0012503&MedGen:C0342801&OMIM:610460&SNOMED_CT:238012003&MedGen:CN517202 MONDO:MONDO:0012503&MedGen:C0342801&OMIM:610460&SNOMED_CT:238012003 criteria_provided&_multiple_submitters&_no_conflicts Likely_benign&_other 12722:drug_response single_nucleotide_variant TPMT:7172 SO:0001583&missense_variant 1 1142345 15 30 50.0 -6 18130687 T C C missense_variant MODERATE TPMT 7172 Transcript NM_001346818.1 protein_coding 8/8 855 674 225 Y/C tAt/tGt -1 EntrezGene T T OK 0 0.993 26.2 3.867206 26.2 0.99807344176072321 9.525838 0.778852102787149 0.88287 9.488735 0.83430422051236 0.88195 -0.21 5.92 0.99622652781227 0.000036 0.5132 0.2149 3.59 2.05255e-07 -5.15 15.1814 0.783 0.94757 0.732398 1.000000 0.877000 4.694000 1.138000 6.2 chr6:18130687-18130687 6310 143292 4.40360e-02 5.46224e-02 5.57220e-02 5.33313e-02 1.55902e-02 1.28205e-02 1.86047e-02 5.09442e-02 5.39581e-02 4.86452e-02 1.83624e-02 2.09989e-02 1.53846e-02 1.08626e-02 8.99032e-03 1.24703e-02 4.50877e-02 2.94174e-02 2.55387e-02 3.06379e-02 4.29172e-02 4.27222e-02 4.20388e-02 4.36622e-02 3.95349e-02 3.81818e-02 4.09524e-02 4.40532e-02 1.87131e-02 2.12014e-02 1.81452e-02 12725 0.03669 0.03914 27764 Thiopurine_methyltransferase_deficiency¬_provided Thiopurine_methyltransferase_deficiency MONDO:MONDO:0012503&MedGen:C0342801&OMIM:610460&SNOMED_CT:238012003&MedGen:CN517202 MONDO:MONDO:0012503&MedGen:C0342801&OMIM:610460&SNOMED_CT:238012003 criteria_provided&_multiple_submitters&_no_conflicts Likely_benign&_other 12722:drug_response single_nucleotide_variant TPMT:7172 SO:0001583&missense_variant 1 1142345 15 30 50.0 -6 18138997 C T T missense_variant MODERATE TPMT 7172 Transcript NM_000367.5 protein_coding 6/9 549 460 154 A/T Gca/Aca -1 EntrezGene C C 0 0.808 23.5 3.037101 23.5 0.99873067351012645 3.326027 0.228502485263353 0.51435 4.195111 0.375510823545504 0.60096 -0.94 -0.479 0.995952767946834 0.000073 0.5215 0.1230 3.665 0.998933 -2.53 11.7517 0.138 0.36246 0.656854 0.949000 1.000000 -0.093000 1.026000 2.25 rs1800460 3748 143284 2.61578e-02 7.34874e-03 7.75399e-03 6.87339e-03 1.55556e-02 1.27660e-02 1.86047e-02 3.91827e-02 4.26685e-02 3.65256e-02 1.74489e-02 1.98638e-02 1.47247e-02 0.00000e+00 0.00000e+00 0.00000e+00 2.65432e-02 2.65927e-02 2.23821e-02 2.79175e-02 2.57481e-02 3.85342e-02 3.76304e-02 3.97777e-02 2.27484e-02 2.18182e-02 2.37192e-02 2.61715e-02 5.57012e-03 7.06714e-03 5.22928e-03 37126 0.02749 0.01278 27761 Thiopurine_methyltransferase_deficiency¬_provided Thiopurine_methyltransferase_deficiency MONDO:MONDO:0012503&MedGen:C0342801&OMIM:610460&SNOMED_CT:238012003&MedGen:CN517202 MONDO:MONDO:0012503&MedGen:C0342801&OMIM:610460&SNOMED_CT:238012003 criteria_provided&_multiple_submitters&_no_conflicts Likely_benign&_other 12722:drug_response single_nucleotide_variant TPMT:7172 SO:0001583&missense_variant 1 1800460 15 30 50.0 -6 18138997 C T T missense_variant MODERATE TPMT 7172 Transcript NM_001346817.1 protein_coding 7/10 1020 460 154 A/T Gca/Aca -1 EntrezGene C C OK 0 0.808 23.5 3.037101 23.5 0.99873067351012645 3.326027 0.228502485263353 0.51435 4.195111 0.375510823545504 0.60096 -0.94 -0.479 0.995952767946834 0.000073 0.5215 0.1230 3.665 0.998933 -2.53 11.7517 0.138 0.36246 0.656854 0.949000 1.000000 -0.093000 1.026000 2.25 rs1800460 3748 143284 2.61578e-02 7.34874e-03 7.75399e-03 6.87339e-03 1.55556e-02 1.27660e-02 1.86047e-02 3.91827e-02 4.26685e-02 3.65256e-02 1.74489e-02 1.98638e-02 1.47247e-02 0.00000e+00 0.00000e+00 0.00000e+00 2.65432e-02 2.65927e-02 2.23821e-02 2.79175e-02 2.57481e-02 3.85342e-02 3.76304e-02 3.97777e-02 2.27484e-02 2.18182e-02 2.37192e-02 2.61715e-02 5.57012e-03 7.06714e-03 5.22928e-03 37126 0.02749 0.01278 27761 Thiopurine_methyltransferase_deficiency¬_provided Thiopurine_methyltransferase_deficiency MONDO:MONDO:0012503&MedGen:C0342801&OMIM:610460&SNOMED_CT:238012003&MedGen:CN517202 MONDO:MONDO:0012503&MedGen:C0342801&OMIM:610460&SNOMED_CT:238012003 criteria_provided&_multiple_submitters&_no_conflicts Likely_benign&_other 12722:drug_response single_nucleotide_variant TPMT:7172 SO:0001583&missense_variant 1 1800460 15 30 50.0 -6 18138997 C T T missense_variant MODERATE TPMT 7172 Transcript NM_001346818.1 protein_coding 6/8 641 460 154 A/T Gca/Aca -1 EntrezGene C C OK 0 0.881 23.5 3.037101 23.5 0.99873067351012645 3.326027 0.228502485263353 0.51435 4.195111 0.375510823545504 0.60096 -0.94 -0.479 0.995952767946834 0.000073 0.5215 0.1230 3.665 0.998933 -2.53 11.7517 0.138 0.36246 0.656854 0.949000 1.000000 -0.093000 1.026000 2.25 rs1800460 3748 143284 2.61578e-02 7.34874e-03 7.75399e-03 6.87339e-03 1.55556e-02 1.27660e-02 1.86047e-02 3.91827e-02 4.26685e-02 3.65256e-02 1.74489e-02 1.98638e-02 1.47247e-02 0.00000e+00 0.00000e+00 0.00000e+00 2.65432e-02 2.65927e-02 2.23821e-02 2.79175e-02 2.57481e-02 3.85342e-02 3.76304e-02 3.97777e-02 2.27484e-02 2.18182e-02 2.37192e-02 2.61715e-02 5.57012e-03 7.06714e-03 5.22928e-03 37126 0.02749 0.01278 27761 Thiopurine_methyltransferase_deficiency¬_provided Thiopurine_methyltransferase_deficiency MONDO:MONDO:0012503&MedGen:C0342801&OMIM:610460&SNOMED_CT:238012003&MedGen:CN517202 MONDO:MONDO:0012503&MedGen:C0342801&OMIM:610460&SNOMED_CT:238012003 criteria_provided&_multiple_submitters&_no_conflicts Likely_benign&_other 12722:drug_response single_nucleotide_variant TPMT:7172 SO:0001583&missense_variant 1 1800460 15 30 50.0 -6 26092913 G A A missense_variant MODERATE HFE 3077 Transcript NM_000410.3 protein_coding 4/6 1005 845 282 C/Y tGc/tAc 1 EntrezGene G G 0 0.759 25.8 3.793231 25.8 0.99446180177943488 8.692576 0.745339606461945 0.85809 10.47997 0.877295728668495 0.90603 -3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68 5.35 0.999999999873201 0.000003 0.9518 1.0733 .&.&.&.&4.395&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1 -8.93&-10.67&-9.03&-8.48&-8.33&-8.88&-8.23&-8.64&-9.14&-8.41 14.7506 0.816&0.762&0.923&0.708&0.746&0.356&0.742&0.839&0.804&0.918 0.94952 0.623552 1.000000 0.821000 5.226000 1.176000 6.35 rs1800562 5448 143266 3.80272e-02 1.13263e-02 1.16720e-02 1.09202e-02 4.55556e-02 3.82979e-02 5.34884e-02 1.89011e-02 1.89509e-02 1.88630e-02 1.35461e-02 1.53409e-02 1.15237e-02 6.38570e-04 6.91563e-04 5.93120e-04 4.07594e-02 3.52234e-02 3.83081e-02 3.42535e-02 3.51206e-02 6.48874e-02 6.57163e-02 6.37468e-02 2.78810e-02 3.00546e-02 2.56167e-02 3.80243e-02 2.62295e-03 1.76678e-03 2.81804e-03 9 0.03243 0.01258 15048 Alzheimer_disease&_susceptibility_to&Porphyria_cutanea_tarda&_susceptibility_to&Porphyria_variegata&_susceptibility_to&Abnormality_of_the_nervous_system&Behavioral_abnormality&Abnormal_peripheral_nervous_system_morphology&Cutaneous_photosensitivity&Abdominal_pain&Alzheimer_disease&Peripheral_neuropathy&Abnormality_of_the_male_genitalia&Porphyrinuria&Pain&Hereditary_hemochromatosis&Microvascular_complications_of_diabetes_7&Hereditary_cancer-predisposing_syndrome&Hemochromatosis_type_2&Hemochromatosis_type_1&Bronze_diabetes&Hemochromatosis&_juvenile&_digenic&Transferrin_serum_level_quantitative_trait_locus_2¬_provided .&.&.&Human_Phenotype_Ontology:HP:0000707&Human_Phenotype_Ontology:HP:0001333&Human_Phenotype_Ontology:HP:0006987&MedGen:C0497552&Human_Phenotype_Ontology:HP:0000708&Human_Phenotype_Ontology:HP:0000715&Human_Phenotype_Ontology:HP:0002368&Human_Phenotype_Ontology:HP:0002456&MedGen:C0233514&Human_Phenotype_Ontology:HP:0000759&Human_Phenotype_Ontology:HP:0003483&MONDO:MONDO:0003620&MedGen:C4025831&Human_Phenotype_Ontology:HP:0000992&Human_Phenotype_Ontology:HP:0005594&Human_Phenotype_Ontology:HP:0006831&Human_Phenotype_Ontology:HP:0007538&MedGen:C0349506&Human_Phenotype_Ontology:HP:0002027&MedGen:C0000737&Human_Phenotype_Ontology:HP:0002511&Human_Phenotype_Ontology:HP:0006878&Human_Phenotype_Ontology:HP:0007213&MONDO:MONDO:0004975&MedGen:C0002395&OMIM:104300&SNOMED_CT:26929004&Human_Phenotype_Ontology:HP:0003157&Human_Phenotype_Ontology:HP:0003407&Human_Phenotype_Ontology:HP:0007088&Human_Phenotype_Ontology:HP:0007235&Human_Phenotype_Ontology:HP:0007355&Human_Phenotype_Ontology:HP:0009830&MONDO:MONDO:0005244&MedGen:C0031117&Human_Phenotype_Ontology:HP:0010461&MedGen:C4023819&Human_Phenotype_Ontology:HP:0010473&MedGen:C0151861&Human_Phenotype_Ontology:HP:0012531&MedGen:C0030193&MONDO:MONDO:0006507&MedGen:C0392514&OMIM:PS235200&SNOMED_CT:35400008&MONDO:MONDO:0012971&MedGen:C2673520&OMIM:612635&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019257&MedGen:CN205842&Orphanet:ORPHA79230&MONDO:MONDO:0021001&MedGen:C3469186&OMIM:235200&Orphanet:ORPHA465508&MedGen:C0018995&MedGen:C3150862&MedGen:C3280096&OMIM:614193&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other&_risk_factor Pathogenic(14)&Uncertain_significance(1) single_nucleotide_variant HFE:3077 SO:0001583&missense_variant&SO:0001627&intron_variant 1 1800562 15 30 50.0 -6 26092913 G A A missense_variant MODERATE HFE 3077 Transcript NM_001300749.2 protein_coding 4/6 857 845 282 C/Y tGc/tAc 1 EntrezGene G G 0.04 0.667 25.8 3.793231 25.8 0.99446180177943488 8.692576 0.745339606461945 0.85809 10.47997 0.877295728668495 0.90603 -3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68 5.35 0.999999999873201 0.000003 0.9518 1.0733 .&.&.&.&4.395&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1 -8.93&-10.67&-9.03&-8.48&-8.33&-8.88&-8.23&-8.64&-9.14&-8.41 14.7506 0.816&0.762&0.923&0.708&0.746&0.356&0.742&0.839&0.804&0.918 0.94952 0.623552 1.000000 0.821000 5.226000 1.176000 6.35 rs1800562 5448 143266 3.80272e-02 1.13263e-02 1.16720e-02 1.09202e-02 4.55556e-02 3.82979e-02 5.34884e-02 1.89011e-02 1.89509e-02 1.88630e-02 1.35461e-02 1.53409e-02 1.15237e-02 6.38570e-04 6.91563e-04 5.93120e-04 4.07594e-02 3.52234e-02 3.83081e-02 3.42535e-02 3.51206e-02 6.48874e-02 6.57163e-02 6.37468e-02 2.78810e-02 3.00546e-02 2.56167e-02 3.80243e-02 2.62295e-03 1.76678e-03 2.81804e-03 9 0.03243 0.01258 15048 Alzheimer_disease&_susceptibility_to&Porphyria_cutanea_tarda&_susceptibility_to&Porphyria_variegata&_susceptibility_to&Abnormality_of_the_nervous_system&Behavioral_abnormality&Abnormal_peripheral_nervous_system_morphology&Cutaneous_photosensitivity&Abdominal_pain&Alzheimer_disease&Peripheral_neuropathy&Abnormality_of_the_male_genitalia&Porphyrinuria&Pain&Hereditary_hemochromatosis&Microvascular_complications_of_diabetes_7&Hereditary_cancer-predisposing_syndrome&Hemochromatosis_type_2&Hemochromatosis_type_1&Bronze_diabetes&Hemochromatosis&_juvenile&_digenic&Transferrin_serum_level_quantitative_trait_locus_2¬_provided .&.&.&Human_Phenotype_Ontology:HP:0000707&Human_Phenotype_Ontology:HP:0001333&Human_Phenotype_Ontology:HP:0006987&MedGen:C0497552&Human_Phenotype_Ontology:HP:0000708&Human_Phenotype_Ontology:HP:0000715&Human_Phenotype_Ontology:HP:0002368&Human_Phenotype_Ontology:HP:0002456&MedGen:C0233514&Human_Phenotype_Ontology:HP:0000759&Human_Phenotype_Ontology:HP:0003483&MONDO:MONDO:0003620&MedGen:C4025831&Human_Phenotype_Ontology:HP:0000992&Human_Phenotype_Ontology:HP:0005594&Human_Phenotype_Ontology:HP:0006831&Human_Phenotype_Ontology:HP:0007538&MedGen:C0349506&Human_Phenotype_Ontology:HP:0002027&MedGen:C0000737&Human_Phenotype_Ontology:HP:0002511&Human_Phenotype_Ontology:HP:0006878&Human_Phenotype_Ontology:HP:0007213&MONDO:MONDO:0004975&MedGen:C0002395&OMIM:104300&SNOMED_CT:26929004&Human_Phenotype_Ontology:HP:0003157&Human_Phenotype_Ontology:HP:0003407&Human_Phenotype_Ontology:HP:0007088&Human_Phenotype_Ontology:HP:0007235&Human_Phenotype_Ontology:HP:0007355&Human_Phenotype_Ontology:HP:0009830&MONDO:MONDO:0005244&MedGen:C0031117&Human_Phenotype_Ontology:HP:0010461&MedGen:C4023819&Human_Phenotype_Ontology:HP:0010473&MedGen:C0151861&Human_Phenotype_Ontology:HP:0012531&MedGen:C0030193&MONDO:MONDO:0006507&MedGen:C0392514&OMIM:PS235200&SNOMED_CT:35400008&MONDO:MONDO:0012971&MedGen:C2673520&OMIM:612635&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019257&MedGen:CN205842&Orphanet:ORPHA79230&MONDO:MONDO:0021001&MedGen:C3469186&OMIM:235200&Orphanet:ORPHA465508&MedGen:C0018995&MedGen:C3150862&MedGen:C3280096&OMIM:614193&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other&_risk_factor Pathogenic(14)&Uncertain_significance(1) single_nucleotide_variant HFE:3077 SO:0001583&missense_variant&SO:0001627&intron_variant 1 1800562 15 30 50.0 -6 26092913 G A A missense_variant MODERATE HFE 3077 Transcript NM_139003.3 protein_coding 3/5 539 527 176 C/Y tGc/tAc 1 EntrezGene G G 0 0.999 25.8 3.793231 25.8 0.99446180177943488 8.692576 0.745339606461945 0.85809 10.47997 0.877295728668495 0.90603 -3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68 5.35 0.999999999873201 0.000003 0.9518 1.0733 .&.&.&.&4.395&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1 -8.93&-10.67&-9.03&-8.48&-8.33&-8.88&-8.23&-8.64&-9.14&-8.41 14.7506 0.816&0.762&0.923&0.708&0.746&0.356&0.742&0.839&0.804&0.918 0.94952 0.623552 1.000000 0.821000 5.226000 1.176000 6.35 rs1800562 5448 143266 3.80272e-02 1.13263e-02 1.16720e-02 1.09202e-02 4.55556e-02 3.82979e-02 5.34884e-02 1.89011e-02 1.89509e-02 1.88630e-02 1.35461e-02 1.53409e-02 1.15237e-02 6.38570e-04 6.91563e-04 5.93120e-04 4.07594e-02 3.52234e-02 3.83081e-02 3.42535e-02 3.51206e-02 6.48874e-02 6.57163e-02 6.37468e-02 2.78810e-02 3.00546e-02 2.56167e-02 3.80243e-02 2.62295e-03 1.76678e-03 2.81804e-03 9 0.03243 0.01258 15048 Alzheimer_disease&_susceptibility_to&Porphyria_cutanea_tarda&_susceptibility_to&Porphyria_variegata&_susceptibility_to&Abnormality_of_the_nervous_system&Behavioral_abnormality&Abnormal_peripheral_nervous_system_morphology&Cutaneous_photosensitivity&Abdominal_pain&Alzheimer_disease&Peripheral_neuropathy&Abnormality_of_the_male_genitalia&Porphyrinuria&Pain&Hereditary_hemochromatosis&Microvascular_complications_of_diabetes_7&Hereditary_cancer-predisposing_syndrome&Hemochromatosis_type_2&Hemochromatosis_type_1&Bronze_diabetes&Hemochromatosis&_juvenile&_digenic&Transferrin_serum_level_quantitative_trait_locus_2¬_provided .&.&.&Human_Phenotype_Ontology:HP:0000707&Human_Phenotype_Ontology:HP:0001333&Human_Phenotype_Ontology:HP:0006987&MedGen:C0497552&Human_Phenotype_Ontology:HP:0000708&Human_Phenotype_Ontology:HP:0000715&Human_Phenotype_Ontology:HP:0002368&Human_Phenotype_Ontology:HP:0002456&MedGen:C0233514&Human_Phenotype_Ontology:HP:0000759&Human_Phenotype_Ontology:HP:0003483&MONDO:MONDO:0003620&MedGen:C4025831&Human_Phenotype_Ontology:HP:0000992&Human_Phenotype_Ontology:HP:0005594&Human_Phenotype_Ontology:HP:0006831&Human_Phenotype_Ontology:HP:0007538&MedGen:C0349506&Human_Phenotype_Ontology:HP:0002027&MedGen:C0000737&Human_Phenotype_Ontology:HP:0002511&Human_Phenotype_Ontology:HP:0006878&Human_Phenotype_Ontology:HP:0007213&MONDO:MONDO:0004975&MedGen:C0002395&OMIM:104300&SNOMED_CT:26929004&Human_Phenotype_Ontology:HP:0003157&Human_Phenotype_Ontology:HP:0003407&Human_Phenotype_Ontology:HP:0007088&Human_Phenotype_Ontology:HP:0007235&Human_Phenotype_Ontology:HP:0007355&Human_Phenotype_Ontology:HP:0009830&MONDO:MONDO:0005244&MedGen:C0031117&Human_Phenotype_Ontology:HP:0010461&MedGen:C4023819&Human_Phenotype_Ontology:HP:0010473&MedGen:C0151861&Human_Phenotype_Ontology:HP:0012531&MedGen:C0030193&MONDO:MONDO:0006507&MedGen:C0392514&OMIM:PS235200&SNOMED_CT:35400008&MONDO:MONDO:0012971&MedGen:C2673520&OMIM:612635&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019257&MedGen:CN205842&Orphanet:ORPHA79230&MONDO:MONDO:0021001&MedGen:C3469186&OMIM:235200&Orphanet:ORPHA465508&MedGen:C0018995&MedGen:C3150862&MedGen:C3280096&OMIM:614193&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other&_risk_factor Pathogenic(14)&Uncertain_significance(1) single_nucleotide_variant HFE:3077 SO:0001583&missense_variant&SO:0001627&intron_variant 1 1800562 15 30 50.0 -6 26092913 G A A missense_variant MODERATE HFE 3077 Transcript NM_139004.3 protein_coding 3/5 581 569 190 C/Y tGc/tAc 1 EntrezGene G G 0 0.999 25.8 3.793231 25.8 0.99446180177943488 8.692576 0.745339606461945 0.85809 10.47997 0.877295728668495 0.90603 -3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68 5.35 0.999999999873201 0.000003 0.9518 1.0733 .&.&.&.&4.395&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1 -8.93&-10.67&-9.03&-8.48&-8.33&-8.88&-8.23&-8.64&-9.14&-8.41 14.7506 0.816&0.762&0.923&0.708&0.746&0.356&0.742&0.839&0.804&0.918 0.94952 0.623552 1.000000 0.821000 5.226000 1.176000 6.35 rs1800562 5448 143266 3.80272e-02 1.13263e-02 1.16720e-02 1.09202e-02 4.55556e-02 3.82979e-02 5.34884e-02 1.89011e-02 1.89509e-02 1.88630e-02 1.35461e-02 1.53409e-02 1.15237e-02 6.38570e-04 6.91563e-04 5.93120e-04 4.07594e-02 3.52234e-02 3.83081e-02 3.42535e-02 3.51206e-02 6.48874e-02 6.57163e-02 6.37468e-02 2.78810e-02 3.00546e-02 2.56167e-02 3.80243e-02 2.62295e-03 1.76678e-03 2.81804e-03 9 0.03243 0.01258 15048 Alzheimer_disease&_susceptibility_to&Porphyria_cutanea_tarda&_susceptibility_to&Porphyria_variegata&_susceptibility_to&Abnormality_of_the_nervous_system&Behavioral_abnormality&Abnormal_peripheral_nervous_system_morphology&Cutaneous_photosensitivity&Abdominal_pain&Alzheimer_disease&Peripheral_neuropathy&Abnormality_of_the_male_genitalia&Porphyrinuria&Pain&Hereditary_hemochromatosis&Microvascular_complications_of_diabetes_7&Hereditary_cancer-predisposing_syndrome&Hemochromatosis_type_2&Hemochromatosis_type_1&Bronze_diabetes&Hemochromatosis&_juvenile&_digenic&Transferrin_serum_level_quantitative_trait_locus_2¬_provided .&.&.&Human_Phenotype_Ontology:HP:0000707&Human_Phenotype_Ontology:HP:0001333&Human_Phenotype_Ontology:HP:0006987&MedGen:C0497552&Human_Phenotype_Ontology:HP:0000708&Human_Phenotype_Ontology:HP:0000715&Human_Phenotype_Ontology:HP:0002368&Human_Phenotype_Ontology:HP:0002456&MedGen:C0233514&Human_Phenotype_Ontology:HP:0000759&Human_Phenotype_Ontology:HP:0003483&MONDO:MONDO:0003620&MedGen:C4025831&Human_Phenotype_Ontology:HP:0000992&Human_Phenotype_Ontology:HP:0005594&Human_Phenotype_Ontology:HP:0006831&Human_Phenotype_Ontology:HP:0007538&MedGen:C0349506&Human_Phenotype_Ontology:HP:0002027&MedGen:C0000737&Human_Phenotype_Ontology:HP:0002511&Human_Phenotype_Ontology:HP:0006878&Human_Phenotype_Ontology:HP:0007213&MONDO:MONDO:0004975&MedGen:C0002395&OMIM:104300&SNOMED_CT:26929004&Human_Phenotype_Ontology:HP:0003157&Human_Phenotype_Ontology:HP:0003407&Human_Phenotype_Ontology:HP:0007088&Human_Phenotype_Ontology:HP:0007235&Human_Phenotype_Ontology:HP:0007355&Human_Phenotype_Ontology:HP:0009830&MONDO:MONDO:0005244&MedGen:C0031117&Human_Phenotype_Ontology:HP:0010461&MedGen:C4023819&Human_Phenotype_Ontology:HP:0010473&MedGen:C0151861&Human_Phenotype_Ontology:HP:0012531&MedGen:C0030193&MONDO:MONDO:0006507&MedGen:C0392514&OMIM:PS235200&SNOMED_CT:35400008&MONDO:MONDO:0012971&MedGen:C2673520&OMIM:612635&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019257&MedGen:CN205842&Orphanet:ORPHA79230&MONDO:MONDO:0021001&MedGen:C3469186&OMIM:235200&Orphanet:ORPHA465508&MedGen:C0018995&MedGen:C3150862&MedGen:C3280096&OMIM:614193&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other&_risk_factor Pathogenic(14)&Uncertain_significance(1) single_nucleotide_variant HFE:3077 SO:0001583&missense_variant&SO:0001627&intron_variant 1 1800562 15 30 50.0 -6 26092913 G A A missense_variant MODERATE HFE 3077 Transcript NM_139006.3 protein_coding 4/6 815 803 268 C/Y tGc/tAc 1 EntrezGene G G 0 0.667 25.8 3.793231 25.8 0.99446180177943488 8.692576 0.745339606461945 0.85809 10.47997 0.877295728668495 0.90603 -3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68 5.35 0.999999999873201 0.000003 0.9518 1.0733 .&.&.&.&4.395&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1 -8.93&-10.67&-9.03&-8.48&-8.33&-8.88&-8.23&-8.64&-9.14&-8.41 14.7506 0.816&0.762&0.923&0.708&0.746&0.356&0.742&0.839&0.804&0.918 0.94952 0.623552 1.000000 0.821000 5.226000 1.176000 6.35 rs1800562 5448 143266 3.80272e-02 1.13263e-02 1.16720e-02 1.09202e-02 4.55556e-02 3.82979e-02 5.34884e-02 1.89011e-02 1.89509e-02 1.88630e-02 1.35461e-02 1.53409e-02 1.15237e-02 6.38570e-04 6.91563e-04 5.93120e-04 4.07594e-02 3.52234e-02 3.83081e-02 3.42535e-02 3.51206e-02 6.48874e-02 6.57163e-02 6.37468e-02 2.78810e-02 3.00546e-02 2.56167e-02 3.80243e-02 2.62295e-03 1.76678e-03 2.81804e-03 9 0.03243 0.01258 15048 Alzheimer_disease&_susceptibility_to&Porphyria_cutanea_tarda&_susceptibility_to&Porphyria_variegata&_susceptibility_to&Abnormality_of_the_nervous_system&Behavioral_abnormality&Abnormal_peripheral_nervous_system_morphology&Cutaneous_photosensitivity&Abdominal_pain&Alzheimer_disease&Peripheral_neuropathy&Abnormality_of_the_male_genitalia&Porphyrinuria&Pain&Hereditary_hemochromatosis&Microvascular_complications_of_diabetes_7&Hereditary_cancer-predisposing_syndrome&Hemochromatosis_type_2&Hemochromatosis_type_1&Bronze_diabetes&Hemochromatosis&_juvenile&_digenic&Transferrin_serum_level_quantitative_trait_locus_2¬_provided .&.&.&Human_Phenotype_Ontology:HP:0000707&Human_Phenotype_Ontology:HP:0001333&Human_Phenotype_Ontology:HP:0006987&MedGen:C0497552&Human_Phenotype_Ontology:HP:0000708&Human_Phenotype_Ontology:HP:0000715&Human_Phenotype_Ontology:HP:0002368&Human_Phenotype_Ontology:HP:0002456&MedGen:C0233514&Human_Phenotype_Ontology:HP:0000759&Human_Phenotype_Ontology:HP:0003483&MONDO:MONDO:0003620&MedGen:C4025831&Human_Phenotype_Ontology:HP:0000992&Human_Phenotype_Ontology:HP:0005594&Human_Phenotype_Ontology:HP:0006831&Human_Phenotype_Ontology:HP:0007538&MedGen:C0349506&Human_Phenotype_Ontology:HP:0002027&MedGen:C0000737&Human_Phenotype_Ontology:HP:0002511&Human_Phenotype_Ontology:HP:0006878&Human_Phenotype_Ontology:HP:0007213&MONDO:MONDO:0004975&MedGen:C0002395&OMIM:104300&SNOMED_CT:26929004&Human_Phenotype_Ontology:HP:0003157&Human_Phenotype_Ontology:HP:0003407&Human_Phenotype_Ontology:HP:0007088&Human_Phenotype_Ontology:HP:0007235&Human_Phenotype_Ontology:HP:0007355&Human_Phenotype_Ontology:HP:0009830&MONDO:MONDO:0005244&MedGen:C0031117&Human_Phenotype_Ontology:HP:0010461&MedGen:C4023819&Human_Phenotype_Ontology:HP:0010473&MedGen:C0151861&Human_Phenotype_Ontology:HP:0012531&MedGen:C0030193&MONDO:MONDO:0006507&MedGen:C0392514&OMIM:PS235200&SNOMED_CT:35400008&MONDO:MONDO:0012971&MedGen:C2673520&OMIM:612635&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019257&MedGen:CN205842&Orphanet:ORPHA79230&MONDO:MONDO:0021001&MedGen:C3469186&OMIM:235200&Orphanet:ORPHA465508&MedGen:C0018995&MedGen:C3150862&MedGen:C3280096&OMIM:614193&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other&_risk_factor Pathogenic(14)&Uncertain_significance(1) single_nucleotide_variant HFE:3077 SO:0001583&missense_variant&SO:0001627&intron_variant 1 1800562 15 30 50.0 -6 26092913 G A A missense_variant MODERATE HFE 3077 Transcript NM_139007.3 protein_coding 3/5 593 581 194 C/Y tGc/tAc 1 EntrezGene G G 0.04 0.882 25.8 3.793231 25.8 0.99446180177943488 8.692576 0.745339606461945 0.85809 10.47997 0.877295728668495 0.90603 -3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68 5.35 0.999999999873201 0.000003 0.9518 1.0733 .&.&.&.&4.395&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1 -8.93&-10.67&-9.03&-8.48&-8.33&-8.88&-8.23&-8.64&-9.14&-8.41 14.7506 0.816&0.762&0.923&0.708&0.746&0.356&0.742&0.839&0.804&0.918 0.94952 0.623552 1.000000 0.821000 5.226000 1.176000 6.35 rs1800562 5448 143266 3.80272e-02 1.13263e-02 1.16720e-02 1.09202e-02 4.55556e-02 3.82979e-02 5.34884e-02 1.89011e-02 1.89509e-02 1.88630e-02 1.35461e-02 1.53409e-02 1.15237e-02 6.38570e-04 6.91563e-04 5.93120e-04 4.07594e-02 3.52234e-02 3.83081e-02 3.42535e-02 3.51206e-02 6.48874e-02 6.57163e-02 6.37468e-02 2.78810e-02 3.00546e-02 2.56167e-02 3.80243e-02 2.62295e-03 1.76678e-03 2.81804e-03 9 0.03243 0.01258 15048 Alzheimer_disease&_susceptibility_to&Porphyria_cutanea_tarda&_susceptibility_to&Porphyria_variegata&_susceptibility_to&Abnormality_of_the_nervous_system&Behavioral_abnormality&Abnormal_peripheral_nervous_system_morphology&Cutaneous_photosensitivity&Abdominal_pain&Alzheimer_disease&Peripheral_neuropathy&Abnormality_of_the_male_genitalia&Porphyrinuria&Pain&Hereditary_hemochromatosis&Microvascular_complications_of_diabetes_7&Hereditary_cancer-predisposing_syndrome&Hemochromatosis_type_2&Hemochromatosis_type_1&Bronze_diabetes&Hemochromatosis&_juvenile&_digenic&Transferrin_serum_level_quantitative_trait_locus_2¬_provided .&.&.&Human_Phenotype_Ontology:HP:0000707&Human_Phenotype_Ontology:HP:0001333&Human_Phenotype_Ontology:HP:0006987&MedGen:C0497552&Human_Phenotype_Ontology:HP:0000708&Human_Phenotype_Ontology:HP:0000715&Human_Phenotype_Ontology:HP:0002368&Human_Phenotype_Ontology:HP:0002456&MedGen:C0233514&Human_Phenotype_Ontology:HP:0000759&Human_Phenotype_Ontology:HP:0003483&MONDO:MONDO:0003620&MedGen:C4025831&Human_Phenotype_Ontology:HP:0000992&Human_Phenotype_Ontology:HP:0005594&Human_Phenotype_Ontology:HP:0006831&Human_Phenotype_Ontology:HP:0007538&MedGen:C0349506&Human_Phenotype_Ontology:HP:0002027&MedGen:C0000737&Human_Phenotype_Ontology:HP:0002511&Human_Phenotype_Ontology:HP:0006878&Human_Phenotype_Ontology:HP:0007213&MONDO:MONDO:0004975&MedGen:C0002395&OMIM:104300&SNOMED_CT:26929004&Human_Phenotype_Ontology:HP:0003157&Human_Phenotype_Ontology:HP:0003407&Human_Phenotype_Ontology:HP:0007088&Human_Phenotype_Ontology:HP:0007235&Human_Phenotype_Ontology:HP:0007355&Human_Phenotype_Ontology:HP:0009830&MONDO:MONDO:0005244&MedGen:C0031117&Human_Phenotype_Ontology:HP:0010461&MedGen:C4023819&Human_Phenotype_Ontology:HP:0010473&MedGen:C0151861&Human_Phenotype_Ontology:HP:0012531&MedGen:C0030193&MONDO:MONDO:0006507&MedGen:C0392514&OMIM:PS235200&SNOMED_CT:35400008&MONDO:MONDO:0012971&MedGen:C2673520&OMIM:612635&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019257&MedGen:CN205842&Orphanet:ORPHA79230&MONDO:MONDO:0021001&MedGen:C3469186&OMIM:235200&Orphanet:ORPHA465508&MedGen:C0018995&MedGen:C3150862&MedGen:C3280096&OMIM:614193&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other&_risk_factor Pathogenic(14)&Uncertain_significance(1) single_nucleotide_variant HFE:3077 SO:0001583&missense_variant&SO:0001627&intron_variant 1 1800562 15 30 50.0 -6 26092913 G A A missense_variant MODERATE HFE 3077 Transcript NM_139008.3 protein_coding 3/5 551 539 180 C/Y tGc/tAc 1 EntrezGene G G 0 1 25.8 3.793231 25.8 0.99446180177943488 8.692576 0.745339606461945 0.85809 10.47997 0.877295728668495 0.90603 -3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68 5.35 0.999999999873201 0.000003 0.9518 1.0733 .&.&.&.&4.395&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1 -8.93&-10.67&-9.03&-8.48&-8.33&-8.88&-8.23&-8.64&-9.14&-8.41 14.7506 0.816&0.762&0.923&0.708&0.746&0.356&0.742&0.839&0.804&0.918 0.94952 0.623552 1.000000 0.821000 5.226000 1.176000 6.35 rs1800562 5448 143266 3.80272e-02 1.13263e-02 1.16720e-02 1.09202e-02 4.55556e-02 3.82979e-02 5.34884e-02 1.89011e-02 1.89509e-02 1.88630e-02 1.35461e-02 1.53409e-02 1.15237e-02 6.38570e-04 6.91563e-04 5.93120e-04 4.07594e-02 3.52234e-02 3.83081e-02 3.42535e-02 3.51206e-02 6.48874e-02 6.57163e-02 6.37468e-02 2.78810e-02 3.00546e-02 2.56167e-02 3.80243e-02 2.62295e-03 1.76678e-03 2.81804e-03 9 0.03243 0.01258 15048 Alzheimer_disease&_susceptibility_to&Porphyria_cutanea_tarda&_susceptibility_to&Porphyria_variegata&_susceptibility_to&Abnormality_of_the_nervous_system&Behavioral_abnormality&Abnormal_peripheral_nervous_system_morphology&Cutaneous_photosensitivity&Abdominal_pain&Alzheimer_disease&Peripheral_neuropathy&Abnormality_of_the_male_genitalia&Porphyrinuria&Pain&Hereditary_hemochromatosis&Microvascular_complications_of_diabetes_7&Hereditary_cancer-predisposing_syndrome&Hemochromatosis_type_2&Hemochromatosis_type_1&Bronze_diabetes&Hemochromatosis&_juvenile&_digenic&Transferrin_serum_level_quantitative_trait_locus_2¬_provided .&.&.&Human_Phenotype_Ontology:HP:0000707&Human_Phenotype_Ontology:HP:0001333&Human_Phenotype_Ontology:HP:0006987&MedGen:C0497552&Human_Phenotype_Ontology:HP:0000708&Human_Phenotype_Ontology:HP:0000715&Human_Phenotype_Ontology:HP:0002368&Human_Phenotype_Ontology:HP:0002456&MedGen:C0233514&Human_Phenotype_Ontology:HP:0000759&Human_Phenotype_Ontology:HP:0003483&MONDO:MONDO:0003620&MedGen:C4025831&Human_Phenotype_Ontology:HP:0000992&Human_Phenotype_Ontology:HP:0005594&Human_Phenotype_Ontology:HP:0006831&Human_Phenotype_Ontology:HP:0007538&MedGen:C0349506&Human_Phenotype_Ontology:HP:0002027&MedGen:C0000737&Human_Phenotype_Ontology:HP:0002511&Human_Phenotype_Ontology:HP:0006878&Human_Phenotype_Ontology:HP:0007213&MONDO:MONDO:0004975&MedGen:C0002395&OMIM:104300&SNOMED_CT:26929004&Human_Phenotype_Ontology:HP:0003157&Human_Phenotype_Ontology:HP:0003407&Human_Phenotype_Ontology:HP:0007088&Human_Phenotype_Ontology:HP:0007235&Human_Phenotype_Ontology:HP:0007355&Human_Phenotype_Ontology:HP:0009830&MONDO:MONDO:0005244&MedGen:C0031117&Human_Phenotype_Ontology:HP:0010461&MedGen:C4023819&Human_Phenotype_Ontology:HP:0010473&MedGen:C0151861&Human_Phenotype_Ontology:HP:0012531&MedGen:C0030193&MONDO:MONDO:0006507&MedGen:C0392514&OMIM:PS235200&SNOMED_CT:35400008&MONDO:MONDO:0012971&MedGen:C2673520&OMIM:612635&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019257&MedGen:CN205842&Orphanet:ORPHA79230&MONDO:MONDO:0021001&MedGen:C3469186&OMIM:235200&Orphanet:ORPHA465508&MedGen:C0018995&MedGen:C3150862&MedGen:C3280096&OMIM:614193&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other&_risk_factor Pathogenic(14)&Uncertain_significance(1) single_nucleotide_variant HFE:3077 SO:0001583&missense_variant&SO:0001627&intron_variant 1 1800562 15 30 50.0 -6 26092913 G A A missense_variant MODERATE HFE 3077 Transcript NM_139009.3 protein_coding 4/6 788 776 259 C/Y tGc/tAc 1 EntrezGene G G 0.04 0.946 25.8 3.793231 25.8 0.99446180177943488 8.692576 0.745339606461945 0.85809 10.47997 0.877295728668495 0.90603 -3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68 5.35 0.999999999873201 0.000003 0.9518 1.0733 .&.&.&.&4.395&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1 -8.93&-10.67&-9.03&-8.48&-8.33&-8.88&-8.23&-8.64&-9.14&-8.41 14.7506 0.816&0.762&0.923&0.708&0.746&0.356&0.742&0.839&0.804&0.918 0.94952 0.623552 1.000000 0.821000 5.226000 1.176000 6.35 rs1800562 5448 143266 3.80272e-02 1.13263e-02 1.16720e-02 1.09202e-02 4.55556e-02 3.82979e-02 5.34884e-02 1.89011e-02 1.89509e-02 1.88630e-02 1.35461e-02 1.53409e-02 1.15237e-02 6.38570e-04 6.91563e-04 5.93120e-04 4.07594e-02 3.52234e-02 3.83081e-02 3.42535e-02 3.51206e-02 6.48874e-02 6.57163e-02 6.37468e-02 2.78810e-02 3.00546e-02 2.56167e-02 3.80243e-02 2.62295e-03 1.76678e-03 2.81804e-03 9 0.03243 0.01258 15048 Alzheimer_disease&_susceptibility_to&Porphyria_cutanea_tarda&_susceptibility_to&Porphyria_variegata&_susceptibility_to&Abnormality_of_the_nervous_system&Behavioral_abnormality&Abnormal_peripheral_nervous_system_morphology&Cutaneous_photosensitivity&Abdominal_pain&Alzheimer_disease&Peripheral_neuropathy&Abnormality_of_the_male_genitalia&Porphyrinuria&Pain&Hereditary_hemochromatosis&Microvascular_complications_of_diabetes_7&Hereditary_cancer-predisposing_syndrome&Hemochromatosis_type_2&Hemochromatosis_type_1&Bronze_diabetes&Hemochromatosis&_juvenile&_digenic&Transferrin_serum_level_quantitative_trait_locus_2¬_provided .&.&.&Human_Phenotype_Ontology:HP:0000707&Human_Phenotype_Ontology:HP:0001333&Human_Phenotype_Ontology:HP:0006987&MedGen:C0497552&Human_Phenotype_Ontology:HP:0000708&Human_Phenotype_Ontology:HP:0000715&Human_Phenotype_Ontology:HP:0002368&Human_Phenotype_Ontology:HP:0002456&MedGen:C0233514&Human_Phenotype_Ontology:HP:0000759&Human_Phenotype_Ontology:HP:0003483&MONDO:MONDO:0003620&MedGen:C4025831&Human_Phenotype_Ontology:HP:0000992&Human_Phenotype_Ontology:HP:0005594&Human_Phenotype_Ontology:HP:0006831&Human_Phenotype_Ontology:HP:0007538&MedGen:C0349506&Human_Phenotype_Ontology:HP:0002027&MedGen:C0000737&Human_Phenotype_Ontology:HP:0002511&Human_Phenotype_Ontology:HP:0006878&Human_Phenotype_Ontology:HP:0007213&MONDO:MONDO:0004975&MedGen:C0002395&OMIM:104300&SNOMED_CT:26929004&Human_Phenotype_Ontology:HP:0003157&Human_Phenotype_Ontology:HP:0003407&Human_Phenotype_Ontology:HP:0007088&Human_Phenotype_Ontology:HP:0007235&Human_Phenotype_Ontology:HP:0007355&Human_Phenotype_Ontology:HP:0009830&MONDO:MONDO:0005244&MedGen:C0031117&Human_Phenotype_Ontology:HP:0010461&MedGen:C4023819&Human_Phenotype_Ontology:HP:0010473&MedGen:C0151861&Human_Phenotype_Ontology:HP:0012531&MedGen:C0030193&MONDO:MONDO:0006507&MedGen:C0392514&OMIM:PS235200&SNOMED_CT:35400008&MONDO:MONDO:0012971&MedGen:C2673520&OMIM:612635&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019257&MedGen:CN205842&Orphanet:ORPHA79230&MONDO:MONDO:0021001&MedGen:C3469186&OMIM:235200&Orphanet:ORPHA465508&MedGen:C0018995&MedGen:C3150862&MedGen:C3280096&OMIM:614193&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other&_risk_factor Pathogenic(14)&Uncertain_significance(1) single_nucleotide_variant HFE:3077 SO:0001583&missense_variant&SO:0001627&intron_variant 1 1800562 15 30 50.0 -6 26092913 G A A missense_variant MODERATE HFE 3077 Transcript NM_139010.3 protein_coding 2/4 317 305 102 C/Y tGc/tAc 1 EntrezGene G G 0 0.999 25.8 3.793231 25.8 0.99446180177943488 8.692576 0.745339606461945 0.85809 10.47997 0.877295728668495 0.90603 -3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68 5.35 0.999999999873201 0.000003 0.9518 1.0733 .&.&.&.&4.395&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1 -8.93&-10.67&-9.03&-8.48&-8.33&-8.88&-8.23&-8.64&-9.14&-8.41 14.7506 0.816&0.762&0.923&0.708&0.746&0.356&0.742&0.839&0.804&0.918 0.94952 0.623552 1.000000 0.821000 5.226000 1.176000 6.35 rs1800562 5448 143266 3.80272e-02 1.13263e-02 1.16720e-02 1.09202e-02 4.55556e-02 3.82979e-02 5.34884e-02 1.89011e-02 1.89509e-02 1.88630e-02 1.35461e-02 1.53409e-02 1.15237e-02 6.38570e-04 6.91563e-04 5.93120e-04 4.07594e-02 3.52234e-02 3.83081e-02 3.42535e-02 3.51206e-02 6.48874e-02 6.57163e-02 6.37468e-02 2.78810e-02 3.00546e-02 2.56167e-02 3.80243e-02 2.62295e-03 1.76678e-03 2.81804e-03 9 0.03243 0.01258 15048 Alzheimer_disease&_susceptibility_to&Porphyria_cutanea_tarda&_susceptibility_to&Porphyria_variegata&_susceptibility_to&Abnormality_of_the_nervous_system&Behavioral_abnormality&Abnormal_peripheral_nervous_system_morphology&Cutaneous_photosensitivity&Abdominal_pain&Alzheimer_disease&Peripheral_neuropathy&Abnormality_of_the_male_genitalia&Porphyrinuria&Pain&Hereditary_hemochromatosis&Microvascular_complications_of_diabetes_7&Hereditary_cancer-predisposing_syndrome&Hemochromatosis_type_2&Hemochromatosis_type_1&Bronze_diabetes&Hemochromatosis&_juvenile&_digenic&Transferrin_serum_level_quantitative_trait_locus_2¬_provided .&.&.&Human_Phenotype_Ontology:HP:0000707&Human_Phenotype_Ontology:HP:0001333&Human_Phenotype_Ontology:HP:0006987&MedGen:C0497552&Human_Phenotype_Ontology:HP:0000708&Human_Phenotype_Ontology:HP:0000715&Human_Phenotype_Ontology:HP:0002368&Human_Phenotype_Ontology:HP:0002456&MedGen:C0233514&Human_Phenotype_Ontology:HP:0000759&Human_Phenotype_Ontology:HP:0003483&MONDO:MONDO:0003620&MedGen:C4025831&Human_Phenotype_Ontology:HP:0000992&Human_Phenotype_Ontology:HP:0005594&Human_Phenotype_Ontology:HP:0006831&Human_Phenotype_Ontology:HP:0007538&MedGen:C0349506&Human_Phenotype_Ontology:HP:0002027&MedGen:C0000737&Human_Phenotype_Ontology:HP:0002511&Human_Phenotype_Ontology:HP:0006878&Human_Phenotype_Ontology:HP:0007213&MONDO:MONDO:0004975&MedGen:C0002395&OMIM:104300&SNOMED_CT:26929004&Human_Phenotype_Ontology:HP:0003157&Human_Phenotype_Ontology:HP:0003407&Human_Phenotype_Ontology:HP:0007088&Human_Phenotype_Ontology:HP:0007235&Human_Phenotype_Ontology:HP:0007355&Human_Phenotype_Ontology:HP:0009830&MONDO:MONDO:0005244&MedGen:C0031117&Human_Phenotype_Ontology:HP:0010461&MedGen:C4023819&Human_Phenotype_Ontology:HP:0010473&MedGen:C0151861&Human_Phenotype_Ontology:HP:0012531&MedGen:C0030193&MONDO:MONDO:0006507&MedGen:C0392514&OMIM:PS235200&SNOMED_CT:35400008&MONDO:MONDO:0012971&MedGen:C2673520&OMIM:612635&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019257&MedGen:CN205842&Orphanet:ORPHA79230&MONDO:MONDO:0021001&MedGen:C3469186&OMIM:235200&Orphanet:ORPHA465508&MedGen:C0018995&MedGen:C3150862&MedGen:C3280096&OMIM:614193&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other&_risk_factor Pathogenic(14)&Uncertain_significance(1) single_nucleotide_variant HFE:3077 SO:0001583&missense_variant&SO:0001627&intron_variant 1 1800562 15 30 50.0 -6 26092913 G A A intron_variant MODIFIER HFE 3077 Transcript NM_139011.3 protein_coding 1/2 1 EntrezGene G G 25.8 3.793231 6.35 rs1800562 5448 143266 3.80272e-02 1.13263e-02 1.16720e-02 1.09202e-02 4.55556e-02 3.82979e-02 5.34884e-02 1.89011e-02 1.89509e-02 1.88630e-02 1.35461e-02 1.53409e-02 1.15237e-02 6.38570e-04 6.91563e-04 5.93120e-04 4.07594e-02 3.52234e-02 3.83081e-02 3.42535e-02 3.51206e-02 6.48874e-02 6.57163e-02 6.37468e-02 2.78810e-02 3.00546e-02 2.56167e-02 3.80243e-02 2.62295e-03 1.76678e-03 2.81804e-03 9 0.03243 0.01258 15048 Alzheimer_disease&_susceptibility_to&Porphyria_cutanea_tarda&_susceptibility_to&Porphyria_variegata&_susceptibility_to&Abnormality_of_the_nervous_system&Behavioral_abnormality&Abnormal_peripheral_nervous_system_morphology&Cutaneous_photosensitivity&Abdominal_pain&Alzheimer_disease&Peripheral_neuropathy&Abnormality_of_the_male_genitalia&Porphyrinuria&Pain&Hereditary_hemochromatosis&Microvascular_complications_of_diabetes_7&Hereditary_cancer-predisposing_syndrome&Hemochromatosis_type_2&Hemochromatosis_type_1&Bronze_diabetes&Hemochromatosis&_juvenile&_digenic&Transferrin_serum_level_quantitative_trait_locus_2¬_provided .&.&.&Human_Phenotype_Ontology:HP:0000707&Human_Phenotype_Ontology:HP:0001333&Human_Phenotype_Ontology:HP:0006987&MedGen:C0497552&Human_Phenotype_Ontology:HP:0000708&Human_Phenotype_Ontology:HP:0000715&Human_Phenotype_Ontology:HP:0002368&Human_Phenotype_Ontology:HP:0002456&MedGen:C0233514&Human_Phenotype_Ontology:HP:0000759&Human_Phenotype_Ontology:HP:0003483&MONDO:MONDO:0003620&MedGen:C4025831&Human_Phenotype_Ontology:HP:0000992&Human_Phenotype_Ontology:HP:0005594&Human_Phenotype_Ontology:HP:0006831&Human_Phenotype_Ontology:HP:0007538&MedGen:C0349506&Human_Phenotype_Ontology:HP:0002027&MedGen:C0000737&Human_Phenotype_Ontology:HP:0002511&Human_Phenotype_Ontology:HP:0006878&Human_Phenotype_Ontology:HP:0007213&MONDO:MONDO:0004975&MedGen:C0002395&OMIM:104300&SNOMED_CT:26929004&Human_Phenotype_Ontology:HP:0003157&Human_Phenotype_Ontology:HP:0003407&Human_Phenotype_Ontology:HP:0007088&Human_Phenotype_Ontology:HP:0007235&Human_Phenotype_Ontology:HP:0007355&Human_Phenotype_Ontology:HP:0009830&MONDO:MONDO:0005244&MedGen:C0031117&Human_Phenotype_Ontology:HP:0010461&MedGen:C4023819&Human_Phenotype_Ontology:HP:0010473&MedGen:C0151861&Human_Phenotype_Ontology:HP:0012531&MedGen:C0030193&MONDO:MONDO:0006507&MedGen:C0392514&OMIM:PS235200&SNOMED_CT:35400008&MONDO:MONDO:0012971&MedGen:C2673520&OMIM:612635&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019257&MedGen:CN205842&Orphanet:ORPHA79230&MONDO:MONDO:0021001&MedGen:C3469186&OMIM:235200&Orphanet:ORPHA465508&MedGen:C0018995&MedGen:C3150862&MedGen:C3280096&OMIM:614193&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other&_risk_factor Pathogenic(14)&Uncertain_significance(1) single_nucleotide_variant HFE:3077 SO:0001583&missense_variant&SO:0001627&intron_variant 1 1800562 15 30 50.0 -6 26092913 G A A upstream_gene_variant MODIFIER LOC108783645 108783645 Transcript NR_144383.1 antisense_RNA 1879 -1 EntrezGene G G 25.8 3.793231 6.35 rs1800562 5448 143266 3.80272e-02 1.13263e-02 1.16720e-02 1.09202e-02 4.55556e-02 3.82979e-02 5.34884e-02 1.89011e-02 1.89509e-02 1.88630e-02 1.35461e-02 1.53409e-02 1.15237e-02 6.38570e-04 6.91563e-04 5.93120e-04 4.07594e-02 3.52234e-02 3.83081e-02 3.42535e-02 3.51206e-02 6.48874e-02 6.57163e-02 6.37468e-02 2.78810e-02 3.00546e-02 2.56167e-02 3.80243e-02 2.62295e-03 1.76678e-03 2.81804e-03 9 0.03243 0.01258 15048 Alzheimer_disease&_susceptibility_to&Porphyria_cutanea_tarda&_susceptibility_to&Porphyria_variegata&_susceptibility_to&Abnormality_of_the_nervous_system&Behavioral_abnormality&Abnormal_peripheral_nervous_system_morphology&Cutaneous_photosensitivity&Abdominal_pain&Alzheimer_disease&Peripheral_neuropathy&Abnormality_of_the_male_genitalia&Porphyrinuria&Pain&Hereditary_hemochromatosis&Microvascular_complications_of_diabetes_7&Hereditary_cancer-predisposing_syndrome&Hemochromatosis_type_2&Hemochromatosis_type_1&Bronze_diabetes&Hemochromatosis&_juvenile&_digenic&Transferrin_serum_level_quantitative_trait_locus_2¬_provided .&.&.&Human_Phenotype_Ontology:HP:0000707&Human_Phenotype_Ontology:HP:0001333&Human_Phenotype_Ontology:HP:0006987&MedGen:C0497552&Human_Phenotype_Ontology:HP:0000708&Human_Phenotype_Ontology:HP:0000715&Human_Phenotype_Ontology:HP:0002368&Human_Phenotype_Ontology:HP:0002456&MedGen:C0233514&Human_Phenotype_Ontology:HP:0000759&Human_Phenotype_Ontology:HP:0003483&MONDO:MONDO:0003620&MedGen:C4025831&Human_Phenotype_Ontology:HP:0000992&Human_Phenotype_Ontology:HP:0005594&Human_Phenotype_Ontology:HP:0006831&Human_Phenotype_Ontology:HP:0007538&MedGen:C0349506&Human_Phenotype_Ontology:HP:0002027&MedGen:C0000737&Human_Phenotype_Ontology:HP:0002511&Human_Phenotype_Ontology:HP:0006878&Human_Phenotype_Ontology:HP:0007213&MONDO:MONDO:0004975&MedGen:C0002395&OMIM:104300&SNOMED_CT:26929004&Human_Phenotype_Ontology:HP:0003157&Human_Phenotype_Ontology:HP:0003407&Human_Phenotype_Ontology:HP:0007088&Human_Phenotype_Ontology:HP:0007235&Human_Phenotype_Ontology:HP:0007355&Human_Phenotype_Ontology:HP:0009830&MONDO:MONDO:0005244&MedGen:C0031117&Human_Phenotype_Ontology:HP:0010461&MedGen:C4023819&Human_Phenotype_Ontology:HP:0010473&MedGen:C0151861&Human_Phenotype_Ontology:HP:0012531&MedGen:C0030193&MONDO:MONDO:0006507&MedGen:C0392514&OMIM:PS235200&SNOMED_CT:35400008&MONDO:MONDO:0012971&MedGen:C2673520&OMIM:612635&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019257&MedGen:CN205842&Orphanet:ORPHA79230&MONDO:MONDO:0021001&MedGen:C3469186&OMIM:235200&Orphanet:ORPHA465508&MedGen:C0018995&MedGen:C3150862&MedGen:C3280096&OMIM:614193&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other&_risk_factor Pathogenic(14)&Uncertain_significance(1) single_nucleotide_variant HFE:3077 SO:0001583&missense_variant&SO:0001627&intron_variant 1 1800562 15 30 50.0 -6 26092913 G A A missense_variant MODERATE HFE 3077 Transcript XM_011514543.3 protein_coding 4/7 939 845 282 C/Y tGc/tAc 1 EntrezGene G G 25.8 3.793231 25.8 0.99446180177943488 8.692576 0.745339606461945 0.85809 10.47997 0.877295728668495 0.90603 -3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68&-3.68 5.35 0.999999999873201 0.000003 0.9518 1.0733 .&.&.&.&4.395&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1 -8.93&-10.67&-9.03&-8.48&-8.33&-8.88&-8.23&-8.64&-9.14&-8.41 14.7506 0.816&0.762&0.923&0.708&0.746&0.356&0.742&0.839&0.804&0.918 0.94952 0.623552 1.000000 0.821000 5.226000 1.176000 6.35 rs1800562 5448 143266 3.80272e-02 1.13263e-02 1.16720e-02 1.09202e-02 4.55556e-02 3.82979e-02 5.34884e-02 1.89011e-02 1.89509e-02 1.88630e-02 1.35461e-02 1.53409e-02 1.15237e-02 6.38570e-04 6.91563e-04 5.93120e-04 4.07594e-02 3.52234e-02 3.83081e-02 3.42535e-02 3.51206e-02 6.48874e-02 6.57163e-02 6.37468e-02 2.78810e-02 3.00546e-02 2.56167e-02 3.80243e-02 2.62295e-03 1.76678e-03 2.81804e-03 9 0.03243 0.01258 15048 Alzheimer_disease&_susceptibility_to&Porphyria_cutanea_tarda&_susceptibility_to&Porphyria_variegata&_susceptibility_to&Abnormality_of_the_nervous_system&Behavioral_abnormality&Abnormal_peripheral_nervous_system_morphology&Cutaneous_photosensitivity&Abdominal_pain&Alzheimer_disease&Peripheral_neuropathy&Abnormality_of_the_male_genitalia&Porphyrinuria&Pain&Hereditary_hemochromatosis&Microvascular_complications_of_diabetes_7&Hereditary_cancer-predisposing_syndrome&Hemochromatosis_type_2&Hemochromatosis_type_1&Bronze_diabetes&Hemochromatosis&_juvenile&_digenic&Transferrin_serum_level_quantitative_trait_locus_2¬_provided .&.&.&Human_Phenotype_Ontology:HP:0000707&Human_Phenotype_Ontology:HP:0001333&Human_Phenotype_Ontology:HP:0006987&MedGen:C0497552&Human_Phenotype_Ontology:HP:0000708&Human_Phenotype_Ontology:HP:0000715&Human_Phenotype_Ontology:HP:0002368&Human_Phenotype_Ontology:HP:0002456&MedGen:C0233514&Human_Phenotype_Ontology:HP:0000759&Human_Phenotype_Ontology:HP:0003483&MONDO:MONDO:0003620&MedGen:C4025831&Human_Phenotype_Ontology:HP:0000992&Human_Phenotype_Ontology:HP:0005594&Human_Phenotype_Ontology:HP:0006831&Human_Phenotype_Ontology:HP:0007538&MedGen:C0349506&Human_Phenotype_Ontology:HP:0002027&MedGen:C0000737&Human_Phenotype_Ontology:HP:0002511&Human_Phenotype_Ontology:HP:0006878&Human_Phenotype_Ontology:HP:0007213&MONDO:MONDO:0004975&MedGen:C0002395&OMIM:104300&SNOMED_CT:26929004&Human_Phenotype_Ontology:HP:0003157&Human_Phenotype_Ontology:HP:0003407&Human_Phenotype_Ontology:HP:0007088&Human_Phenotype_Ontology:HP:0007235&Human_Phenotype_Ontology:HP:0007355&Human_Phenotype_Ontology:HP:0009830&MONDO:MONDO:0005244&MedGen:C0031117&Human_Phenotype_Ontology:HP:0010461&MedGen:C4023819&Human_Phenotype_Ontology:HP:0010473&MedGen:C0151861&Human_Phenotype_Ontology:HP:0012531&MedGen:C0030193&MONDO:MONDO:0006507&MedGen:C0392514&OMIM:PS235200&SNOMED_CT:35400008&MONDO:MONDO:0012971&MedGen:C2673520&OMIM:612635&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019257&MedGen:CN205842&Orphanet:ORPHA79230&MONDO:MONDO:0021001&MedGen:C3469186&OMIM:235200&Orphanet:ORPHA465508&MedGen:C0018995&MedGen:C3150862&MedGen:C3280096&OMIM:614193&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other&_risk_factor Pathogenic(14)&Uncertain_significance(1) single_nucleotide_variant HFE:3077 SO:0001583&missense_variant&SO:0001627&intron_variant 1 1800562 15 30 50.0 -6 26092913 G A A non_coding_transcript_exon_variant MODIFIER HFE 3077 Transcript XR_241893.4 misc_RNA 4/5 939 1 EntrezGene G G 25.8 3.793231 6.35 rs1800562 5448 143266 3.80272e-02 1.13263e-02 1.16720e-02 1.09202e-02 4.55556e-02 3.82979e-02 5.34884e-02 1.89011e-02 1.89509e-02 1.88630e-02 1.35461e-02 1.53409e-02 1.15237e-02 6.38570e-04 6.91563e-04 5.93120e-04 4.07594e-02 3.52234e-02 3.83081e-02 3.42535e-02 3.51206e-02 6.48874e-02 6.57163e-02 6.37468e-02 2.78810e-02 3.00546e-02 2.56167e-02 3.80243e-02 2.62295e-03 1.76678e-03 2.81804e-03 9 0.03243 0.01258 15048 Alzheimer_disease&_susceptibility_to&Porphyria_cutanea_tarda&_susceptibility_to&Porphyria_variegata&_susceptibility_to&Abnormality_of_the_nervous_system&Behavioral_abnormality&Abnormal_peripheral_nervous_system_morphology&Cutaneous_photosensitivity&Abdominal_pain&Alzheimer_disease&Peripheral_neuropathy&Abnormality_of_the_male_genitalia&Porphyrinuria&Pain&Hereditary_hemochromatosis&Microvascular_complications_of_diabetes_7&Hereditary_cancer-predisposing_syndrome&Hemochromatosis_type_2&Hemochromatosis_type_1&Bronze_diabetes&Hemochromatosis&_juvenile&_digenic&Transferrin_serum_level_quantitative_trait_locus_2¬_provided .&.&.&Human_Phenotype_Ontology:HP:0000707&Human_Phenotype_Ontology:HP:0001333&Human_Phenotype_Ontology:HP:0006987&MedGen:C0497552&Human_Phenotype_Ontology:HP:0000708&Human_Phenotype_Ontology:HP:0000715&Human_Phenotype_Ontology:HP:0002368&Human_Phenotype_Ontology:HP:0002456&MedGen:C0233514&Human_Phenotype_Ontology:HP:0000759&Human_Phenotype_Ontology:HP:0003483&MONDO:MONDO:0003620&MedGen:C4025831&Human_Phenotype_Ontology:HP:0000992&Human_Phenotype_Ontology:HP:0005594&Human_Phenotype_Ontology:HP:0006831&Human_Phenotype_Ontology:HP:0007538&MedGen:C0349506&Human_Phenotype_Ontology:HP:0002027&MedGen:C0000737&Human_Phenotype_Ontology:HP:0002511&Human_Phenotype_Ontology:HP:0006878&Human_Phenotype_Ontology:HP:0007213&MONDO:MONDO:0004975&MedGen:C0002395&OMIM:104300&SNOMED_CT:26929004&Human_Phenotype_Ontology:HP:0003157&Human_Phenotype_Ontology:HP:0003407&Human_Phenotype_Ontology:HP:0007088&Human_Phenotype_Ontology:HP:0007235&Human_Phenotype_Ontology:HP:0007355&Human_Phenotype_Ontology:HP:0009830&MONDO:MONDO:0005244&MedGen:C0031117&Human_Phenotype_Ontology:HP:0010461&MedGen:C4023819&Human_Phenotype_Ontology:HP:0010473&MedGen:C0151861&Human_Phenotype_Ontology:HP:0012531&MedGen:C0030193&MONDO:MONDO:0006507&MedGen:C0392514&OMIM:PS235200&SNOMED_CT:35400008&MONDO:MONDO:0012971&MedGen:C2673520&OMIM:612635&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019257&MedGen:CN205842&Orphanet:ORPHA79230&MONDO:MONDO:0021001&MedGen:C3469186&OMIM:235200&Orphanet:ORPHA465508&MedGen:C0018995&MedGen:C3150862&MedGen:C3280096&OMIM:614193&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other&_risk_factor Pathogenic(14)&Uncertain_significance(1) single_nucleotide_variant HFE:3077 SO:0001583&missense_variant&SO:0001627&intron_variant 1 1800562 15 30 50.0 -6 26893059 C G G intron_variant&non_coding_transcript_variant MODIFIER GUSBP2 387036 Transcript NR_003504.3 transcribed_pseudogene 3/8 -1 EntrezGene C C 3.646 0.245072 -0.0478 chr6:26893059-26893059 121633 124076 9.80310e-01 9.47560e-01 9.49700e-01 9.45007e-01 1.00000e+00 1.00000e+00 1.00000e+00 9.89698e-01 9.88473e-01 9.90657e-01 9.91997e-01 9.89260e-01 9.95166e-01 9.44485e-01 9.56288e-01 9.33752e-01 9.81351e-01 1.00000e+00 1.00000e+00 1.00000e+00 9.79136e-01 9.97338e-01 9.97425e-01 9.97212e-01 9.84013e-01 9.83640e-01 9.84450e-01 9.81054e-01 9.49355e-01 9.51872e-01 9.48780e-01 15 30 50.0 -6 32062442 GTTCT G - frameshift_variant HIGH TNXB 7148 Transcript NM_001365276.2 protein_coding 20/44 7045-7048 6879-6882 2293-2294 TE/X acAGAA/ac -1 EntrezGene TTCT TTCT -2.69&0.127&2.06&0.346 15 30 50.0 -6 32062442 GTTCT G - frameshift_variant HIGH TNXB 7148 Transcript NM_019105.8 protein_coding 20/44 7045-7048 6879-6882 2293-2294 TE/X acAGAA/ac -1 EntrezGene TTCT TTCT -2.69&0.127&2.06&0.346 15 30 50.0 -6 32491972 A C C intergenic_variant MODIFIER 3.086 0.196684 0.296 chr6:32491972-32491972 13152 86680 1.51731e-01 1.19162e-01 1.23577e-01 1.14037e-01 2.47588e-01 2.42236e-01 2.53333e-01 1.90602e-01 1.90344e-01 1.90801e-01 1.94554e-01 1.87269e-01 2.02991e-01 1.49911e-01 1.42992e-01 1.56040e-01 1.53329e-01 1.72701e-01 1.73881e-01 1.72323e-01 1.50002e-01 1.55400e-01 1.58778e-01 1.50730e-01 1.56154e-01 1.56627e-01 1.55660e-01 1.51678e-01 1.30699e-01 1.27841e-01 1.31319e-01 15 30 50.0 -6 35509302 TTTGG T - frameshift_variant HIGH TULP1 7287 Transcript NM_001289395.2 protein_coding 7/14 604-607 566-569 189-190 PK/X cCCAAa/ca -1 EntrezGene TTGG TTGG 29.3 4.267830 5.34&4.17&2.88&4.76 rs771723580 4 143204 2.79322e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35472e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32351e-05 6.19675e-05 2.67508e-05 1.10424e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.79033e-05 0.00000e+00 0.00000e+00 0.00000e+00 198784 0.00001 195944 not_provided MedGen:CN517202 criteria_provided&_single_submitter Pathogenic Deletion TULP1:7287 SO:0001589&frameshift_variant 1 771723580 15 30 50.0 -6 35509302 TTTGG T - frameshift_variant HIGH TULP1 7287 Transcript NM_003322.6 protein_coding 8/15 763-766 725-728 242-243 PK/X cCCAAa/ca -1 EntrezGene TTGG TTGG 29.3 4.267830 5.34&4.17&2.88&4.76 rs771723580 4 143204 2.79322e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35472e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32351e-05 6.19675e-05 2.67508e-05 1.10424e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.79033e-05 0.00000e+00 0.00000e+00 0.00000e+00 198784 0.00001 195944 not_provided MedGen:CN517202 criteria_provided&_single_submitter Pathogenic Deletion TULP1:7287 SO:0001589&frameshift_variant 1 771723580 15 30 50.0 -6 38931857 TTTACA T - frameshift_variant HIGH DNAH8 1769 Transcript NM_001206927.2 protein_coding 76/93 11461-11465 11322-11326 3774-3776 LYI/LX ctTTACAtt/cttt 1 EntrezGene TTACA TTACA 2.56&6.51&-0.683&6.51 15 30 50.0 -6 38931857 TTTACA T - frameshift_variant HIGH DNAH8 1769 Transcript NM_001371.4 protein_coding 75/92 11376-11380 10671-10675 3557-3559 LYI/LX ctTTACAtt/cttt 1 EntrezGene TTACA TTACA 2.56&6.51&-0.683&6.51 15 30 50.0 -6 38931857 TTTACA T - intron_variant&non_coding_transcript_variant MODIFIER LOC100131047 100131047 Transcript NR_038401.1 lncRNA 2/4 -1 EntrezGene TTACA TTACA OK 2.56&6.51&-0.683&6.51 15 30 50.0 -6 38931857 TTTACA T - frameshift_variant HIGH DNAH8 1769 Transcript XM_011514318.2 protein_coding 75/92 11379-11383 11259-11263 3753-3755 LYI/LX ctTTACAtt/cttt 1 EntrezGene TTACA TTACA 2.56&6.51&-0.683&6.51 15 30 50.0 -6 38931857 TTTACA T - frameshift_variant HIGH DNAH8 1769 Transcript XM_011514319.2 protein_coding 75/92 11334-11338 11214-11218 3738-3740 LYI/LX ctTTACAtt/cttt 1 EntrezGene TTACA TTACA 2.56&6.51&-0.683&6.51 15 30 50.0 -6 38931857 TTTACA T - frameshift_variant HIGH DNAH8 1769 Transcript XM_011514320.2 protein_coding 74/91 11205-11209 11085-11089 3695-3697 LYI/LX ctTTACAtt/cttt 1 EntrezGene TTACA TTACA 2.56&6.51&-0.683&6.51 15 30 50.0 -6 38931857 TTTACA T - frameshift_variant HIGH DNAH8 1769 Transcript XM_017010325.1 protein_coding 76/89 11442-11446 11322-11326 3774-3776 LYI/LX ctTTACAtt/cttt 1 EntrezGene TTACA TTACA 2.56&6.51&-0.683&6.51 15 30 50.0 -6 38931857 TTTACA T - frameshift_variant HIGH DNAH8 1769 Transcript XM_017010326.1 protein_coding 76/81 11442-11446 11322-11326 3774-3776 LYI/LX ctTTACAtt/cttt 1 EntrezGene TTACA TTACA 2.56&6.51&-0.683&6.51 15 30 50.0 -6 38931857 TTTACA T - non_coding_transcript_exon_variant MODIFIER DNAH8 1769 Transcript XR_926078.2 misc_RNA 76/94 11442-11446 1 EntrezGene TTACA TTACA 2.56&6.51&-0.683&6.51 15 30 50.0 -6 80168945 G C C missense_variant MODERATE BCKDHB 594 Transcript NM_000056.4 protein_coding 5/11 632 548 183 R/P cGg/cCg 1 EntrezGene G G OK 0 1 32 4.464748 32 0.99690737436039811 17.58642 0.978550296663869 0.98168 17.57304 1.10154906757908 0.98162 -3.14&-3.14&-3.14 5.1 0.999997921034991 0.000001 0.815083 0.9364 1.1092 4.805&4.805&4.805 1&1&1&1 -6.88&-6.81&-6.81 17.5522 0.976&0.966&0.966 0.98907 0.706298 1.000000 0.994000 9.486000 1.172000 6.3 chr6:80168945-80168945 14 143222 9.77503e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.91331e-03 5.11364e-03 2.56082e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21928e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20378e-05 1.54909e-05 0.00000e+00 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 9.76522e-05 0.00000e+00 0.00000e+00 0.00000e+00 11937 0.00031 0.00020 26976 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B&MAPLE_SYRUP_URINE_DISEASE&_CLASSIC&_TYPE_IB¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:C4016442&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 79761867 15 30 50.0 -6 80168945 G C C missense_variant MODERATE BCKDHB 594 Transcript NM_001318975.1 protein_coding 5/10 426 338 113 R/P cGg/cCg 1 EntrezGene G G OK 0 1 32 4.464748 32 0.99690737436039811 17.58642 0.978550296663869 0.98168 17.57304 1.10154906757908 0.98162 -3.14&-3.14&-3.14 5.1 0.999997921034991 0.000001 0.815083 0.9364 1.1092 4.805&4.805&4.805 1&1&1&1 -6.88&-6.81&-6.81 17.5522 0.976&0.966&0.966 0.98907 0.706298 1.000000 0.994000 9.486000 1.172000 6.3 chr6:80168945-80168945 14 143222 9.77503e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.91331e-03 5.11364e-03 2.56082e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21928e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20378e-05 1.54909e-05 0.00000e+00 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 9.76522e-05 0.00000e+00 0.00000e+00 0.00000e+00 11937 0.00031 0.00020 26976 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B&MAPLE_SYRUP_URINE_DISEASE&_CLASSIC&_TYPE_IB¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:C4016442&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 79761867 15 30 50.0 -6 80168945 G C C missense_variant MODERATE BCKDHB 594 Transcript NM_183050.4 protein_coding 5/10 571 548 183 R/P cGg/cCg 1 EntrezGene G G 0 1 32 4.464748 32 0.99690737436039811 17.58642 0.978550296663869 0.98168 17.57304 1.10154906757908 0.98162 -3.14&-3.14&-3.14 5.1 0.999997921034991 0.000001 0.815083 0.9364 1.1092 4.805&4.805&4.805 1&1&1&1 -6.88&-6.81&-6.81 17.5522 0.976&0.966&0.966 0.98907 0.706298 1.000000 0.994000 9.486000 1.172000 6.3 chr6:80168945-80168945 14 143222 9.77503e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.91331e-03 5.11364e-03 2.56082e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21928e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20378e-05 1.54909e-05 0.00000e+00 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 9.76522e-05 0.00000e+00 0.00000e+00 0.00000e+00 11937 0.00031 0.00020 26976 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B&MAPLE_SYRUP_URINE_DISEASE&_CLASSIC&_TYPE_IB¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:C4016442&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 79761867 15 30 50.0 -6 80168945 G C C non_coding_transcript_exon_variant MODIFIER BCKDHB 594 Transcript NR_134945.2 misc_RNA 5/11 571 1 EntrezGene G G 32 4.464748 6.3 chr6:80168945-80168945 14 143222 9.77503e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.91331e-03 5.11364e-03 2.56082e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21928e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20378e-05 1.54909e-05 0.00000e+00 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 9.76522e-05 0.00000e+00 0.00000e+00 0.00000e+00 11937 0.00031 0.00020 26976 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B&MAPLE_SYRUP_URINE_DISEASE&_CLASSIC&_TYPE_IB¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:C4016442&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 79761867 15 30 50.0 -6 80168945 G C C missense_variant MODERATE BCKDHB 594 Transcript XM_005248756.5 protein_coding 5/10 578 548 183 R/P cGg/cCg 1 EntrezGene G G 32 4.464748 32 0.99690737436039811 17.58642 0.978550296663869 0.98168 17.57304 1.10154906757908 0.98162 -3.14&-3.14&-3.14 5.1 0.999997921034991 0.000001 0.815083 0.9364 1.1092 4.805&4.805&4.805 1&1&1&1 -6.88&-6.81&-6.81 17.5522 0.976&0.966&0.966 0.98907 0.706298 1.000000 0.994000 9.486000 1.172000 6.3 chr6:80168945-80168945 14 143222 9.77503e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.91331e-03 5.11364e-03 2.56082e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21928e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20378e-05 1.54909e-05 0.00000e+00 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 9.76522e-05 0.00000e+00 0.00000e+00 0.00000e+00 11937 0.00031 0.00020 26976 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B&MAPLE_SYRUP_URINE_DISEASE&_CLASSIC&_TYPE_IB¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:C4016442&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 79761867 15 30 50.0 -6 80168945 G C C missense_variant MODERATE BCKDHB 594 Transcript XM_011536023.3 protein_coding 5/10 578 548 183 R/P cGg/cCg 1 EntrezGene G G 32 4.464748 32 0.99690737436039811 17.58642 0.978550296663869 0.98168 17.57304 1.10154906757908 0.98162 -3.14&-3.14&-3.14 5.1 0.999997921034991 0.000001 0.815083 0.9364 1.1092 4.805&4.805&4.805 1&1&1&1 -6.88&-6.81&-6.81 17.5522 0.976&0.966&0.966 0.98907 0.706298 1.000000 0.994000 9.486000 1.172000 6.3 chr6:80168945-80168945 14 143222 9.77503e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.91331e-03 5.11364e-03 2.56082e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21928e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20378e-05 1.54909e-05 0.00000e+00 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 9.76522e-05 0.00000e+00 0.00000e+00 0.00000e+00 11937 0.00031 0.00020 26976 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B&MAPLE_SYRUP_URINE_DISEASE&_CLASSIC&_TYPE_IB¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:C4016442&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 79761867 15 30 50.0 -6 80168945 G C C missense_variant MODERATE BCKDHB 594 Transcript XM_011536024.3 protein_coding 5/11 578 548 183 R/P cGg/cCg 1 EntrezGene G G 32 4.464748 32 0.99690737436039811 17.58642 0.978550296663869 0.98168 17.57304 1.10154906757908 0.98162 -3.14&-3.14&-3.14 5.1 0.999997921034991 0.000001 0.815083 0.9364 1.1092 4.805&4.805&4.805 1&1&1&1 -6.88&-6.81&-6.81 17.5522 0.976&0.966&0.966 0.98907 0.706298 1.000000 0.994000 9.486000 1.172000 6.3 chr6:80168945-80168945 14 143222 9.77503e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.91331e-03 5.11364e-03 2.56082e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21928e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20378e-05 1.54909e-05 0.00000e+00 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 9.76522e-05 0.00000e+00 0.00000e+00 0.00000e+00 11937 0.00031 0.00020 26976 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B&MAPLE_SYRUP_URINE_DISEASE&_CLASSIC&_TYPE_IB¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:C4016442&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 79761867 15 30 50.0 -6 80168945 G C C missense_variant MODERATE BCKDHB 594 Transcript XM_011536025.3 protein_coding 5/10 578 548 183 R/P cGg/cCg 1 EntrezGene G G 32 4.464748 32 0.99690737436039811 17.58642 0.978550296663869 0.98168 17.57304 1.10154906757908 0.98162 -3.14&-3.14&-3.14 5.1 0.999997921034991 0.000001 0.815083 0.9364 1.1092 4.805&4.805&4.805 1&1&1&1 -6.88&-6.81&-6.81 17.5522 0.976&0.966&0.966 0.98907 0.706298 1.000000 0.994000 9.486000 1.172000 6.3 chr6:80168945-80168945 14 143222 9.77503e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.91331e-03 5.11364e-03 2.56082e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21928e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20378e-05 1.54909e-05 0.00000e+00 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 9.76522e-05 0.00000e+00 0.00000e+00 0.00000e+00 11937 0.00031 0.00020 26976 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B&MAPLE_SYRUP_URINE_DISEASE&_CLASSIC&_TYPE_IB¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:C4016442&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 79761867 15 30 50.0 -6 80168945 G C C non_coding_transcript_exon_variant MODIFIER BCKDHB 594 Transcript XR_001743546.2 misc_RNA 5/11 578 1 EntrezGene G G 32 4.464748 6.3 chr6:80168945-80168945 14 143222 9.77503e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.91331e-03 5.11364e-03 2.56082e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21928e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20378e-05 1.54909e-05 0.00000e+00 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 9.76522e-05 0.00000e+00 0.00000e+00 0.00000e+00 11937 0.00031 0.00020 26976 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B&MAPLE_SYRUP_URINE_DISEASE&_CLASSIC&_TYPE_IB¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:C4016442&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 79761867 15 30 50.0 -6 80168945 G C C non_coding_transcript_exon_variant MODIFIER BCKDHB 594 Transcript XR_001743547.2 misc_RNA 5/14 578 1 EntrezGene G G 32 4.464748 6.3 chr6:80168945-80168945 14 143222 9.77503e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.91331e-03 5.11364e-03 2.56082e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21928e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20378e-05 1.54909e-05 0.00000e+00 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 9.76522e-05 0.00000e+00 0.00000e+00 0.00000e+00 11937 0.00031 0.00020 26976 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B&MAPLE_SYRUP_URINE_DISEASE&_CLASSIC&_TYPE_IB¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:C4016442&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 79761867 15 30 50.0 -6 80168945 G C C non_coding_transcript_exon_variant MODIFIER BCKDHB 594 Transcript XR_001743548.2 misc_RNA 5/12 578 1 EntrezGene G G 32 4.464748 6.3 chr6:80168945-80168945 14 143222 9.77503e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.91331e-03 5.11364e-03 2.56082e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21928e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20378e-05 1.54909e-05 0.00000e+00 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 9.76522e-05 0.00000e+00 0.00000e+00 0.00000e+00 11937 0.00031 0.00020 26976 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B&MAPLE_SYRUP_URINE_DISEASE&_CLASSIC&_TYPE_IB¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:C4016442&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 79761867 15 30 50.0 -6 80168945 G C C non_coding_transcript_exon_variant MODIFIER BCKDHB 594 Transcript XR_001743549.2 misc_RNA 5/15 578 1 EntrezGene G G 32 4.464748 6.3 chr6:80168945-80168945 14 143222 9.77503e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.91331e-03 5.11364e-03 2.56082e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21928e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20378e-05 1.54909e-05 0.00000e+00 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 9.76522e-05 0.00000e+00 0.00000e+00 0.00000e+00 11937 0.00031 0.00020 26976 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B&MAPLE_SYRUP_URINE_DISEASE&_CLASSIC&_TYPE_IB¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:C4016442&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 79761867 15 30 50.0 -6 80168945 G C C non_coding_transcript_exon_variant MODIFIER BCKDHB 594 Transcript XR_002956292.1 misc_RNA 5/13 578 1 EntrezGene G G 32 4.464748 6.3 chr6:80168945-80168945 14 143222 9.77503e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.91331e-03 5.11364e-03 2.56082e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21928e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20378e-05 1.54909e-05 0.00000e+00 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 9.76522e-05 0.00000e+00 0.00000e+00 0.00000e+00 11937 0.00031 0.00020 26976 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B&MAPLE_SYRUP_URINE_DISEASE&_CLASSIC&_TYPE_IB¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:C4016442&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 79761867 15 30 50.0 -6 80201023 G A A missense_variant MODERATE BCKDHB 594 Transcript NM_000056.4 protein_coding 7/11 916 832 278 G/S Ggc/Agc 1 EntrezGene G G OK 0.02 1 28.3 4.160994 28.3 0.99879767570751898 14.54571 0.91805288005139 0.96312 11.57696 0.919902591919361 0.92700 -5.95&-5.95 6.17 0.999999462048569 0.000000 0.57878 0.9813 1.0912 2.08&2.08 1&1&1 -5.74&-5.74 18.3732 0.985&0.984 0.98247 0.706298 1.000000 1.000000 9.435000 1.176000 6.51 rs386834233 86 143102 6.00970e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.06570e-04 6.76819e-04 9.05797e-04 1.20555e-03 1.70843e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.33710e-04 3.73850e-03 2.40385e-03 4.15827e-03 7.78996e-04 4.95832e-04 5.08402e-04 4.78539e-04 0.00000e+00 0.00000e+00 0.00000e+00 6.00006e-04 0.00000e+00 0.00000e+00 0.00000e+00 65771 0.00008 0.00048 76679 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 386834233 15 30 50.0 -6 80201023 G A A missense_variant MODERATE BCKDHB 594 Transcript NM_001318975.1 protein_coding 7/10 710 622 208 G/S Ggc/Agc 1 EntrezGene G G OK 0.03 1 28.3 4.160994 28.3 0.99879767570751898 14.54571 0.91805288005139 0.96312 11.57696 0.919902591919361 0.92700 -5.95&-5.95 6.17 0.999999462048569 0.000000 0.57878 0.9813 1.0912 2.08&2.08 1&1&1 -5.74&-5.74 18.3732 0.985&0.984 0.98247 0.706298 1.000000 1.000000 9.435000 1.176000 6.51 rs386834233 86 143102 6.00970e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.06570e-04 6.76819e-04 9.05797e-04 1.20555e-03 1.70843e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.33710e-04 3.73850e-03 2.40385e-03 4.15827e-03 7.78996e-04 4.95832e-04 5.08402e-04 4.78539e-04 0.00000e+00 0.00000e+00 0.00000e+00 6.00006e-04 0.00000e+00 0.00000e+00 0.00000e+00 65771 0.00008 0.00048 76679 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 386834233 15 30 50.0 -6 80201023 G A A missense_variant MODERATE BCKDHB 594 Transcript NM_183050.4 protein_coding 7/10 855 832 278 G/S Ggc/Agc 1 EntrezGene G G 0.02 1 28.3 4.160994 28.3 0.99879767570751898 14.54571 0.91805288005139 0.96312 11.57696 0.919902591919361 0.92700 -5.95&-5.95 6.17 0.999999462048569 0.000000 0.57878 0.9813 1.0912 2.08&2.08 1&1&1 -5.74&-5.74 18.3732 0.985&0.984 0.98247 0.706298 1.000000 1.000000 9.435000 1.176000 6.51 rs386834233 86 143102 6.00970e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.06570e-04 6.76819e-04 9.05797e-04 1.20555e-03 1.70843e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.33710e-04 3.73850e-03 2.40385e-03 4.15827e-03 7.78996e-04 4.95832e-04 5.08402e-04 4.78539e-04 0.00000e+00 0.00000e+00 0.00000e+00 6.00006e-04 0.00000e+00 0.00000e+00 0.00000e+00 65771 0.00008 0.00048 76679 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 386834233 15 30 50.0 -6 80201023 G A A non_coding_transcript_exon_variant MODIFIER BCKDHB 594 Transcript NR_134945.2 misc_RNA 8/11 949 1 EntrezGene G G 28.3 4.160994 6.51 rs386834233 86 143102 6.00970e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.06570e-04 6.76819e-04 9.05797e-04 1.20555e-03 1.70843e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.33710e-04 3.73850e-03 2.40385e-03 4.15827e-03 7.78996e-04 4.95832e-04 5.08402e-04 4.78539e-04 0.00000e+00 0.00000e+00 0.00000e+00 6.00006e-04 0.00000e+00 0.00000e+00 0.00000e+00 65771 0.00008 0.00048 76679 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 386834233 15 30 50.0 -6 80201023 G A A missense_variant MODERATE BCKDHB 594 Transcript XM_005248756.5 protein_coding 7/10 862 832 278 G/S Ggc/Agc 1 EntrezGene G G 28.3 4.160994 28.3 0.99879767570751898 14.54571 0.91805288005139 0.96312 11.57696 0.919902591919361 0.92700 -5.95&-5.95 6.17 0.999999462048569 0.000000 0.57878 0.9813 1.0912 2.08&2.08 1&1&1 -5.74&-5.74 18.3732 0.985&0.984 0.98247 0.706298 1.000000 1.000000 9.435000 1.176000 6.51 rs386834233 86 143102 6.00970e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.06570e-04 6.76819e-04 9.05797e-04 1.20555e-03 1.70843e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.33710e-04 3.73850e-03 2.40385e-03 4.15827e-03 7.78996e-04 4.95832e-04 5.08402e-04 4.78539e-04 0.00000e+00 0.00000e+00 0.00000e+00 6.00006e-04 0.00000e+00 0.00000e+00 0.00000e+00 65771 0.00008 0.00048 76679 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 386834233 15 30 50.0 -6 80201023 G A A missense_variant MODERATE BCKDHB 594 Transcript XM_011536023.3 protein_coding 7/10 862 832 278 G/S Ggc/Agc 1 EntrezGene G G 28.3 4.160994 28.3 0.99879767570751898 14.54571 0.91805288005139 0.96312 11.57696 0.919902591919361 0.92700 -5.95&-5.95 6.17 0.999999462048569 0.000000 0.57878 0.9813 1.0912 2.08&2.08 1&1&1 -5.74&-5.74 18.3732 0.985&0.984 0.98247 0.706298 1.000000 1.000000 9.435000 1.176000 6.51 rs386834233 86 143102 6.00970e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.06570e-04 6.76819e-04 9.05797e-04 1.20555e-03 1.70843e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.33710e-04 3.73850e-03 2.40385e-03 4.15827e-03 7.78996e-04 4.95832e-04 5.08402e-04 4.78539e-04 0.00000e+00 0.00000e+00 0.00000e+00 6.00006e-04 0.00000e+00 0.00000e+00 0.00000e+00 65771 0.00008 0.00048 76679 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 386834233 15 30 50.0 -6 80201023 G A A missense_variant MODERATE BCKDHB 594 Transcript XM_011536024.3 protein_coding 7/11 862 832 278 G/S Ggc/Agc 1 EntrezGene G G 28.3 4.160994 28.3 0.99879767570751898 14.54571 0.91805288005139 0.96312 11.57696 0.919902591919361 0.92700 -5.95&-5.95 6.17 0.999999462048569 0.000000 0.57878 0.9813 1.0912 2.08&2.08 1&1&1 -5.74&-5.74 18.3732 0.985&0.984 0.98247 0.706298 1.000000 1.000000 9.435000 1.176000 6.51 rs386834233 86 143102 6.00970e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.06570e-04 6.76819e-04 9.05797e-04 1.20555e-03 1.70843e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.33710e-04 3.73850e-03 2.40385e-03 4.15827e-03 7.78996e-04 4.95832e-04 5.08402e-04 4.78539e-04 0.00000e+00 0.00000e+00 0.00000e+00 6.00006e-04 0.00000e+00 0.00000e+00 0.00000e+00 65771 0.00008 0.00048 76679 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 386834233 15 30 50.0 -6 80201023 G A A missense_variant MODERATE BCKDHB 594 Transcript XM_011536025.3 protein_coding 7/10 862 832 278 G/S Ggc/Agc 1 EntrezGene G G 28.3 4.160994 28.3 0.99879767570751898 14.54571 0.91805288005139 0.96312 11.57696 0.919902591919361 0.92700 -5.95&-5.95 6.17 0.999999462048569 0.000000 0.57878 0.9813 1.0912 2.08&2.08 1&1&1 -5.74&-5.74 18.3732 0.985&0.984 0.98247 0.706298 1.000000 1.000000 9.435000 1.176000 6.51 rs386834233 86 143102 6.00970e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.06570e-04 6.76819e-04 9.05797e-04 1.20555e-03 1.70843e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.33710e-04 3.73850e-03 2.40385e-03 4.15827e-03 7.78996e-04 4.95832e-04 5.08402e-04 4.78539e-04 0.00000e+00 0.00000e+00 0.00000e+00 6.00006e-04 0.00000e+00 0.00000e+00 0.00000e+00 65771 0.00008 0.00048 76679 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 386834233 15 30 50.0 -6 80201023 G A A non_coding_transcript_exon_variant MODIFIER BCKDHB 594 Transcript XR_001743546.2 misc_RNA 7/11 862 1 EntrezGene G G 28.3 4.160994 6.51 rs386834233 86 143102 6.00970e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.06570e-04 6.76819e-04 9.05797e-04 1.20555e-03 1.70843e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.33710e-04 3.73850e-03 2.40385e-03 4.15827e-03 7.78996e-04 4.95832e-04 5.08402e-04 4.78539e-04 0.00000e+00 0.00000e+00 0.00000e+00 6.00006e-04 0.00000e+00 0.00000e+00 0.00000e+00 65771 0.00008 0.00048 76679 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 386834233 15 30 50.0 -6 80201023 G A A non_coding_transcript_exon_variant MODIFIER BCKDHB 594 Transcript XR_001743547.2 misc_RNA 7/14 862 1 EntrezGene G G 28.3 4.160994 6.51 rs386834233 86 143102 6.00970e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.06570e-04 6.76819e-04 9.05797e-04 1.20555e-03 1.70843e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.33710e-04 3.73850e-03 2.40385e-03 4.15827e-03 7.78996e-04 4.95832e-04 5.08402e-04 4.78539e-04 0.00000e+00 0.00000e+00 0.00000e+00 6.00006e-04 0.00000e+00 0.00000e+00 0.00000e+00 65771 0.00008 0.00048 76679 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 386834233 15 30 50.0 -6 80201023 G A A non_coding_transcript_exon_variant MODIFIER BCKDHB 594 Transcript XR_001743548.2 misc_RNA 7/12 862 1 EntrezGene G G 28.3 4.160994 6.51 rs386834233 86 143102 6.00970e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.06570e-04 6.76819e-04 9.05797e-04 1.20555e-03 1.70843e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.33710e-04 3.73850e-03 2.40385e-03 4.15827e-03 7.78996e-04 4.95832e-04 5.08402e-04 4.78539e-04 0.00000e+00 0.00000e+00 0.00000e+00 6.00006e-04 0.00000e+00 0.00000e+00 0.00000e+00 65771 0.00008 0.00048 76679 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 386834233 15 30 50.0 -6 80201023 G A A non_coding_transcript_exon_variant MODIFIER BCKDHB 594 Transcript XR_001743549.2 misc_RNA 7/15 862 1 EntrezGene G G 28.3 4.160994 6.51 rs386834233 86 143102 6.00970e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.06570e-04 6.76819e-04 9.05797e-04 1.20555e-03 1.70843e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.33710e-04 3.73850e-03 2.40385e-03 4.15827e-03 7.78996e-04 4.95832e-04 5.08402e-04 4.78539e-04 0.00000e+00 0.00000e+00 0.00000e+00 6.00006e-04 0.00000e+00 0.00000e+00 0.00000e+00 65771 0.00008 0.00048 76679 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 386834233 15 30 50.0 -6 80201023 G A A non_coding_transcript_exon_variant MODIFIER BCKDHB 594 Transcript XR_002956292.1 misc_RNA 7/13 862 1 EntrezGene G G 28.3 4.160994 6.51 rs386834233 86 143102 6.00970e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.06570e-04 6.76819e-04 9.05797e-04 1.20555e-03 1.70843e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.33710e-04 3.73850e-03 2.40385e-03 4.15827e-03 7.78996e-04 4.95832e-04 5.08402e-04 4.78539e-04 0.00000e+00 0.00000e+00 0.00000e+00 6.00006e-04 0.00000e+00 0.00000e+00 0.00000e+00 65771 0.00008 0.00048 76679 Maple_syrup_urine_disease&Maple_syrup_urine_disease_type_1B¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MONDO:MONDO:0023692&MedGen:C2930990&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 386834233 15 30 50.0 -6 80343739 G T T stop_gained HIGH BCKDHB 594 Transcript NM_000056.4 protein_coding 10/11 1198 1114 372 E/* Gaa/Taa 1 EntrezGene G G OK 48 9.210012 48 0.99726688519544859 36.10446 1.15896569789883 0.99974 35.84526 1.28470626543286 0.99972 .&. 5.96 0.999999999932261 0.000000 .&. 1&1&1 .&. 19.4074 0.742&0.683 0.98785 0.693126 1.000000 1.000000 9.441000 1.176000 6.54 65770 76678 Maple_syrup_urine_disease¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001587&nonsense&SO:0001619&non-coding_transcript_variant 1 386834234 15 30 50.0 -6 80343739 G T T stop_gained HIGH BCKDHB 594 Transcript NM_001318975.1 protein_coding 10/10 992 904 302 E/* Gaa/Taa 1 EntrezGene G G OK 48 9.210012 48 0.99726688519544859 36.10446 1.15896569789883 0.99974 35.84526 1.28470626543286 0.99972 .&. 5.96 0.999999999932261 0.000000 .&. 1&1&1 .&. 19.4074 0.742&0.683 0.98785 0.693126 1.000000 1.000000 9.441000 1.176000 6.54 65770 76678 Maple_syrup_urine_disease¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001587&nonsense&SO:0001619&non-coding_transcript_variant 1 386834234 15 30 50.0 -6 80343739 G T T stop_gained HIGH BCKDHB 594 Transcript NM_183050.4 protein_coding 10/10 1137 1114 372 E/* Gaa/Taa 1 EntrezGene G G 48 9.210012 48 0.99726688519544859 36.10446 1.15896569789883 0.99974 35.84526 1.28470626543286 0.99972 .&. 5.96 0.999999999932261 0.000000 .&. 1&1&1 .&. 19.4074 0.742&0.683 0.98785 0.693126 1.000000 1.000000 9.441000 1.176000 6.54 65770 76678 Maple_syrup_urine_disease¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001587&nonsense&SO:0001619&non-coding_transcript_variant 1 386834234 15 30 50.0 -6 80343739 G T T non_coding_transcript_exon_variant MODIFIER BCKDHB 594 Transcript NR_134945.2 misc_RNA 11/11 1231 1 EntrezGene G G 48 9.210012 6.54 65770 76678 Maple_syrup_urine_disease¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001587&nonsense&SO:0001619&non-coding_transcript_variant 1 386834234 15 30 50.0 -6 80343739 G T T 3_prime_UTR_variant MODIFIER BCKDHB 594 Transcript XM_011536024.3 protein_coding 11/11 1212 1 EntrezGene G G 48 9.210012 6.54 65770 76678 Maple_syrup_urine_disease¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001587&nonsense&SO:0001619&non-coding_transcript_variant 1 386834234 15 30 50.0 -6 80343739 G T T intron_variant&non_coding_transcript_variant MODIFIER BCKDHB 594 Transcript XR_001743546.2 misc_RNA 9/10 1 EntrezGene G G 48 9.210012 6.54 65770 76678 Maple_syrup_urine_disease¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001587&nonsense&SO:0001619&non-coding_transcript_variant 1 386834234 15 30 50.0 -6 80343739 G T T intron_variant&non_coding_transcript_variant MODIFIER BCKDHB 594 Transcript XR_001743547.2 misc_RNA 9/13 1 EntrezGene G G 48 9.210012 6.54 65770 76678 Maple_syrup_urine_disease¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001587&nonsense&SO:0001619&non-coding_transcript_variant 1 386834234 15 30 50.0 -6 80343739 G T T intron_variant&non_coding_transcript_variant MODIFIER BCKDHB 594 Transcript XR_001743548.2 misc_RNA 9/11 1 EntrezGene G G 48 9.210012 6.54 65770 76678 Maple_syrup_urine_disease¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001587&nonsense&SO:0001619&non-coding_transcript_variant 1 386834234 15 30 50.0 -6 80343739 G T T intron_variant&non_coding_transcript_variant MODIFIER BCKDHB 594 Transcript XR_001743549.2 misc_RNA 9/14 1 EntrezGene G G 48 9.210012 6.54 65770 76678 Maple_syrup_urine_disease¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001587&nonsense&SO:0001619&non-coding_transcript_variant 1 386834234 15 30 50.0 -6 80343739 G T T intron_variant&non_coding_transcript_variant MODIFIER BCKDHB 594 Transcript XR_002956292.1 misc_RNA 9/12 1 EntrezGene G G 48 9.210012 6.54 65770 76678 Maple_syrup_urine_disease¬_provided MONDO:MONDO:0009563&MeSH:D008375&MedGen:C0024776&OMIM:248600&OMIM:PS248600&Orphanet:ORPHA511&SNOMED_CT:27718001&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant BCKDHB:594 SO:0001587&nonsense&SO:0001619&non-coding_transcript_variant 1 386834234 15 30 50.0 -6 87545676 CTTT C - 5_prime_UTR_variant MODIFIER RARS2 57038 Transcript NM_001318785.1 protein_coding 6/19 712-714 -1 EntrezGene TTT TTT OK 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - inframe_deletion MODERATE RARS2 57038 Transcript NM_001350505.1 protein_coding 7/21 547-549 472-474 158 K/- AAA/- -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - 5_prime_UTR_variant MODIFIER RARS2 57038 Transcript NM_001350506.1 protein_coding 7/21 768-770 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - 5_prime_UTR_variant MODIFIER RARS2 57038 Transcript NM_001350507.1 protein_coding 8/21 891-893 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - 5_prime_UTR_variant MODIFIER RARS2 57038 Transcript NM_001350508.1 protein_coding 8/21 930-932 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - 5_prime_UTR_variant MODIFIER RARS2 57038 Transcript NM_001350509.1 protein_coding 5/18 638-640 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - 5_prime_UTR_variant MODIFIER RARS2 57038 Transcript NM_001350510.1 protein_coding 7/20 768-770 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - 5_prime_UTR_variant MODIFIER RARS2 57038 Transcript NM_001350511.1 protein_coding 8/21 887-889 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - inframe_deletion MODERATE RARS2 57038 Transcript NM_020320.5 protein_coding 7/20 502-504 472-474 158 K/- AAA/- -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - non_coding_transcript_exon_variant MODIFIER RARS2 57038 Transcript NR_134857.1 misc_RNA 7/20 547-549 -1 EntrezGene TTT TTT OK 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - non_coding_transcript_exon_variant MODIFIER RARS2 57038 Transcript NR_146738.1 misc_RNA 7/21 768-770 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - intron_variant&non_coding_transcript_variant MODIFIER RARS2 57038 Transcript NR_146739.1 misc_RNA 5/17 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - non_coding_transcript_exon_variant MODIFIER RARS2 57038 Transcript NR_146740.1 misc_RNA 7/22 768-770 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - intron_variant&non_coding_transcript_variant MODIFIER RARS2 57038 Transcript NR_146741.1 misc_RNA 4/16 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - non_coding_transcript_exon_variant MODIFIER RARS2 57038 Transcript NR_146742.1 misc_RNA 8/21 930-932 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - non_coding_transcript_exon_variant MODIFIER RARS2 57038 Transcript NR_146743.1 misc_RNA 7/20 768-770 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - non_coding_transcript_exon_variant MODIFIER RARS2 57038 Transcript NR_146744.1 misc_RNA 7/22 768-770 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - intron_variant&non_coding_transcript_variant MODIFIER RARS2 57038 Transcript NR_146745.1 misc_RNA 5/16 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - non_coding_transcript_exon_variant MODIFIER RARS2 57038 Transcript NR_146746.1 misc_RNA 8/21 986-988 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - intron_variant&non_coding_transcript_variant MODIFIER RARS2 57038 Transcript NR_146747.1 misc_RNA 5/16 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - non_coding_transcript_exon_variant MODIFIER RARS2 57038 Transcript NR_146748.1 misc_RNA 7/20 768-770 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - non_coding_transcript_exon_variant MODIFIER RARS2 57038 Transcript NR_146749.1 misc_RNA 7/21 768-770 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - non_coding_transcript_exon_variant MODIFIER RARS2 57038 Transcript NR_146750.1 misc_RNA 7/21 768-770 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - non_coding_transcript_exon_variant MODIFIER RARS2 57038 Transcript NR_146751.1 misc_RNA 7/20 768-770 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - non_coding_transcript_exon_variant MODIFIER RARS2 57038 Transcript NR_146752.1 misc_RNA 6/20 712-714 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - intron_variant&non_coding_transcript_variant MODIFIER RARS2 57038 Transcript NR_146753.1 misc_RNA 6/18 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - intron_variant&non_coding_transcript_variant MODIFIER RARS2 57038 Transcript NR_146754.1 misc_RNA 5/18 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - non_coding_transcript_exon_variant MODIFIER RARS2 57038 Transcript NR_146755.1 misc_RNA 7/21 768-770 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - non_coding_transcript_exon_variant MODIFIER RARS2 57038 Transcript NR_146756.1 misc_RNA 7/20 547-549 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - non_coding_transcript_exon_variant MODIFIER RARS2 57038 Transcript NR_146757.1 misc_RNA 6/20 694-696 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - intron_variant&non_coding_transcript_variant MODIFIER RARS2 57038 Transcript NR_146758.1 misc_RNA 5/17 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - intron_variant&non_coding_transcript_variant MODIFIER RARS2 57038 Transcript NR_146759.1 misc_RNA 5/16 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - inframe_deletion MODERATE RARS2 57038 Transcript XM_011535949.3 protein_coding 7/17 511-513 472-474 158 K/- AAA/- -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - 5_prime_UTR_variant MODIFIER RARS2 57038 Transcript XM_017011073.1 protein_coding 8/22 931-933 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - 5_prime_UTR_variant MODIFIER RARS2 57038 Transcript XM_017011074.2 protein_coding 7/21 529-531 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - 5_prime_UTR_variant MODIFIER RARS2 57038 Transcript XM_017011075.2 protein_coding 6/20 657-659 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - 5_prime_UTR_variant MODIFIER RARS2 57038 Transcript XM_017011076.2 protein_coding 8/22 875-877 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - 5_prime_UTR_variant MODIFIER RARS2 57038 Transcript XM_017011077.2 protein_coding 6/20 434-436 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - 5_prime_UTR_variant MODIFIER RARS2 57038 Transcript XM_017011078.2 protein_coding 6/20 473-475 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - 5_prime_UTR_variant MODIFIER RARS2 57038 Transcript XM_024446494.1 protein_coding 8/21 718-720 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 87545676 CTTT C - non_coding_transcript_exon_variant MODIFIER RARS2 57038 Transcript XR_001743517.2 misc_RNA 7/14 511-513 -1 EntrezGene TTT TTT 19.61 2.046918 5.39&6.54&5.4 rs757743894 22 143232 1.53597e-04 7.13776e-05 4.40451e-05 1.03488e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.66300e-04 3.38295e-04 3.87697e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.21908e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.87304e-04 2.16846e-04 1.60496e-04 2.94356e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53468e-04 0.00000e+00 0.00000e+00 0.00000e+00 215072 0.00024 0.00010 211288 Pontocerebellar_hypoplasia_type_6¬_provided MONDO:MONDO:0012683&MedGen:C1969084&OMIM:611523&Orphanet:ORPHA166073&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(1) Deletion RARS2:57038 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001623&5_prime_UTR_variant 1 757743894 15 30 50.0 -6 94330597 CAC C - intergenic_variant MODIFIER 0.211&-0.422 15 30 50.0 -6 135466360 CTAATAGTG C - frameshift_variant HIGH AHI1 54806 Transcript NM_001134830.1 protein_coding 5/27 432-439 195-202 65-68 DTIR/EX gaCACTATTAga/gaga -1 EntrezGene TAATAGTG TAATAGTG OK 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - frameshift_variant HIGH AHI1 54806 Transcript NM_001134831.2 protein_coding 7/29 554-561 195-202 65-68 DTIR/EX gaCACTATTAga/gaga -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - frameshift_variant HIGH AHI1 54806 Transcript NM_001134832.1 protein_coding 6/23 494-501 195-202 65-68 DTIR/EX gaCACTATTAga/gaga -1 EntrezGene TAATAGTG TAATAGTG OK 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - frameshift_variant HIGH AHI1 54806 Transcript NM_001350503.1 protein_coding 7/29 688-695 195-202 65-68 DTIR/EX gaCACTATTAga/gaga -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - frameshift_variant HIGH AHI1 54806 Transcript NM_001350504.1 protein_coding 7/28 579-586 195-202 65-68 DTIR/EX gaCACTATTAga/gaga -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - frameshift_variant HIGH AHI1 54806 Transcript NM_017651.4 protein_coding 6/28 494-501 195-202 65-68 DTIR/EX gaCACTATTAga/gaga -1 EntrezGene TAATAGTG TAATAGTG OK 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - frameshift_variant HIGH AHI1 54806 Transcript XM_011535910.3 protein_coding 8/30 755-762 195-202 65-68 DTIR/EX gaCACTATTAga/gaga -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - frameshift_variant HIGH AHI1 54806 Transcript XM_011535911.3 protein_coding 6/28 480-487 195-202 65-68 DTIR/EX gaCACTATTAga/gaga -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - frameshift_variant HIGH AHI1 54806 Transcript XM_017010978.2 protein_coding 7/28 585-592 195-202 65-68 DTIR/EX gaCACTATTAga/gaga -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - frameshift_variant HIGH AHI1 54806 Transcript XM_017010979.2 protein_coding 7/27 585-592 195-202 65-68 DTIR/EX gaCACTATTAga/gaga -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - frameshift_variant HIGH AHI1 54806 Transcript XM_017010980.2 protein_coding 7/29 585-592 195-202 65-68 DTIR/EX gaCACTATTAga/gaga -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - frameshift_variant HIGH AHI1 54806 Transcript XM_017010981.2 protein_coding 6/28 531-538 141-148 47-50 DTIR/EX gaCACTATTAga/gaga -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - frameshift_variant HIGH AHI1 54806 Transcript XM_017010984.2 protein_coding 7/24 585-592 195-202 65-68 DTIR/EX gaCACTATTAga/gaga -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - frameshift_variant HIGH AHI1 54806 Transcript XM_024446479.1 protein_coding 5/27 435-442 141-148 47-50 DTIR/EX gaCACTATTAga/gaga -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - frameshift_variant HIGH AHI1 54806 Transcript XM_024446480.1 protein_coding 7/25 585-592 195-202 65-68 DTIR/EX gaCACTATTAga/gaga -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - non_coding_transcript_exon_variant MODIFIER AHI1 54806 Transcript XR_001743479.2 misc_RNA 7/31 585-592 -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - non_coding_transcript_exon_variant MODIFIER AHI1 54806 Transcript XR_001743480.2 misc_RNA 7/32 585-592 -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - non_coding_transcript_exon_variant MODIFIER AHI1 54806 Transcript XR_001743481.2 misc_RNA 7/32 585-592 -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - non_coding_transcript_exon_variant MODIFIER AHI1 54806 Transcript XR_001743482.2 misc_RNA 7/31 585-592 -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - non_coding_transcript_exon_variant MODIFIER AHI1 54806 Transcript XR_001743483.2 misc_RNA 7/30 585-592 -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - non_coding_transcript_exon_variant MODIFIER AHI1 54806 Transcript XR_001743484.2 misc_RNA 7/30 585-592 -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - non_coding_transcript_exon_variant MODIFIER AHI1 54806 Transcript XR_001743485.2 misc_RNA 7/30 585-592 -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - non_coding_transcript_exon_variant MODIFIER AHI1 54806 Transcript XR_001743486.2 misc_RNA 7/29 585-592 -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - non_coding_transcript_exon_variant MODIFIER AHI1 54806 Transcript XR_001743487.2 misc_RNA 7/31 585-592 -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - non_coding_transcript_exon_variant MODIFIER AHI1 54806 Transcript XR_001743488.1 misc_RNA 7/31 827-834 -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - non_coding_transcript_exon_variant MODIFIER AHI1 54806 Transcript XR_001743489.2 misc_RNA 7/29 585-592 -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - non_coding_transcript_exon_variant MODIFIER AHI1 54806 Transcript XR_001743490.2 misc_RNA 7/31 585-592 -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - non_coding_transcript_exon_variant MODIFIER AHI1 54806 Transcript XR_002956286.1 misc_RNA 7/28 585-592 -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -6 135466360 CTAATAGTG C - non_coding_transcript_exon_variant MODIFIER AHI1 54806 Transcript XR_002956287.1 misc_RNA 7/27 585-592 -1 EntrezGene TAATAGTG TAATAGTG 1.26&2.61&1.05&-3.66&-6.31&2.56&-7.19&-0.99 15 30 50.0 -7 2513251 G GGATG GATG frameshift_variant HIGH LFNG 3955 Transcript NM_001166355.1 protein_coding 2/9 268-269 142-143 48 G/GWX gga/gGATGga 1 EntrezGene OK 1.13&3.57 15 30 50.0 -7 2513251 G GGATG GATG upstream_gene_variant MODIFIER LFNG 3955 Transcript NM_002304.2 protein_coding 4611 1 EntrezGene OK 1.13&3.57 15 30 50.0 -7 2513251 G GGATG GATG upstream_gene_variant MODIFIER LOC107986759 107986759 Transcript XR_001745066.1 lncRNA 4180 -1 EntrezGene 1.13&3.57 15 30 50.0 -7 2513251 G GGATG GATG upstream_gene_variant MODIFIER LOC107986759 107986759 Transcript XR_001745067.1 lncRNA 4180 -1 EntrezGene 1.13&3.57 15 30 50.0 -7 24142825 GGT G - intron_variant&non_coding_transcript_variant MODIFIER LOC107986777 107986777 Transcript XR_001745132.1 lncRNA 3/3 -1 EntrezGene GT GT 0.396 -0.251286 -2.47&-5.49 rs1491428662 4582 138646 3.30482e-02 7.16446e-03 7.69724e-03 6.53630e-03 9.17226e-02 1.00000e-01 8.25472e-02 3.53512e-02 3.75966e-02 3.36449e-02 6.30713e-02 7.20046e-02 5.30401e-02 3.58774e-03 3.51124e-03 3.65408e-03 3.22333e-02 8.73667e-02 9.81350e-02 8.41333e-02 3.39208e-02 4.09504e-02 4.08263e-02 4.11223e-02 3.48948e-02 2.52336e-02 4.50098e-02 3.53228e-02 5.23378e-03 1.12360e-02 3.85935e-03 15 30 50.0 -7 107893264 A TA TA frameshift_variant HIGH DLD 1738 Transcript NM_000108.5 protein_coding 2/14 172 104 35 Y/LX tAc/tTAc 1 EntrezGene A A 6.38 15 30 50.0 -7 107893264 A TA TA 5_prime_UTR_variant MODIFIER DLD 1738 Transcript NM_001289750.1 protein_coding 2/12 248 1 EntrezGene A A 6.38 15 30 50.0 -7 107893264 A TA TA frameshift_variant HIGH DLD 1738 Transcript NM_001289751.1 protein_coding 2/13 248 104 35 Y/LX tAc/tTAc 1 EntrezGene A A 6.38 15 30 50.0 -7 107893264 A TA TA frameshift_variant HIGH DLD 1738 Transcript NM_001289752.1 protein_coding 2/13 248 104 35 Y/LX tAc/tTAc 1 EntrezGene A A 6.38 15 30 50.0 -7 107893265 C G G stop_gained HIGH DLD 1738 Transcript NM_000108.5 protein_coding 2/14 173 105 35 Y/* taC/taG 1 EntrezGene C C 23.8 3.189569 23.8 0.92052932539789212 0.400689 -1.07608160284541 0.08172 0.7999412 -0.707591935158337 0.15807 .&.&.&. -10.1 0.999999998855842 0.000046 .&.&.&. 1&1&1&1&1 .&.&.&. 18.9039 0.787&.&0.73&0.728 0.24294 0.706298 0.489000 0.428000 -0.706000 -3.704000 -9.4 chr7:107893265-107893265 1 143292 6.97876e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35406e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97496e-06 3.27869e-04 1.76678e-03 0.00000e+00 421142 406953 Maple_syrup_urine_disease&_type_3¬_provided MONDO:MONDO:0009529&MedGen:CN043137&OMIM:246900&Orphanet:ORPHA2394&MedGen:CN517202 criteria_provided&_single_submitter Likely_pathogenic single_nucleotide_variant DLD:1738 SO:0001587&nonsense&SO:0001623&5_prime_UTR_variant 1 747810875 15 30 50.0 -7 107893265 C G G 5_prime_UTR_variant MODIFIER DLD 1738 Transcript NM_001289750.1 protein_coding 2/12 249 1 EntrezGene C C 23.8 3.189569 -9.4 chr7:107893265-107893265 1 143292 6.97876e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35406e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97496e-06 3.27869e-04 1.76678e-03 0.00000e+00 421142 406953 Maple_syrup_urine_disease&_type_3¬_provided MONDO:MONDO:0009529&MedGen:CN043137&OMIM:246900&Orphanet:ORPHA2394&MedGen:CN517202 criteria_provided&_single_submitter Likely_pathogenic single_nucleotide_variant DLD:1738 SO:0001587&nonsense&SO:0001623&5_prime_UTR_variant 1 747810875 15 30 50.0 -7 107893265 C G G stop_gained HIGH DLD 1738 Transcript NM_001289751.1 protein_coding 2/13 249 105 35 Y/* taC/taG 1 EntrezGene C C 23.8 3.189569 23.8 0.92052932539789212 0.400689 -1.07608160284541 0.08172 0.7999412 -0.707591935158337 0.15807 .&.&.&. -10.1 0.999999998855842 0.000046 .&.&.&. 1&1&1&1&1 .&.&.&. 18.9039 0.787&.&0.73&0.728 0.24294 0.706298 0.489000 0.428000 -0.706000 -3.704000 -9.4 chr7:107893265-107893265 1 143292 6.97876e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35406e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97496e-06 3.27869e-04 1.76678e-03 0.00000e+00 421142 406953 Maple_syrup_urine_disease&_type_3¬_provided MONDO:MONDO:0009529&MedGen:CN043137&OMIM:246900&Orphanet:ORPHA2394&MedGen:CN517202 criteria_provided&_single_submitter Likely_pathogenic single_nucleotide_variant DLD:1738 SO:0001587&nonsense&SO:0001623&5_prime_UTR_variant 1 747810875 15 30 50.0 -7 107893265 C G G stop_gained HIGH DLD 1738 Transcript NM_001289752.1 protein_coding 2/13 249 105 35 Y/* taC/taG 1 EntrezGene C C 23.8 3.189569 23.8 0.92052932539789212 0.400689 -1.07608160284541 0.08172 0.7999412 -0.707591935158337 0.15807 .&.&.&. -10.1 0.999999998855842 0.000046 .&.&.&. 1&1&1&1&1 .&.&.&. 18.9039 0.787&.&0.73&0.728 0.24294 0.706298 0.489000 0.428000 -0.706000 -3.704000 -9.4 chr7:107893265-107893265 1 143292 6.97876e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35406e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97496e-06 3.27869e-04 1.76678e-03 0.00000e+00 421142 406953 Maple_syrup_urine_disease&_type_3¬_provided MONDO:MONDO:0009529&MedGen:CN043137&OMIM:246900&Orphanet:ORPHA2394&MedGen:CN517202 criteria_provided&_single_submitter Likely_pathogenic single_nucleotide_variant DLD:1738 SO:0001587&nonsense&SO:0001623&5_prime_UTR_variant 1 747810875 15 30 50.0 -7 107915506 G T T missense_variant&splice_region_variant MODERATE DLD 1738 Transcript NM_000108.5 protein_coding 9/14 753 685 229 G/C Ggt/Tgt 1 EntrezGene G G 0 1 33 5.318444 33 0.99698125198454934 21.64359 1.0414330019259 0.99280 18.54174 1.12248600078907 0.98529 0.3&0.3&0.3 5.79 1.0 0.000000 0.21052 0.5773 0.4252 3.815&.&. 1&1&1&1&1 -8.48&-8.42&-8.65 20.0349 0.966&0.938&0.926 0.99319 0.732398 1.000000 0.986000 10.003000 1.176000 6.54 chr7:107915506-107915506 25 143134 1.74662e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.22022e-03 7.94552e-03 6.40205e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.89779e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.58584e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.74415e-04 3.28947e-04 0.00000e+00 4.03226e-04 11966 0.00026 27005 Maple_syrup_urine_disease&_type_3&Inborn_genetic_diseases&DLD-Related_Disorders¬_provided MONDO:MONDO:0009529&MedGen:CN043137&OMIM:246900&Orphanet:ORPHA2394&MeSH:D030342&MedGen:C0950123&MedGen:CN239383&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant DLD:1738 SO:0001583&missense_variant 1 121964990 15 30 50.0 -7 107915506 G T T missense_variant&splice_region_variant MODERATE DLD 1738 Transcript NM_001289750.1 protein_coding 7/12 680 388 130 G/C Ggt/Tgt 1 EntrezGene G G 0 1 33 5.318444 33 0.99698125198454934 21.64359 1.0414330019259 0.99280 18.54174 1.12248600078907 0.98529 0.3&0.3&0.3 5.79 1.0 0.000000 0.21052 0.5773 0.4252 3.815&.&. 1&1&1&1&1 -8.48&-8.42&-8.65 20.0349 0.966&0.938&0.926 0.99319 0.732398 1.000000 0.986000 10.003000 1.176000 6.54 chr7:107915506-107915506 25 143134 1.74662e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.22022e-03 7.94552e-03 6.40205e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.89779e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.58584e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.74415e-04 3.28947e-04 0.00000e+00 4.03226e-04 11966 0.00026 27005 Maple_syrup_urine_disease&_type_3&Inborn_genetic_diseases&DLD-Related_Disorders¬_provided MONDO:MONDO:0009529&MedGen:CN043137&OMIM:246900&Orphanet:ORPHA2394&MeSH:D030342&MedGen:C0950123&MedGen:CN239383&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant DLD:1738 SO:0001583&missense_variant 1 121964990 15 30 50.0 -7 107915506 G T T missense_variant&splice_region_variant MODERATE DLD 1738 Transcript NM_001289751.1 protein_coding 8/13 760 616 206 G/C Ggt/Tgt 1 EntrezGene G G 0 1 33 5.318444 33 0.99698125198454934 21.64359 1.0414330019259 0.99280 18.54174 1.12248600078907 0.98529 0.3&0.3&0.3 5.79 1.0 0.000000 0.21052 0.5773 0.4252 3.815&.&. 1&1&1&1&1 -8.48&-8.42&-8.65 20.0349 0.966&0.938&0.926 0.99319 0.732398 1.000000 0.986000 10.003000 1.176000 6.54 chr7:107915506-107915506 25 143134 1.74662e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.22022e-03 7.94552e-03 6.40205e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.89779e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.58584e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.74415e-04 3.28947e-04 0.00000e+00 4.03226e-04 11966 0.00026 27005 Maple_syrup_urine_disease&_type_3&Inborn_genetic_diseases&DLD-Related_Disorders¬_provided MONDO:MONDO:0009529&MedGen:CN043137&OMIM:246900&Orphanet:ORPHA2394&MeSH:D030342&MedGen:C0950123&MedGen:CN239383&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant DLD:1738 SO:0001583&missense_variant 1 121964990 15 30 50.0 -7 107915506 G T T missense_variant&splice_region_variant MODERATE DLD 1738 Transcript NM_001289752.1 protein_coding 8/13 685 541 181 G/C Ggt/Tgt 1 EntrezGene G G 0 1 33 5.318444 33 0.99698125198454934 21.64359 1.0414330019259 0.99280 18.54174 1.12248600078907 0.98529 0.3&0.3&0.3 5.79 1.0 0.000000 0.21052 0.5773 0.4252 3.815&.&. 1&1&1&1&1 -8.48&-8.42&-8.65 20.0349 0.966&0.938&0.926 0.99319 0.732398 1.000000 0.986000 10.003000 1.176000 6.54 chr7:107915506-107915506 25 143134 1.74662e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.22022e-03 7.94552e-03 6.40205e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.89779e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.58584e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.74415e-04 3.28947e-04 0.00000e+00 4.03226e-04 11966 0.00026 27005 Maple_syrup_urine_disease&_type_3&Inborn_genetic_diseases&DLD-Related_Disorders¬_provided MONDO:MONDO:0009529&MedGen:CN043137&OMIM:246900&Orphanet:ORPHA2394&MeSH:D030342&MedGen:C0950123&MedGen:CN239383&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant DLD:1738 SO:0001583&missense_variant 1 121964990 15 30 50.0 -7 117559590 ATCT A - inframe_deletion MODERATE CFTR 1080 Transcript NM_000492.4 protein_coding 11/27 1590-1592 1520-1522 507-508 IF/I aTCTtt/att 1 EntrezGene TCT TCT 19.21 1.993640 6.54 rs113993960 1140 143214 7.96012e-03 2.61743e-03 2.02732e-03 3.30989e-03 1.11111e-03 2.12766e-03 0.00000e+00 3.00234e-03 2.87745e-03 3.09757e-03 4.21179e-03 6.24291e-03 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.50856e-03 1.62400e-03 2.79553e-03 1.25565e-03 7.37688e-03 1.44000e-02 1.42903e-02 1.45509e-02 8.37209e-03 9.12409e-03 7.59013e-03 8.00195e-03 3.28515e-03 3.54610e-03 3.22581e-03 7105 22144 Duodenal_stenosis&Recurrent_pancreatitis&Hereditary_pancreatitis&Cystic_fibrosis&Congenital_bilateral_aplasia_of_vas_deferens_from_CFTR_mutation&Inborn_genetic_diseases¬_specified&ivacaftor_response_-_Efficacy&ivacaftor_/_lumacaftor_response_-_Efficacy&Bronchiectasis_with_or_without_elevated_sweat_chloride_1&_modifier_of¬_provided Cystic_fibrosis Human_Phenotype_Ontology:HP:0005205&Human_Phenotype_Ontology:HP:0010449&Human_Phenotype_Ontology:HP:0100867&MedGen:C0238093&Human_Phenotype_Ontology:HP:0100027&MedGen:C4551632&MONDO:MONDO:0008185&MedGen:C0238339&OMIM:167800&Orphanet:ORPHA676&SNOMED_CT:68072000&MONDO:MONDO:0009061&MedGen:C0010674&OMIM:219700&Orphanet:ORPHA586&SNOMED_CT:190905008&MONDO:MONDO:0010178&MedGen:C0403814&OMIM:277180&Orphanet:ORPHA48&MeSH:D030342&MedGen:C0950123&MedGen:CN169374&MedGen:CN236562&MedGen:CN240582&MedGen:CN258830&MedGen:CN517202 MONDO:MONDO:0009061&MedGen:C0010674&OMIM:219700&Orphanet:ORPHA586&SNOMED_CT:190905008 practice_guideline Pathogenic 634837:Pathogenic Deletion CFTR:1080&CFTR-AS1:111082987 29 113993960 15 30 50.0 -7 117559590 ATCT A - intron_variant&non_coding_transcript_variant MODIFIER CFTR-AS1 111082987 Transcript NR_149084.1 lncRNA 2/2 -1 EntrezGene TCT TCT 19.21 1.993640 6.54 rs113993960 1140 143214 7.96012e-03 2.61743e-03 2.02732e-03 3.30989e-03 1.11111e-03 2.12766e-03 0.00000e+00 3.00234e-03 2.87745e-03 3.09757e-03 4.21179e-03 6.24291e-03 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.50856e-03 1.62400e-03 2.79553e-03 1.25565e-03 7.37688e-03 1.44000e-02 1.42903e-02 1.45509e-02 8.37209e-03 9.12409e-03 7.59013e-03 8.00195e-03 3.28515e-03 3.54610e-03 3.22581e-03 7105 22144 Duodenal_stenosis&Recurrent_pancreatitis&Hereditary_pancreatitis&Cystic_fibrosis&Congenital_bilateral_aplasia_of_vas_deferens_from_CFTR_mutation&Inborn_genetic_diseases¬_specified&ivacaftor_response_-_Efficacy&ivacaftor_/_lumacaftor_response_-_Efficacy&Bronchiectasis_with_or_without_elevated_sweat_chloride_1&_modifier_of¬_provided Cystic_fibrosis Human_Phenotype_Ontology:HP:0005205&Human_Phenotype_Ontology:HP:0010449&Human_Phenotype_Ontology:HP:0100867&MedGen:C0238093&Human_Phenotype_Ontology:HP:0100027&MedGen:C4551632&MONDO:MONDO:0008185&MedGen:C0238339&OMIM:167800&Orphanet:ORPHA676&SNOMED_CT:68072000&MONDO:MONDO:0009061&MedGen:C0010674&OMIM:219700&Orphanet:ORPHA586&SNOMED_CT:190905008&MONDO:MONDO:0010178&MedGen:C0403814&OMIM:277180&Orphanet:ORPHA48&MeSH:D030342&MedGen:C0950123&MedGen:CN169374&MedGen:CN236562&MedGen:CN240582&MedGen:CN258830&MedGen:CN517202 MONDO:MONDO:0009061&MedGen:C0010674&OMIM:219700&Orphanet:ORPHA586&SNOMED_CT:190905008 practice_guideline Pathogenic 634837:Pathogenic Deletion CFTR:1080&CFTR-AS1:111082987 29 113993960 15 30 50.0 -7 141649323 A ATAAC TAAC frameshift_variant HIGH AGK 55750 Transcript NM_001364948.2 protein_coding 14/15 1075-1076 1036-1037 346 I/ITX ata/aTAACta 1 EntrezGene 33 4.836857 5.41&6.54 rs778049466 2 143286 1.39581e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35424e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44001e-05 3.09799e-05 2.67508e-05 3.67972e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.39517e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -7 141649323 A ATAAC TAAC frameshift_variant HIGH AGK 55750 Transcript NM_018238.4 protein_coding 14/16 1075-1076 1036-1037 346 I/ITX ata/aTAACta 1 EntrezGene 33 4.836857 5.41&6.54 rs778049466 2 143286 1.39581e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35424e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44001e-05 3.09799e-05 2.67508e-05 3.67972e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.39517e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -7 141649323 A ATAAC TAAC frameshift_variant HIGH AGK 55750 Transcript XM_011516397.3 protein_coding 14/16 1673-1674 1036-1037 346 I/ITX ata/aTAACta 1 EntrezGene 33 4.836857 5.41&6.54 rs778049466 2 143286 1.39581e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35424e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44001e-05 3.09799e-05 2.67508e-05 3.67972e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.39517e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -7 141649323 A ATAAC TAAC frameshift_variant HIGH AGK 55750 Transcript XM_024446835.1 protein_coding 14/16 1227-1228 1036-1037 346 I/ITX ata/aTAACta 1 EntrezGene 33 4.836857 5.41&6.54 rs778049466 2 143286 1.39581e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35424e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44001e-05 3.09799e-05 2.67508e-05 3.67972e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.39517e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -7 141649323 A ATAAC TAAC downstream_gene_variant MODIFIER KIAA1147 57189 Transcript XR_001744838.1 misc_RNA 3057 -1 EntrezGene 33 4.836857 5.41&6.54 rs778049466 2 143286 1.39581e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35424e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44001e-05 3.09799e-05 2.67508e-05 3.67972e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.39517e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript NM_170606.3 protein_coding 14/59 2664-2665 2447-2448 816 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_005250025.4 protein_coding 15/62 2472-2473 2450-2451 817 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_005250026.3 protein_coding 15/62 2506-2507 2447-2448 816 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_005250027.4 protein_coding 14/61 2680-2681 2450-2451 817 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_005250028.4 protein_coding 14/61 2680-2681 2450-2451 817 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_005250031.4 protein_coding 14/60 2680-2681 2450-2451 817 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_006716077.3 protein_coding 14/61 2680-2681 2450-2451 817 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_006716078.3 protein_coding 14/61 2680-2681 2450-2451 817 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_006716079.3 protein_coding 14/60 2680-2681 2450-2451 817 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_011516450.2 protein_coding 14/60 2680-2681 2450-2451 817 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_011516451.2 protein_coding 14/60 2680-2681 2450-2451 817 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_011516452.2 protein_coding 14/59 2680-2681 2450-2451 817 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_011516453.2 protein_coding 14/60 2680-2681 2450-2451 817 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_011516454.2 protein_coding 9/56 1675-1676 1535-1536 512 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_011516456.2 protein_coding 14/60 2680-2681 2450-2451 817 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_017012480.1 protein_coding 15/62 2509-2510 2450-2451 817 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_017012481.1 protein_coding 15/62 2469-2470 2447-2448 816 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_017012482.1 protein_coding 14/61 2680-2681 2450-2451 817 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_017012483.1 protein_coding 14/61 2680-2681 2450-2451 817 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_017012484.1 protein_coding 14/61 2647-2648 2417-2418 806 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_017012485.1 protein_coding 14/60 2677-2678 2447-2448 816 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_017012486.1 protein_coding 14/61 2680-2681 2450-2451 817 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_017012487.1 protein_coding 14/61 2648-2649 2303-2304 768 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_017012488.1 protein_coding 14/60 2680-2681 2450-2451 817 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_024446852.1 protein_coding 14/61 2680-2681 2450-2451 817 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T stop_gained&frameshift_variant HIGH KMT2C 58508 Transcript XM_024446853.1 protein_coding 14/61 2680-2681 2450-2451 817 Y/* tac/taAc -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 152247986 G GT T non_coding_transcript_exon_variant MODIFIER KMT2C 58508 Transcript XR_428183.3 misc_RNA 14/49 2682-2683 -1 EntrezGene 33 5.304347 3.74&5.39 rs150073007 67964 137408 4.94615e-01 4.96522e-01 4.96390e-01 4.96677e-01 4.98848e-01 5.00000e-01 4.97596e-01 4.94983e-01 4.94538e-01 4.95321e-01 4.94014e-01 4.92874e-01 4.95302e-01 5.00000e-01 5.00000e-01 5.00000e-01 4.94670e-01 4.93691e-01 4.93729e-01 4.93679e-01 4.94556e-01 4.93048e-01 4.93483e-01 4.92452e-01 4.92118e-01 4.93371e-01 4.90760e-01 4.94524e-01 4.97987e-01 4.98182e-01 4.97942e-01 403020 0.49007 390552 Intellectual_disability¬_specified Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication KMT2C:58508 SO:0001587&nonsense 129 150073007 15 30 50.0 -7 117559590 ATCT A - inframe_deletion MODERATE CFTR 1080 Transcript NM_000492.4 protein_coding 11/27 1590-1592 1520-1522 507-508 IF/I aTCTtt/att 1 EntrezGene TCT TCT 19.21 1.993640 6.54 rs113993960 1140 143214 7.96012e-03 2.61743e-03 2.02732e-03 3.30989e-03 1.11111e-03 2.12766e-03 0.00000e+00 3.00234e-03 2.87745e-03 3.09757e-03 4.21179e-03 6.24291e-03 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.50856e-03 1.62400e-03 2.79553e-03 1.25565e-03 7.37688e-03 1.44000e-02 1.42903e-02 1.45509e-02 8.37209e-03 9.12409e-03 7.59013e-03 8.00195e-03 3.28515e-03 3.54610e-03 3.22581e-03 7105 22144 Duodenal_stenosis&Recurrent_pancreatitis&Hereditary_pancreatitis&Cystic_fibrosis&Congenital_bilateral_aplasia_of_vas_deferens_from_CFTR_mutation&Inborn_genetic_diseases¬_specified&ivacaftor_response_-_Efficacy&ivacaftor_/_lumacaftor_response_-_Efficacy&Bronchiectasis_with_or_without_elevated_sweat_chloride_1&_modifier_of¬_provided Cystic_fibrosis Human_Phenotype_Ontology:HP:0005205&Human_Phenotype_Ontology:HP:0010449&Human_Phenotype_Ontology:HP:0100867&MedGen:C0238093&Human_Phenotype_Ontology:HP:0100027&MedGen:C4551632&MONDO:MONDO:0008185&MedGen:C0238339&OMIM:167800&Orphanet:ORPHA676&SNOMED_CT:68072000&MONDO:MONDO:0009061&MedGen:C0010674&OMIM:219700&Orphanet:ORPHA586&SNOMED_CT:190905008&MONDO:MONDO:0010178&MedGen:C0403814&OMIM:277180&Orphanet:ORPHA48&MeSH:D030342&MedGen:C0950123&MedGen:CN169374&MedGen:CN236562&MedGen:CN240582&MedGen:CN258830&MedGen:CN517202 MONDO:MONDO:0009061&MedGen:C0010674&OMIM:219700&Orphanet:ORPHA586&SNOMED_CT:190905008 practice_guideline Pathogenic 634837:Pathogenic Deletion CFTR:1080&CFTR-AS1:111082987 29 113993960 15 30 50.0 -7 117559590 ATCT A - intron_variant&non_coding_transcript_variant MODIFIER CFTR-AS1 111082987 Transcript NR_149084.1 lncRNA 2/2 -1 EntrezGene TCT TCT 19.21 1.993640 6.54 rs113993960 1140 143214 7.96012e-03 2.61743e-03 2.02732e-03 3.30989e-03 1.11111e-03 2.12766e-03 0.00000e+00 3.00234e-03 2.87745e-03 3.09757e-03 4.21179e-03 6.24291e-03 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.50856e-03 1.62400e-03 2.79553e-03 1.25565e-03 7.37688e-03 1.44000e-02 1.42903e-02 1.45509e-02 8.37209e-03 9.12409e-03 7.59013e-03 8.00195e-03 3.28515e-03 3.54610e-03 3.22581e-03 7105 22144 Duodenal_stenosis&Recurrent_pancreatitis&Hereditary_pancreatitis&Cystic_fibrosis&Congenital_bilateral_aplasia_of_vas_deferens_from_CFTR_mutation&Inborn_genetic_diseases¬_specified&ivacaftor_response_-_Efficacy&ivacaftor_/_lumacaftor_response_-_Efficacy&Bronchiectasis_with_or_without_elevated_sweat_chloride_1&_modifier_of¬_provided Cystic_fibrosis Human_Phenotype_Ontology:HP:0005205&Human_Phenotype_Ontology:HP:0010449&Human_Phenotype_Ontology:HP:0100867&MedGen:C0238093&Human_Phenotype_Ontology:HP:0100027&MedGen:C4551632&MONDO:MONDO:0008185&MedGen:C0238339&OMIM:167800&Orphanet:ORPHA676&SNOMED_CT:68072000&MONDO:MONDO:0009061&MedGen:C0010674&OMIM:219700&Orphanet:ORPHA586&SNOMED_CT:190905008&MONDO:MONDO:0010178&MedGen:C0403814&OMIM:277180&Orphanet:ORPHA48&MeSH:D030342&MedGen:C0950123&MedGen:CN169374&MedGen:CN236562&MedGen:CN240582&MedGen:CN258830&MedGen:CN517202 MONDO:MONDO:0009061&MedGen:C0010674&OMIM:219700&Orphanet:ORPHA586&SNOMED_CT:190905008 practice_guideline Pathogenic 634837:Pathogenic Deletion CFTR:1080&CFTR-AS1:111082987 29 113993960 15 30 50.0 -8 29593089 A AAAAAG AAAAG upstream_gene_variant MODIFIER LOC105379352 105379352 Transcript XR_001745862.2 lncRNA 2082 1 EntrezGene 0.929 -0.076197 0.637 chr8:29593090-29593090 13067 133160 9.81301e-02 3.83513e-02 3.82499e-02 3.84698e-02 7.09220e-02 7.39910e-02 6.75000e-02 1.27980e-01 1.24201e-01 1.30918e-01 9.46372e-02 9.62220e-02 9.28184e-02 9.91408e-04 7.14286e-04 1.23001e-03 1.00557e-01 7.85135e-02 8.29519e-02 7.71408e-02 9.54683e-02 1.39259e-01 1.40770e-01 1.37152e-01 1.04352e-01 9.69582e-02 1.12371e-01 1.01293e-01 9.70735e-02 8.49421e-02 9.98249e-02 15 30 50.0 -8 29593089 A AAAAAG AAAAG upstream_gene_variant MODIFIER LOC105379352 105379352 Transcript XR_949626.3 lncRNA 2082 1 EntrezGene 0.929 -0.076197 0.637 chr8:29593090-29593090 13067 133160 9.81301e-02 3.83513e-02 3.82499e-02 3.84698e-02 7.09220e-02 7.39910e-02 6.75000e-02 1.27980e-01 1.24201e-01 1.30918e-01 9.46372e-02 9.62220e-02 9.28184e-02 9.91408e-04 7.14286e-04 1.23001e-03 1.00557e-01 7.85135e-02 8.29519e-02 7.71408e-02 9.54683e-02 1.39259e-01 1.40770e-01 1.37152e-01 1.04352e-01 9.69582e-02 1.12371e-01 1.01293e-01 9.70735e-02 8.49421e-02 9.98249e-02 15 30 50.0 -8 34004551 G A A intron_variant&non_coding_transcript_variant MODIFIER LOC105379364 105379364 Transcript XR_002956701.1 lncRNA 2/3 1 EntrezGene G G 5.314 0.389905 0.219 chr8:34004551-34004551 91282 143146 6.37685e-01 3.21563e-01 3.21927e-01 3.21137e-01 6.47778e-01 6.59574e-01 6.34884e-01 7.25681e-01 7.31043e-01 7.21591e-01 8.43769e-01 8.29545e-01 8.59795e-01 6.74024e-01 6.73343e-01 6.74613e-01 6.30818e-01 7.91068e-01 7.79647e-01 7.94659e-01 6.44989e-01 7.79690e-01 7.77962e-01 7.82069e-01 6.59387e-01 6.47541e-01 6.71727e-01 6.37611e-01 7.84211e-01 7.85461e-01 7.83926e-01 15 30 50.0 -8 63065942 T TAA AA frameshift_variant HIGH TTPA 7274 Transcript NM_000370.3 protein_coding 3/5 545-546 513-514 171-172 -/X -/TT -1 EntrezGene 28.8 4.216295 5.38&-1.16 rs397515379 14 143184 9.77763e-05 4.76327e-05 4.40956e-05 5.17866e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.33138e-05 1.69377e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 5.41991e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44130e-04 1.70389e-04 5.35074e-05 3.31126e-04 0.00000e+00 0.00000e+00 0.00000e+00 9.76563e-05 0.00000e+00 0.00000e+00 0.00000e+00 9139 0.00008 0.00017 24178 Familial_isolated_deficiency_of_vitamin_E&Ataxia&_Friedreich-like&_with_isolated_vitamin_E_deficiency¬_provided MONDO:MONDO:0010188&MedGen:C1848533&OMIM:277460&Orphanet:ORPHA96&MedGen:C4016662&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Insertion TTPA:7274 SO:0001589&frameshift_variant 1 397515379 15 30 50.0 -8 63065942 T TAA AA intron_variant MODIFIER TTPA 7274 Transcript XM_006716468.4 protein_coding 1/2 -1 EntrezGene 28.8 4.216295 5.38&-1.16 rs397515379 14 143184 9.77763e-05 4.76327e-05 4.40956e-05 5.17866e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.33138e-05 1.69377e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 5.41991e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44130e-04 1.70389e-04 5.35074e-05 3.31126e-04 0.00000e+00 0.00000e+00 0.00000e+00 9.76563e-05 0.00000e+00 0.00000e+00 0.00000e+00 9139 0.00008 0.00017 24178 Familial_isolated_deficiency_of_vitamin_E&Ataxia&_Friedreich-like&_with_isolated_vitamin_E_deficiency¬_provided MONDO:MONDO:0010188&MedGen:C1848533&OMIM:277460&Orphanet:ORPHA96&MedGen:C4016662&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Insertion TTPA:7274 SO:0001589&frameshift_variant 1 397515379 15 30 50.0 -8 86643780 AG A - frameshift_variant HIGH CNGB3 54714 Transcript NM_019098.4 protein_coding 10/18 1196 1148 383 T/X aCt/at -1 EntrezGene G G OK 33 4.871124 6.54 rs397515360 252 140224 1.79712e-03 4.14149e-04 4.96032e-04 3.17931e-04 0.00000e+00 0.00000e+00 0.00000e+00 6.84932e-04 1.05226e-03 4.03334e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.04110e-03 2.65122e-03 4.02576e-03 2.20779e-03 1.53587e-03 3.03612e-03 3.19870e-03 2.81131e-03 2.38322e-03 2.77778e-03 1.96464e-03 1.78928e-03 3.43643e-04 0.00000e+00 4.21230e-04 5225 0.00232 0.00185 20264 Abnormality_of_the_eye&Retinitis_pigmentosa&Cone-rod_dystrophy&Retinal_dystrophy&Achromatopsia&Achromatopsia_3&Leber_congenital_amaurosis&Stargardt_Disease&_Recessive&CNGB3-Related_Disorders¬_provided Human_Phenotype_Ontology:HP:0000478&MONDO:MONDO:0005328&MedGen:C4316870&Human_Phenotype_Ontology:HP:0000547&MONDO:MONDO:0019200&MeSH:D012174&MedGen:C0035334&OMIM:268000&OMIM:PS268000&Orphanet:ORPHA791&SNOMED_CT:28835009&Human_Phenotype_Ontology:HP:0000548&MONDO:MONDO:0015993&MedGen:C4085590&OMIM:PS120970&Orphanet:ORPHA1872&Human_Phenotype_Ontology:HP:0000556&Human_Phenotype_Ontology:HP:0007736&Human_Phenotype_Ontology:HP:0007910&Human_Phenotype_Ontology:HP:0007974&Human_Phenotype_Ontology:HP:0007982&MONDO:MONDO:0019118&MeSH:D058499&MedGen:C0854723&Orphanet:ORPHA71862&SNOMED_CT:314407005&Human_Phenotype_Ontology:HP:0011516&MONDO:MONDO:0018852&MedGen:C0152200&Orphanet:ORPHA49382&SNOMED_CT:56852002&MONDO:MONDO:0009875&MedGen:C1849792&OMIM:262300&MONDO:MONDO:0018998&MeSH:D057130&MedGen:C0339527&OMIM:PS204000&Orphanet:ORPHA65&SNOMED_CT:193413001&MedGen:CN239312&MedGen:CN239340&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Pathogenic(14)&Uncertain_significance(1) Deletion CNGB3:54714 5 397515360 15 30 50.0 -8 86643780 AG A - frameshift_variant HIGH CNGB3 54714 Transcript XM_011517138.2 protein_coding 8/16 851 734 245 T/X aCt/at -1 EntrezGene G G 33 4.871124 6.54 rs397515360 252 140224 1.79712e-03 4.14149e-04 4.96032e-04 3.17931e-04 0.00000e+00 0.00000e+00 0.00000e+00 6.84932e-04 1.05226e-03 4.03334e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.04110e-03 2.65122e-03 4.02576e-03 2.20779e-03 1.53587e-03 3.03612e-03 3.19870e-03 2.81131e-03 2.38322e-03 2.77778e-03 1.96464e-03 1.78928e-03 3.43643e-04 0.00000e+00 4.21230e-04 5225 0.00232 0.00185 20264 Abnormality_of_the_eye&Retinitis_pigmentosa&Cone-rod_dystrophy&Retinal_dystrophy&Achromatopsia&Achromatopsia_3&Leber_congenital_amaurosis&Stargardt_Disease&_Recessive&CNGB3-Related_Disorders¬_provided Human_Phenotype_Ontology:HP:0000478&MONDO:MONDO:0005328&MedGen:C4316870&Human_Phenotype_Ontology:HP:0000547&MONDO:MONDO:0019200&MeSH:D012174&MedGen:C0035334&OMIM:268000&OMIM:PS268000&Orphanet:ORPHA791&SNOMED_CT:28835009&Human_Phenotype_Ontology:HP:0000548&MONDO:MONDO:0015993&MedGen:C4085590&OMIM:PS120970&Orphanet:ORPHA1872&Human_Phenotype_Ontology:HP:0000556&Human_Phenotype_Ontology:HP:0007736&Human_Phenotype_Ontology:HP:0007910&Human_Phenotype_Ontology:HP:0007974&Human_Phenotype_Ontology:HP:0007982&MONDO:MONDO:0019118&MeSH:D058499&MedGen:C0854723&Orphanet:ORPHA71862&SNOMED_CT:314407005&Human_Phenotype_Ontology:HP:0011516&MONDO:MONDO:0018852&MedGen:C0152200&Orphanet:ORPHA49382&SNOMED_CT:56852002&MONDO:MONDO:0009875&MedGen:C1849792&OMIM:262300&MONDO:MONDO:0018998&MeSH:D057130&MedGen:C0339527&OMIM:PS204000&Orphanet:ORPHA65&SNOMED_CT:193413001&MedGen:CN239312&MedGen:CN239340&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Pathogenic(14)&Uncertain_significance(1) Deletion CNGB3:54714 5 397515360 15 30 50.0 -8 96330228 CTCTG C - frameshift_variant HIGH PTDSS1 9791 Transcript NM_001290225.2 protein_coding 9/11 1159-1162 752-755 251-252 LC/X cTCTGt/ct 1 EntrezGene TCTG TCTG 34 5.462302 6.28&4.49&6.28 rs1251777396 3 143218 2.09471e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70988e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44063e-05 4.64684e-05 5.35103e-05 3.67863e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.09284e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -8 96330228 CTCTG C - frameshift_variant HIGH PTDSS1 9791 Transcript NM_014754.3 protein_coding 11/13 1329-1332 1190-1193 397-398 LC/X cTCTGt/ct 1 EntrezGene TCTG TCTG 34 5.462302 6.28&4.49&6.28 rs1251777396 3 143218 2.09471e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70988e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44063e-05 4.64684e-05 5.35103e-05 3.67863e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.09284e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -9 34648170 A G G missense_variant&splice_region_variant MODERATE GALT 2592 Transcript NM_000155.4 protein_coding 6/11 593 563 188 Q/R cAg/cGg 1 EntrezGene A A 0 0.999 33 5.031992 33 0.99810344958828123 10.26274 0.804652464039255 0.90113 11.84661 0.929771393409939 0.93139 -8.53&-8.53&-8.53 4.77 0.999999999916865 0.000004 0.558786 0.9984 0.9086 .&3.89&. 1&1 -3.99&-3.9&-3.27 13.6236 0.966&0.975&. 0.98412 0.660085 1.000000 1.000000 6.039000 1.312000 6.54 rs75391579 269 143284 1.87739e-03 8.08754e-04 8.36857e-04 7.75755e-04 5.55556e-03 4.25532e-03 6.97674e-03 1.17233e-03 1.52439e-03 9.03926e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.23450e-03 1.90803e-04 3.99680e-04 1.25313e-04 1.49765e-03 3.20632e-03 3.53054e-03 2.76019e-03 2.32342e-03 1.82149e-03 2.84630e-03 1.87632e-03 0.00000e+00 0.00000e+00 0.00000e+00 3614 0.00132 0.00060 18653 Galactosemia&Deficiency_of_UDPglucose-hexose-1-phosphate_uridylyltransferase¬_provided Human_Phenotype_Ontology:HP:0004919&MONDO:MONDO:0018116&MedGen:C0016952&OMIM:PS230400&Orphanet:ORPHA352&SNOMED_CT:190745006&MONDO:MONDO:0009258&MedGen:C0268151&OMIM:230400&Orphanet:ORPHA79239&SNOMED_CT:124354006&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Pathogenic(16)&Uncertain_significance(1) single_nucleotide_variant GALT:2592 SO:0001583&missense_variant 17 75391579 15 30 50.0 -9 34648170 A G G upstream_gene_variant MODIFIER IL11RA 3590 Transcript NM_001142784.3 protein_coding 4015 1 EntrezGene A A 33 5.031992 6.54 rs75391579 269 143284 1.87739e-03 8.08754e-04 8.36857e-04 7.75755e-04 5.55556e-03 4.25532e-03 6.97674e-03 1.17233e-03 1.52439e-03 9.03926e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.23450e-03 1.90803e-04 3.99680e-04 1.25313e-04 1.49765e-03 3.20632e-03 3.53054e-03 2.76019e-03 2.32342e-03 1.82149e-03 2.84630e-03 1.87632e-03 0.00000e+00 0.00000e+00 0.00000e+00 3614 0.00132 0.00060 18653 Galactosemia&Deficiency_of_UDPglucose-hexose-1-phosphate_uridylyltransferase¬_provided Human_Phenotype_Ontology:HP:0004919&MONDO:MONDO:0018116&MedGen:C0016952&OMIM:PS230400&Orphanet:ORPHA352&SNOMED_CT:190745006&MONDO:MONDO:0009258&MedGen:C0268151&OMIM:230400&Orphanet:ORPHA79239&SNOMED_CT:124354006&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Pathogenic(16)&Uncertain_significance(1) single_nucleotide_variant GALT:2592 SO:0001583&missense_variant 17 75391579 15 30 50.0 -9 34648170 A G G missense_variant&splice_region_variant MODERATE GALT 2592 Transcript NM_001258332.1 protein_coding 4/9 554 236 79 Q/R cAg/cGg 1 EntrezGene A A OK 0 1 33 5.031992 33 0.99810344958828123 10.26274 0.804652464039255 0.90113 11.84661 0.929771393409939 0.93139 -8.53&-8.53&-8.53 4.77 0.999999999916865 0.000004 0.558786 0.9984 0.9086 .&3.89&. 1&1 -3.99&-3.9&-3.27 13.6236 0.966&0.975&. 0.98412 0.660085 1.000000 1.000000 6.039000 1.312000 6.54 rs75391579 269 143284 1.87739e-03 8.08754e-04 8.36857e-04 7.75755e-04 5.55556e-03 4.25532e-03 6.97674e-03 1.17233e-03 1.52439e-03 9.03926e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.23450e-03 1.90803e-04 3.99680e-04 1.25313e-04 1.49765e-03 3.20632e-03 3.53054e-03 2.76019e-03 2.32342e-03 1.82149e-03 2.84630e-03 1.87632e-03 0.00000e+00 0.00000e+00 0.00000e+00 3614 0.00132 0.00060 18653 Galactosemia&Deficiency_of_UDPglucose-hexose-1-phosphate_uridylyltransferase¬_provided Human_Phenotype_Ontology:HP:0004919&MONDO:MONDO:0018116&MedGen:C0016952&OMIM:PS230400&Orphanet:ORPHA352&SNOMED_CT:190745006&MONDO:MONDO:0009258&MedGen:C0268151&OMIM:230400&Orphanet:ORPHA79239&SNOMED_CT:124354006&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Pathogenic(16)&Uncertain_significance(1) single_nucleotide_variant GALT:2592 SO:0001583&missense_variant 17 75391579 15 30 50.0 -9 35657753 A G G upstream_gene_variant MODIFIER CCDC107 203260 Transcript NM_001195200.1 protein_coding 537 1 EntrezGene A A OK 4.088 0.282778 -4.39 chr9:35657753-35657753 4 143338 2.79061e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 0.00000e+00 1.29066e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70783e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87861e-05 4.64554e-05 5.34874e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79006e-05 0.00000e+00 0.00000e+00 0.00000e+00 972235 955827 Anauxetic_dysplasia MONDO:MONDO:0011773&MedGen:C1846796&OMIM:PS607095&Orphanet:ORPHA93347 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant RMRP:6023 SO:0001619&non-coding_transcript_variant 1 15 30 50.0 -9 35657753 A G G upstream_gene_variant MODIFIER CCDC107 203260 Transcript NM_001195201.2 protein_coding 539 1 EntrezGene A A 4.088 0.282778 -4.39 chr9:35657753-35657753 4 143338 2.79061e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 0.00000e+00 1.29066e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70783e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87861e-05 4.64554e-05 5.34874e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79006e-05 0.00000e+00 0.00000e+00 0.00000e+00 972235 955827 Anauxetic_dysplasia MONDO:MONDO:0011773&MedGen:C1846796&OMIM:PS607095&Orphanet:ORPHA93347 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant RMRP:6023 SO:0001619&non-coding_transcript_variant 1 15 30 50.0 -9 35657753 A G G upstream_gene_variant MODIFIER CCDC107 203260 Transcript NM_001195217.2 protein_coding 539 1 EntrezGene A A 4.088 0.282778 -4.39 chr9:35657753-35657753 4 143338 2.79061e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 0.00000e+00 1.29066e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70783e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87861e-05 4.64554e-05 5.34874e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79006e-05 0.00000e+00 0.00000e+00 0.00000e+00 972235 955827 Anauxetic_dysplasia MONDO:MONDO:0011773&MedGen:C1846796&OMIM:PS607095&Orphanet:ORPHA93347 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant RMRP:6023 SO:0001619&non-coding_transcript_variant 1 15 30 50.0 -9 35657753 A G G downstream_gene_variant MODIFIER ARHGEF39 84904 Transcript NM_032818.3 protein_coding 1590 -1 EntrezGene A A 4.088 0.282778 -4.39 chr9:35657753-35657753 4 143338 2.79061e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 0.00000e+00 1.29066e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70783e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87861e-05 4.64554e-05 5.34874e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79006e-05 0.00000e+00 0.00000e+00 0.00000e+00 972235 955827 Anauxetic_dysplasia MONDO:MONDO:0011773&MedGen:C1846796&OMIM:PS607095&Orphanet:ORPHA93347 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant RMRP:6023 SO:0001619&non-coding_transcript_variant 1 15 30 50.0 -9 35657753 A G G upstream_gene_variant MODIFIER CCDC107 203260 Transcript NM_174923.3 protein_coding 539 1 EntrezGene A A 4.088 0.282778 -4.39 chr9:35657753-35657753 4 143338 2.79061e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 0.00000e+00 1.29066e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70783e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87861e-05 4.64554e-05 5.34874e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79006e-05 0.00000e+00 0.00000e+00 0.00000e+00 972235 955827 Anauxetic_dysplasia MONDO:MONDO:0011773&MedGen:C1846796&OMIM:PS607095&Orphanet:ORPHA93347 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant RMRP:6023 SO:0001619&non-coding_transcript_variant 1 15 30 50.0 -9 35657753 A G G non_coding_transcript_exon_variant MODIFIER RMRP 6023 Transcript NR_003051.3 RNase_MRP_RNA 1/1 266 -1 EntrezGene A A OK 4.088 0.282778 -4.39 chr9:35657753-35657753 4 143338 2.79061e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 0.00000e+00 1.29066e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70783e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87861e-05 4.64554e-05 5.34874e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79006e-05 0.00000e+00 0.00000e+00 0.00000e+00 972235 955827 Anauxetic_dysplasia MONDO:MONDO:0011773&MedGen:C1846796&OMIM:PS607095&Orphanet:ORPHA93347 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant RMRP:6023 SO:0001619&non-coding_transcript_variant 1 15 30 50.0 -9 35657753 A G G upstream_gene_variant MODIFIER CCDC107 203260 Transcript XM_005251403.5 protein_coding 551 1 EntrezGene A A 4.088 0.282778 -4.39 chr9:35657753-35657753 4 143338 2.79061e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 0.00000e+00 1.29066e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70783e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87861e-05 4.64554e-05 5.34874e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79006e-05 0.00000e+00 0.00000e+00 0.00000e+00 972235 955827 Anauxetic_dysplasia MONDO:MONDO:0011773&MedGen:C1846796&OMIM:PS607095&Orphanet:ORPHA93347 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant RMRP:6023 SO:0001619&non-coding_transcript_variant 1 15 30 50.0 -9 35657753 A G G downstream_gene_variant MODIFIER ARHGEF39 84904 Transcript XM_011518057.2 protein_coding 2256 -1 EntrezGene A A 4.088 0.282778 -4.39 chr9:35657753-35657753 4 143338 2.79061e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 0.00000e+00 1.29066e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70783e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87861e-05 4.64554e-05 5.34874e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79006e-05 0.00000e+00 0.00000e+00 0.00000e+00 972235 955827 Anauxetic_dysplasia MONDO:MONDO:0011773&MedGen:C1846796&OMIM:PS607095&Orphanet:ORPHA93347 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant RMRP:6023 SO:0001619&non-coding_transcript_variant 1 15 30 50.0 -9 35657753 A G G downstream_gene_variant MODIFIER ARHGEF39 84904 Transcript XM_017015225.1 protein_coding 2256 -1 EntrezGene A A 4.088 0.282778 -4.39 chr9:35657753-35657753 4 143338 2.79061e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 0.00000e+00 1.29066e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70783e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87861e-05 4.64554e-05 5.34874e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79006e-05 0.00000e+00 0.00000e+00 0.00000e+00 972235 955827 Anauxetic_dysplasia MONDO:MONDO:0011773&MedGen:C1846796&OMIM:PS607095&Orphanet:ORPHA93347 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant RMRP:6023 SO:0001619&non-coding_transcript_variant 1 15 30 50.0 -9 35657753 A G G downstream_gene_variant MODIFIER ARHGEF39 84904 Transcript XM_017015226.1 protein_coding 3641 -1 EntrezGene A A 4.088 0.282778 -4.39 chr9:35657753-35657753 4 143338 2.79061e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 0.00000e+00 1.29066e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70783e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87861e-05 4.64554e-05 5.34874e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79006e-05 0.00000e+00 0.00000e+00 0.00000e+00 972235 955827 Anauxetic_dysplasia MONDO:MONDO:0011773&MedGen:C1846796&OMIM:PS607095&Orphanet:ORPHA93347 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant RMRP:6023 SO:0001619&non-coding_transcript_variant 1 15 30 50.0 -9 35657753 A G G downstream_gene_variant MODIFIER ARHGEF39 84904 Transcript XM_017015227.1 protein_coding 2256 -1 EntrezGene A A 4.088 0.282778 -4.39 chr9:35657753-35657753 4 143338 2.79061e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 0.00000e+00 1.29066e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70783e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87861e-05 4.64554e-05 5.34874e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79006e-05 0.00000e+00 0.00000e+00 0.00000e+00 972235 955827 Anauxetic_dysplasia MONDO:MONDO:0011773&MedGen:C1846796&OMIM:PS607095&Orphanet:ORPHA93347 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant RMRP:6023 SO:0001619&non-coding_transcript_variant 1 15 30 50.0 -9 35657753 A G G downstream_gene_variant MODIFIER ARHGEF39 84904 Transcript XM_017015228.1 protein_coding 4895 -1 EntrezGene A A 4.088 0.282778 -4.39 chr9:35657753-35657753 4 143338 2.79061e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 0.00000e+00 1.29066e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70783e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87861e-05 4.64554e-05 5.34874e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79006e-05 0.00000e+00 0.00000e+00 0.00000e+00 972235 955827 Anauxetic_dysplasia MONDO:MONDO:0011773&MedGen:C1846796&OMIM:PS607095&Orphanet:ORPHA93347 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant RMRP:6023 SO:0001619&non-coding_transcript_variant 1 15 30 50.0 -9 35657753 A G G downstream_gene_variant MODIFIER ARHGEF39 84904 Transcript XR_001746399.2 misc_RNA 1125 -1 EntrezGene A A 4.088 0.282778 -4.39 chr9:35657753-35657753 4 143338 2.79061e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 0.00000e+00 1.29066e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70783e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87861e-05 4.64554e-05 5.34874e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79006e-05 0.00000e+00 0.00000e+00 0.00000e+00 972235 955827 Anauxetic_dysplasia MONDO:MONDO:0011773&MedGen:C1846796&OMIM:PS607095&Orphanet:ORPHA93347 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant RMRP:6023 SO:0001619&non-coding_transcript_variant 1 15 30 50.0 -9 35657753 A G G downstream_gene_variant MODIFIER ARHGEF39 84904 Transcript XR_001746400.1 misc_RNA 2256 -1 EntrezGene A A 4.088 0.282778 -4.39 chr9:35657753-35657753 4 143338 2.79061e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 0.00000e+00 1.29066e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70783e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87861e-05 4.64554e-05 5.34874e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79006e-05 0.00000e+00 0.00000e+00 0.00000e+00 972235 955827 Anauxetic_dysplasia MONDO:MONDO:0011773&MedGen:C1846796&OMIM:PS607095&Orphanet:ORPHA93347 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant RMRP:6023 SO:0001619&non-coding_transcript_variant 1 15 30 50.0 -9 35657753 A G G downstream_gene_variant MODIFIER ARHGEF39 84904 Transcript XR_001746401.1 misc_RNA 2256 -1 EntrezGene A A 4.088 0.282778 -4.39 chr9:35657753-35657753 4 143338 2.79061e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 0.00000e+00 1.29066e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70783e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87861e-05 4.64554e-05 5.34874e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79006e-05 0.00000e+00 0.00000e+00 0.00000e+00 972235 955827 Anauxetic_dysplasia MONDO:MONDO:0011773&MedGen:C1846796&OMIM:PS607095&Orphanet:ORPHA93347 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant RMRP:6023 SO:0001619&non-coding_transcript_variant 1 15 30 50.0 -9 35657753 A G G upstream_gene_variant MODIFIER CCDC107 203260 Transcript XR_929218.3 misc_RNA 551 1 EntrezGene A A 4.088 0.282778 -4.39 chr9:35657753-35657753 4 143338 2.79061e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 0.00000e+00 1.29066e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70783e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87861e-05 4.64554e-05 5.34874e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79006e-05 0.00000e+00 0.00000e+00 0.00000e+00 972235 955827 Anauxetic_dysplasia MONDO:MONDO:0011773&MedGen:C1846796&OMIM:PS607095&Orphanet:ORPHA93347 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant RMRP:6023 SO:0001619&non-coding_transcript_variant 1 15 30 50.0 -9 35657753 A G G downstream_gene_variant MODIFIER ARHGEF39 84904 Transcript XR_929352.2 misc_RNA 1122 -1 EntrezGene A A 4.088 0.282778 -4.39 chr9:35657753-35657753 4 143338 2.79061e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 0.00000e+00 1.29066e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70783e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87861e-05 4.64554e-05 5.34874e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79006e-05 0.00000e+00 0.00000e+00 0.00000e+00 972235 955827 Anauxetic_dysplasia MONDO:MONDO:0011773&MedGen:C1846796&OMIM:PS607095&Orphanet:ORPHA93347 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant RMRP:6023 SO:0001619&non-coding_transcript_variant 1 15 30 50.0 -9 35657753 A G G downstream_gene_variant MODIFIER ARHGEF39 84904 Transcript XR_929354.2 misc_RNA 1122 -1 EntrezGene A A 4.088 0.282778 -4.39 chr9:35657753-35657753 4 143338 2.79061e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 0.00000e+00 1.29066e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70783e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87861e-05 4.64554e-05 5.34874e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79006e-05 0.00000e+00 0.00000e+00 0.00000e+00 972235 955827 Anauxetic_dysplasia MONDO:MONDO:0011773&MedGen:C1846796&OMIM:PS607095&Orphanet:ORPHA93347 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant RMRP:6023 SO:0001619&non-coding_transcript_variant 1 15 30 50.0 -9 35657753 A G G downstream_gene_variant MODIFIER ARHGEF39 84904 Transcript XR_929366.3 misc_RNA 1125 -1 EntrezGene A A 4.088 0.282778 -4.39 chr9:35657753-35657753 4 143338 2.79061e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 0.00000e+00 1.29066e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70783e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87861e-05 4.64554e-05 5.34874e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79006e-05 0.00000e+00 0.00000e+00 0.00000e+00 972235 955827 Anauxetic_dysplasia MONDO:MONDO:0011773&MedGen:C1846796&OMIM:PS607095&Orphanet:ORPHA93347 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant RMRP:6023 SO:0001619&non-coding_transcript_variant 1 15 30 50.0 -9 69221309 GGCCT G - frameshift_variant HIGH TJP2 9414 Transcript NM_001170414.2 protein_coding 6/22 929-932 697-700 233-234 AY/X GCCTac/ac 1 EntrezGene GCCT GCCT -1.16&3.01&4.09&5.92 139627 143250 Progressive_familial_intrahepatic_cholestasis_4¬_provided MONDO:MONDO:0014381&MedGen:C2931067&OMIM:615878&Orphanet:ORPHA480483&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic Deletion TJP2:9414 SO:0001589&frameshift_variant 1 587777518 15 30 50.0 -9 69221309 GGCCT G - frameshift_variant HIGH TJP2 9414 Transcript NM_001170415.1 protein_coding 5/22 929-932 778-781 260-261 AY/X GCCTac/ac 1 EntrezGene GCCT GCCT -1.16&3.01&4.09&5.92 139627 143250 Progressive_familial_intrahepatic_cholestasis_4¬_provided MONDO:MONDO:0014381&MedGen:C2931067&OMIM:615878&Orphanet:ORPHA480483&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic Deletion TJP2:9414 SO:0001589&frameshift_variant 1 587777518 15 30 50.0 -9 69221309 GGCCT G - frameshift_variant HIGH TJP2 9414 Transcript NM_001170416.2 protein_coding 5/23 866-869 859-862 287-288 AY/X GCCTac/ac 1 EntrezGene GCCT GCCT -1.16&3.01&4.09&5.92 139627 143250 Progressive_familial_intrahepatic_cholestasis_4¬_provided MONDO:MONDO:0014381&MedGen:C2931067&OMIM:615878&Orphanet:ORPHA480483&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic Deletion TJP2:9414 SO:0001589&frameshift_variant 1 587777518 15 30 50.0 -9 69221309 GGCCT G - frameshift_variant HIGH TJP2 9414 Transcript NM_001369870.1 protein_coding 6/24 1054-1057 697-700 233-234 AY/X GCCTac/ac 1 EntrezGene GCCT GCCT -1.16&3.01&4.09&5.92 139627 143250 Progressive_familial_intrahepatic_cholestasis_4¬_provided MONDO:MONDO:0014381&MedGen:C2931067&OMIM:615878&Orphanet:ORPHA480483&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic Deletion TJP2:9414 SO:0001589&frameshift_variant 1 587777518 15 30 50.0 -9 69221309 GGCCT G - frameshift_variant HIGH TJP2 9414 Transcript NM_001369871.1 protein_coding 7/25 1044-1047 697-700 233-234 AY/X GCCTac/ac 1 EntrezGene GCCT GCCT -1.16&3.01&4.09&5.92 139627 143250 Progressive_familial_intrahepatic_cholestasis_4¬_provided MONDO:MONDO:0014381&MedGen:C2931067&OMIM:615878&Orphanet:ORPHA480483&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic Deletion TJP2:9414 SO:0001589&frameshift_variant 1 587777518 15 30 50.0 -9 69221309 GGCCT G - frameshift_variant HIGH TJP2 9414 Transcript NM_001369872.1 protein_coding 5/22 862-865 766-769 256-257 AY/X GCCTac/ac 1 EntrezGene GCCT GCCT -1.16&3.01&4.09&5.92 139627 143250 Progressive_familial_intrahepatic_cholestasis_4¬_provided MONDO:MONDO:0014381&MedGen:C2931067&OMIM:615878&Orphanet:ORPHA480483&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic Deletion TJP2:9414 SO:0001589&frameshift_variant 1 587777518 15 30 50.0 -9 69221309 GGCCT G - frameshift_variant HIGH TJP2 9414 Transcript NM_001369873.1 protein_coding 5/21 862-865 766-769 256-257 AY/X GCCTac/ac 1 EntrezGene GCCT GCCT -1.16&3.01&4.09&5.92 139627 143250 Progressive_familial_intrahepatic_cholestasis_4¬_provided MONDO:MONDO:0014381&MedGen:C2931067&OMIM:615878&Orphanet:ORPHA480483&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic Deletion TJP2:9414 SO:0001589&frameshift_variant 1 587777518 15 30 50.0 -9 69221309 GGCCT G - frameshift_variant HIGH TJP2 9414 Transcript NM_001369874.1 protein_coding 5/21 785-788 778-781 260-261 AY/X GCCTac/ac 1 EntrezGene GCCT GCCT -1.16&3.01&4.09&5.92 139627 143250 Progressive_familial_intrahepatic_cholestasis_4¬_provided MONDO:MONDO:0014381&MedGen:C2931067&OMIM:615878&Orphanet:ORPHA480483&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic Deletion TJP2:9414 SO:0001589&frameshift_variant 1 587777518 15 30 50.0 -9 69221309 GGCCT G - frameshift_variant HIGH TJP2 9414 Transcript NM_001369875.1 protein_coding 5/23 785-788 778-781 260-261 AY/X GCCTac/ac 1 EntrezGene GCCT GCCT -1.16&3.01&4.09&5.92 139627 143250 Progressive_familial_intrahepatic_cholestasis_4¬_provided MONDO:MONDO:0014381&MedGen:C2931067&OMIM:615878&Orphanet:ORPHA480483&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic Deletion TJP2:9414 SO:0001589&frameshift_variant 1 587777518 15 30 50.0 -9 69221309 GGCCT G - frameshift_variant HIGH TJP2 9414 Transcript NM_004817.4 protein_coding 5/23 862-865 766-769 256-257 AY/X GCCTac/ac 1 EntrezGene GCCT GCCT -1.16&3.01&4.09&5.92 139627 143250 Progressive_familial_intrahepatic_cholestasis_4¬_provided MONDO:MONDO:0014381&MedGen:C2931067&OMIM:615878&Orphanet:ORPHA480483&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic Deletion TJP2:9414 SO:0001589&frameshift_variant 1 587777518 15 30 50.0 -9 69221309 GGCCT G - frameshift_variant HIGH TJP2 9414 Transcript NM_201629.3 protein_coding 5/21 1084-1087 766-769 256-257 AY/X GCCTac/ac 1 EntrezGene GCCT GCCT -1.16&3.01&4.09&5.92 139627 143250 Progressive_familial_intrahepatic_cholestasis_4¬_provided MONDO:MONDO:0014381&MedGen:C2931067&OMIM:615878&Orphanet:ORPHA480483&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic Deletion TJP2:9414 SO:0001589&frameshift_variant 1 587777518 15 30 50.0 -9 69221309 GGCCT G - frameshift_variant HIGH TJP2 9414 Transcript XM_011519204.1 protein_coding 5/22 827-830 697-700 233-234 AY/X GCCTac/ac 1 EntrezGene GCCT GCCT -1.16&3.01&4.09&5.92 139627 143250 Progressive_familial_intrahepatic_cholestasis_4¬_provided MONDO:MONDO:0014381&MedGen:C2931067&OMIM:615878&Orphanet:ORPHA480483&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic Deletion TJP2:9414 SO:0001589&frameshift_variant 1 587777518 15 30 50.0 -9 69221309 GGCCT G - frameshift_variant HIGH TJP2 9414 Transcript XM_011519206.2 protein_coding 7/25 1429-1432 697-700 233-234 AY/X GCCTac/ac 1 EntrezGene GCCT GCCT -1.16&3.01&4.09&5.92 139627 143250 Progressive_familial_intrahepatic_cholestasis_4¬_provided MONDO:MONDO:0014381&MedGen:C2931067&OMIM:615878&Orphanet:ORPHA480483&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic Deletion TJP2:9414 SO:0001589&frameshift_variant 1 587777518 15 30 50.0 -9 69221309 GGCCT G - frameshift_variant HIGH TJP2 9414 Transcript XM_011519207.2 protein_coding 6/24 925-928 697-700 233-234 AY/X GCCTac/ac 1 EntrezGene GCCT GCCT -1.16&3.01&4.09&5.92 139627 143250 Progressive_familial_intrahepatic_cholestasis_4¬_provided MONDO:MONDO:0014381&MedGen:C2931067&OMIM:615878&Orphanet:ORPHA480483&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic Deletion TJP2:9414 SO:0001589&frameshift_variant 1 587777518 15 30 50.0 -9 69221309 GGCCT G - frameshift_variant HIGH TJP2 9414 Transcript XM_011519208.2 protein_coding 6/24 885-888 697-700 233-234 AY/X GCCTac/ac 1 EntrezGene GCCT GCCT -1.16&3.01&4.09&5.92 139627 143250 Progressive_familial_intrahepatic_cholestasis_4¬_provided MONDO:MONDO:0014381&MedGen:C2931067&OMIM:615878&Orphanet:ORPHA480483&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic Deletion TJP2:9414 SO:0001589&frameshift_variant 1 587777518 15 30 50.0 -9 69221309 GGCCT G - frameshift_variant HIGH TJP2 9414 Transcript XM_011519209.2 protein_coding 6/24 714-717 697-700 233-234 AY/X GCCTac/ac 1 EntrezGene GCCT GCCT -1.16&3.01&4.09&5.92 139627 143250 Progressive_familial_intrahepatic_cholestasis_4¬_provided MONDO:MONDO:0014381&MedGen:C2931067&OMIM:615878&Orphanet:ORPHA480483&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic Deletion TJP2:9414 SO:0001589&frameshift_variant 1 587777518 15 30 50.0 -9 94639191 T TGTGCAG GTGCAG inframe_insertion MODERATE FBP1 2203 Transcript NM_000507.4 protein_coding 1/7 327-328 119-120 40 T/TCT aca/acCTGCACa -1 EntrezGene -11.1&6.48 419848 407727 Fructose-biphosphatase_deficiency¬_provided MONDO:MONDO:0009251&MedGen:C0016756&OMIM:229700&Orphanet:ORPHA348&SNOMED_CT:28183005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Duplication FBP1:2203 SO:0001821&inframe_insertion 1 1554682769 15 30 50.0 -9 94639191 T TGTGCAG GTGCAG inframe_insertion MODERATE FBP1 2203 Transcript NM_001127628.2 protein_coding 2/8 363-364 119-120 40 T/TCT aca/acCTGCACa -1 EntrezGene -11.1&6.48 419848 407727 Fructose-biphosphatase_deficiency¬_provided MONDO:MONDO:0009251&MedGen:C0016756&OMIM:229700&Orphanet:ORPHA348&SNOMED_CT:28183005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Duplication FBP1:2203 SO:0001821&inframe_insertion 1 1554682769 15 30 50.0 -9 94639191 T TGTGCAG GTGCAG intron_variant MODIFIER FBP1 2203 Transcript XM_006717005.4 protein_coding 1/6 -1 EntrezGene -11.1&6.48 419848 407727 Fructose-biphosphatase_deficiency¬_provided MONDO:MONDO:0009251&MedGen:C0016756&OMIM:229700&Orphanet:ORPHA348&SNOMED_CT:28183005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Duplication FBP1:2203 SO:0001821&inframe_insertion 1 1554682769 15 30 50.0 -9 95249224 TC T - frameshift_variant HIGH FANCC 2176 Transcript NM_000136.3 protein_coding 2/15 329 67 23 D/X Gat/at -1 EntrezGene C C 23.2 2.884301 3.25 rs104886459 22 143282 1.53543e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48959e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.58419e-04 3.40716e-04 2.94228e-04 4.04650e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53434e-04 0.00000e+00 0.00000e+00 0.00000e+00 12049 27088 Fanconi_anemia&_complementation_group_C&Hereditary_cancer-predisposing_syndrome&Fanconi_anemia¬_provided MONDO:MONDO:0009213&MedGen:C3468041&OMIM:227645&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019391&MedGen:C0015625&OMIM:PS227650&Orphanet:ORPHA84&SNOMED_CT:30575002&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion FANCC:2176 SO:0001589&frameshift_variant 1 104886459 15 30 50.0 -9 95249224 TC T - frameshift_variant HIGH FANCC 2176 Transcript NM_001243743.1 protein_coding 2/15 271 67 23 D/X Gat/at -1 EntrezGene C C OK 23.2 2.884301 3.25 rs104886459 22 143282 1.53543e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48959e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.58419e-04 3.40716e-04 2.94228e-04 4.04650e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53434e-04 0.00000e+00 0.00000e+00 0.00000e+00 12049 27088 Fanconi_anemia&_complementation_group_C&Hereditary_cancer-predisposing_syndrome&Fanconi_anemia¬_provided MONDO:MONDO:0009213&MedGen:C3468041&OMIM:227645&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019391&MedGen:C0015625&OMIM:PS227650&Orphanet:ORPHA84&SNOMED_CT:30575002&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion FANCC:2176 SO:0001589&frameshift_variant 1 104886459 15 30 50.0 -9 95249224 TC T - frameshift_variant HIGH FANCC 2176 Transcript NM_001243744.1 protein_coding 2/14 329 67 23 D/X Gat/at -1 EntrezGene C C OK 23.2 2.884301 3.25 rs104886459 22 143282 1.53543e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48959e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.58419e-04 3.40716e-04 2.94228e-04 4.04650e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53434e-04 0.00000e+00 0.00000e+00 0.00000e+00 12049 27088 Fanconi_anemia&_complementation_group_C&Hereditary_cancer-predisposing_syndrome&Fanconi_anemia¬_provided MONDO:MONDO:0009213&MedGen:C3468041&OMIM:227645&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019391&MedGen:C0015625&OMIM:PS227650&Orphanet:ORPHA84&SNOMED_CT:30575002&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion FANCC:2176 SO:0001589&frameshift_variant 1 104886459 15 30 50.0 -9 95249224 TC T - frameshift_variant HIGH FANCC 2176 Transcript XM_006717001.3 protein_coding 2/14 329 67 23 D/X Gat/at -1 EntrezGene C C 23.2 2.884301 3.25 rs104886459 22 143282 1.53543e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48959e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.58419e-04 3.40716e-04 2.94228e-04 4.04650e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53434e-04 0.00000e+00 0.00000e+00 0.00000e+00 12049 27088 Fanconi_anemia&_complementation_group_C&Hereditary_cancer-predisposing_syndrome&Fanconi_anemia¬_provided MONDO:MONDO:0009213&MedGen:C3468041&OMIM:227645&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019391&MedGen:C0015625&OMIM:PS227650&Orphanet:ORPHA84&SNOMED_CT:30575002&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion FANCC:2176 SO:0001589&frameshift_variant 1 104886459 15 30 50.0 -9 95249224 TC T - frameshift_variant HIGH FANCC 2176 Transcript XM_006717002.4 protein_coding 2/14 329 67 23 D/X Gat/at -1 EntrezGene C C 23.2 2.884301 3.25 rs104886459 22 143282 1.53543e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48959e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.58419e-04 3.40716e-04 2.94228e-04 4.04650e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53434e-04 0.00000e+00 0.00000e+00 0.00000e+00 12049 27088 Fanconi_anemia&_complementation_group_C&Hereditary_cancer-predisposing_syndrome&Fanconi_anemia¬_provided MONDO:MONDO:0009213&MedGen:C3468041&OMIM:227645&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019391&MedGen:C0015625&OMIM:PS227650&Orphanet:ORPHA84&SNOMED_CT:30575002&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion FANCC:2176 SO:0001589&frameshift_variant 1 104886459 15 30 50.0 -9 95249224 TC T - frameshift_variant HIGH FANCC 2176 Transcript XM_006717004.4 protein_coding 2/12 329 67 23 D/X Gat/at -1 EntrezGene C C 23.2 2.884301 3.25 rs104886459 22 143282 1.53543e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48959e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.58419e-04 3.40716e-04 2.94228e-04 4.04650e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53434e-04 0.00000e+00 0.00000e+00 0.00000e+00 12049 27088 Fanconi_anemia&_complementation_group_C&Hereditary_cancer-predisposing_syndrome&Fanconi_anemia¬_provided MONDO:MONDO:0009213&MedGen:C3468041&OMIM:227645&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019391&MedGen:C0015625&OMIM:PS227650&Orphanet:ORPHA84&SNOMED_CT:30575002&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion FANCC:2176 SO:0001589&frameshift_variant 1 104886459 15 30 50.0 -9 95249224 TC T - frameshift_variant HIGH FANCC 2176 Transcript XM_011518365.3 protein_coding 2/15 2138 67 23 D/X Gat/at -1 EntrezGene C C 23.2 2.884301 3.25 rs104886459 22 143282 1.53543e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48959e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.58419e-04 3.40716e-04 2.94228e-04 4.04650e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53434e-04 0.00000e+00 0.00000e+00 0.00000e+00 12049 27088 Fanconi_anemia&_complementation_group_C&Hereditary_cancer-predisposing_syndrome&Fanconi_anemia¬_provided MONDO:MONDO:0009213&MedGen:C3468041&OMIM:227645&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019391&MedGen:C0015625&OMIM:PS227650&Orphanet:ORPHA84&SNOMED_CT:30575002&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion FANCC:2176 SO:0001589&frameshift_variant 1 104886459 15 30 50.0 -9 95249224 TC T - frameshift_variant HIGH FANCC 2176 Transcript XM_011518366.3 protein_coding 2/14 329 67 23 D/X Gat/at -1 EntrezGene C C 23.2 2.884301 3.25 rs104886459 22 143282 1.53543e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48959e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.58419e-04 3.40716e-04 2.94228e-04 4.04650e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53434e-04 0.00000e+00 0.00000e+00 0.00000e+00 12049 27088 Fanconi_anemia&_complementation_group_C&Hereditary_cancer-predisposing_syndrome&Fanconi_anemia¬_provided MONDO:MONDO:0009213&MedGen:C3468041&OMIM:227645&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019391&MedGen:C0015625&OMIM:PS227650&Orphanet:ORPHA84&SNOMED_CT:30575002&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion FANCC:2176 SO:0001589&frameshift_variant 1 104886459 15 30 50.0 -9 95249224 TC T - 5_prime_UTR_variant MODIFIER FANCC 2176 Transcript XM_011518367.2 protein_coding 2/16 296 -1 EntrezGene C C 23.2 2.884301 3.25 rs104886459 22 143282 1.53543e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48959e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.58419e-04 3.40716e-04 2.94228e-04 4.04650e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53434e-04 0.00000e+00 0.00000e+00 0.00000e+00 12049 27088 Fanconi_anemia&_complementation_group_C&Hereditary_cancer-predisposing_syndrome&Fanconi_anemia¬_provided MONDO:MONDO:0009213&MedGen:C3468041&OMIM:227645&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019391&MedGen:C0015625&OMIM:PS227650&Orphanet:ORPHA84&SNOMED_CT:30575002&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion FANCC:2176 SO:0001589&frameshift_variant 1 104886459 15 30 50.0 -9 95249224 TC T - 5_prime_UTR_variant MODIFIER FANCC 2176 Transcript XM_017014452.2 protein_coding 2/16 313 -1 EntrezGene C C 23.2 2.884301 3.25 rs104886459 22 143282 1.53543e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48959e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.58419e-04 3.40716e-04 2.94228e-04 4.04650e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53434e-04 0.00000e+00 0.00000e+00 0.00000e+00 12049 27088 Fanconi_anemia&_complementation_group_C&Hereditary_cancer-predisposing_syndrome&Fanconi_anemia¬_provided MONDO:MONDO:0009213&MedGen:C3468041&OMIM:227645&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019391&MedGen:C0015625&OMIM:PS227650&Orphanet:ORPHA84&SNOMED_CT:30575002&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion FANCC:2176 SO:0001589&frameshift_variant 1 104886459 15 30 50.0 -9 95249224 TC T - 5_prime_UTR_variant MODIFIER FANCC 2176 Transcript XM_017014453.1 protein_coding 2/16 258 -1 EntrezGene C C 23.2 2.884301 3.25 rs104886459 22 143282 1.53543e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48959e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.58419e-04 3.40716e-04 2.94228e-04 4.04650e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53434e-04 0.00000e+00 0.00000e+00 0.00000e+00 12049 27088 Fanconi_anemia&_complementation_group_C&Hereditary_cancer-predisposing_syndrome&Fanconi_anemia¬_provided MONDO:MONDO:0009213&MedGen:C3468041&OMIM:227645&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019391&MedGen:C0015625&OMIM:PS227650&Orphanet:ORPHA84&SNOMED_CT:30575002&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion FANCC:2176 SO:0001589&frameshift_variant 1 104886459 15 30 50.0 -9 95249224 TC T - 5_prime_UTR_variant MODIFIER FANCC 2176 Transcript XM_017014454.1 protein_coding 2/15 350 -1 EntrezGene C C 23.2 2.884301 3.25 rs104886459 22 143282 1.53543e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48959e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.58419e-04 3.40716e-04 2.94228e-04 4.04650e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53434e-04 0.00000e+00 0.00000e+00 0.00000e+00 12049 27088 Fanconi_anemia&_complementation_group_C&Hereditary_cancer-predisposing_syndrome&Fanconi_anemia¬_provided MONDO:MONDO:0009213&MedGen:C3468041&OMIM:227645&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019391&MedGen:C0015625&OMIM:PS227650&Orphanet:ORPHA84&SNOMED_CT:30575002&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion FANCC:2176 SO:0001589&frameshift_variant 1 104886459 15 30 50.0 -9 95249224 TC T - frameshift_variant HIGH FANCC 2176 Transcript XM_024447451.1 protein_coding 2/15 303 67 23 D/X Gat/at -1 EntrezGene C C 23.2 2.884301 3.25 rs104886459 22 143282 1.53543e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48959e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.58419e-04 3.40716e-04 2.94228e-04 4.04650e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.53434e-04 0.00000e+00 0.00000e+00 0.00000e+00 12049 27088 Fanconi_anemia&_complementation_group_C&Hereditary_cancer-predisposing_syndrome&Fanconi_anemia¬_provided MONDO:MONDO:0009213&MedGen:C3468041&OMIM:227645&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0019391&MedGen:C0015625&OMIM:PS227650&Orphanet:ORPHA84&SNOMED_CT:30575002&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion FANCC:2176 SO:0001589&frameshift_variant 1 104886459 15 30 50.0 -9 104794512 T TAA AA splice_region_variant&intron_variant LOW ABCA1 19 Transcript NM_005502.4 protein_coding 39/49 -1 EntrezGene 15.21 1.419672 6.53&0 chr9:104794513-104794513 6549 120012 5.45695e-02 1.42624e-01 1.41915e-01 1.43467e-01 1.16009e-03 2.20264e-03 0.00000e+00 3.03774e-02 3.32064e-02 2.80995e-02 4.34923e-02 4.23216e-02 4.48276e-02 1.94938e-02 1.90616e-02 1.98718e-02 5.48214e-02 7.13668e-03 2.07469e-02 3.55191e-03 5.42801e-02 1.46216e-02 1.40642e-02 1.54189e-02 6.30682e-02 6.45161e-02 6.14458e-02 5.75621e-02 2.30235e-02 1.62037e-02 2.45989e-02 928609 917468 not_specified MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication ABCA1:19 SO:0001627&intron_variant 1 15 30 50.0 -9 104794512 T TAA AA splice_region_variant&intron_variant LOW ABCA1 19 Transcript XM_005251773.3 protein_coding 39/49 -1 EntrezGene 15.21 1.419672 6.53&0 chr9:104794513-104794513 6549 120012 5.45695e-02 1.42624e-01 1.41915e-01 1.43467e-01 1.16009e-03 2.20264e-03 0.00000e+00 3.03774e-02 3.32064e-02 2.80995e-02 4.34923e-02 4.23216e-02 4.48276e-02 1.94938e-02 1.90616e-02 1.98718e-02 5.48214e-02 7.13668e-03 2.07469e-02 3.55191e-03 5.42801e-02 1.46216e-02 1.40642e-02 1.54189e-02 6.30682e-02 6.45161e-02 6.14458e-02 5.75621e-02 2.30235e-02 1.62037e-02 2.45989e-02 928609 917468 not_specified MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication ABCA1:19 SO:0001627&intron_variant 1 15 30 50.0 -9 104794512 T TAA AA splice_region_variant&intron_variant LOW ABCA1 19 Transcript XM_005251776.3 protein_coding 38/48 -1 EntrezGene 15.21 1.419672 6.53&0 chr9:104794513-104794513 6549 120012 5.45695e-02 1.42624e-01 1.41915e-01 1.43467e-01 1.16009e-03 2.20264e-03 0.00000e+00 3.03774e-02 3.32064e-02 2.80995e-02 4.34923e-02 4.23216e-02 4.48276e-02 1.94938e-02 1.90616e-02 1.98718e-02 5.48214e-02 7.13668e-03 2.07469e-02 3.55191e-03 5.42801e-02 1.46216e-02 1.40642e-02 1.54189e-02 6.30682e-02 6.45161e-02 6.14458e-02 5.75621e-02 2.30235e-02 1.62037e-02 2.45989e-02 928609 917468 not_specified MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication ABCA1:19 SO:0001627&intron_variant 1 15 30 50.0 -9 104794512 T TAA AA splice_region_variant&intron_variant LOW ABCA1 19 Transcript XM_011518339.3 protein_coding 40/50 -1 EntrezGene 15.21 1.419672 6.53&0 chr9:104794513-104794513 6549 120012 5.45695e-02 1.42624e-01 1.41915e-01 1.43467e-01 1.16009e-03 2.20264e-03 0.00000e+00 3.03774e-02 3.32064e-02 2.80995e-02 4.34923e-02 4.23216e-02 4.48276e-02 1.94938e-02 1.90616e-02 1.98718e-02 5.48214e-02 7.13668e-03 2.07469e-02 3.55191e-03 5.42801e-02 1.46216e-02 1.40642e-02 1.54189e-02 6.30682e-02 6.45161e-02 6.14458e-02 5.75621e-02 2.30235e-02 1.62037e-02 2.45989e-02 928609 917468 not_specified MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication ABCA1:19 SO:0001627&intron_variant 1 15 30 50.0 -9 104794512 T TAA AA splice_region_variant&intron_variant LOW ABCA1 19 Transcript XM_011518340.3 protein_coding 41/51 -1 EntrezGene 15.21 1.419672 6.53&0 chr9:104794513-104794513 6549 120012 5.45695e-02 1.42624e-01 1.41915e-01 1.43467e-01 1.16009e-03 2.20264e-03 0.00000e+00 3.03774e-02 3.32064e-02 2.80995e-02 4.34923e-02 4.23216e-02 4.48276e-02 1.94938e-02 1.90616e-02 1.98718e-02 5.48214e-02 7.13668e-03 2.07469e-02 3.55191e-03 5.42801e-02 1.46216e-02 1.40642e-02 1.54189e-02 6.30682e-02 6.45161e-02 6.14458e-02 5.75621e-02 2.30235e-02 1.62037e-02 2.45989e-02 928609 917468 not_specified MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication ABCA1:19 SO:0001627&intron_variant 1 15 30 50.0 -9 104794512 T TAA AA splice_region_variant&intron_variant LOW ABCA1 19 Transcript XM_011518341.3 protein_coding 40/50 -1 EntrezGene 15.21 1.419672 6.53&0 chr9:104794513-104794513 6549 120012 5.45695e-02 1.42624e-01 1.41915e-01 1.43467e-01 1.16009e-03 2.20264e-03 0.00000e+00 3.03774e-02 3.32064e-02 2.80995e-02 4.34923e-02 4.23216e-02 4.48276e-02 1.94938e-02 1.90616e-02 1.98718e-02 5.48214e-02 7.13668e-03 2.07469e-02 3.55191e-03 5.42801e-02 1.46216e-02 1.40642e-02 1.54189e-02 6.30682e-02 6.45161e-02 6.14458e-02 5.75621e-02 2.30235e-02 1.62037e-02 2.45989e-02 928609 917468 not_specified MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication ABCA1:19 SO:0001627&intron_variant 1 15 30 50.0 -9 104794512 T TAA AA splice_region_variant&intron_variant LOW ABCA1 19 Transcript XM_011518342.3 protein_coding 37/47 -1 EntrezGene 15.21 1.419672 6.53&0 chr9:104794513-104794513 6549 120012 5.45695e-02 1.42624e-01 1.41915e-01 1.43467e-01 1.16009e-03 2.20264e-03 0.00000e+00 3.03774e-02 3.32064e-02 2.80995e-02 4.34923e-02 4.23216e-02 4.48276e-02 1.94938e-02 1.90616e-02 1.98718e-02 5.48214e-02 7.13668e-03 2.07469e-02 3.55191e-03 5.42801e-02 1.46216e-02 1.40642e-02 1.54189e-02 6.30682e-02 6.45161e-02 6.14458e-02 5.75621e-02 2.30235e-02 1.62037e-02 2.45989e-02 928609 917468 not_specified MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication ABCA1:19 SO:0001627&intron_variant 1 15 30 50.0 -9 104794512 T TAA AA splice_region_variant&intron_variant LOW ABCA1 19 Transcript XM_017014378.2 protein_coding 40/50 -1 EntrezGene 15.21 1.419672 6.53&0 chr9:104794513-104794513 6549 120012 5.45695e-02 1.42624e-01 1.41915e-01 1.43467e-01 1.16009e-03 2.20264e-03 0.00000e+00 3.03774e-02 3.32064e-02 2.80995e-02 4.34923e-02 4.23216e-02 4.48276e-02 1.94938e-02 1.90616e-02 1.98718e-02 5.48214e-02 7.13668e-03 2.07469e-02 3.55191e-03 5.42801e-02 1.46216e-02 1.40642e-02 1.54189e-02 6.30682e-02 6.45161e-02 6.14458e-02 5.75621e-02 2.30235e-02 1.62037e-02 2.45989e-02 928609 917468 not_specified MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication ABCA1:19 SO:0001627&intron_variant 1 15 30 50.0 -9 104794512 T TAA AA splice_region_variant&intron_variant LOW ABCA1 19 Transcript XM_017014379.2 protein_coding 40/50 -1 EntrezGene 15.21 1.419672 6.53&0 chr9:104794513-104794513 6549 120012 5.45695e-02 1.42624e-01 1.41915e-01 1.43467e-01 1.16009e-03 2.20264e-03 0.00000e+00 3.03774e-02 3.32064e-02 2.80995e-02 4.34923e-02 4.23216e-02 4.48276e-02 1.94938e-02 1.90616e-02 1.98718e-02 5.48214e-02 7.13668e-03 2.07469e-02 3.55191e-03 5.42801e-02 1.46216e-02 1.40642e-02 1.54189e-02 6.30682e-02 6.45161e-02 6.14458e-02 5.75621e-02 2.30235e-02 1.62037e-02 2.45989e-02 928609 917468 not_specified MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication ABCA1:19 SO:0001627&intron_variant 1 15 30 50.0 -9 104794512 T TAA AA splice_region_variant&intron_variant LOW ABCA1 19 Transcript XM_017014380.2 protein_coding 40/50 -1 EntrezGene 15.21 1.419672 6.53&0 chr9:104794513-104794513 6549 120012 5.45695e-02 1.42624e-01 1.41915e-01 1.43467e-01 1.16009e-03 2.20264e-03 0.00000e+00 3.03774e-02 3.32064e-02 2.80995e-02 4.34923e-02 4.23216e-02 4.48276e-02 1.94938e-02 1.90616e-02 1.98718e-02 5.48214e-02 7.13668e-03 2.07469e-02 3.55191e-03 5.42801e-02 1.46216e-02 1.40642e-02 1.54189e-02 6.30682e-02 6.45161e-02 6.14458e-02 5.75621e-02 2.30235e-02 1.62037e-02 2.45989e-02 928609 917468 not_specified MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication ABCA1:19 SO:0001627&intron_variant 1 15 30 50.0 -9 104794512 T TAA AA splice_region_variant&intron_variant LOW ABCA1 19 Transcript XM_017014381.2 protein_coding 40/50 -1 EntrezGene 15.21 1.419672 6.53&0 chr9:104794513-104794513 6549 120012 5.45695e-02 1.42624e-01 1.41915e-01 1.43467e-01 1.16009e-03 2.20264e-03 0.00000e+00 3.03774e-02 3.32064e-02 2.80995e-02 4.34923e-02 4.23216e-02 4.48276e-02 1.94938e-02 1.90616e-02 1.98718e-02 5.48214e-02 7.13668e-03 2.07469e-02 3.55191e-03 5.42801e-02 1.46216e-02 1.40642e-02 1.54189e-02 6.30682e-02 6.45161e-02 6.14458e-02 5.75621e-02 2.30235e-02 1.62037e-02 2.45989e-02 928609 917468 not_specified MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication ABCA1:19 SO:0001627&intron_variant 1 15 30 50.0 -9 104794512 T TAA AA splice_region_variant&intron_variant LOW ABCA1 19 Transcript XM_017014382.2 protein_coding 38/48 -1 EntrezGene 15.21 1.419672 6.53&0 chr9:104794513-104794513 6549 120012 5.45695e-02 1.42624e-01 1.41915e-01 1.43467e-01 1.16009e-03 2.20264e-03 0.00000e+00 3.03774e-02 3.32064e-02 2.80995e-02 4.34923e-02 4.23216e-02 4.48276e-02 1.94938e-02 1.90616e-02 1.98718e-02 5.48214e-02 7.13668e-03 2.07469e-02 3.55191e-03 5.42801e-02 1.46216e-02 1.40642e-02 1.54189e-02 6.30682e-02 6.45161e-02 6.14458e-02 5.75621e-02 2.30235e-02 1.62037e-02 2.45989e-02 928609 917468 not_specified MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication ABCA1:19 SO:0001627&intron_variant 1 15 30 50.0 -9 104794512 T TAA AA splice_region_variant&intron_variant&non_coding_transcript_variant LOW ABCA1 19 Transcript XR_001746223.1 misc_RNA 40/48 -1 EntrezGene 15.21 1.419672 6.53&0 chr9:104794513-104794513 6549 120012 5.45695e-02 1.42624e-01 1.41915e-01 1.43467e-01 1.16009e-03 2.20264e-03 0.00000e+00 3.03774e-02 3.32064e-02 2.80995e-02 4.34923e-02 4.23216e-02 4.48276e-02 1.94938e-02 1.90616e-02 1.98718e-02 5.48214e-02 7.13668e-03 2.07469e-02 3.55191e-03 5.42801e-02 1.46216e-02 1.40642e-02 1.54189e-02 6.30682e-02 6.45161e-02 6.14458e-02 5.75621e-02 2.30235e-02 1.62037e-02 2.45989e-02 928609 917468 not_specified MedGen:CN169374 criteria_provided&_single_submitter Benign Duplication ABCA1:19 SO:0001627&intron_variant 1 15 30 50.0 -9 108899816 A G G splice_region_variant&intron_variant LOW ELP1 8518 Transcript NM_001318360.2 protein_coding 20/36 -1 EntrezGene A A 17.75 1.802146 4.05 rs111033171 72 143334 5.02323e-04 2.37767e-05 4.40257e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.31957e-05 0.00000e+00 1.29066e-04 1.83514e-02 1.58910e-02 2.11268e-02 0.00000e+00 0.00000e+00 0.00000e+00 4.19668e-04 9.54016e-05 0.00000e+00 1.25376e-04 5.90217e-04 9.29167e-05 2.67437e-05 1.83945e-04 9.30233e-04 9.12409e-04 9.48767e-04 5.02155e-04 0.00000e+00 0.00000e+00 0.00000e+00 6085 0.00065 21124 Familial_dysautonomia&Charcot-Marie-Tooth_disease¬_provided MONDO:MONDO:0009131&MedGen:C0013364&OMIM:223900&Orphanet:ORPHA1764&SNOMED_CT:29159009&MONDO:MONDO:0015626&MedGen:C0007959&OMIM:PS118220&Orphanet:ORPHA166&SNOMED_CT:50548001&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ELP1:8518 SO:0001627&intron_variant 1 111033171 15 30 50.0 -9 108899816 A G G splice_region_variant&intron_variant LOW ELP1 8518 Transcript NM_001330749.2 protein_coding 18/34 -1 EntrezGene A A 17.75 1.802146 4.05 rs111033171 72 143334 5.02323e-04 2.37767e-05 4.40257e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.31957e-05 0.00000e+00 1.29066e-04 1.83514e-02 1.58910e-02 2.11268e-02 0.00000e+00 0.00000e+00 0.00000e+00 4.19668e-04 9.54016e-05 0.00000e+00 1.25376e-04 5.90217e-04 9.29167e-05 2.67437e-05 1.83945e-04 9.30233e-04 9.12409e-04 9.48767e-04 5.02155e-04 0.00000e+00 0.00000e+00 0.00000e+00 6085 0.00065 21124 Familial_dysautonomia&Charcot-Marie-Tooth_disease¬_provided MONDO:MONDO:0009131&MedGen:C0013364&OMIM:223900&Orphanet:ORPHA1764&SNOMED_CT:29159009&MONDO:MONDO:0015626&MedGen:C0007959&OMIM:PS118220&Orphanet:ORPHA166&SNOMED_CT:50548001&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ELP1:8518 SO:0001627&intron_variant 1 111033171 15 30 50.0 -9 108899816 A G G splice_region_variant&intron_variant LOW ELP1 8518 Transcript NM_003640.5 protein_coding 20/36 -1 EntrezGene A A 17.75 1.802146 4.05 rs111033171 72 143334 5.02323e-04 2.37767e-05 4.40257e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.31957e-05 0.00000e+00 1.29066e-04 1.83514e-02 1.58910e-02 2.11268e-02 0.00000e+00 0.00000e+00 0.00000e+00 4.19668e-04 9.54016e-05 0.00000e+00 1.25376e-04 5.90217e-04 9.29167e-05 2.67437e-05 1.83945e-04 9.30233e-04 9.12409e-04 9.48767e-04 5.02155e-04 0.00000e+00 0.00000e+00 0.00000e+00 6085 0.00065 21124 Familial_dysautonomia&Charcot-Marie-Tooth_disease¬_provided MONDO:MONDO:0009131&MedGen:C0013364&OMIM:223900&Orphanet:ORPHA1764&SNOMED_CT:29159009&MONDO:MONDO:0015626&MedGen:C0007959&OMIM:PS118220&Orphanet:ORPHA166&SNOMED_CT:50548001&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ELP1:8518 SO:0001627&intron_variant 1 111033171 15 30 50.0 -9 108899816 A G G splice_region_variant&intron_variant LOW ELP1 8518 Transcript XM_011519136.2 protein_coding 20/36 -1 EntrezGene A A 17.75 1.802146 4.05 rs111033171 72 143334 5.02323e-04 2.37767e-05 4.40257e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.31957e-05 0.00000e+00 1.29066e-04 1.83514e-02 1.58910e-02 2.11268e-02 0.00000e+00 0.00000e+00 0.00000e+00 4.19668e-04 9.54016e-05 0.00000e+00 1.25376e-04 5.90217e-04 9.29167e-05 2.67437e-05 1.83945e-04 9.30233e-04 9.12409e-04 9.48767e-04 5.02155e-04 0.00000e+00 0.00000e+00 0.00000e+00 6085 0.00065 21124 Familial_dysautonomia&Charcot-Marie-Tooth_disease¬_provided MONDO:MONDO:0009131&MedGen:C0013364&OMIM:223900&Orphanet:ORPHA1764&SNOMED_CT:29159009&MONDO:MONDO:0015626&MedGen:C0007959&OMIM:PS118220&Orphanet:ORPHA166&SNOMED_CT:50548001&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ELP1:8518 SO:0001627&intron_variant 1 111033171 15 30 50.0 -9 108899816 A G G splice_region_variant&intron_variant LOW ELP1 8518 Transcript XM_011519137.1 protein_coding 20/36 -1 EntrezGene A A 17.75 1.802146 4.05 rs111033171 72 143334 5.02323e-04 2.37767e-05 4.40257e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.31957e-05 0.00000e+00 1.29066e-04 1.83514e-02 1.58910e-02 2.11268e-02 0.00000e+00 0.00000e+00 0.00000e+00 4.19668e-04 9.54016e-05 0.00000e+00 1.25376e-04 5.90217e-04 9.29167e-05 2.67437e-05 1.83945e-04 9.30233e-04 9.12409e-04 9.48767e-04 5.02155e-04 0.00000e+00 0.00000e+00 0.00000e+00 6085 0.00065 21124 Familial_dysautonomia&Charcot-Marie-Tooth_disease¬_provided MONDO:MONDO:0009131&MedGen:C0013364&OMIM:223900&Orphanet:ORPHA1764&SNOMED_CT:29159009&MONDO:MONDO:0015626&MedGen:C0007959&OMIM:PS118220&Orphanet:ORPHA166&SNOMED_CT:50548001&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ELP1:8518 SO:0001627&intron_variant 1 111033171 15 30 50.0 -9 108899816 A G G splice_region_variant&intron_variant&non_coding_transcript_variant LOW ELP1 8518 Transcript XR_929859.3 misc_RNA 20/36 -1 EntrezGene A A 17.75 1.802146 4.05 rs111033171 72 143334 5.02323e-04 2.37767e-05 4.40257e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.31957e-05 0.00000e+00 1.29066e-04 1.83514e-02 1.58910e-02 2.11268e-02 0.00000e+00 0.00000e+00 0.00000e+00 4.19668e-04 9.54016e-05 0.00000e+00 1.25376e-04 5.90217e-04 9.29167e-05 2.67437e-05 1.83945e-04 9.30233e-04 9.12409e-04 9.48767e-04 5.02155e-04 0.00000e+00 0.00000e+00 0.00000e+00 6085 0.00065 21124 Familial_dysautonomia&Charcot-Marie-Tooth_disease¬_provided MONDO:MONDO:0009131&MedGen:C0013364&OMIM:223900&Orphanet:ORPHA1764&SNOMED_CT:29159009&MONDO:MONDO:0015626&MedGen:C0007959&OMIM:PS118220&Orphanet:ORPHA166&SNOMED_CT:50548001&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ELP1:8518 SO:0001627&intron_variant 1 111033171 15 30 50.0 -9 108900303 C G G missense_variant MODERATE ELP1 8518 Transcript NM_001318360.2 protein_coding 19/37 2258 1745 582 R/P cGg/cCg -1 EntrezGene C C 0 1 27.8 4.106990 27.8 0.99762160686477885 10.23535 0.803741598428636 0.90054 10.34074 0.871540240210268 0.90297 1.52&1.52 5.24 0.999999999999669 0.000000 0.088499 0.2615 -0.4398 3.655&. 0.999435&0.999435 -4.82&-4.12 16.6948 0.963&0.988 0.98573 0.706548 1.000000 0.999000 5.579000 1.026000 6.47 6086 21125 Familial_dysautonomia&Charcot-Marie-Tooth_disease MONDO:MONDO:0009131&MedGen:C0013364&OMIM:223900&Orphanet:ORPHA1764&SNOMED_CT:29159009&MONDO:MONDO:0015626&MedGen:C0007959&OMIM:PS118220&Orphanet:ORPHA166&SNOMED_CT:50548001 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant ELP1:8518 SO:0001583&missense_variant 1 137853022 15 30 50.0 -9 108900303 C G G missense_variant MODERATE ELP1 8518 Transcript NM_001330749.2 protein_coding 17/35 2215 1040 347 R/P cGg/cCg -1 EntrezGene C C 0 1 27.8 4.106990 27.8 0.99762160686477885 10.23535 0.803741598428636 0.90054 10.34074 0.871540240210268 0.90297 1.52&1.52 5.24 0.999999999999669 0.000000 0.088499 0.2615 -0.4398 3.655&. 0.999435&0.999435 -4.82&-4.12 16.6948 0.963&0.988 0.98573 0.706548 1.000000 0.999000 5.579000 1.026000 6.47 6086 21125 Familial_dysautonomia&Charcot-Marie-Tooth_disease MONDO:MONDO:0009131&MedGen:C0013364&OMIM:223900&Orphanet:ORPHA1764&SNOMED_CT:29159009&MONDO:MONDO:0015626&MedGen:C0007959&OMIM:PS118220&Orphanet:ORPHA166&SNOMED_CT:50548001 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant ELP1:8518 SO:0001583&missense_variant 1 137853022 15 30 50.0 -9 108900303 C G G missense_variant MODERATE ELP1 8518 Transcript NM_003640.5 protein_coding 19/37 2403 2087 696 R/P cGg/cCg -1 EntrezGene C C 0 1 27.8 4.106990 27.8 0.99762160686477885 10.23535 0.803741598428636 0.90054 10.34074 0.871540240210268 0.90297 1.52&1.52 5.24 0.999999999999669 0.000000 0.088499 0.2615 -0.4398 3.655&. 0.999435&0.999435 -4.82&-4.12 16.6948 0.963&0.988 0.98573 0.706548 1.000000 0.999000 5.579000 1.026000 6.47 6086 21125 Familial_dysautonomia&Charcot-Marie-Tooth_disease MONDO:MONDO:0009131&MedGen:C0013364&OMIM:223900&Orphanet:ORPHA1764&SNOMED_CT:29159009&MONDO:MONDO:0015626&MedGen:C0007959&OMIM:PS118220&Orphanet:ORPHA166&SNOMED_CT:50548001 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant ELP1:8518 SO:0001583&missense_variant 1 137853022 15 30 50.0 -9 108900303 C G G missense_variant MODERATE ELP1 8518 Transcript XM_011519136.2 protein_coding 19/37 2414 2087 696 R/P cGg/cCg -1 EntrezGene C C 0 0.943 27.8 4.106990 27.8 0.99762160686477885 10.23535 0.803741598428636 0.90054 10.34074 0.871540240210268 0.90297 1.52&1.52 5.24 0.999999999999669 0.000000 0.088499 0.2615 -0.4398 3.655&. 0.999435&0.999435 -4.82&-4.12 16.6948 0.963&0.988 0.98573 0.706548 1.000000 0.999000 5.579000 1.026000 6.47 6086 21125 Familial_dysautonomia&Charcot-Marie-Tooth_disease MONDO:MONDO:0009131&MedGen:C0013364&OMIM:223900&Orphanet:ORPHA1764&SNOMED_CT:29159009&MONDO:MONDO:0015626&MedGen:C0007959&OMIM:PS118220&Orphanet:ORPHA166&SNOMED_CT:50548001 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant ELP1:8518 SO:0001583&missense_variant 1 137853022 15 30 50.0 -9 108900303 C G G missense_variant MODERATE ELP1 8518 Transcript XM_011519137.1 protein_coding 19/37 2177 1745 582 R/P cGg/cCg -1 EntrezGene C C 0 0.923 27.8 4.106990 27.8 0.99762160686477885 10.23535 0.803741598428636 0.90054 10.34074 0.871540240210268 0.90297 1.52&1.52 5.24 0.999999999999669 0.000000 0.088499 0.2615 -0.4398 3.655&. 0.999435&0.999435 -4.82&-4.12 16.6948 0.963&0.988 0.98573 0.706548 1.000000 0.999000 5.579000 1.026000 6.47 6086 21125 Familial_dysautonomia&Charcot-Marie-Tooth_disease MONDO:MONDO:0009131&MedGen:C0013364&OMIM:223900&Orphanet:ORPHA1764&SNOMED_CT:29159009&MONDO:MONDO:0015626&MedGen:C0007959&OMIM:PS118220&Orphanet:ORPHA166&SNOMED_CT:50548001 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant ELP1:8518 SO:0001583&missense_variant 1 137853022 15 30 50.0 -9 108900303 C G G non_coding_transcript_exon_variant MODIFIER ELP1 8518 Transcript XR_929859.3 misc_RNA 19/37 2414 -1 EntrezGene C C 27.8 4.106990 6.47 6086 21125 Familial_dysautonomia&Charcot-Marie-Tooth_disease MONDO:MONDO:0009131&MedGen:C0013364&OMIM:223900&Orphanet:ORPHA1764&SNOMED_CT:29159009&MONDO:MONDO:0015626&MedGen:C0007959&OMIM:PS118220&Orphanet:ORPHA166&SNOMED_CT:50548001 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant ELP1:8518 SO:0001583&missense_variant 1 137853022 15 30 50.0 -9 131764714 T TTCCTCCTCCTCCTCGTCCTCCTCCTCCTCG TCCTCCTCCTCCTCGTCCTCCTCCTCCTCG intergenic_variant MODIFIER 2.652 0.157270 -1.25&2.63 chr9:131764715-131764715 39998 133746 2.99059e-01 3.37739e-01 3.37716e-01 3.37766e-01 1.39140e-01 1.34783e-01 1.43868e-01 3.70391e-01 3.82914e-01 3.60845e-01 1.81818e-01 1.84354e-01 1.78906e-01 3.44482e-01 3.57550e-01 3.32913e-01 3.03435e-01 2.18802e-01 2.18401e-01 2.18929e-01 2.94364e-01 2.76226e-01 2.79968e-01 2.71063e-01 3.10070e-01 3.18798e-01 3.00821e-01 3.00841e-01 2.90232e-01 2.75210e-01 2.93769e-01 15 30 50.0 -9 133454548 C T T missense_variant MODERATE ADAMTS13 11093 Transcript NM_139025.5 protein_coding 24/29 3255 3178 1060 R/W Cgg/Tgg 1 EntrezGene C C 0 0.915 24.2 3.344595 24.2 0.9989525273943205 3.300916 0.223590908723831 0.51160 4.322379 0.396636622914473 0.61239 0.04&0.04&0.04 3.17 0.986196307515833 0.272392 0.4563 -0.0193 2.87&2.87&. 0.999834&0.933707&0.933707&0.933707 -6.13&-5.57&-5.44 10.1638 0.406&0.358&0.367 0.82208 0.706298 0.970000 0.005000 3.185000 1.026000 5.44 chr9:133454548-133454548 125 143342 8.72040e-04 5.23087e-04 5.72385e-04 4.65212e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.78349e-04 1.69147e-03 2.58065e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.19081e-04 0.00000e+00 5.93120e-04 1.09655e-03 1.90803e-04 3.99042e-04 1.25376e-04 6.33330e-04 1.34712e-03 1.52431e-03 1.10343e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.71773e-04 3.27869e-04 0.00000e+00 4.02576e-04 68815 0.00077 0.00095 0.00080 79706 Upshaw-Schulman_syndrome¬_provided MONDO:MONDO:0010122&MedGen:C1268935&OMIM:274150&Orphanet:ORPHA93583&SNOMED_CT:373420004&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant ADAMTS13:11093 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 142572218 15 30 50.0 -9 133454548 C T T missense_variant MODERATE ADAMTS13 11093 Transcript NM_139026.6 protein_coding 24/29 3162 3085 1029 R/W Cgg/Tgg 1 EntrezGene C C 0 0.949 24.2 3.344595 24.2 0.9989525273943205 3.300916 0.223590908723831 0.51160 4.322379 0.396636622914473 0.61239 0.04&0.04&0.04 3.17 0.986196307515833 0.272392 0.4563 -0.0193 2.87&2.87&. 0.999834&0.933707&0.933707&0.933707 -6.13&-5.57&-5.44 10.1638 0.406&0.358&0.367 0.82208 0.706298 0.970000 0.005000 3.185000 1.026000 5.44 chr9:133454548-133454548 125 143342 8.72040e-04 5.23087e-04 5.72385e-04 4.65212e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.78349e-04 1.69147e-03 2.58065e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.19081e-04 0.00000e+00 5.93120e-04 1.09655e-03 1.90803e-04 3.99042e-04 1.25376e-04 6.33330e-04 1.34712e-03 1.52431e-03 1.10343e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.71773e-04 3.27869e-04 0.00000e+00 4.02576e-04 68815 0.00077 0.00095 0.00080 79706 Upshaw-Schulman_syndrome¬_provided MONDO:MONDO:0010122&MedGen:C1268935&OMIM:274150&Orphanet:ORPHA93583&SNOMED_CT:373420004&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant ADAMTS13:11093 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 142572218 15 30 50.0 -9 133454548 C T T missense_variant MODERATE ADAMTS13 11093 Transcript NM_139027.6 protein_coding 24/29 3255 3178 1060 R/W Cgg/Tgg 1 EntrezGene C C 0 0.931 24.2 3.344595 24.2 0.9989525273943205 3.300916 0.223590908723831 0.51160 4.322379 0.396636622914473 0.61239 0.04&0.04&0.04 3.17 0.986196307515833 0.272392 0.4563 -0.0193 2.87&2.87&. 0.999834&0.933707&0.933707&0.933707 -6.13&-5.57&-5.44 10.1638 0.406&0.358&0.367 0.82208 0.706298 0.970000 0.005000 3.185000 1.026000 5.44 chr9:133454548-133454548 125 143342 8.72040e-04 5.23087e-04 5.72385e-04 4.65212e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.78349e-04 1.69147e-03 2.58065e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.19081e-04 0.00000e+00 5.93120e-04 1.09655e-03 1.90803e-04 3.99042e-04 1.25376e-04 6.33330e-04 1.34712e-03 1.52431e-03 1.10343e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.71773e-04 3.27869e-04 0.00000e+00 4.02576e-04 68815 0.00077 0.00095 0.00080 79706 Upshaw-Schulman_syndrome¬_provided MONDO:MONDO:0010122&MedGen:C1268935&OMIM:274150&Orphanet:ORPHA93583&SNOMED_CT:373420004&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant ADAMTS13:11093 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 142572218 15 30 50.0 -9 133454548 C T T non_coding_transcript_exon_variant MODIFIER ADAMTS13 11093 Transcript NR_024514.3 misc_RNA 14/18 2015 1 EntrezGene C C 24.2 3.344595 5.44 chr9:133454548-133454548 125 143342 8.72040e-04 5.23087e-04 5.72385e-04 4.65212e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.78349e-04 1.69147e-03 2.58065e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.19081e-04 0.00000e+00 5.93120e-04 1.09655e-03 1.90803e-04 3.99042e-04 1.25376e-04 6.33330e-04 1.34712e-03 1.52431e-03 1.10343e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.71773e-04 3.27869e-04 0.00000e+00 4.02576e-04 68815 0.00077 0.00095 0.00080 79706 Upshaw-Schulman_syndrome¬_provided MONDO:MONDO:0010122&MedGen:C1268935&OMIM:274150&Orphanet:ORPHA93583&SNOMED_CT:373420004&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant ADAMTS13:11093 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 142572218 15 30 50.0 -9 133454548 C T T missense_variant MODERATE ADAMTS13 11093 Transcript XM_011518176.3 protein_coding 17/22 2274 2194 732 R/W Cgg/Tgg 1 EntrezGene C C 24.2 3.344595 24.2 0.9989525273943205 3.300916 0.223590908723831 0.51160 4.322379 0.396636622914473 0.61239 0.04&0.04&0.04 3.17 0.986196307515833 0.272392 0.4563 -0.0193 2.87&2.87&. 0.999834&0.933707&0.933707&0.933707 -6.13&-5.57&-5.44 10.1638 0.406&0.358&0.367 0.82208 0.706298 0.970000 0.005000 3.185000 1.026000 5.44 chr9:133454548-133454548 125 143342 8.72040e-04 5.23087e-04 5.72385e-04 4.65212e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.78349e-04 1.69147e-03 2.58065e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.19081e-04 0.00000e+00 5.93120e-04 1.09655e-03 1.90803e-04 3.99042e-04 1.25376e-04 6.33330e-04 1.34712e-03 1.52431e-03 1.10343e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.71773e-04 3.27869e-04 0.00000e+00 4.02576e-04 68815 0.00077 0.00095 0.00080 79706 Upshaw-Schulman_syndrome¬_provided MONDO:MONDO:0010122&MedGen:C1268935&OMIM:274150&Orphanet:ORPHA93583&SNOMED_CT:373420004&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant ADAMTS13:11093 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 142572218 15 30 50.0 -9 133454548 C T T missense_variant MODERATE ADAMTS13 11093 Transcript XM_011518178.2 protein_coding 14/19 1948 1843 615 R/W Cgg/Tgg 1 EntrezGene C C 24.2 3.344595 24.2 0.9989525273943205 3.300916 0.223590908723831 0.51160 4.322379 0.396636622914473 0.61239 0.04&0.04&0.04 3.17 0.986196307515833 0.272392 0.4563 -0.0193 2.87&2.87&. 0.999834&0.933707&0.933707&0.933707 -6.13&-5.57&-5.44 10.1638 0.406&0.358&0.367 0.82208 0.706298 0.970000 0.005000 3.185000 1.026000 5.44 chr9:133454548-133454548 125 143342 8.72040e-04 5.23087e-04 5.72385e-04 4.65212e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.78349e-04 1.69147e-03 2.58065e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.19081e-04 0.00000e+00 5.93120e-04 1.09655e-03 1.90803e-04 3.99042e-04 1.25376e-04 6.33330e-04 1.34712e-03 1.52431e-03 1.10343e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.71773e-04 3.27869e-04 0.00000e+00 4.02576e-04 68815 0.00077 0.00095 0.00080 79706 Upshaw-Schulman_syndrome¬_provided MONDO:MONDO:0010122&MedGen:C1268935&OMIM:274150&Orphanet:ORPHA93583&SNOMED_CT:373420004&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant ADAMTS13:11093 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 142572218 15 30 50.0 -9 133454548 C T T missense_variant MODERATE ADAMTS13 11093 Transcript XM_011518179.1 protein_coding 14/19 1991 1843 615 R/W Cgg/Tgg 1 EntrezGene C C 24.2 3.344595 24.2 0.9989525273943205 3.300916 0.223590908723831 0.51160 4.322379 0.396636622914473 0.61239 0.04&0.04&0.04 3.17 0.986196307515833 0.272392 0.4563 -0.0193 2.87&2.87&. 0.999834&0.933707&0.933707&0.933707 -6.13&-5.57&-5.44 10.1638 0.406&0.358&0.367 0.82208 0.706298 0.970000 0.005000 3.185000 1.026000 5.44 chr9:133454548-133454548 125 143342 8.72040e-04 5.23087e-04 5.72385e-04 4.65212e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.78349e-04 1.69147e-03 2.58065e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.19081e-04 0.00000e+00 5.93120e-04 1.09655e-03 1.90803e-04 3.99042e-04 1.25376e-04 6.33330e-04 1.34712e-03 1.52431e-03 1.10343e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.71773e-04 3.27869e-04 0.00000e+00 4.02576e-04 68815 0.00077 0.00095 0.00080 79706 Upshaw-Schulman_syndrome¬_provided MONDO:MONDO:0010122&MedGen:C1268935&OMIM:274150&Orphanet:ORPHA93583&SNOMED_CT:373420004&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant ADAMTS13:11093 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 142572218 15 30 50.0 -9 133454548 C T T missense_variant MODERATE ADAMTS13 11093 Transcript XM_017014232.1 protein_coding 24/29 3833 3166 1056 R/W Cgg/Tgg 1 EntrezGene C C 24.2 3.344595 24.2 0.9989525273943205 3.300916 0.223590908723831 0.51160 4.322379 0.396636622914473 0.61239 0.04&0.04&0.04 3.17 0.986196307515833 0.272392 0.4563 -0.0193 2.87&2.87&. 0.999834&0.933707&0.933707&0.933707 -6.13&-5.57&-5.44 10.1638 0.406&0.358&0.367 0.82208 0.706298 0.970000 0.005000 3.185000 1.026000 5.44 chr9:133454548-133454548 125 143342 8.72040e-04 5.23087e-04 5.72385e-04 4.65212e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.78349e-04 1.69147e-03 2.58065e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.19081e-04 0.00000e+00 5.93120e-04 1.09655e-03 1.90803e-04 3.99042e-04 1.25376e-04 6.33330e-04 1.34712e-03 1.52431e-03 1.10343e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.71773e-04 3.27869e-04 0.00000e+00 4.02576e-04 68815 0.00077 0.00095 0.00080 79706 Upshaw-Schulman_syndrome¬_provided MONDO:MONDO:0010122&MedGen:C1268935&OMIM:274150&Orphanet:ORPHA93583&SNOMED_CT:373420004&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant ADAMTS13:11093 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 142572218 15 30 50.0 -9 133454548 C T T missense_variant MODERATE ADAMTS13 11093 Transcript XM_017014233.1 protein_coding 24/29 3234 2788 930 R/W Cgg/Tgg 1 EntrezGene C C 24.2 3.344595 24.2 0.9989525273943205 3.300916 0.223590908723831 0.51160 4.322379 0.396636622914473 0.61239 0.04&0.04&0.04 3.17 0.986196307515833 0.272392 0.4563 -0.0193 2.87&2.87&. 0.999834&0.933707&0.933707&0.933707 -6.13&-5.57&-5.44 10.1638 0.406&0.358&0.367 0.82208 0.706298 0.970000 0.005000 3.185000 1.026000 5.44 chr9:133454548-133454548 125 143342 8.72040e-04 5.23087e-04 5.72385e-04 4.65212e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.78349e-04 1.69147e-03 2.58065e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.19081e-04 0.00000e+00 5.93120e-04 1.09655e-03 1.90803e-04 3.99042e-04 1.25376e-04 6.33330e-04 1.34712e-03 1.52431e-03 1.10343e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.71773e-04 3.27869e-04 0.00000e+00 4.02576e-04 68815 0.00077 0.00095 0.00080 79706 Upshaw-Schulman_syndrome¬_provided MONDO:MONDO:0010122&MedGen:C1268935&OMIM:274150&Orphanet:ORPHA93583&SNOMED_CT:373420004&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant ADAMTS13:11093 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 142572218 15 30 50.0 -9 133454548 C T T missense_variant MODERATE ADAMTS13 11093 Transcript XM_017014234.2 protein_coding 18/23 2422 2188 730 R/W Cgg/Tgg 1 EntrezGene C C 24.2 3.344595 24.2 0.9989525273943205 3.300916 0.223590908723831 0.51160 4.322379 0.396636622914473 0.61239 0.04&0.04&0.04 3.17 0.986196307515833 0.272392 0.4563 -0.0193 2.87&2.87&. 0.999834&0.933707&0.933707&0.933707 -6.13&-5.57&-5.44 10.1638 0.406&0.358&0.367 0.82208 0.706298 0.970000 0.005000 3.185000 1.026000 5.44 chr9:133454548-133454548 125 143342 8.72040e-04 5.23087e-04 5.72385e-04 4.65212e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.78349e-04 1.69147e-03 2.58065e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.19081e-04 0.00000e+00 5.93120e-04 1.09655e-03 1.90803e-04 3.99042e-04 1.25376e-04 6.33330e-04 1.34712e-03 1.52431e-03 1.10343e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.71773e-04 3.27869e-04 0.00000e+00 4.02576e-04 68815 0.00077 0.00095 0.00080 79706 Upshaw-Schulman_syndrome¬_provided MONDO:MONDO:0010122&MedGen:C1268935&OMIM:274150&Orphanet:ORPHA93583&SNOMED_CT:373420004&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant ADAMTS13:11093 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 142572218 15 30 50.0 -9 133454548 C T T downstream_gene_variant MODIFIER ADAMTS13 11093 Transcript XR_001746171.1 misc_RNA 82 1 EntrezGene C C 24.2 3.344595 5.44 chr9:133454548-133454548 125 143342 8.72040e-04 5.23087e-04 5.72385e-04 4.65212e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.78349e-04 1.69147e-03 2.58065e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.19081e-04 0.00000e+00 5.93120e-04 1.09655e-03 1.90803e-04 3.99042e-04 1.25376e-04 6.33330e-04 1.34712e-03 1.52431e-03 1.10343e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.71773e-04 3.27869e-04 0.00000e+00 4.02576e-04 68815 0.00077 0.00095 0.00080 79706 Upshaw-Schulman_syndrome¬_provided MONDO:MONDO:0010122&MedGen:C1268935&OMIM:274150&Orphanet:ORPHA93583&SNOMED_CT:373420004&MedGen:CN517202 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant ADAMTS13:11093 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 142572218 15 30 50.0 -9 136687357 T TGGCCC GGCCC 5_prime_UTR_variant MODIFIER AGPAT2 10555 Transcript NM_001012727.2 protein_coding 1/5 100-101 -1 EntrezGene 16.54 1.626740 2.5&1.8 rs886063723 3 143014 2.09770e-05 2.38231e-05 4.41151e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.01205e-04 5.68182e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.71312e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44304e-05 1.55140e-05 0.00000e+00 3.68732e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.09325e-05 0.00000e+00 0.00000e+00 0.00000e+00 365935 311934 Congenital_generalized_lipodystrophy_(disease) Human_Phenotype_Ontology:HP:0009059&MONDO:MONDO:0006536&MedGen:C0221032&OMIM:PS608594 criteria_provided&_single_submitter Uncertain_significance Microsatellite AGPAT2:10555 SO:0001589&frameshift_variant 1 886063723 15 30 50.0 -9 136687357 T TGGCCC GGCCC 5_prime_UTR_variant MODIFIER AGPAT2 10555 Transcript NM_006412.4 protein_coding 1/6 100-101 -1 EntrezGene 16.54 1.626740 2.5&1.8 rs886063723 3 143014 2.09770e-05 2.38231e-05 4.41151e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.01205e-04 5.68182e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.71312e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44304e-05 1.55140e-05 0.00000e+00 3.68732e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.09325e-05 0.00000e+00 0.00000e+00 0.00000e+00 365935 311934 Congenital_generalized_lipodystrophy_(disease) Human_Phenotype_Ontology:HP:0009059&MONDO:MONDO:0006536&MedGen:C0221032&OMIM:PS608594 criteria_provided&_single_submitter Uncertain_significance Microsatellite AGPAT2:10555 SO:0001589&frameshift_variant 1 886063723 15 30 50.0 -9 137007944 CGTG GTCTGCGGTA GTCTGCGGTA inframe_insertion MODERATE ABCA2 20 Transcript NM_001606.5 protein_coding 49/49 7390-7393 7293-7296 2431-2432 NT/NTAD aaCACG/aaTACCGCAGAC -1 EntrezGene CGTG CGTG -2.93&6.25&3.49 15 30 50.0 -9 137007944 CGTG GTCTGCGGTA GTCTGCGGTA inframe_insertion MODERATE ABCA2 20 Transcript NM_212533.3 protein_coding 49/49 7433-7436 7383-7386 2461-2462 NT/NTAD aaCACG/aaTACCGCAGAC -1 EntrezGene CGTG CGTG -2.93&6.25&3.49 15 30 50.0 -9 137007944 CGTG GTCTGCGGTA GTCTGCGGTA inframe_insertion MODERATE ABCA2 20 Transcript XM_006716996.4 protein_coding 48/48 7290-7293 7290-7293 2430-2431 NT/NTAD aaCACG/aaTACCGCAGAC -1 EntrezGene CGTG CGTG -2.93&6.25&3.49 15 30 50.0 -9 137007944 CGTG GTCTGCGGTA GTCTGCGGTA non_coding_transcript_exon_variant MODIFIER ABCA2 20 Transcript XR_001746224.1 misc_RNA 47/47 7497-7500 -1 EntrezGene CGTG CGTG -2.93&6.25&3.49 15 30 50.0 -9 137028134 AGC TTT TTT missense_variant MODERATE ABCA2 20 Transcript NM_001606.5 protein_coding 1/49 102-104 5-7 2-3 GF/EI gGCTtc/gAAAtc -1 EntrezGene AGC AGC 2.8&1.89&2.8 15 30 50.0 -9 137028134 AGC TTT TTT downstream_gene_variant MODIFIER FUT7 2529 Transcript NM_004479.4 protein_coding 2038 -1 EntrezGene AGC AGC 2.8&1.89&2.8 15 30 50.0 -9 137028134 AGC TTT TTT intron_variant MODIFIER C9orf139 401563 Transcript NM_207511.2 protein_coding 1/2 1 EntrezGene AGC AGC 2.8&1.89&2.8 15 30 50.0 -9 137028134 AGC TTT TTT intron_variant MODIFIER ABCA2 20 Transcript NM_212533.3 protein_coding 1/48 -1 EntrezGene AGC AGC 2.8&1.89&2.8 15 30 50.0 -9 137028134 AGC TTT TTT missense_variant MODERATE ABCA2 20 Transcript XM_006716996.4 protein_coding 1/48 5-7 5-7 2-3 GF/EI gGCTtc/gAAAtc -1 EntrezGene AGC AGC 2.8&1.89&2.8 15 30 50.0 -9 137028134 AGC TTT TTT upstream_gene_variant MODIFIER C9orf139 401563 Transcript XM_017014717.1 protein_coding 3913 1 EntrezGene AGC AGC 2.8&1.89&2.8 15 30 50.0 -9 137028134 AGC TTT TTT intron_variant&non_coding_transcript_variant MODIFIER ABCA2 20 Transcript XR_001746224.1 misc_RNA 1/46 -1 EntrezGene AGC AGC 2.8&1.89&2.8 15 30 50.0 -9 137028134 AGC TTT TTT upstream_gene_variant MODIFIER C9orf139 401563 Transcript XR_001746294.1 misc_RNA 4440 1 EntrezGene AGC AGC 2.8&1.89&2.8 15 30 50.0 -9 137028134 AGC TTT TTT upstream_gene_variant MODIFIER C9orf139 401563 Transcript XR_929799.1 misc_RNA 4440 1 EntrezGene AGC AGC 2.8&1.89&2.8 15 30 50.0 -9 137879160 G GACGACACGGAGCCCTATTTCATCGGGATCTTTTGCTTCGAGGCAGGGATCAAAATCATCGC ACGACACGGAGCCCTATTTCATCGGGATCTTTTGCTTCGAGGCAGGGATCAAAATCATCGC splice_donor_variant HIGH CACNA1B 774 Transcript NM_000718.4 protein_coding 2/46 1 EntrezGene 6 15 30 50.0 -9 137879160 G GACGACACGGAGCCCTATTTCATCGGGATCTTTTGCTTCGAGGCAGGGATCAAAATCATCGC ACGACACGGAGCCCTATTTCATCGGGATCTTTTGCTTCGAGGCAGGGATCAAAATCATCGC splice_donor_variant HIGH CACNA1B 774 Transcript NM_001243812.2 protein_coding 2/46 1 EntrezGene 6 15 30 50.0 -9 137879160 G GACGACACGGAGCCCTATTTCATCGGGATCTTTTGCTTCGAGGCAGGGATCAAAATCATCGC ACGACACGGAGCCCTATTTCATCGGGATCTTTTGCTTCGAGGCAGGGATCAAAATCATCGC intron_variant&non_coding_transcript_variant MODIFIER LOC100133077 100133077 Transcript NR_121583.1 lncRNA 2/3 -1 EntrezGene 6 15 30 50.0 -10 17071457 C T T missense_variant MODERATE CUBN 8029 Transcript NM_001081.4 protein_coding 19/67 2640 2594 865 S/N aGt/aAt -1 EntrezGene C C 0.48 0.006 0.710 -0.132458 0.710 0.86535495772032101 0.4221397 -1.05645910652824 0.08572 0.3291508 -1.07499693678541 0.07107 2.24 -4.35 0.990266353850165 0.140464 0.0131 -0.9833 0.695 1 0.38 11.8837 0.659 0.03089 0.559995 0.000000 0.965000 -0.871000 0.103000 -6.02 chr10:17071457-17071457 1066 143198 7.44424e-03 2.07182e-03 2.11696e-03 2.01884e-03 3.00000e-02 2.76596e-02 3.25581e-02 3.81064e-03 4.40230e-03 3.35917e-03 6.91937e-03 6.24291e-03 7.68246e-03 0.00000e+00 0.00000e+00 0.00000e+00 7.57412e-03 5.26921e-03 5.19584e-03 5.29234e-03 7.30611e-03 1.24048e-02 1.17959e-02 1.32421e-02 6.51769e-03 6.39854e-03 6.64137e-03 7.44394e-03 2.29961e-03 0.00000e+00 2.82031e-03 265086 0.00846 0.00319 259935 Imerslund-Gräsbeck_syndrome&Imerslund-Gräsbeck_syndrome_1¬_specified MONDO:MONDO:0009853&MedGen:C4551825&OMIM:PS261100&Orphanet:ORPHA35858&SNOMED_CT:34925000&MONDO:MONDO:0100156&MedGen:C4016819&OMIM:261100&MedGen:CN169374 criteria_provided&_multiple_submitters&_no_conflicts Benign/Likely_benign single_nucleotide_variant CUBN:8029 SO:0001583&missense_variant 1 138083522 15 30 50.0 -10 17071457 C T T missense_variant MODERATE CUBN 8029 Transcript XM_011519708.2 protein_coding 19/55 3321 2594 865 S/N aGt/aAt -1 EntrezGene C C 0.710 -0.132458 0.710 0.86535495772032101 0.4221397 -1.05645910652824 0.08572 0.3291508 -1.07499693678541 0.07107 2.24 -4.35 0.990266353850165 0.140464 0.0131 -0.9833 0.695 1 0.38 11.8837 0.659 0.03089 0.559995 0.000000 0.965000 -0.871000 0.103000 -6.02 chr10:17071457-17071457 1066 143198 7.44424e-03 2.07182e-03 2.11696e-03 2.01884e-03 3.00000e-02 2.76596e-02 3.25581e-02 3.81064e-03 4.40230e-03 3.35917e-03 6.91937e-03 6.24291e-03 7.68246e-03 0.00000e+00 0.00000e+00 0.00000e+00 7.57412e-03 5.26921e-03 5.19584e-03 5.29234e-03 7.30611e-03 1.24048e-02 1.17959e-02 1.32421e-02 6.51769e-03 6.39854e-03 6.64137e-03 7.44394e-03 2.29961e-03 0.00000e+00 2.82031e-03 265086 0.00846 0.00319 259935 Imerslund-Gräsbeck_syndrome&Imerslund-Gräsbeck_syndrome_1¬_specified MONDO:MONDO:0009853&MedGen:C4551825&OMIM:PS261100&Orphanet:ORPHA35858&SNOMED_CT:34925000&MONDO:MONDO:0100156&MedGen:C4016819&OMIM:261100&MedGen:CN169374 criteria_provided&_multiple_submitters&_no_conflicts Benign/Likely_benign single_nucleotide_variant CUBN:8029 SO:0001583&missense_variant 1 138083522 15 30 50.0 -10 27034798 CTCTT C - frameshift_variant HIGH ANKRD26 22852 Transcript NM_001256053.2 protein_coding 24/34 3813-3816 3645-3648 1215-1216 ER/X gaAAGA/ga -1 EntrezGene TCTT TCTT 33 5.022240 6.05&4.22&4.91&6.05 rs1416417454 1 143066 6.98978e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.33999e-05 0.00000e+00 1.29433e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44267e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97846e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -10 27034798 CTCTT C - frameshift_variant HIGH ANKRD26 22852 Transcript NM_014915.3 protein_coding 24/34 3816-3819 3648-3651 1216-1217 ER/X gaAAGA/ga -1 EntrezGene TCTT TCTT 33 5.022240 6.05&4.22&4.91&6.05 rs1416417454 1 143066 6.98978e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.33999e-05 0.00000e+00 1.29433e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44267e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97846e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -10 27034798 CTCTT C - frameshift_variant HIGH ANKRD26 22852 Transcript XM_006717423.2 protein_coding 25/35 4906-4909 4734-4737 1578-1579 ER/X gaAAGA/ga -1 EntrezGene TCTT TCTT 33 5.022240 6.05&4.22&4.91&6.05 rs1416417454 1 143066 6.98978e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.33999e-05 0.00000e+00 1.29433e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44267e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97846e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -10 27034798 CTCTT C - frameshift_variant HIGH ANKRD26 22852 Transcript XM_006717425.4 protein_coding 25/35 4906-4909 4734-4737 1578-1579 ER/X gaAAGA/ga -1 EntrezGene TCTT TCTT 33 5.022240 6.05&4.22&4.91&6.05 rs1416417454 1 143066 6.98978e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.33999e-05 0.00000e+00 1.29433e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44267e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97846e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -10 27034798 CTCTT C - frameshift_variant HIGH ANKRD26 22852 Transcript XM_011519416.2 protein_coding 25/35 4906-4909 4734-4737 1578-1579 ER/X gaAAGA/ga -1 EntrezGene TCTT TCTT 33 5.022240 6.05&4.22&4.91&6.05 rs1416417454 1 143066 6.98978e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.33999e-05 0.00000e+00 1.29433e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44267e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97846e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -10 27034798 CTCTT C - frameshift_variant HIGH ANKRD26 22852 Transcript XM_017015928.1 protein_coding 25/40 4906-4909 4734-4737 1578-1579 ER/X gaAAGA/ga -1 EntrezGene TCTT TCTT 33 5.022240 6.05&4.22&4.91&6.05 rs1416417454 1 143066 6.98978e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.33999e-05 0.00000e+00 1.29433e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44267e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97846e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -10 27034798 CTCTT C - frameshift_variant HIGH ANKRD26 22852 Transcript XM_017015929.1 protein_coding 26/41 4894-4897 4722-4725 1574-1575 ER/X gaAAGA/ga -1 EntrezGene TCTT TCTT 33 5.022240 6.05&4.22&4.91&6.05 rs1416417454 1 143066 6.98978e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.33999e-05 0.00000e+00 1.29433e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44267e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97846e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -10 27034798 CTCTT C - frameshift_variant HIGH ANKRD26 22852 Transcript XM_017015930.1 protein_coding 25/37 4906-4909 4734-4737 1578-1579 ER/X gaAAGA/ga -1 EntrezGene TCTT TCTT 33 5.022240 6.05&4.22&4.91&6.05 rs1416417454 1 143066 6.98978e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.33999e-05 0.00000e+00 1.29433e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44267e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97846e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -10 27034798 CTCTT C - frameshift_variant HIGH ANKRD26 22852 Transcript XM_017015931.1 protein_coding 25/36 4906-4909 4734-4737 1578-1579 ER/X gaAAGA/ga -1 EntrezGene TCTT TCTT 33 5.022240 6.05&4.22&4.91&6.05 rs1416417454 1 143066 6.98978e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.33999e-05 0.00000e+00 1.29433e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44267e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97846e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -10 27034798 CTCTT C - frameshift_variant HIGH ANKRD26 22852 Transcript XM_017015932.1 protein_coding 25/36 4906-4909 4734-4737 1578-1579 ER/X gaAAGA/ga -1 EntrezGene TCTT TCTT 33 5.022240 6.05&4.22&4.91&6.05 rs1416417454 1 143066 6.98978e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.33999e-05 0.00000e+00 1.29433e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44267e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97846e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -10 27034798 CTCTT C - frameshift_variant HIGH ANKRD26 22852 Transcript XM_017015933.1 protein_coding 25/36 4906-4909 4734-4737 1578-1579 ER/X gaAAGA/ga -1 EntrezGene TCTT TCTT 33 5.022240 6.05&4.22&4.91&6.05 rs1416417454 1 143066 6.98978e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.33999e-05 0.00000e+00 1.29433e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44267e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97846e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -10 54317413 C T T missense_variant MODERATE PCDH15 65217 Transcript NM_001142763.2 protein_coding 9/35 1084 749 250 R/Q cGa/cAa -1 EntrezGene C C 0.26 0.983 22.8 2.697595 22.8 0.99864774195444861 3.662166 0.290529302031111 0.54985 4.047424 0.349871119654677 0.58732 0.67&.&.&.&.&.&0.67&0.67&.&0.67&.&0.67&0.67&.&0.36&.&.&0.67&0.67&0.67&.&0.67&0.67&0.67 4.77 0.00626453593096664 0.091221 0.3022 -0.6830 .&.&.&.&0.765&.&.&.&.&.&.&0.765&.&.&.&.&.&.&.&.&.&.&0.765&0.765 0.999636&0.999636&0.999636&0.999636&0.999636&0.999795&0.999636&0.999636&0.99915&0.999636&0.999636&0.999636&0.999636&0.99915&1&0.900257 -2.12&.&.&.&.&.&-2.29&-2.34&.&-2.32&.&-2.12&-2.12&.&-2.04&.&.&-2.12&-2.12&-2.12&.&-2.12&-2.12&-2.31 17.7447 0.245&.&0.294&0.228&0.256&0.295&0.291&0.315&0.233&0.43&0.293&0.345&0.251&0.301&0.272&0.301&0.36&0.319&0.308&0.311&0.333&0.368&0.317&0.267 0.92852 0.553676 0.655000 0.880000 1.425000 0.130000 5.35 chr10:54317413-54317413 7 143098 4.89175e-05 2.38254e-05 4.41112e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.34322e-05 1.69492e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13184e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44271e-05 7.74617e-05 1.07026e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.88452e-05 0.00000e+00 0.00000e+00 0.00000e+00 164932 0.00040 174670 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant PCDH15:65217 SO:0001583&missense_variant 1 562377533 15 30 50.0 -10 54317413 C T T missense_variant MODERATE PCDH15 65217 Transcript NM_001142764.2 protein_coding 8/34 1069 734 245 R/Q cGa/cAa -1 EntrezGene C C 0.26 0.989 22.8 2.697595 22.8 0.99864774195444861 3.662166 0.290529302031111 0.54985 4.047424 0.349871119654677 0.58732 0.67&.&.&.&.&.&0.67&0.67&.&0.67&.&0.67&0.67&.&0.36&.&.&0.67&0.67&0.67&.&0.67&0.67&0.67 4.77 0.00626453593096664 0.091221 0.3022 -0.6830 .&.&.&.&0.765&.&.&.&.&.&.&0.765&.&.&.&.&.&.&.&.&.&.&0.765&0.765 0.999636&0.999636&0.999636&0.999636&0.999636&0.999795&0.999636&0.999636&0.99915&0.999636&0.999636&0.999636&0.999636&0.99915&1&0.900257 -2.12&.&.&.&.&.&-2.29&-2.34&.&-2.32&.&-2.12&-2.12&.&-2.04&.&.&-2.12&-2.12&-2.12&.&-2.12&-2.12&-2.31 17.7447 0.245&.&0.294&0.228&0.256&0.295&0.291&0.315&0.233&0.43&0.293&0.345&0.251&0.301&0.272&0.301&0.36&0.319&0.308&0.311&0.333&0.368&0.317&0.267 0.92852 0.553676 0.655000 0.880000 1.425000 0.130000 5.35 chr10:54317413-54317413 7 143098 4.89175e-05 2.38254e-05 4.41112e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.34322e-05 1.69492e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13184e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44271e-05 7.74617e-05 1.07026e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.88452e-05 0.00000e+00 0.00000e+00 0.00000e+00 164932 0.00040 174670 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant PCDH15:65217 SO:0001583&missense_variant 1 562377533 15 30 50.0 -10 54317413 C T T missense_variant MODERATE PCDH15 65217 Transcript NM_001142765.2 protein_coding 8/32 1069 734 245 R/Q cGa/cAa -1 EntrezGene C C 0.25 0.982 22.8 2.697595 22.8 0.99864774195444861 3.662166 0.290529302031111 0.54985 4.047424 0.349871119654677 0.58732 0.67&.&.&.&.&.&0.67&0.67&.&0.67&.&0.67&0.67&.&0.36&.&.&0.67&0.67&0.67&.&0.67&0.67&0.67 4.77 0.00626453593096664 0.091221 0.3022 -0.6830 .&.&.&.&0.765&.&.&.&.&.&.&0.765&.&.&.&.&.&.&.&.&.&.&0.765&0.765 0.999636&0.999636&0.999636&0.999636&0.999636&0.999795&0.999636&0.999636&0.99915&0.999636&0.999636&0.999636&0.999636&0.99915&1&0.900257 -2.12&.&.&.&.&.&-2.29&-2.34&.&-2.32&.&-2.12&-2.12&.&-2.04&.&.&-2.12&-2.12&-2.12&.&-2.12&-2.12&-2.31 17.7447 0.245&.&0.294&0.228&0.256&0.295&0.291&0.315&0.233&0.43&0.293&0.345&0.251&0.301&0.272&0.301&0.36&0.319&0.308&0.311&0.333&0.368&0.317&0.267 0.92852 0.553676 0.655000 0.880000 1.425000 0.130000 5.35 chr10:54317413-54317413 7 143098 4.89175e-05 2.38254e-05 4.41112e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.34322e-05 1.69492e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13184e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44271e-05 7.74617e-05 1.07026e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.88452e-05 0.00000e+00 0.00000e+00 0.00000e+00 164932 0.00040 174670 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant PCDH15:65217 SO:0001583&missense_variant 1 562377533 15 30 50.0 -10 54317413 C T T missense_variant MODERATE PCDH15 65217 Transcript NM_001142766.2 protein_coding 8/32 1069 734 245 R/Q cGa/cAa -1 EntrezGene C C 0.28 0.998 22.8 2.697595 22.8 0.99864774195444861 3.662166 0.290529302031111 0.54985 4.047424 0.349871119654677 0.58732 0.67&.&.&.&.&.&0.67&0.67&.&0.67&.&0.67&0.67&.&0.36&.&.&0.67&0.67&0.67&.&0.67&0.67&0.67 4.77 0.00626453593096664 0.091221 0.3022 -0.6830 .&.&.&.&0.765&.&.&.&.&.&.&0.765&.&.&.&.&.&.&.&.&.&.&0.765&0.765 0.999636&0.999636&0.999636&0.999636&0.999636&0.999795&0.999636&0.999636&0.99915&0.999636&0.999636&0.999636&0.999636&0.99915&1&0.900257 -2.12&.&.&.&.&.&-2.29&-2.34&.&-2.32&.&-2.12&-2.12&.&-2.04&.&.&-2.12&-2.12&-2.12&.&-2.12&-2.12&-2.31 17.7447 0.245&.&0.294&0.228&0.256&0.295&0.291&0.315&0.233&0.43&0.293&0.345&0.251&0.301&0.272&0.301&0.36&0.319&0.308&0.311&0.333&0.368&0.317&0.267 0.92852 0.553676 0.655000 0.880000 1.425000 0.130000 5.35 chr10:54317413-54317413 7 143098 4.89175e-05 2.38254e-05 4.41112e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.34322e-05 1.69492e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13184e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44271e-05 7.74617e-05 1.07026e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.88452e-05 0.00000e+00 0.00000e+00 0.00000e+00 164932 0.00040 174670 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant PCDH15:65217 SO:0001583&missense_variant 1 562377533 15 30 50.0 -10 54317413 C T T missense_variant MODERATE PCDH15 65217 Transcript NM_001142767.2 protein_coding 7/31 958 623 208 R/Q cGa/cAa -1 EntrezGene C C 0.26 0.97 22.8 2.697595 22.8 0.99864774195444861 3.662166 0.290529302031111 0.54985 4.047424 0.349871119654677 0.58732 0.67&.&.&.&.&.&0.67&0.67&.&0.67&.&0.67&0.67&.&0.36&.&.&0.67&0.67&0.67&.&0.67&0.67&0.67 4.77 0.00626453593096664 0.091221 0.3022 -0.6830 .&.&.&.&0.765&.&.&.&.&.&.&0.765&.&.&.&.&.&.&.&.&.&.&0.765&0.765 0.999636&0.999636&0.999636&0.999636&0.999636&0.999795&0.999636&0.999636&0.99915&0.999636&0.999636&0.999636&0.999636&0.99915&1&0.900257 -2.12&.&.&.&.&.&-2.29&-2.34&.&-2.32&.&-2.12&-2.12&.&-2.04&.&.&-2.12&-2.12&-2.12&.&-2.12&-2.12&-2.31 17.7447 0.245&.&0.294&0.228&0.256&0.295&0.291&0.315&0.233&0.43&0.293&0.345&0.251&0.301&0.272&0.301&0.36&0.319&0.308&0.311&0.333&0.368&0.317&0.267 0.92852 0.553676 0.655000 0.880000 1.425000 0.130000 5.35 chr10:54317413-54317413 7 143098 4.89175e-05 2.38254e-05 4.41112e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.34322e-05 1.69492e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13184e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44271e-05 7.74617e-05 1.07026e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.88452e-05 0.00000e+00 0.00000e+00 0.00000e+00 164932 0.00040 174670 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant PCDH15:65217 SO:0001583&missense_variant 1 562377533 15 30 50.0 -10 54317413 C T T missense_variant MODERATE PCDH15 65217 Transcript NM_001142768.2 protein_coding 7/33 1003 668 223 R/Q cGa/cAa -1 EntrezGene C C 0.27 0.983 22.8 2.697595 22.8 0.99864774195444861 3.662166 0.290529302031111 0.54985 4.047424 0.349871119654677 0.58732 0.67&.&.&.&.&.&0.67&0.67&.&0.67&.&0.67&0.67&.&0.36&.&.&0.67&0.67&0.67&.&0.67&0.67&0.67 4.77 0.00626453593096664 0.091221 0.3022 -0.6830 .&.&.&.&0.765&.&.&.&.&.&.&0.765&.&.&.&.&.&.&.&.&.&.&0.765&0.765 0.999636&0.999636&0.999636&0.999636&0.999636&0.999795&0.999636&0.999636&0.99915&0.999636&0.999636&0.999636&0.999636&0.99915&1&0.900257 -2.12&.&.&.&.&.&-2.29&-2.34&.&-2.32&.&-2.12&-2.12&.&-2.04&.&.&-2.12&-2.12&-2.12&.&-2.12&-2.12&-2.31 17.7447 0.245&.&0.294&0.228&0.256&0.295&0.291&0.315&0.233&0.43&0.293&0.345&0.251&0.301&0.272&0.301&0.36&0.319&0.308&0.311&0.333&0.368&0.317&0.267 0.92852 0.553676 0.655000 0.880000 1.425000 0.130000 5.35 chr10:54317413-54317413 7 143098 4.89175e-05 2.38254e-05 4.41112e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.34322e-05 1.69492e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13184e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44271e-05 7.74617e-05 1.07026e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.88452e-05 0.00000e+00 0.00000e+00 0.00000e+00 164932 0.00040 174670 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant PCDH15:65217 SO:0001583&missense_variant 1 562377533 15 30 50.0 -10 54317413 C T T missense_variant MODERATE PCDH15 65217 Transcript NM_001142769.3 protein_coding 9/37 1084 749 250 R/Q cGa/cAa -1 EntrezGene C C 0.22 0.848 22.8 2.697595 22.8 0.99864774195444861 3.662166 0.290529302031111 0.54985 4.047424 0.349871119654677 0.58732 0.67&.&.&.&.&.&0.67&0.67&.&0.67&.&0.67&0.67&.&0.36&.&.&0.67&0.67&0.67&.&0.67&0.67&0.67 4.77 0.00626453593096664 0.091221 0.3022 -0.6830 .&.&.&.&0.765&.&.&.&.&.&.&0.765&.&.&.&.&.&.&.&.&.&.&0.765&0.765 0.999636&0.999636&0.999636&0.999636&0.999636&0.999795&0.999636&0.999636&0.99915&0.999636&0.999636&0.999636&0.999636&0.99915&1&0.900257 -2.12&.&.&.&.&.&-2.29&-2.34&.&-2.32&.&-2.12&-2.12&.&-2.04&.&.&-2.12&-2.12&-2.12&.&-2.12&-2.12&-2.31 17.7447 0.245&.&0.294&0.228&0.256&0.295&0.291&0.315&0.233&0.43&0.293&0.345&0.251&0.301&0.272&0.301&0.36&0.319&0.308&0.311&0.333&0.368&0.317&0.267 0.92852 0.553676 0.655000 0.880000 1.425000 0.130000 5.35 chr10:54317413-54317413 7 143098 4.89175e-05 2.38254e-05 4.41112e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.34322e-05 1.69492e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13184e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44271e-05 7.74617e-05 1.07026e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.88452e-05 0.00000e+00 0.00000e+00 0.00000e+00 164932 0.00040 174670 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant PCDH15:65217 SO:0001583&missense_variant 1 562377533 15 30 50.0 -10 54317413 C T T missense_variant MODERATE PCDH15 65217 Transcript NM_001142770.3 protein_coding 8/36 1069 734 245 R/Q cGa/cAa -1 EntrezGene C C 0.23 0.974 22.8 2.697595 22.8 0.99864774195444861 3.662166 0.290529302031111 0.54985 4.047424 0.349871119654677 0.58732 0.67&.&.&.&.&.&0.67&0.67&.&0.67&.&0.67&0.67&.&0.36&.&.&0.67&0.67&0.67&.&0.67&0.67&0.67 4.77 0.00626453593096664 0.091221 0.3022 -0.6830 .&.&.&.&0.765&.&.&.&.&.&.&0.765&.&.&.&.&.&.&.&.&.&.&0.765&0.765 0.999636&0.999636&0.999636&0.999636&0.999636&0.999795&0.999636&0.999636&0.99915&0.999636&0.999636&0.999636&0.999636&0.99915&1&0.900257 -2.12&.&.&.&.&.&-2.29&-2.34&.&-2.32&.&-2.12&-2.12&.&-2.04&.&.&-2.12&-2.12&-2.12&.&-2.12&-2.12&-2.31 17.7447 0.245&.&0.294&0.228&0.256&0.295&0.291&0.315&0.233&0.43&0.293&0.345&0.251&0.301&0.272&0.301&0.36&0.319&0.308&0.311&0.333&0.368&0.317&0.267 0.92852 0.553676 0.655000 0.880000 1.425000 0.130000 5.35 chr10:54317413-54317413 7 143098 4.89175e-05 2.38254e-05 4.41112e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.34322e-05 1.69492e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13184e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44271e-05 7.74617e-05 1.07026e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.88452e-05 0.00000e+00 0.00000e+00 0.00000e+00 164932 0.00040 174670 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant PCDH15:65217 SO:0001583&missense_variant 1 562377533 15 30 50.0 -10 54317413 C T T missense_variant MODERATE PCDH15 65217 Transcript NM_001142771.2 protein_coding 9/36 1084 749 250 R/Q cGa/cAa -1 EntrezGene C C 0.23 0.999 22.8 2.697595 22.8 0.99864774195444861 3.662166 0.290529302031111 0.54985 4.047424 0.349871119654677 0.58732 0.67&.&.&.&.&.&0.67&0.67&.&0.67&.&0.67&0.67&.&0.36&.&.&0.67&0.67&0.67&.&0.67&0.67&0.67 4.77 0.00626453593096664 0.091221 0.3022 -0.6830 .&.&.&.&0.765&.&.&.&.&.&.&0.765&.&.&.&.&.&.&.&.&.&.&0.765&0.765 0.999636&0.999636&0.999636&0.999636&0.999636&0.999795&0.999636&0.999636&0.99915&0.999636&0.999636&0.999636&0.999636&0.99915&1&0.900257 -2.12&.&.&.&.&.&-2.29&-2.34&.&-2.32&.&-2.12&-2.12&.&-2.04&.&.&-2.12&-2.12&-2.12&.&-2.12&-2.12&-2.31 17.7447 0.245&.&0.294&0.228&0.256&0.295&0.291&0.315&0.233&0.43&0.293&0.345&0.251&0.301&0.272&0.301&0.36&0.319&0.308&0.311&0.333&0.368&0.317&0.267 0.92852 0.553676 0.655000 0.880000 1.425000 0.130000 5.35 chr10:54317413-54317413 7 143098 4.89175e-05 2.38254e-05 4.41112e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.34322e-05 1.69492e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13184e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44271e-05 7.74617e-05 1.07026e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.88452e-05 0.00000e+00 0.00000e+00 0.00000e+00 164932 0.00040 174670 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant PCDH15:65217 SO:0001583&missense_variant 1 562377533 15 30 50.0 -10 54317413 C T T missense_variant MODERATE PCDH15 65217 Transcript NM_001142772.2 protein_coding 8/35 1069 734 245 R/Q cGa/cAa -1 EntrezGene C C 0.23 0.951 22.8 2.697595 22.8 0.99864774195444861 3.662166 0.290529302031111 0.54985 4.047424 0.349871119654677 0.58732 0.67&.&.&.&.&.&0.67&0.67&.&0.67&.&0.67&0.67&.&0.36&.&.&0.67&0.67&0.67&.&0.67&0.67&0.67 4.77 0.00626453593096664 0.091221 0.3022 -0.6830 .&.&.&.&0.765&.&.&.&.&.&.&0.765&.&.&.&.&.&.&.&.&.&.&0.765&0.765 0.999636&0.999636&0.999636&0.999636&0.999636&0.999795&0.999636&0.999636&0.99915&0.999636&0.999636&0.999636&0.999636&0.99915&1&0.900257 -2.12&.&.&.&.&.&-2.29&-2.34&.&-2.32&.&-2.12&-2.12&.&-2.04&.&.&-2.12&-2.12&-2.12&.&-2.12&-2.12&-2.31 17.7447 0.245&.&0.294&0.228&0.256&0.295&0.291&0.315&0.233&0.43&0.293&0.345&0.251&0.301&0.272&0.301&0.36&0.319&0.308&0.311&0.333&0.368&0.317&0.267 0.92852 0.553676 0.655000 0.880000 1.425000 0.130000 5.35 chr10:54317413-54317413 7 143098 4.89175e-05 2.38254e-05 4.41112e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.34322e-05 1.69492e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13184e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44271e-05 7.74617e-05 1.07026e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.88452e-05 0.00000e+00 0.00000e+00 0.00000e+00 164932 0.00040 174670 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant PCDH15:65217 SO:0001583&missense_variant 1 562377533 15 30 50.0 -10 54317413 C T T missense_variant MODERATE PCDH15 65217 Transcript NM_001142773.2 protein_coding 7/32 1003 668 223 R/Q cGa/cAa -1 EntrezGene C C 0.26 0.998 22.8 2.697595 22.8 0.99864774195444861 3.662166 0.290529302031111 0.54985 4.047424 0.349871119654677 0.58732 0.67&.&.&.&.&.&0.67&0.67&.&0.67&.&0.67&0.67&.&0.36&.&.&0.67&0.67&0.67&.&0.67&0.67&0.67 4.77 0.00626453593096664 0.091221 0.3022 -0.6830 .&.&.&.&0.765&.&.&.&.&.&.&0.765&.&.&.&.&.&.&.&.&.&.&0.765&0.765 0.999636&0.999636&0.999636&0.999636&0.999636&0.999795&0.999636&0.999636&0.99915&0.999636&0.999636&0.999636&0.999636&0.99915&1&0.900257 -2.12&.&.&.&.&.&-2.29&-2.34&.&-2.32&.&-2.12&-2.12&.&-2.04&.&.&-2.12&-2.12&-2.12&.&-2.12&-2.12&-2.31 17.7447 0.245&.&0.294&0.228&0.256&0.295&0.291&0.315&0.233&0.43&0.293&0.345&0.251&0.301&0.272&0.301&0.36&0.319&0.308&0.311&0.333&0.368&0.317&0.267 0.92852 0.553676 0.655000 0.880000 1.425000 0.130000 5.35 chr10:54317413-54317413 7 143098 4.89175e-05 2.38254e-05 4.41112e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.34322e-05 1.69492e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13184e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44271e-05 7.74617e-05 1.07026e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.88452e-05 0.00000e+00 0.00000e+00 0.00000e+00 164932 0.00040 174670 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant PCDH15:65217 SO:0001583&missense_variant 1 562377533 15 30 50.0 -10 54317413 C T T missense_variant MODERATE PCDH15 65217 Transcript NM_001354404.2 protein_coding 10/35 1030 668 223 R/Q cGa/cAa -1 EntrezGene C C 22.8 2.697595 22.8 0.99864774195444861 3.662166 0.290529302031111 0.54985 4.047424 0.349871119654677 0.58732 0.67&.&.&.&.&.&0.67&0.67&.&0.67&.&0.67&0.67&.&0.36&.&.&0.67&0.67&0.67&.&0.67&0.67&0.67 4.77 0.00626453593096664 0.091221 0.3022 -0.6830 .&.&.&.&0.765&.&.&.&.&.&.&0.765&.&.&.&.&.&.&.&.&.&.&0.765&0.765 0.999636&0.999636&0.999636&0.999636&0.999636&0.999795&0.999636&0.999636&0.99915&0.999636&0.999636&0.999636&0.999636&0.99915&1&0.900257 -2.12&.&.&.&.&.&-2.29&-2.34&.&-2.32&.&-2.12&-2.12&.&-2.04&.&.&-2.12&-2.12&-2.12&.&-2.12&-2.12&-2.31 17.7447 0.245&.&0.294&0.228&0.256&0.295&0.291&0.315&0.233&0.43&0.293&0.345&0.251&0.301&0.272&0.301&0.36&0.319&0.308&0.311&0.333&0.368&0.317&0.267 0.92852 0.553676 0.655000 0.880000 1.425000 0.130000 5.35 chr10:54317413-54317413 7 143098 4.89175e-05 2.38254e-05 4.41112e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.34322e-05 1.69492e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13184e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44271e-05 7.74617e-05 1.07026e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.88452e-05 0.00000e+00 0.00000e+00 0.00000e+00 164932 0.00040 174670 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant PCDH15:65217 SO:0001583&missense_variant 1 562377533 15 30 50.0 -10 54317413 C T T missense_variant MODERATE PCDH15 65217 Transcript NM_001354411.2 protein_coding 8/35 1069 734 245 R/Q cGa/cAa -1 EntrezGene C C 0.19 0.986 22.8 2.697595 22.8 0.99864774195444861 3.662166 0.290529302031111 0.54985 4.047424 0.349871119654677 0.58732 0.67&.&.&.&.&.&0.67&0.67&.&0.67&.&0.67&0.67&.&0.36&.&.&0.67&0.67&0.67&.&0.67&0.67&0.67 4.77 0.00626453593096664 0.091221 0.3022 -0.6830 .&.&.&.&0.765&.&.&.&.&.&.&0.765&.&.&.&.&.&.&.&.&.&.&0.765&0.765 0.999636&0.999636&0.999636&0.999636&0.999636&0.999795&0.999636&0.999636&0.99915&0.999636&0.999636&0.999636&0.999636&0.99915&1&0.900257 -2.12&.&.&.&.&.&-2.29&-2.34&.&-2.32&.&-2.12&-2.12&.&-2.04&.&.&-2.12&-2.12&-2.12&.&-2.12&-2.12&-2.31 17.7447 0.245&.&0.294&0.228&0.256&0.295&0.291&0.315&0.233&0.43&0.293&0.345&0.251&0.301&0.272&0.301&0.36&0.319&0.308&0.311&0.333&0.368&0.317&0.267 0.92852 0.553676 0.655000 0.880000 1.425000 0.130000 5.35 chr10:54317413-54317413 7 143098 4.89175e-05 2.38254e-05 4.41112e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.34322e-05 1.69492e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13184e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44271e-05 7.74617e-05 1.07026e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.88452e-05 0.00000e+00 0.00000e+00 0.00000e+00 164932 0.00040 174670 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant PCDH15:65217 SO:0001583&missense_variant 1 562377533 15 30 50.0 -10 54317413 C T T missense_variant MODERATE PCDH15 65217 Transcript NM_001354420.2 protein_coding 8/34 1069 734 245 R/Q cGa/cAa -1 EntrezGene C C 0.24 0.974 22.8 2.697595 22.8 0.99864774195444861 3.662166 0.290529302031111 0.54985 4.047424 0.349871119654677 0.58732 0.67&.&.&.&.&.&0.67&0.67&.&0.67&.&0.67&0.67&.&0.36&.&.&0.67&0.67&0.67&.&0.67&0.67&0.67 4.77 0.00626453593096664 0.091221 0.3022 -0.6830 .&.&.&.&0.765&.&.&.&.&.&.&0.765&.&.&.&.&.&.&.&.&.&.&0.765&0.765 0.999636&0.999636&0.999636&0.999636&0.999636&0.999795&0.999636&0.999636&0.99915&0.999636&0.999636&0.999636&0.999636&0.99915&1&0.900257 -2.12&.&.&.&.&.&-2.29&-2.34&.&-2.32&.&-2.12&-2.12&.&-2.04&.&.&-2.12&-2.12&-2.12&.&-2.12&-2.12&-2.31 17.7447 0.245&.&0.294&0.228&0.256&0.295&0.291&0.315&0.233&0.43&0.293&0.345&0.251&0.301&0.272&0.301&0.36&0.319&0.308&0.311&0.333&0.368&0.317&0.267 0.92852 0.553676 0.655000 0.880000 1.425000 0.130000 5.35 chr10:54317413-54317413 7 143098 4.89175e-05 2.38254e-05 4.41112e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.34322e-05 1.69492e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13184e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44271e-05 7.74617e-05 1.07026e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.88452e-05 0.00000e+00 0.00000e+00 0.00000e+00 164932 0.00040 174670 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant PCDH15:65217 SO:0001583&missense_variant 1 562377533 15 30 50.0 -10 54317413 C T T missense_variant MODERATE PCDH15 65217 Transcript NM_001354429.2 protein_coding 8/37 1069 734 245 R/Q cGa/cAa -1 EntrezGene C C 22.8 2.697595 22.8 0.99864774195444861 3.662166 0.290529302031111 0.54985 4.047424 0.349871119654677 0.58732 0.67&.&.&.&.&.&0.67&0.67&.&0.67&.&0.67&0.67&.&0.36&.&.&0.67&0.67&0.67&.&0.67&0.67&0.67 4.77 0.00626453593096664 0.091221 0.3022 -0.6830 .&.&.&.&0.765&.&.&.&.&.&.&0.765&.&.&.&.&.&.&.&.&.&.&0.765&0.765 0.999636&0.999636&0.999636&0.999636&0.999636&0.999795&0.999636&0.999636&0.99915&0.999636&0.999636&0.999636&0.999636&0.99915&1&0.900257 -2.12&.&.&.&.&.&-2.29&-2.34&.&-2.32&.&-2.12&-2.12&.&-2.04&.&.&-2.12&-2.12&-2.12&.&-2.12&-2.12&-2.31 17.7447 0.245&.&0.294&0.228&0.256&0.295&0.291&0.315&0.233&0.43&0.293&0.345&0.251&0.301&0.272&0.301&0.36&0.319&0.308&0.311&0.333&0.368&0.317&0.267 0.92852 0.553676 0.655000 0.880000 1.425000 0.130000 5.35 chr10:54317413-54317413 7 143098 4.89175e-05 2.38254e-05 4.41112e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.34322e-05 1.69492e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13184e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44271e-05 7.74617e-05 1.07026e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.88452e-05 0.00000e+00 0.00000e+00 0.00000e+00 164932 0.00040 174670 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant PCDH15:65217 SO:0001583&missense_variant 1 562377533 15 30 50.0 -10 54317413 C T T missense_variant MODERATE PCDH15 65217 Transcript NM_001354430.2 protein_coding 8/21 1069 734 245 R/Q cGa/cAa -1 EntrezGene C C 0.03 0.559 22.8 2.697595 22.8 0.99864774195444861 3.662166 0.290529302031111 0.54985 4.047424 0.349871119654677 0.58732 0.67&.&.&.&.&.&0.67&0.67&.&0.67&.&0.67&0.67&.&0.36&.&.&0.67&0.67&0.67&.&0.67&0.67&0.67 4.77 0.00626453593096664 0.091221 0.3022 -0.6830 .&.&.&.&0.765&.&.&.&.&.&.&0.765&.&.&.&.&.&.&.&.&.&.&0.765&0.765 0.999636&0.999636&0.999636&0.999636&0.999636&0.999795&0.999636&0.999636&0.99915&0.999636&0.999636&0.999636&0.999636&0.99915&1&0.900257 -2.12&.&.&.&.&.&-2.29&-2.34&.&-2.32&.&-2.12&-2.12&.&-2.04&.&.&-2.12&-2.12&-2.12&.&-2.12&-2.12&-2.31 17.7447 0.245&.&0.294&0.228&0.256&0.295&0.291&0.315&0.233&0.43&0.293&0.345&0.251&0.301&0.272&0.301&0.36&0.319&0.308&0.311&0.333&0.368&0.317&0.267 0.92852 0.553676 0.655000 0.880000 1.425000 0.130000 5.35 chr10:54317413-54317413 7 143098 4.89175e-05 2.38254e-05 4.41112e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.34322e-05 1.69492e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13184e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44271e-05 7.74617e-05 1.07026e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.88452e-05 0.00000e+00 0.00000e+00 0.00000e+00 164932 0.00040 174670 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant PCDH15:65217 SO:0001583&missense_variant 1 562377533 15 30 50.0 -10 54317413 C T T missense_variant MODERATE PCDH15 65217 Transcript NM_033056.4 protein_coding 8/33 1069 734 245 R/Q cGa/cAa -1 EntrezGene C C 0.28 0.986 22.8 2.697595 22.8 0.99864774195444861 3.662166 0.290529302031111 0.54985 4.047424 0.349871119654677 0.58732 0.67&.&.&.&.&.&0.67&0.67&.&0.67&.&0.67&0.67&.&0.36&.&.&0.67&0.67&0.67&.&0.67&0.67&0.67 4.77 0.00626453593096664 0.091221 0.3022 -0.6830 .&.&.&.&0.765&.&.&.&.&.&.&0.765&.&.&.&.&.&.&.&.&.&.&0.765&0.765 0.999636&0.999636&0.999636&0.999636&0.999636&0.999795&0.999636&0.999636&0.99915&0.999636&0.999636&0.999636&0.999636&0.99915&1&0.900257 -2.12&.&.&.&.&.&-2.29&-2.34&.&-2.32&.&-2.12&-2.12&.&-2.04&.&.&-2.12&-2.12&-2.12&.&-2.12&-2.12&-2.31 17.7447 0.245&.&0.294&0.228&0.256&0.295&0.291&0.315&0.233&0.43&0.293&0.345&0.251&0.301&0.272&0.301&0.36&0.319&0.308&0.311&0.333&0.368&0.317&0.267 0.92852 0.553676 0.655000 0.880000 1.425000 0.130000 5.35 chr10:54317413-54317413 7 143098 4.89175e-05 2.38254e-05 4.41112e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.34322e-05 1.69492e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13184e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44271e-05 7.74617e-05 1.07026e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.88452e-05 0.00000e+00 0.00000e+00 0.00000e+00 164932 0.00040 174670 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant PCDH15:65217 SO:0001583&missense_variant 1 562377533 15 30 50.0 -10 54317413 C T T missense_variant MODERATE PCDH15 65217 Transcript XM_017016573.2 protein_coding 15/42 4526 749 250 R/Q cGa/cAa -1 EntrezGene C C 22.8 2.697595 22.8 0.99864774195444861 3.662166 0.290529302031111 0.54985 4.047424 0.349871119654677 0.58732 0.67&.&.&.&.&.&0.67&0.67&.&0.67&.&0.67&0.67&.&0.36&.&.&0.67&0.67&0.67&.&0.67&0.67&0.67 4.77 0.00626453593096664 0.091221 0.3022 -0.6830 .&.&.&.&0.765&.&.&.&.&.&.&0.765&.&.&.&.&.&.&.&.&.&.&0.765&0.765 0.999636&0.999636&0.999636&0.999636&0.999636&0.999795&0.999636&0.999636&0.99915&0.999636&0.999636&0.999636&0.999636&0.99915&1&0.900257 -2.12&.&.&.&.&.&-2.29&-2.34&.&-2.32&.&-2.12&-2.12&.&-2.04&.&.&-2.12&-2.12&-2.12&.&-2.12&-2.12&-2.31 17.7447 0.245&.&0.294&0.228&0.256&0.295&0.291&0.315&0.233&0.43&0.293&0.345&0.251&0.301&0.272&0.301&0.36&0.319&0.308&0.311&0.333&0.368&0.317&0.267 0.92852 0.553676 0.655000 0.880000 1.425000 0.130000 5.35 chr10:54317413-54317413 7 143098 4.89175e-05 2.38254e-05 4.41112e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.34322e-05 1.69492e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13184e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44271e-05 7.74617e-05 1.07026e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.88452e-05 0.00000e+00 0.00000e+00 0.00000e+00 164932 0.00040 174670 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant PCDH15:65217 SO:0001583&missense_variant 1 562377533 15 30 50.0 -10 54317413 C T T non_coding_transcript_exon_variant MODIFIER PCDH15 65217 Transcript XR_001747192.2 misc_RNA 8/37 1747 -1 EntrezGene C C 22.8 2.697595 5.35 chr10:54317413-54317413 7 143098 4.89175e-05 2.38254e-05 4.41112e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.34322e-05 1.69492e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13184e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44271e-05 7.74617e-05 1.07026e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.88452e-05 0.00000e+00 0.00000e+00 0.00000e+00 164932 0.00040 174670 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant PCDH15:65217 SO:0001583&missense_variant 1 562377533 15 30 50.0 -10 54317413 C T T non_coding_transcript_exon_variant MODIFIER PCDH15 65217 Transcript XR_001747193.2 misc_RNA 8/36 1747 -1 EntrezGene C C 22.8 2.697595 5.35 chr10:54317413-54317413 7 143098 4.89175e-05 2.38254e-05 4.41112e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.34322e-05 1.69492e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.13184e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44271e-05 7.74617e-05 1.07026e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.88452e-05 0.00000e+00 0.00000e+00 0.00000e+00 164932 0.00040 174670 not_specified MedGen:CN169374 criteria_provided&_single_submitter Uncertain_significance single_nucleotide_variant PCDH15:65217 SO:0001583&missense_variant 1 562377533 15 30 50.0 -10 54317414 G A A stop_gained HIGH PCDH15 65217 Transcript NM_001142763.2 protein_coding 9/35 1083 748 250 R/* Cga/Tga -1 EntrezGene G G 44 8.691248 44 0.99654250520425236 2.794224 0.114371242427073 0.45282 3.88419 0.320039808724762 0.57176 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 2.52 5.2068738684452E-6 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1&1&1&1&1&1 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 12.0829 0.841&.&0.843&0.842&0.84&0.839&0.865&0.866&0.833&0.86&0.85&0.824&0.842&0.838&0.857&0.856&0.765&0.811&0.82&0.817&0.831&0.762&0.82&0.86 0.96198 0.553676 0.763000 0.873000 3.334000 0.194000 6.24 chr10:54317414-54317414 17 143154 1.18753e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.61446e-03 3.97727e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.49051e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.65127e-05 7.74665e-05 1.07038e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.18601e-04 0.00000e+00 0.00000e+00 0.00000e+00 4933 19972 Usher_syndrome_type_1&Usher_syndrome_type_1D&Usher_syndrome_type_1F&Usher_syndrome&_type_1G&Deafness&_autosomal_recessive_23¬_provided&Rare_genetic_deafness MONDO:MONDO:0010168&MedGen:C1568247&OMIM:276900&Orphanet:ORPHA231169&MONDO:MONDO:0010984&MedGen:C1832845&OMIM:601067&MONDO:MONDO:0011186&MedGen:C1865885&OMIM:602083&MONDO:MONDO:0011748&MedGen:C1847089&OMIM:606943&MONDO:MONDO:0012293&MedGen:C1836027&OMIM:609533&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant PCDH15:65217 SO:0001587&nonsense 5 111033260 15 30 50.0 -10 54317414 G A A stop_gained HIGH PCDH15 65217 Transcript NM_001142764.2 protein_coding 8/34 1068 733 245 R/* Cga/Tga -1 EntrezGene G G 44 8.691248 44 0.99654250520425236 2.794224 0.114371242427073 0.45282 3.88419 0.320039808724762 0.57176 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 2.52 5.2068738684452E-6 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1&1&1&1&1&1 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 12.0829 0.841&.&0.843&0.842&0.84&0.839&0.865&0.866&0.833&0.86&0.85&0.824&0.842&0.838&0.857&0.856&0.765&0.811&0.82&0.817&0.831&0.762&0.82&0.86 0.96198 0.553676 0.763000 0.873000 3.334000 0.194000 6.24 chr10:54317414-54317414 17 143154 1.18753e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.61446e-03 3.97727e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.49051e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.65127e-05 7.74665e-05 1.07038e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.18601e-04 0.00000e+00 0.00000e+00 0.00000e+00 4933 19972 Usher_syndrome_type_1&Usher_syndrome_type_1D&Usher_syndrome_type_1F&Usher_syndrome&_type_1G&Deafness&_autosomal_recessive_23¬_provided&Rare_genetic_deafness MONDO:MONDO:0010168&MedGen:C1568247&OMIM:276900&Orphanet:ORPHA231169&MONDO:MONDO:0010984&MedGen:C1832845&OMIM:601067&MONDO:MONDO:0011186&MedGen:C1865885&OMIM:602083&MONDO:MONDO:0011748&MedGen:C1847089&OMIM:606943&MONDO:MONDO:0012293&MedGen:C1836027&OMIM:609533&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant PCDH15:65217 SO:0001587&nonsense 5 111033260 15 30 50.0 -10 54317414 G A A stop_gained HIGH PCDH15 65217 Transcript NM_001142765.2 protein_coding 8/32 1068 733 245 R/* Cga/Tga -1 EntrezGene G G 44 8.691248 44 0.99654250520425236 2.794224 0.114371242427073 0.45282 3.88419 0.320039808724762 0.57176 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 2.52 5.2068738684452E-6 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1&1&1&1&1&1 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 12.0829 0.841&.&0.843&0.842&0.84&0.839&0.865&0.866&0.833&0.86&0.85&0.824&0.842&0.838&0.857&0.856&0.765&0.811&0.82&0.817&0.831&0.762&0.82&0.86 0.96198 0.553676 0.763000 0.873000 3.334000 0.194000 6.24 chr10:54317414-54317414 17 143154 1.18753e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.61446e-03 3.97727e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.49051e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.65127e-05 7.74665e-05 1.07038e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.18601e-04 0.00000e+00 0.00000e+00 0.00000e+00 4933 19972 Usher_syndrome_type_1&Usher_syndrome_type_1D&Usher_syndrome_type_1F&Usher_syndrome&_type_1G&Deafness&_autosomal_recessive_23¬_provided&Rare_genetic_deafness MONDO:MONDO:0010168&MedGen:C1568247&OMIM:276900&Orphanet:ORPHA231169&MONDO:MONDO:0010984&MedGen:C1832845&OMIM:601067&MONDO:MONDO:0011186&MedGen:C1865885&OMIM:602083&MONDO:MONDO:0011748&MedGen:C1847089&OMIM:606943&MONDO:MONDO:0012293&MedGen:C1836027&OMIM:609533&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant PCDH15:65217 SO:0001587&nonsense 5 111033260 15 30 50.0 -10 54317414 G A A stop_gained HIGH PCDH15 65217 Transcript NM_001142766.2 protein_coding 8/32 1068 733 245 R/* Cga/Tga -1 EntrezGene G G 44 8.691248 44 0.99654250520425236 2.794224 0.114371242427073 0.45282 3.88419 0.320039808724762 0.57176 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 2.52 5.2068738684452E-6 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1&1&1&1&1&1 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 12.0829 0.841&.&0.843&0.842&0.84&0.839&0.865&0.866&0.833&0.86&0.85&0.824&0.842&0.838&0.857&0.856&0.765&0.811&0.82&0.817&0.831&0.762&0.82&0.86 0.96198 0.553676 0.763000 0.873000 3.334000 0.194000 6.24 chr10:54317414-54317414 17 143154 1.18753e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.61446e-03 3.97727e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.49051e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.65127e-05 7.74665e-05 1.07038e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.18601e-04 0.00000e+00 0.00000e+00 0.00000e+00 4933 19972 Usher_syndrome_type_1&Usher_syndrome_type_1D&Usher_syndrome_type_1F&Usher_syndrome&_type_1G&Deafness&_autosomal_recessive_23¬_provided&Rare_genetic_deafness MONDO:MONDO:0010168&MedGen:C1568247&OMIM:276900&Orphanet:ORPHA231169&MONDO:MONDO:0010984&MedGen:C1832845&OMIM:601067&MONDO:MONDO:0011186&MedGen:C1865885&OMIM:602083&MONDO:MONDO:0011748&MedGen:C1847089&OMIM:606943&MONDO:MONDO:0012293&MedGen:C1836027&OMIM:609533&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant PCDH15:65217 SO:0001587&nonsense 5 111033260 15 30 50.0 -10 54317414 G A A stop_gained HIGH PCDH15 65217 Transcript NM_001142767.2 protein_coding 7/31 957 622 208 R/* Cga/Tga -1 EntrezGene G G 44 8.691248 44 0.99654250520425236 2.794224 0.114371242427073 0.45282 3.88419 0.320039808724762 0.57176 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 2.52 5.2068738684452E-6 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1&1&1&1&1&1 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 12.0829 0.841&.&0.843&0.842&0.84&0.839&0.865&0.866&0.833&0.86&0.85&0.824&0.842&0.838&0.857&0.856&0.765&0.811&0.82&0.817&0.831&0.762&0.82&0.86 0.96198 0.553676 0.763000 0.873000 3.334000 0.194000 6.24 chr10:54317414-54317414 17 143154 1.18753e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.61446e-03 3.97727e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.49051e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.65127e-05 7.74665e-05 1.07038e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.18601e-04 0.00000e+00 0.00000e+00 0.00000e+00 4933 19972 Usher_syndrome_type_1&Usher_syndrome_type_1D&Usher_syndrome_type_1F&Usher_syndrome&_type_1G&Deafness&_autosomal_recessive_23¬_provided&Rare_genetic_deafness MONDO:MONDO:0010168&MedGen:C1568247&OMIM:276900&Orphanet:ORPHA231169&MONDO:MONDO:0010984&MedGen:C1832845&OMIM:601067&MONDO:MONDO:0011186&MedGen:C1865885&OMIM:602083&MONDO:MONDO:0011748&MedGen:C1847089&OMIM:606943&MONDO:MONDO:0012293&MedGen:C1836027&OMIM:609533&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant PCDH15:65217 SO:0001587&nonsense 5 111033260 15 30 50.0 -10 54317414 G A A stop_gained HIGH PCDH15 65217 Transcript NM_001142768.2 protein_coding 7/33 1002 667 223 R/* Cga/Tga -1 EntrezGene G G 44 8.691248 44 0.99654250520425236 2.794224 0.114371242427073 0.45282 3.88419 0.320039808724762 0.57176 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 2.52 5.2068738684452E-6 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1&1&1&1&1&1 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 12.0829 0.841&.&0.843&0.842&0.84&0.839&0.865&0.866&0.833&0.86&0.85&0.824&0.842&0.838&0.857&0.856&0.765&0.811&0.82&0.817&0.831&0.762&0.82&0.86 0.96198 0.553676 0.763000 0.873000 3.334000 0.194000 6.24 chr10:54317414-54317414 17 143154 1.18753e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.61446e-03 3.97727e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.49051e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.65127e-05 7.74665e-05 1.07038e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.18601e-04 0.00000e+00 0.00000e+00 0.00000e+00 4933 19972 Usher_syndrome_type_1&Usher_syndrome_type_1D&Usher_syndrome_type_1F&Usher_syndrome&_type_1G&Deafness&_autosomal_recessive_23¬_provided&Rare_genetic_deafness MONDO:MONDO:0010168&MedGen:C1568247&OMIM:276900&Orphanet:ORPHA231169&MONDO:MONDO:0010984&MedGen:C1832845&OMIM:601067&MONDO:MONDO:0011186&MedGen:C1865885&OMIM:602083&MONDO:MONDO:0011748&MedGen:C1847089&OMIM:606943&MONDO:MONDO:0012293&MedGen:C1836027&OMIM:609533&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant PCDH15:65217 SO:0001587&nonsense 5 111033260 15 30 50.0 -10 54317414 G A A stop_gained HIGH PCDH15 65217 Transcript NM_001142769.3 protein_coding 9/37 1083 748 250 R/* Cga/Tga -1 EntrezGene G G 44 8.691248 44 0.99654250520425236 2.794224 0.114371242427073 0.45282 3.88419 0.320039808724762 0.57176 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 2.52 5.2068738684452E-6 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1&1&1&1&1&1 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 12.0829 0.841&.&0.843&0.842&0.84&0.839&0.865&0.866&0.833&0.86&0.85&0.824&0.842&0.838&0.857&0.856&0.765&0.811&0.82&0.817&0.831&0.762&0.82&0.86 0.96198 0.553676 0.763000 0.873000 3.334000 0.194000 6.24 chr10:54317414-54317414 17 143154 1.18753e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.61446e-03 3.97727e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.49051e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.65127e-05 7.74665e-05 1.07038e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.18601e-04 0.00000e+00 0.00000e+00 0.00000e+00 4933 19972 Usher_syndrome_type_1&Usher_syndrome_type_1D&Usher_syndrome_type_1F&Usher_syndrome&_type_1G&Deafness&_autosomal_recessive_23¬_provided&Rare_genetic_deafness MONDO:MONDO:0010168&MedGen:C1568247&OMIM:276900&Orphanet:ORPHA231169&MONDO:MONDO:0010984&MedGen:C1832845&OMIM:601067&MONDO:MONDO:0011186&MedGen:C1865885&OMIM:602083&MONDO:MONDO:0011748&MedGen:C1847089&OMIM:606943&MONDO:MONDO:0012293&MedGen:C1836027&OMIM:609533&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant PCDH15:65217 SO:0001587&nonsense 5 111033260 15 30 50.0 -10 54317414 G A A stop_gained HIGH PCDH15 65217 Transcript NM_001142770.3 protein_coding 8/36 1068 733 245 R/* Cga/Tga -1 EntrezGene G G 44 8.691248 44 0.99654250520425236 2.794224 0.114371242427073 0.45282 3.88419 0.320039808724762 0.57176 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 2.52 5.2068738684452E-6 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1&1&1&1&1&1 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 12.0829 0.841&.&0.843&0.842&0.84&0.839&0.865&0.866&0.833&0.86&0.85&0.824&0.842&0.838&0.857&0.856&0.765&0.811&0.82&0.817&0.831&0.762&0.82&0.86 0.96198 0.553676 0.763000 0.873000 3.334000 0.194000 6.24 chr10:54317414-54317414 17 143154 1.18753e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.61446e-03 3.97727e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.49051e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.65127e-05 7.74665e-05 1.07038e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.18601e-04 0.00000e+00 0.00000e+00 0.00000e+00 4933 19972 Usher_syndrome_type_1&Usher_syndrome_type_1D&Usher_syndrome_type_1F&Usher_syndrome&_type_1G&Deafness&_autosomal_recessive_23¬_provided&Rare_genetic_deafness MONDO:MONDO:0010168&MedGen:C1568247&OMIM:276900&Orphanet:ORPHA231169&MONDO:MONDO:0010984&MedGen:C1832845&OMIM:601067&MONDO:MONDO:0011186&MedGen:C1865885&OMIM:602083&MONDO:MONDO:0011748&MedGen:C1847089&OMIM:606943&MONDO:MONDO:0012293&MedGen:C1836027&OMIM:609533&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant PCDH15:65217 SO:0001587&nonsense 5 111033260 15 30 50.0 -10 54317414 G A A stop_gained HIGH PCDH15 65217 Transcript NM_001142771.2 protein_coding 9/36 1083 748 250 R/* Cga/Tga -1 EntrezGene G G 44 8.691248 44 0.99654250520425236 2.794224 0.114371242427073 0.45282 3.88419 0.320039808724762 0.57176 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 2.52 5.2068738684452E-6 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1&1&1&1&1&1 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 12.0829 0.841&.&0.843&0.842&0.84&0.839&0.865&0.866&0.833&0.86&0.85&0.824&0.842&0.838&0.857&0.856&0.765&0.811&0.82&0.817&0.831&0.762&0.82&0.86 0.96198 0.553676 0.763000 0.873000 3.334000 0.194000 6.24 chr10:54317414-54317414 17 143154 1.18753e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.61446e-03 3.97727e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.49051e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.65127e-05 7.74665e-05 1.07038e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.18601e-04 0.00000e+00 0.00000e+00 0.00000e+00 4933 19972 Usher_syndrome_type_1&Usher_syndrome_type_1D&Usher_syndrome_type_1F&Usher_syndrome&_type_1G&Deafness&_autosomal_recessive_23¬_provided&Rare_genetic_deafness MONDO:MONDO:0010168&MedGen:C1568247&OMIM:276900&Orphanet:ORPHA231169&MONDO:MONDO:0010984&MedGen:C1832845&OMIM:601067&MONDO:MONDO:0011186&MedGen:C1865885&OMIM:602083&MONDO:MONDO:0011748&MedGen:C1847089&OMIM:606943&MONDO:MONDO:0012293&MedGen:C1836027&OMIM:609533&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant PCDH15:65217 SO:0001587&nonsense 5 111033260 15 30 50.0 -10 54317414 G A A stop_gained HIGH PCDH15 65217 Transcript NM_001142772.2 protein_coding 8/35 1068 733 245 R/* Cga/Tga -1 EntrezGene G G 44 8.691248 44 0.99654250520425236 2.794224 0.114371242427073 0.45282 3.88419 0.320039808724762 0.57176 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 2.52 5.2068738684452E-6 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1&1&1&1&1&1 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 12.0829 0.841&.&0.843&0.842&0.84&0.839&0.865&0.866&0.833&0.86&0.85&0.824&0.842&0.838&0.857&0.856&0.765&0.811&0.82&0.817&0.831&0.762&0.82&0.86 0.96198 0.553676 0.763000 0.873000 3.334000 0.194000 6.24 chr10:54317414-54317414 17 143154 1.18753e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.61446e-03 3.97727e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.49051e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.65127e-05 7.74665e-05 1.07038e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.18601e-04 0.00000e+00 0.00000e+00 0.00000e+00 4933 19972 Usher_syndrome_type_1&Usher_syndrome_type_1D&Usher_syndrome_type_1F&Usher_syndrome&_type_1G&Deafness&_autosomal_recessive_23¬_provided&Rare_genetic_deafness MONDO:MONDO:0010168&MedGen:C1568247&OMIM:276900&Orphanet:ORPHA231169&MONDO:MONDO:0010984&MedGen:C1832845&OMIM:601067&MONDO:MONDO:0011186&MedGen:C1865885&OMIM:602083&MONDO:MONDO:0011748&MedGen:C1847089&OMIM:606943&MONDO:MONDO:0012293&MedGen:C1836027&OMIM:609533&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant PCDH15:65217 SO:0001587&nonsense 5 111033260 15 30 50.0 -10 54317414 G A A stop_gained HIGH PCDH15 65217 Transcript NM_001142773.2 protein_coding 7/32 1002 667 223 R/* Cga/Tga -1 EntrezGene G G 44 8.691248 44 0.99654250520425236 2.794224 0.114371242427073 0.45282 3.88419 0.320039808724762 0.57176 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 2.52 5.2068738684452E-6 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1&1&1&1&1&1 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 12.0829 0.841&.&0.843&0.842&0.84&0.839&0.865&0.866&0.833&0.86&0.85&0.824&0.842&0.838&0.857&0.856&0.765&0.811&0.82&0.817&0.831&0.762&0.82&0.86 0.96198 0.553676 0.763000 0.873000 3.334000 0.194000 6.24 chr10:54317414-54317414 17 143154 1.18753e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.61446e-03 3.97727e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.49051e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.65127e-05 7.74665e-05 1.07038e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.18601e-04 0.00000e+00 0.00000e+00 0.00000e+00 4933 19972 Usher_syndrome_type_1&Usher_syndrome_type_1D&Usher_syndrome_type_1F&Usher_syndrome&_type_1G&Deafness&_autosomal_recessive_23¬_provided&Rare_genetic_deafness MONDO:MONDO:0010168&MedGen:C1568247&OMIM:276900&Orphanet:ORPHA231169&MONDO:MONDO:0010984&MedGen:C1832845&OMIM:601067&MONDO:MONDO:0011186&MedGen:C1865885&OMIM:602083&MONDO:MONDO:0011748&MedGen:C1847089&OMIM:606943&MONDO:MONDO:0012293&MedGen:C1836027&OMIM:609533&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant PCDH15:65217 SO:0001587&nonsense 5 111033260 15 30 50.0 -10 54317414 G A A stop_gained HIGH PCDH15 65217 Transcript NM_001354404.2 protein_coding 10/35 1029 667 223 R/* Cga/Tga -1 EntrezGene G G 44 8.691248 44 0.99654250520425236 2.794224 0.114371242427073 0.45282 3.88419 0.320039808724762 0.57176 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 2.52 5.2068738684452E-6 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1&1&1&1&1&1 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 12.0829 0.841&.&0.843&0.842&0.84&0.839&0.865&0.866&0.833&0.86&0.85&0.824&0.842&0.838&0.857&0.856&0.765&0.811&0.82&0.817&0.831&0.762&0.82&0.86 0.96198 0.553676 0.763000 0.873000 3.334000 0.194000 6.24 chr10:54317414-54317414 17 143154 1.18753e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.61446e-03 3.97727e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.49051e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.65127e-05 7.74665e-05 1.07038e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.18601e-04 0.00000e+00 0.00000e+00 0.00000e+00 4933 19972 Usher_syndrome_type_1&Usher_syndrome_type_1D&Usher_syndrome_type_1F&Usher_syndrome&_type_1G&Deafness&_autosomal_recessive_23¬_provided&Rare_genetic_deafness MONDO:MONDO:0010168&MedGen:C1568247&OMIM:276900&Orphanet:ORPHA231169&MONDO:MONDO:0010984&MedGen:C1832845&OMIM:601067&MONDO:MONDO:0011186&MedGen:C1865885&OMIM:602083&MONDO:MONDO:0011748&MedGen:C1847089&OMIM:606943&MONDO:MONDO:0012293&MedGen:C1836027&OMIM:609533&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant PCDH15:65217 SO:0001587&nonsense 5 111033260 15 30 50.0 -10 54317414 G A A stop_gained HIGH PCDH15 65217 Transcript NM_001354411.2 protein_coding 8/35 1068 733 245 R/* Cga/Tga -1 EntrezGene G G 44 8.691248 44 0.99654250520425236 2.794224 0.114371242427073 0.45282 3.88419 0.320039808724762 0.57176 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 2.52 5.2068738684452E-6 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1&1&1&1&1&1 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 12.0829 0.841&.&0.843&0.842&0.84&0.839&0.865&0.866&0.833&0.86&0.85&0.824&0.842&0.838&0.857&0.856&0.765&0.811&0.82&0.817&0.831&0.762&0.82&0.86 0.96198 0.553676 0.763000 0.873000 3.334000 0.194000 6.24 chr10:54317414-54317414 17 143154 1.18753e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.61446e-03 3.97727e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.49051e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.65127e-05 7.74665e-05 1.07038e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.18601e-04 0.00000e+00 0.00000e+00 0.00000e+00 4933 19972 Usher_syndrome_type_1&Usher_syndrome_type_1D&Usher_syndrome_type_1F&Usher_syndrome&_type_1G&Deafness&_autosomal_recessive_23¬_provided&Rare_genetic_deafness MONDO:MONDO:0010168&MedGen:C1568247&OMIM:276900&Orphanet:ORPHA231169&MONDO:MONDO:0010984&MedGen:C1832845&OMIM:601067&MONDO:MONDO:0011186&MedGen:C1865885&OMIM:602083&MONDO:MONDO:0011748&MedGen:C1847089&OMIM:606943&MONDO:MONDO:0012293&MedGen:C1836027&OMIM:609533&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant PCDH15:65217 SO:0001587&nonsense 5 111033260 15 30 50.0 -10 54317414 G A A stop_gained HIGH PCDH15 65217 Transcript NM_001354420.2 protein_coding 8/34 1068 733 245 R/* Cga/Tga -1 EntrezGene G G 44 8.691248 44 0.99654250520425236 2.794224 0.114371242427073 0.45282 3.88419 0.320039808724762 0.57176 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 2.52 5.2068738684452E-6 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1&1&1&1&1&1 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 12.0829 0.841&.&0.843&0.842&0.84&0.839&0.865&0.866&0.833&0.86&0.85&0.824&0.842&0.838&0.857&0.856&0.765&0.811&0.82&0.817&0.831&0.762&0.82&0.86 0.96198 0.553676 0.763000 0.873000 3.334000 0.194000 6.24 chr10:54317414-54317414 17 143154 1.18753e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.61446e-03 3.97727e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.49051e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.65127e-05 7.74665e-05 1.07038e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.18601e-04 0.00000e+00 0.00000e+00 0.00000e+00 4933 19972 Usher_syndrome_type_1&Usher_syndrome_type_1D&Usher_syndrome_type_1F&Usher_syndrome&_type_1G&Deafness&_autosomal_recessive_23¬_provided&Rare_genetic_deafness MONDO:MONDO:0010168&MedGen:C1568247&OMIM:276900&Orphanet:ORPHA231169&MONDO:MONDO:0010984&MedGen:C1832845&OMIM:601067&MONDO:MONDO:0011186&MedGen:C1865885&OMIM:602083&MONDO:MONDO:0011748&MedGen:C1847089&OMIM:606943&MONDO:MONDO:0012293&MedGen:C1836027&OMIM:609533&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant PCDH15:65217 SO:0001587&nonsense 5 111033260 15 30 50.0 -10 54317414 G A A stop_gained HIGH PCDH15 65217 Transcript NM_001354429.2 protein_coding 8/37 1068 733 245 R/* Cga/Tga -1 EntrezGene G G 44 8.691248 44 0.99654250520425236 2.794224 0.114371242427073 0.45282 3.88419 0.320039808724762 0.57176 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 2.52 5.2068738684452E-6 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1&1&1&1&1&1 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 12.0829 0.841&.&0.843&0.842&0.84&0.839&0.865&0.866&0.833&0.86&0.85&0.824&0.842&0.838&0.857&0.856&0.765&0.811&0.82&0.817&0.831&0.762&0.82&0.86 0.96198 0.553676 0.763000 0.873000 3.334000 0.194000 6.24 chr10:54317414-54317414 17 143154 1.18753e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.61446e-03 3.97727e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.49051e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.65127e-05 7.74665e-05 1.07038e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.18601e-04 0.00000e+00 0.00000e+00 0.00000e+00 4933 19972 Usher_syndrome_type_1&Usher_syndrome_type_1D&Usher_syndrome_type_1F&Usher_syndrome&_type_1G&Deafness&_autosomal_recessive_23¬_provided&Rare_genetic_deafness MONDO:MONDO:0010168&MedGen:C1568247&OMIM:276900&Orphanet:ORPHA231169&MONDO:MONDO:0010984&MedGen:C1832845&OMIM:601067&MONDO:MONDO:0011186&MedGen:C1865885&OMIM:602083&MONDO:MONDO:0011748&MedGen:C1847089&OMIM:606943&MONDO:MONDO:0012293&MedGen:C1836027&OMIM:609533&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant PCDH15:65217 SO:0001587&nonsense 5 111033260 15 30 50.0 -10 54317414 G A A stop_gained HIGH PCDH15 65217 Transcript NM_001354430.2 protein_coding 8/21 1068 733 245 R/* Cga/Tga -1 EntrezGene G G 44 8.691248 44 0.99654250520425236 2.794224 0.114371242427073 0.45282 3.88419 0.320039808724762 0.57176 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 2.52 5.2068738684452E-6 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1&1&1&1&1&1 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 12.0829 0.841&.&0.843&0.842&0.84&0.839&0.865&0.866&0.833&0.86&0.85&0.824&0.842&0.838&0.857&0.856&0.765&0.811&0.82&0.817&0.831&0.762&0.82&0.86 0.96198 0.553676 0.763000 0.873000 3.334000 0.194000 6.24 chr10:54317414-54317414 17 143154 1.18753e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.61446e-03 3.97727e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.49051e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.65127e-05 7.74665e-05 1.07038e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.18601e-04 0.00000e+00 0.00000e+00 0.00000e+00 4933 19972 Usher_syndrome_type_1&Usher_syndrome_type_1D&Usher_syndrome_type_1F&Usher_syndrome&_type_1G&Deafness&_autosomal_recessive_23¬_provided&Rare_genetic_deafness MONDO:MONDO:0010168&MedGen:C1568247&OMIM:276900&Orphanet:ORPHA231169&MONDO:MONDO:0010984&MedGen:C1832845&OMIM:601067&MONDO:MONDO:0011186&MedGen:C1865885&OMIM:602083&MONDO:MONDO:0011748&MedGen:C1847089&OMIM:606943&MONDO:MONDO:0012293&MedGen:C1836027&OMIM:609533&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant PCDH15:65217 SO:0001587&nonsense 5 111033260 15 30 50.0 -10 54317414 G A A stop_gained HIGH PCDH15 65217 Transcript NM_033056.4 protein_coding 8/33 1068 733 245 R/* Cga/Tga -1 EntrezGene G G 44 8.691248 44 0.99654250520425236 2.794224 0.114371242427073 0.45282 3.88419 0.320039808724762 0.57176 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 2.52 5.2068738684452E-6 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1&1&1&1&1&1 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 12.0829 0.841&.&0.843&0.842&0.84&0.839&0.865&0.866&0.833&0.86&0.85&0.824&0.842&0.838&0.857&0.856&0.765&0.811&0.82&0.817&0.831&0.762&0.82&0.86 0.96198 0.553676 0.763000 0.873000 3.334000 0.194000 6.24 chr10:54317414-54317414 17 143154 1.18753e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.61446e-03 3.97727e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.49051e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.65127e-05 7.74665e-05 1.07038e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.18601e-04 0.00000e+00 0.00000e+00 0.00000e+00 4933 19972 Usher_syndrome_type_1&Usher_syndrome_type_1D&Usher_syndrome_type_1F&Usher_syndrome&_type_1G&Deafness&_autosomal_recessive_23¬_provided&Rare_genetic_deafness MONDO:MONDO:0010168&MedGen:C1568247&OMIM:276900&Orphanet:ORPHA231169&MONDO:MONDO:0010984&MedGen:C1832845&OMIM:601067&MONDO:MONDO:0011186&MedGen:C1865885&OMIM:602083&MONDO:MONDO:0011748&MedGen:C1847089&OMIM:606943&MONDO:MONDO:0012293&MedGen:C1836027&OMIM:609533&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant PCDH15:65217 SO:0001587&nonsense 5 111033260 15 30 50.0 -10 54317414 G A A stop_gained HIGH PCDH15 65217 Transcript XM_017016573.2 protein_coding 15/42 4525 748 250 R/* Cga/Tga -1 EntrezGene G G 44 8.691248 44 0.99654250520425236 2.794224 0.114371242427073 0.45282 3.88419 0.320039808724762 0.57176 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 2.52 5.2068738684452E-6 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 1&1&1&1&1&1&1&1&1&1&1&1&1&1&1&1 .&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&.&. 12.0829 0.841&.&0.843&0.842&0.84&0.839&0.865&0.866&0.833&0.86&0.85&0.824&0.842&0.838&0.857&0.856&0.765&0.811&0.82&0.817&0.831&0.762&0.82&0.86 0.96198 0.553676 0.763000 0.873000 3.334000 0.194000 6.24 chr10:54317414-54317414 17 143154 1.18753e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.61446e-03 3.97727e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.49051e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.65127e-05 7.74665e-05 1.07038e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.18601e-04 0.00000e+00 0.00000e+00 0.00000e+00 4933 19972 Usher_syndrome_type_1&Usher_syndrome_type_1D&Usher_syndrome_type_1F&Usher_syndrome&_type_1G&Deafness&_autosomal_recessive_23¬_provided&Rare_genetic_deafness MONDO:MONDO:0010168&MedGen:C1568247&OMIM:276900&Orphanet:ORPHA231169&MONDO:MONDO:0010984&MedGen:C1832845&OMIM:601067&MONDO:MONDO:0011186&MedGen:C1865885&OMIM:602083&MONDO:MONDO:0011748&MedGen:C1847089&OMIM:606943&MONDO:MONDO:0012293&MedGen:C1836027&OMIM:609533&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant PCDH15:65217 SO:0001587&nonsense 5 111033260 15 30 50.0 -10 54317414 G A A non_coding_transcript_exon_variant MODIFIER PCDH15 65217 Transcript XR_001747192.2 misc_RNA 8/37 1746 -1 EntrezGene G G 44 8.691248 6.24 chr10:54317414-54317414 17 143154 1.18753e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.61446e-03 3.97727e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.49051e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.65127e-05 7.74665e-05 1.07038e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.18601e-04 0.00000e+00 0.00000e+00 0.00000e+00 4933 19972 Usher_syndrome_type_1&Usher_syndrome_type_1D&Usher_syndrome_type_1F&Usher_syndrome&_type_1G&Deafness&_autosomal_recessive_23¬_provided&Rare_genetic_deafness MONDO:MONDO:0010168&MedGen:C1568247&OMIM:276900&Orphanet:ORPHA231169&MONDO:MONDO:0010984&MedGen:C1832845&OMIM:601067&MONDO:MONDO:0011186&MedGen:C1865885&OMIM:602083&MONDO:MONDO:0011748&MedGen:C1847089&OMIM:606943&MONDO:MONDO:0012293&MedGen:C1836027&OMIM:609533&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant PCDH15:65217 SO:0001587&nonsense 5 111033260 15 30 50.0 -10 54317414 G A A non_coding_transcript_exon_variant MODIFIER PCDH15 65217 Transcript XR_001747193.2 misc_RNA 8/36 1746 -1 EntrezGene G G 44 8.691248 6.24 chr10:54317414-54317414 17 143154 1.18753e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.61446e-03 3.97727e-03 3.20513e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.49051e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.65127e-05 7.74665e-05 1.07038e-04 3.67999e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.18601e-04 0.00000e+00 0.00000e+00 0.00000e+00 4933 19972 Usher_syndrome_type_1&Usher_syndrome_type_1D&Usher_syndrome_type_1F&Usher_syndrome&_type_1G&Deafness&_autosomal_recessive_23¬_provided&Rare_genetic_deafness MONDO:MONDO:0010168&MedGen:C1568247&OMIM:276900&Orphanet:ORPHA231169&MONDO:MONDO:0010984&MedGen:C1832845&OMIM:601067&MONDO:MONDO:0011186&MedGen:C1865885&OMIM:602083&MONDO:MONDO:0011748&MedGen:C1847089&OMIM:606943&MONDO:MONDO:0012293&MedGen:C1836027&OMIM:609533&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant PCDH15:65217 SO:0001587&nonsense 5 111033260 15 30 50.0 -10 70598966 T C C missense_variant MODERATE PRF1 5551 Transcript NM_001083116.3 protein_coding 3/3 882 755 252 N/S aAc/aGc -1 EntrezGene T T 0.03 0.001 12.88 1.111113 12.88 0.58046360684472142 0.623943 -0.897161219354332 0.12161 0.3941355 -1.00939047839436 0.08409 -1.65&-1.65 4.69 0.999999999996607 0.261829 0.1609 -0.7851 -0.205&-0.205 1&1&1 -0.59&-0.59 10.0841 0.041&0.041 0.17351 0.554377 0.001000 0.003000 0.935000 1.138000 2.86 rs28933375 1078 143340 7.52058e-03 9.20027e-03 9.50704e-03 8.83995e-03 1.24444e-01 1.19149e-01 1.30233e-01 7.39169e-03 8.45737e-03 6.57895e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.96079e-03 4.76826e-04 7.98085e-04 3.75940e-04 7.05259e-03 6.81368e-03 6.66025e-03 7.02464e-03 7.42804e-03 9.09091e-03 5.69260e-03 7.51838e-03 5.58476e-03 8.92857e-03 4.83092e-03 13716 0.00517 0.00759 28755 Familial_hemophagocytic_lymphohistiocytosis_2¬_specified¬_provided MONDO:MONDO:0011337&MedGen:C1863727&OMIM:603553&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(2)&Likely_benign(3)&Uncertain_significance(1) single_nucleotide_variant PRF1:5551 SO:0001583&missense_variant 1 28933375 15 30 50.0 -10 70598966 T C C missense_variant MODERATE PRF1 5551 Transcript NM_005041.5 protein_coding 3/3 903 755 252 N/S aAc/aGc -1 EntrezGene T T OK 0.03 0.001 12.88 1.111113 12.88 0.58046360684472142 0.623943 -0.897161219354332 0.12161 0.3941355 -1.00939047839436 0.08409 -1.65&-1.65 4.69 0.999999999996607 0.261829 0.1609 -0.7851 -0.205&-0.205 1&1&1 -0.59&-0.59 10.0841 0.041&0.041 0.17351 0.554377 0.001000 0.003000 0.935000 1.138000 2.86 rs28933375 1078 143340 7.52058e-03 9.20027e-03 9.50704e-03 8.83995e-03 1.24444e-01 1.19149e-01 1.30233e-01 7.39169e-03 8.45737e-03 6.57895e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.96079e-03 4.76826e-04 7.98085e-04 3.75940e-04 7.05259e-03 6.81368e-03 6.66025e-03 7.02464e-03 7.42804e-03 9.09091e-03 5.69260e-03 7.51838e-03 5.58476e-03 8.92857e-03 4.83092e-03 13716 0.00517 0.00759 28755 Familial_hemophagocytic_lymphohistiocytosis_2¬_specified¬_provided MONDO:MONDO:0011337&MedGen:C1863727&OMIM:603553&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(2)&Likely_benign(3)&Uncertain_significance(1) single_nucleotide_variant PRF1:5551 SO:0001583&missense_variant 1 28933375 15 30 50.0 -10 87460988 A C C intron_variant&non_coding_transcript_variant MODIFIER LOC112268063 112268063 Transcript XR_002957093.1 lncRNA 2/2 1 EntrezGene A A 13.20 1.147393 0.0574 chr10:87460988-87460988 82330 131744 6.24924e-01 6.18233e-01 6.16318e-01 6.20500e-01 5.48387e-01 5.55066e-01 5.41063e-01 6.40655e-01 6.38592e-01 6.42218e-01 5.85296e-01 5.72376e-01 5.99730e-01 5.91421e-01 6.01838e-01 5.82575e-01 6.22550e-01 6.30634e-01 6.20893e-01 6.33746e-01 6.27461e-01 6.28945e-01 6.27354e-01 6.31143e-01 6.26620e-01 6.28627e-01 6.24486e-01 6.28786e-01 6.33310e-01 6.26953e-01 6.34733e-01 15 30 50.0 -10 98435647 ATACAGGAAGT A - frameshift_variant HIGH HPS1 3257 Transcript NM_000195.5 protein_coding 4/20 467-476 233-242 78-81 NFLY/X aACTTCCTGTAt/at -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - 5_prime_UTR_variant MODIFIER HPS1 3257 Transcript NM_001311345.1 protein_coding 4/19 504-513 -1 EntrezGene TACAGGAAGT TACAGGAAGT OK 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - frameshift_variant HIGH HPS1 3257 Transcript NM_001322476.1 protein_coding 4/20 424-433 233-242 78-81 NFLY/X aACTTCCTGTAt/at -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - frameshift_variant HIGH HPS1 3257 Transcript NM_001322477.1 protein_coding 4/20 504-513 233-242 78-81 NFLY/X aACTTCCTGTAt/at -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - frameshift_variant HIGH HPS1 3257 Transcript NM_001322478.1 protein_coding 4/19 424-433 233-242 78-81 NFLY/X aACTTCCTGTAt/at -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - frameshift_variant HIGH HPS1 3257 Transcript NM_001322479.1 protein_coding 4/19 504-513 233-242 78-81 NFLY/X aACTTCCTGTAt/at -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - frameshift_variant HIGH HPS1 3257 Transcript NM_001322480.1 protein_coding 4/18 424-433 233-242 78-81 NFLY/X aACTTCCTGTAt/at -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - frameshift_variant HIGH HPS1 3257 Transcript NM_001322481.1 protein_coding 4/18 504-513 233-242 78-81 NFLY/X aACTTCCTGTAt/at -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - frameshift_variant HIGH HPS1 3257 Transcript NM_001322482.1 protein_coding 4/17 504-513 233-242 78-81 NFLY/X aACTTCCTGTAt/at -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - frameshift_variant HIGH HPS1 3257 Transcript NM_001322483.1 protein_coding 4/19 424-433 7-16 3-6 TSCM/X ACTTCCTGTAtg/tg -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - frameshift_variant HIGH HPS1 3257 Transcript NM_001322484.1 protein_coding 4/19 504-513 7-16 3-6 TSCM/X ACTTCCTGTAtg/tg -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - frameshift_variant HIGH HPS1 3257 Transcript NM_001322485.1 protein_coding 4/18 495-504 7-16 3-6 TSCM/X ACTTCCTGTAtg/tg -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - 5_prime_UTR_variant MODIFIER HPS1 3257 Transcript NM_001322487.1 protein_coding 4/20 504-513 -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - 5_prime_UTR_variant MODIFIER HPS1 3257 Transcript NM_001322489.1 protein_coding 4/18 504-513 -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - frameshift_variant HIGH HPS1 3257 Transcript NM_001322490.1 protein_coding 4/9 504-513 233-242 78-81 NFLY/X aACTTCCTGTAt/at -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - frameshift_variant HIGH HPS1 3257 Transcript NM_001322491.1 protein_coding 4/9 424-433 233-242 78-81 NFLY/X aACTTCCTGTAt/at -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - frameshift_variant HIGH HPS1 3257 Transcript NM_001322492.1 protein_coding 4/8 424-433 233-242 78-81 NFLY/X aACTTCCTGTAt/at -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - frameshift_variant HIGH HPS1 3257 Transcript NM_182639.3 protein_coding 4/10 504-513 233-242 78-81 NFLY/X aACTTCCTGTAt/at -1 EntrezGene TACAGGAAGT TACAGGAAGT OK 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - upstream_gene_variant MODIFIER MIR4685 100616482 Transcript NR_039833.2 miRNA 4288 -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - frameshift_variant HIGH HPS1 3257 Transcript XM_005269757.4 protein_coding 4/20 437-446 233-242 78-81 NFLY/X aACTTCCTGTAt/at -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - frameshift_variant HIGH HPS1 3257 Transcript XM_017016170.1 protein_coding 4/17 411-420 233-242 78-81 NFLY/X aACTTCCTGTAt/at -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - frameshift_variant HIGH HPS1 3257 Transcript XM_017016171.2 protein_coding 4/18 456-465 7-16 3-6 TSCM/X ACTTCCTGTAtg/tg -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - 5_prime_UTR_variant MODIFIER HPS1 3257 Transcript XM_017016172.2 protein_coding 4/17 444-453 -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - frameshift_variant HIGH HPS1 3257 Transcript XM_017016173.1 protein_coding 4/8 502-511 233-242 78-81 NFLY/X aACTTCCTGTAt/at -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - frameshift_variant HIGH HPS1 3257 Transcript XM_024447971.1 protein_coding 4/18 424-433 233-242 78-81 NFLY/X aACTTCCTGTAt/at -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - 5_prime_UTR_variant MODIFIER HPS1 3257 Transcript XM_024447972.1 protein_coding 4/19 456-465 -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - non_coding_transcript_exon_variant MODIFIER HPS1 3257 Transcript XR_001747098.1 misc_RNA 4/21 502-511 -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - non_coding_transcript_exon_variant MODIFIER HPS1 3257 Transcript XR_001747099.2 misc_RNA 4/20 502-511 -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - non_coding_transcript_exon_variant MODIFIER HPS1 3257 Transcript XR_001747100.2 misc_RNA 4/16 502-511 -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98435647 ATACAGGAAGT A - non_coding_transcript_exon_variant MODIFIER HPS1 3257 Transcript XR_001747101.2 misc_RNA 4/19 502-511 -1 EntrezGene TACAGGAAGT TACAGGAAGT 6.54&5.42&0.704&6.54&4.73&3.62&-1.03&-2.47&2.8&-5.11 435451 429155 Hermansky-Pudlak_syndrome_1¬_provided MONDO:MONDO:0008748&MedGen:C2931875&OMIM:203300&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPS1:3257 SO:0001589&frameshift_variant&SO:0001623&5_prime_UTR_variant 1 773323079 15 30 50.0 -10 98490050 CTT C - frameshift_variant&splice_region_variant HIGH HPSE2 60495 Transcript NM_001166244.1 protein_coding 9/11 1364-1365 1291-1292 431 N/X AAc/c -1 EntrezGene TT TT 37 7.123968 5.42&6.54 rs397515338 26 143294 1.81445e-04 9.51475e-05 1.32182e-04 5.16956e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.30215e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.29590e-04 3.40705e-04 3.74472e-04 2.94269e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.88327e-04 0.00000e+00 0.00000e+00 0.00000e+00 84 0.00032 0.00016 15123 Urofacial_syndrome_1¬_provided MONDO:MONDO:0009368&MedGen:CN033872&OMIM:236730&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPSE2:60495 SO:0001589&frameshift_variant 17 397515338 15 30 50.0 -10 98490050 CTT C - frameshift_variant&splice_region_variant HIGH HPSE2 60495 Transcript NM_001166245.1 protein_coding 8/10 1202-1203 1129-1130 377 N/X AAc/c -1 EntrezGene TT TT 37 7.123968 5.42&6.54 rs397515338 26 143294 1.81445e-04 9.51475e-05 1.32182e-04 5.16956e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.30215e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.29590e-04 3.40705e-04 3.74472e-04 2.94269e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.88327e-04 0.00000e+00 0.00000e+00 0.00000e+00 84 0.00032 0.00016 15123 Urofacial_syndrome_1¬_provided MONDO:MONDO:0009368&MedGen:CN033872&OMIM:236730&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPSE2:60495 SO:0001589&frameshift_variant 17 397515338 15 30 50.0 -10 98490050 CTT C - frameshift_variant&splice_region_variant HIGH HPSE2 60495 Transcript NM_001166246.1 protein_coding 10/13 1538-1539 1465-1466 489 N/X AAc/c -1 EntrezGene TT TT 37 7.123968 5.42&6.54 rs397515338 26 143294 1.81445e-04 9.51475e-05 1.32182e-04 5.16956e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.30215e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.29590e-04 3.40705e-04 3.74472e-04 2.94269e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.88327e-04 0.00000e+00 0.00000e+00 0.00000e+00 84 0.00032 0.00016 15123 Urofacial_syndrome_1¬_provided MONDO:MONDO:0009368&MedGen:CN033872&OMIM:236730&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPSE2:60495 SO:0001589&frameshift_variant 17 397515338 15 30 50.0 -10 98490050 CTT C - frameshift_variant&splice_region_variant HIGH HPSE2 60495 Transcript NM_021828.5 protein_coding 10/12 1515-1516 1465-1466 489 N/X AAc/c -1 EntrezGene TT TT 37 7.123968 5.42&6.54 rs397515338 26 143294 1.81445e-04 9.51475e-05 1.32182e-04 5.16956e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.30215e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.29590e-04 3.40705e-04 3.74472e-04 2.94269e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.88327e-04 0.00000e+00 0.00000e+00 0.00000e+00 84 0.00032 0.00016 15123 Urofacial_syndrome_1¬_provided MONDO:MONDO:0009368&MedGen:CN033872&OMIM:236730&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPSE2:60495 SO:0001589&frameshift_variant 17 397515338 15 30 50.0 -10 98490050 CTT C - frameshift_variant&splice_region_variant HIGH HPSE2 60495 Transcript XM_006717937.2 protein_coding 10/12 1598-1599 949-950 317 N/X AAc/c -1 EntrezGene TT TT 37 7.123968 5.42&6.54 rs397515338 26 143294 1.81445e-04 9.51475e-05 1.32182e-04 5.16956e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.30215e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.29590e-04 3.40705e-04 3.74472e-04 2.94269e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.88327e-04 0.00000e+00 0.00000e+00 0.00000e+00 84 0.00032 0.00016 15123 Urofacial_syndrome_1¬_provided MONDO:MONDO:0009368&MedGen:CN033872&OMIM:236730&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPSE2:60495 SO:0001589&frameshift_variant 17 397515338 15 30 50.0 -10 98490050 CTT C - frameshift_variant&splice_region_variant HIGH HPSE2 60495 Transcript XM_011540029.1 protein_coding 10/11 1538-1539 1465-1466 489 K/X AAg/g -1 EntrezGene TT TT 37 7.123968 5.42&6.54 rs397515338 26 143294 1.81445e-04 9.51475e-05 1.32182e-04 5.16956e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.30215e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.29590e-04 3.40705e-04 3.74472e-04 2.94269e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.88327e-04 0.00000e+00 0.00000e+00 0.00000e+00 84 0.00032 0.00016 15123 Urofacial_syndrome_1¬_provided MONDO:MONDO:0009368&MedGen:CN033872&OMIM:236730&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPSE2:60495 SO:0001589&frameshift_variant 17 397515338 15 30 50.0 -10 98490050 CTT C - frameshift_variant&splice_region_variant HIGH HPSE2 60495 Transcript XM_011540030.1 protein_coding 9/11 1363-1364 1303-1304 435 N/X AAc/c -1 EntrezGene TT TT 37 7.123968 5.42&6.54 rs397515338 26 143294 1.81445e-04 9.51475e-05 1.32182e-04 5.16956e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.30215e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.29590e-04 3.40705e-04 3.74472e-04 2.94269e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.88327e-04 0.00000e+00 0.00000e+00 0.00000e+00 84 0.00032 0.00016 15123 Urofacial_syndrome_1¬_provided MONDO:MONDO:0009368&MedGen:CN033872&OMIM:236730&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPSE2:60495 SO:0001589&frameshift_variant 17 397515338 15 30 50.0 -10 98490050 CTT C - frameshift_variant&splice_region_variant HIGH HPSE2 60495 Transcript XM_011540031.2 protein_coding 9/11 1147-1148 949-950 317 N/X AAc/c -1 EntrezGene TT TT 37 7.123968 5.42&6.54 rs397515338 26 143294 1.81445e-04 9.51475e-05 1.32182e-04 5.16956e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.30215e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.29590e-04 3.40705e-04 3.74472e-04 2.94269e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.88327e-04 0.00000e+00 0.00000e+00 0.00000e+00 84 0.00032 0.00016 15123 Urofacial_syndrome_1¬_provided MONDO:MONDO:0009368&MedGen:CN033872&OMIM:236730&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPSE2:60495 SO:0001589&frameshift_variant 17 397515338 15 30 50.0 -10 98490050 CTT C - frameshift_variant&splice_region_variant HIGH HPSE2 60495 Transcript XM_017016495.1 protein_coding 10/12 1538-1539 1465-1466 489 N/X AAc/c -1 EntrezGene TT TT 37 7.123968 5.42&6.54 rs397515338 26 143294 1.81445e-04 9.51475e-05 1.32182e-04 5.16956e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.30215e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.29590e-04 3.40705e-04 3.74472e-04 2.94269e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.88327e-04 0.00000e+00 0.00000e+00 0.00000e+00 84 0.00032 0.00016 15123 Urofacial_syndrome_1¬_provided MONDO:MONDO:0009368&MedGen:CN033872&OMIM:236730&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPSE2:60495 SO:0001589&frameshift_variant 17 397515338 15 30 50.0 -10 98490050 CTT C - frameshift_variant&splice_region_variant HIGH HPSE2 60495 Transcript XM_017016497.1 protein_coding 9/11 1131-1132 949-950 317 N/X AAc/c -1 EntrezGene TT TT 37 7.123968 5.42&6.54 rs397515338 26 143294 1.81445e-04 9.51475e-05 1.32182e-04 5.16956e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.30215e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.29590e-04 3.40705e-04 3.74472e-04 2.94269e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.88327e-04 0.00000e+00 0.00000e+00 0.00000e+00 84 0.00032 0.00016 15123 Urofacial_syndrome_1¬_provided MONDO:MONDO:0009368&MedGen:CN033872&OMIM:236730&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPSE2:60495 SO:0001589&frameshift_variant 17 397515338 15 30 50.0 -10 98490050 CTT C - frameshift_variant&splice_region_variant HIGH HPSE2 60495 Transcript XM_017016498.1 protein_coding 9/11 1432-1433 661-662 221 N/X AAc/c -1 EntrezGene TT TT 37 7.123968 5.42&6.54 rs397515338 26 143294 1.81445e-04 9.51475e-05 1.32182e-04 5.16956e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.30215e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.29590e-04 3.40705e-04 3.74472e-04 2.94269e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.88327e-04 0.00000e+00 0.00000e+00 0.00000e+00 84 0.00032 0.00016 15123 Urofacial_syndrome_1¬_provided MONDO:MONDO:0009368&MedGen:CN033872&OMIM:236730&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPSE2:60495 SO:0001589&frameshift_variant 17 397515338 15 30 50.0 -10 98490050 CTT C - frameshift_variant&splice_region_variant HIGH HPSE2 60495 Transcript XM_024448119.1 protein_coding 10/12 1257-1258 949-950 317 N/X AAc/c -1 EntrezGene TT TT 37 7.123968 5.42&6.54 rs397515338 26 143294 1.81445e-04 9.51475e-05 1.32182e-04 5.16956e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.30215e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.29590e-04 3.40705e-04 3.74472e-04 2.94269e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.88327e-04 0.00000e+00 0.00000e+00 0.00000e+00 84 0.00032 0.00016 15123 Urofacial_syndrome_1¬_provided MONDO:MONDO:0009368&MedGen:CN033872&OMIM:236730&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPSE2:60495 SO:0001589&frameshift_variant 17 397515338 15 30 50.0 -10 98490050 CTT C - frameshift_variant&splice_region_variant HIGH HPSE2 60495 Transcript XM_024448120.1 protein_coding 10/12 1594-1595 661-662 221 N/X AAc/c -1 EntrezGene TT TT 37 7.123968 5.42&6.54 rs397515338 26 143294 1.81445e-04 9.51475e-05 1.32182e-04 5.16956e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.30215e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.29590e-04 3.40705e-04 3.74472e-04 2.94269e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.88327e-04 0.00000e+00 0.00000e+00 0.00000e+00 84 0.00032 0.00016 15123 Urofacial_syndrome_1¬_provided MONDO:MONDO:0009368&MedGen:CN033872&OMIM:236730&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPSE2:60495 SO:0001589&frameshift_variant 17 397515338 15 30 50.0 -10 98490050 CTT C - splice_region_variant&non_coding_transcript_exon_variant LOW HPSE2 60495 Transcript XR_001747170.1 misc_RNA 10/12 1542-1543 -1 EntrezGene TT TT 37 7.123968 5.42&6.54 rs397515338 26 143294 1.81445e-04 9.51475e-05 1.32182e-04 5.16956e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.30215e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.29590e-04 3.40705e-04 3.74472e-04 2.94269e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.88327e-04 0.00000e+00 0.00000e+00 0.00000e+00 84 0.00032 0.00016 15123 Urofacial_syndrome_1¬_provided MONDO:MONDO:0009368&MedGen:CN033872&OMIM:236730&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion HPSE2:60495 SO:0001589&frameshift_variant 17 397515338 15 30 50.0 -10 102065939 A AGCAGCCGCTT GCAGCCGCTT frameshift_variant HIGH HPS6 79803 Transcript NM_024747.6 protein_coding 1/1 591-592 465-466 155-156 -/AAAX -/GCAGCCGCTT 1 EntrezGene 12.73 1.095619 -7.41&-8.87 rs1462863412 2 143272 1.39595e-05 2.37937e-05 0.00000e+00 5.17438e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35417e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44038e-05 1.54904e-05 2.67480e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39488e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -10 104479773 ACGGGAAG A - intron_variant&non_coding_transcript_variant MODIFIER LOC101927523 101927523 Transcript NR_120624.1 lncRNA 1/2 -1 EntrezGene CGGGAAG CGGGAAG 0.802 -0.107092 0 rs5787523 32768 32854 9.97382e-01 9.89997e-01 9.89398e-01 9.90683e-01 1.00000e+00 1.00000e+00 1.00000e+00 9.98368e-01 9.97908e-01 9.98866e-01 1.00000e+00 1.00000e+00 1.00000e+00 1.00000e+00 1.00000e+00 1.00000e+00 9.97415e-01 1.00000e+00 1.00000e+00 1.00000e+00 9.97340e-01 9.99690e-01 9.99741e-01 9.99612e-01 9.97696e-01 1.00000e+00 9.94792e-01 9.96825e-01 1.00000e+00 1.00000e+00 1.00000e+00 15 30 50.0 -11 5227002 T A A missense_variant MODERATE HBB 3043 Transcript NM_000518.5 protein_coding 1/3 70 20 7 E/V gAg/gTg -1 EntrezGene T T 0.01 0.007 13.13 1.139333 13.13 0.95986345036160714 0.6916179 -0.847724503913206 0.13343 0.7414193 -0.743216459724792 0.14819 -2.47&.&.&-2.47 0.149 0.442133975431412 0.0719 -0.5961 3.625&3.625&.&. 1 -4.85&.&.&-4.79 8.3887 0.537&.&.&. 0.40197 0.487112 0.107000 0.003000 0.690000 0.141000 -1.08 chr11:5227002-5227002 1826 143280 1.27443e-02 4.08537e-02 4.09836e-02 4.07013e-02 0.00000e+00 0.00000e+00 0.00000e+00 4.90483e-03 5.41089e-03 4.51846e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.32591e-02 0.00000e+00 0.00000e+00 0.00000e+00 1.21969e-02 1.70337e-04 1.33726e-04 2.20686e-04 1.16387e-02 9.14077e-03 1.42315e-02 1.27713e-02 1.96850e-03 3.53357e-03 1.61160e-03 15333 0.00438 0.02736 30372 Anemia&Heinz_body_anemia&Hb_SS_disease&Sickle_cell_disease_and_related_diseases&Susceptibility_to_malaria&Inborn_genetic_diseases&beta_Thalassemia&Hemoglobin_E&HEMOGLOBIN_S&Fetal_hemoglobin_quantitative_trait_locus_1&Beta-thalassemia&_dominant_inclusion_body_type&Malaria&_resistance_to¬_specified¬_provided HEMOGLOBIN_JAMAICA_PLAIN&HEMOGLOBIN_S_(ANTILLES)&HEMOGLOBIN_S_(CAMEROON)&HEMOGLOBIN_S_(PROVIDENCE)&HEMOGLOBIN_S_(TRAVIS)&HEMOGLOBIN_ZIGUINCHOR&Sickle_cell-Hemoglobin_O_Arab_disease Human_Phenotype_Ontology:HP:0001903&Human_Phenotype_Ontology:HP:0001926&Human_Phenotype_Ontology:HP:0003136&Human_Phenotype_Ontology:HP:0005509&MedGen:C0002871&Human_Phenotype_Ontology:HP:0005511&MONDO:MONDO:0007705&MedGen:C0700299&OMIM:140700&MONDO:MONDO:0011382&MedGen:C0002895&OMIM:603903&Orphanet:ORPHA232&SNOMED_CT:127040003&MONDO:MONDO:0017146&MedGen:CN202572&Orphanet:ORPHA275752&MONDO:MONDO:0021024&MedGen:C1970028&OMIM:611162&MeSH:D030342&MedGen:C0950123&MedGen:C0005283&OMIM:613985&Orphanet:ORPHA848&SNOMED_CT:65959000&MedGen:C0019024&SNOMED_CT:83815000&MedGen:C0019043&MedGen:C1841621&OMIM:141749&MedGen:C1858990&OMIM:603902&MedGen:C2720293&MedGen:CN169374&MedGen:CN517202 .&.&.&.&.&.&MedGen:C1264000&SNOMED_CT:127048005 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_benign(3)&Pathogenic(17) 446730:other&446731:other&446735:Pathogenic&446736:Pathogenic&446737:Pathogenic&446738:Pathogenic&446747:Pathogenic&446748:other single_nucleotide_variant HBB:3043&LOC106099062:106099062 SO:0001583&missense_variant 25 334 15 30 50.0 -11 6391976 T C C missense_variant MODERATE SMPD1 6609 Transcript NM_000543.5 protein_coding 2/6 1036 911 304 L/P cTt/cCt 1 EntrezGene T T 0.03 0.841 25.0 3.599817 25.0 0.99861388825256914 5.192884 0.50372418208901 0.68241 5.565679 0.562907516462299 0.70872 -2.08&-2.08&-2.08 4.91 0.999999999997208 0.000003 0.39241 0.7703 0.7839 .&.&. 1&1&1&1 -4.67&-5.98&-4.67 13.3621 0.977&.&0.971 0.97249 0.706548 0.999000 0.925000 3.842000 1.135000 6.54 rs120074124 5 143264 3.49006e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32040e-05 3.09799e-05 5.34960e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.48724e-05 0.00000e+00 0.00000e+00 0.00000e+00 2989 18028 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 120074124 15 30 50.0 -11 6391976 T C C missense_variant MODERATE SMPD1 6609 Transcript NM_001007593.3 protein_coding 2/6 1033 908 303 L/P cTt/cCt 1 EntrezGene T T 0.03 0.663 25.0 3.599817 25.0 0.99861388825256914 5.192884 0.50372418208901 0.68241 5.565679 0.562907516462299 0.70872 -2.08&-2.08&-2.08 4.91 0.999999999997208 0.000003 0.39241 0.7703 0.7839 .&.&. 1&1&1&1 -4.67&-5.98&-4.67 13.3621 0.977&.&0.971 0.97249 0.706548 0.999000 0.925000 3.842000 1.135000 6.54 rs120074124 5 143264 3.49006e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32040e-05 3.09799e-05 5.34960e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.48724e-05 0.00000e+00 0.00000e+00 0.00000e+00 2989 18028 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 120074124 15 30 50.0 -11 6391976 T C C downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001164.5 protein_coding 3149 -1 EntrezGene T T 25.0 3.599817 6.54 rs120074124 5 143264 3.49006e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32040e-05 3.09799e-05 5.34960e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.48724e-05 0.00000e+00 0.00000e+00 0.00000e+00 2989 18028 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 120074124 15 30 50.0 -11 6391976 T C C downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257319.2 protein_coding 3148 -1 EntrezGene T T OK 25.0 3.599817 6.54 rs120074124 5 143264 3.49006e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32040e-05 3.09799e-05 5.34960e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.48724e-05 0.00000e+00 0.00000e+00 0.00000e+00 2989 18028 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 120074124 15 30 50.0 -11 6391976 T C C downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257320.2 protein_coding 3148 -1 EntrezGene T T OK 25.0 3.599817 6.54 rs120074124 5 143264 3.49006e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32040e-05 3.09799e-05 5.34960e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.48724e-05 0.00000e+00 0.00000e+00 0.00000e+00 2989 18028 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 120074124 15 30 50.0 -11 6391976 T C C downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257321.2 protein_coding 3148 -1 EntrezGene T T OK 25.0 3.599817 6.54 rs120074124 5 143264 3.49006e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32040e-05 3.09799e-05 5.34960e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.48724e-05 0.00000e+00 0.00000e+00 0.00000e+00 2989 18028 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 120074124 15 30 50.0 -11 6391976 T C C downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257323.2 protein_coding 3148 -1 EntrezGene T T OK 25.0 3.599817 6.54 rs120074124 5 143264 3.49006e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32040e-05 3.09799e-05 5.34960e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.48724e-05 0.00000e+00 0.00000e+00 0.00000e+00 2989 18028 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 120074124 15 30 50.0 -11 6391976 T C C downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257325.2 protein_coding 3148 -1 EntrezGene T T OK 25.0 3.599817 6.54 rs120074124 5 143264 3.49006e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32040e-05 3.09799e-05 5.34960e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.48724e-05 0.00000e+00 0.00000e+00 0.00000e+00 2989 18028 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 120074124 15 30 50.0 -11 6391976 T C C downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257326.2 protein_coding 3148 -1 EntrezGene T T OK 25.0 3.599817 6.54 rs120074124 5 143264 3.49006e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32040e-05 3.09799e-05 5.34960e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.48724e-05 0.00000e+00 0.00000e+00 0.00000e+00 2989 18028 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 120074124 15 30 50.0 -11 6391976 T C C missense_variant MODERATE SMPD1 6609 Transcript NM_001318087.2 protein_coding 2/6 1036 911 304 L/P cTt/cCt 1 EntrezGene T T 0.02 0.766 25.0 3.599817 25.0 0.99861388825256914 5.192884 0.50372418208901 0.68241 5.565679 0.562907516462299 0.70872 -2.08&-2.08&-2.08 4.91 0.999999999997208 0.000003 0.39241 0.7703 0.7839 .&.&. 1&1&1&1 -4.67&-5.98&-4.67 13.3621 0.977&.&0.971 0.97249 0.706548 0.999000 0.925000 3.842000 1.135000 6.54 rs120074124 5 143264 3.49006e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32040e-05 3.09799e-05 5.34960e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.48724e-05 0.00000e+00 0.00000e+00 0.00000e+00 2989 18028 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 120074124 15 30 50.0 -11 6391976 T C C 5_prime_UTR_variant MODIFIER SMPD1 6609 Transcript NM_001318088.2 protein_coding 2/6 1036 1 EntrezGene T T 25.0 3.599817 6.54 rs120074124 5 143264 3.49006e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32040e-05 3.09799e-05 5.34960e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.48724e-05 0.00000e+00 0.00000e+00 0.00000e+00 2989 18028 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 120074124 15 30 50.0 -11 6391976 T C C missense_variant MODERATE SMPD1 6609 Transcript NM_001365135.2 protein_coding 2/5 1036 911 304 L/P cTt/cCt 1 EntrezGene T T 0.03 0.609 25.0 3.599817 25.0 0.99861388825256914 5.192884 0.50372418208901 0.68241 5.565679 0.562907516462299 0.70872 -2.08&-2.08&-2.08 4.91 0.999999999997208 0.000003 0.39241 0.7703 0.7839 .&.&. 1&1&1&1 -4.67&-5.98&-4.67 13.3621 0.977&.&0.971 0.97249 0.706548 0.999000 0.925000 3.842000 1.135000 6.54 rs120074124 5 143264 3.49006e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32040e-05 3.09799e-05 5.34960e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.48724e-05 0.00000e+00 0.00000e+00 0.00000e+00 2989 18028 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 120074124 15 30 50.0 -11 6391976 T C C downstream_gene_variant MODIFIER APBB1 322 Transcript NM_145689.3 protein_coding 3149 -1 EntrezGene T T 25.0 3.599817 6.54 rs120074124 5 143264 3.49006e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32040e-05 3.09799e-05 5.34960e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.48724e-05 0.00000e+00 0.00000e+00 0.00000e+00 2989 18028 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 120074124 15 30 50.0 -11 6391976 T C C non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript NR_027400.3 misc_RNA 2/5 1036 1 EntrezGene T T 25.0 3.599817 6.54 rs120074124 5 143264 3.49006e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32040e-05 3.09799e-05 5.34960e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.48724e-05 0.00000e+00 0.00000e+00 0.00000e+00 2989 18028 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 120074124 15 30 50.0 -11 6391976 T C C downstream_gene_variant MODIFIER APBB1 322 Transcript NR_047512.2 misc_RNA 3148 -1 EntrezGene T T OK 25.0 3.599817 6.54 rs120074124 5 143264 3.49006e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32040e-05 3.09799e-05 5.34960e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.48724e-05 0.00000e+00 0.00000e+00 0.00000e+00 2989 18028 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 120074124 15 30 50.0 -11 6391976 T C C intron_variant&non_coding_transcript_variant MODIFIER SMPD1 6609 Transcript NR_134502.2 misc_RNA 2/5 1 EntrezGene T T 25.0 3.599817 6.54 rs120074124 5 143264 3.49006e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32040e-05 3.09799e-05 5.34960e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.48724e-05 0.00000e+00 0.00000e+00 0.00000e+00 2989 18028 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 120074124 15 30 50.0 -11 6391976 T C C missense_variant MODERATE SMPD1 6609 Transcript XM_011520304.2 protein_coding 2/5 1036 911 304 L/P cTt/cCt 1 EntrezGene T T 25.0 3.599817 25.0 0.99861388825256914 5.192884 0.50372418208901 0.68241 5.565679 0.562907516462299 0.70872 -2.08&-2.08&-2.08 4.91 0.999999999997208 0.000003 0.39241 0.7703 0.7839 .&.&. 1&1&1&1 -4.67&-5.98&-4.67 13.3621 0.977&.&0.971 0.97249 0.706548 0.999000 0.925000 3.842000 1.135000 6.54 rs120074124 5 143264 3.49006e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32040e-05 3.09799e-05 5.34960e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.48724e-05 0.00000e+00 0.00000e+00 0.00000e+00 2989 18028 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 120074124 15 30 50.0 -11 6391976 T C C non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript XR_001747940.2 misc_RNA 2/6 1036 1 EntrezGene T T 25.0 3.599817 6.54 rs120074124 5 143264 3.49006e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32040e-05 3.09799e-05 5.34960e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.48724e-05 0.00000e+00 0.00000e+00 0.00000e+00 2989 18028 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 120074124 15 30 50.0 -11 6391976 T C C non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript XR_002957158.1 misc_RNA 2/5 1036 1 EntrezGene T T 25.0 3.599817 6.54 rs120074124 5 143264 3.49006e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32040e-05 3.09799e-05 5.34960e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.48724e-05 0.00000e+00 0.00000e+00 0.00000e+00 2989 18028 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 120074124 15 30 50.0 -11 6392055 TC T - frameshift_variant HIGH SMPD1 6609 Transcript NM_000543.5 protein_coding 2/6 1116 991 331 P/X Ccc/cc 1 EntrezGene C C 33 4.804609 6.54 rs1408292116 2 142860 1.39997e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.03136e-04 5.69476e-04 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44513e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39600e-05 0.00000e+00 0.00000e+00 0.00000e+00 2990 18029 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SMPD1:6609 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 387906289 15 30 50.0 -11 6392055 TC T - frameshift_variant HIGH SMPD1 6609 Transcript NM_001007593.3 protein_coding 2/6 1113 988 330 P/X Ccc/cc 1 EntrezGene C C 33 4.804609 6.54 rs1408292116 2 142860 1.39997e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.03136e-04 5.69476e-04 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44513e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39600e-05 0.00000e+00 0.00000e+00 0.00000e+00 2990 18029 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SMPD1:6609 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 387906289 15 30 50.0 -11 6392055 TC T - downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001164.5 protein_coding 3069 -1 EntrezGene C C 33 4.804609 6.54 rs1408292116 2 142860 1.39997e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.03136e-04 5.69476e-04 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44513e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39600e-05 0.00000e+00 0.00000e+00 0.00000e+00 2990 18029 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SMPD1:6609 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 387906289 15 30 50.0 -11 6392055 TC T - downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257319.2 protein_coding 3068 -1 EntrezGene C C OK 33 4.804609 6.54 rs1408292116 2 142860 1.39997e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.03136e-04 5.69476e-04 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44513e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39600e-05 0.00000e+00 0.00000e+00 0.00000e+00 2990 18029 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SMPD1:6609 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 387906289 15 30 50.0 -11 6392055 TC T - downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257320.2 protein_coding 3068 -1 EntrezGene C C OK 33 4.804609 6.54 rs1408292116 2 142860 1.39997e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.03136e-04 5.69476e-04 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44513e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39600e-05 0.00000e+00 0.00000e+00 0.00000e+00 2990 18029 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SMPD1:6609 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 387906289 15 30 50.0 -11 6392055 TC T - downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257321.2 protein_coding 3068 -1 EntrezGene C C OK 33 4.804609 6.54 rs1408292116 2 142860 1.39997e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.03136e-04 5.69476e-04 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44513e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39600e-05 0.00000e+00 0.00000e+00 0.00000e+00 2990 18029 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SMPD1:6609 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 387906289 15 30 50.0 -11 6392055 TC T - downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257323.2 protein_coding 3068 -1 EntrezGene C C OK 33 4.804609 6.54 rs1408292116 2 142860 1.39997e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.03136e-04 5.69476e-04 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44513e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39600e-05 0.00000e+00 0.00000e+00 0.00000e+00 2990 18029 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SMPD1:6609 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 387906289 15 30 50.0 -11 6392055 TC T - downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257325.2 protein_coding 3068 -1 EntrezGene C C OK 33 4.804609 6.54 rs1408292116 2 142860 1.39997e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.03136e-04 5.69476e-04 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44513e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39600e-05 0.00000e+00 0.00000e+00 0.00000e+00 2990 18029 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SMPD1:6609 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 387906289 15 30 50.0 -11 6392055 TC T - downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257326.2 protein_coding 3068 -1 EntrezGene C C OK 33 4.804609 6.54 rs1408292116 2 142860 1.39997e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.03136e-04 5.69476e-04 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44513e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39600e-05 0.00000e+00 0.00000e+00 0.00000e+00 2990 18029 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SMPD1:6609 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 387906289 15 30 50.0 -11 6392055 TC T - frameshift_variant HIGH SMPD1 6609 Transcript NM_001318087.2 protein_coding 2/6 1116 991 331 P/X Ccc/cc 1 EntrezGene C C 33 4.804609 6.54 rs1408292116 2 142860 1.39997e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.03136e-04 5.69476e-04 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44513e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39600e-05 0.00000e+00 0.00000e+00 0.00000e+00 2990 18029 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SMPD1:6609 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 387906289 15 30 50.0 -11 6392055 TC T - frameshift_variant HIGH SMPD1 6609 Transcript NM_001318088.2 protein_coding 2/6 1116 30 10 L/X ctC/ct 1 EntrezGene C C 33 4.804609 6.54 rs1408292116 2 142860 1.39997e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.03136e-04 5.69476e-04 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44513e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39600e-05 0.00000e+00 0.00000e+00 0.00000e+00 2990 18029 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SMPD1:6609 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 387906289 15 30 50.0 -11 6392055 TC T - frameshift_variant HIGH SMPD1 6609 Transcript NM_001365135.2 protein_coding 2/5 1116 991 331 P/X Ccc/cc 1 EntrezGene C C 33 4.804609 6.54 rs1408292116 2 142860 1.39997e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.03136e-04 5.69476e-04 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44513e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39600e-05 0.00000e+00 0.00000e+00 0.00000e+00 2990 18029 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SMPD1:6609 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 387906289 15 30 50.0 -11 6392055 TC T - downstream_gene_variant MODIFIER APBB1 322 Transcript NM_145689.3 protein_coding 3069 -1 EntrezGene C C 33 4.804609 6.54 rs1408292116 2 142860 1.39997e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.03136e-04 5.69476e-04 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44513e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39600e-05 0.00000e+00 0.00000e+00 0.00000e+00 2990 18029 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SMPD1:6609 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 387906289 15 30 50.0 -11 6392055 TC T - non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript NR_027400.3 misc_RNA 2/5 1116 1 EntrezGene C C 33 4.804609 6.54 rs1408292116 2 142860 1.39997e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.03136e-04 5.69476e-04 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44513e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39600e-05 0.00000e+00 0.00000e+00 0.00000e+00 2990 18029 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SMPD1:6609 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 387906289 15 30 50.0 -11 6392055 TC T - downstream_gene_variant MODIFIER APBB1 322 Transcript NR_047512.2 misc_RNA 3068 -1 EntrezGene C C OK 33 4.804609 6.54 rs1408292116 2 142860 1.39997e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.03136e-04 5.69476e-04 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44513e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39600e-05 0.00000e+00 0.00000e+00 0.00000e+00 2990 18029 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SMPD1:6609 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 387906289 15 30 50.0 -11 6392055 TC T - intron_variant&non_coding_transcript_variant MODIFIER SMPD1 6609 Transcript NR_134502.2 misc_RNA 2/5 1 EntrezGene C C 33 4.804609 6.54 rs1408292116 2 142860 1.39997e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.03136e-04 5.69476e-04 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44513e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39600e-05 0.00000e+00 0.00000e+00 0.00000e+00 2990 18029 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SMPD1:6609 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 387906289 15 30 50.0 -11 6392055 TC T - frameshift_variant HIGH SMPD1 6609 Transcript XM_011520304.2 protein_coding 2/5 1116 991 331 P/X Ccc/cc 1 EntrezGene C C 33 4.804609 6.54 rs1408292116 2 142860 1.39997e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.03136e-04 5.69476e-04 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44513e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39600e-05 0.00000e+00 0.00000e+00 0.00000e+00 2990 18029 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SMPD1:6609 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 387906289 15 30 50.0 -11 6392055 TC T - non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript XR_001747940.2 misc_RNA 2/6 1116 1 EntrezGene C C 33 4.804609 6.54 rs1408292116 2 142860 1.39997e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.03136e-04 5.69476e-04 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44513e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39600e-05 0.00000e+00 0.00000e+00 0.00000e+00 2990 18029 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SMPD1:6609 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 387906289 15 30 50.0 -11 6392055 TC T - non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript XR_002957158.1 misc_RNA 2/5 1116 1 EntrezGene C C 33 4.804609 6.54 rs1408292116 2 142860 1.39997e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.03136e-04 5.69476e-04 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35755e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44513e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39600e-05 0.00000e+00 0.00000e+00 0.00000e+00 2990 18029 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SMPD1:6609 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 387906289 15 30 50.0 -11 6394203 C T T missense_variant MODERATE SMPD1 6609 Transcript NM_000543.5 protein_coding 6/6 1617 1492 498 R/C Cgt/Tgt 1 EntrezGene C C 0 0.998 26.4 3.911540 26.4 0.99917932109665963 4.949336 0.476204251655119 0.66417 6.139187 0.620552069957094 0.74474 -5.81&-5.81 3.76 0.999789358486881 0.000054 0.85746 0.9810 1.0778 .&. 1&1&1&1 -7.16&-7.16 10.3319 0.97&0.971 0.96135 0.67177 1.000000 0.587000 5.601000 1.026000 6.54 rs769904764 3 143266 2.09401e-05 2.38005e-05 0.00000e+00 5.17545e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70812e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44063e-05 3.09751e-05 5.34788e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09252e-05 0.00000e+00 0.00000e+00 0.00000e+00 198095 0.00002 195256 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001819&synonymous_variant 1 769904764 15 30 50.0 -11 6394203 C T T missense_variant MODERATE SMPD1 6609 Transcript NM_001007593.3 protein_coding 6/6 1614 1489 497 R/C Cgt/Tgt 1 EntrezGene C C 0 0.996 26.4 3.911540 26.4 0.99917932109665963 4.949336 0.476204251655119 0.66417 6.139187 0.620552069957094 0.74474 -5.81&-5.81 3.76 0.999789358486881 0.000054 0.85746 0.9810 1.0778 .&. 1&1&1&1 -7.16&-7.16 10.3319 0.97&0.971 0.96135 0.67177 1.000000 0.587000 5.601000 1.026000 6.54 rs769904764 3 143266 2.09401e-05 2.38005e-05 0.00000e+00 5.17545e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70812e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44063e-05 3.09751e-05 5.34788e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09252e-05 0.00000e+00 0.00000e+00 0.00000e+00 198095 0.00002 195256 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001819&synonymous_variant 1 769904764 15 30 50.0 -11 6394203 C T T downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001164.5 protein_coding 922 -1 EntrezGene C C 26.4 3.911540 6.54 rs769904764 3 143266 2.09401e-05 2.38005e-05 0.00000e+00 5.17545e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70812e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44063e-05 3.09751e-05 5.34788e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09252e-05 0.00000e+00 0.00000e+00 0.00000e+00 198095 0.00002 195256 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001819&synonymous_variant 1 769904764 15 30 50.0 -11 6394203 C T T downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257319.2 protein_coding 921 -1 EntrezGene C C OK 26.4 3.911540 6.54 rs769904764 3 143266 2.09401e-05 2.38005e-05 0.00000e+00 5.17545e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70812e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44063e-05 3.09751e-05 5.34788e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09252e-05 0.00000e+00 0.00000e+00 0.00000e+00 198095 0.00002 195256 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001819&synonymous_variant 1 769904764 15 30 50.0 -11 6394203 C T T downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257320.2 protein_coding 921 -1 EntrezGene C C OK 26.4 3.911540 6.54 rs769904764 3 143266 2.09401e-05 2.38005e-05 0.00000e+00 5.17545e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70812e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44063e-05 3.09751e-05 5.34788e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09252e-05 0.00000e+00 0.00000e+00 0.00000e+00 198095 0.00002 195256 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001819&synonymous_variant 1 769904764 15 30 50.0 -11 6394203 C T T downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257321.2 protein_coding 921 -1 EntrezGene C C OK 26.4 3.911540 6.54 rs769904764 3 143266 2.09401e-05 2.38005e-05 0.00000e+00 5.17545e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70812e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44063e-05 3.09751e-05 5.34788e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09252e-05 0.00000e+00 0.00000e+00 0.00000e+00 198095 0.00002 195256 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001819&synonymous_variant 1 769904764 15 30 50.0 -11 6394203 C T T downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257323.2 protein_coding 921 -1 EntrezGene C C OK 26.4 3.911540 6.54 rs769904764 3 143266 2.09401e-05 2.38005e-05 0.00000e+00 5.17545e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70812e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44063e-05 3.09751e-05 5.34788e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09252e-05 0.00000e+00 0.00000e+00 0.00000e+00 198095 0.00002 195256 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001819&synonymous_variant 1 769904764 15 30 50.0 -11 6394203 C T T downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257325.2 protein_coding 921 -1 EntrezGene C C OK 26.4 3.911540 6.54 rs769904764 3 143266 2.09401e-05 2.38005e-05 0.00000e+00 5.17545e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70812e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44063e-05 3.09751e-05 5.34788e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09252e-05 0.00000e+00 0.00000e+00 0.00000e+00 198095 0.00002 195256 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001819&synonymous_variant 1 769904764 15 30 50.0 -11 6394203 C T T downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257326.2 protein_coding 921 -1 EntrezGene C C OK 26.4 3.911540 6.54 rs769904764 3 143266 2.09401e-05 2.38005e-05 0.00000e+00 5.17545e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70812e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44063e-05 3.09751e-05 5.34788e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09252e-05 0.00000e+00 0.00000e+00 0.00000e+00 198095 0.00002 195256 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001819&synonymous_variant 1 769904764 15 30 50.0 -11 6394203 C T T synonymous_variant LOW SMPD1 6609 Transcript NM_001318087.2 protein_coding 6/6 1637 1512 504 T acC/acT 1 EntrezGene C C 26.4 3.911540 6.54 rs769904764 3 143266 2.09401e-05 2.38005e-05 0.00000e+00 5.17545e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70812e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44063e-05 3.09751e-05 5.34788e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09252e-05 0.00000e+00 0.00000e+00 0.00000e+00 198095 0.00002 195256 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001819&synonymous_variant 1 769904764 15 30 50.0 -11 6394203 C T T missense_variant MODERATE SMPD1 6609 Transcript NM_001318088.2 protein_coding 6/6 1657 571 191 R/C Cgt/Tgt 1 EntrezGene C C 0 0.998 26.4 3.911540 26.4 0.99917932109665963 4.949336 0.476204251655119 0.66417 6.139187 0.620552069957094 0.74474 -5.81&-5.81 3.76 0.999789358486881 0.000054 0.85746 0.9810 1.0778 .&. 1&1&1&1 -7.16&-7.16 10.3319 0.97&0.971 0.96135 0.67177 1.000000 0.587000 5.601000 1.026000 6.54 rs769904764 3 143266 2.09401e-05 2.38005e-05 0.00000e+00 5.17545e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70812e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44063e-05 3.09751e-05 5.34788e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09252e-05 0.00000e+00 0.00000e+00 0.00000e+00 198095 0.00002 195256 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001819&synonymous_variant 1 769904764 15 30 50.0 -11 6394203 C T T missense_variant MODERATE SMPD1 6609 Transcript NM_001365135.2 protein_coding 5/5 1485 1360 454 R/C Cgt/Tgt 1 EntrezGene C C 0 1 26.4 3.911540 26.4 0.99917932109665963 4.949336 0.476204251655119 0.66417 6.139187 0.620552069957094 0.74474 -5.81&-5.81 3.76 0.999789358486881 0.000054 0.85746 0.9810 1.0778 .&. 1&1&1&1 -7.16&-7.16 10.3319 0.97&0.971 0.96135 0.67177 1.000000 0.587000 5.601000 1.026000 6.54 rs769904764 3 143266 2.09401e-05 2.38005e-05 0.00000e+00 5.17545e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70812e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44063e-05 3.09751e-05 5.34788e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09252e-05 0.00000e+00 0.00000e+00 0.00000e+00 198095 0.00002 195256 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001819&synonymous_variant 1 769904764 15 30 50.0 -11 6394203 C T T downstream_gene_variant MODIFIER APBB1 322 Transcript NM_145689.3 protein_coding 922 -1 EntrezGene C C 26.4 3.911540 6.54 rs769904764 3 143266 2.09401e-05 2.38005e-05 0.00000e+00 5.17545e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70812e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44063e-05 3.09751e-05 5.34788e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09252e-05 0.00000e+00 0.00000e+00 0.00000e+00 198095 0.00002 195256 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001819&synonymous_variant 1 769904764 15 30 50.0 -11 6394203 C T T non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript NR_027400.3 misc_RNA 5/5 1445 1 EntrezGene C C 26.4 3.911540 6.54 rs769904764 3 143266 2.09401e-05 2.38005e-05 0.00000e+00 5.17545e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70812e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44063e-05 3.09751e-05 5.34788e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09252e-05 0.00000e+00 0.00000e+00 0.00000e+00 198095 0.00002 195256 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001819&synonymous_variant 1 769904764 15 30 50.0 -11 6394203 C T T downstream_gene_variant MODIFIER APBB1 322 Transcript NR_047512.2 misc_RNA 921 -1 EntrezGene C C OK 26.4 3.911540 6.54 rs769904764 3 143266 2.09401e-05 2.38005e-05 0.00000e+00 5.17545e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70812e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44063e-05 3.09751e-05 5.34788e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09252e-05 0.00000e+00 0.00000e+00 0.00000e+00 198095 0.00002 195256 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001819&synonymous_variant 1 769904764 15 30 50.0 -11 6394203 C T T non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript NR_134502.2 misc_RNA 6/6 984 1 EntrezGene C C 26.4 3.911540 6.54 rs769904764 3 143266 2.09401e-05 2.38005e-05 0.00000e+00 5.17545e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70812e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44063e-05 3.09751e-05 5.34788e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09252e-05 0.00000e+00 0.00000e+00 0.00000e+00 198095 0.00002 195256 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001819&synonymous_variant 1 769904764 15 30 50.0 -11 6394203 C T T synonymous_variant LOW SMPD1 6609 Transcript XM_011520304.2 protein_coding 5/5 1505 1380 460 T acC/acT 1 EntrezGene C C 26.4 3.911540 6.54 rs769904764 3 143266 2.09401e-05 2.38005e-05 0.00000e+00 5.17545e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70812e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44063e-05 3.09751e-05 5.34788e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09252e-05 0.00000e+00 0.00000e+00 0.00000e+00 198095 0.00002 195256 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001819&synonymous_variant 1 769904764 15 30 50.0 -11 6394203 C T T non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript XR_001747940.2 misc_RNA 6/6 1677 1 EntrezGene C C 26.4 3.911540 6.54 rs769904764 3 143266 2.09401e-05 2.38005e-05 0.00000e+00 5.17545e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70812e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44063e-05 3.09751e-05 5.34788e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09252e-05 0.00000e+00 0.00000e+00 0.00000e+00 198095 0.00002 195256 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001819&synonymous_variant 1 769904764 15 30 50.0 -11 6394203 C T T non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript XR_002957158.1 misc_RNA 5/5 1859 1 EntrezGene C C 26.4 3.911540 6.54 rs769904764 3 143266 2.09401e-05 2.38005e-05 0.00000e+00 5.17545e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70812e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44063e-05 3.09751e-05 5.34788e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09252e-05 0.00000e+00 0.00000e+00 0.00000e+00 198095 0.00002 195256 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001819&synonymous_variant 1 769904764 15 30 50.0 -11 6394204 G A A missense_variant MODERATE SMPD1 6609 Transcript NM_000543.5 protein_coding 6/6 1618 1493 498 R/H cGt/cAt 1 EntrezGene G G 0 0.994 26.7 3.963767 26.7 0.99925801194668662 4.80111 0.458393060776743 0.65260 4.914361 0.484079028265117 0.66161 -5.8&-5.8 4.68 0.999999994644076 0.000054 0.339581 0.5207 0.1806 .&. 1&1&1&1 -4.63&-4.63 15.1283 0.972&0.959 0.98984 0.67177 1.000000 0.460000 9.374000 1.176000 6.54 167712 177005 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G A A missense_variant MODERATE SMPD1 6609 Transcript NM_001007593.3 protein_coding 6/6 1615 1490 497 R/H cGt/cAt 1 EntrezGene G G 0 0.991 26.7 3.963767 26.7 0.99925801194668662 4.80111 0.458393060776743 0.65260 4.914361 0.484079028265117 0.66161 -5.8&-5.8 4.68 0.999999994644076 0.000054 0.339581 0.5207 0.1806 .&. 1&1&1&1 -4.63&-4.63 15.1283 0.972&0.959 0.98984 0.67177 1.000000 0.460000 9.374000 1.176000 6.54 167712 177005 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G A A downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001164.5 protein_coding 921 -1 EntrezGene G G 26.7 3.963767 6.54 167712 177005 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G A A downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257319.2 protein_coding 920 -1 EntrezGene G G OK 26.7 3.963767 6.54 167712 177005 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G A A downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257320.2 protein_coding 920 -1 EntrezGene G G OK 26.7 3.963767 6.54 167712 177005 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G A A downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257321.2 protein_coding 920 -1 EntrezGene G G OK 26.7 3.963767 6.54 167712 177005 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G A A downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257323.2 protein_coding 920 -1 EntrezGene G G OK 26.7 3.963767 6.54 167712 177005 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G A A downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257325.2 protein_coding 920 -1 EntrezGene G G OK 26.7 3.963767 6.54 167712 177005 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G A A downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257326.2 protein_coding 920 -1 EntrezGene G G OK 26.7 3.963767 6.54 167712 177005 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G A A missense_variant MODERATE SMPD1 6609 Transcript NM_001318087.2 protein_coding 6/6 1638 1513 505 V/M Gtg/Atg 1 EntrezGene G G 0 0.106 26.7 3.963767 26.7 0.99925801194668662 4.80111 0.458393060776743 0.65260 4.914361 0.484079028265117 0.66161 -5.8&-5.8 4.68 0.999999994644076 0.000054 0.339581 0.5207 0.1806 .&. 1&1&1&1 -4.63&-4.63 15.1283 0.972&0.959 0.98984 0.67177 1.000000 0.460000 9.374000 1.176000 6.54 167712 177005 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G A A missense_variant MODERATE SMPD1 6609 Transcript NM_001318088.2 protein_coding 6/6 1658 572 191 R/H cGt/cAt 1 EntrezGene G G 0 0.996 26.7 3.963767 26.7 0.99925801194668662 4.80111 0.458393060776743 0.65260 4.914361 0.484079028265117 0.66161 -5.8&-5.8 4.68 0.999999994644076 0.000054 0.339581 0.5207 0.1806 .&. 1&1&1&1 -4.63&-4.63 15.1283 0.972&0.959 0.98984 0.67177 1.000000 0.460000 9.374000 1.176000 6.54 167712 177005 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G A A missense_variant MODERATE SMPD1 6609 Transcript NM_001365135.2 protein_coding 5/5 1486 1361 454 R/H cGt/cAt 1 EntrezGene G G 0 0.998 26.7 3.963767 26.7 0.99925801194668662 4.80111 0.458393060776743 0.65260 4.914361 0.484079028265117 0.66161 -5.8&-5.8 4.68 0.999999994644076 0.000054 0.339581 0.5207 0.1806 .&. 1&1&1&1 -4.63&-4.63 15.1283 0.972&0.959 0.98984 0.67177 1.000000 0.460000 9.374000 1.176000 6.54 167712 177005 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G A A downstream_gene_variant MODIFIER APBB1 322 Transcript NM_145689.3 protein_coding 921 -1 EntrezGene G G 26.7 3.963767 6.54 167712 177005 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G A A non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript NR_027400.3 misc_RNA 5/5 1446 1 EntrezGene G G 26.7 3.963767 6.54 167712 177005 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G A A downstream_gene_variant MODIFIER APBB1 322 Transcript NR_047512.2 misc_RNA 920 -1 EntrezGene G G OK 26.7 3.963767 6.54 167712 177005 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G A A non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript NR_134502.2 misc_RNA 6/6 985 1 EntrezGene G G 26.7 3.963767 6.54 167712 177005 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G A A missense_variant MODERATE SMPD1 6609 Transcript XM_011520304.2 protein_coding 5/5 1506 1381 461 V/M Gtg/Atg 1 EntrezGene G G 26.7 3.963767 26.7 0.99925801194668662 4.80111 0.458393060776743 0.65260 4.914361 0.484079028265117 0.66161 -5.8&-5.8 4.68 0.999999994644076 0.000054 0.339581 0.5207 0.1806 .&. 1&1&1&1 -4.63&-4.63 15.1283 0.972&0.959 0.98984 0.67177 1.000000 0.460000 9.374000 1.176000 6.54 167712 177005 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G A A non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript XR_001747940.2 misc_RNA 6/6 1678 1 EntrezGene G G 26.7 3.963767 6.54 167712 177005 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G A A non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript XR_002957158.1 misc_RNA 5/5 1860 1 EntrezGene G G 26.7 3.963767 6.54 167712 177005 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G T T missense_variant MODERATE SMPD1 6609 Transcript NM_000543.5 protein_coding 6/6 1618 1493 498 R/L cGt/cTt 1 EntrezGene G G 0 0.983 27.1 4.011427 27.1 0.9984371345258336 4.998959 0.481972823184279 0.66796 5.202772 0.521001478713605 0.68333 -5.8&-5.8 4.68 0.999999994644076 0.000054 0.449561 0.5692 0.4340 .&. 1&1&1&1 -6.42&-6.42 15.1283 0.985&0.985 0.99130 0.67177 1.000000 0.460000 9.374000 1.176000 6.54 chr11:6394204-6394204 14 143238 9.77394e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70921e-03 3.97727e-03 1.28041e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21882e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20503e-05 4.64742e-05 5.34902e-05 3.68161e-05 9.30233e-04 0.00000e+00 1.90114e-03 9.76549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2980 0.00012 18019 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant APBB1:322&SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G T T missense_variant MODERATE SMPD1 6609 Transcript NM_001007593.3 protein_coding 6/6 1615 1490 497 R/L cGt/cTt 1 EntrezGene G G 0 0.99 27.1 4.011427 27.1 0.9984371345258336 4.998959 0.481972823184279 0.66796 5.202772 0.521001478713605 0.68333 -5.8&-5.8 4.68 0.999999994644076 0.000054 0.449561 0.5692 0.4340 .&. 1&1&1&1 -6.42&-6.42 15.1283 0.985&0.985 0.99130 0.67177 1.000000 0.460000 9.374000 1.176000 6.54 chr11:6394204-6394204 14 143238 9.77394e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70921e-03 3.97727e-03 1.28041e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21882e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20503e-05 4.64742e-05 5.34902e-05 3.68161e-05 9.30233e-04 0.00000e+00 1.90114e-03 9.76549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2980 0.00012 18019 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant APBB1:322&SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G T T downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001164.5 protein_coding 921 -1 EntrezGene G G 27.1 4.011427 6.54 chr11:6394204-6394204 14 143238 9.77394e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70921e-03 3.97727e-03 1.28041e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21882e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20503e-05 4.64742e-05 5.34902e-05 3.68161e-05 9.30233e-04 0.00000e+00 1.90114e-03 9.76549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2980 0.00012 18019 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant APBB1:322&SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G T T downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257319.2 protein_coding 920 -1 EntrezGene G G OK 27.1 4.011427 6.54 chr11:6394204-6394204 14 143238 9.77394e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70921e-03 3.97727e-03 1.28041e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21882e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20503e-05 4.64742e-05 5.34902e-05 3.68161e-05 9.30233e-04 0.00000e+00 1.90114e-03 9.76549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2980 0.00012 18019 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant APBB1:322&SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G T T downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257320.2 protein_coding 920 -1 EntrezGene G G OK 27.1 4.011427 6.54 chr11:6394204-6394204 14 143238 9.77394e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70921e-03 3.97727e-03 1.28041e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21882e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20503e-05 4.64742e-05 5.34902e-05 3.68161e-05 9.30233e-04 0.00000e+00 1.90114e-03 9.76549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2980 0.00012 18019 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant APBB1:322&SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G T T downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257321.2 protein_coding 920 -1 EntrezGene G G OK 27.1 4.011427 6.54 chr11:6394204-6394204 14 143238 9.77394e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70921e-03 3.97727e-03 1.28041e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21882e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20503e-05 4.64742e-05 5.34902e-05 3.68161e-05 9.30233e-04 0.00000e+00 1.90114e-03 9.76549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2980 0.00012 18019 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant APBB1:322&SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G T T downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257323.2 protein_coding 920 -1 EntrezGene G G OK 27.1 4.011427 6.54 chr11:6394204-6394204 14 143238 9.77394e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70921e-03 3.97727e-03 1.28041e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21882e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20503e-05 4.64742e-05 5.34902e-05 3.68161e-05 9.30233e-04 0.00000e+00 1.90114e-03 9.76549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2980 0.00012 18019 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant APBB1:322&SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G T T downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257325.2 protein_coding 920 -1 EntrezGene G G OK 27.1 4.011427 6.54 chr11:6394204-6394204 14 143238 9.77394e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70921e-03 3.97727e-03 1.28041e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21882e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20503e-05 4.64742e-05 5.34902e-05 3.68161e-05 9.30233e-04 0.00000e+00 1.90114e-03 9.76549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2980 0.00012 18019 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant APBB1:322&SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G T T downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257326.2 protein_coding 920 -1 EntrezGene G G OK 27.1 4.011427 6.54 chr11:6394204-6394204 14 143238 9.77394e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70921e-03 3.97727e-03 1.28041e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21882e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20503e-05 4.64742e-05 5.34902e-05 3.68161e-05 9.30233e-04 0.00000e+00 1.90114e-03 9.76549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2980 0.00012 18019 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant APBB1:322&SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G T T missense_variant MODERATE SMPD1 6609 Transcript NM_001318087.2 protein_coding 6/6 1638 1513 505 V/L Gtg/Ttg 1 EntrezGene G G 0 0.003 27.1 4.011427 27.1 0.9984371345258336 4.998959 0.481972823184279 0.66796 5.202772 0.521001478713605 0.68333 -5.8&-5.8 4.68 0.999999994644076 0.000054 0.449561 0.5692 0.4340 .&. 1&1&1&1 -6.42&-6.42 15.1283 0.985&0.985 0.99130 0.67177 1.000000 0.460000 9.374000 1.176000 6.54 chr11:6394204-6394204 14 143238 9.77394e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70921e-03 3.97727e-03 1.28041e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21882e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20503e-05 4.64742e-05 5.34902e-05 3.68161e-05 9.30233e-04 0.00000e+00 1.90114e-03 9.76549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2980 0.00012 18019 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant APBB1:322&SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G T T missense_variant MODERATE SMPD1 6609 Transcript NM_001318088.2 protein_coding 6/6 1658 572 191 R/L cGt/cTt 1 EntrezGene G G 0 0.994 27.1 4.011427 27.1 0.9984371345258336 4.998959 0.481972823184279 0.66796 5.202772 0.521001478713605 0.68333 -5.8&-5.8 4.68 0.999999994644076 0.000054 0.449561 0.5692 0.4340 .&. 1&1&1&1 -6.42&-6.42 15.1283 0.985&0.985 0.99130 0.67177 1.000000 0.460000 9.374000 1.176000 6.54 chr11:6394204-6394204 14 143238 9.77394e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70921e-03 3.97727e-03 1.28041e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21882e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20503e-05 4.64742e-05 5.34902e-05 3.68161e-05 9.30233e-04 0.00000e+00 1.90114e-03 9.76549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2980 0.00012 18019 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant APBB1:322&SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G T T missense_variant MODERATE SMPD1 6609 Transcript NM_001365135.2 protein_coding 5/5 1486 1361 454 R/L cGt/cTt 1 EntrezGene G G 0 0.999 27.1 4.011427 27.1 0.9984371345258336 4.998959 0.481972823184279 0.66796 5.202772 0.521001478713605 0.68333 -5.8&-5.8 4.68 0.999999994644076 0.000054 0.449561 0.5692 0.4340 .&. 1&1&1&1 -6.42&-6.42 15.1283 0.985&0.985 0.99130 0.67177 1.000000 0.460000 9.374000 1.176000 6.54 chr11:6394204-6394204 14 143238 9.77394e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70921e-03 3.97727e-03 1.28041e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21882e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20503e-05 4.64742e-05 5.34902e-05 3.68161e-05 9.30233e-04 0.00000e+00 1.90114e-03 9.76549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2980 0.00012 18019 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant APBB1:322&SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G T T downstream_gene_variant MODIFIER APBB1 322 Transcript NM_145689.3 protein_coding 921 -1 EntrezGene G G 27.1 4.011427 6.54 chr11:6394204-6394204 14 143238 9.77394e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70921e-03 3.97727e-03 1.28041e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21882e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20503e-05 4.64742e-05 5.34902e-05 3.68161e-05 9.30233e-04 0.00000e+00 1.90114e-03 9.76549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2980 0.00012 18019 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant APBB1:322&SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G T T non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript NR_027400.3 misc_RNA 5/5 1446 1 EntrezGene G G 27.1 4.011427 6.54 chr11:6394204-6394204 14 143238 9.77394e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70921e-03 3.97727e-03 1.28041e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21882e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20503e-05 4.64742e-05 5.34902e-05 3.68161e-05 9.30233e-04 0.00000e+00 1.90114e-03 9.76549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2980 0.00012 18019 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant APBB1:322&SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G T T downstream_gene_variant MODIFIER APBB1 322 Transcript NR_047512.2 misc_RNA 920 -1 EntrezGene G G OK 27.1 4.011427 6.54 chr11:6394204-6394204 14 143238 9.77394e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70921e-03 3.97727e-03 1.28041e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21882e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20503e-05 4.64742e-05 5.34902e-05 3.68161e-05 9.30233e-04 0.00000e+00 1.90114e-03 9.76549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2980 0.00012 18019 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant APBB1:322&SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G T T non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript NR_134502.2 misc_RNA 6/6 985 1 EntrezGene G G 27.1 4.011427 6.54 chr11:6394204-6394204 14 143238 9.77394e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70921e-03 3.97727e-03 1.28041e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21882e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20503e-05 4.64742e-05 5.34902e-05 3.68161e-05 9.30233e-04 0.00000e+00 1.90114e-03 9.76549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2980 0.00012 18019 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant APBB1:322&SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G T T missense_variant MODERATE SMPD1 6609 Transcript XM_011520304.2 protein_coding 5/5 1506 1381 461 V/L Gtg/Ttg 1 EntrezGene G G 27.1 4.011427 27.1 0.9984371345258336 4.998959 0.481972823184279 0.66796 5.202772 0.521001478713605 0.68333 -5.8&-5.8 4.68 0.999999994644076 0.000054 0.449561 0.5692 0.4340 .&. 1&1&1&1 -6.42&-6.42 15.1283 0.985&0.985 0.99130 0.67177 1.000000 0.460000 9.374000 1.176000 6.54 chr11:6394204-6394204 14 143238 9.77394e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70921e-03 3.97727e-03 1.28041e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21882e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20503e-05 4.64742e-05 5.34902e-05 3.68161e-05 9.30233e-04 0.00000e+00 1.90114e-03 9.76549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2980 0.00012 18019 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant APBB1:322&SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G T T non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript XR_001747940.2 misc_RNA 6/6 1678 1 EntrezGene G G 27.1 4.011427 6.54 chr11:6394204-6394204 14 143238 9.77394e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70921e-03 3.97727e-03 1.28041e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21882e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20503e-05 4.64742e-05 5.34902e-05 3.68161e-05 9.30233e-04 0.00000e+00 1.90114e-03 9.76549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2980 0.00012 18019 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant APBB1:322&SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394204 G T T non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript XR_002957158.1 misc_RNA 5/5 1860 1 EntrezGene G G 27.1 4.011427 6.54 chr11:6394204-6394204 14 143238 9.77394e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70921e-03 3.97727e-03 1.28041e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.21882e-04 0.00000e+00 0.00000e+00 0.00000e+00 7.20503e-05 4.64742e-05 5.34902e-05 3.68161e-05 9.30233e-04 0.00000e+00 1.90114e-03 9.76549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2980 0.00012 18019 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant APBB1:322&SMPD1:6609 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 120074117 15 30 50.0 -11 6394536 TGCC T - inframe_deletion MODERATE SMPD1 6609 Transcript NM_000543.5 protein_coding 6/6 1951-1953 1826-1828 609-610 CR/C tGCCgc/tgc 1 EntrezGene GCC GCC 20.5 2.146023 6.54&1.25&6.54 rs1479332885 41 143328 2.86057e-04 4.75602e-05 8.80669e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.48902e-03 1.18483e-03 3.48297e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48922e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.31878e-04 7.74281e-05 5.34845e-05 1.10367e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.85933e-04 0.00000e+00 0.00000e+00 0.00000e+00 198093 195254 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_specified¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite APBB1:322&SMPD1:6609 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 120074118 15 30 50.0 -11 6394536 TGCC T - inframe_deletion MODERATE SMPD1 6609 Transcript NM_001007593.3 protein_coding 6/6 1948-1950 1823-1825 608-609 CR/C tGCCgc/tgc 1 EntrezGene GCC GCC 20.5 2.146023 6.54&1.25&6.54 rs1479332885 41 143328 2.86057e-04 4.75602e-05 8.80669e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.48902e-03 1.18483e-03 3.48297e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48922e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.31878e-04 7.74281e-05 5.34845e-05 1.10367e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.85933e-04 0.00000e+00 0.00000e+00 0.00000e+00 198093 195254 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_specified¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite APBB1:322&SMPD1:6609 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 120074118 15 30 50.0 -11 6394536 TGCC T - downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001164.5 protein_coding 586 -1 EntrezGene GCC GCC 20.5 2.146023 6.54&1.25&6.54 rs1479332885 41 143328 2.86057e-04 4.75602e-05 8.80669e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.48902e-03 1.18483e-03 3.48297e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48922e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.31878e-04 7.74281e-05 5.34845e-05 1.10367e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.85933e-04 0.00000e+00 0.00000e+00 0.00000e+00 198093 195254 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_specified¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite APBB1:322&SMPD1:6609 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 120074118 15 30 50.0 -11 6394536 TGCC T - downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257319.2 protein_coding 585 -1 EntrezGene GCC GCC OK 20.5 2.146023 6.54&1.25&6.54 rs1479332885 41 143328 2.86057e-04 4.75602e-05 8.80669e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.48902e-03 1.18483e-03 3.48297e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48922e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.31878e-04 7.74281e-05 5.34845e-05 1.10367e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.85933e-04 0.00000e+00 0.00000e+00 0.00000e+00 198093 195254 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_specified¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite APBB1:322&SMPD1:6609 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 120074118 15 30 50.0 -11 6394536 TGCC T - downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257320.2 protein_coding 585 -1 EntrezGene GCC GCC OK 20.5 2.146023 6.54&1.25&6.54 rs1479332885 41 143328 2.86057e-04 4.75602e-05 8.80669e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.48902e-03 1.18483e-03 3.48297e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48922e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.31878e-04 7.74281e-05 5.34845e-05 1.10367e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.85933e-04 0.00000e+00 0.00000e+00 0.00000e+00 198093 195254 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_specified¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite APBB1:322&SMPD1:6609 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 120074118 15 30 50.0 -11 6394536 TGCC T - downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257321.2 protein_coding 585 -1 EntrezGene GCC GCC OK 20.5 2.146023 6.54&1.25&6.54 rs1479332885 41 143328 2.86057e-04 4.75602e-05 8.80669e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.48902e-03 1.18483e-03 3.48297e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48922e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.31878e-04 7.74281e-05 5.34845e-05 1.10367e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.85933e-04 0.00000e+00 0.00000e+00 0.00000e+00 198093 195254 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_specified¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite APBB1:322&SMPD1:6609 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 120074118 15 30 50.0 -11 6394536 TGCC T - downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257323.2 protein_coding 585 -1 EntrezGene GCC GCC OK 20.5 2.146023 6.54&1.25&6.54 rs1479332885 41 143328 2.86057e-04 4.75602e-05 8.80669e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.48902e-03 1.18483e-03 3.48297e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48922e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.31878e-04 7.74281e-05 5.34845e-05 1.10367e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.85933e-04 0.00000e+00 0.00000e+00 0.00000e+00 198093 195254 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_specified¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite APBB1:322&SMPD1:6609 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 120074118 15 30 50.0 -11 6394536 TGCC T - downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257325.2 protein_coding 585 -1 EntrezGene GCC GCC OK 20.5 2.146023 6.54&1.25&6.54 rs1479332885 41 143328 2.86057e-04 4.75602e-05 8.80669e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.48902e-03 1.18483e-03 3.48297e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48922e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.31878e-04 7.74281e-05 5.34845e-05 1.10367e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.85933e-04 0.00000e+00 0.00000e+00 0.00000e+00 198093 195254 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_specified¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite APBB1:322&SMPD1:6609 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 120074118 15 30 50.0 -11 6394536 TGCC T - downstream_gene_variant MODIFIER APBB1 322 Transcript NM_001257326.2 protein_coding 585 -1 EntrezGene GCC GCC OK 20.5 2.146023 6.54&1.25&6.54 rs1479332885 41 143328 2.86057e-04 4.75602e-05 8.80669e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.48902e-03 1.18483e-03 3.48297e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48922e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.31878e-04 7.74281e-05 5.34845e-05 1.10367e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.85933e-04 0.00000e+00 0.00000e+00 0.00000e+00 198093 195254 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_specified¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite APBB1:322&SMPD1:6609 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 120074118 15 30 50.0 -11 6394536 TGCC T - 3_prime_UTR_variant MODIFIER SMPD1 6609 Transcript NM_001318087.2 protein_coding 6/6 1971-1973 1 EntrezGene GCC GCC 20.5 2.146023 6.54&1.25&6.54 rs1479332885 41 143328 2.86057e-04 4.75602e-05 8.80669e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.48902e-03 1.18483e-03 3.48297e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48922e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.31878e-04 7.74281e-05 5.34845e-05 1.10367e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.85933e-04 0.00000e+00 0.00000e+00 0.00000e+00 198093 195254 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_specified¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite APBB1:322&SMPD1:6609 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 120074118 15 30 50.0 -11 6394536 TGCC T - inframe_deletion MODERATE SMPD1 6609 Transcript NM_001318088.2 protein_coding 6/6 1991-1993 905-907 302-303 CR/C tGCCgc/tgc 1 EntrezGene GCC GCC 20.5 2.146023 6.54&1.25&6.54 rs1479332885 41 143328 2.86057e-04 4.75602e-05 8.80669e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.48902e-03 1.18483e-03 3.48297e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48922e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.31878e-04 7.74281e-05 5.34845e-05 1.10367e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.85933e-04 0.00000e+00 0.00000e+00 0.00000e+00 198093 195254 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_specified¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite APBB1:322&SMPD1:6609 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 120074118 15 30 50.0 -11 6394536 TGCC T - inframe_deletion MODERATE SMPD1 6609 Transcript NM_001365135.2 protein_coding 5/5 1819-1821 1694-1696 565-566 CR/C tGCCgc/tgc 1 EntrezGene GCC GCC 20.5 2.146023 6.54&1.25&6.54 rs1479332885 41 143328 2.86057e-04 4.75602e-05 8.80669e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.48902e-03 1.18483e-03 3.48297e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48922e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.31878e-04 7.74281e-05 5.34845e-05 1.10367e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.85933e-04 0.00000e+00 0.00000e+00 0.00000e+00 198093 195254 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_specified¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite APBB1:322&SMPD1:6609 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 120074118 15 30 50.0 -11 6394536 TGCC T - downstream_gene_variant MODIFIER APBB1 322 Transcript NM_145689.3 protein_coding 586 -1 EntrezGene GCC GCC 20.5 2.146023 6.54&1.25&6.54 rs1479332885 41 143328 2.86057e-04 4.75602e-05 8.80669e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.48902e-03 1.18483e-03 3.48297e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48922e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.31878e-04 7.74281e-05 5.34845e-05 1.10367e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.85933e-04 0.00000e+00 0.00000e+00 0.00000e+00 198093 195254 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_specified¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite APBB1:322&SMPD1:6609 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 120074118 15 30 50.0 -11 6394536 TGCC T - non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript NR_027400.3 misc_RNA 5/5 1779-1781 1 EntrezGene GCC GCC 20.5 2.146023 6.54&1.25&6.54 rs1479332885 41 143328 2.86057e-04 4.75602e-05 8.80669e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.48902e-03 1.18483e-03 3.48297e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48922e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.31878e-04 7.74281e-05 5.34845e-05 1.10367e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.85933e-04 0.00000e+00 0.00000e+00 0.00000e+00 198093 195254 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_specified¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite APBB1:322&SMPD1:6609 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 120074118 15 30 50.0 -11 6394536 TGCC T - downstream_gene_variant MODIFIER APBB1 322 Transcript NR_047512.2 misc_RNA 585 -1 EntrezGene GCC GCC OK 20.5 2.146023 6.54&1.25&6.54 rs1479332885 41 143328 2.86057e-04 4.75602e-05 8.80669e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.48902e-03 1.18483e-03 3.48297e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48922e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.31878e-04 7.74281e-05 5.34845e-05 1.10367e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.85933e-04 0.00000e+00 0.00000e+00 0.00000e+00 198093 195254 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_specified¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite APBB1:322&SMPD1:6609 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 120074118 15 30 50.0 -11 6394536 TGCC T - non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript NR_134502.2 misc_RNA 6/6 1318-1320 1 EntrezGene GCC GCC 20.5 2.146023 6.54&1.25&6.54 rs1479332885 41 143328 2.86057e-04 4.75602e-05 8.80669e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.48902e-03 1.18483e-03 3.48297e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48922e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.31878e-04 7.74281e-05 5.34845e-05 1.10367e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.85933e-04 0.00000e+00 0.00000e+00 0.00000e+00 198093 195254 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_specified¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite APBB1:322&SMPD1:6609 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 120074118 15 30 50.0 -11 6394536 TGCC T - 3_prime_UTR_variant MODIFIER SMPD1 6609 Transcript XM_011520304.2 protein_coding 5/5 1839-1841 1 EntrezGene GCC GCC 20.5 2.146023 6.54&1.25&6.54 rs1479332885 41 143328 2.86057e-04 4.75602e-05 8.80669e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.48902e-03 1.18483e-03 3.48297e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48922e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.31878e-04 7.74281e-05 5.34845e-05 1.10367e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.85933e-04 0.00000e+00 0.00000e+00 0.00000e+00 198093 195254 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_specified¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite APBB1:322&SMPD1:6609 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 120074118 15 30 50.0 -11 6394536 TGCC T - non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript XR_001747940.2 misc_RNA 6/6 2011-2013 1 EntrezGene GCC GCC 20.5 2.146023 6.54&1.25&6.54 rs1479332885 41 143328 2.86057e-04 4.75602e-05 8.80669e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.48902e-03 1.18483e-03 3.48297e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48922e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.31878e-04 7.74281e-05 5.34845e-05 1.10367e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.85933e-04 0.00000e+00 0.00000e+00 0.00000e+00 198093 195254 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_specified¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite APBB1:322&SMPD1:6609 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 120074118 15 30 50.0 -11 6394536 TGCC T - non_coding_transcript_exon_variant MODIFIER SMPD1 6609 Transcript XR_002957158.1 misc_RNA 5/5 2193-2195 1 EntrezGene GCC GCC 20.5 2.146023 6.54&1.25&6.54 rs1479332885 41 143328 2.86057e-04 4.75602e-05 8.80669e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.48902e-03 1.18483e-03 3.48297e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48922e-04 0.00000e+00 0.00000e+00 0.00000e+00 4.31878e-04 7.74281e-05 5.34845e-05 1.10367e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.85933e-04 0.00000e+00 0.00000e+00 0.00000e+00 198093 195254 Sphingomyelin/cholesterol_lipidosis&Niemann-Pick_disease&_type_A&Niemann-Pick_disease&_type_B¬_specified¬_provided MONDO:MONDO:0001982&MedGen:C0028064&SNOMED_CT:58459009&MONDO:MONDO:0009756&MedGen:C0268242&OMIM:257200&Orphanet:ORPHA77292&SNOMED_CT:52165006&MONDO:MONDO:0011871&MedGen:C0268243&OMIM:607616&Orphanet:ORPHA77293&SNOMED_CT:39390005&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite APBB1:322&SMPD1:6609 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 120074118 15 30 50.0 -11 17395887 GAGA G - inframe_deletion MODERATE ABCC8 6833 Transcript NM_000352.6 protein_coding 34/39 4229-4231 4160-4162 1387-1388 FS/S tTCTct/tct -1 EntrezGene AGA AGA 21.1 2.227712 6.54&3.7&6.54 rs1213140867 4 143310 2.79115e-05 2.37812e-05 0.00000e+00 5.17063e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87985e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.78959e-05 0.00000e+00 0.00000e+00 0.00000e+00 196880 194041 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Familial_hyperinsulinism¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion 1 151344624 15 30 50.0 -11 17395887 GAGA G - inframe_deletion MODERATE ABCC8 6833 Transcript NM_001287174.2 protein_coding 34/39 4232-4234 4163-4165 1388-1389 FS/S tTCTct/tct -1 EntrezGene AGA AGA 21.1 2.227712 6.54&3.7&6.54 rs1213140867 4 143310 2.79115e-05 2.37812e-05 0.00000e+00 5.17063e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87985e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.78959e-05 0.00000e+00 0.00000e+00 0.00000e+00 196880 194041 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Familial_hyperinsulinism¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion 1 151344624 15 30 50.0 -11 17395887 GAGA G - inframe_deletion MODERATE ABCC8 6833 Transcript NM_001351295.2 protein_coding 34/39 4295-4297 4226-4228 1409-1410 FS/S tTCTct/tct -1 EntrezGene AGA AGA 21.1 2.227712 6.54&3.7&6.54 rs1213140867 4 143310 2.79115e-05 2.37812e-05 0.00000e+00 5.17063e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87985e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.78959e-05 0.00000e+00 0.00000e+00 0.00000e+00 196880 194041 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Familial_hyperinsulinism¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion 1 151344624 15 30 50.0 -11 17395887 GAGA G - inframe_deletion MODERATE ABCC8 6833 Transcript NM_001351296.2 protein_coding 34/39 4229-4231 4160-4162 1387-1388 FS/S tTCTct/tct -1 EntrezGene AGA AGA 21.1 2.227712 6.54&3.7&6.54 rs1213140867 4 143310 2.79115e-05 2.37812e-05 0.00000e+00 5.17063e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87985e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.78959e-05 0.00000e+00 0.00000e+00 0.00000e+00 196880 194041 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Familial_hyperinsulinism¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion 1 151344624 15 30 50.0 -11 17395887 GAGA G - inframe_deletion MODERATE ABCC8 6833 Transcript NM_001351297.2 protein_coding 34/39 4226-4228 4157-4159 1386-1387 FS/S tTCTct/tct -1 EntrezGene AGA AGA 21.1 2.227712 6.54&3.7&6.54 rs1213140867 4 143310 2.79115e-05 2.37812e-05 0.00000e+00 5.17063e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87985e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.78959e-05 0.00000e+00 0.00000e+00 0.00000e+00 196880 194041 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Familial_hyperinsulinism¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion 1 151344624 15 30 50.0 -11 17395887 GAGA G - non_coding_transcript_exon_variant MODIFIER ABCC8 6833 Transcript NR_147094.2 misc_RNA 33/38 4455-4457 -1 EntrezGene AGA AGA 21.1 2.227712 6.54&3.7&6.54 rs1213140867 4 143310 2.79115e-05 2.37812e-05 0.00000e+00 5.17063e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87985e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.78959e-05 0.00000e+00 0.00000e+00 0.00000e+00 196880 194041 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Familial_hyperinsulinism¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion 1 151344624 15 30 50.0 -11 17395887 GAGA G - inframe_deletion MODERATE ABCC8 6833 Transcript XM_017018197.2 protein_coding 34/39 4301-4303 4229-4231 1410-1411 FS/S tTCTct/tct -1 EntrezGene AGA AGA 21.1 2.227712 6.54&3.7&6.54 rs1213140867 4 143310 2.79115e-05 2.37812e-05 0.00000e+00 5.17063e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87985e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.78959e-05 0.00000e+00 0.00000e+00 0.00000e+00 196880 194041 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Familial_hyperinsulinism¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion 1 151344624 15 30 50.0 -11 17395887 GAGA G - inframe_deletion MODERATE ABCC8 6833 Transcript XM_017018199.1 protein_coding 34/39 4297-4299 4226-4228 1409-1410 FS/S tTCTct/tct -1 EntrezGene AGA AGA 21.1 2.227712 6.54&3.7&6.54 rs1213140867 4 143310 2.79115e-05 2.37812e-05 0.00000e+00 5.17063e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87985e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.78959e-05 0.00000e+00 0.00000e+00 0.00000e+00 196880 194041 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Familial_hyperinsulinism¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion 1 151344624 15 30 50.0 -11 17395887 GAGA G - inframe_deletion MODERATE ABCC8 6833 Transcript XM_017018201.2 protein_coding 34/36 4301-4303 4229-4231 1410-1411 FS/S tTCTct/tct -1 EntrezGene AGA AGA 21.1 2.227712 6.54&3.7&6.54 rs1213140867 4 143310 2.79115e-05 2.37812e-05 0.00000e+00 5.17063e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87985e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.78959e-05 0.00000e+00 0.00000e+00 0.00000e+00 196880 194041 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Familial_hyperinsulinism¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion 1 151344624 15 30 50.0 -11 17395887 GAGA G - inframe_deletion MODERATE ABCC8 6833 Transcript XM_017018202.1 protein_coding 33/38 4093-4095 2726-2728 909-910 FS/S tTCTct/tct -1 EntrezGene AGA AGA 21.1 2.227712 6.54&3.7&6.54 rs1213140867 4 143310 2.79115e-05 2.37812e-05 0.00000e+00 5.17063e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87985e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.78959e-05 0.00000e+00 0.00000e+00 0.00000e+00 196880 194041 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Familial_hyperinsulinism¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion 1 151344624 15 30 50.0 -11 17395887 GAGA G - inframe_deletion MODERATE ABCC8 6833 Transcript XM_017018204.1 protein_coding 28/33 3205-3207 2117-2119 706-707 FS/S tTCTct/tct -1 EntrezGene AGA AGA 21.1 2.227712 6.54&3.7&6.54 rs1213140867 4 143310 2.79115e-05 2.37812e-05 0.00000e+00 5.17063e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87985e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.78959e-05 0.00000e+00 0.00000e+00 0.00000e+00 196880 194041 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Familial_hyperinsulinism¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion 1 151344624 15 30 50.0 -11 17395887 GAGA G - inframe_deletion MODERATE ABCC8 6833 Transcript XM_024448668.1 protein_coding 33/38 5244-5246 2528-2530 843-844 FS/S tTCTct/tct -1 EntrezGene AGA AGA 21.1 2.227712 6.54&3.7&6.54 rs1213140867 4 143310 2.79115e-05 2.37812e-05 0.00000e+00 5.17063e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87985e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.78959e-05 0.00000e+00 0.00000e+00 0.00000e+00 196880 194041 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Familial_hyperinsulinism¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion 1 151344624 15 30 50.0 -11 17395887 GAGA G - non_coding_transcript_exon_variant MODIFIER ABCC8 6833 Transcript XR_001747945.2 misc_RNA 34/38 4301-4303 -1 EntrezGene AGA AGA 21.1 2.227712 6.54&3.7&6.54 rs1213140867 4 143310 2.79115e-05 2.37812e-05 0.00000e+00 5.17063e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87985e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.78959e-05 0.00000e+00 0.00000e+00 0.00000e+00 196880 194041 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Familial_hyperinsulinism¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion 1 151344624 15 30 50.0 -11 17395887 GAGA G - non_coding_transcript_exon_variant MODIFIER ABCC8 6833 Transcript XR_001747946.2 misc_RNA 34/38 4232-4234 -1 EntrezGene AGA AGA 21.1 2.227712 6.54&3.7&6.54 rs1213140867 4 143310 2.79115e-05 2.37812e-05 0.00000e+00 5.17063e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87985e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.78959e-05 0.00000e+00 0.00000e+00 0.00000e+00 196880 194041 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Familial_hyperinsulinism¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion 1 151344624 15 30 50.0 -11 17395887 GAGA G - non_coding_transcript_exon_variant MODIFIER ABCC8 6833 Transcript XR_002957189.1 misc_RNA 31/34 5882-5884 -1 EntrezGene AGA AGA 21.1 2.227712 6.54&3.7&6.54 rs1213140867 4 143310 2.79115e-05 2.37812e-05 0.00000e+00 5.17063e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.02527e-04 1.13507e-03 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87985e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.78959e-05 0.00000e+00 0.00000e+00 0.00000e+00 196880 194041 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Familial_hyperinsulinism¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001822&inframe_deletion 1 151344624 15 30 50.0 -11 17397055 C T T intron_variant MODIFIER ABCC8 6833 Transcript NM_000352.6 protein_coding 32/38 -1 EntrezGene C C 20.1 2.103902 1.41 chr11:17397055-17397055 26 143296 1.81443e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.52106e-03 8.51305e-03 6.40205e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.16632e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44013e-04 1.54885e-05 2.67437e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.81324e-04 0.00000e+00 0.00000e+00 0.00000e+00 9088 0.00018 0.00020 24127 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Transient_neonatal_diabetes_mellitus_2&Familial_hyperinsulinism&Permanent_neonatal_diabetes_mellitus&Inborn_genetic_diseases¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0012480&MedGen:C1835887&OMIM:610374&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MONDO:MONDO:0100164&MedGen:C1833104&OMIM:PS606176&Orphanet:ORPHA99885&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_benign(2)&Pathogenic(7) single_nucleotide_variant ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 151344623 15 30 50.0 -11 17397055 C T T intron_variant MODIFIER ABCC8 6833 Transcript NM_001287174.2 protein_coding 32/38 -1 EntrezGene C C 20.1 2.103902 1.41 chr11:17397055-17397055 26 143296 1.81443e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.52106e-03 8.51305e-03 6.40205e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.16632e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44013e-04 1.54885e-05 2.67437e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.81324e-04 0.00000e+00 0.00000e+00 0.00000e+00 9088 0.00018 0.00020 24127 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Transient_neonatal_diabetes_mellitus_2&Familial_hyperinsulinism&Permanent_neonatal_diabetes_mellitus&Inborn_genetic_diseases¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0012480&MedGen:C1835887&OMIM:610374&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MONDO:MONDO:0100164&MedGen:C1833104&OMIM:PS606176&Orphanet:ORPHA99885&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_benign(2)&Pathogenic(7) single_nucleotide_variant ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 151344623 15 30 50.0 -11 17397055 C T T intron_variant MODIFIER ABCC8 6833 Transcript NM_001351295.2 protein_coding 32/38 -1 EntrezGene C C 20.1 2.103902 1.41 chr11:17397055-17397055 26 143296 1.81443e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.52106e-03 8.51305e-03 6.40205e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.16632e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44013e-04 1.54885e-05 2.67437e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.81324e-04 0.00000e+00 0.00000e+00 0.00000e+00 9088 0.00018 0.00020 24127 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Transient_neonatal_diabetes_mellitus_2&Familial_hyperinsulinism&Permanent_neonatal_diabetes_mellitus&Inborn_genetic_diseases¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0012480&MedGen:C1835887&OMIM:610374&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MONDO:MONDO:0100164&MedGen:C1833104&OMIM:PS606176&Orphanet:ORPHA99885&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_benign(2)&Pathogenic(7) single_nucleotide_variant ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 151344623 15 30 50.0 -11 17397055 C T T intron_variant MODIFIER ABCC8 6833 Transcript NM_001351296.2 protein_coding 32/38 -1 EntrezGene C C 20.1 2.103902 1.41 chr11:17397055-17397055 26 143296 1.81443e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.52106e-03 8.51305e-03 6.40205e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.16632e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44013e-04 1.54885e-05 2.67437e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.81324e-04 0.00000e+00 0.00000e+00 0.00000e+00 9088 0.00018 0.00020 24127 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Transient_neonatal_diabetes_mellitus_2&Familial_hyperinsulinism&Permanent_neonatal_diabetes_mellitus&Inborn_genetic_diseases¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0012480&MedGen:C1835887&OMIM:610374&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MONDO:MONDO:0100164&MedGen:C1833104&OMIM:PS606176&Orphanet:ORPHA99885&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_benign(2)&Pathogenic(7) single_nucleotide_variant ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 151344623 15 30 50.0 -11 17397055 C T T intron_variant MODIFIER ABCC8 6833 Transcript NM_001351297.2 protein_coding 32/38 -1 EntrezGene C C 20.1 2.103902 1.41 chr11:17397055-17397055 26 143296 1.81443e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.52106e-03 8.51305e-03 6.40205e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.16632e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44013e-04 1.54885e-05 2.67437e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.81324e-04 0.00000e+00 0.00000e+00 0.00000e+00 9088 0.00018 0.00020 24127 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Transient_neonatal_diabetes_mellitus_2&Familial_hyperinsulinism&Permanent_neonatal_diabetes_mellitus&Inborn_genetic_diseases¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0012480&MedGen:C1835887&OMIM:610374&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MONDO:MONDO:0100164&MedGen:C1833104&OMIM:PS606176&Orphanet:ORPHA99885&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_benign(2)&Pathogenic(7) single_nucleotide_variant ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 151344623 15 30 50.0 -11 17397055 C T T non_coding_transcript_exon_variant MODIFIER ABCC8 6833 Transcript NR_147094.2 misc_RNA 32/38 4275 -1 EntrezGene C C 20.1 2.103902 1.41 chr11:17397055-17397055 26 143296 1.81443e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.52106e-03 8.51305e-03 6.40205e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.16632e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44013e-04 1.54885e-05 2.67437e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.81324e-04 0.00000e+00 0.00000e+00 0.00000e+00 9088 0.00018 0.00020 24127 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Transient_neonatal_diabetes_mellitus_2&Familial_hyperinsulinism&Permanent_neonatal_diabetes_mellitus&Inborn_genetic_diseases¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0012480&MedGen:C1835887&OMIM:610374&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MONDO:MONDO:0100164&MedGen:C1833104&OMIM:PS606176&Orphanet:ORPHA99885&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_benign(2)&Pathogenic(7) single_nucleotide_variant ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 151344623 15 30 50.0 -11 17397055 C T T intron_variant MODIFIER ABCC8 6833 Transcript XM_017018197.2 protein_coding 32/38 -1 EntrezGene C C 20.1 2.103902 1.41 chr11:17397055-17397055 26 143296 1.81443e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.52106e-03 8.51305e-03 6.40205e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.16632e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44013e-04 1.54885e-05 2.67437e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.81324e-04 0.00000e+00 0.00000e+00 0.00000e+00 9088 0.00018 0.00020 24127 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Transient_neonatal_diabetes_mellitus_2&Familial_hyperinsulinism&Permanent_neonatal_diabetes_mellitus&Inborn_genetic_diseases¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0012480&MedGen:C1835887&OMIM:610374&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MONDO:MONDO:0100164&MedGen:C1833104&OMIM:PS606176&Orphanet:ORPHA99885&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_benign(2)&Pathogenic(7) single_nucleotide_variant ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 151344623 15 30 50.0 -11 17397055 C T T intron_variant MODIFIER ABCC8 6833 Transcript XM_017018199.1 protein_coding 32/38 -1 EntrezGene C C 20.1 2.103902 1.41 chr11:17397055-17397055 26 143296 1.81443e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.52106e-03 8.51305e-03 6.40205e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.16632e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44013e-04 1.54885e-05 2.67437e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.81324e-04 0.00000e+00 0.00000e+00 0.00000e+00 9088 0.00018 0.00020 24127 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Transient_neonatal_diabetes_mellitus_2&Familial_hyperinsulinism&Permanent_neonatal_diabetes_mellitus&Inborn_genetic_diseases¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0012480&MedGen:C1835887&OMIM:610374&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MONDO:MONDO:0100164&MedGen:C1833104&OMIM:PS606176&Orphanet:ORPHA99885&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_benign(2)&Pathogenic(7) single_nucleotide_variant ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 151344623 15 30 50.0 -11 17397055 C T T intron_variant MODIFIER ABCC8 6833 Transcript XM_017018201.2 protein_coding 32/35 -1 EntrezGene C C 20.1 2.103902 1.41 chr11:17397055-17397055 26 143296 1.81443e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.52106e-03 8.51305e-03 6.40205e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.16632e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44013e-04 1.54885e-05 2.67437e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.81324e-04 0.00000e+00 0.00000e+00 0.00000e+00 9088 0.00018 0.00020 24127 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Transient_neonatal_diabetes_mellitus_2&Familial_hyperinsulinism&Permanent_neonatal_diabetes_mellitus&Inborn_genetic_diseases¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0012480&MedGen:C1835887&OMIM:610374&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MONDO:MONDO:0100164&MedGen:C1833104&OMIM:PS606176&Orphanet:ORPHA99885&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_benign(2)&Pathogenic(7) single_nucleotide_variant ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 151344623 15 30 50.0 -11 17397055 C T T intron_variant MODIFIER ABCC8 6833 Transcript XM_017018202.1 protein_coding 31/37 -1 EntrezGene C C 20.1 2.103902 1.41 chr11:17397055-17397055 26 143296 1.81443e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.52106e-03 8.51305e-03 6.40205e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.16632e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44013e-04 1.54885e-05 2.67437e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.81324e-04 0.00000e+00 0.00000e+00 0.00000e+00 9088 0.00018 0.00020 24127 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Transient_neonatal_diabetes_mellitus_2&Familial_hyperinsulinism&Permanent_neonatal_diabetes_mellitus&Inborn_genetic_diseases¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0012480&MedGen:C1835887&OMIM:610374&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MONDO:MONDO:0100164&MedGen:C1833104&OMIM:PS606176&Orphanet:ORPHA99885&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_benign(2)&Pathogenic(7) single_nucleotide_variant ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 151344623 15 30 50.0 -11 17397055 C T T intron_variant MODIFIER ABCC8 6833 Transcript XM_017018204.1 protein_coding 26/32 -1 EntrezGene C C 20.1 2.103902 1.41 chr11:17397055-17397055 26 143296 1.81443e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.52106e-03 8.51305e-03 6.40205e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.16632e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44013e-04 1.54885e-05 2.67437e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.81324e-04 0.00000e+00 0.00000e+00 0.00000e+00 9088 0.00018 0.00020 24127 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Transient_neonatal_diabetes_mellitus_2&Familial_hyperinsulinism&Permanent_neonatal_diabetes_mellitus&Inborn_genetic_diseases¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0012480&MedGen:C1835887&OMIM:610374&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MONDO:MONDO:0100164&MedGen:C1833104&OMIM:PS606176&Orphanet:ORPHA99885&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_benign(2)&Pathogenic(7) single_nucleotide_variant ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 151344623 15 30 50.0 -11 17397055 C T T intron_variant MODIFIER ABCC8 6833 Transcript XM_024448668.1 protein_coding 31/37 -1 EntrezGene C C 20.1 2.103902 1.41 chr11:17397055-17397055 26 143296 1.81443e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.52106e-03 8.51305e-03 6.40205e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.16632e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44013e-04 1.54885e-05 2.67437e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.81324e-04 0.00000e+00 0.00000e+00 0.00000e+00 9088 0.00018 0.00020 24127 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Transient_neonatal_diabetes_mellitus_2&Familial_hyperinsulinism&Permanent_neonatal_diabetes_mellitus&Inborn_genetic_diseases¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0012480&MedGen:C1835887&OMIM:610374&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MONDO:MONDO:0100164&MedGen:C1833104&OMIM:PS606176&Orphanet:ORPHA99885&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_benign(2)&Pathogenic(7) single_nucleotide_variant ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 151344623 15 30 50.0 -11 17397055 C T T intron_variant&non_coding_transcript_variant MODIFIER ABCC8 6833 Transcript XR_001747945.2 misc_RNA 32/37 -1 EntrezGene C C 20.1 2.103902 1.41 chr11:17397055-17397055 26 143296 1.81443e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.52106e-03 8.51305e-03 6.40205e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.16632e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44013e-04 1.54885e-05 2.67437e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.81324e-04 0.00000e+00 0.00000e+00 0.00000e+00 9088 0.00018 0.00020 24127 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Transient_neonatal_diabetes_mellitus_2&Familial_hyperinsulinism&Permanent_neonatal_diabetes_mellitus&Inborn_genetic_diseases¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0012480&MedGen:C1835887&OMIM:610374&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MONDO:MONDO:0100164&MedGen:C1833104&OMIM:PS606176&Orphanet:ORPHA99885&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_benign(2)&Pathogenic(7) single_nucleotide_variant ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 151344623 15 30 50.0 -11 17397055 C T T intron_variant&non_coding_transcript_variant MODIFIER ABCC8 6833 Transcript XR_001747946.2 misc_RNA 32/37 -1 EntrezGene C C 20.1 2.103902 1.41 chr11:17397055-17397055 26 143296 1.81443e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.52106e-03 8.51305e-03 6.40205e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.16632e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44013e-04 1.54885e-05 2.67437e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.81324e-04 0.00000e+00 0.00000e+00 0.00000e+00 9088 0.00018 0.00020 24127 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Transient_neonatal_diabetes_mellitus_2&Familial_hyperinsulinism&Permanent_neonatal_diabetes_mellitus&Inborn_genetic_diseases¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0012480&MedGen:C1835887&OMIM:610374&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MONDO:MONDO:0100164&MedGen:C1833104&OMIM:PS606176&Orphanet:ORPHA99885&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_benign(2)&Pathogenic(7) single_nucleotide_variant ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 151344623 15 30 50.0 -11 17397055 C T T non_coding_transcript_exon_variant MODIFIER ABCC8 6833 Transcript XR_002957189.1 misc_RNA 31/34 4717 -1 EntrezGene C C 20.1 2.103902 1.41 chr11:17397055-17397055 26 143296 1.81443e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.52106e-03 8.51305e-03 6.40205e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.16632e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44013e-04 1.54885e-05 2.67437e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.81324e-04 0.00000e+00 0.00000e+00 0.00000e+00 9088 0.00018 0.00020 24127 Hereditary_hyperinsulinism&Hyperinsulinemic_hypoglycemia&_familial&_1&Transient_neonatal_diabetes_mellitus_2&Familial_hyperinsulinism&Permanent_neonatal_diabetes_mellitus&Inborn_genetic_diseases¬_provided .&MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MONDO:MONDO:0012480&MedGen:C1835887&OMIM:610374&MONDO:MONDO:0017182&MedGen:C3888018&Orphanet:ORPHA276525&MONDO:MONDO:0100164&MedGen:C1833104&OMIM:PS606176&Orphanet:ORPHA99885&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_benign(2)&Pathogenic(7) single_nucleotide_variant ABCC8:6833 SO:0001619&non-coding_transcript_variant&SO:0001627&intron_variant 1 151344623 15 30 50.0 -11 17463457 A T T missense_variant MODERATE ABCC8 6833 Transcript NM_000352.6 protein_coding 4/39 629 560 187 V/D gTc/gAc -1 EntrezGene A A 0 0.03 27.3 4.042775 27.3 0.98134480473551289 3.711349 0.299040383757597 0.55485 2.987196 0.125261804709211 0.47643 .&.&-4.14&-4.14&.&.&.&. 4.98 0.999999999999999 0.111277 0.600782 0.7856 0.7383 .&1.355&1.355&1.355&.&.&.&. 0.999969&0.999969 .&.&-3.88&-3.88&.&.&.&. 15.1345 .&.&0.892&0.901&.&.&.&. 0.91942 0.554377 1.000000 0.999000 9.325000 1.312000 6.54 rs137852672 31 143084 2.16656e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.48972e-05 2.87246e-03 2.39617e-03 3.02267e-03 3.46220e-04 1.54957e-05 2.67537e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16299e-04 0.00000e+00 0.00000e+00 0.00000e+00 9099 0.00021 24138 Hyperinsulinemic_hypoglycemia&_familial&_1¬_provided MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ABCC8:6833 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 137852672 15 30 50.0 -11 17463457 A T T missense_variant MODERATE ABCC8 6833 Transcript NM_001287174.2 protein_coding 4/39 629 560 187 V/D gTc/gAc -1 EntrezGene A A 0 0.116 27.3 4.042775 27.3 0.98134480473551289 3.711349 0.299040383757597 0.55485 2.987196 0.125261804709211 0.47643 .&.&-4.14&-4.14&.&.&.&. 4.98 0.999999999999999 0.111277 0.600782 0.7856 0.7383 .&1.355&1.355&1.355&.&.&.&. 0.999969&0.999969 .&.&-3.88&-3.88&.&.&.&. 15.1345 .&.&0.892&0.901&.&.&.&. 0.91942 0.554377 1.000000 0.999000 9.325000 1.312000 6.54 rs137852672 31 143084 2.16656e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.48972e-05 2.87246e-03 2.39617e-03 3.02267e-03 3.46220e-04 1.54957e-05 2.67537e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16299e-04 0.00000e+00 0.00000e+00 0.00000e+00 9099 0.00021 24138 Hyperinsulinemic_hypoglycemia&_familial&_1¬_provided MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ABCC8:6833 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 137852672 15 30 50.0 -11 17463457 A T T missense_variant MODERATE ABCC8 6833 Transcript NM_001351295.2 protein_coding 4/39 629 560 187 V/D gTc/gAc -1 EntrezGene A A 0 0.394 27.3 4.042775 27.3 0.98134480473551289 3.711349 0.299040383757597 0.55485 2.987196 0.125261804709211 0.47643 .&.&-4.14&-4.14&.&.&.&. 4.98 0.999999999999999 0.111277 0.600782 0.7856 0.7383 .&1.355&1.355&1.355&.&.&.&. 0.999969&0.999969 .&.&-3.88&-3.88&.&.&.&. 15.1345 .&.&0.892&0.901&.&.&.&. 0.91942 0.554377 1.000000 0.999000 9.325000 1.312000 6.54 rs137852672 31 143084 2.16656e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.48972e-05 2.87246e-03 2.39617e-03 3.02267e-03 3.46220e-04 1.54957e-05 2.67537e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16299e-04 0.00000e+00 0.00000e+00 0.00000e+00 9099 0.00021 24138 Hyperinsulinemic_hypoglycemia&_familial&_1¬_provided MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ABCC8:6833 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 137852672 15 30 50.0 -11 17463457 A T T missense_variant MODERATE ABCC8 6833 Transcript NM_001351296.2 protein_coding 4/39 629 560 187 V/D gTc/gAc -1 EntrezGene A A 0 0.251 27.3 4.042775 27.3 0.98134480473551289 3.711349 0.299040383757597 0.55485 2.987196 0.125261804709211 0.47643 .&.&-4.14&-4.14&.&.&.&. 4.98 0.999999999999999 0.111277 0.600782 0.7856 0.7383 .&1.355&1.355&1.355&.&.&.&. 0.999969&0.999969 .&.&-3.88&-3.88&.&.&.&. 15.1345 .&.&0.892&0.901&.&.&.&. 0.91942 0.554377 1.000000 0.999000 9.325000 1.312000 6.54 rs137852672 31 143084 2.16656e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.48972e-05 2.87246e-03 2.39617e-03 3.02267e-03 3.46220e-04 1.54957e-05 2.67537e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16299e-04 0.00000e+00 0.00000e+00 0.00000e+00 9099 0.00021 24138 Hyperinsulinemic_hypoglycemia&_familial&_1¬_provided MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ABCC8:6833 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 137852672 15 30 50.0 -11 17463457 A T T missense_variant MODERATE ABCC8 6833 Transcript NM_001351297.2 protein_coding 4/39 629 560 187 V/D gTc/gAc -1 EntrezGene A A 0 0.251 27.3 4.042775 27.3 0.98134480473551289 3.711349 0.299040383757597 0.55485 2.987196 0.125261804709211 0.47643 .&.&-4.14&-4.14&.&.&.&. 4.98 0.999999999999999 0.111277 0.600782 0.7856 0.7383 .&1.355&1.355&1.355&.&.&.&. 0.999969&0.999969 .&.&-3.88&-3.88&.&.&.&. 15.1345 .&.&0.892&0.901&.&.&.&. 0.91942 0.554377 1.000000 0.999000 9.325000 1.312000 6.54 rs137852672 31 143084 2.16656e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.48972e-05 2.87246e-03 2.39617e-03 3.02267e-03 3.46220e-04 1.54957e-05 2.67537e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16299e-04 0.00000e+00 0.00000e+00 0.00000e+00 9099 0.00021 24138 Hyperinsulinemic_hypoglycemia&_familial&_1¬_provided MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ABCC8:6833 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 137852672 15 30 50.0 -11 17463457 A T T non_coding_transcript_exon_variant MODIFIER ABCC8 6833 Transcript NR_147094.2 misc_RNA 4/38 629 -1 EntrezGene A A 27.3 4.042775 6.54 rs137852672 31 143084 2.16656e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.48972e-05 2.87246e-03 2.39617e-03 3.02267e-03 3.46220e-04 1.54957e-05 2.67537e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16299e-04 0.00000e+00 0.00000e+00 0.00000e+00 9099 0.00021 24138 Hyperinsulinemic_hypoglycemia&_familial&_1¬_provided MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ABCC8:6833 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 137852672 15 30 50.0 -11 17463457 A T T missense_variant MODERATE ABCC8 6833 Transcript XM_017018197.2 protein_coding 4/39 632 560 187 V/D gTc/gAc -1 EntrezGene A A 27.3 4.042775 27.3 0.98134480473551289 3.711349 0.299040383757597 0.55485 2.987196 0.125261804709211 0.47643 .&.&-4.14&-4.14&.&.&.&. 4.98 0.999999999999999 0.111277 0.600782 0.7856 0.7383 .&1.355&1.355&1.355&.&.&.&. 0.999969&0.999969 .&.&-3.88&-3.88&.&.&.&. 15.1345 .&.&0.892&0.901&.&.&.&. 0.91942 0.554377 1.000000 0.999000 9.325000 1.312000 6.54 rs137852672 31 143084 2.16656e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.48972e-05 2.87246e-03 2.39617e-03 3.02267e-03 3.46220e-04 1.54957e-05 2.67537e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16299e-04 0.00000e+00 0.00000e+00 0.00000e+00 9099 0.00021 24138 Hyperinsulinemic_hypoglycemia&_familial&_1¬_provided MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ABCC8:6833 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 137852672 15 30 50.0 -11 17463457 A T T missense_variant MODERATE ABCC8 6833 Transcript XM_017018199.1 protein_coding 4/39 631 560 187 V/D gTc/gAc -1 EntrezGene A A 27.3 4.042775 27.3 0.98134480473551289 3.711349 0.299040383757597 0.55485 2.987196 0.125261804709211 0.47643 .&.&-4.14&-4.14&.&.&.&. 4.98 0.999999999999999 0.111277 0.600782 0.7856 0.7383 .&1.355&1.355&1.355&.&.&.&. 0.999969&0.999969 .&.&-3.88&-3.88&.&.&.&. 15.1345 .&.&0.892&0.901&.&.&.&. 0.91942 0.554377 1.000000 0.999000 9.325000 1.312000 6.54 rs137852672 31 143084 2.16656e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.48972e-05 2.87246e-03 2.39617e-03 3.02267e-03 3.46220e-04 1.54957e-05 2.67537e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16299e-04 0.00000e+00 0.00000e+00 0.00000e+00 9099 0.00021 24138 Hyperinsulinemic_hypoglycemia&_familial&_1¬_provided MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ABCC8:6833 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 137852672 15 30 50.0 -11 17463457 A T T missense_variant MODERATE ABCC8 6833 Transcript XM_017018201.2 protein_coding 4/36 632 560 187 V/D gTc/gAc -1 EntrezGene A A 27.3 4.042775 27.3 0.98134480473551289 3.711349 0.299040383757597 0.55485 2.987196 0.125261804709211 0.47643 .&.&-4.14&-4.14&.&.&.&. 4.98 0.999999999999999 0.111277 0.600782 0.7856 0.7383 .&1.355&1.355&1.355&.&.&.&. 0.999969&0.999969 .&.&-3.88&-3.88&.&.&.&. 15.1345 .&.&0.892&0.901&.&.&.&. 0.91942 0.554377 1.000000 0.999000 9.325000 1.312000 6.54 rs137852672 31 143084 2.16656e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.48972e-05 2.87246e-03 2.39617e-03 3.02267e-03 3.46220e-04 1.54957e-05 2.67537e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16299e-04 0.00000e+00 0.00000e+00 0.00000e+00 9099 0.00021 24138 Hyperinsulinemic_hypoglycemia&_familial&_1¬_provided MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ABCC8:6833 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 137852672 15 30 50.0 -11 17463457 A T T 5_prime_UTR_variant MODIFIER ABCC8 6833 Transcript XM_017018202.1 protein_coding 3/38 410 -1 EntrezGene A A 27.3 4.042775 6.54 rs137852672 31 143084 2.16656e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.48972e-05 2.87246e-03 2.39617e-03 3.02267e-03 3.46220e-04 1.54957e-05 2.67537e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16299e-04 0.00000e+00 0.00000e+00 0.00000e+00 9099 0.00021 24138 Hyperinsulinemic_hypoglycemia&_familial&_1¬_provided MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ABCC8:6833 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 137852672 15 30 50.0 -11 17463457 A T T 5_prime_UTR_variant MODIFIER ABCC8 6833 Transcript XM_024448668.1 protein_coding 4/38 633 -1 EntrezGene A A 27.3 4.042775 6.54 rs137852672 31 143084 2.16656e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.48972e-05 2.87246e-03 2.39617e-03 3.02267e-03 3.46220e-04 1.54957e-05 2.67537e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16299e-04 0.00000e+00 0.00000e+00 0.00000e+00 9099 0.00021 24138 Hyperinsulinemic_hypoglycemia&_familial&_1¬_provided MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ABCC8:6833 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 137852672 15 30 50.0 -11 17463457 A T T non_coding_transcript_exon_variant MODIFIER ABCC8 6833 Transcript XR_001747945.2 misc_RNA 4/38 632 -1 EntrezGene A A 27.3 4.042775 6.54 rs137852672 31 143084 2.16656e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.48972e-05 2.87246e-03 2.39617e-03 3.02267e-03 3.46220e-04 1.54957e-05 2.67537e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16299e-04 0.00000e+00 0.00000e+00 0.00000e+00 9099 0.00021 24138 Hyperinsulinemic_hypoglycemia&_familial&_1¬_provided MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ABCC8:6833 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 137852672 15 30 50.0 -11 17463457 A T T non_coding_transcript_exon_variant MODIFIER ABCC8 6833 Transcript XR_001747946.2 misc_RNA 4/38 632 -1 EntrezGene A A 27.3 4.042775 6.54 rs137852672 31 143084 2.16656e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.48972e-05 2.87246e-03 2.39617e-03 3.02267e-03 3.46220e-04 1.54957e-05 2.67537e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16299e-04 0.00000e+00 0.00000e+00 0.00000e+00 9099 0.00021 24138 Hyperinsulinemic_hypoglycemia&_familial&_1¬_provided MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ABCC8:6833 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 137852672 15 30 50.0 -11 17463457 A T T non_coding_transcript_exon_variant MODIFIER ABCC8 6833 Transcript XR_002957189.1 misc_RNA 4/34 632 -1 EntrezGene A A 27.3 4.042775 6.54 rs137852672 31 143084 2.16656e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.48972e-05 2.87246e-03 2.39617e-03 3.02267e-03 3.46220e-04 1.54957e-05 2.67537e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16299e-04 0.00000e+00 0.00000e+00 0.00000e+00 9099 0.00021 24138 Hyperinsulinemic_hypoglycemia&_familial&_1¬_provided MONDO:MONDO:0009734&MedGen:C2931832&OMIM:256450&SNOMED_CT:360339005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ABCC8:6833 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 137852672 15 30 50.0 -11 42403298 A AT T intergenic_variant MODIFIER 2.429 0.135674 2.29&0.942 chr11:42403299-42403299 54313 142836 3.80247e-01 1.86068e-01 1.85737e-01 1.86456e-01 4.12027e-01 4.23077e-01 4.00000e-01 4.56499e-01 4.62428e-01 4.51973e-01 6.18516e-01 6.08324e-01 6.29962e-01 1.38604e-01 1.39004e-01 1.38260e-01 3.82069e-01 3.92726e-01 4.09639e-01 3.87379e-01 3.78307e-01 4.86829e-01 4.85191e-01 4.89084e-01 4.10531e-01 3.79781e-01 4.42748e-01 3.80200e-01 3.71297e-01 3.58156e-01 3.74293e-01 15 30 50.0 -11 46739505 G A A 3_prime_UTR_variant MODIFIER F2 2147 Transcript NM_000506.5 protein_coding 14/14 1989 1 EntrezGene G G 5.341 0.392295 3.36 rs1799963 1377 143280 9.61055e-03 2.59240e-03 2.99480e-03 2.11996e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.51648e-02 1.45615e-02 1.56250e-02 2.92169e-02 2.83768e-02 3.01669e-02 0.00000e+00 0.00000e+00 0.00000e+00 1.02784e-02 5.25210e-03 5.19169e-03 5.27108e-03 8.90028e-03 1.35047e-02 1.41749e-02 1.25828e-02 1.53346e-02 1.00182e-02 2.08729e-02 9.60425e-03 1.31666e-03 1.77936e-03 1.21163e-03 13310 0.00359 28349 Ischemic_stroke&_susceptibility_to&Venous_thromboembolism&Thrombophilia_due_to_thrombin_defect&Prothrombin_deficiency&_congenital&Pregnancy_loss&_recurrent&_susceptibility_to&_2&Hereditary_factor_II_deficiency_disease¬_provided .&MONDO:MONDO:0005399&MeSH:D054556&MedGen:C1861172&MONDO:MONDO:0008559&MedGen:C3160733&OMIM:188050&MONDO:MONDO:0013361&MedGen:C0020640&OMIM:613679&Orphanet:ORPHA325&SNOMED_CT:73975000&MONDO:MONDO:0013728&MedGen:C3280672&OMIM:614390&MONDO:MONDO:0024307&MedGen:C0272317&SNOMED_CT:33297000&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_risk_factor Benign(1)&Pathogenic(4)&Uncertain_significance(2) single_nucleotide_variant F2:2147 SO:0001624&3_prime_UTR_variant 1 1799963 15 30 50.0 -11 46739505 G A A downstream_gene_variant MODIFIER CKAP5 9793 Transcript NM_001008938.4 protein_coding 3543 -1 EntrezGene G G 5.341 0.392295 3.36 rs1799963 1377 143280 9.61055e-03 2.59240e-03 2.99480e-03 2.11996e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.51648e-02 1.45615e-02 1.56250e-02 2.92169e-02 2.83768e-02 3.01669e-02 0.00000e+00 0.00000e+00 0.00000e+00 1.02784e-02 5.25210e-03 5.19169e-03 5.27108e-03 8.90028e-03 1.35047e-02 1.41749e-02 1.25828e-02 1.53346e-02 1.00182e-02 2.08729e-02 9.60425e-03 1.31666e-03 1.77936e-03 1.21163e-03 13310 0.00359 28349 Ischemic_stroke&_susceptibility_to&Venous_thromboembolism&Thrombophilia_due_to_thrombin_defect&Prothrombin_deficiency&_congenital&Pregnancy_loss&_recurrent&_susceptibility_to&_2&Hereditary_factor_II_deficiency_disease¬_provided .&MONDO:MONDO:0005399&MeSH:D054556&MedGen:C1861172&MONDO:MONDO:0008559&MedGen:C3160733&OMIM:188050&MONDO:MONDO:0013361&MedGen:C0020640&OMIM:613679&Orphanet:ORPHA325&SNOMED_CT:73975000&MONDO:MONDO:0013728&MedGen:C3280672&OMIM:614390&MONDO:MONDO:0024307&MedGen:C0272317&SNOMED_CT:33297000&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_risk_factor Benign(1)&Pathogenic(4)&Uncertain_significance(2) single_nucleotide_variant F2:2147 SO:0001624&3_prime_UTR_variant 1 1799963 15 30 50.0 -11 46739505 G A A downstream_gene_variant MODIFIER CKAP5 9793 Transcript NM_014756.3 protein_coding 4029 -1 EntrezGene G G OK 5.341 0.392295 3.36 rs1799963 1377 143280 9.61055e-03 2.59240e-03 2.99480e-03 2.11996e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.51648e-02 1.45615e-02 1.56250e-02 2.92169e-02 2.83768e-02 3.01669e-02 0.00000e+00 0.00000e+00 0.00000e+00 1.02784e-02 5.25210e-03 5.19169e-03 5.27108e-03 8.90028e-03 1.35047e-02 1.41749e-02 1.25828e-02 1.53346e-02 1.00182e-02 2.08729e-02 9.60425e-03 1.31666e-03 1.77936e-03 1.21163e-03 13310 0.00359 28349 Ischemic_stroke&_susceptibility_to&Venous_thromboembolism&Thrombophilia_due_to_thrombin_defect&Prothrombin_deficiency&_congenital&Pregnancy_loss&_recurrent&_susceptibility_to&_2&Hereditary_factor_II_deficiency_disease¬_provided .&MONDO:MONDO:0005399&MeSH:D054556&MedGen:C1861172&MONDO:MONDO:0008559&MedGen:C3160733&OMIM:188050&MONDO:MONDO:0013361&MedGen:C0020640&OMIM:613679&Orphanet:ORPHA325&SNOMED_CT:73975000&MONDO:MONDO:0013728&MedGen:C3280672&OMIM:614390&MONDO:MONDO:0024307&MedGen:C0272317&SNOMED_CT:33297000&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_risk_factor Benign(1)&Pathogenic(4)&Uncertain_significance(2) single_nucleotide_variant F2:2147 SO:0001624&3_prime_UTR_variant 1 1799963 15 30 50.0 -11 59842516 GTTC G - inframe_deletion MODERATE GIF 2694 Transcript NM_005142.3 protein_coding 4/9 481-483 435-437 145-146 KN/N aaGAAc/aac -1 EntrezGene TTC TTC 17.26 1.733971 4.11&4.14&1.52 rs770530971 237 143332 1.65350e-03 3.56633e-04 3.52237e-04 3.61795e-04 0.00000e+00 0.00000e+00 0.00000e+00 5.41886e-03 4.06366e-03 6.45161e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.67894e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.62646e-03 2.24535e-03 2.43368e-03 1.98632e-03 9.31099e-04 0.00000e+00 1.90114e-03 1.65295e-03 3.27869e-04 1.76678e-03 0.00000e+00 208192 204450 Intrinsic_factor_deficiency MedGen:C1394891&OMIM:261000 criteria_provided&_single_submitter Likely_benign Microsatellite CBLIF:2694 SO:0001822&inframe_deletion 1 770530971 15 30 50.0 -11 59842516 GTTC G - inframe_deletion MODERATE GIF 2694 Transcript XM_011544939.3 protein_coding 4/9 483-485 435-437 145-146 KN/N aaGAAc/aac -1 EntrezGene TTC TTC 17.26 1.733971 4.11&4.14&1.52 rs770530971 237 143332 1.65350e-03 3.56633e-04 3.52237e-04 3.61795e-04 0.00000e+00 0.00000e+00 0.00000e+00 5.41886e-03 4.06366e-03 6.45161e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.67894e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.62646e-03 2.24535e-03 2.43368e-03 1.98632e-03 9.31099e-04 0.00000e+00 1.90114e-03 1.65295e-03 3.27869e-04 1.76678e-03 0.00000e+00 208192 204450 Intrinsic_factor_deficiency MedGen:C1394891&OMIM:261000 criteria_provided&_single_submitter Likely_benign Microsatellite CBLIF:2694 SO:0001822&inframe_deletion 1 770530971 15 30 50.0 -11 61393964 C T T missense_variant MODERATE TMEM216 51259 Transcript NM_001173990.3 protein_coding 3/5 262 217 73 R/C Cgc/Tgc 1 EntrezGene C C 0 1 27.1 4.015988 27.1 0.9992459018653217 4.294784 0.390937751491167 0.61006 5.089541 0.506901004329847 0.67496 -3.42&-3.42&-3.42 4.99 0.99999982561307 0.000000 0.207319 0.9046 0.9681 3.235&3.235&. 0.999999&0.999997&0.999999 -7.27&-7.27&-7.58 11.5558 0.977&0.976&0.969 0.45305 0.732398 0.615000 0.998000 0.928000 1.025000 5.56 217705 0.00002 214310 Joubert_syndrome&Joubert_syndrome_2¬_provided Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0011963&MedGen:C1842577&OMIM:608091&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Pathogenic(2)&Uncertain_significance(1) single_nucleotide_variant TMEM216:51259 SO:0001583&missense_variant 1 779526456 15 30 50.0 -11 61393964 C T T missense_variant MODERATE TMEM216 51259 Transcript NM_001173991.2 protein_coding 3/5 489 217 73 R/C Cgc/Tgc 1 EntrezGene C C 0 0.997 27.1 4.015988 27.1 0.9992459018653217 4.294784 0.390937751491167 0.61006 5.089541 0.506901004329847 0.67496 -3.42&-3.42&-3.42 4.99 0.99999982561307 0.000000 0.207319 0.9046 0.9681 3.235&3.235&. 0.999999&0.999997&0.999999 -7.27&-7.27&-7.58 11.5558 0.977&0.976&0.969 0.45305 0.732398 0.615000 0.998000 0.928000 1.025000 5.56 217705 0.00002 214310 Joubert_syndrome&Joubert_syndrome_2¬_provided Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0011963&MedGen:C1842577&OMIM:608091&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Pathogenic(2)&Uncertain_significance(1) single_nucleotide_variant TMEM216:51259 SO:0001583&missense_variant 1 779526456 15 30 50.0 -11 61393964 C T T missense_variant MODERATE TMEM216 51259 Transcript NM_001330285.1 protein_coding 3/5 503 34 12 R/C Cgc/Tgc 1 EntrezGene C C 0 0.98 27.1 4.015988 27.1 0.9992459018653217 4.294784 0.390937751491167 0.61006 5.089541 0.506901004329847 0.67496 -3.42&-3.42&-3.42 4.99 0.99999982561307 0.000000 0.207319 0.9046 0.9681 3.235&3.235&. 0.999999&0.999997&0.999999 -7.27&-7.27&-7.58 11.5558 0.977&0.976&0.969 0.45305 0.732398 0.615000 0.998000 0.928000 1.025000 5.56 217705 0.00002 214310 Joubert_syndrome&Joubert_syndrome_2¬_provided Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0011963&MedGen:C1842577&OMIM:608091&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Pathogenic(2)&Uncertain_significance(1) single_nucleotide_variant TMEM216:51259 SO:0001583&missense_variant 1 779526456 15 30 50.0 -11 61393964 C T T missense_variant MODERATE TMEM216 51259 Transcript NM_016499.5 protein_coding 3/5 470 34 12 R/C Cgc/Tgc 1 EntrezGene C C 0 0.972 27.1 4.015988 27.1 0.9992459018653217 4.294784 0.390937751491167 0.61006 5.089541 0.506901004329847 0.67496 -3.42&-3.42&-3.42 4.99 0.99999982561307 0.000000 0.207319 0.9046 0.9681 3.235&3.235&. 0.999999&0.999997&0.999999 -7.27&-7.27&-7.58 11.5558 0.977&0.976&0.969 0.45305 0.732398 0.615000 0.998000 0.928000 1.025000 5.56 217705 0.00002 214310 Joubert_syndrome&Joubert_syndrome_2¬_provided Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0011963&MedGen:C1842577&OMIM:608091&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Pathogenic(2)&Uncertain_significance(1) single_nucleotide_variant TMEM216:51259 SO:0001583&missense_variant 1 779526456 15 30 50.0 -11 61393964 C T T missense_variant MODERATE TMEM216 51259 Transcript XM_005274039.4 protein_coding 4/6 1015 34 12 R/C Cgc/Tgc 1 EntrezGene C C 0 0.972 27.1 4.015988 27.1 0.9992459018653217 4.294784 0.390937751491167 0.61006 5.089541 0.506901004329847 0.67496 -3.42&-3.42&-3.42 4.99 0.99999982561307 0.000000 0.207319 0.9046 0.9681 3.235&3.235&. 0.999999&0.999997&0.999999 -7.27&-7.27&-7.58 11.5558 0.977&0.976&0.969 0.45305 0.732398 0.615000 0.998000 0.928000 1.025000 5.56 217705 0.00002 214310 Joubert_syndrome&Joubert_syndrome_2¬_provided Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0011963&MedGen:C1842577&OMIM:608091&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Pathogenic(2)&Uncertain_significance(1) single_nucleotide_variant TMEM216:51259 SO:0001583&missense_variant 1 779526456 15 30 50.0 -11 61393965 G A A missense_variant MODERATE TMEM216 51259 Transcript NM_001173990.3 protein_coding 3/5 263 218 73 R/H cGc/cAc 1 EntrezGene G G 0 0.998 31 4.371833 31 0.99657610397503871 2.294229 -0.0193510852615356 0.38841 2.516833 0.00160896020901942 0.41927 -3.41&-3.41&-3.41 3.12 0.999973729472172 0.000000 0.334581 0.7788 0.4941 2.685&2.685&. 0.999323&0.998484&0.999323 -4.28&-4.28&-4.71 8.6011 0.966&0.968&0.909 0.89030 0.732398 0.972000 0.998000 6.698000 1.175000 6.47 chr11:61393965-61393965 1 143240 6.98129e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.19489e-04 0.00000e+00 5.93824e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44080e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97574e-06 0.00000e+00 0.00000e+00 0.00000e+00 198 15237 Joubert_syndrome&Meckel_syndrome&_type_2&Joubert_syndrome_2 Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0011296&MedGen:C1864148&OMIM:603194&MONDO:MONDO:0011963&MedGen:C1842577&OMIM:608091 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant TMEM216:51259 SO:0001583&missense_variant 1 201108965 15 30 50.0 -11 61393965 G A A missense_variant MODERATE TMEM216 51259 Transcript NM_001173991.2 protein_coding 3/5 490 218 73 R/H cGc/cAc 1 EntrezGene G G 0 0.997 31 4.371833 31 0.99657610397503871 2.294229 -0.0193510852615356 0.38841 2.516833 0.00160896020901942 0.41927 -3.41&-3.41&-3.41 3.12 0.999973729472172 0.000000 0.334581 0.7788 0.4941 2.685&2.685&. 0.999323&0.998484&0.999323 -4.28&-4.28&-4.71 8.6011 0.966&0.968&0.909 0.89030 0.732398 0.972000 0.998000 6.698000 1.175000 6.47 chr11:61393965-61393965 1 143240 6.98129e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.19489e-04 0.00000e+00 5.93824e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44080e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97574e-06 0.00000e+00 0.00000e+00 0.00000e+00 198 15237 Joubert_syndrome&Meckel_syndrome&_type_2&Joubert_syndrome_2 Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0011296&MedGen:C1864148&OMIM:603194&MONDO:MONDO:0011963&MedGen:C1842577&OMIM:608091 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant TMEM216:51259 SO:0001583&missense_variant 1 201108965 15 30 50.0 -11 61393965 G A A missense_variant MODERATE TMEM216 51259 Transcript NM_001330285.1 protein_coding 3/5 504 35 12 R/H cGc/cAc 1 EntrezGene G G 0 0.506 31 4.371833 31 0.99657610397503871 2.294229 -0.0193510852615356 0.38841 2.516833 0.00160896020901942 0.41927 -3.41&-3.41&-3.41 3.12 0.999973729472172 0.000000 0.334581 0.7788 0.4941 2.685&2.685&. 0.999323&0.998484&0.999323 -4.28&-4.28&-4.71 8.6011 0.966&0.968&0.909 0.89030 0.732398 0.972000 0.998000 6.698000 1.175000 6.47 chr11:61393965-61393965 1 143240 6.98129e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.19489e-04 0.00000e+00 5.93824e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44080e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97574e-06 0.00000e+00 0.00000e+00 0.00000e+00 198 15237 Joubert_syndrome&Meckel_syndrome&_type_2&Joubert_syndrome_2 Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0011296&MedGen:C1864148&OMIM:603194&MONDO:MONDO:0011963&MedGen:C1842577&OMIM:608091 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant TMEM216:51259 SO:0001583&missense_variant 1 201108965 15 30 50.0 -11 61393965 G A A missense_variant MODERATE TMEM216 51259 Transcript NM_016499.5 protein_coding 3/5 471 35 12 R/H cGc/cAc 1 EntrezGene G G 0 0.972 31 4.371833 31 0.99657610397503871 2.294229 -0.0193510852615356 0.38841 2.516833 0.00160896020901942 0.41927 -3.41&-3.41&-3.41 3.12 0.999973729472172 0.000000 0.334581 0.7788 0.4941 2.685&2.685&. 0.999323&0.998484&0.999323 -4.28&-4.28&-4.71 8.6011 0.966&0.968&0.909 0.89030 0.732398 0.972000 0.998000 6.698000 1.175000 6.47 chr11:61393965-61393965 1 143240 6.98129e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.19489e-04 0.00000e+00 5.93824e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44080e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97574e-06 0.00000e+00 0.00000e+00 0.00000e+00 198 15237 Joubert_syndrome&Meckel_syndrome&_type_2&Joubert_syndrome_2 Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0011296&MedGen:C1864148&OMIM:603194&MONDO:MONDO:0011963&MedGen:C1842577&OMIM:608091 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant TMEM216:51259 SO:0001583&missense_variant 1 201108965 15 30 50.0 -11 61393965 G A A missense_variant MODERATE TMEM216 51259 Transcript XM_005274039.4 protein_coding 4/6 1016 35 12 R/H cGc/cAc 1 EntrezGene G G 0 0.972 31 4.371833 31 0.99657610397503871 2.294229 -0.0193510852615356 0.38841 2.516833 0.00160896020901942 0.41927 -3.41&-3.41&-3.41 3.12 0.999973729472172 0.000000 0.334581 0.7788 0.4941 2.685&2.685&. 0.999323&0.998484&0.999323 -4.28&-4.28&-4.71 8.6011 0.966&0.968&0.909 0.89030 0.732398 0.972000 0.998000 6.698000 1.175000 6.47 chr11:61393965-61393965 1 143240 6.98129e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.19489e-04 0.00000e+00 5.93824e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44080e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97574e-06 0.00000e+00 0.00000e+00 0.00000e+00 198 15237 Joubert_syndrome&Meckel_syndrome&_type_2&Joubert_syndrome_2 Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0011296&MedGen:C1864148&OMIM:603194&MONDO:MONDO:0011963&MedGen:C1842577&OMIM:608091 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant TMEM216:51259 SO:0001583&missense_variant 1 201108965 15 30 50.0 -11 61393965 G T T missense_variant MODERATE TMEM216 51259 Transcript NM_001173990.3 protein_coding 3/5 263 218 73 R/L cGc/cTc 1 EntrezGene G G 0 0.999 29.5 4.292084 29.5 0.99698831393416332 3.15327 0.193852547460893 0.49509 3.821084 0.308072748681267 0.56562 -3.4&-3.4&-3.4 3.12 0.999973729472172 0.000000 0.461694 0.8597 0.7658 3.235&3.235&. 0.999712&0.999354&0.999712 -6.18&-6.18&-6.63 8.6011 0.976&0.98&0.968 0.91600 0.732398 0.972000 0.998000 6.698000 1.175000 6.47 chr11:61393965-61393965 19 143240 1.32645e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.51264e-03 4.54030e-03 4.48143e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.35439e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.29672e-04 4.64612e-05 2.67423e-05 7.35943e-05 4.64253e-04 9.09091e-04 0.00000e+00 1.32539e-04 0.00000e+00 0.00000e+00 0.00000e+00 197 15236 Joubert_syndrome_type_2&TMEM216-Related_Disorders&Joubert_syndrome&Meckel_syndrome&_type_2&Joubert_syndrome_2&Inborn_genetic_diseases¬_provided .&.&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0011296&MedGen:C1864148&OMIM:603194&MONDO:MONDO:0011963&MedGen:C1842577&OMIM:608091&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant TMEM216:51259 SO:0001583&missense_variant 1 201108965 15 30 50.0 -11 61393965 G T T missense_variant MODERATE TMEM216 51259 Transcript NM_001173991.2 protein_coding 3/5 490 218 73 R/L cGc/cTc 1 EntrezGene G G 0 0.986 29.5 4.292084 29.5 0.99698831393416332 3.15327 0.193852547460893 0.49509 3.821084 0.308072748681267 0.56562 -3.4&-3.4&-3.4 3.12 0.999973729472172 0.000000 0.461694 0.8597 0.7658 3.235&3.235&. 0.999712&0.999354&0.999712 -6.18&-6.18&-6.63 8.6011 0.976&0.98&0.968 0.91600 0.732398 0.972000 0.998000 6.698000 1.175000 6.47 chr11:61393965-61393965 19 143240 1.32645e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.51264e-03 4.54030e-03 4.48143e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.35439e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.29672e-04 4.64612e-05 2.67423e-05 7.35943e-05 4.64253e-04 9.09091e-04 0.00000e+00 1.32539e-04 0.00000e+00 0.00000e+00 0.00000e+00 197 15236 Joubert_syndrome_type_2&TMEM216-Related_Disorders&Joubert_syndrome&Meckel_syndrome&_type_2&Joubert_syndrome_2&Inborn_genetic_diseases¬_provided .&.&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0011296&MedGen:C1864148&OMIM:603194&MONDO:MONDO:0011963&MedGen:C1842577&OMIM:608091&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant TMEM216:51259 SO:0001583&missense_variant 1 201108965 15 30 50.0 -11 61393965 G T T missense_variant MODERATE TMEM216 51259 Transcript NM_001330285.1 protein_coding 3/5 504 35 12 R/L cGc/cTc 1 EntrezGene G G 0 0.823 29.5 4.292084 29.5 0.99698831393416332 3.15327 0.193852547460893 0.49509 3.821084 0.308072748681267 0.56562 -3.4&-3.4&-3.4 3.12 0.999973729472172 0.000000 0.461694 0.8597 0.7658 3.235&3.235&. 0.999712&0.999354&0.999712 -6.18&-6.18&-6.63 8.6011 0.976&0.98&0.968 0.91600 0.732398 0.972000 0.998000 6.698000 1.175000 6.47 chr11:61393965-61393965 19 143240 1.32645e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.51264e-03 4.54030e-03 4.48143e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.35439e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.29672e-04 4.64612e-05 2.67423e-05 7.35943e-05 4.64253e-04 9.09091e-04 0.00000e+00 1.32539e-04 0.00000e+00 0.00000e+00 0.00000e+00 197 15236 Joubert_syndrome_type_2&TMEM216-Related_Disorders&Joubert_syndrome&Meckel_syndrome&_type_2&Joubert_syndrome_2&Inborn_genetic_diseases¬_provided .&.&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0011296&MedGen:C1864148&OMIM:603194&MONDO:MONDO:0011963&MedGen:C1842577&OMIM:608091&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant TMEM216:51259 SO:0001583&missense_variant 1 201108965 15 30 50.0 -11 61393965 G T T missense_variant MODERATE TMEM216 51259 Transcript NM_016499.5 protein_coding 3/5 471 35 12 R/L cGc/cTc 1 EntrezGene G G 0 0.953 29.5 4.292084 29.5 0.99698831393416332 3.15327 0.193852547460893 0.49509 3.821084 0.308072748681267 0.56562 -3.4&-3.4&-3.4 3.12 0.999973729472172 0.000000 0.461694 0.8597 0.7658 3.235&3.235&. 0.999712&0.999354&0.999712 -6.18&-6.18&-6.63 8.6011 0.976&0.98&0.968 0.91600 0.732398 0.972000 0.998000 6.698000 1.175000 6.47 chr11:61393965-61393965 19 143240 1.32645e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.51264e-03 4.54030e-03 4.48143e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.35439e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.29672e-04 4.64612e-05 2.67423e-05 7.35943e-05 4.64253e-04 9.09091e-04 0.00000e+00 1.32539e-04 0.00000e+00 0.00000e+00 0.00000e+00 197 15236 Joubert_syndrome_type_2&TMEM216-Related_Disorders&Joubert_syndrome&Meckel_syndrome&_type_2&Joubert_syndrome_2&Inborn_genetic_diseases¬_provided .&.&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0011296&MedGen:C1864148&OMIM:603194&MONDO:MONDO:0011963&MedGen:C1842577&OMIM:608091&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant TMEM216:51259 SO:0001583&missense_variant 1 201108965 15 30 50.0 -11 61393965 G T T missense_variant MODERATE TMEM216 51259 Transcript XM_005274039.4 protein_coding 4/6 1016 35 12 R/L cGc/cTc 1 EntrezGene G G 0 0.953 29.5 4.292084 29.5 0.99698831393416332 3.15327 0.193852547460893 0.49509 3.821084 0.308072748681267 0.56562 -3.4&-3.4&-3.4 3.12 0.999973729472172 0.000000 0.461694 0.8597 0.7658 3.235&3.235&. 0.999712&0.999354&0.999712 -6.18&-6.18&-6.63 8.6011 0.976&0.98&0.968 0.91600 0.732398 0.972000 0.998000 6.698000 1.175000 6.47 chr11:61393965-61393965 19 143240 1.32645e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.51264e-03 4.54030e-03 4.48143e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.35439e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.29672e-04 4.64612e-05 2.67423e-05 7.35943e-05 4.64253e-04 9.09091e-04 0.00000e+00 1.32539e-04 0.00000e+00 0.00000e+00 0.00000e+00 197 15236 Joubert_syndrome_type_2&TMEM216-Related_Disorders&Joubert_syndrome&Meckel_syndrome&_type_2&Joubert_syndrome_2&Inborn_genetic_diseases¬_provided .&.&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0011296&MedGen:C1864148&OMIM:603194&MONDO:MONDO:0011963&MedGen:C1842577&OMIM:608091&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant TMEM216:51259 SO:0001583&missense_variant 1 201108965 15 30 50.0 -11 95882497 CTAAAA C - splice_acceptor_variant&intron_variant HIGH MTMR2 8898 Transcript NM_001243571.2 protein_coding 4/17 -1 EntrezGene TAAAA TAAAA 0.717 -0.130491 0.475&-0.951&0.111&0.453 rs112378876 92692 136350 6.79809e-01 6.50612e-01 6.53220e-01 6.47546e-01 7.47696e-01 7.45575e-01 7.50000e-01 6.47512e-01 6.41126e-01 6.52374e-01 7.38636e-01 7.34067e-01 7.43791e-01 5.52632e-01 5.55085e-01 5.50493e-01 6.86722e-01 6.36878e-01 6.58649e-01 6.30188e-01 6.72364e-01 7.19313e-01 7.18806e-01 7.20015e-01 6.65020e-01 6.68288e-01 6.61647e-01 6.80614e-01 5.51524e-01 5.96457e-01 5.41370e-01 306546 315574 Charcot-Marie-Tooth_disease_type_4 MONDO:MONDO:0018995&MedGen:C4082197&Orphanet:ORPHA64749&SNOMED_CT:715795005 criteria_provided&_single_submitter Benign Microsatellite MTMR2:8898 SO:0001574&splice_acceptor_variant&SO:0001627&intron_variant 1 112378876 15 30 50.0 -11 95882497 CTAAAA C - intron_variant MODIFIER MTMR2 8898 Transcript NM_016156.6 protein_coding 2/14 -1 EntrezGene TAAAA TAAAA 0.717 -0.130491 0.475&-0.951&0.111&0.453 rs112378876 92692 136350 6.79809e-01 6.50612e-01 6.53220e-01 6.47546e-01 7.47696e-01 7.45575e-01 7.50000e-01 6.47512e-01 6.41126e-01 6.52374e-01 7.38636e-01 7.34067e-01 7.43791e-01 5.52632e-01 5.55085e-01 5.50493e-01 6.86722e-01 6.36878e-01 6.58649e-01 6.30188e-01 6.72364e-01 7.19313e-01 7.18806e-01 7.20015e-01 6.65020e-01 6.68288e-01 6.61647e-01 6.80614e-01 5.51524e-01 5.96457e-01 5.41370e-01 306546 315574 Charcot-Marie-Tooth_disease_type_4 MONDO:MONDO:0018995&MedGen:C4082197&Orphanet:ORPHA64749&SNOMED_CT:715795005 criteria_provided&_single_submitter Benign Microsatellite MTMR2:8898 SO:0001574&splice_acceptor_variant&SO:0001627&intron_variant 1 112378876 15 30 50.0 -11 95882497 CTAAAA C - splice_acceptor_variant&intron_variant HIGH MTMR2 8898 Transcript NM_201278.3 protein_coding 3/16 -1 EntrezGene TAAAA TAAAA 0.717 -0.130491 0.475&-0.951&0.111&0.453 rs112378876 92692 136350 6.79809e-01 6.50612e-01 6.53220e-01 6.47546e-01 7.47696e-01 7.45575e-01 7.50000e-01 6.47512e-01 6.41126e-01 6.52374e-01 7.38636e-01 7.34067e-01 7.43791e-01 5.52632e-01 5.55085e-01 5.50493e-01 6.86722e-01 6.36878e-01 6.58649e-01 6.30188e-01 6.72364e-01 7.19313e-01 7.18806e-01 7.20015e-01 6.65020e-01 6.68288e-01 6.61647e-01 6.80614e-01 5.51524e-01 5.96457e-01 5.41370e-01 306546 315574 Charcot-Marie-Tooth_disease_type_4 MONDO:MONDO:0018995&MedGen:C4082197&Orphanet:ORPHA64749&SNOMED_CT:715795005 criteria_provided&_single_submitter Benign Microsatellite MTMR2:8898 SO:0001574&splice_acceptor_variant&SO:0001627&intron_variant 1 112378876 15 30 50.0 -11 95882497 CTAAAA C - intron_variant MODIFIER MTMR2 8898 Transcript NM_201281.3 protein_coding 3/15 -1 EntrezGene TAAAA TAAAA 0.717 -0.130491 0.475&-0.951&0.111&0.453 rs112378876 92692 136350 6.79809e-01 6.50612e-01 6.53220e-01 6.47546e-01 7.47696e-01 7.45575e-01 7.50000e-01 6.47512e-01 6.41126e-01 6.52374e-01 7.38636e-01 7.34067e-01 7.43791e-01 5.52632e-01 5.55085e-01 5.50493e-01 6.86722e-01 6.36878e-01 6.58649e-01 6.30188e-01 6.72364e-01 7.19313e-01 7.18806e-01 7.20015e-01 6.65020e-01 6.68288e-01 6.61647e-01 6.80614e-01 5.51524e-01 5.96457e-01 5.41370e-01 306546 315574 Charcot-Marie-Tooth_disease_type_4 MONDO:MONDO:0018995&MedGen:C4082197&Orphanet:ORPHA64749&SNOMED_CT:715795005 criteria_provided&_single_submitter Benign Microsatellite MTMR2:8898 SO:0001574&splice_acceptor_variant&SO:0001627&intron_variant 1 112378876 15 30 50.0 -11 95882497 CTAAAA C - intron_variant MODIFIER MTMR2 8898 Transcript XM_005274374.3 protein_coding 3/15 -1 EntrezGene TAAAA TAAAA 0.717 -0.130491 0.475&-0.951&0.111&0.453 rs112378876 92692 136350 6.79809e-01 6.50612e-01 6.53220e-01 6.47546e-01 7.47696e-01 7.45575e-01 7.50000e-01 6.47512e-01 6.41126e-01 6.52374e-01 7.38636e-01 7.34067e-01 7.43791e-01 5.52632e-01 5.55085e-01 5.50493e-01 6.86722e-01 6.36878e-01 6.58649e-01 6.30188e-01 6.72364e-01 7.19313e-01 7.18806e-01 7.20015e-01 6.65020e-01 6.68288e-01 6.61647e-01 6.80614e-01 5.51524e-01 5.96457e-01 5.41370e-01 306546 315574 Charcot-Marie-Tooth_disease_type_4 MONDO:MONDO:0018995&MedGen:C4082197&Orphanet:ORPHA64749&SNOMED_CT:715795005 criteria_provided&_single_submitter Benign Microsatellite MTMR2:8898 SO:0001574&splice_acceptor_variant&SO:0001627&intron_variant 1 112378876 15 30 50.0 -11 95882497 CTAAAA C - splice_acceptor_variant&intron_variant HIGH MTMR2 8898 Transcript XM_005274375.3 protein_coding 2/15 -1 EntrezGene TAAAA TAAAA 0.717 -0.130491 0.475&-0.951&0.111&0.453 rs112378876 92692 136350 6.79809e-01 6.50612e-01 6.53220e-01 6.47546e-01 7.47696e-01 7.45575e-01 7.50000e-01 6.47512e-01 6.41126e-01 6.52374e-01 7.38636e-01 7.34067e-01 7.43791e-01 5.52632e-01 5.55085e-01 5.50493e-01 6.86722e-01 6.36878e-01 6.58649e-01 6.30188e-01 6.72364e-01 7.19313e-01 7.18806e-01 7.20015e-01 6.65020e-01 6.68288e-01 6.61647e-01 6.80614e-01 5.51524e-01 5.96457e-01 5.41370e-01 306546 315574 Charcot-Marie-Tooth_disease_type_4 MONDO:MONDO:0018995&MedGen:C4082197&Orphanet:ORPHA64749&SNOMED_CT:715795005 criteria_provided&_single_submitter Benign Microsatellite MTMR2:8898 SO:0001574&splice_acceptor_variant&SO:0001627&intron_variant 1 112378876 15 30 50.0 -11 108249050 G GTAAT TAAT frameshift_variant HIGH ATM 472 Transcript NM_000051.4 protein_coding 9/63 1333-1334 1183-1184 395 V/VIX gta/gTAATta 1 EntrezGene 5.62&6.53 15 30 50.0 -11 108249050 G GTAAT TAAT frameshift_variant HIGH ATM 472 Transcript NM_001351834.2 protein_coding 10/64 1421-1422 1183-1184 395 V/VIX gta/gTAATta 1 EntrezGene 5.62&6.53 15 30 50.0 -11 108249050 G GTAAT TAAT frameshift_variant HIGH ATM 472 Transcript XM_005271562.5 protein_coding 9/63 1702-1703 1183-1184 395 V/VIX gta/gTAATta 1 EntrezGene 5.62&6.53 15 30 50.0 -11 108249050 G GTAAT TAAT frameshift_variant HIGH ATM 472 Transcript XM_006718843.4 protein_coding 9/63 1335-1336 1183-1184 395 V/VIX gta/gTAATta 1 EntrezGene 5.62&6.53 15 30 50.0 -11 108249050 G GTAAT TAAT frameshift_variant HIGH ATM 472 Transcript XM_011542840.3 protein_coding 10/64 1423-1424 1183-1184 395 V/VIX gta/gTAATta 1 EntrezGene 5.62&6.53 15 30 50.0 -11 108249050 G GTAAT TAAT frameshift_variant HIGH ATM 472 Transcript XM_011542842.3 protein_coding 8/62 1398-1399 1018-1019 340 V/VIX gta/gTAATta 1 EntrezGene 5.62&6.53 15 30 50.0 -11 108249050 G GTAAT TAAT frameshift_variant HIGH ATM 472 Transcript XM_011542843.2 protein_coding 9/60 1916-1917 1183-1184 395 V/VIX gta/gTAATta 1 EntrezGene 5.62&6.53 15 30 50.0 -11 108249050 G GTAAT TAAT frameshift_variant HIGH ATM 472 Transcript XM_011542844.3 protein_coding 9/63 1519-1520 139-140 47 V/VIX gta/gTAATta 1 EntrezGene 5.62&6.53 15 30 50.0 -11 108249050 G GTAAT TAAT intron_variant MODIFIER ATM 472 Transcript XM_011542845.2 protein_coding 1/54 1 EntrezGene 5.62&6.53 15 30 50.0 -11 108249050 G GTAAT TAAT frameshift_variant HIGH ATM 472 Transcript XM_017017789.2 protein_coding 10/64 2126-2127 1183-1184 395 V/VIX gta/gTAATta 1 EntrezGene 5.62&6.53 15 30 50.0 -11 108249050 G GTAAT TAAT frameshift_variant HIGH ATM 472 Transcript XM_017017790.2 protein_coding 9/63 1523-1524 1183-1184 395 V/VIX gta/gTAATta 1 EntrezGene 5.62&6.53 15 30 50.0 -11 108249050 G GTAAT TAAT frameshift_variant HIGH ATM 472 Transcript XM_017017791.1 protein_coding 9/45 1916-1917 1183-1184 395 V/VIX gta/gTAATta 1 EntrezGene 5.62&6.53 15 30 50.0 -11 108249050 G GTAAT TAAT frameshift_variant HIGH ATM 472 Transcript XM_017017792.2 protein_coding 9/36 1916-1917 1183-1184 395 V/VIX gta/gTAATta 1 EntrezGene 5.62&6.53 15 30 50.0 -11 108249050 G GTAAT TAAT non_coding_transcript_exon_variant MODIFIER ATM 472 Transcript XR_002957150.1 misc_RNA 9/39 1916-1917 1 EntrezGene 5.62&6.53 15 30 50.0 -11 22221100 C CA A frameshift_variant HIGH ANO5 203859 Transcript NM_001142649.2 protein_coding 5/22 589-590 181-182 61 Q/QX caa/cAaa 1 EntrezGene 25.4 3.685970 2.23&2.99 rs1265883666 165 142942 1.15431e-03 3.57466e-04 4.41579e-04 2.58853e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.17664e-03 8.49473e-04 1.42635e-03 6.02773e-04 1.13636e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.27554e-03 9.58405e-05 0.00000e+00 1.26072e-04 1.02530e-03 1.97040e-03 2.00879e-03 1.91755e-03 1.86741e-03 1.83150e-03 1.90476e-03 1.15140e-03 0.00000e+00 0.00000e+00 0.00000e+00 2164 17203 Polycystic_kidney_dysplasia&Intellectual_disability&Achilles_tendon_contracture&Lower_limb_muscle_weakness&Elevated_serum_creatine_phosphokinase&Myopathy&Lower_limb_amyotrophy&Gnathodiaphyseal_dysplasia&Limb-girdle_muscular_dystrophy&_type_2L&Miyoshi_muscular_dystrophy_3&ANO5-Related_Disorders¬_provided Limb-girdle_muscular_dystrophy&_type_2L&Miyoshi_muscular_dystrophy_3 Human_Phenotype_Ontology:HP:0000113&Human_Phenotype_Ontology:HP:0004716&Human_Phenotype_Ontology:HP:0004739&Human_Phenotype_Ontology:HP:0004740&Human_Phenotype_Ontology:HP:0008645&Human_Phenotype_Ontology:HP:0008673&Human_Phenotype_Ontology:HP:0008699&MedGen:C0022680&OMIM:PS173900&Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&Human_Phenotype_Ontology:HP:0001771&Human_Phenotype_Ontology:HP:0004711&Human_Phenotype_Ontology:HP:0005031&Human_Phenotype_Ontology:HP:0006430&MedGen:C0410264&Human_Phenotype_Ontology:HP:0002065&Human_Phenotype_Ontology:HP:0002477&Human_Phenotype_Ontology:HP:0007340&Human_Phenotype_Ontology:HP:0009047&MedGen:C1836296&Human_Phenotype_Ontology:HP:0002147&Human_Phenotype_Ontology:HP:0002906&Human_Phenotype_Ontology:HP:0003078&Human_Phenotype_Ontology:HP:0003236&Human_Phenotype_Ontology:HP:0003525&Human_Phenotype_Ontology:HP:0003531&Human_Phenotype_Ontology:HP:0008164&MONDO:MONDO:0007402&MedGen:C0241005&OMIM:123320&Human_Phenotype_Ontology:HP:0003198&Human_Phenotype_Ontology:HP:0003569&Human_Phenotype_Ontology:HP:0003705&Human_Phenotype_Ontology:HP:0003742&Human_Phenotype_Ontology:HP:0003802&MONDO:MONDO:0005336&MedGen:C0026848&Human_Phenotype_Ontology:HP:0007210&MedGen:C4024921&MONDO:MONDO:0008151&MedGen:C1833736&OMIM:166260&Orphanet:ORPHA53697&MONDO:MONDO:0012652&MedGen:C1969785&OMIM:611307&Orphanet:ORPHA206549&MONDO:MONDO:0013222&MedGen:C2750076&OMIM:613319&Orphanet:ORPHA399096&MedGen:CN239193&MedGen:CN517202 MONDO:MONDO:0012652&MedGen:C1969785&OMIM:611307&Orphanet:ORPHA206549&MONDO:MONDO:0013222&MedGen:C2750076&OMIM:613319&Orphanet:ORPHA399096 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 424764:Likely_pathogenic Duplication ANO5:203859 SO:0001589&frameshift_variant 133 137854521 15 30 50.0 -11 22221100 C CA A frameshift_variant HIGH ANO5 203859 Transcript NM_213599.3 protein_coding 5/22 592-593 184-185 62 Q/QX caa/cAaa 1 EntrezGene 25.4 3.685970 2.23&2.99 rs1265883666 165 142942 1.15431e-03 3.57466e-04 4.41579e-04 2.58853e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.17664e-03 8.49473e-04 1.42635e-03 6.02773e-04 1.13636e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.27554e-03 9.58405e-05 0.00000e+00 1.26072e-04 1.02530e-03 1.97040e-03 2.00879e-03 1.91755e-03 1.86741e-03 1.83150e-03 1.90476e-03 1.15140e-03 0.00000e+00 0.00000e+00 0.00000e+00 2164 17203 Polycystic_kidney_dysplasia&Intellectual_disability&Achilles_tendon_contracture&Lower_limb_muscle_weakness&Elevated_serum_creatine_phosphokinase&Myopathy&Lower_limb_amyotrophy&Gnathodiaphyseal_dysplasia&Limb-girdle_muscular_dystrophy&_type_2L&Miyoshi_muscular_dystrophy_3&ANO5-Related_Disorders¬_provided Limb-girdle_muscular_dystrophy&_type_2L&Miyoshi_muscular_dystrophy_3 Human_Phenotype_Ontology:HP:0000113&Human_Phenotype_Ontology:HP:0004716&Human_Phenotype_Ontology:HP:0004739&Human_Phenotype_Ontology:HP:0004740&Human_Phenotype_Ontology:HP:0008645&Human_Phenotype_Ontology:HP:0008673&Human_Phenotype_Ontology:HP:0008699&MedGen:C0022680&OMIM:PS173900&Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&Human_Phenotype_Ontology:HP:0001771&Human_Phenotype_Ontology:HP:0004711&Human_Phenotype_Ontology:HP:0005031&Human_Phenotype_Ontology:HP:0006430&MedGen:C0410264&Human_Phenotype_Ontology:HP:0002065&Human_Phenotype_Ontology:HP:0002477&Human_Phenotype_Ontology:HP:0007340&Human_Phenotype_Ontology:HP:0009047&MedGen:C1836296&Human_Phenotype_Ontology:HP:0002147&Human_Phenotype_Ontology:HP:0002906&Human_Phenotype_Ontology:HP:0003078&Human_Phenotype_Ontology:HP:0003236&Human_Phenotype_Ontology:HP:0003525&Human_Phenotype_Ontology:HP:0003531&Human_Phenotype_Ontology:HP:0008164&MONDO:MONDO:0007402&MedGen:C0241005&OMIM:123320&Human_Phenotype_Ontology:HP:0003198&Human_Phenotype_Ontology:HP:0003569&Human_Phenotype_Ontology:HP:0003705&Human_Phenotype_Ontology:HP:0003742&Human_Phenotype_Ontology:HP:0003802&MONDO:MONDO:0005336&MedGen:C0026848&Human_Phenotype_Ontology:HP:0007210&MedGen:C4024921&MONDO:MONDO:0008151&MedGen:C1833736&OMIM:166260&Orphanet:ORPHA53697&MONDO:MONDO:0012652&MedGen:C1969785&OMIM:611307&Orphanet:ORPHA206549&MONDO:MONDO:0013222&MedGen:C2750076&OMIM:613319&Orphanet:ORPHA399096&MedGen:CN239193&MedGen:CN517202 MONDO:MONDO:0012652&MedGen:C1969785&OMIM:611307&Orphanet:ORPHA206549&MONDO:MONDO:0013222&MedGen:C2750076&OMIM:613319&Orphanet:ORPHA399096 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 424764:Likely_pathogenic Duplication ANO5:203859 SO:0001589&frameshift_variant 133 137854521 15 30 50.0 -11 22221100 C CA A frameshift_variant HIGH ANO5 203859 Transcript XM_005252820.3 protein_coding 4/21 605-606 142-143 48 Q/QX caa/cAaa 1 EntrezGene 25.4 3.685970 2.23&2.99 rs1265883666 165 142942 1.15431e-03 3.57466e-04 4.41579e-04 2.58853e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.17664e-03 8.49473e-04 1.42635e-03 6.02773e-04 1.13636e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.27554e-03 9.58405e-05 0.00000e+00 1.26072e-04 1.02530e-03 1.97040e-03 2.00879e-03 1.91755e-03 1.86741e-03 1.83150e-03 1.90476e-03 1.15140e-03 0.00000e+00 0.00000e+00 0.00000e+00 2164 17203 Polycystic_kidney_dysplasia&Intellectual_disability&Achilles_tendon_contracture&Lower_limb_muscle_weakness&Elevated_serum_creatine_phosphokinase&Myopathy&Lower_limb_amyotrophy&Gnathodiaphyseal_dysplasia&Limb-girdle_muscular_dystrophy&_type_2L&Miyoshi_muscular_dystrophy_3&ANO5-Related_Disorders¬_provided Limb-girdle_muscular_dystrophy&_type_2L&Miyoshi_muscular_dystrophy_3 Human_Phenotype_Ontology:HP:0000113&Human_Phenotype_Ontology:HP:0004716&Human_Phenotype_Ontology:HP:0004739&Human_Phenotype_Ontology:HP:0004740&Human_Phenotype_Ontology:HP:0008645&Human_Phenotype_Ontology:HP:0008673&Human_Phenotype_Ontology:HP:0008699&MedGen:C0022680&OMIM:PS173900&Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&Human_Phenotype_Ontology:HP:0001771&Human_Phenotype_Ontology:HP:0004711&Human_Phenotype_Ontology:HP:0005031&Human_Phenotype_Ontology:HP:0006430&MedGen:C0410264&Human_Phenotype_Ontology:HP:0002065&Human_Phenotype_Ontology:HP:0002477&Human_Phenotype_Ontology:HP:0007340&Human_Phenotype_Ontology:HP:0009047&MedGen:C1836296&Human_Phenotype_Ontology:HP:0002147&Human_Phenotype_Ontology:HP:0002906&Human_Phenotype_Ontology:HP:0003078&Human_Phenotype_Ontology:HP:0003236&Human_Phenotype_Ontology:HP:0003525&Human_Phenotype_Ontology:HP:0003531&Human_Phenotype_Ontology:HP:0008164&MONDO:MONDO:0007402&MedGen:C0241005&OMIM:123320&Human_Phenotype_Ontology:HP:0003198&Human_Phenotype_Ontology:HP:0003569&Human_Phenotype_Ontology:HP:0003705&Human_Phenotype_Ontology:HP:0003742&Human_Phenotype_Ontology:HP:0003802&MONDO:MONDO:0005336&MedGen:C0026848&Human_Phenotype_Ontology:HP:0007210&MedGen:C4024921&MONDO:MONDO:0008151&MedGen:C1833736&OMIM:166260&Orphanet:ORPHA53697&MONDO:MONDO:0012652&MedGen:C1969785&OMIM:611307&Orphanet:ORPHA206549&MONDO:MONDO:0013222&MedGen:C2750076&OMIM:613319&Orphanet:ORPHA399096&MedGen:CN239193&MedGen:CN517202 MONDO:MONDO:0012652&MedGen:C1969785&OMIM:611307&Orphanet:ORPHA206549&MONDO:MONDO:0013222&MedGen:C2750076&OMIM:613319&Orphanet:ORPHA399096 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 424764:Likely_pathogenic Duplication ANO5:203859 SO:0001589&frameshift_variant 133 137854521 15 30 50.0 -11 22221100 C CA A frameshift_variant HIGH ANO5 203859 Transcript XM_005252821.3 protein_coding 4/21 602-603 139-140 47 Q/QX caa/cAaa 1 EntrezGene 25.4 3.685970 2.23&2.99 rs1265883666 165 142942 1.15431e-03 3.57466e-04 4.41579e-04 2.58853e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.17664e-03 8.49473e-04 1.42635e-03 6.02773e-04 1.13636e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.27554e-03 9.58405e-05 0.00000e+00 1.26072e-04 1.02530e-03 1.97040e-03 2.00879e-03 1.91755e-03 1.86741e-03 1.83150e-03 1.90476e-03 1.15140e-03 0.00000e+00 0.00000e+00 0.00000e+00 2164 17203 Polycystic_kidney_dysplasia&Intellectual_disability&Achilles_tendon_contracture&Lower_limb_muscle_weakness&Elevated_serum_creatine_phosphokinase&Myopathy&Lower_limb_amyotrophy&Gnathodiaphyseal_dysplasia&Limb-girdle_muscular_dystrophy&_type_2L&Miyoshi_muscular_dystrophy_3&ANO5-Related_Disorders¬_provided Limb-girdle_muscular_dystrophy&_type_2L&Miyoshi_muscular_dystrophy_3 Human_Phenotype_Ontology:HP:0000113&Human_Phenotype_Ontology:HP:0004716&Human_Phenotype_Ontology:HP:0004739&Human_Phenotype_Ontology:HP:0004740&Human_Phenotype_Ontology:HP:0008645&Human_Phenotype_Ontology:HP:0008673&Human_Phenotype_Ontology:HP:0008699&MedGen:C0022680&OMIM:PS173900&Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&Human_Phenotype_Ontology:HP:0001771&Human_Phenotype_Ontology:HP:0004711&Human_Phenotype_Ontology:HP:0005031&Human_Phenotype_Ontology:HP:0006430&MedGen:C0410264&Human_Phenotype_Ontology:HP:0002065&Human_Phenotype_Ontology:HP:0002477&Human_Phenotype_Ontology:HP:0007340&Human_Phenotype_Ontology:HP:0009047&MedGen:C1836296&Human_Phenotype_Ontology:HP:0002147&Human_Phenotype_Ontology:HP:0002906&Human_Phenotype_Ontology:HP:0003078&Human_Phenotype_Ontology:HP:0003236&Human_Phenotype_Ontology:HP:0003525&Human_Phenotype_Ontology:HP:0003531&Human_Phenotype_Ontology:HP:0008164&MONDO:MONDO:0007402&MedGen:C0241005&OMIM:123320&Human_Phenotype_Ontology:HP:0003198&Human_Phenotype_Ontology:HP:0003569&Human_Phenotype_Ontology:HP:0003705&Human_Phenotype_Ontology:HP:0003742&Human_Phenotype_Ontology:HP:0003802&MONDO:MONDO:0005336&MedGen:C0026848&Human_Phenotype_Ontology:HP:0007210&MedGen:C4024921&MONDO:MONDO:0008151&MedGen:C1833736&OMIM:166260&Orphanet:ORPHA53697&MONDO:MONDO:0012652&MedGen:C1969785&OMIM:611307&Orphanet:ORPHA206549&MONDO:MONDO:0013222&MedGen:C2750076&OMIM:613319&Orphanet:ORPHA399096&MedGen:CN239193&MedGen:CN517202 MONDO:MONDO:0012652&MedGen:C1969785&OMIM:611307&Orphanet:ORPHA206549&MONDO:MONDO:0013222&MedGen:C2750076&OMIM:613319&Orphanet:ORPHA399096 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 424764:Likely_pathogenic Duplication ANO5:203859 SO:0001589&frameshift_variant 133 137854521 15 30 50.0 -11 22221100 C CA A frameshift_variant HIGH ANO5 203859 Transcript XM_005252822.4 protein_coding 5/22 386-387 106-107 36 Q/QX caa/cAaa 1 EntrezGene 25.4 3.685970 2.23&2.99 rs1265883666 165 142942 1.15431e-03 3.57466e-04 4.41579e-04 2.58853e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.17664e-03 8.49473e-04 1.42635e-03 6.02773e-04 1.13636e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.27554e-03 9.58405e-05 0.00000e+00 1.26072e-04 1.02530e-03 1.97040e-03 2.00879e-03 1.91755e-03 1.86741e-03 1.83150e-03 1.90476e-03 1.15140e-03 0.00000e+00 0.00000e+00 0.00000e+00 2164 17203 Polycystic_kidney_dysplasia&Intellectual_disability&Achilles_tendon_contracture&Lower_limb_muscle_weakness&Elevated_serum_creatine_phosphokinase&Myopathy&Lower_limb_amyotrophy&Gnathodiaphyseal_dysplasia&Limb-girdle_muscular_dystrophy&_type_2L&Miyoshi_muscular_dystrophy_3&ANO5-Related_Disorders¬_provided Limb-girdle_muscular_dystrophy&_type_2L&Miyoshi_muscular_dystrophy_3 Human_Phenotype_Ontology:HP:0000113&Human_Phenotype_Ontology:HP:0004716&Human_Phenotype_Ontology:HP:0004739&Human_Phenotype_Ontology:HP:0004740&Human_Phenotype_Ontology:HP:0008645&Human_Phenotype_Ontology:HP:0008673&Human_Phenotype_Ontology:HP:0008699&MedGen:C0022680&OMIM:PS173900&Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&Human_Phenotype_Ontology:HP:0001771&Human_Phenotype_Ontology:HP:0004711&Human_Phenotype_Ontology:HP:0005031&Human_Phenotype_Ontology:HP:0006430&MedGen:C0410264&Human_Phenotype_Ontology:HP:0002065&Human_Phenotype_Ontology:HP:0002477&Human_Phenotype_Ontology:HP:0007340&Human_Phenotype_Ontology:HP:0009047&MedGen:C1836296&Human_Phenotype_Ontology:HP:0002147&Human_Phenotype_Ontology:HP:0002906&Human_Phenotype_Ontology:HP:0003078&Human_Phenotype_Ontology:HP:0003236&Human_Phenotype_Ontology:HP:0003525&Human_Phenotype_Ontology:HP:0003531&Human_Phenotype_Ontology:HP:0008164&MONDO:MONDO:0007402&MedGen:C0241005&OMIM:123320&Human_Phenotype_Ontology:HP:0003198&Human_Phenotype_Ontology:HP:0003569&Human_Phenotype_Ontology:HP:0003705&Human_Phenotype_Ontology:HP:0003742&Human_Phenotype_Ontology:HP:0003802&MONDO:MONDO:0005336&MedGen:C0026848&Human_Phenotype_Ontology:HP:0007210&MedGen:C4024921&MONDO:MONDO:0008151&MedGen:C1833736&OMIM:166260&Orphanet:ORPHA53697&MONDO:MONDO:0012652&MedGen:C1969785&OMIM:611307&Orphanet:ORPHA206549&MONDO:MONDO:0013222&MedGen:C2750076&OMIM:613319&Orphanet:ORPHA399096&MedGen:CN239193&MedGen:CN517202 MONDO:MONDO:0012652&MedGen:C1969785&OMIM:611307&Orphanet:ORPHA206549&MONDO:MONDO:0013222&MedGen:C2750076&OMIM:613319&Orphanet:ORPHA399096 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 424764:Likely_pathogenic Duplication ANO5:203859 SO:0001589&frameshift_variant 133 137854521 15 30 50.0 -11 22221100 C CA A frameshift_variant HIGH ANO5 203859 Transcript XM_011519949.2 protein_coding 3/20 554-555 91-92 31 Q/QX caa/cAaa 1 EntrezGene 25.4 3.685970 2.23&2.99 rs1265883666 165 142942 1.15431e-03 3.57466e-04 4.41579e-04 2.58853e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.17664e-03 8.49473e-04 1.42635e-03 6.02773e-04 1.13636e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.27554e-03 9.58405e-05 0.00000e+00 1.26072e-04 1.02530e-03 1.97040e-03 2.00879e-03 1.91755e-03 1.86741e-03 1.83150e-03 1.90476e-03 1.15140e-03 0.00000e+00 0.00000e+00 0.00000e+00 2164 17203 Polycystic_kidney_dysplasia&Intellectual_disability&Achilles_tendon_contracture&Lower_limb_muscle_weakness&Elevated_serum_creatine_phosphokinase&Myopathy&Lower_limb_amyotrophy&Gnathodiaphyseal_dysplasia&Limb-girdle_muscular_dystrophy&_type_2L&Miyoshi_muscular_dystrophy_3&ANO5-Related_Disorders¬_provided Limb-girdle_muscular_dystrophy&_type_2L&Miyoshi_muscular_dystrophy_3 Human_Phenotype_Ontology:HP:0000113&Human_Phenotype_Ontology:HP:0004716&Human_Phenotype_Ontology:HP:0004739&Human_Phenotype_Ontology:HP:0004740&Human_Phenotype_Ontology:HP:0008645&Human_Phenotype_Ontology:HP:0008673&Human_Phenotype_Ontology:HP:0008699&MedGen:C0022680&OMIM:PS173900&Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&Human_Phenotype_Ontology:HP:0001771&Human_Phenotype_Ontology:HP:0004711&Human_Phenotype_Ontology:HP:0005031&Human_Phenotype_Ontology:HP:0006430&MedGen:C0410264&Human_Phenotype_Ontology:HP:0002065&Human_Phenotype_Ontology:HP:0002477&Human_Phenotype_Ontology:HP:0007340&Human_Phenotype_Ontology:HP:0009047&MedGen:C1836296&Human_Phenotype_Ontology:HP:0002147&Human_Phenotype_Ontology:HP:0002906&Human_Phenotype_Ontology:HP:0003078&Human_Phenotype_Ontology:HP:0003236&Human_Phenotype_Ontology:HP:0003525&Human_Phenotype_Ontology:HP:0003531&Human_Phenotype_Ontology:HP:0008164&MONDO:MONDO:0007402&MedGen:C0241005&OMIM:123320&Human_Phenotype_Ontology:HP:0003198&Human_Phenotype_Ontology:HP:0003569&Human_Phenotype_Ontology:HP:0003705&Human_Phenotype_Ontology:HP:0003742&Human_Phenotype_Ontology:HP:0003802&MONDO:MONDO:0005336&MedGen:C0026848&Human_Phenotype_Ontology:HP:0007210&MedGen:C4024921&MONDO:MONDO:0008151&MedGen:C1833736&OMIM:166260&Orphanet:ORPHA53697&MONDO:MONDO:0012652&MedGen:C1969785&OMIM:611307&Orphanet:ORPHA206549&MONDO:MONDO:0013222&MedGen:C2750076&OMIM:613319&Orphanet:ORPHA399096&MedGen:CN239193&MedGen:CN517202 MONDO:MONDO:0012652&MedGen:C1969785&OMIM:611307&Orphanet:ORPHA206549&MONDO:MONDO:0013222&MedGen:C2750076&OMIM:613319&Orphanet:ORPHA399096 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 424764:Likely_pathogenic Duplication ANO5:203859 SO:0001589&frameshift_variant 133 137854521 15 30 50.0 -12 6034812 C T T missense_variant MODERATE VWF 7450 Transcript NM_000552.5 protein_coding 20/52 2811 2561 854 R/Q cGg/cAg -1 EntrezGene C C 0.02 0.994 29.5 4.292149 29.5 0.99952562310062276 9.021527 0.759223423163341 0.86844 8.011943 0.757866534861481 0.83419 -0.16 5.56 0.999999999305559 0.003642 0.097239 0.3092 -0.3951 2.105 0.999999 -3.58 17.027 0.911 0.93935 0.67177 1.000000 1.000000 7.130000 1.026000 6.47 rs41276738 530 143340 3.69750e-03 8.79612e-04 9.24377e-04 8.27044e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.00059e-03 4.05817e-03 2.19355e-03 1.80505e-03 2.27015e-03 1.28041e-03 0.00000e+00 0.00000e+00 0.00000e+00 3.85771e-03 5.63407e-03 4.38947e-03 6.02561e-03 3.52711e-03 5.91478e-03 5.96320e-03 5.84817e-03 1.85874e-03 1.82149e-03 1.89753e-03 3.69626e-03 3.28299e-04 0.00000e+00 4.03226e-04 296 0.00308 0.00180 15335 Thrombocytopenia&Abnormal_bleeding&Abnormality_of_coagulation&von_Willebrand_disease_type_1&von_Willebrand_disease_type_2&von_Willebrand_disease_type_2N&von_Willebrand_disorder&Von_Willebrand_disease&_recessive_form¬_provided Human_Phenotype_Ontology:HP:0001873&Human_Phenotype_Ontology:HP:0001906&Human_Phenotype_Ontology:HP:0004838&Human_Phenotype_Ontology:HP:0008175&Human_Phenotype_Ontology:HP:0008268&Human_Phenotype_Ontology:HP:0008302&MONDO:MONDO:0002049&MedGen:C0040034&Human_Phenotype_Ontology:HP:0001892&Human_Phenotype_Ontology:HP:0004830&Human_Phenotype_Ontology:HP:0004834&Human_Phenotype_Ontology:HP:0004849&Human_Phenotype_Ontology:HP:0004862&Human_Phenotype_Ontology:HP:0004865&Human_Phenotype_Ontology:HP:0008183&MedGen:C1458140&Human_Phenotype_Ontology:HP:0001928&MedGen:C1846821&MONDO:MONDO:0008668&MedGen:C1264039&OMIM:193400&Orphanet:ORPHA166078&SNOMED_CT:128106003&MONDO:MONDO:0013304&MedGen:C1264040&OMIM:613554&Orphanet:ORPHA166081&SNOMED_CT:128107007&MONDO:MONDO:0015631&MedGen:C1282975&Orphanet:ORPHA166093&SNOMED_CT:359732009&MONDO:MONDO:0019565&MedGen:C0042974&Orphanet:ORPHA903&SNOMED_CT:128105004&MedGen:C1848525&OMIM:277480&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant VWF:7450 SO:0001583&missense_variant 9 41276738 15 30 50.0 -12 6333477 C T T missense_variant MODERATE TNFRSF1A 7132 Transcript NM_001065.4 protein_coding 4/10 624 362 121 R/Q cGg/cAg -1 EntrezGene C C 0.66 0.023 2.720 0.163610 2.720 0.97949681967343849 0.410596 -1.06692660514733 0.08357 0.4222508 -0.983207588847318 0.08962 -2.68&-2.68&-2.68&-2.68&-2.68&-2.68 -3.82 0.99999972459515 0.266583 0.2667 -0.5384 0.17&.&.&0.17&.&. 1&1&1 -0.59&-0.62&-0.46&-0.52&-0.29&-0.43 7.519 0.096&0.046&.&0.08&.&. 0.02845 0.722319 0.000000 0.066000 -1.463000 -0.214000 -8.9 chr12:6333477-6333477 1694 142676 1.18731e-02 3.44531e-03 3.14326e-03 3.80050e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.15424e-02 1.32383e-02 1.02464e-02 2.05066e-02 2.21843e-02 1.86136e-02 0.00000e+00 0.00000e+00 0.00000e+00 1.23009e-02 8.23913e-03 1.08086e-02 7.43073e-03 1.14176e-02 1.88166e-02 1.82920e-02 1.95390e-02 8.87850e-03 7.32601e-03 1.04962e-02 1.18434e-02 2.98607e-03 0.00000e+00 3.66450e-03 217017 0.01411 0.00599 213624 TNF_receptor-associated_periodic_fever_syndrome_(TRAPS)&Multiple_sclerosis&_susceptibility_to&_5¬_specified¬_provided MONDO:MONDO:0007727&MedGen:C1275126&OMIM:142680&Orphanet:ORPHA32960&SNOMED_CT:403833009&MONDO:MONDO:0013893&MedGen:C3553728&OMIM:614810&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(3)&Likely_benign(1)&Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(5) single_nucleotide_variant TNFRSF1A:7132 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 4149584 15 30 50.0 -12 6333477 C T T downstream_gene_variant MODIFIER PLEKHG6 55200 Transcript NM_001144856.1 protein_coding 4971 1 EntrezGene C C OK 2.720 0.163610 -8.9 chr12:6333477-6333477 1694 142676 1.18731e-02 3.44531e-03 3.14326e-03 3.80050e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.15424e-02 1.32383e-02 1.02464e-02 2.05066e-02 2.21843e-02 1.86136e-02 0.00000e+00 0.00000e+00 0.00000e+00 1.23009e-02 8.23913e-03 1.08086e-02 7.43073e-03 1.14176e-02 1.88166e-02 1.82920e-02 1.95390e-02 8.87850e-03 7.32601e-03 1.04962e-02 1.18434e-02 2.98607e-03 0.00000e+00 3.66450e-03 217017 0.01411 0.00599 213624 TNF_receptor-associated_periodic_fever_syndrome_(TRAPS)&Multiple_sclerosis&_susceptibility_to&_5¬_specified¬_provided MONDO:MONDO:0007727&MedGen:C1275126&OMIM:142680&Orphanet:ORPHA32960&SNOMED_CT:403833009&MONDO:MONDO:0013893&MedGen:C3553728&OMIM:614810&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(3)&Likely_benign(1)&Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(5) single_nucleotide_variant TNFRSF1A:7132 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 4149584 15 30 50.0 -12 6333477 C T T downstream_gene_variant MODIFIER PLEKHG6 55200 Transcript NM_001144857.1 protein_coding 4971 1 EntrezGene C C OK 2.720 0.163610 -8.9 chr12:6333477-6333477 1694 142676 1.18731e-02 3.44531e-03 3.14326e-03 3.80050e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.15424e-02 1.32383e-02 1.02464e-02 2.05066e-02 2.21843e-02 1.86136e-02 0.00000e+00 0.00000e+00 0.00000e+00 1.23009e-02 8.23913e-03 1.08086e-02 7.43073e-03 1.14176e-02 1.88166e-02 1.82920e-02 1.95390e-02 8.87850e-03 7.32601e-03 1.04962e-02 1.18434e-02 2.98607e-03 0.00000e+00 3.66450e-03 217017 0.01411 0.00599 213624 TNF_receptor-associated_periodic_fever_syndrome_(TRAPS)&Multiple_sclerosis&_susceptibility_to&_5¬_specified¬_provided MONDO:MONDO:0007727&MedGen:C1275126&OMIM:142680&Orphanet:ORPHA32960&SNOMED_CT:403833009&MONDO:MONDO:0013893&MedGen:C3553728&OMIM:614810&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(3)&Likely_benign(1)&Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(5) single_nucleotide_variant TNFRSF1A:7132 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 4149584 15 30 50.0 -12 6333477 C T T missense_variant MODERATE TNFRSF1A 7132 Transcript NM_001346091.2 protein_coding 3/9 470 38 13 R/Q cGg/cAg -1 EntrezGene C C 0.82 0.023 2.720 0.163610 2.720 0.97949681967343849 0.410596 -1.06692660514733 0.08357 0.4222508 -0.983207588847318 0.08962 -2.68&-2.68&-2.68&-2.68&-2.68&-2.68 -3.82 0.99999972459515 0.266583 0.2667 -0.5384 0.17&.&.&0.17&.&. 1&1&1 -0.59&-0.62&-0.46&-0.52&-0.29&-0.43 7.519 0.096&0.046&.&0.08&.&. 0.02845 0.722319 0.000000 0.066000 -1.463000 -0.214000 -8.9 chr12:6333477-6333477 1694 142676 1.18731e-02 3.44531e-03 3.14326e-03 3.80050e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.15424e-02 1.32383e-02 1.02464e-02 2.05066e-02 2.21843e-02 1.86136e-02 0.00000e+00 0.00000e+00 0.00000e+00 1.23009e-02 8.23913e-03 1.08086e-02 7.43073e-03 1.14176e-02 1.88166e-02 1.82920e-02 1.95390e-02 8.87850e-03 7.32601e-03 1.04962e-02 1.18434e-02 2.98607e-03 0.00000e+00 3.66450e-03 217017 0.01411 0.00599 213624 TNF_receptor-associated_periodic_fever_syndrome_(TRAPS)&Multiple_sclerosis&_susceptibility_to&_5¬_specified¬_provided MONDO:MONDO:0007727&MedGen:C1275126&OMIM:142680&Orphanet:ORPHA32960&SNOMED_CT:403833009&MONDO:MONDO:0013893&MedGen:C3553728&OMIM:614810&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(3)&Likely_benign(1)&Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(5) single_nucleotide_variant TNFRSF1A:7132 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 4149584 15 30 50.0 -12 6333477 C T T 5_prime_UTR_variant MODIFIER TNFRSF1A 7132 Transcript NM_001346092.2 protein_coding 4/11 624 -1 EntrezGene C C 2.720 0.163610 -8.9 chr12:6333477-6333477 1694 142676 1.18731e-02 3.44531e-03 3.14326e-03 3.80050e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.15424e-02 1.32383e-02 1.02464e-02 2.05066e-02 2.21843e-02 1.86136e-02 0.00000e+00 0.00000e+00 0.00000e+00 1.23009e-02 8.23913e-03 1.08086e-02 7.43073e-03 1.14176e-02 1.88166e-02 1.82920e-02 1.95390e-02 8.87850e-03 7.32601e-03 1.04962e-02 1.18434e-02 2.98607e-03 0.00000e+00 3.66450e-03 217017 0.01411 0.00599 213624 TNF_receptor-associated_periodic_fever_syndrome_(TRAPS)&Multiple_sclerosis&_susceptibility_to&_5¬_specified¬_provided MONDO:MONDO:0007727&MedGen:C1275126&OMIM:142680&Orphanet:ORPHA32960&SNOMED_CT:403833009&MONDO:MONDO:0013893&MedGen:C3553728&OMIM:614810&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(3)&Likely_benign(1)&Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(5) single_nucleotide_variant TNFRSF1A:7132 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 4149584 15 30 50.0 -12 6333477 C T T downstream_gene_variant MODIFIER PLEKHG6 55200 Transcript NM_018173.3 protein_coding 4971 1 EntrezGene C C OK 2.720 0.163610 -8.9 chr12:6333477-6333477 1694 142676 1.18731e-02 3.44531e-03 3.14326e-03 3.80050e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.15424e-02 1.32383e-02 1.02464e-02 2.05066e-02 2.21843e-02 1.86136e-02 0.00000e+00 0.00000e+00 0.00000e+00 1.23009e-02 8.23913e-03 1.08086e-02 7.43073e-03 1.14176e-02 1.88166e-02 1.82920e-02 1.95390e-02 8.87850e-03 7.32601e-03 1.04962e-02 1.18434e-02 2.98607e-03 0.00000e+00 3.66450e-03 217017 0.01411 0.00599 213624 TNF_receptor-associated_periodic_fever_syndrome_(TRAPS)&Multiple_sclerosis&_susceptibility_to&_5¬_specified¬_provided MONDO:MONDO:0007727&MedGen:C1275126&OMIM:142680&Orphanet:ORPHA32960&SNOMED_CT:403833009&MONDO:MONDO:0013893&MedGen:C3553728&OMIM:614810&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(3)&Likely_benign(1)&Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(5) single_nucleotide_variant TNFRSF1A:7132 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 4149584 15 30 50.0 -12 6333477 C T T non_coding_transcript_exon_variant MODIFIER TNFRSF1A 7132 Transcript NR_144351.2 misc_RNA 4/9 624 -1 EntrezGene C C 2.720 0.163610 -8.9 chr12:6333477-6333477 1694 142676 1.18731e-02 3.44531e-03 3.14326e-03 3.80050e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.15424e-02 1.32383e-02 1.02464e-02 2.05066e-02 2.21843e-02 1.86136e-02 0.00000e+00 0.00000e+00 0.00000e+00 1.23009e-02 8.23913e-03 1.08086e-02 7.43073e-03 1.14176e-02 1.88166e-02 1.82920e-02 1.95390e-02 8.87850e-03 7.32601e-03 1.04962e-02 1.18434e-02 2.98607e-03 0.00000e+00 3.66450e-03 217017 0.01411 0.00599 213624 TNF_receptor-associated_periodic_fever_syndrome_(TRAPS)&Multiple_sclerosis&_susceptibility_to&_5¬_specified¬_provided MONDO:MONDO:0007727&MedGen:C1275126&OMIM:142680&Orphanet:ORPHA32960&SNOMED_CT:403833009&MONDO:MONDO:0013893&MedGen:C3553728&OMIM:614810&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(3)&Likely_benign(1)&Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(5) single_nucleotide_variant TNFRSF1A:7132 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 4149584 15 30 50.0 -12 6333477 C T T downstream_gene_variant MODIFIER PLEKHG6 55200 Transcript XM_005253704.4 protein_coding 4971 1 EntrezGene C C 2.720 0.163610 -8.9 chr12:6333477-6333477 1694 142676 1.18731e-02 3.44531e-03 3.14326e-03 3.80050e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.15424e-02 1.32383e-02 1.02464e-02 2.05066e-02 2.21843e-02 1.86136e-02 0.00000e+00 0.00000e+00 0.00000e+00 1.23009e-02 8.23913e-03 1.08086e-02 7.43073e-03 1.14176e-02 1.88166e-02 1.82920e-02 1.95390e-02 8.87850e-03 7.32601e-03 1.04962e-02 1.18434e-02 2.98607e-03 0.00000e+00 3.66450e-03 217017 0.01411 0.00599 213624 TNF_receptor-associated_periodic_fever_syndrome_(TRAPS)&Multiple_sclerosis&_susceptibility_to&_5¬_specified¬_provided MONDO:MONDO:0007727&MedGen:C1275126&OMIM:142680&Orphanet:ORPHA32960&SNOMED_CT:403833009&MONDO:MONDO:0013893&MedGen:C3553728&OMIM:614810&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(3)&Likely_benign(1)&Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(5) single_nucleotide_variant TNFRSF1A:7132 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 4149584 15 30 50.0 -12 6333477 C T T downstream_gene_variant MODIFIER PLEKHG6 55200 Transcript XM_006718985.3 protein_coding 4971 1 EntrezGene C C 2.720 0.163610 -8.9 chr12:6333477-6333477 1694 142676 1.18731e-02 3.44531e-03 3.14326e-03 3.80050e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.15424e-02 1.32383e-02 1.02464e-02 2.05066e-02 2.21843e-02 1.86136e-02 0.00000e+00 0.00000e+00 0.00000e+00 1.23009e-02 8.23913e-03 1.08086e-02 7.43073e-03 1.14176e-02 1.88166e-02 1.82920e-02 1.95390e-02 8.87850e-03 7.32601e-03 1.04962e-02 1.18434e-02 2.98607e-03 0.00000e+00 3.66450e-03 217017 0.01411 0.00599 213624 TNF_receptor-associated_periodic_fever_syndrome_(TRAPS)&Multiple_sclerosis&_susceptibility_to&_5¬_specified¬_provided MONDO:MONDO:0007727&MedGen:C1275126&OMIM:142680&Orphanet:ORPHA32960&SNOMED_CT:403833009&MONDO:MONDO:0013893&MedGen:C3553728&OMIM:614810&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(3)&Likely_benign(1)&Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(5) single_nucleotide_variant TNFRSF1A:7132 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 4149584 15 30 50.0 -12 6333477 C T T downstream_gene_variant MODIFIER PLEKHG6 55200 Transcript XM_011520967.2 protein_coding 4971 1 EntrezGene C C 2.720 0.163610 -8.9 chr12:6333477-6333477 1694 142676 1.18731e-02 3.44531e-03 3.14326e-03 3.80050e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.15424e-02 1.32383e-02 1.02464e-02 2.05066e-02 2.21843e-02 1.86136e-02 0.00000e+00 0.00000e+00 0.00000e+00 1.23009e-02 8.23913e-03 1.08086e-02 7.43073e-03 1.14176e-02 1.88166e-02 1.82920e-02 1.95390e-02 8.87850e-03 7.32601e-03 1.04962e-02 1.18434e-02 2.98607e-03 0.00000e+00 3.66450e-03 217017 0.01411 0.00599 213624 TNF_receptor-associated_periodic_fever_syndrome_(TRAPS)&Multiple_sclerosis&_susceptibility_to&_5¬_specified¬_provided MONDO:MONDO:0007727&MedGen:C1275126&OMIM:142680&Orphanet:ORPHA32960&SNOMED_CT:403833009&MONDO:MONDO:0013893&MedGen:C3553728&OMIM:614810&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(3)&Likely_benign(1)&Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(5) single_nucleotide_variant TNFRSF1A:7132 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 4149584 15 30 50.0 -12 6333477 C T T downstream_gene_variant MODIFIER PLEKHG6 55200 Transcript XR_931514.2 misc_RNA 4979 1 EntrezGene C C 2.720 0.163610 -8.9 chr12:6333477-6333477 1694 142676 1.18731e-02 3.44531e-03 3.14326e-03 3.80050e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.15424e-02 1.32383e-02 1.02464e-02 2.05066e-02 2.21843e-02 1.86136e-02 0.00000e+00 0.00000e+00 0.00000e+00 1.23009e-02 8.23913e-03 1.08086e-02 7.43073e-03 1.14176e-02 1.88166e-02 1.82920e-02 1.95390e-02 8.87850e-03 7.32601e-03 1.04962e-02 1.18434e-02 2.98607e-03 0.00000e+00 3.66450e-03 217017 0.01411 0.00599 213624 TNF_receptor-associated_periodic_fever_syndrome_(TRAPS)&Multiple_sclerosis&_susceptibility_to&_5¬_specified¬_provided MONDO:MONDO:0007727&MedGen:C1275126&OMIM:142680&Orphanet:ORPHA32960&SNOMED_CT:403833009&MONDO:MONDO:0013893&MedGen:C3553728&OMIM:614810&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(3)&Likely_benign(1)&Likely_pathogenic(1)&Pathogenic(1)&Uncertain_significance(5) single_nucleotide_variant TNFRSF1A:7132 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant&SO:0001623&5_prime_UTR_variant 1 4149584 15 30 50.0 -12 20855146 T TAATTG AATTG frameshift_variant HIGH SLCO1B3 28234 Transcript NM_001349920.2 protein_coding 2/14 245-246 119-120 40 L/LIX tta/ttAATTGa 1 EntrezGene 26.7 3.956812 6.04&-1.42 rs558592800 569 143334 3.96975e-03 7.84593e-04 7.92323e-04 7.75514e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.31752e-03 1.35272e-03 1.29066e-03 6.01685e-04 5.67537e-04 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.28930e-03 1.89958e-02 1.67866e-02 1.96890e-02 4.69348e-03 4.75394e-03 4.46524e-03 5.15123e-03 4.18216e-03 6.37523e-03 1.89753e-03 3.98226e-03 3.27654e-04 0.00000e+00 4.02253e-04 712105 725039 Rotor_syndrome¬_provided MONDO:MONDO:0009379&MedGen:C0220991&OMIM:237450&Orphanet:ORPHA3111&SNOMED_CT:32891000&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Uncertain_significance(1) Duplication SLCO1B3:28234&SLCO1B3-SLCO1B7:115072896 SO:0001589&frameshift_variant 1 558592800 15 30 50.0 -12 20855146 T TAATTG AATTG frameshift_variant HIGH SLCO1B3-SLCO1B7 115072896 Transcript NM_001371097.1 protein_coding 2/16 268-269 203-204 68 L/LIX tta/ttAATTGa 1 EntrezGene 26.7 3.956812 6.04&-1.42 rs558592800 569 143334 3.96975e-03 7.84593e-04 7.92323e-04 7.75514e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.31752e-03 1.35272e-03 1.29066e-03 6.01685e-04 5.67537e-04 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.28930e-03 1.89958e-02 1.67866e-02 1.96890e-02 4.69348e-03 4.75394e-03 4.46524e-03 5.15123e-03 4.18216e-03 6.37523e-03 1.89753e-03 3.98226e-03 3.27654e-04 0.00000e+00 4.02253e-04 712105 725039 Rotor_syndrome¬_provided MONDO:MONDO:0009379&MedGen:C0220991&OMIM:237450&Orphanet:ORPHA3111&SNOMED_CT:32891000&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Uncertain_significance(1) Duplication SLCO1B3:28234&SLCO1B3-SLCO1B7:115072896 SO:0001589&frameshift_variant 1 558592800 15 30 50.0 -12 20855146 T TAATTG AATTG frameshift_variant HIGH SLCO1B3 28234 Transcript NM_019844.4 protein_coding 4/16 443-444 203-204 68 L/LIX tta/ttAATTGa 1 EntrezGene 26.7 3.956812 6.04&-1.42 rs558592800 569 143334 3.96975e-03 7.84593e-04 7.92323e-04 7.75514e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.31752e-03 1.35272e-03 1.29066e-03 6.01685e-04 5.67537e-04 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.28930e-03 1.89958e-02 1.67866e-02 1.96890e-02 4.69348e-03 4.75394e-03 4.46524e-03 5.15123e-03 4.18216e-03 6.37523e-03 1.89753e-03 3.98226e-03 3.27654e-04 0.00000e+00 4.02253e-04 712105 725039 Rotor_syndrome¬_provided MONDO:MONDO:0009379&MedGen:C0220991&OMIM:237450&Orphanet:ORPHA3111&SNOMED_CT:32891000&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Uncertain_significance(1) Duplication SLCO1B3:28234&SLCO1B3-SLCO1B7:115072896 SO:0001589&frameshift_variant 1 558592800 15 30 50.0 -12 75996946 CAAGGATGATG C - intergenic_variant MODIFIER 7.788 0.628642 -6.01&2.33&2.94&3.03&2.46&0.318&2.86&-3.73&-2.88&2.69 rs5799246 71078 142492 4.98821e-01 5.43395e-01 5.42502e-01 5.44442e-01 4.14027e-01 4.17031e-01 4.10798e-01 4.98748e-01 4.97445e-01 4.99741e-01 4.45987e-01 4.54338e-01 4.36620e-01 3.82542e-01 3.73611e-01 3.90215e-01 5.06926e-01 4.36477e-01 4.53200e-01 4.31179e-01 4.90188e-01 4.99984e-01 5.03488e-01 4.95150e-01 4.86007e-01 4.79964e-01 4.92352e-01 4.99121e-01 2.85432e-01 2.79152e-01 2.86872e-01 15 30 50.0 -12 88049400 CTTCT C - 3_prime_UTR_variant MODIFIER C12orf29 91298 Transcript NM_001009894.3 protein_coding 7/7 2070-2073 1 EntrezGene TTCT TTCT 34 5.740274 4.25&6.53&2.94&4.23 rs763762899 6 143014 4.19539e-05 2.38129e-05 4.40995e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.78021e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44363e-05 7.75964e-05 1.07112e-04 3.69113e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18579e-05 0.00000e+00 0.00000e+00 0.00000e+00 418123 408757 Nephronophthisis&Micrognathia&Global_developmental_delay&Muscular_hypotonia&Abnormal_facial_shape&Joubert_syndrome&Meckel-Gruber_syndrome¬_provided Human_Phenotype_Ontology:HP:0000090&Human_Phenotype_Ontology:HP:0004748&MONDO:MONDO:0019005&MedGen:C0687120&OMIM:PS256100&Orphanet:ORPHA655&SNOMED_CT:204958008&Human_Phenotype_Ontology:HP:0000210&Human_Phenotype_Ontology:HP:0000330&Human_Phenotype_Ontology:HP:0000345&Human_Phenotype_Ontology:HP:0000347&Human_Phenotype_Ontology:HP:0002005&Human_Phenotype_Ontology:HP:0002674&Human_Phenotype_Ontology:HP:0004669&Human_Phenotype_Ontology:HP:0005460&Human_Phenotype_Ontology:HP:0005470&MedGen:C0025990&Human_Phenotype_Ontology:HP:0000754&Human_Phenotype_Ontology:HP:0001255&Human_Phenotype_Ontology:HP:0001263&Human_Phenotype_Ontology:HP:0001277&Human_Phenotype_Ontology:HP:0001292&Human_Phenotype_Ontology:HP:0002433&Human_Phenotype_Ontology:HP:0002473&Human_Phenotype_Ontology:HP:0002532&Human_Phenotype_Ontology:HP:0006793&Human_Phenotype_Ontology:HP:0006867&Human_Phenotype_Ontology:HP:0006885&Human_Phenotype_Ontology:HP:0006935&Human_Phenotype_Ontology:HP:0007005&Human_Phenotype_Ontology:HP:0007094&Human_Phenotype_Ontology:HP:0007106&Human_Phenotype_Ontology:HP:0007174&Human_Phenotype_Ontology:HP:0007224&Human_Phenotype_Ontology:HP:0007228&Human_Phenotype_Ontology:HP:0007342&MedGen:C0557874&Human_Phenotype_Ontology:HP:0001252&Human_Phenotype_Ontology:HP:0001318&MedGen:C0026827&Human_Phenotype_Ontology:HP:0001999&Human_Phenotype_Ontology:HP:0002004&Human_Phenotype_Ontology:HP:0002260&Human_Phenotype_Ontology:HP:0004643&Human_Phenotype_Ontology:HP:0004649&Human_Phenotype_Ontology:HP:0004652&Human_Phenotype_Ontology:HP:0004655&Human_Phenotype_Ontology:HP:0004675&Human_Phenotype_Ontology:HP:0005124&MedGen:C0424503&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0018921&MedGen:C0265215&OMIM:PS249000&Orphanet:ORPHA564&SNOMED_CT:29076005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic Deletion CEP290:80184&C12orf29:91298 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 763762899 15 30 50.0 -12 88049400 CTTCT C - frameshift_variant HIGH CEP290 80184 Transcript NM_025114.4 protein_coding 54/54 7436-7439 7220-7223 2407-2408 KK/X aAGAAg/ag -1 EntrezGene TTCT TTCT 34 5.740274 4.25&6.53&2.94&4.23 rs763762899 6 143014 4.19539e-05 2.38129e-05 4.40995e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.78021e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44363e-05 7.75964e-05 1.07112e-04 3.69113e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18579e-05 0.00000e+00 0.00000e+00 0.00000e+00 418123 408757 Nephronophthisis&Micrognathia&Global_developmental_delay&Muscular_hypotonia&Abnormal_facial_shape&Joubert_syndrome&Meckel-Gruber_syndrome¬_provided Human_Phenotype_Ontology:HP:0000090&Human_Phenotype_Ontology:HP:0004748&MONDO:MONDO:0019005&MedGen:C0687120&OMIM:PS256100&Orphanet:ORPHA655&SNOMED_CT:204958008&Human_Phenotype_Ontology:HP:0000210&Human_Phenotype_Ontology:HP:0000330&Human_Phenotype_Ontology:HP:0000345&Human_Phenotype_Ontology:HP:0000347&Human_Phenotype_Ontology:HP:0002005&Human_Phenotype_Ontology:HP:0002674&Human_Phenotype_Ontology:HP:0004669&Human_Phenotype_Ontology:HP:0005460&Human_Phenotype_Ontology:HP:0005470&MedGen:C0025990&Human_Phenotype_Ontology:HP:0000754&Human_Phenotype_Ontology:HP:0001255&Human_Phenotype_Ontology:HP:0001263&Human_Phenotype_Ontology:HP:0001277&Human_Phenotype_Ontology:HP:0001292&Human_Phenotype_Ontology:HP:0002433&Human_Phenotype_Ontology:HP:0002473&Human_Phenotype_Ontology:HP:0002532&Human_Phenotype_Ontology:HP:0006793&Human_Phenotype_Ontology:HP:0006867&Human_Phenotype_Ontology:HP:0006885&Human_Phenotype_Ontology:HP:0006935&Human_Phenotype_Ontology:HP:0007005&Human_Phenotype_Ontology:HP:0007094&Human_Phenotype_Ontology:HP:0007106&Human_Phenotype_Ontology:HP:0007174&Human_Phenotype_Ontology:HP:0007224&Human_Phenotype_Ontology:HP:0007228&Human_Phenotype_Ontology:HP:0007342&MedGen:C0557874&Human_Phenotype_Ontology:HP:0001252&Human_Phenotype_Ontology:HP:0001318&MedGen:C0026827&Human_Phenotype_Ontology:HP:0001999&Human_Phenotype_Ontology:HP:0002004&Human_Phenotype_Ontology:HP:0002260&Human_Phenotype_Ontology:HP:0004643&Human_Phenotype_Ontology:HP:0004649&Human_Phenotype_Ontology:HP:0004652&Human_Phenotype_Ontology:HP:0004655&Human_Phenotype_Ontology:HP:0004675&Human_Phenotype_Ontology:HP:0005124&MedGen:C0424503&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0018921&MedGen:C0265215&OMIM:PS249000&Orphanet:ORPHA564&SNOMED_CT:29076005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic Deletion CEP290:80184&C12orf29:91298 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 763762899 15 30 50.0 -12 88049400 CTTCT C - frameshift_variant HIGH CEP290 80184 Transcript XM_011538756.3 protein_coding 57/57 8434-8437 8090-8093 2697-2698 KK/X aAGAAg/ag -1 EntrezGene TTCT TTCT 34 5.740274 4.25&6.53&2.94&4.23 rs763762899 6 143014 4.19539e-05 2.38129e-05 4.40995e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.78021e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44363e-05 7.75964e-05 1.07112e-04 3.69113e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18579e-05 0.00000e+00 0.00000e+00 0.00000e+00 418123 408757 Nephronophthisis&Micrognathia&Global_developmental_delay&Muscular_hypotonia&Abnormal_facial_shape&Joubert_syndrome&Meckel-Gruber_syndrome¬_provided Human_Phenotype_Ontology:HP:0000090&Human_Phenotype_Ontology:HP:0004748&MONDO:MONDO:0019005&MedGen:C0687120&OMIM:PS256100&Orphanet:ORPHA655&SNOMED_CT:204958008&Human_Phenotype_Ontology:HP:0000210&Human_Phenotype_Ontology:HP:0000330&Human_Phenotype_Ontology:HP:0000345&Human_Phenotype_Ontology:HP:0000347&Human_Phenotype_Ontology:HP:0002005&Human_Phenotype_Ontology:HP:0002674&Human_Phenotype_Ontology:HP:0004669&Human_Phenotype_Ontology:HP:0005460&Human_Phenotype_Ontology:HP:0005470&MedGen:C0025990&Human_Phenotype_Ontology:HP:0000754&Human_Phenotype_Ontology:HP:0001255&Human_Phenotype_Ontology:HP:0001263&Human_Phenotype_Ontology:HP:0001277&Human_Phenotype_Ontology:HP:0001292&Human_Phenotype_Ontology:HP:0002433&Human_Phenotype_Ontology:HP:0002473&Human_Phenotype_Ontology:HP:0002532&Human_Phenotype_Ontology:HP:0006793&Human_Phenotype_Ontology:HP:0006867&Human_Phenotype_Ontology:HP:0006885&Human_Phenotype_Ontology:HP:0006935&Human_Phenotype_Ontology:HP:0007005&Human_Phenotype_Ontology:HP:0007094&Human_Phenotype_Ontology:HP:0007106&Human_Phenotype_Ontology:HP:0007174&Human_Phenotype_Ontology:HP:0007224&Human_Phenotype_Ontology:HP:0007228&Human_Phenotype_Ontology:HP:0007342&MedGen:C0557874&Human_Phenotype_Ontology:HP:0001252&Human_Phenotype_Ontology:HP:0001318&MedGen:C0026827&Human_Phenotype_Ontology:HP:0001999&Human_Phenotype_Ontology:HP:0002004&Human_Phenotype_Ontology:HP:0002260&Human_Phenotype_Ontology:HP:0004643&Human_Phenotype_Ontology:HP:0004649&Human_Phenotype_Ontology:HP:0004652&Human_Phenotype_Ontology:HP:0004655&Human_Phenotype_Ontology:HP:0004675&Human_Phenotype_Ontology:HP:0005124&MedGen:C0424503&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0018921&MedGen:C0265215&OMIM:PS249000&Orphanet:ORPHA564&SNOMED_CT:29076005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic Deletion CEP290:80184&C12orf29:91298 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 763762899 15 30 50.0 -12 88049400 CTTCT C - frameshift_variant HIGH CEP290 80184 Transcript XM_011538757.3 protein_coding 57/57 8242-8245 8090-8093 2697-2698 KK/X aAGAAg/ag -1 EntrezGene TTCT TTCT 34 5.740274 4.25&6.53&2.94&4.23 rs763762899 6 143014 4.19539e-05 2.38129e-05 4.40995e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.78021e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44363e-05 7.75964e-05 1.07112e-04 3.69113e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18579e-05 0.00000e+00 0.00000e+00 0.00000e+00 418123 408757 Nephronophthisis&Micrognathia&Global_developmental_delay&Muscular_hypotonia&Abnormal_facial_shape&Joubert_syndrome&Meckel-Gruber_syndrome¬_provided Human_Phenotype_Ontology:HP:0000090&Human_Phenotype_Ontology:HP:0004748&MONDO:MONDO:0019005&MedGen:C0687120&OMIM:PS256100&Orphanet:ORPHA655&SNOMED_CT:204958008&Human_Phenotype_Ontology:HP:0000210&Human_Phenotype_Ontology:HP:0000330&Human_Phenotype_Ontology:HP:0000345&Human_Phenotype_Ontology:HP:0000347&Human_Phenotype_Ontology:HP:0002005&Human_Phenotype_Ontology:HP:0002674&Human_Phenotype_Ontology:HP:0004669&Human_Phenotype_Ontology:HP:0005460&Human_Phenotype_Ontology:HP:0005470&MedGen:C0025990&Human_Phenotype_Ontology:HP:0000754&Human_Phenotype_Ontology:HP:0001255&Human_Phenotype_Ontology:HP:0001263&Human_Phenotype_Ontology:HP:0001277&Human_Phenotype_Ontology:HP:0001292&Human_Phenotype_Ontology:HP:0002433&Human_Phenotype_Ontology:HP:0002473&Human_Phenotype_Ontology:HP:0002532&Human_Phenotype_Ontology:HP:0006793&Human_Phenotype_Ontology:HP:0006867&Human_Phenotype_Ontology:HP:0006885&Human_Phenotype_Ontology:HP:0006935&Human_Phenotype_Ontology:HP:0007005&Human_Phenotype_Ontology:HP:0007094&Human_Phenotype_Ontology:HP:0007106&Human_Phenotype_Ontology:HP:0007174&Human_Phenotype_Ontology:HP:0007224&Human_Phenotype_Ontology:HP:0007228&Human_Phenotype_Ontology:HP:0007342&MedGen:C0557874&Human_Phenotype_Ontology:HP:0001252&Human_Phenotype_Ontology:HP:0001318&MedGen:C0026827&Human_Phenotype_Ontology:HP:0001999&Human_Phenotype_Ontology:HP:0002004&Human_Phenotype_Ontology:HP:0002260&Human_Phenotype_Ontology:HP:0004643&Human_Phenotype_Ontology:HP:0004649&Human_Phenotype_Ontology:HP:0004652&Human_Phenotype_Ontology:HP:0004655&Human_Phenotype_Ontology:HP:0004675&Human_Phenotype_Ontology:HP:0005124&MedGen:C0424503&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0018921&MedGen:C0265215&OMIM:PS249000&Orphanet:ORPHA564&SNOMED_CT:29076005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic Deletion CEP290:80184&C12orf29:91298 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 763762899 15 30 50.0 -12 88049400 CTTCT C - frameshift_variant HIGH CEP290 80184 Transcript XM_011538758.3 protein_coding 57/57 8431-8434 8087-8090 2696-2697 KK/X aAGAAg/ag -1 EntrezGene TTCT TTCT 34 5.740274 4.25&6.53&2.94&4.23 rs763762899 6 143014 4.19539e-05 2.38129e-05 4.40995e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.78021e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44363e-05 7.75964e-05 1.07112e-04 3.69113e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18579e-05 0.00000e+00 0.00000e+00 0.00000e+00 418123 408757 Nephronophthisis&Micrognathia&Global_developmental_delay&Muscular_hypotonia&Abnormal_facial_shape&Joubert_syndrome&Meckel-Gruber_syndrome¬_provided Human_Phenotype_Ontology:HP:0000090&Human_Phenotype_Ontology:HP:0004748&MONDO:MONDO:0019005&MedGen:C0687120&OMIM:PS256100&Orphanet:ORPHA655&SNOMED_CT:204958008&Human_Phenotype_Ontology:HP:0000210&Human_Phenotype_Ontology:HP:0000330&Human_Phenotype_Ontology:HP:0000345&Human_Phenotype_Ontology:HP:0000347&Human_Phenotype_Ontology:HP:0002005&Human_Phenotype_Ontology:HP:0002674&Human_Phenotype_Ontology:HP:0004669&Human_Phenotype_Ontology:HP:0005460&Human_Phenotype_Ontology:HP:0005470&MedGen:C0025990&Human_Phenotype_Ontology:HP:0000754&Human_Phenotype_Ontology:HP:0001255&Human_Phenotype_Ontology:HP:0001263&Human_Phenotype_Ontology:HP:0001277&Human_Phenotype_Ontology:HP:0001292&Human_Phenotype_Ontology:HP:0002433&Human_Phenotype_Ontology:HP:0002473&Human_Phenotype_Ontology:HP:0002532&Human_Phenotype_Ontology:HP:0006793&Human_Phenotype_Ontology:HP:0006867&Human_Phenotype_Ontology:HP:0006885&Human_Phenotype_Ontology:HP:0006935&Human_Phenotype_Ontology:HP:0007005&Human_Phenotype_Ontology:HP:0007094&Human_Phenotype_Ontology:HP:0007106&Human_Phenotype_Ontology:HP:0007174&Human_Phenotype_Ontology:HP:0007224&Human_Phenotype_Ontology:HP:0007228&Human_Phenotype_Ontology:HP:0007342&MedGen:C0557874&Human_Phenotype_Ontology:HP:0001252&Human_Phenotype_Ontology:HP:0001318&MedGen:C0026827&Human_Phenotype_Ontology:HP:0001999&Human_Phenotype_Ontology:HP:0002004&Human_Phenotype_Ontology:HP:0002260&Human_Phenotype_Ontology:HP:0004643&Human_Phenotype_Ontology:HP:0004649&Human_Phenotype_Ontology:HP:0004652&Human_Phenotype_Ontology:HP:0004655&Human_Phenotype_Ontology:HP:0004675&Human_Phenotype_Ontology:HP:0005124&MedGen:C0424503&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0018921&MedGen:C0265215&OMIM:PS249000&Orphanet:ORPHA564&SNOMED_CT:29076005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic Deletion CEP290:80184&C12orf29:91298 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 763762899 15 30 50.0 -12 88049400 CTTCT C - frameshift_variant HIGH CEP290 80184 Transcript XM_011538759.2 protein_coding 56/56 8425-8428 8081-8084 2694-2695 KK/X aAGAAg/ag -1 EntrezGene TTCT TTCT 34 5.740274 4.25&6.53&2.94&4.23 rs763762899 6 143014 4.19539e-05 2.38129e-05 4.40995e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.78021e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44363e-05 7.75964e-05 1.07112e-04 3.69113e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18579e-05 0.00000e+00 0.00000e+00 0.00000e+00 418123 408757 Nephronophthisis&Micrognathia&Global_developmental_delay&Muscular_hypotonia&Abnormal_facial_shape&Joubert_syndrome&Meckel-Gruber_syndrome¬_provided Human_Phenotype_Ontology:HP:0000090&Human_Phenotype_Ontology:HP:0004748&MONDO:MONDO:0019005&MedGen:C0687120&OMIM:PS256100&Orphanet:ORPHA655&SNOMED_CT:204958008&Human_Phenotype_Ontology:HP:0000210&Human_Phenotype_Ontology:HP:0000330&Human_Phenotype_Ontology:HP:0000345&Human_Phenotype_Ontology:HP:0000347&Human_Phenotype_Ontology:HP:0002005&Human_Phenotype_Ontology:HP:0002674&Human_Phenotype_Ontology:HP:0004669&Human_Phenotype_Ontology:HP:0005460&Human_Phenotype_Ontology:HP:0005470&MedGen:C0025990&Human_Phenotype_Ontology:HP:0000754&Human_Phenotype_Ontology:HP:0001255&Human_Phenotype_Ontology:HP:0001263&Human_Phenotype_Ontology:HP:0001277&Human_Phenotype_Ontology:HP:0001292&Human_Phenotype_Ontology:HP:0002433&Human_Phenotype_Ontology:HP:0002473&Human_Phenotype_Ontology:HP:0002532&Human_Phenotype_Ontology:HP:0006793&Human_Phenotype_Ontology:HP:0006867&Human_Phenotype_Ontology:HP:0006885&Human_Phenotype_Ontology:HP:0006935&Human_Phenotype_Ontology:HP:0007005&Human_Phenotype_Ontology:HP:0007094&Human_Phenotype_Ontology:HP:0007106&Human_Phenotype_Ontology:HP:0007174&Human_Phenotype_Ontology:HP:0007224&Human_Phenotype_Ontology:HP:0007228&Human_Phenotype_Ontology:HP:0007342&MedGen:C0557874&Human_Phenotype_Ontology:HP:0001252&Human_Phenotype_Ontology:HP:0001318&MedGen:C0026827&Human_Phenotype_Ontology:HP:0001999&Human_Phenotype_Ontology:HP:0002004&Human_Phenotype_Ontology:HP:0002260&Human_Phenotype_Ontology:HP:0004643&Human_Phenotype_Ontology:HP:0004649&Human_Phenotype_Ontology:HP:0004652&Human_Phenotype_Ontology:HP:0004655&Human_Phenotype_Ontology:HP:0004675&Human_Phenotype_Ontology:HP:0005124&MedGen:C0424503&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0018921&MedGen:C0265215&OMIM:PS249000&Orphanet:ORPHA564&SNOMED_CT:29076005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic Deletion CEP290:80184&C12orf29:91298 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 763762899 15 30 50.0 -12 88049400 CTTCT C - frameshift_variant HIGH CEP290 80184 Transcript XM_011538760.2 protein_coding 56/56 8311-8314 7967-7970 2656-2657 KK/X aAGAAg/ag -1 EntrezGene TTCT TTCT 34 5.740274 4.25&6.53&2.94&4.23 rs763762899 6 143014 4.19539e-05 2.38129e-05 4.40995e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.78021e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44363e-05 7.75964e-05 1.07112e-04 3.69113e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18579e-05 0.00000e+00 0.00000e+00 0.00000e+00 418123 408757 Nephronophthisis&Micrognathia&Global_developmental_delay&Muscular_hypotonia&Abnormal_facial_shape&Joubert_syndrome&Meckel-Gruber_syndrome¬_provided Human_Phenotype_Ontology:HP:0000090&Human_Phenotype_Ontology:HP:0004748&MONDO:MONDO:0019005&MedGen:C0687120&OMIM:PS256100&Orphanet:ORPHA655&SNOMED_CT:204958008&Human_Phenotype_Ontology:HP:0000210&Human_Phenotype_Ontology:HP:0000330&Human_Phenotype_Ontology:HP:0000345&Human_Phenotype_Ontology:HP:0000347&Human_Phenotype_Ontology:HP:0002005&Human_Phenotype_Ontology:HP:0002674&Human_Phenotype_Ontology:HP:0004669&Human_Phenotype_Ontology:HP:0005460&Human_Phenotype_Ontology:HP:0005470&MedGen:C0025990&Human_Phenotype_Ontology:HP:0000754&Human_Phenotype_Ontology:HP:0001255&Human_Phenotype_Ontology:HP:0001263&Human_Phenotype_Ontology:HP:0001277&Human_Phenotype_Ontology:HP:0001292&Human_Phenotype_Ontology:HP:0002433&Human_Phenotype_Ontology:HP:0002473&Human_Phenotype_Ontology:HP:0002532&Human_Phenotype_Ontology:HP:0006793&Human_Phenotype_Ontology:HP:0006867&Human_Phenotype_Ontology:HP:0006885&Human_Phenotype_Ontology:HP:0006935&Human_Phenotype_Ontology:HP:0007005&Human_Phenotype_Ontology:HP:0007094&Human_Phenotype_Ontology:HP:0007106&Human_Phenotype_Ontology:HP:0007174&Human_Phenotype_Ontology:HP:0007224&Human_Phenotype_Ontology:HP:0007228&Human_Phenotype_Ontology:HP:0007342&MedGen:C0557874&Human_Phenotype_Ontology:HP:0001252&Human_Phenotype_Ontology:HP:0001318&MedGen:C0026827&Human_Phenotype_Ontology:HP:0001999&Human_Phenotype_Ontology:HP:0002004&Human_Phenotype_Ontology:HP:0002260&Human_Phenotype_Ontology:HP:0004643&Human_Phenotype_Ontology:HP:0004649&Human_Phenotype_Ontology:HP:0004652&Human_Phenotype_Ontology:HP:0004655&Human_Phenotype_Ontology:HP:0004675&Human_Phenotype_Ontology:HP:0005124&MedGen:C0424503&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0018921&MedGen:C0265215&OMIM:PS249000&Orphanet:ORPHA564&SNOMED_CT:29076005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic Deletion CEP290:80184&C12orf29:91298 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 763762899 15 30 50.0 -12 88049400 CTTCT C - frameshift_variant HIGH CEP290 80184 Transcript XM_011538761.2 protein_coding 56/56 8269-8272 7925-7928 2642-2643 KK/X aAGAAg/ag -1 EntrezGene TTCT TTCT 34 5.740274 4.25&6.53&2.94&4.23 rs763762899 6 143014 4.19539e-05 2.38129e-05 4.40995e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.78021e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44363e-05 7.75964e-05 1.07112e-04 3.69113e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18579e-05 0.00000e+00 0.00000e+00 0.00000e+00 418123 408757 Nephronophthisis&Micrognathia&Global_developmental_delay&Muscular_hypotonia&Abnormal_facial_shape&Joubert_syndrome&Meckel-Gruber_syndrome¬_provided Human_Phenotype_Ontology:HP:0000090&Human_Phenotype_Ontology:HP:0004748&MONDO:MONDO:0019005&MedGen:C0687120&OMIM:PS256100&Orphanet:ORPHA655&SNOMED_CT:204958008&Human_Phenotype_Ontology:HP:0000210&Human_Phenotype_Ontology:HP:0000330&Human_Phenotype_Ontology:HP:0000345&Human_Phenotype_Ontology:HP:0000347&Human_Phenotype_Ontology:HP:0002005&Human_Phenotype_Ontology:HP:0002674&Human_Phenotype_Ontology:HP:0004669&Human_Phenotype_Ontology:HP:0005460&Human_Phenotype_Ontology:HP:0005470&MedGen:C0025990&Human_Phenotype_Ontology:HP:0000754&Human_Phenotype_Ontology:HP:0001255&Human_Phenotype_Ontology:HP:0001263&Human_Phenotype_Ontology:HP:0001277&Human_Phenotype_Ontology:HP:0001292&Human_Phenotype_Ontology:HP:0002433&Human_Phenotype_Ontology:HP:0002473&Human_Phenotype_Ontology:HP:0002532&Human_Phenotype_Ontology:HP:0006793&Human_Phenotype_Ontology:HP:0006867&Human_Phenotype_Ontology:HP:0006885&Human_Phenotype_Ontology:HP:0006935&Human_Phenotype_Ontology:HP:0007005&Human_Phenotype_Ontology:HP:0007094&Human_Phenotype_Ontology:HP:0007106&Human_Phenotype_Ontology:HP:0007174&Human_Phenotype_Ontology:HP:0007224&Human_Phenotype_Ontology:HP:0007228&Human_Phenotype_Ontology:HP:0007342&MedGen:C0557874&Human_Phenotype_Ontology:HP:0001252&Human_Phenotype_Ontology:HP:0001318&MedGen:C0026827&Human_Phenotype_Ontology:HP:0001999&Human_Phenotype_Ontology:HP:0002004&Human_Phenotype_Ontology:HP:0002260&Human_Phenotype_Ontology:HP:0004643&Human_Phenotype_Ontology:HP:0004649&Human_Phenotype_Ontology:HP:0004652&Human_Phenotype_Ontology:HP:0004655&Human_Phenotype_Ontology:HP:0004675&Human_Phenotype_Ontology:HP:0005124&MedGen:C0424503&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0018921&MedGen:C0265215&OMIM:PS249000&Orphanet:ORPHA564&SNOMED_CT:29076005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic Deletion CEP290:80184&C12orf29:91298 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 763762899 15 30 50.0 -12 88049400 CTTCT C - frameshift_variant HIGH CEP290 80184 Transcript XM_011538762.3 protein_coding 56/56 7666-7669 7322-7325 2441-2442 KK/X aAGAAg/ag -1 EntrezGene TTCT TTCT 34 5.740274 4.25&6.53&2.94&4.23 rs763762899 6 143014 4.19539e-05 2.38129e-05 4.40995e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.78021e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44363e-05 7.75964e-05 1.07112e-04 3.69113e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18579e-05 0.00000e+00 0.00000e+00 0.00000e+00 418123 408757 Nephronophthisis&Micrognathia&Global_developmental_delay&Muscular_hypotonia&Abnormal_facial_shape&Joubert_syndrome&Meckel-Gruber_syndrome¬_provided Human_Phenotype_Ontology:HP:0000090&Human_Phenotype_Ontology:HP:0004748&MONDO:MONDO:0019005&MedGen:C0687120&OMIM:PS256100&Orphanet:ORPHA655&SNOMED_CT:204958008&Human_Phenotype_Ontology:HP:0000210&Human_Phenotype_Ontology:HP:0000330&Human_Phenotype_Ontology:HP:0000345&Human_Phenotype_Ontology:HP:0000347&Human_Phenotype_Ontology:HP:0002005&Human_Phenotype_Ontology:HP:0002674&Human_Phenotype_Ontology:HP:0004669&Human_Phenotype_Ontology:HP:0005460&Human_Phenotype_Ontology:HP:0005470&MedGen:C0025990&Human_Phenotype_Ontology:HP:0000754&Human_Phenotype_Ontology:HP:0001255&Human_Phenotype_Ontology:HP:0001263&Human_Phenotype_Ontology:HP:0001277&Human_Phenotype_Ontology:HP:0001292&Human_Phenotype_Ontology:HP:0002433&Human_Phenotype_Ontology:HP:0002473&Human_Phenotype_Ontology:HP:0002532&Human_Phenotype_Ontology:HP:0006793&Human_Phenotype_Ontology:HP:0006867&Human_Phenotype_Ontology:HP:0006885&Human_Phenotype_Ontology:HP:0006935&Human_Phenotype_Ontology:HP:0007005&Human_Phenotype_Ontology:HP:0007094&Human_Phenotype_Ontology:HP:0007106&Human_Phenotype_Ontology:HP:0007174&Human_Phenotype_Ontology:HP:0007224&Human_Phenotype_Ontology:HP:0007228&Human_Phenotype_Ontology:HP:0007342&MedGen:C0557874&Human_Phenotype_Ontology:HP:0001252&Human_Phenotype_Ontology:HP:0001318&MedGen:C0026827&Human_Phenotype_Ontology:HP:0001999&Human_Phenotype_Ontology:HP:0002004&Human_Phenotype_Ontology:HP:0002260&Human_Phenotype_Ontology:HP:0004643&Human_Phenotype_Ontology:HP:0004649&Human_Phenotype_Ontology:HP:0004652&Human_Phenotype_Ontology:HP:0004655&Human_Phenotype_Ontology:HP:0004675&Human_Phenotype_Ontology:HP:0005124&MedGen:C0424503&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0018921&MedGen:C0265215&OMIM:PS249000&Orphanet:ORPHA564&SNOMED_CT:29076005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic Deletion CEP290:80184&C12orf29:91298 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 763762899 15 30 50.0 -12 88049400 CTTCT C - frameshift_variant HIGH CEP290 80184 Transcript XM_011538763.3 protein_coding 55/55 7573-7576 7229-7232 2410-2411 KK/X aAGAAg/ag -1 EntrezGene TTCT TTCT 34 5.740274 4.25&6.53&2.94&4.23 rs763762899 6 143014 4.19539e-05 2.38129e-05 4.40995e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.78021e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44363e-05 7.75964e-05 1.07112e-04 3.69113e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18579e-05 0.00000e+00 0.00000e+00 0.00000e+00 418123 408757 Nephronophthisis&Micrognathia&Global_developmental_delay&Muscular_hypotonia&Abnormal_facial_shape&Joubert_syndrome&Meckel-Gruber_syndrome¬_provided Human_Phenotype_Ontology:HP:0000090&Human_Phenotype_Ontology:HP:0004748&MONDO:MONDO:0019005&MedGen:C0687120&OMIM:PS256100&Orphanet:ORPHA655&SNOMED_CT:204958008&Human_Phenotype_Ontology:HP:0000210&Human_Phenotype_Ontology:HP:0000330&Human_Phenotype_Ontology:HP:0000345&Human_Phenotype_Ontology:HP:0000347&Human_Phenotype_Ontology:HP:0002005&Human_Phenotype_Ontology:HP:0002674&Human_Phenotype_Ontology:HP:0004669&Human_Phenotype_Ontology:HP:0005460&Human_Phenotype_Ontology:HP:0005470&MedGen:C0025990&Human_Phenotype_Ontology:HP:0000754&Human_Phenotype_Ontology:HP:0001255&Human_Phenotype_Ontology:HP:0001263&Human_Phenotype_Ontology:HP:0001277&Human_Phenotype_Ontology:HP:0001292&Human_Phenotype_Ontology:HP:0002433&Human_Phenotype_Ontology:HP:0002473&Human_Phenotype_Ontology:HP:0002532&Human_Phenotype_Ontology:HP:0006793&Human_Phenotype_Ontology:HP:0006867&Human_Phenotype_Ontology:HP:0006885&Human_Phenotype_Ontology:HP:0006935&Human_Phenotype_Ontology:HP:0007005&Human_Phenotype_Ontology:HP:0007094&Human_Phenotype_Ontology:HP:0007106&Human_Phenotype_Ontology:HP:0007174&Human_Phenotype_Ontology:HP:0007224&Human_Phenotype_Ontology:HP:0007228&Human_Phenotype_Ontology:HP:0007342&MedGen:C0557874&Human_Phenotype_Ontology:HP:0001252&Human_Phenotype_Ontology:HP:0001318&MedGen:C0026827&Human_Phenotype_Ontology:HP:0001999&Human_Phenotype_Ontology:HP:0002004&Human_Phenotype_Ontology:HP:0002260&Human_Phenotype_Ontology:HP:0004643&Human_Phenotype_Ontology:HP:0004649&Human_Phenotype_Ontology:HP:0004652&Human_Phenotype_Ontology:HP:0004655&Human_Phenotype_Ontology:HP:0004675&Human_Phenotype_Ontology:HP:0005124&MedGen:C0424503&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0018921&MedGen:C0265215&OMIM:PS249000&Orphanet:ORPHA564&SNOMED_CT:29076005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic Deletion CEP290:80184&C12orf29:91298 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 763762899 15 30 50.0 -12 88049400 CTTCT C - frameshift_variant HIGH CEP290 80184 Transcript XM_011538766.3 protein_coding 43/43 6661-6664 6551-6554 2184-2185 KK/X aAGAAg/ag -1 EntrezGene TTCT TTCT 34 5.740274 4.25&6.53&2.94&4.23 rs763762899 6 143014 4.19539e-05 2.38129e-05 4.40995e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.78021e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44363e-05 7.75964e-05 1.07112e-04 3.69113e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18579e-05 0.00000e+00 0.00000e+00 0.00000e+00 418123 408757 Nephronophthisis&Micrognathia&Global_developmental_delay&Muscular_hypotonia&Abnormal_facial_shape&Joubert_syndrome&Meckel-Gruber_syndrome¬_provided Human_Phenotype_Ontology:HP:0000090&Human_Phenotype_Ontology:HP:0004748&MONDO:MONDO:0019005&MedGen:C0687120&OMIM:PS256100&Orphanet:ORPHA655&SNOMED_CT:204958008&Human_Phenotype_Ontology:HP:0000210&Human_Phenotype_Ontology:HP:0000330&Human_Phenotype_Ontology:HP:0000345&Human_Phenotype_Ontology:HP:0000347&Human_Phenotype_Ontology:HP:0002005&Human_Phenotype_Ontology:HP:0002674&Human_Phenotype_Ontology:HP:0004669&Human_Phenotype_Ontology:HP:0005460&Human_Phenotype_Ontology:HP:0005470&MedGen:C0025990&Human_Phenotype_Ontology:HP:0000754&Human_Phenotype_Ontology:HP:0001255&Human_Phenotype_Ontology:HP:0001263&Human_Phenotype_Ontology:HP:0001277&Human_Phenotype_Ontology:HP:0001292&Human_Phenotype_Ontology:HP:0002433&Human_Phenotype_Ontology:HP:0002473&Human_Phenotype_Ontology:HP:0002532&Human_Phenotype_Ontology:HP:0006793&Human_Phenotype_Ontology:HP:0006867&Human_Phenotype_Ontology:HP:0006885&Human_Phenotype_Ontology:HP:0006935&Human_Phenotype_Ontology:HP:0007005&Human_Phenotype_Ontology:HP:0007094&Human_Phenotype_Ontology:HP:0007106&Human_Phenotype_Ontology:HP:0007174&Human_Phenotype_Ontology:HP:0007224&Human_Phenotype_Ontology:HP:0007228&Human_Phenotype_Ontology:HP:0007342&MedGen:C0557874&Human_Phenotype_Ontology:HP:0001252&Human_Phenotype_Ontology:HP:0001318&MedGen:C0026827&Human_Phenotype_Ontology:HP:0001999&Human_Phenotype_Ontology:HP:0002004&Human_Phenotype_Ontology:HP:0002260&Human_Phenotype_Ontology:HP:0004643&Human_Phenotype_Ontology:HP:0004649&Human_Phenotype_Ontology:HP:0004652&Human_Phenotype_Ontology:HP:0004655&Human_Phenotype_Ontology:HP:0004675&Human_Phenotype_Ontology:HP:0005124&MedGen:C0424503&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0018921&MedGen:C0265215&OMIM:PS249000&Orphanet:ORPHA564&SNOMED_CT:29076005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic Deletion CEP290:80184&C12orf29:91298 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 763762899 15 30 50.0 -12 88049400 CTTCT C - frameshift_variant HIGH CEP290 80184 Transcript XM_017019980.2 protein_coding 55/55 8302-8305 7958-7961 2653-2654 KK/X aAGAAg/ag -1 EntrezGene TTCT TTCT 34 5.740274 4.25&6.53&2.94&4.23 rs763762899 6 143014 4.19539e-05 2.38129e-05 4.40995e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.78021e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44363e-05 7.75964e-05 1.07112e-04 3.69113e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18579e-05 0.00000e+00 0.00000e+00 0.00000e+00 418123 408757 Nephronophthisis&Micrognathia&Global_developmental_delay&Muscular_hypotonia&Abnormal_facial_shape&Joubert_syndrome&Meckel-Gruber_syndrome¬_provided Human_Phenotype_Ontology:HP:0000090&Human_Phenotype_Ontology:HP:0004748&MONDO:MONDO:0019005&MedGen:C0687120&OMIM:PS256100&Orphanet:ORPHA655&SNOMED_CT:204958008&Human_Phenotype_Ontology:HP:0000210&Human_Phenotype_Ontology:HP:0000330&Human_Phenotype_Ontology:HP:0000345&Human_Phenotype_Ontology:HP:0000347&Human_Phenotype_Ontology:HP:0002005&Human_Phenotype_Ontology:HP:0002674&Human_Phenotype_Ontology:HP:0004669&Human_Phenotype_Ontology:HP:0005460&Human_Phenotype_Ontology:HP:0005470&MedGen:C0025990&Human_Phenotype_Ontology:HP:0000754&Human_Phenotype_Ontology:HP:0001255&Human_Phenotype_Ontology:HP:0001263&Human_Phenotype_Ontology:HP:0001277&Human_Phenotype_Ontology:HP:0001292&Human_Phenotype_Ontology:HP:0002433&Human_Phenotype_Ontology:HP:0002473&Human_Phenotype_Ontology:HP:0002532&Human_Phenotype_Ontology:HP:0006793&Human_Phenotype_Ontology:HP:0006867&Human_Phenotype_Ontology:HP:0006885&Human_Phenotype_Ontology:HP:0006935&Human_Phenotype_Ontology:HP:0007005&Human_Phenotype_Ontology:HP:0007094&Human_Phenotype_Ontology:HP:0007106&Human_Phenotype_Ontology:HP:0007174&Human_Phenotype_Ontology:HP:0007224&Human_Phenotype_Ontology:HP:0007228&Human_Phenotype_Ontology:HP:0007342&MedGen:C0557874&Human_Phenotype_Ontology:HP:0001252&Human_Phenotype_Ontology:HP:0001318&MedGen:C0026827&Human_Phenotype_Ontology:HP:0001999&Human_Phenotype_Ontology:HP:0002004&Human_Phenotype_Ontology:HP:0002260&Human_Phenotype_Ontology:HP:0004643&Human_Phenotype_Ontology:HP:0004649&Human_Phenotype_Ontology:HP:0004652&Human_Phenotype_Ontology:HP:0004655&Human_Phenotype_Ontology:HP:0004675&Human_Phenotype_Ontology:HP:0005124&MedGen:C0424503&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0018921&MedGen:C0265215&OMIM:PS249000&Orphanet:ORPHA564&SNOMED_CT:29076005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic Deletion CEP290:80184&C12orf29:91298 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 763762899 15 30 50.0 -12 88049400 CTTCT C - frameshift_variant HIGH CEP290 80184 Transcript XM_017019981.2 protein_coding 55/55 8260-8263 7916-7919 2639-2640 KK/X aAGAAg/ag -1 EntrezGene TTCT TTCT 34 5.740274 4.25&6.53&2.94&4.23 rs763762899 6 143014 4.19539e-05 2.38129e-05 4.40995e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.78021e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44363e-05 7.75964e-05 1.07112e-04 3.69113e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18579e-05 0.00000e+00 0.00000e+00 0.00000e+00 418123 408757 Nephronophthisis&Micrognathia&Global_developmental_delay&Muscular_hypotonia&Abnormal_facial_shape&Joubert_syndrome&Meckel-Gruber_syndrome¬_provided Human_Phenotype_Ontology:HP:0000090&Human_Phenotype_Ontology:HP:0004748&MONDO:MONDO:0019005&MedGen:C0687120&OMIM:PS256100&Orphanet:ORPHA655&SNOMED_CT:204958008&Human_Phenotype_Ontology:HP:0000210&Human_Phenotype_Ontology:HP:0000330&Human_Phenotype_Ontology:HP:0000345&Human_Phenotype_Ontology:HP:0000347&Human_Phenotype_Ontology:HP:0002005&Human_Phenotype_Ontology:HP:0002674&Human_Phenotype_Ontology:HP:0004669&Human_Phenotype_Ontology:HP:0005460&Human_Phenotype_Ontology:HP:0005470&MedGen:C0025990&Human_Phenotype_Ontology:HP:0000754&Human_Phenotype_Ontology:HP:0001255&Human_Phenotype_Ontology:HP:0001263&Human_Phenotype_Ontology:HP:0001277&Human_Phenotype_Ontology:HP:0001292&Human_Phenotype_Ontology:HP:0002433&Human_Phenotype_Ontology:HP:0002473&Human_Phenotype_Ontology:HP:0002532&Human_Phenotype_Ontology:HP:0006793&Human_Phenotype_Ontology:HP:0006867&Human_Phenotype_Ontology:HP:0006885&Human_Phenotype_Ontology:HP:0006935&Human_Phenotype_Ontology:HP:0007005&Human_Phenotype_Ontology:HP:0007094&Human_Phenotype_Ontology:HP:0007106&Human_Phenotype_Ontology:HP:0007174&Human_Phenotype_Ontology:HP:0007224&Human_Phenotype_Ontology:HP:0007228&Human_Phenotype_Ontology:HP:0007342&MedGen:C0557874&Human_Phenotype_Ontology:HP:0001252&Human_Phenotype_Ontology:HP:0001318&MedGen:C0026827&Human_Phenotype_Ontology:HP:0001999&Human_Phenotype_Ontology:HP:0002004&Human_Phenotype_Ontology:HP:0002260&Human_Phenotype_Ontology:HP:0004643&Human_Phenotype_Ontology:HP:0004649&Human_Phenotype_Ontology:HP:0004652&Human_Phenotype_Ontology:HP:0004655&Human_Phenotype_Ontology:HP:0004675&Human_Phenotype_Ontology:HP:0005124&MedGen:C0424503&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0018921&MedGen:C0265215&OMIM:PS249000&Orphanet:ORPHA564&SNOMED_CT:29076005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic Deletion CEP290:80184&C12orf29:91298 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 763762899 15 30 50.0 -12 88049400 CTTCT C - downstream_gene_variant MODIFIER CEP290 80184 Transcript XM_017019982.1 protein_coding 4254 -1 EntrezGene TTCT TTCT 34 5.740274 4.25&6.53&2.94&4.23 rs763762899 6 143014 4.19539e-05 2.38129e-05 4.40995e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.78021e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44363e-05 7.75964e-05 1.07112e-04 3.69113e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18579e-05 0.00000e+00 0.00000e+00 0.00000e+00 418123 408757 Nephronophthisis&Micrognathia&Global_developmental_delay&Muscular_hypotonia&Abnormal_facial_shape&Joubert_syndrome&Meckel-Gruber_syndrome¬_provided Human_Phenotype_Ontology:HP:0000090&Human_Phenotype_Ontology:HP:0004748&MONDO:MONDO:0019005&MedGen:C0687120&OMIM:PS256100&Orphanet:ORPHA655&SNOMED_CT:204958008&Human_Phenotype_Ontology:HP:0000210&Human_Phenotype_Ontology:HP:0000330&Human_Phenotype_Ontology:HP:0000345&Human_Phenotype_Ontology:HP:0000347&Human_Phenotype_Ontology:HP:0002005&Human_Phenotype_Ontology:HP:0002674&Human_Phenotype_Ontology:HP:0004669&Human_Phenotype_Ontology:HP:0005460&Human_Phenotype_Ontology:HP:0005470&MedGen:C0025990&Human_Phenotype_Ontology:HP:0000754&Human_Phenotype_Ontology:HP:0001255&Human_Phenotype_Ontology:HP:0001263&Human_Phenotype_Ontology:HP:0001277&Human_Phenotype_Ontology:HP:0001292&Human_Phenotype_Ontology:HP:0002433&Human_Phenotype_Ontology:HP:0002473&Human_Phenotype_Ontology:HP:0002532&Human_Phenotype_Ontology:HP:0006793&Human_Phenotype_Ontology:HP:0006867&Human_Phenotype_Ontology:HP:0006885&Human_Phenotype_Ontology:HP:0006935&Human_Phenotype_Ontology:HP:0007005&Human_Phenotype_Ontology:HP:0007094&Human_Phenotype_Ontology:HP:0007106&Human_Phenotype_Ontology:HP:0007174&Human_Phenotype_Ontology:HP:0007224&Human_Phenotype_Ontology:HP:0007228&Human_Phenotype_Ontology:HP:0007342&MedGen:C0557874&Human_Phenotype_Ontology:HP:0001252&Human_Phenotype_Ontology:HP:0001318&MedGen:C0026827&Human_Phenotype_Ontology:HP:0001999&Human_Phenotype_Ontology:HP:0002004&Human_Phenotype_Ontology:HP:0002260&Human_Phenotype_Ontology:HP:0004643&Human_Phenotype_Ontology:HP:0004649&Human_Phenotype_Ontology:HP:0004652&Human_Phenotype_Ontology:HP:0004655&Human_Phenotype_Ontology:HP:0004675&Human_Phenotype_Ontology:HP:0005124&MedGen:C0424503&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0018921&MedGen:C0265215&OMIM:PS249000&Orphanet:ORPHA564&SNOMED_CT:29076005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic Deletion CEP290:80184&C12orf29:91298 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 763762899 15 30 50.0 -12 88049400 CTTCT C - frameshift_variant HIGH CEP290 80184 Transcript XM_017019983.2 protein_coding 54/54 7552-7555 7208-7211 2403-2404 KK/X aAGAAg/ag -1 EntrezGene TTCT TTCT 34 5.740274 4.25&6.53&2.94&4.23 rs763762899 6 143014 4.19539e-05 2.38129e-05 4.40995e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.78021e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44363e-05 7.75964e-05 1.07112e-04 3.69113e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18579e-05 0.00000e+00 0.00000e+00 0.00000e+00 418123 408757 Nephronophthisis&Micrognathia&Global_developmental_delay&Muscular_hypotonia&Abnormal_facial_shape&Joubert_syndrome&Meckel-Gruber_syndrome¬_provided Human_Phenotype_Ontology:HP:0000090&Human_Phenotype_Ontology:HP:0004748&MONDO:MONDO:0019005&MedGen:C0687120&OMIM:PS256100&Orphanet:ORPHA655&SNOMED_CT:204958008&Human_Phenotype_Ontology:HP:0000210&Human_Phenotype_Ontology:HP:0000330&Human_Phenotype_Ontology:HP:0000345&Human_Phenotype_Ontology:HP:0000347&Human_Phenotype_Ontology:HP:0002005&Human_Phenotype_Ontology:HP:0002674&Human_Phenotype_Ontology:HP:0004669&Human_Phenotype_Ontology:HP:0005460&Human_Phenotype_Ontology:HP:0005470&MedGen:C0025990&Human_Phenotype_Ontology:HP:0000754&Human_Phenotype_Ontology:HP:0001255&Human_Phenotype_Ontology:HP:0001263&Human_Phenotype_Ontology:HP:0001277&Human_Phenotype_Ontology:HP:0001292&Human_Phenotype_Ontology:HP:0002433&Human_Phenotype_Ontology:HP:0002473&Human_Phenotype_Ontology:HP:0002532&Human_Phenotype_Ontology:HP:0006793&Human_Phenotype_Ontology:HP:0006867&Human_Phenotype_Ontology:HP:0006885&Human_Phenotype_Ontology:HP:0006935&Human_Phenotype_Ontology:HP:0007005&Human_Phenotype_Ontology:HP:0007094&Human_Phenotype_Ontology:HP:0007106&Human_Phenotype_Ontology:HP:0007174&Human_Phenotype_Ontology:HP:0007224&Human_Phenotype_Ontology:HP:0007228&Human_Phenotype_Ontology:HP:0007342&MedGen:C0557874&Human_Phenotype_Ontology:HP:0001252&Human_Phenotype_Ontology:HP:0001318&MedGen:C0026827&Human_Phenotype_Ontology:HP:0001999&Human_Phenotype_Ontology:HP:0002004&Human_Phenotype_Ontology:HP:0002260&Human_Phenotype_Ontology:HP:0004643&Human_Phenotype_Ontology:HP:0004649&Human_Phenotype_Ontology:HP:0004652&Human_Phenotype_Ontology:HP:0004655&Human_Phenotype_Ontology:HP:0004675&Human_Phenotype_Ontology:HP:0005124&MedGen:C0424503&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0018921&MedGen:C0265215&OMIM:PS249000&Orphanet:ORPHA564&SNOMED_CT:29076005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic Deletion CEP290:80184&C12orf29:91298 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 763762899 15 30 50.0 -12 88049400 CTTCT C - non_coding_transcript_exon_variant MODIFIER CEP290 80184 Transcript XR_001748869.1 misc_RNA 55/55 8351-8354 -1 EntrezGene TTCT TTCT 34 5.740274 4.25&6.53&2.94&4.23 rs763762899 6 143014 4.19539e-05 2.38129e-05 4.40995e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.78021e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44363e-05 7.75964e-05 1.07112e-04 3.69113e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18579e-05 0.00000e+00 0.00000e+00 0.00000e+00 418123 408757 Nephronophthisis&Micrognathia&Global_developmental_delay&Muscular_hypotonia&Abnormal_facial_shape&Joubert_syndrome&Meckel-Gruber_syndrome¬_provided Human_Phenotype_Ontology:HP:0000090&Human_Phenotype_Ontology:HP:0004748&MONDO:MONDO:0019005&MedGen:C0687120&OMIM:PS256100&Orphanet:ORPHA655&SNOMED_CT:204958008&Human_Phenotype_Ontology:HP:0000210&Human_Phenotype_Ontology:HP:0000330&Human_Phenotype_Ontology:HP:0000345&Human_Phenotype_Ontology:HP:0000347&Human_Phenotype_Ontology:HP:0002005&Human_Phenotype_Ontology:HP:0002674&Human_Phenotype_Ontology:HP:0004669&Human_Phenotype_Ontology:HP:0005460&Human_Phenotype_Ontology:HP:0005470&MedGen:C0025990&Human_Phenotype_Ontology:HP:0000754&Human_Phenotype_Ontology:HP:0001255&Human_Phenotype_Ontology:HP:0001263&Human_Phenotype_Ontology:HP:0001277&Human_Phenotype_Ontology:HP:0001292&Human_Phenotype_Ontology:HP:0002433&Human_Phenotype_Ontology:HP:0002473&Human_Phenotype_Ontology:HP:0002532&Human_Phenotype_Ontology:HP:0006793&Human_Phenotype_Ontology:HP:0006867&Human_Phenotype_Ontology:HP:0006885&Human_Phenotype_Ontology:HP:0006935&Human_Phenotype_Ontology:HP:0007005&Human_Phenotype_Ontology:HP:0007094&Human_Phenotype_Ontology:HP:0007106&Human_Phenotype_Ontology:HP:0007174&Human_Phenotype_Ontology:HP:0007224&Human_Phenotype_Ontology:HP:0007228&Human_Phenotype_Ontology:HP:0007342&MedGen:C0557874&Human_Phenotype_Ontology:HP:0001252&Human_Phenotype_Ontology:HP:0001318&MedGen:C0026827&Human_Phenotype_Ontology:HP:0001999&Human_Phenotype_Ontology:HP:0002004&Human_Phenotype_Ontology:HP:0002260&Human_Phenotype_Ontology:HP:0004643&Human_Phenotype_Ontology:HP:0004649&Human_Phenotype_Ontology:HP:0004652&Human_Phenotype_Ontology:HP:0004655&Human_Phenotype_Ontology:HP:0004675&Human_Phenotype_Ontology:HP:0005124&MedGen:C0424503&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0018921&MedGen:C0265215&OMIM:PS249000&Orphanet:ORPHA564&SNOMED_CT:29076005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic Deletion CEP290:80184&C12orf29:91298 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 763762899 15 30 50.0 -12 88049400 CTTCT C - non_coding_transcript_exon_variant MODIFIER CEP290 80184 Transcript XR_001748870.2 misc_RNA 54/54 8186-8189 -1 EntrezGene TTCT TTCT 34 5.740274 4.25&6.53&2.94&4.23 rs763762899 6 143014 4.19539e-05 2.38129e-05 4.40995e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.78021e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44363e-05 7.75964e-05 1.07112e-04 3.69113e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18579e-05 0.00000e+00 0.00000e+00 0.00000e+00 418123 408757 Nephronophthisis&Micrognathia&Global_developmental_delay&Muscular_hypotonia&Abnormal_facial_shape&Joubert_syndrome&Meckel-Gruber_syndrome¬_provided Human_Phenotype_Ontology:HP:0000090&Human_Phenotype_Ontology:HP:0004748&MONDO:MONDO:0019005&MedGen:C0687120&OMIM:PS256100&Orphanet:ORPHA655&SNOMED_CT:204958008&Human_Phenotype_Ontology:HP:0000210&Human_Phenotype_Ontology:HP:0000330&Human_Phenotype_Ontology:HP:0000345&Human_Phenotype_Ontology:HP:0000347&Human_Phenotype_Ontology:HP:0002005&Human_Phenotype_Ontology:HP:0002674&Human_Phenotype_Ontology:HP:0004669&Human_Phenotype_Ontology:HP:0005460&Human_Phenotype_Ontology:HP:0005470&MedGen:C0025990&Human_Phenotype_Ontology:HP:0000754&Human_Phenotype_Ontology:HP:0001255&Human_Phenotype_Ontology:HP:0001263&Human_Phenotype_Ontology:HP:0001277&Human_Phenotype_Ontology:HP:0001292&Human_Phenotype_Ontology:HP:0002433&Human_Phenotype_Ontology:HP:0002473&Human_Phenotype_Ontology:HP:0002532&Human_Phenotype_Ontology:HP:0006793&Human_Phenotype_Ontology:HP:0006867&Human_Phenotype_Ontology:HP:0006885&Human_Phenotype_Ontology:HP:0006935&Human_Phenotype_Ontology:HP:0007005&Human_Phenotype_Ontology:HP:0007094&Human_Phenotype_Ontology:HP:0007106&Human_Phenotype_Ontology:HP:0007174&Human_Phenotype_Ontology:HP:0007224&Human_Phenotype_Ontology:HP:0007228&Human_Phenotype_Ontology:HP:0007342&MedGen:C0557874&Human_Phenotype_Ontology:HP:0001252&Human_Phenotype_Ontology:HP:0001318&MedGen:C0026827&Human_Phenotype_Ontology:HP:0001999&Human_Phenotype_Ontology:HP:0002004&Human_Phenotype_Ontology:HP:0002260&Human_Phenotype_Ontology:HP:0004643&Human_Phenotype_Ontology:HP:0004649&Human_Phenotype_Ontology:HP:0004652&Human_Phenotype_Ontology:HP:0004655&Human_Phenotype_Ontology:HP:0004675&Human_Phenotype_Ontology:HP:0005124&MedGen:C0424503&Human_Phenotype_Ontology:HP:0002335&Human_Phenotype_Ontology:HP:0007125&MONDO:MONDO:0018772&MedGen:C0431399&OMIM:PS213300&Orphanet:ORPHA475&SNOMED_CT:253175003&SNOMED_CT:716997004&MONDO:MONDO:0018921&MedGen:C0265215&OMIM:PS249000&Orphanet:ORPHA564&SNOMED_CT:29076005&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic Deletion CEP290:80184&C12orf29:91298 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 763762899 15 30 50.0 -12 102852875 C T T missense_variant MODERATE PAH 5053 Transcript NM_000277.3 protein_coding 7/13 896 782 261 R/Q cGa/cAa -1 EntrezGene C C 0 0.997 29.2 4.259784 29.2 0.99958587196391924 16.30177 0.954531501491271 0.97537 14.70244 1.02430945680376 0.96444 -6.71&-6.71 5.72 0.999999999999476 0.000000 0.447948 0.9938 0.9598 3.98&. 1&1 -3.75&-3.75 19.8761 0.946&0.942 0.98424 0.638212 1.000000 1.000000 7.905000 1.026000 6.54 chr12:102852875-102852875 31 143140 2.16571e-04 7.14864e-05 4.40995e-05 1.03681e-04 1.11111e-02 1.27660e-02 9.30233e-03 7.33245e-05 0.00000e+00 1.29166e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.30384e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.01875e-04 2.32356e-04 2.14030e-04 2.57561e-04 9.28505e-04 1.81818e-03 0.00000e+00 2.16266e-04 0.00000e+00 0.00000e+00 0.00000e+00 582 0.00027 0.00020 15621 Phenylketonuria¬_provided MedGen:C0751434&OMIM:261600&Orphanet:ORPHA716&SNOMED_CT:154735006&MedGen:CN517202 reviewed_by_expert_panel Pathogenic single_nucleotide_variant PAH:5053 SO:0001583&missense_variant 1 5030849 15 30 50.0 -12 102852875 C T T missense_variant MODERATE PAH 5053 Transcript NM_001354304.2 protein_coding 8/14 1124 782 261 R/Q cGa/cAa -1 EntrezGene C C 0 0.997 29.2 4.259784 29.2 0.99958587196391924 16.30177 0.954531501491271 0.97537 14.70244 1.02430945680376 0.96444 -6.71&-6.71 5.72 0.999999999999476 0.000000 0.447948 0.9938 0.9598 3.98&. 1&1 -3.75&-3.75 19.8761 0.946&0.942 0.98424 0.638212 1.000000 1.000000 7.905000 1.026000 6.54 chr12:102852875-102852875 31 143140 2.16571e-04 7.14864e-05 4.40995e-05 1.03681e-04 1.11111e-02 1.27660e-02 9.30233e-03 7.33245e-05 0.00000e+00 1.29166e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.30384e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.01875e-04 2.32356e-04 2.14030e-04 2.57561e-04 9.28505e-04 1.81818e-03 0.00000e+00 2.16266e-04 0.00000e+00 0.00000e+00 0.00000e+00 582 0.00027 0.00020 15621 Phenylketonuria¬_provided MedGen:C0751434&OMIM:261600&Orphanet:ORPHA716&SNOMED_CT:154735006&MedGen:CN517202 reviewed_by_expert_panel Pathogenic single_nucleotide_variant PAH:5053 SO:0001583&missense_variant 1 5030849 15 30 50.0 -12 102852875 C T T downstream_gene_variant MODIFIER PAH 5053 Transcript XM_017019370.2 protein_coding 953 -1 EntrezGene C C 29.2 4.259784 6.54 chr12:102852875-102852875 31 143140 2.16571e-04 7.14864e-05 4.40995e-05 1.03681e-04 1.11111e-02 1.27660e-02 9.30233e-03 7.33245e-05 0.00000e+00 1.29166e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.30384e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.01875e-04 2.32356e-04 2.14030e-04 2.57561e-04 9.28505e-04 1.81818e-03 0.00000e+00 2.16266e-04 0.00000e+00 0.00000e+00 0.00000e+00 582 0.00027 0.00020 15621 Phenylketonuria¬_provided MedGen:C0751434&OMIM:261600&Orphanet:ORPHA716&SNOMED_CT:154735006&MedGen:CN517202 reviewed_by_expert_panel Pathogenic single_nucleotide_variant PAH:5053 SO:0001583&missense_variant 1 5030849 15 30 50.0 -12 109596515 G A A missense_variant MODERATE MVK 4598 Transcript NM_000431.4 protein_coding 11/11 1223 1129 377 V/I Gtc/Atc 1 EntrezGene G G 0.22 0.12 15.11 1.404695 15.11 0.98086582992945504 1.433153 -0.366718995224718 0.25996 1.354331 -0.413182622011983 0.25007 -3.42&-3.42&-3.42&.&. 3.17 0.999790733823998 0.000004 0.08625 0.5804 0.0709 .&2.035&.&.&2.035 0.999863&0.999965&0.999965&0.999887&0.999863&0.999847 -0.86&-0.56&-0.53&.&. 7.5391 0.21&0.546&0.157&0.179&0.662 0.93189 0.634777 1.000000 0.010000 5.077000 0.222000 4.72 rs28934897 219 143294 1.52833e-03 4.04319e-04 6.16577e-04 1.55119e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.31830e-03 1.52284e-03 1.16219e-03 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.63832e-03 1.43212e-03 7.98085e-04 1.63153e-03 1.41133e-03 2.53980e-03 2.51377e-03 2.57561e-03 9.29368e-04 1.81818e-03 0.00000e+00 1.52752e-03 0.00000e+00 0.00000e+00 0.00000e+00 11929 0.00141 26968 Porokeratosis_3&_disseminated_superficial_actinic_type&Hyperimmunoglobulin_D_with_periodic_fever&Mevalonic_aciduria¬_specified&MVK-Related_Disorders¬_provided MONDO:MONDO:0008293&MedGen:C1867981&OMIM:175900&MONDO:MONDO:0009849&MedGen:C0398691&OMIM:260920&Orphanet:ORPHA343&SNOMED_CT:234538002&MedGen:C1959626&OMIM:610377&MedGen:CN169374&MedGen:CN239294&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(16)&Uncertain_significance(2) single_nucleotide_variant MVK:4598 SO:0001583&missense_variant 9 28934897 15 30 50.0 -12 109596515 G A A missense_variant MODERATE MVK 4598 Transcript NM_001114185.3 protein_coding 11/11 1214 1129 377 V/I Gtc/Atc 1 EntrezGene G G 0.22 0.12 15.11 1.404695 15.11 0.98086582992945504 1.433153 -0.366718995224718 0.25996 1.354331 -0.413182622011983 0.25007 -3.42&-3.42&-3.42&.&. 3.17 0.999790733823998 0.000004 0.08625 0.5804 0.0709 .&2.035&.&.&2.035 0.999863&0.999965&0.999965&0.999887&0.999863&0.999847 -0.86&-0.56&-0.53&.&. 7.5391 0.21&0.546&0.157&0.179&0.662 0.93189 0.634777 1.000000 0.010000 5.077000 0.222000 4.72 rs28934897 219 143294 1.52833e-03 4.04319e-04 6.16577e-04 1.55119e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.31830e-03 1.52284e-03 1.16219e-03 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.63832e-03 1.43212e-03 7.98085e-04 1.63153e-03 1.41133e-03 2.53980e-03 2.51377e-03 2.57561e-03 9.29368e-04 1.81818e-03 0.00000e+00 1.52752e-03 0.00000e+00 0.00000e+00 0.00000e+00 11929 0.00141 26968 Porokeratosis_3&_disseminated_superficial_actinic_type&Hyperimmunoglobulin_D_with_periodic_fever&Mevalonic_aciduria¬_specified&MVK-Related_Disorders¬_provided MONDO:MONDO:0008293&MedGen:C1867981&OMIM:175900&MONDO:MONDO:0009849&MedGen:C0398691&OMIM:260920&Orphanet:ORPHA343&SNOMED_CT:234538002&MedGen:C1959626&OMIM:610377&MedGen:CN169374&MedGen:CN239294&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(16)&Uncertain_significance(2) single_nucleotide_variant MVK:4598 SO:0001583&missense_variant 9 28934897 15 30 50.0 -12 109596515 G A A missense_variant MODERATE MVK 4598 Transcript NM_001301182.2 protein_coding 10/10 1067 973 325 V/I Gtc/Atc 1 EntrezGene G G 0.13 0.132 15.11 1.404695 15.11 0.98086582992945504 1.433153 -0.366718995224718 0.25996 1.354331 -0.413182622011983 0.25007 -3.42&-3.42&-3.42&.&. 3.17 0.999790733823998 0.000004 0.08625 0.5804 0.0709 .&2.035&.&.&2.035 0.999863&0.999965&0.999965&0.999887&0.999863&0.999847 -0.86&-0.56&-0.53&.&. 7.5391 0.21&0.546&0.157&0.179&0.662 0.93189 0.634777 1.000000 0.010000 5.077000 0.222000 4.72 rs28934897 219 143294 1.52833e-03 4.04319e-04 6.16577e-04 1.55119e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.31830e-03 1.52284e-03 1.16219e-03 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.63832e-03 1.43212e-03 7.98085e-04 1.63153e-03 1.41133e-03 2.53980e-03 2.51377e-03 2.57561e-03 9.29368e-04 1.81818e-03 0.00000e+00 1.52752e-03 0.00000e+00 0.00000e+00 0.00000e+00 11929 0.00141 26968 Porokeratosis_3&_disseminated_superficial_actinic_type&Hyperimmunoglobulin_D_with_periodic_fever&Mevalonic_aciduria¬_specified&MVK-Related_Disorders¬_provided MONDO:MONDO:0008293&MedGen:C1867981&OMIM:175900&MONDO:MONDO:0009849&MedGen:C0398691&OMIM:260920&Orphanet:ORPHA343&SNOMED_CT:234538002&MedGen:C1959626&OMIM:610377&MedGen:CN169374&MedGen:CN239294&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(16)&Uncertain_significance(2) single_nucleotide_variant MVK:4598 SO:0001583&missense_variant 9 28934897 15 30 50.0 -12 109596515 G A A missense_variant MODERATE MVK 4598 Transcript XM_017019313.2 protein_coding 10/10 1061 973 325 V/I Gtc/Atc 1 EntrezGene G G 0.13 0.132 15.11 1.404695 15.11 0.98086582992945504 1.433153 -0.366718995224718 0.25996 1.354331 -0.413182622011983 0.25007 -3.42&-3.42&-3.42&.&. 3.17 0.999790733823998 0.000004 0.08625 0.5804 0.0709 .&2.035&.&.&2.035 0.999863&0.999965&0.999965&0.999887&0.999863&0.999847 -0.86&-0.56&-0.53&.&. 7.5391 0.21&0.546&0.157&0.179&0.662 0.93189 0.634777 1.000000 0.010000 5.077000 0.222000 4.72 rs28934897 219 143294 1.52833e-03 4.04319e-04 6.16577e-04 1.55119e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.31830e-03 1.52284e-03 1.16219e-03 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.63832e-03 1.43212e-03 7.98085e-04 1.63153e-03 1.41133e-03 2.53980e-03 2.51377e-03 2.57561e-03 9.29368e-04 1.81818e-03 0.00000e+00 1.52752e-03 0.00000e+00 0.00000e+00 0.00000e+00 11929 0.00141 26968 Porokeratosis_3&_disseminated_superficial_actinic_type&Hyperimmunoglobulin_D_with_periodic_fever&Mevalonic_aciduria¬_specified&MVK-Related_Disorders¬_provided MONDO:MONDO:0008293&MedGen:C1867981&OMIM:175900&MONDO:MONDO:0009849&MedGen:C0398691&OMIM:260920&Orphanet:ORPHA343&SNOMED_CT:234538002&MedGen:C1959626&OMIM:610377&MedGen:CN169374&MedGen:CN239294&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(16)&Uncertain_significance(2) single_nucleotide_variant MVK:4598 SO:0001583&missense_variant 9 28934897 15 30 50.0 -12 109596515 G A A missense_variant MODERATE MVK 4598 Transcript XM_017019314.1 protein_coding 11/11 1788 1129 377 V/I Gtc/Atc 1 EntrezGene G G 0.22 0.12 15.11 1.404695 15.11 0.98086582992945504 1.433153 -0.366718995224718 0.25996 1.354331 -0.413182622011983 0.25007 -3.42&-3.42&-3.42&.&. 3.17 0.999790733823998 0.000004 0.08625 0.5804 0.0709 .&2.035&.&.&2.035 0.999863&0.999965&0.999965&0.999887&0.999863&0.999847 -0.86&-0.56&-0.53&.&. 7.5391 0.21&0.546&0.157&0.179&0.662 0.93189 0.634777 1.000000 0.010000 5.077000 0.222000 4.72 rs28934897 219 143294 1.52833e-03 4.04319e-04 6.16577e-04 1.55119e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.31830e-03 1.52284e-03 1.16219e-03 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.63832e-03 1.43212e-03 7.98085e-04 1.63153e-03 1.41133e-03 2.53980e-03 2.51377e-03 2.57561e-03 9.29368e-04 1.81818e-03 0.00000e+00 1.52752e-03 0.00000e+00 0.00000e+00 0.00000e+00 11929 0.00141 26968 Porokeratosis_3&_disseminated_superficial_actinic_type&Hyperimmunoglobulin_D_with_periodic_fever&Mevalonic_aciduria¬_specified&MVK-Related_Disorders¬_provided MONDO:MONDO:0008293&MedGen:C1867981&OMIM:175900&MONDO:MONDO:0009849&MedGen:C0398691&OMIM:260920&Orphanet:ORPHA343&SNOMED_CT:234538002&MedGen:C1959626&OMIM:610377&MedGen:CN169374&MedGen:CN239294&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(16)&Uncertain_significance(2) single_nucleotide_variant MVK:4598 SO:0001583&missense_variant 9 28934897 15 30 50.0 -12 109596515 G A A downstream_gene_variant MODIFIER MVK 4598 Transcript XM_024448982.1 protein_coding 4435 1 EntrezGene G G 15.11 1.404695 4.72 rs28934897 219 143294 1.52833e-03 4.04319e-04 6.16577e-04 1.55119e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.31830e-03 1.52284e-03 1.16219e-03 9.03070e-04 0.00000e+00 1.92061e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.63832e-03 1.43212e-03 7.98085e-04 1.63153e-03 1.41133e-03 2.53980e-03 2.51377e-03 2.57561e-03 9.29368e-04 1.81818e-03 0.00000e+00 1.52752e-03 0.00000e+00 0.00000e+00 0.00000e+00 11929 0.00141 26968 Porokeratosis_3&_disseminated_superficial_actinic_type&Hyperimmunoglobulin_D_with_periodic_fever&Mevalonic_aciduria¬_specified&MVK-Related_Disorders¬_provided MONDO:MONDO:0008293&MedGen:C1867981&OMIM:175900&MONDO:MONDO:0009849&MedGen:C0398691&OMIM:260920&Orphanet:ORPHA343&SNOMED_CT:234538002&MedGen:C1959626&OMIM:610377&MedGen:CN169374&MedGen:CN239294&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(16)&Uncertain_significance(2) single_nucleotide_variant MVK:4598 SO:0001583&missense_variant 9 28934897 15 30 50.0 -13 20189546 AC A - frameshift_variant HIGH GJB2 2706 Transcript NM_004004.6 protein_coding 2/2 213 35 12 G/X gGt/gt -1 EntrezGene C C 25.0 3.600222 6.4 rs80338939 919 142874 6.43224e-03 1.38630e-03 1.37192e-03 1.40318e-03 2.22222e-03 4.25532e-03 0.00000e+00 5.94452e-03 6.61914e-03 5.43057e-03 4.21433e-03 5.68182e-03 2.56082e-03 0.00000e+00 0.00000e+00 0.00000e+00 6.68006e-03 9.69476e-03 1.07914e-02 9.34816e-03 6.16856e-03 9.94014e-03 9.96251e-03 9.90938e-03 9.35454e-03 1.01103e-02 8.57143e-03 6.41554e-03 6.61813e-04 0.00000e+00 8.12348e-04 17004 32043 Autosomal_recessive_deafness_type_1A&Hearing_impairment&Bilateral_conductive_hearing_impairment&Bilateral_sensorineural_hearing_impairment&Severe_sensorineural_hearing_impairment&Mutilating_keratoderma&Keratitis-ichthyosis-deafness_syndrome&_autosomal_dominant&Palmoplantar_keratoderma-deafness_syndrome&Knuckle_pads&_deafness_AND_leukonychia_syndrome&Deafness&_autosomal_recessive_1A&Deafness&_autosomal_dominant_3a&Hystrix-like_ichthyosis_with_deafness&Deafness&_autosomal_recessive_1b&Nonsyndromic_hearing_loss_and_deafness&Deafness&_autosomal_recessive&Inborn_genetic_diseases&Deafness&Deafness&_digenic&_GJB2/GJB6&Hearing_loss¬_specified&Nonsyndromic_Hearing_Loss&_Recessive¬_provided&Rare_genetic_deafness .&Human_Phenotype_Ontology:HP:0000365&Human_Phenotype_Ontology:HP:0000404&Human_Phenotype_Ontology:HP:0001728&Human_Phenotype_Ontology:HP:0001729&Human_Phenotype_Ontology:HP:0001754&Human_Phenotype_Ontology:HP:0008560&Human_Phenotype_Ontology:HP:0008563&MONDO:MONDO:0005365&MedGen:C1384666&Human_Phenotype_Ontology:HP:0008513&Human_Phenotype_Ontology:HP:0008536&MedGen:C0452136&Human_Phenotype_Ontology:HP:0008530&Human_Phenotype_Ontology:HP:0008539&Human_Phenotype_Ontology:HP:0008579&Human_Phenotype_Ontology:HP:0008585&Human_Phenotype_Ontology:HP:0008619&MedGen:C0452138&Human_Phenotype_Ontology:HP:0008534&Human_Phenotype_Ontology:HP:0008574&Human_Phenotype_Ontology:HP:0008625&MedGen:C4021533&MONDO:MONDO:0007422&MedGen:C0265964&OMIM:124500&Orphanet:ORPHA494&SNOMED_CT:24559001&MONDO:MONDO:0007850&MedGen:C0265336&OMIM:148210&MONDO:MONDO:0007852&MedGen:C1835672&OMIM:148350&Orphanet:ORPHA2202&MONDO:MONDO:0007866&MedGen:C0266004&OMIM:149200&Orphanet:ORPHA2698&SNOMED_CT:1271009&MONDO:MONDO:0009076&MedGen:C2673759&OMIM:220290&MONDO:MONDO:0011103&MedGen:C2675750&OMIM:601544&MONDO:MONDO:0011245&MedGen:C1865234&OMIM:602540&MONDO:MONDO:0012977&MedGen:C2675235&OMIM:612645&MONDO:MONDO:0019497&MedGen:CN043648&Orphanet:ORPHA87884&MONDO:MONDO:0019588&MedGen:C1846647&OMIM:607197&OMIM:PS220290&Orphanet:ORPHA90636&MeSH:D030342&MedGen:C0950123&MedGen:C0011053&MedGen:C2673760&MedGen:C3887873&MedGen:CN169374&MedGen:CN239439&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 reviewed_by_expert_panel Pathogenic Deletion GJB2:2706 21 80338939 15 30 50.0 -13 20189546 AC A - frameshift_variant HIGH GJB2 2706 Transcript XM_011535049.2 protein_coding 2/2 241 35 12 G/X gGt/gt -1 EntrezGene C C 25.0 3.600222 6.4 rs80338939 919 142874 6.43224e-03 1.38630e-03 1.37192e-03 1.40318e-03 2.22222e-03 4.25532e-03 0.00000e+00 5.94452e-03 6.61914e-03 5.43057e-03 4.21433e-03 5.68182e-03 2.56082e-03 0.00000e+00 0.00000e+00 0.00000e+00 6.68006e-03 9.69476e-03 1.07914e-02 9.34816e-03 6.16856e-03 9.94014e-03 9.96251e-03 9.90938e-03 9.35454e-03 1.01103e-02 8.57143e-03 6.41554e-03 6.61813e-04 0.00000e+00 8.12348e-04 17004 32043 Autosomal_recessive_deafness_type_1A&Hearing_impairment&Bilateral_conductive_hearing_impairment&Bilateral_sensorineural_hearing_impairment&Severe_sensorineural_hearing_impairment&Mutilating_keratoderma&Keratitis-ichthyosis-deafness_syndrome&_autosomal_dominant&Palmoplantar_keratoderma-deafness_syndrome&Knuckle_pads&_deafness_AND_leukonychia_syndrome&Deafness&_autosomal_recessive_1A&Deafness&_autosomal_dominant_3a&Hystrix-like_ichthyosis_with_deafness&Deafness&_autosomal_recessive_1b&Nonsyndromic_hearing_loss_and_deafness&Deafness&_autosomal_recessive&Inborn_genetic_diseases&Deafness&Deafness&_digenic&_GJB2/GJB6&Hearing_loss¬_specified&Nonsyndromic_Hearing_Loss&_Recessive¬_provided&Rare_genetic_deafness .&Human_Phenotype_Ontology:HP:0000365&Human_Phenotype_Ontology:HP:0000404&Human_Phenotype_Ontology:HP:0001728&Human_Phenotype_Ontology:HP:0001729&Human_Phenotype_Ontology:HP:0001754&Human_Phenotype_Ontology:HP:0008560&Human_Phenotype_Ontology:HP:0008563&MONDO:MONDO:0005365&MedGen:C1384666&Human_Phenotype_Ontology:HP:0008513&Human_Phenotype_Ontology:HP:0008536&MedGen:C0452136&Human_Phenotype_Ontology:HP:0008530&Human_Phenotype_Ontology:HP:0008539&Human_Phenotype_Ontology:HP:0008579&Human_Phenotype_Ontology:HP:0008585&Human_Phenotype_Ontology:HP:0008619&MedGen:C0452138&Human_Phenotype_Ontology:HP:0008534&Human_Phenotype_Ontology:HP:0008574&Human_Phenotype_Ontology:HP:0008625&MedGen:C4021533&MONDO:MONDO:0007422&MedGen:C0265964&OMIM:124500&Orphanet:ORPHA494&SNOMED_CT:24559001&MONDO:MONDO:0007850&MedGen:C0265336&OMIM:148210&MONDO:MONDO:0007852&MedGen:C1835672&OMIM:148350&Orphanet:ORPHA2202&MONDO:MONDO:0007866&MedGen:C0266004&OMIM:149200&Orphanet:ORPHA2698&SNOMED_CT:1271009&MONDO:MONDO:0009076&MedGen:C2673759&OMIM:220290&MONDO:MONDO:0011103&MedGen:C2675750&OMIM:601544&MONDO:MONDO:0011245&MedGen:C1865234&OMIM:602540&MONDO:MONDO:0012977&MedGen:C2675235&OMIM:612645&MONDO:MONDO:0019497&MedGen:CN043648&Orphanet:ORPHA87884&MONDO:MONDO:0019588&MedGen:C1846647&OMIM:607197&OMIM:PS220290&Orphanet:ORPHA90636&MeSH:D030342&MedGen:C0950123&MedGen:C0011053&MedGen:C2673760&MedGen:C3887873&MedGen:CN169374&MedGen:CN239439&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 reviewed_by_expert_panel Pathogenic Deletion GJB2:2706 21 80338939 15 30 50.0 -13 20189546 AC A - upstream_gene_variant MODIFIER LOC107984553 107984553 Transcript XR_001749956.1 lncRNA 3356 -1 EntrezGene C C 25.0 3.600222 6.4 rs80338939 919 142874 6.43224e-03 1.38630e-03 1.37192e-03 1.40318e-03 2.22222e-03 4.25532e-03 0.00000e+00 5.94452e-03 6.61914e-03 5.43057e-03 4.21433e-03 5.68182e-03 2.56082e-03 0.00000e+00 0.00000e+00 0.00000e+00 6.68006e-03 9.69476e-03 1.07914e-02 9.34816e-03 6.16856e-03 9.94014e-03 9.96251e-03 9.90938e-03 9.35454e-03 1.01103e-02 8.57143e-03 6.41554e-03 6.61813e-04 0.00000e+00 8.12348e-04 17004 32043 Autosomal_recessive_deafness_type_1A&Hearing_impairment&Bilateral_conductive_hearing_impairment&Bilateral_sensorineural_hearing_impairment&Severe_sensorineural_hearing_impairment&Mutilating_keratoderma&Keratitis-ichthyosis-deafness_syndrome&_autosomal_dominant&Palmoplantar_keratoderma-deafness_syndrome&Knuckle_pads&_deafness_AND_leukonychia_syndrome&Deafness&_autosomal_recessive_1A&Deafness&_autosomal_dominant_3a&Hystrix-like_ichthyosis_with_deafness&Deafness&_autosomal_recessive_1b&Nonsyndromic_hearing_loss_and_deafness&Deafness&_autosomal_recessive&Inborn_genetic_diseases&Deafness&Deafness&_digenic&_GJB2/GJB6&Hearing_loss¬_specified&Nonsyndromic_Hearing_Loss&_Recessive¬_provided&Rare_genetic_deafness .&Human_Phenotype_Ontology:HP:0000365&Human_Phenotype_Ontology:HP:0000404&Human_Phenotype_Ontology:HP:0001728&Human_Phenotype_Ontology:HP:0001729&Human_Phenotype_Ontology:HP:0001754&Human_Phenotype_Ontology:HP:0008560&Human_Phenotype_Ontology:HP:0008563&MONDO:MONDO:0005365&MedGen:C1384666&Human_Phenotype_Ontology:HP:0008513&Human_Phenotype_Ontology:HP:0008536&MedGen:C0452136&Human_Phenotype_Ontology:HP:0008530&Human_Phenotype_Ontology:HP:0008539&Human_Phenotype_Ontology:HP:0008579&Human_Phenotype_Ontology:HP:0008585&Human_Phenotype_Ontology:HP:0008619&MedGen:C0452138&Human_Phenotype_Ontology:HP:0008534&Human_Phenotype_Ontology:HP:0008574&Human_Phenotype_Ontology:HP:0008625&MedGen:C4021533&MONDO:MONDO:0007422&MedGen:C0265964&OMIM:124500&Orphanet:ORPHA494&SNOMED_CT:24559001&MONDO:MONDO:0007850&MedGen:C0265336&OMIM:148210&MONDO:MONDO:0007852&MedGen:C1835672&OMIM:148350&Orphanet:ORPHA2202&MONDO:MONDO:0007866&MedGen:C0266004&OMIM:149200&Orphanet:ORPHA2698&SNOMED_CT:1271009&MONDO:MONDO:0009076&MedGen:C2673759&OMIM:220290&MONDO:MONDO:0011103&MedGen:C2675750&OMIM:601544&MONDO:MONDO:0011245&MedGen:C1865234&OMIM:602540&MONDO:MONDO:0012977&MedGen:C2675235&OMIM:612645&MONDO:MONDO:0019497&MedGen:CN043648&Orphanet:ORPHA87884&MONDO:MONDO:0019588&MedGen:C1846647&OMIM:607197&OMIM:PS220290&Orphanet:ORPHA90636&MeSH:D030342&MedGen:C0950123&MedGen:C0011053&MedGen:C2673760&MedGen:C3887873&MedGen:CN169374&MedGen:CN239439&MedGen:CN517202&MedGen:CN826980&Orphanet:ORPHA96210 reviewed_by_expert_panel Pathogenic Deletion GJB2:2706 21 80338939 15 30 50.0 -13 24246861 CAA C - intron_variant MODIFIER SPATA13 221178 Transcript NM_001166271.3 protein_coding 2/12 1 EntrezGene AA AA 0.644 -0.152658 0.126&0.065 chr13:24246862-24246864 0 133270 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.69527e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -13 24246861 CAA C - intron_variant MODIFIER SPATA13 221178 Transcript NM_001286792.2 protein_coding 4/14 1 EntrezGene AA AA 0.644 -0.152658 0.126&0.065 chr13:24246862-24246864 0 133270 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.69527e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -13 24246861 CAA C - intron_variant MODIFIER SPATA13 221178 Transcript NM_153023.4 protein_coding 1/11 1 EntrezGene AA AA 0.644 -0.152658 0.126&0.065 chr13:24246862-24246864 0 133270 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.69527e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -13 36329415 GT G - frameshift_variant HIGH SPART 23111 Transcript NM_001142294.1 protein_coding 4/9 1204 1110 370 K/X aaA/aa -1 EntrezGene T T OK 24.2 3.346285 2.1 rs1060499524 2 143260 1.39606e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35439e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44038e-05 1.54880e-05 0.00000e+00 3.67945e-05 4.65116e-04 9.10747e-04 0.00000e+00 1.39509e-05 0.00000e+00 0.00000e+00 0.00000e+00 3457 18496 Troyer_syndrome¬_provided MONDO:MONDO:0010156&MedGen:C0393559&OMIM:275900&Orphanet:ORPHA101000&SNOMED_CT:230264003&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SPART:23111 SO:0001589&frameshift_variant 1 1060499524 15 30 50.0 -13 36329415 GT G - frameshift_variant HIGH SPART 23111 Transcript NM_001142295.1 protein_coding 4/9 1328 1110 370 K/X aaA/aa -1 EntrezGene T T OK 24.2 3.346285 2.1 rs1060499524 2 143260 1.39606e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35439e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44038e-05 1.54880e-05 0.00000e+00 3.67945e-05 4.65116e-04 9.10747e-04 0.00000e+00 1.39509e-05 0.00000e+00 0.00000e+00 0.00000e+00 3457 18496 Troyer_syndrome¬_provided MONDO:MONDO:0010156&MedGen:C0393559&OMIM:275900&Orphanet:ORPHA101000&SNOMED_CT:230264003&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SPART:23111 SO:0001589&frameshift_variant 1 1060499524 15 30 50.0 -13 36329415 GT G - frameshift_variant HIGH SPART 23111 Transcript NM_001142296.2 protein_coding 5/10 1265 1110 370 K/X aaA/aa -1 EntrezGene T T 24.2 3.346285 2.1 rs1060499524 2 143260 1.39606e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35439e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44038e-05 1.54880e-05 0.00000e+00 3.67945e-05 4.65116e-04 9.10747e-04 0.00000e+00 1.39509e-05 0.00000e+00 0.00000e+00 0.00000e+00 3457 18496 Troyer_syndrome¬_provided MONDO:MONDO:0010156&MedGen:C0393559&OMIM:275900&Orphanet:ORPHA101000&SNOMED_CT:230264003&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SPART:23111 SO:0001589&frameshift_variant 1 1060499524 15 30 50.0 -13 36329415 GT G - frameshift_variant HIGH SPART 23111 Transcript NM_015087.5 protein_coding 4/9 1282 1110 370 K/X aaA/aa -1 EntrezGene T T 24.2 3.346285 2.1 rs1060499524 2 143260 1.39606e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35439e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44038e-05 1.54880e-05 0.00000e+00 3.67945e-05 4.65116e-04 9.10747e-04 0.00000e+00 1.39509e-05 0.00000e+00 0.00000e+00 0.00000e+00 3457 18496 Troyer_syndrome¬_provided MONDO:MONDO:0010156&MedGen:C0393559&OMIM:275900&Orphanet:ORPHA101000&SNOMED_CT:230264003&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SPART:23111 SO:0001589&frameshift_variant 1 1060499524 15 30 50.0 -13 36329415 GT G - frameshift_variant HIGH SPART 23111 Transcript XM_005266313.5 protein_coding 4/9 1267 1110 370 K/X aaA/aa -1 EntrezGene T T 24.2 3.346285 2.1 rs1060499524 2 143260 1.39606e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35439e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44038e-05 1.54880e-05 0.00000e+00 3.67945e-05 4.65116e-04 9.10747e-04 0.00000e+00 1.39509e-05 0.00000e+00 0.00000e+00 0.00000e+00 3457 18496 Troyer_syndrome¬_provided MONDO:MONDO:0010156&MedGen:C0393559&OMIM:275900&Orphanet:ORPHA101000&SNOMED_CT:230264003&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SPART:23111 SO:0001589&frameshift_variant 1 1060499524 15 30 50.0 -13 36329415 GT G - frameshift_variant HIGH SPART 23111 Transcript XM_005266314.3 protein_coding 5/10 1371 1110 370 K/X aaA/aa -1 EntrezGene T T 24.2 3.346285 2.1 rs1060499524 2 143260 1.39606e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35439e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44038e-05 1.54880e-05 0.00000e+00 3.67945e-05 4.65116e-04 9.10747e-04 0.00000e+00 1.39509e-05 0.00000e+00 0.00000e+00 0.00000e+00 3457 18496 Troyer_syndrome¬_provided MONDO:MONDO:0010156&MedGen:C0393559&OMIM:275900&Orphanet:ORPHA101000&SNOMED_CT:230264003&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SPART:23111 SO:0001589&frameshift_variant 1 1060499524 15 30 50.0 -13 36329415 GT G - frameshift_variant HIGH SPART 23111 Transcript XM_005266315.3 protein_coding 5/10 1225 1110 370 K/X aaA/aa -1 EntrezGene T T 24.2 3.346285 2.1 rs1060499524 2 143260 1.39606e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35439e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44038e-05 1.54880e-05 0.00000e+00 3.67945e-05 4.65116e-04 9.10747e-04 0.00000e+00 1.39509e-05 0.00000e+00 0.00000e+00 0.00000e+00 3457 18496 Troyer_syndrome¬_provided MONDO:MONDO:0010156&MedGen:C0393559&OMIM:275900&Orphanet:ORPHA101000&SNOMED_CT:230264003&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SPART:23111 SO:0001589&frameshift_variant 1 1060499524 15 30 50.0 -13 36329415 GT G - frameshift_variant HIGH SPART 23111 Transcript XM_005266317.3 protein_coding 4/9 1155 1110 370 K/X aaA/aa -1 EntrezGene T T 24.2 3.346285 2.1 rs1060499524 2 143260 1.39606e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35439e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44038e-05 1.54880e-05 0.00000e+00 3.67945e-05 4.65116e-04 9.10747e-04 0.00000e+00 1.39509e-05 0.00000e+00 0.00000e+00 0.00000e+00 3457 18496 Troyer_syndrome¬_provided MONDO:MONDO:0010156&MedGen:C0393559&OMIM:275900&Orphanet:ORPHA101000&SNOMED_CT:230264003&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SPART:23111 SO:0001589&frameshift_variant 1 1060499524 15 30 50.0 -13 36329415 GT G - frameshift_variant HIGH SPART 23111 Transcript XM_011535012.2 protein_coding 4/9 1308 1110 370 K/X aaA/aa -1 EntrezGene T T 24.2 3.346285 2.1 rs1060499524 2 143260 1.39606e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35439e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44038e-05 1.54880e-05 0.00000e+00 3.67945e-05 4.65116e-04 9.10747e-04 0.00000e+00 1.39509e-05 0.00000e+00 0.00000e+00 0.00000e+00 3457 18496 Troyer_syndrome¬_provided MONDO:MONDO:0010156&MedGen:C0393559&OMIM:275900&Orphanet:ORPHA101000&SNOMED_CT:230264003&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SPART:23111 SO:0001589&frameshift_variant 1 1060499524 15 30 50.0 -13 36329415 GT G - frameshift_variant HIGH SPART 23111 Transcript XM_024449334.1 protein_coding 5/10 1522 1110 370 K/X aaA/aa -1 EntrezGene T T 24.2 3.346285 2.1 rs1060499524 2 143260 1.39606e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35439e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44038e-05 1.54880e-05 0.00000e+00 3.67945e-05 4.65116e-04 9.10747e-04 0.00000e+00 1.39509e-05 0.00000e+00 0.00000e+00 0.00000e+00 3457 18496 Troyer_syndrome¬_provided MONDO:MONDO:0010156&MedGen:C0393559&OMIM:275900&Orphanet:ORPHA101000&SNOMED_CT:230264003&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SPART:23111 SO:0001589&frameshift_variant 1 1060499524 15 30 50.0 -13 36329415 GT G - non_coding_transcript_exon_variant MODIFIER SPART 23111 Transcript XR_001749523.2 misc_RNA 4/10 1338 -1 EntrezGene T T 24.2 3.346285 2.1 rs1060499524 2 143260 1.39606e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35439e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44038e-05 1.54880e-05 0.00000e+00 3.67945e-05 4.65116e-04 9.10747e-04 0.00000e+00 1.39509e-05 0.00000e+00 0.00000e+00 0.00000e+00 3457 18496 Troyer_syndrome¬_provided MONDO:MONDO:0010156&MedGen:C0393559&OMIM:275900&Orphanet:ORPHA101000&SNOMED_CT:230264003&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SPART:23111 SO:0001589&frameshift_variant 1 1060499524 15 30 50.0 -13 38151605 CTG C - intergenic_variant MODIFIER 1.041 -0.052354 -0.541&0.271 rs145099021 826 143250 5.76614e-03 1.66492e-03 1.85006e-03 1.44763e-03 2.55556e-02 2.12766e-02 3.02326e-02 6.30406e-03 5.41455e-03 6.98396e-03 3.91331e-03 2.27015e-03 5.76923e-03 0.00000e+00 0.00000e+00 0.00000e+00 6.41928e-03 3.92119e-03 6.80000e-03 3.01659e-03 5.07132e-03 9.06162e-03 9.78976e-03 8.05977e-03 2.32126e-03 2.72727e-03 1.89753e-03 5.77567e-03 9.86193e-04 0.00000e+00 1.21065e-03 15 30 50.0 -13 108209518 ATCTTT A - frameshift_variant HIGH LIG4 3981 Transcript NM_001098268.2 protein_coding 2/2 1962-1966 1746-1750 582-584 EKI/DX gaAAAGAta/gata -1 EntrezGene TCTTT TCTTT 32 4.754795 6.35&3.78&0.0783&5.17&-12.7 rs759829934 3 143250 2.09424e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44042e-05 4.64540e-05 5.34845e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.09284e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -13 108209518 ATCTTT A - frameshift_variant HIGH LIG4 3981 Transcript NM_001330595.1 protein_coding 3/3 1818-1822 1545-1549 515-517 EKI/DX gaAAAGAta/gata -1 EntrezGene TCTTT TCTTT OK 32 4.754795 6.35&3.78&0.0783&5.17&-12.7 rs759829934 3 143250 2.09424e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44042e-05 4.64540e-05 5.34845e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.09284e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -13 108209518 ATCTTT A - frameshift_variant HIGH LIG4 3981 Transcript NM_001352598.2 protein_coding 4/4 2379-2383 1746-1750 582-584 EKI/DX gaAAAGAta/gata -1 EntrezGene TCTTT TCTTT 32 4.754795 6.35&3.78&0.0783&5.17&-12.7 rs759829934 3 143250 2.09424e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44042e-05 4.64540e-05 5.34845e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.09284e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -13 108209518 ATCTTT A - frameshift_variant HIGH LIG4 3981 Transcript NM_001352599.2 protein_coding 4/4 2403-2407 1746-1750 582-584 EKI/DX gaAAAGAta/gata -1 EntrezGene TCTTT TCTTT 32 4.754795 6.35&3.78&0.0783&5.17&-12.7 rs759829934 3 143250 2.09424e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44042e-05 4.64540e-05 5.34845e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.09284e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -13 108209518 ATCTTT A - frameshift_variant HIGH LIG4 3981 Transcript NM_001352600.2 protein_coding 4/4 2295-2299 1746-1750 582-584 EKI/DX gaAAAGAta/gata -1 EntrezGene TCTTT TCTTT 32 4.754795 6.35&3.78&0.0783&5.17&-12.7 rs759829934 3 143250 2.09424e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44042e-05 4.64540e-05 5.34845e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.09284e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -13 108209518 ATCTTT A - frameshift_variant HIGH LIG4 3981 Transcript NM_001352601.2 protein_coding 3/3 2004-2008 1746-1750 582-584 EKI/DX gaAAAGAta/gata -1 EntrezGene TCTTT TCTTT 32 4.754795 6.35&3.78&0.0783&5.17&-12.7 rs759829934 3 143250 2.09424e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44042e-05 4.64540e-05 5.34845e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.09284e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -13 108209518 ATCTTT A - frameshift_variant HIGH LIG4 3981 Transcript NM_001352602.2 protein_coding 3/3 1896-1900 1746-1750 582-584 EKI/DX gaAAAGAta/gata -1 EntrezGene TCTTT TCTTT 32 4.754795 6.35&3.78&0.0783&5.17&-12.7 rs759829934 3 143250 2.09424e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44042e-05 4.64540e-05 5.34845e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.09284e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -13 108209518 ATCTTT A - frameshift_variant HIGH LIG4 3981 Transcript NM_001352603.1 protein_coding 3/3 1874-1878 1746-1750 582-584 EKI/DX gaAAAGAta/gata -1 EntrezGene TCTTT TCTTT 32 4.754795 6.35&3.78&0.0783&5.17&-12.7 rs759829934 3 143250 2.09424e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44042e-05 4.64540e-05 5.34845e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.09284e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -13 108209518 ATCTTT A - frameshift_variant HIGH LIG4 3981 Transcript NM_001352604.1 protein_coding 3/3 1953-1957 1782-1786 594-596 EKI/DX gaAAAGAta/gata -1 EntrezGene TCTTT TCTTT 32 4.754795 6.35&3.78&0.0783&5.17&-12.7 rs759829934 3 143250 2.09424e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44042e-05 4.64540e-05 5.34845e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.09284e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -13 108209518 ATCTTT A - frameshift_variant HIGH LIG4 3981 Transcript NM_001379095.1 protein_coding 3/3 2097-2101 1746-1750 582-584 EKI/DX gaAAAGAta/gata -1 EntrezGene TCTTT TCTTT 32 4.754795 6.35&3.78&0.0783&5.17&-12.7 rs759829934 3 143250 2.09424e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44042e-05 4.64540e-05 5.34845e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.09284e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -13 108209518 ATCTTT A - frameshift_variant HIGH LIG4 3981 Transcript NM_002312.3 protein_coding 2/2 2019-2023 1746-1750 582-584 EKI/DX gaAAAGAta/gata -1 EntrezGene TCTTT TCTTT OK 32 4.754795 6.35&3.78&0.0783&5.17&-12.7 rs759829934 3 143250 2.09424e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44042e-05 4.64540e-05 5.34845e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.09284e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -13 108209518 ATCTTT A - frameshift_variant HIGH LIG4 3981 Transcript NM_206937.2 protein_coding 3/3 1900-1904 1746-1750 582-584 EKI/DX gaAAAGAta/gata -1 EntrezGene TCTTT TCTTT 32 4.754795 6.35&3.78&0.0783&5.17&-12.7 rs759829934 3 143250 2.09424e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70907e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44042e-05 4.64540e-05 5.34845e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.09284e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -13 108209993 CTCTTT C - frameshift_variant HIGH LIG4 3981 Transcript NM_001098268.2 protein_coding 2/2 1487-1491 1271-1275 424-425 KR/X aAAAGA/a -1 EntrezGene TCTTT TCTTT 29.7 4.317105 2.68&6.29&5.11&-2.71&6.29 rs772226399 37 143320 2.58164e-04 3.32984e-04 4.40490e-04 2.06804e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.19555e-04 3.38295e-04 1.28999e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.84391e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.30289e-04 2.78767e-04 2.14007e-04 3.67809e-04 4.64253e-04 9.09091e-04 0.00000e+00 2.58056e-04 3.27869e-04 0.00000e+00 4.02253e-04 279838 264505 Lig4_syndrome&LIG4-Related_Disorders¬_provided MONDO:MONDO:0011686&MedGen:C1847827&OMIM:606593&Orphanet:ORPHA99812&MedGen:CN239380&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion LIG4:3981 SO:0001589&frameshift_variant 13 772226399 15 30 50.0 -13 108209993 CTCTTT C - frameshift_variant HIGH LIG4 3981 Transcript NM_001330595.1 protein_coding 3/3 1343-1347 1070-1074 357-358 KR/X aAAAGA/a -1 EntrezGene TCTTT TCTTT OK 29.7 4.317105 2.68&6.29&5.11&-2.71&6.29 rs772226399 37 143320 2.58164e-04 3.32984e-04 4.40490e-04 2.06804e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.19555e-04 3.38295e-04 1.28999e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.84391e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.30289e-04 2.78767e-04 2.14007e-04 3.67809e-04 4.64253e-04 9.09091e-04 0.00000e+00 2.58056e-04 3.27869e-04 0.00000e+00 4.02253e-04 279838 264505 Lig4_syndrome&LIG4-Related_Disorders¬_provided MONDO:MONDO:0011686&MedGen:C1847827&OMIM:606593&Orphanet:ORPHA99812&MedGen:CN239380&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion LIG4:3981 SO:0001589&frameshift_variant 13 772226399 15 30 50.0 -13 108209993 CTCTTT C - frameshift_variant HIGH LIG4 3981 Transcript NM_001352598.2 protein_coding 4/4 1904-1908 1271-1275 424-425 KR/X aAAAGA/a -1 EntrezGene TCTTT TCTTT 29.7 4.317105 2.68&6.29&5.11&-2.71&6.29 rs772226399 37 143320 2.58164e-04 3.32984e-04 4.40490e-04 2.06804e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.19555e-04 3.38295e-04 1.28999e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.84391e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.30289e-04 2.78767e-04 2.14007e-04 3.67809e-04 4.64253e-04 9.09091e-04 0.00000e+00 2.58056e-04 3.27869e-04 0.00000e+00 4.02253e-04 279838 264505 Lig4_syndrome&LIG4-Related_Disorders¬_provided MONDO:MONDO:0011686&MedGen:C1847827&OMIM:606593&Orphanet:ORPHA99812&MedGen:CN239380&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion LIG4:3981 SO:0001589&frameshift_variant 13 772226399 15 30 50.0 -13 108209993 CTCTTT C - frameshift_variant HIGH LIG4 3981 Transcript NM_001352599.2 protein_coding 4/4 1928-1932 1271-1275 424-425 KR/X aAAAGA/a -1 EntrezGene TCTTT TCTTT 29.7 4.317105 2.68&6.29&5.11&-2.71&6.29 rs772226399 37 143320 2.58164e-04 3.32984e-04 4.40490e-04 2.06804e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.19555e-04 3.38295e-04 1.28999e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.84391e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.30289e-04 2.78767e-04 2.14007e-04 3.67809e-04 4.64253e-04 9.09091e-04 0.00000e+00 2.58056e-04 3.27869e-04 0.00000e+00 4.02253e-04 279838 264505 Lig4_syndrome&LIG4-Related_Disorders¬_provided MONDO:MONDO:0011686&MedGen:C1847827&OMIM:606593&Orphanet:ORPHA99812&MedGen:CN239380&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion LIG4:3981 SO:0001589&frameshift_variant 13 772226399 15 30 50.0 -13 108209993 CTCTTT C - frameshift_variant HIGH LIG4 3981 Transcript NM_001352600.2 protein_coding 4/4 1820-1824 1271-1275 424-425 KR/X aAAAGA/a -1 EntrezGene TCTTT TCTTT 29.7 4.317105 2.68&6.29&5.11&-2.71&6.29 rs772226399 37 143320 2.58164e-04 3.32984e-04 4.40490e-04 2.06804e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.19555e-04 3.38295e-04 1.28999e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.84391e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.30289e-04 2.78767e-04 2.14007e-04 3.67809e-04 4.64253e-04 9.09091e-04 0.00000e+00 2.58056e-04 3.27869e-04 0.00000e+00 4.02253e-04 279838 264505 Lig4_syndrome&LIG4-Related_Disorders¬_provided MONDO:MONDO:0011686&MedGen:C1847827&OMIM:606593&Orphanet:ORPHA99812&MedGen:CN239380&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion LIG4:3981 SO:0001589&frameshift_variant 13 772226399 15 30 50.0 -13 108209993 CTCTTT C - frameshift_variant HIGH LIG4 3981 Transcript NM_001352601.2 protein_coding 3/3 1529-1533 1271-1275 424-425 KR/X aAAAGA/a -1 EntrezGene TCTTT TCTTT 29.7 4.317105 2.68&6.29&5.11&-2.71&6.29 rs772226399 37 143320 2.58164e-04 3.32984e-04 4.40490e-04 2.06804e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.19555e-04 3.38295e-04 1.28999e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.84391e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.30289e-04 2.78767e-04 2.14007e-04 3.67809e-04 4.64253e-04 9.09091e-04 0.00000e+00 2.58056e-04 3.27869e-04 0.00000e+00 4.02253e-04 279838 264505 Lig4_syndrome&LIG4-Related_Disorders¬_provided MONDO:MONDO:0011686&MedGen:C1847827&OMIM:606593&Orphanet:ORPHA99812&MedGen:CN239380&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion LIG4:3981 SO:0001589&frameshift_variant 13 772226399 15 30 50.0 -13 108209993 CTCTTT C - frameshift_variant HIGH LIG4 3981 Transcript NM_001352602.2 protein_coding 3/3 1421-1425 1271-1275 424-425 KR/X aAAAGA/a -1 EntrezGene TCTTT TCTTT 29.7 4.317105 2.68&6.29&5.11&-2.71&6.29 rs772226399 37 143320 2.58164e-04 3.32984e-04 4.40490e-04 2.06804e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.19555e-04 3.38295e-04 1.28999e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.84391e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.30289e-04 2.78767e-04 2.14007e-04 3.67809e-04 4.64253e-04 9.09091e-04 0.00000e+00 2.58056e-04 3.27869e-04 0.00000e+00 4.02253e-04 279838 264505 Lig4_syndrome&LIG4-Related_Disorders¬_provided MONDO:MONDO:0011686&MedGen:C1847827&OMIM:606593&Orphanet:ORPHA99812&MedGen:CN239380&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion LIG4:3981 SO:0001589&frameshift_variant 13 772226399 15 30 50.0 -13 108209993 CTCTTT C - frameshift_variant HIGH LIG4 3981 Transcript NM_001352603.1 protein_coding 3/3 1399-1403 1271-1275 424-425 KR/X aAAAGA/a -1 EntrezGene TCTTT TCTTT 29.7 4.317105 2.68&6.29&5.11&-2.71&6.29 rs772226399 37 143320 2.58164e-04 3.32984e-04 4.40490e-04 2.06804e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.19555e-04 3.38295e-04 1.28999e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.84391e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.30289e-04 2.78767e-04 2.14007e-04 3.67809e-04 4.64253e-04 9.09091e-04 0.00000e+00 2.58056e-04 3.27869e-04 0.00000e+00 4.02253e-04 279838 264505 Lig4_syndrome&LIG4-Related_Disorders¬_provided MONDO:MONDO:0011686&MedGen:C1847827&OMIM:606593&Orphanet:ORPHA99812&MedGen:CN239380&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion LIG4:3981 SO:0001589&frameshift_variant 13 772226399 15 30 50.0 -13 108209993 CTCTTT C - frameshift_variant HIGH LIG4 3981 Transcript NM_001352604.1 protein_coding 3/3 1478-1482 1307-1311 436-437 KR/X aAAAGA/a -1 EntrezGene TCTTT TCTTT 29.7 4.317105 2.68&6.29&5.11&-2.71&6.29 rs772226399 37 143320 2.58164e-04 3.32984e-04 4.40490e-04 2.06804e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.19555e-04 3.38295e-04 1.28999e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.84391e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.30289e-04 2.78767e-04 2.14007e-04 3.67809e-04 4.64253e-04 9.09091e-04 0.00000e+00 2.58056e-04 3.27869e-04 0.00000e+00 4.02253e-04 279838 264505 Lig4_syndrome&LIG4-Related_Disorders¬_provided MONDO:MONDO:0011686&MedGen:C1847827&OMIM:606593&Orphanet:ORPHA99812&MedGen:CN239380&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion LIG4:3981 SO:0001589&frameshift_variant 13 772226399 15 30 50.0 -13 108209993 CTCTTT C - frameshift_variant HIGH LIG4 3981 Transcript NM_001379095.1 protein_coding 3/3 1622-1626 1271-1275 424-425 KR/X aAAAGA/a -1 EntrezGene TCTTT TCTTT 29.7 4.317105 2.68&6.29&5.11&-2.71&6.29 rs772226399 37 143320 2.58164e-04 3.32984e-04 4.40490e-04 2.06804e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.19555e-04 3.38295e-04 1.28999e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.84391e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.30289e-04 2.78767e-04 2.14007e-04 3.67809e-04 4.64253e-04 9.09091e-04 0.00000e+00 2.58056e-04 3.27869e-04 0.00000e+00 4.02253e-04 279838 264505 Lig4_syndrome&LIG4-Related_Disorders¬_provided MONDO:MONDO:0011686&MedGen:C1847827&OMIM:606593&Orphanet:ORPHA99812&MedGen:CN239380&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion LIG4:3981 SO:0001589&frameshift_variant 13 772226399 15 30 50.0 -13 108209993 CTCTTT C - frameshift_variant HIGH LIG4 3981 Transcript NM_002312.3 protein_coding 2/2 1544-1548 1271-1275 424-425 KR/X aAAAGA/a -1 EntrezGene TCTTT TCTTT OK 29.7 4.317105 2.68&6.29&5.11&-2.71&6.29 rs772226399 37 143320 2.58164e-04 3.32984e-04 4.40490e-04 2.06804e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.19555e-04 3.38295e-04 1.28999e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.84391e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.30289e-04 2.78767e-04 2.14007e-04 3.67809e-04 4.64253e-04 9.09091e-04 0.00000e+00 2.58056e-04 3.27869e-04 0.00000e+00 4.02253e-04 279838 264505 Lig4_syndrome&LIG4-Related_Disorders¬_provided MONDO:MONDO:0011686&MedGen:C1847827&OMIM:606593&Orphanet:ORPHA99812&MedGen:CN239380&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion LIG4:3981 SO:0001589&frameshift_variant 13 772226399 15 30 50.0 -13 108209993 CTCTTT C - frameshift_variant HIGH LIG4 3981 Transcript NM_206937.2 protein_coding 3/3 1425-1429 1271-1275 424-425 KR/X aAAAGA/a -1 EntrezGene TCTTT TCTTT 29.7 4.317105 2.68&6.29&5.11&-2.71&6.29 rs772226399 37 143320 2.58164e-04 3.32984e-04 4.40490e-04 2.06804e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.19555e-04 3.38295e-04 1.28999e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.84391e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.30289e-04 2.78767e-04 2.14007e-04 3.67809e-04 4.64253e-04 9.09091e-04 0.00000e+00 2.58056e-04 3.27869e-04 0.00000e+00 4.02253e-04 279838 264505 Lig4_syndrome&LIG4-Related_Disorders¬_provided MONDO:MONDO:0011686&MedGen:C1847827&OMIM:606593&Orphanet:ORPHA99812&MedGen:CN239380&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion LIG4:3981 SO:0001589&frameshift_variant 13 772226399 15 30 50.0 -14 64081559 TCACCATGCTAG T - frameshift_variant HIGH SYNE2 23224 Transcript NM_015180.5 protein_coding 57/115 11655-11665 11464-11474 3822-3825 HHAS/X CACCATGCTAGc/c 1 EntrezGene CACCATGCTAG CACCATGCTAG 33 5.256979 6.54&5.41&-0.473&6.54&1.89&-11.9&6.54&-7.96&1.75&3.77 rs747315463 1 143278 6.97944e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44034e-05 1.54861e-05 0.00000e+00 3.67918e-05 0.00000e+00 0.00000e+00 0.00000e+00 6.97525e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -14 64081559 TCACCATGCTAG T - frameshift_variant HIGH SYNE2 23224 Transcript NM_182914.3 protein_coding 57/116 11655-11665 11464-11474 3822-3825 HHAS/X CACCATGCTAGc/c 1 EntrezGene CACCATGCTAG CACCATGCTAG 33 5.256979 6.54&5.41&-0.473&6.54&1.89&-11.9&6.54&-7.96&1.75&3.77 rs747315463 1 143278 6.97944e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44034e-05 1.54861e-05 0.00000e+00 3.67918e-05 0.00000e+00 0.00000e+00 0.00000e+00 6.97525e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -14 64081559 TCACCATGCTAG T - frameshift_variant HIGH SYNE2 23224 Transcript XM_005267454.1 protein_coding 57/116 11694-11704 11464-11474 3822-3825 HHAS/X CACCATGCTAGc/c 1 EntrezGene CACCATGCTAG CACCATGCTAG 33 5.256979 6.54&5.41&-0.473&6.54&1.89&-11.9&6.54&-7.96&1.75&3.77 rs747315463 1 143278 6.97944e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44034e-05 1.54861e-05 0.00000e+00 3.67918e-05 0.00000e+00 0.00000e+00 0.00000e+00 6.97525e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -14 64081559 TCACCATGCTAG T - frameshift_variant HIGH SYNE2 23224 Transcript XM_005267456.1 protein_coding 57/116 11694-11704 11464-11474 3822-3825 HHAS/X CACCATGCTAGc/c 1 EntrezGene CACCATGCTAG CACCATGCTAG 33 5.256979 6.54&5.41&-0.473&6.54&1.89&-11.9&6.54&-7.96&1.75&3.77 rs747315463 1 143278 6.97944e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44034e-05 1.54861e-05 0.00000e+00 3.67918e-05 0.00000e+00 0.00000e+00 0.00000e+00 6.97525e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -14 64081559 TCACCATGCTAG T - frameshift_variant HIGH SYNE2 23224 Transcript XM_005267457.1 protein_coding 57/116 11694-11704 11464-11474 3822-3825 HHAS/X CACCATGCTAGc/c 1 EntrezGene CACCATGCTAG CACCATGCTAG 33 5.256979 6.54&5.41&-0.473&6.54&1.89&-11.9&6.54&-7.96&1.75&3.77 rs747315463 1 143278 6.97944e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44034e-05 1.54861e-05 0.00000e+00 3.67918e-05 0.00000e+00 0.00000e+00 0.00000e+00 6.97525e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -14 64081559 TCACCATGCTAG T - frameshift_variant HIGH SYNE2 23224 Transcript XM_005267458.1 protein_coding 57/115 11694-11704 11464-11474 3822-3825 HHAS/X CACCATGCTAGc/c 1 EntrezGene CACCATGCTAG CACCATGCTAG 33 5.256979 6.54&5.41&-0.473&6.54&1.89&-11.9&6.54&-7.96&1.75&3.77 rs747315463 1 143278 6.97944e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44034e-05 1.54861e-05 0.00000e+00 3.67918e-05 0.00000e+00 0.00000e+00 0.00000e+00 6.97525e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -14 64081559 TCACCATGCTAG T - frameshift_variant HIGH SYNE2 23224 Transcript XM_005267459.1 protein_coding 57/115 11694-11704 11464-11474 3822-3825 HHAS/X CACCATGCTAGc/c 1 EntrezGene CACCATGCTAG CACCATGCTAG 33 5.256979 6.54&5.41&-0.473&6.54&1.89&-11.9&6.54&-7.96&1.75&3.77 rs747315463 1 143278 6.97944e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44034e-05 1.54861e-05 0.00000e+00 3.67918e-05 0.00000e+00 0.00000e+00 0.00000e+00 6.97525e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -14 64081559 TCACCATGCTAG T - frameshift_variant HIGH SYNE2 23224 Transcript XM_011536574.1 protein_coding 57/116 11694-11704 11464-11474 3822-3825 HHAS/X CACCATGCTAGc/c 1 EntrezGene CACCATGCTAG CACCATGCTAG 33 5.256979 6.54&5.41&-0.473&6.54&1.89&-11.9&6.54&-7.96&1.75&3.77 rs747315463 1 143278 6.97944e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44034e-05 1.54861e-05 0.00000e+00 3.67918e-05 0.00000e+00 0.00000e+00 0.00000e+00 6.97525e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -14 64081559 TCACCATGCTAG T - frameshift_variant HIGH SYNE2 23224 Transcript XM_011536575.2 protein_coding 57/116 11671-11681 11464-11474 3822-3825 HHAS/X CACCATGCTAGc/c 1 EntrezGene CACCATGCTAG CACCATGCTAG 33 5.256979 6.54&5.41&-0.473&6.54&1.89&-11.9&6.54&-7.96&1.75&3.77 rs747315463 1 143278 6.97944e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44034e-05 1.54861e-05 0.00000e+00 3.67918e-05 0.00000e+00 0.00000e+00 0.00000e+00 6.97525e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -14 64081559 TCACCATGCTAG T - frameshift_variant HIGH SYNE2 23224 Transcript XM_011536576.2 protein_coding 58/117 11851-11861 11464-11474 3822-3825 HHAS/X CACCATGCTAGc/c 1 EntrezGene CACCATGCTAG CACCATGCTAG 33 5.256979 6.54&5.41&-0.473&6.54&1.89&-11.9&6.54&-7.96&1.75&3.77 rs747315463 1 143278 6.97944e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44034e-05 1.54861e-05 0.00000e+00 3.67918e-05 0.00000e+00 0.00000e+00 0.00000e+00 6.97525e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -14 64081559 TCACCATGCTAG T - frameshift_variant HIGH SYNE2 23224 Transcript XM_011536577.2 protein_coding 57/116 11618-11628 11464-11474 3822-3825 HHAS/X CACCATGCTAGc/c 1 EntrezGene CACCATGCTAG CACCATGCTAG 33 5.256979 6.54&5.41&-0.473&6.54&1.89&-11.9&6.54&-7.96&1.75&3.77 rs747315463 1 143278 6.97944e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44034e-05 1.54861e-05 0.00000e+00 3.67918e-05 0.00000e+00 0.00000e+00 0.00000e+00 6.97525e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -14 64081559 TCACCATGCTAG T - frameshift_variant HIGH SYNE2 23224 Transcript XM_011536578.1 protein_coding 57/116 11694-11704 11464-11474 3822-3825 HHAS/X CACCATGCTAGc/c 1 EntrezGene CACCATGCTAG CACCATGCTAG 33 5.256979 6.54&5.41&-0.473&6.54&1.89&-11.9&6.54&-7.96&1.75&3.77 rs747315463 1 143278 6.97944e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44034e-05 1.54861e-05 0.00000e+00 3.67918e-05 0.00000e+00 0.00000e+00 0.00000e+00 6.97525e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -14 64081559 TCACCATGCTAG T - frameshift_variant HIGH SYNE2 23224 Transcript XM_011536579.1 protein_coding 57/116 11694-11704 11464-11474 3822-3825 HHAS/X CACCATGCTAGc/c 1 EntrezGene CACCATGCTAG CACCATGCTAG 33 5.256979 6.54&5.41&-0.473&6.54&1.89&-11.9&6.54&-7.96&1.75&3.77 rs747315463 1 143278 6.97944e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44034e-05 1.54861e-05 0.00000e+00 3.67918e-05 0.00000e+00 0.00000e+00 0.00000e+00 6.97525e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -14 64081559 TCACCATGCTAG T - frameshift_variant HIGH SYNE2 23224 Transcript XM_011536580.2 protein_coding 57/116 11694-11704 11464-11474 3822-3825 HHAS/X CACCATGCTAGc/c 1 EntrezGene CACCATGCTAG CACCATGCTAG 33 5.256979 6.54&5.41&-0.473&6.54&1.89&-11.9&6.54&-7.96&1.75&3.77 rs747315463 1 143278 6.97944e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44034e-05 1.54861e-05 0.00000e+00 3.67918e-05 0.00000e+00 0.00000e+00 0.00000e+00 6.97525e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -14 64081559 TCACCATGCTAG T - frameshift_variant HIGH SYNE2 23224 Transcript XM_011536581.1 protein_coding 57/115 11694-11704 11464-11474 3822-3825 HHAS/X CACCATGCTAGc/c 1 EntrezGene CACCATGCTAG CACCATGCTAG 33 5.256979 6.54&5.41&-0.473&6.54&1.89&-11.9&6.54&-7.96&1.75&3.77 rs747315463 1 143278 6.97944e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44034e-05 1.54861e-05 0.00000e+00 3.67918e-05 0.00000e+00 0.00000e+00 0.00000e+00 6.97525e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -14 64081559 TCACCATGCTAG T - frameshift_variant HIGH SYNE2 23224 Transcript XM_011536582.1 protein_coding 57/115 11694-11704 11464-11474 3822-3825 HHAS/X CACCATGCTAGc/c 1 EntrezGene CACCATGCTAG CACCATGCTAG 33 5.256979 6.54&5.41&-0.473&6.54&1.89&-11.9&6.54&-7.96&1.75&3.77 rs747315463 1 143278 6.97944e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44034e-05 1.54861e-05 0.00000e+00 3.67918e-05 0.00000e+00 0.00000e+00 0.00000e+00 6.97525e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -14 64081559 TCACCATGCTAG T - frameshift_variant HIGH SYNE2 23224 Transcript XM_011536584.2 protein_coding 57/58 11694-11704 11464-11474 3822-3825 HHAS/X CACCATGCTAGc/c 1 EntrezGene CACCATGCTAG CACCATGCTAG 33 5.256979 6.54&5.41&-0.473&6.54&1.89&-11.9&6.54&-7.96&1.75&3.77 rs747315463 1 143278 6.97944e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44034e-05 1.54861e-05 0.00000e+00 3.67918e-05 0.00000e+00 0.00000e+00 0.00000e+00 6.97525e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -14 64081559 TCACCATGCTAG T - frameshift_variant HIGH SYNE2 23224 Transcript XM_017021101.1 protein_coding 57/116 11696-11706 11464-11474 3822-3825 HHAS/X CACCATGCTAGc/c 1 EntrezGene CACCATGCTAG CACCATGCTAG 33 5.256979 6.54&5.41&-0.473&6.54&1.89&-11.9&6.54&-7.96&1.75&3.77 rs747315463 1 143278 6.97944e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44034e-05 1.54861e-05 0.00000e+00 3.67918e-05 0.00000e+00 0.00000e+00 0.00000e+00 6.97525e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -14 64081559 TCACCATGCTAG T - frameshift_variant HIGH SYNE2 23224 Transcript XM_017021102.1 protein_coding 56/115 11739-11749 11395-11405 3799-3802 HHAS/X CACCATGCTAGc/c 1 EntrezGene CACCATGCTAG CACCATGCTAG 33 5.256979 6.54&5.41&-0.473&6.54&1.89&-11.9&6.54&-7.96&1.75&3.77 rs747315463 1 143278 6.97944e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44034e-05 1.54861e-05 0.00000e+00 3.67918e-05 0.00000e+00 0.00000e+00 0.00000e+00 6.97525e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -14 64081559 TCACCATGCTAG T - non_coding_transcript_exon_variant MODIFIER SYNE2 23224 Transcript XR_001750198.1 misc_RNA 57/78 11694-11704 1 EntrezGene CACCATGCTAG CACCATGCTAG 33 5.256979 6.54&5.41&-0.473&6.54&1.89&-11.9&6.54&-7.96&1.75&3.77 rs747315463 1 143278 6.97944e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.44034e-05 1.54861e-05 0.00000e+00 3.67918e-05 0.00000e+00 0.00000e+00 0.00000e+00 6.97525e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -14 67729336 CGCCCT C - frameshift_variant HIGH RDH12 145226 Transcript NM_152443.3 protein_coding 8/9 1129-1133 805-809 269-270 AL/X GCCCTg/g 1 EntrezGene GCCCT GCCCT 33 4.991036 5.65&6.54&-2.82&5.66&6.54 rs386834261 13 143326 9.07023e-05 4.75579e-05 4.40412e-05 5.16849e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.31850e-05 0.00000e+00 1.28999e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.12392e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.00763e-04 1.54851e-04 1.33726e-04 1.83905e-04 0.00000e+00 0.00000e+00 0.00000e+00 9.06631e-05 0.00000e+00 0.00000e+00 0.00000e+00 2047 17086 RDH12-Related_Disorders&Abnormality_of_the_eye&Retinitis_pigmentosa&Retinal_dystrophy&Leber_congenital_amaurosis_13&Leber_congenital_amaurosis¬_provided .&Human_Phenotype_Ontology:HP:0000478&MONDO:MONDO:0005328&MedGen:C4316870&Human_Phenotype_Ontology:HP:0000547&MONDO:MONDO:0019200&MeSH:D012174&MedGen:C0035334&OMIM:268000&OMIM:PS268000&Orphanet:ORPHA791&SNOMED_CT:28835009&Human_Phenotype_Ontology:HP:0000556&Human_Phenotype_Ontology:HP:0007736&Human_Phenotype_Ontology:HP:0007910&Human_Phenotype_Ontology:HP:0007974&Human_Phenotype_Ontology:HP:0007982&MONDO:MONDO:0019118&MeSH:D058499&MedGen:C0854723&Orphanet:ORPHA71862&SNOMED_CT:314407005&MONDO:MONDO:0012990&MedGen:C2675186&OMIM:612712&MONDO:MONDO:0018998&MeSH:D057130&MedGen:C0339527&OMIM:PS204000&Orphanet:ORPHA65&SNOMED_CT:193413001&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion ZFYVE26:23503&RDH12:145226 SO:0001589&frameshift_variant 5 386834261 15 30 50.0 -14 67729336 CGCCCT C - 3_prime_UTR_variant MODIFIER ZFYVE26 23503 Transcript XM_017021125.1 protein_coding 42/42 8053-8057 -1 EntrezGene GCCCT GCCCT 33 4.991036 5.65&6.54&-2.82&5.66&6.54 rs386834261 13 143326 9.07023e-05 4.75579e-05 4.40412e-05 5.16849e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.31850e-05 0.00000e+00 1.28999e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.12392e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.00763e-04 1.54851e-04 1.33726e-04 1.83905e-04 0.00000e+00 0.00000e+00 0.00000e+00 9.06631e-05 0.00000e+00 0.00000e+00 0.00000e+00 2047 17086 RDH12-Related_Disorders&Abnormality_of_the_eye&Retinitis_pigmentosa&Retinal_dystrophy&Leber_congenital_amaurosis_13&Leber_congenital_amaurosis¬_provided .&Human_Phenotype_Ontology:HP:0000478&MONDO:MONDO:0005328&MedGen:C4316870&Human_Phenotype_Ontology:HP:0000547&MONDO:MONDO:0019200&MeSH:D012174&MedGen:C0035334&OMIM:268000&OMIM:PS268000&Orphanet:ORPHA791&SNOMED_CT:28835009&Human_Phenotype_Ontology:HP:0000556&Human_Phenotype_Ontology:HP:0007736&Human_Phenotype_Ontology:HP:0007910&Human_Phenotype_Ontology:HP:0007974&Human_Phenotype_Ontology:HP:0007982&MONDO:MONDO:0019118&MeSH:D058499&MedGen:C0854723&Orphanet:ORPHA71862&SNOMED_CT:314407005&MONDO:MONDO:0012990&MedGen:C2675186&OMIM:612712&MONDO:MONDO:0018998&MeSH:D057130&MedGen:C0339527&OMIM:PS204000&Orphanet:ORPHA65&SNOMED_CT:193413001&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion ZFYVE26:23503&RDH12:145226 SO:0001589&frameshift_variant 5 386834261 15 30 50.0 -14 87219614 T C C intergenic_variant MODIFIER 8.895 0.748120 0.872 chr14:87219614-87219614 23887 143162 1.66853e-01 1.69525e-01 1.69417e-01 1.69652e-01 2.43333e-01 2.57447e-01 2.27907e-01 1.30897e-01 1.32363e-01 1.29778e-01 1.48616e-01 1.53802e-01 1.42766e-01 9.18106e-02 7.76699e-02 1.03919e-01 1.70484e-01 1.30647e-01 1.27498e-01 1.31639e-01 1.62991e-01 1.83530e-01 1.83803e-01 1.83153e-01 1.60298e-01 1.66971e-01 1.53333e-01 1.67143e-01 1.40933e-01 1.44876e-01 1.40032e-01 15 30 50.0 -14 94378547 C CG G frameshift_variant HIGH SERPINA1 5265 Transcript NM_000295.5 protein_coding 5/5 1205-1206 1158-1159 386-387 -/X -/C -1 EntrezGene 18.62 1.917365 -12.4&-5.72 rs764325655 6 142970 4.19668e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.20102e-04 0.00000e+00 5.94530e-04 4.07067e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.33075e-05 6.20424e-05 5.35791e-05 7.36811e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18760e-05 3.29381e-04 1.77305e-03 0.00000e+00 188845 186924 Alpha-1-antitrypsin_deficiency Alpha-1-antitrypsin_deficiency MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 626305:Pathogenic Duplication SERPINA1:5265 SO:0001589&frameshift_variant 1 764325655 15 30 50.0 -14 94378547 C CG G frameshift_variant HIGH SERPINA1 5265 Transcript NM_001002235.3 protein_coding 5/5 1341-1342 1158-1159 386-387 -/X -/C -1 EntrezGene 18.62 1.917365 -12.4&-5.72 rs764325655 6 142970 4.19668e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.20102e-04 0.00000e+00 5.94530e-04 4.07067e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.33075e-05 6.20424e-05 5.35791e-05 7.36811e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18760e-05 3.29381e-04 1.77305e-03 0.00000e+00 188845 186924 Alpha-1-antitrypsin_deficiency Alpha-1-antitrypsin_deficiency MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 626305:Pathogenic Duplication SERPINA1:5265 SO:0001589&frameshift_variant 1 764325655 15 30 50.0 -14 94378547 C CG G frameshift_variant HIGH SERPINA1 5265 Transcript NM_001002236.3 protein_coding 7/7 1655-1656 1158-1159 386-387 -/X -/C -1 EntrezGene 18.62 1.917365 -12.4&-5.72 rs764325655 6 142970 4.19668e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.20102e-04 0.00000e+00 5.94530e-04 4.07067e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.33075e-05 6.20424e-05 5.35791e-05 7.36811e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18760e-05 3.29381e-04 1.77305e-03 0.00000e+00 188845 186924 Alpha-1-antitrypsin_deficiency Alpha-1-antitrypsin_deficiency MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 626305:Pathogenic Duplication SERPINA1:5265 SO:0001589&frameshift_variant 1 764325655 15 30 50.0 -14 94378547 C CG G frameshift_variant HIGH SERPINA1 5265 Transcript NM_001127700.2 protein_coding 5/5 1378-1379 1158-1159 386-387 -/X -/C -1 EntrezGene 18.62 1.917365 -12.4&-5.72 rs764325655 6 142970 4.19668e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.20102e-04 0.00000e+00 5.94530e-04 4.07067e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.33075e-05 6.20424e-05 5.35791e-05 7.36811e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18760e-05 3.29381e-04 1.77305e-03 0.00000e+00 188845 186924 Alpha-1-antitrypsin_deficiency Alpha-1-antitrypsin_deficiency MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 626305:Pathogenic Duplication SERPINA1:5265 SO:0001589&frameshift_variant 1 764325655 15 30 50.0 -14 94378547 C CG G frameshift_variant HIGH SERPINA1 5265 Transcript NM_001127701.2 protein_coding 7/7 1674-1675 1158-1159 386-387 -/X -/C -1 EntrezGene 18.62 1.917365 -12.4&-5.72 rs764325655 6 142970 4.19668e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.20102e-04 0.00000e+00 5.94530e-04 4.07067e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.33075e-05 6.20424e-05 5.35791e-05 7.36811e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18760e-05 3.29381e-04 1.77305e-03 0.00000e+00 188845 186924 Alpha-1-antitrypsin_deficiency Alpha-1-antitrypsin_deficiency MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 626305:Pathogenic Duplication SERPINA1:5265 SO:0001589&frameshift_variant 1 764325655 15 30 50.0 -14 94378547 C CG G frameshift_variant HIGH SERPINA1 5265 Transcript NM_001127702.2 protein_coding 6/6 1482-1483 1158-1159 386-387 -/X -/C -1 EntrezGene 18.62 1.917365 -12.4&-5.72 rs764325655 6 142970 4.19668e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.20102e-04 0.00000e+00 5.94530e-04 4.07067e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.33075e-05 6.20424e-05 5.35791e-05 7.36811e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18760e-05 3.29381e-04 1.77305e-03 0.00000e+00 188845 186924 Alpha-1-antitrypsin_deficiency Alpha-1-antitrypsin_deficiency MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 626305:Pathogenic Duplication SERPINA1:5265 SO:0001589&frameshift_variant 1 764325655 15 30 50.0 -14 94378547 C CG G frameshift_variant HIGH SERPINA1 5265 Transcript NM_001127703.2 protein_coding 7/7 1637-1638 1158-1159 386-387 -/X -/C -1 EntrezGene 18.62 1.917365 -12.4&-5.72 rs764325655 6 142970 4.19668e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.20102e-04 0.00000e+00 5.94530e-04 4.07067e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.33075e-05 6.20424e-05 5.35791e-05 7.36811e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18760e-05 3.29381e-04 1.77305e-03 0.00000e+00 188845 186924 Alpha-1-antitrypsin_deficiency Alpha-1-antitrypsin_deficiency MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 626305:Pathogenic Duplication SERPINA1:5265 SO:0001589&frameshift_variant 1 764325655 15 30 50.0 -14 94378547 C CG G frameshift_variant HIGH SERPINA1 5265 Transcript NM_001127704.2 protein_coding 7/7 1634-1635 1158-1159 386-387 -/X -/C -1 EntrezGene 18.62 1.917365 -12.4&-5.72 rs764325655 6 142970 4.19668e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.20102e-04 0.00000e+00 5.94530e-04 4.07067e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.33075e-05 6.20424e-05 5.35791e-05 7.36811e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18760e-05 3.29381e-04 1.77305e-03 0.00000e+00 188845 186924 Alpha-1-antitrypsin_deficiency Alpha-1-antitrypsin_deficiency MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 626305:Pathogenic Duplication SERPINA1:5265 SO:0001589&frameshift_variant 1 764325655 15 30 50.0 -14 94378547 C CG G frameshift_variant HIGH SERPINA1 5265 Transcript NM_001127705.2 protein_coding 7/7 1652-1653 1158-1159 386-387 -/X -/C -1 EntrezGene 18.62 1.917365 -12.4&-5.72 rs764325655 6 142970 4.19668e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.20102e-04 0.00000e+00 5.94530e-04 4.07067e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.33075e-05 6.20424e-05 5.35791e-05 7.36811e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18760e-05 3.29381e-04 1.77305e-03 0.00000e+00 188845 186924 Alpha-1-antitrypsin_deficiency Alpha-1-antitrypsin_deficiency MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 626305:Pathogenic Duplication SERPINA1:5265 SO:0001589&frameshift_variant 1 764325655 15 30 50.0 -14 94378547 C CG G frameshift_variant HIGH SERPINA1 5265 Transcript NM_001127706.2 protein_coding 6/6 1445-1446 1158-1159 386-387 -/X -/C -1 EntrezGene 18.62 1.917365 -12.4&-5.72 rs764325655 6 142970 4.19668e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.20102e-04 0.00000e+00 5.94530e-04 4.07067e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.33075e-05 6.20424e-05 5.35791e-05 7.36811e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18760e-05 3.29381e-04 1.77305e-03 0.00000e+00 188845 186924 Alpha-1-antitrypsin_deficiency Alpha-1-antitrypsin_deficiency MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 626305:Pathogenic Duplication SERPINA1:5265 SO:0001589&frameshift_variant 1 764325655 15 30 50.0 -14 94378547 C CG G frameshift_variant HIGH SERPINA1 5265 Transcript NM_001127707.2 protein_coding 6/6 1442-1443 1158-1159 386-387 -/X -/C -1 EntrezGene 18.62 1.917365 -12.4&-5.72 rs764325655 6 142970 4.19668e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.20102e-04 0.00000e+00 5.94530e-04 4.07067e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.33075e-05 6.20424e-05 5.35791e-05 7.36811e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18760e-05 3.29381e-04 1.77305e-03 0.00000e+00 188845 186924 Alpha-1-antitrypsin_deficiency Alpha-1-antitrypsin_deficiency MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 626305:Pathogenic Duplication SERPINA1:5265 SO:0001589&frameshift_variant 1 764325655 15 30 50.0 -14 94378547 C CG G frameshift_variant HIGH SERPINA1 5265 Transcript XM_017021370.1 protein_coding 6/6 1599-1600 1158-1159 386-387 -/X -/C -1 EntrezGene 18.62 1.917365 -12.4&-5.72 rs764325655 6 142970 4.19668e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.20102e-04 0.00000e+00 5.94530e-04 4.07067e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.33075e-05 6.20424e-05 5.35791e-05 7.36811e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.18760e-05 3.29381e-04 1.77305e-03 0.00000e+00 188845 186924 Alpha-1-antitrypsin_deficiency Alpha-1-antitrypsin_deficiency MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic 626305:Pathogenic Duplication SERPINA1:5265 SO:0001589&frameshift_variant 1 764325655 15 30 50.0 -14 94378610 C T T missense_variant MODERATE SERPINA1 5265 Transcript NM_000295.5 protein_coding 5/5 1143 1096 366 E/K Gag/Aag -1 EntrezGene C C 0.07 1 23.5 3.048880 23.5 0.99806662942656288 2.425494 0.0190238184345999 0.40597 3.597016 0.263532611203965 0.54312 -2.93&-2.93&-2.93&-2.93&-2.93&.&-2.93&-2.93&-2.93 2.95 0.999997244866832 0.007687 0.8205 0.6007 3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82 0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164 -3.55&-3.55&-3.55&-3.55&-3.55&.&-3.55&-3.55&-3.55 8.4892 0.709&0.708&0.707&0.71&0.769&.&0.776&0.766&0.735 0.91636 0.516011 1.000000 0.095000 5.270000 0.126000 5.31 chr14:94378610-94378610 1799 143250 1.25585e-02 2.71377e-03 3.12886e-03 2.22613e-03 7.79510e-03 4.25532e-03 1.16822e-02 5.92885e-03 6.42760e-03 5.54839e-03 9.32611e-03 9.64813e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.25274e-02 1.74818e-02 1.43885e-02 1.84534e-02 1.25915e-02 2.10037e-02 1.99508e-02 2.24529e-02 1.20818e-02 1.36612e-02 1.04364e-02 1.25764e-02 3.28299e-04 0.00000e+00 4.02901e-04 17967 0.01170 0.00399 33006 PI_Z&PI_Z(AUGSBURG)&PI_Z(TUN)&Chronic_obstructive_pulmonary_disease&FRAXE&Alpha-1-antitrypsin_deficiency&Inborn_genetic_diseases¬_provided Alpha-1-antitrypsin_deficiency .&.&.&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0010659&MedGen:C0751157&OMIM:309548&Orphanet:ORPHA100973&SNOMED_CT:254288000&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic&_risk_factor 626304:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 9 28929474 15 30 50.0 -14 94378610 C T T missense_variant MODERATE SERPINA1 5265 Transcript NM_001002235.3 protein_coding 5/5 1279 1096 366 E/K Gag/Aag -1 EntrezGene C C 0.07 1 23.5 3.048880 23.5 0.99806662942656288 2.425494 0.0190238184345999 0.40597 3.597016 0.263532611203965 0.54312 -2.93&-2.93&-2.93&-2.93&-2.93&.&-2.93&-2.93&-2.93 2.95 0.999997244866832 0.007687 0.8205 0.6007 3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82 0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164 -3.55&-3.55&-3.55&-3.55&-3.55&.&-3.55&-3.55&-3.55 8.4892 0.709&0.708&0.707&0.71&0.769&.&0.776&0.766&0.735 0.91636 0.516011 1.000000 0.095000 5.270000 0.126000 5.31 chr14:94378610-94378610 1799 143250 1.25585e-02 2.71377e-03 3.12886e-03 2.22613e-03 7.79510e-03 4.25532e-03 1.16822e-02 5.92885e-03 6.42760e-03 5.54839e-03 9.32611e-03 9.64813e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.25274e-02 1.74818e-02 1.43885e-02 1.84534e-02 1.25915e-02 2.10037e-02 1.99508e-02 2.24529e-02 1.20818e-02 1.36612e-02 1.04364e-02 1.25764e-02 3.28299e-04 0.00000e+00 4.02901e-04 17967 0.01170 0.00399 33006 PI_Z&PI_Z(AUGSBURG)&PI_Z(TUN)&Chronic_obstructive_pulmonary_disease&FRAXE&Alpha-1-antitrypsin_deficiency&Inborn_genetic_diseases¬_provided Alpha-1-antitrypsin_deficiency .&.&.&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0010659&MedGen:C0751157&OMIM:309548&Orphanet:ORPHA100973&SNOMED_CT:254288000&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic&_risk_factor 626304:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 9 28929474 15 30 50.0 -14 94378610 C T T missense_variant MODERATE SERPINA1 5265 Transcript NM_001002236.3 protein_coding 7/7 1593 1096 366 E/K Gag/Aag -1 EntrezGene C C 0.07 1 23.5 3.048880 23.5 0.99806662942656288 2.425494 0.0190238184345999 0.40597 3.597016 0.263532611203965 0.54312 -2.93&-2.93&-2.93&-2.93&-2.93&.&-2.93&-2.93&-2.93 2.95 0.999997244866832 0.007687 0.8205 0.6007 3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82 0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164 -3.55&-3.55&-3.55&-3.55&-3.55&.&-3.55&-3.55&-3.55 8.4892 0.709&0.708&0.707&0.71&0.769&.&0.776&0.766&0.735 0.91636 0.516011 1.000000 0.095000 5.270000 0.126000 5.31 chr14:94378610-94378610 1799 143250 1.25585e-02 2.71377e-03 3.12886e-03 2.22613e-03 7.79510e-03 4.25532e-03 1.16822e-02 5.92885e-03 6.42760e-03 5.54839e-03 9.32611e-03 9.64813e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.25274e-02 1.74818e-02 1.43885e-02 1.84534e-02 1.25915e-02 2.10037e-02 1.99508e-02 2.24529e-02 1.20818e-02 1.36612e-02 1.04364e-02 1.25764e-02 3.28299e-04 0.00000e+00 4.02901e-04 17967 0.01170 0.00399 33006 PI_Z&PI_Z(AUGSBURG)&PI_Z(TUN)&Chronic_obstructive_pulmonary_disease&FRAXE&Alpha-1-antitrypsin_deficiency&Inborn_genetic_diseases¬_provided Alpha-1-antitrypsin_deficiency .&.&.&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0010659&MedGen:C0751157&OMIM:309548&Orphanet:ORPHA100973&SNOMED_CT:254288000&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic&_risk_factor 626304:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 9 28929474 15 30 50.0 -14 94378610 C T T missense_variant MODERATE SERPINA1 5265 Transcript NM_001127700.2 protein_coding 5/5 1316 1096 366 E/K Gag/Aag -1 EntrezGene C C 0.07 1 23.5 3.048880 23.5 0.99806662942656288 2.425494 0.0190238184345999 0.40597 3.597016 0.263532611203965 0.54312 -2.93&-2.93&-2.93&-2.93&-2.93&.&-2.93&-2.93&-2.93 2.95 0.999997244866832 0.007687 0.8205 0.6007 3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82 0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164 -3.55&-3.55&-3.55&-3.55&-3.55&.&-3.55&-3.55&-3.55 8.4892 0.709&0.708&0.707&0.71&0.769&.&0.776&0.766&0.735 0.91636 0.516011 1.000000 0.095000 5.270000 0.126000 5.31 chr14:94378610-94378610 1799 143250 1.25585e-02 2.71377e-03 3.12886e-03 2.22613e-03 7.79510e-03 4.25532e-03 1.16822e-02 5.92885e-03 6.42760e-03 5.54839e-03 9.32611e-03 9.64813e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.25274e-02 1.74818e-02 1.43885e-02 1.84534e-02 1.25915e-02 2.10037e-02 1.99508e-02 2.24529e-02 1.20818e-02 1.36612e-02 1.04364e-02 1.25764e-02 3.28299e-04 0.00000e+00 4.02901e-04 17967 0.01170 0.00399 33006 PI_Z&PI_Z(AUGSBURG)&PI_Z(TUN)&Chronic_obstructive_pulmonary_disease&FRAXE&Alpha-1-antitrypsin_deficiency&Inborn_genetic_diseases¬_provided Alpha-1-antitrypsin_deficiency .&.&.&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0010659&MedGen:C0751157&OMIM:309548&Orphanet:ORPHA100973&SNOMED_CT:254288000&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic&_risk_factor 626304:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 9 28929474 15 30 50.0 -14 94378610 C T T missense_variant MODERATE SERPINA1 5265 Transcript NM_001127701.2 protein_coding 7/7 1612 1096 366 E/K Gag/Aag -1 EntrezGene C C 0.07 1 23.5 3.048880 23.5 0.99806662942656288 2.425494 0.0190238184345999 0.40597 3.597016 0.263532611203965 0.54312 -2.93&-2.93&-2.93&-2.93&-2.93&.&-2.93&-2.93&-2.93 2.95 0.999997244866832 0.007687 0.8205 0.6007 3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82 0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164 -3.55&-3.55&-3.55&-3.55&-3.55&.&-3.55&-3.55&-3.55 8.4892 0.709&0.708&0.707&0.71&0.769&.&0.776&0.766&0.735 0.91636 0.516011 1.000000 0.095000 5.270000 0.126000 5.31 chr14:94378610-94378610 1799 143250 1.25585e-02 2.71377e-03 3.12886e-03 2.22613e-03 7.79510e-03 4.25532e-03 1.16822e-02 5.92885e-03 6.42760e-03 5.54839e-03 9.32611e-03 9.64813e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.25274e-02 1.74818e-02 1.43885e-02 1.84534e-02 1.25915e-02 2.10037e-02 1.99508e-02 2.24529e-02 1.20818e-02 1.36612e-02 1.04364e-02 1.25764e-02 3.28299e-04 0.00000e+00 4.02901e-04 17967 0.01170 0.00399 33006 PI_Z&PI_Z(AUGSBURG)&PI_Z(TUN)&Chronic_obstructive_pulmonary_disease&FRAXE&Alpha-1-antitrypsin_deficiency&Inborn_genetic_diseases¬_provided Alpha-1-antitrypsin_deficiency .&.&.&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0010659&MedGen:C0751157&OMIM:309548&Orphanet:ORPHA100973&SNOMED_CT:254288000&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic&_risk_factor 626304:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 9 28929474 15 30 50.0 -14 94378610 C T T missense_variant MODERATE SERPINA1 5265 Transcript NM_001127702.2 protein_coding 6/6 1420 1096 366 E/K Gag/Aag -1 EntrezGene C C 0.07 1 23.5 3.048880 23.5 0.99806662942656288 2.425494 0.0190238184345999 0.40597 3.597016 0.263532611203965 0.54312 -2.93&-2.93&-2.93&-2.93&-2.93&.&-2.93&-2.93&-2.93 2.95 0.999997244866832 0.007687 0.8205 0.6007 3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82 0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164 -3.55&-3.55&-3.55&-3.55&-3.55&.&-3.55&-3.55&-3.55 8.4892 0.709&0.708&0.707&0.71&0.769&.&0.776&0.766&0.735 0.91636 0.516011 1.000000 0.095000 5.270000 0.126000 5.31 chr14:94378610-94378610 1799 143250 1.25585e-02 2.71377e-03 3.12886e-03 2.22613e-03 7.79510e-03 4.25532e-03 1.16822e-02 5.92885e-03 6.42760e-03 5.54839e-03 9.32611e-03 9.64813e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.25274e-02 1.74818e-02 1.43885e-02 1.84534e-02 1.25915e-02 2.10037e-02 1.99508e-02 2.24529e-02 1.20818e-02 1.36612e-02 1.04364e-02 1.25764e-02 3.28299e-04 0.00000e+00 4.02901e-04 17967 0.01170 0.00399 33006 PI_Z&PI_Z(AUGSBURG)&PI_Z(TUN)&Chronic_obstructive_pulmonary_disease&FRAXE&Alpha-1-antitrypsin_deficiency&Inborn_genetic_diseases¬_provided Alpha-1-antitrypsin_deficiency .&.&.&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0010659&MedGen:C0751157&OMIM:309548&Orphanet:ORPHA100973&SNOMED_CT:254288000&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic&_risk_factor 626304:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 9 28929474 15 30 50.0 -14 94378610 C T T missense_variant MODERATE SERPINA1 5265 Transcript NM_001127703.2 protein_coding 7/7 1575 1096 366 E/K Gag/Aag -1 EntrezGene C C 0.07 1 23.5 3.048880 23.5 0.99806662942656288 2.425494 0.0190238184345999 0.40597 3.597016 0.263532611203965 0.54312 -2.93&-2.93&-2.93&-2.93&-2.93&.&-2.93&-2.93&-2.93 2.95 0.999997244866832 0.007687 0.8205 0.6007 3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82 0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164 -3.55&-3.55&-3.55&-3.55&-3.55&.&-3.55&-3.55&-3.55 8.4892 0.709&0.708&0.707&0.71&0.769&.&0.776&0.766&0.735 0.91636 0.516011 1.000000 0.095000 5.270000 0.126000 5.31 chr14:94378610-94378610 1799 143250 1.25585e-02 2.71377e-03 3.12886e-03 2.22613e-03 7.79510e-03 4.25532e-03 1.16822e-02 5.92885e-03 6.42760e-03 5.54839e-03 9.32611e-03 9.64813e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.25274e-02 1.74818e-02 1.43885e-02 1.84534e-02 1.25915e-02 2.10037e-02 1.99508e-02 2.24529e-02 1.20818e-02 1.36612e-02 1.04364e-02 1.25764e-02 3.28299e-04 0.00000e+00 4.02901e-04 17967 0.01170 0.00399 33006 PI_Z&PI_Z(AUGSBURG)&PI_Z(TUN)&Chronic_obstructive_pulmonary_disease&FRAXE&Alpha-1-antitrypsin_deficiency&Inborn_genetic_diseases¬_provided Alpha-1-antitrypsin_deficiency .&.&.&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0010659&MedGen:C0751157&OMIM:309548&Orphanet:ORPHA100973&SNOMED_CT:254288000&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic&_risk_factor 626304:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 9 28929474 15 30 50.0 -14 94378610 C T T missense_variant MODERATE SERPINA1 5265 Transcript NM_001127704.2 protein_coding 7/7 1572 1096 366 E/K Gag/Aag -1 EntrezGene C C 0.07 1 23.5 3.048880 23.5 0.99806662942656288 2.425494 0.0190238184345999 0.40597 3.597016 0.263532611203965 0.54312 -2.93&-2.93&-2.93&-2.93&-2.93&.&-2.93&-2.93&-2.93 2.95 0.999997244866832 0.007687 0.8205 0.6007 3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82 0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164 -3.55&-3.55&-3.55&-3.55&-3.55&.&-3.55&-3.55&-3.55 8.4892 0.709&0.708&0.707&0.71&0.769&.&0.776&0.766&0.735 0.91636 0.516011 1.000000 0.095000 5.270000 0.126000 5.31 chr14:94378610-94378610 1799 143250 1.25585e-02 2.71377e-03 3.12886e-03 2.22613e-03 7.79510e-03 4.25532e-03 1.16822e-02 5.92885e-03 6.42760e-03 5.54839e-03 9.32611e-03 9.64813e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.25274e-02 1.74818e-02 1.43885e-02 1.84534e-02 1.25915e-02 2.10037e-02 1.99508e-02 2.24529e-02 1.20818e-02 1.36612e-02 1.04364e-02 1.25764e-02 3.28299e-04 0.00000e+00 4.02901e-04 17967 0.01170 0.00399 33006 PI_Z&PI_Z(AUGSBURG)&PI_Z(TUN)&Chronic_obstructive_pulmonary_disease&FRAXE&Alpha-1-antitrypsin_deficiency&Inborn_genetic_diseases¬_provided Alpha-1-antitrypsin_deficiency .&.&.&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0010659&MedGen:C0751157&OMIM:309548&Orphanet:ORPHA100973&SNOMED_CT:254288000&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic&_risk_factor 626304:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 9 28929474 15 30 50.0 -14 94378610 C T T missense_variant MODERATE SERPINA1 5265 Transcript NM_001127705.2 protein_coding 7/7 1590 1096 366 E/K Gag/Aag -1 EntrezGene C C 0.07 1 23.5 3.048880 23.5 0.99806662942656288 2.425494 0.0190238184345999 0.40597 3.597016 0.263532611203965 0.54312 -2.93&-2.93&-2.93&-2.93&-2.93&.&-2.93&-2.93&-2.93 2.95 0.999997244866832 0.007687 0.8205 0.6007 3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82 0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164 -3.55&-3.55&-3.55&-3.55&-3.55&.&-3.55&-3.55&-3.55 8.4892 0.709&0.708&0.707&0.71&0.769&.&0.776&0.766&0.735 0.91636 0.516011 1.000000 0.095000 5.270000 0.126000 5.31 chr14:94378610-94378610 1799 143250 1.25585e-02 2.71377e-03 3.12886e-03 2.22613e-03 7.79510e-03 4.25532e-03 1.16822e-02 5.92885e-03 6.42760e-03 5.54839e-03 9.32611e-03 9.64813e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.25274e-02 1.74818e-02 1.43885e-02 1.84534e-02 1.25915e-02 2.10037e-02 1.99508e-02 2.24529e-02 1.20818e-02 1.36612e-02 1.04364e-02 1.25764e-02 3.28299e-04 0.00000e+00 4.02901e-04 17967 0.01170 0.00399 33006 PI_Z&PI_Z(AUGSBURG)&PI_Z(TUN)&Chronic_obstructive_pulmonary_disease&FRAXE&Alpha-1-antitrypsin_deficiency&Inborn_genetic_diseases¬_provided Alpha-1-antitrypsin_deficiency .&.&.&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0010659&MedGen:C0751157&OMIM:309548&Orphanet:ORPHA100973&SNOMED_CT:254288000&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic&_risk_factor 626304:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 9 28929474 15 30 50.0 -14 94378610 C T T missense_variant MODERATE SERPINA1 5265 Transcript NM_001127706.2 protein_coding 6/6 1383 1096 366 E/K Gag/Aag -1 EntrezGene C C 0.07 1 23.5 3.048880 23.5 0.99806662942656288 2.425494 0.0190238184345999 0.40597 3.597016 0.263532611203965 0.54312 -2.93&-2.93&-2.93&-2.93&-2.93&.&-2.93&-2.93&-2.93 2.95 0.999997244866832 0.007687 0.8205 0.6007 3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82 0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164 -3.55&-3.55&-3.55&-3.55&-3.55&.&-3.55&-3.55&-3.55 8.4892 0.709&0.708&0.707&0.71&0.769&.&0.776&0.766&0.735 0.91636 0.516011 1.000000 0.095000 5.270000 0.126000 5.31 chr14:94378610-94378610 1799 143250 1.25585e-02 2.71377e-03 3.12886e-03 2.22613e-03 7.79510e-03 4.25532e-03 1.16822e-02 5.92885e-03 6.42760e-03 5.54839e-03 9.32611e-03 9.64813e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.25274e-02 1.74818e-02 1.43885e-02 1.84534e-02 1.25915e-02 2.10037e-02 1.99508e-02 2.24529e-02 1.20818e-02 1.36612e-02 1.04364e-02 1.25764e-02 3.28299e-04 0.00000e+00 4.02901e-04 17967 0.01170 0.00399 33006 PI_Z&PI_Z(AUGSBURG)&PI_Z(TUN)&Chronic_obstructive_pulmonary_disease&FRAXE&Alpha-1-antitrypsin_deficiency&Inborn_genetic_diseases¬_provided Alpha-1-antitrypsin_deficiency .&.&.&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0010659&MedGen:C0751157&OMIM:309548&Orphanet:ORPHA100973&SNOMED_CT:254288000&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic&_risk_factor 626304:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 9 28929474 15 30 50.0 -14 94378610 C T T missense_variant MODERATE SERPINA1 5265 Transcript NM_001127707.2 protein_coding 6/6 1380 1096 366 E/K Gag/Aag -1 EntrezGene C C 0.07 1 23.5 3.048880 23.5 0.99806662942656288 2.425494 0.0190238184345999 0.40597 3.597016 0.263532611203965 0.54312 -2.93&-2.93&-2.93&-2.93&-2.93&.&-2.93&-2.93&-2.93 2.95 0.999997244866832 0.007687 0.8205 0.6007 3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82 0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164 -3.55&-3.55&-3.55&-3.55&-3.55&.&-3.55&-3.55&-3.55 8.4892 0.709&0.708&0.707&0.71&0.769&.&0.776&0.766&0.735 0.91636 0.516011 1.000000 0.095000 5.270000 0.126000 5.31 chr14:94378610-94378610 1799 143250 1.25585e-02 2.71377e-03 3.12886e-03 2.22613e-03 7.79510e-03 4.25532e-03 1.16822e-02 5.92885e-03 6.42760e-03 5.54839e-03 9.32611e-03 9.64813e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.25274e-02 1.74818e-02 1.43885e-02 1.84534e-02 1.25915e-02 2.10037e-02 1.99508e-02 2.24529e-02 1.20818e-02 1.36612e-02 1.04364e-02 1.25764e-02 3.28299e-04 0.00000e+00 4.02901e-04 17967 0.01170 0.00399 33006 PI_Z&PI_Z(AUGSBURG)&PI_Z(TUN)&Chronic_obstructive_pulmonary_disease&FRAXE&Alpha-1-antitrypsin_deficiency&Inborn_genetic_diseases¬_provided Alpha-1-antitrypsin_deficiency .&.&.&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0010659&MedGen:C0751157&OMIM:309548&Orphanet:ORPHA100973&SNOMED_CT:254288000&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic&_risk_factor 626304:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 9 28929474 15 30 50.0 -14 94378610 C T T missense_variant MODERATE SERPINA1 5265 Transcript XM_017021370.1 protein_coding 6/6 1537 1096 366 E/K Gag/Aag -1 EntrezGene C C 0.07 1 23.5 3.048880 23.5 0.99806662942656288 2.425494 0.0190238184345999 0.40597 3.597016 0.263532611203965 0.54312 -2.93&-2.93&-2.93&-2.93&-2.93&.&-2.93&-2.93&-2.93 2.95 0.999997244866832 0.007687 0.8205 0.6007 3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82&3.82 0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164&0.999164 -3.55&-3.55&-3.55&-3.55&-3.55&.&-3.55&-3.55&-3.55 8.4892 0.709&0.708&0.707&0.71&0.769&.&0.776&0.766&0.735 0.91636 0.516011 1.000000 0.095000 5.270000 0.126000 5.31 chr14:94378610-94378610 1799 143250 1.25585e-02 2.71377e-03 3.12886e-03 2.22613e-03 7.79510e-03 4.25532e-03 1.16822e-02 5.92885e-03 6.42760e-03 5.54839e-03 9.32611e-03 9.64813e-03 8.96287e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.25274e-02 1.74818e-02 1.43885e-02 1.84534e-02 1.25915e-02 2.10037e-02 1.99508e-02 2.24529e-02 1.20818e-02 1.36612e-02 1.04364e-02 1.25764e-02 3.28299e-04 0.00000e+00 4.02901e-04 17967 0.01170 0.00399 33006 PI_Z&PI_Z(AUGSBURG)&PI_Z(TUN)&Chronic_obstructive_pulmonary_disease&FRAXE&Alpha-1-antitrypsin_deficiency&Inborn_genetic_diseases¬_provided Alpha-1-antitrypsin_deficiency .&.&.&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0010659&MedGen:C0751157&OMIM:309548&Orphanet:ORPHA100973&SNOMED_CT:254288000&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MeSH:D030342&MedGen:C0950123&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic&_risk_factor 626304:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 9 28929474 15 30 50.0 -14 94380925 T A A missense_variant MODERATE SERPINA1 5265 Transcript NM_000295.5 protein_coding 3/5 910 863 288 E/V gAa/gTa -1 EntrezGene T T 0 0.996 32 4.522644 32 0.99125751745836055 4.060671 0.356045358213489 0.58872 5.370522 0.540959054635916 0.69532 -2.54&-2.54&-2.54&-2.54&-2.54&.&-2.54&-2.54&-2.54&-2.54 5.18 0.999999999804063 0.000193 0.2843 -0.1620 3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615 0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935 -6.8&-6.8&-6.8&-6.8&-6.8&.&-6.8&-6.8&-6.8&-6.71 14.3171 0.277&0.272&0.27&0.276&0.281&.&0.281&0.28&0.271&0.394 0.98481 0.553676 1.000000 0.029000 6.461000 1.138000 5.98 rs17580 4138 143306 2.88753e-02 8.93919e-03 9.37748e-03 8.42464e-03 1.18889e-01 8.93617e-02 1.51163e-01 5.55230e-02 5.29790e-02 5.74638e-02 1.02348e-02 1.02157e-02 1.02564e-02 0.00000e+00 0.00000e+00 0.00000e+00 3.00322e-02 7.82293e-03 8.77893e-03 7.52257e-03 2.76450e-02 4.15931e-02 4.19988e-02 4.10349e-02 4.46097e-02 3.64299e-02 5.31309e-02 2.89223e-02 0.00000e+00 0.00000e+00 0.00000e+00 17969 0.02007 0.01957 33008 PI_S&Chronic_obstructive_pulmonary_disease&Cystic_fibrosis&Alpha-1-antitrypsin_deficiency¬_specified¬_provided Alpha-1-antitrypsin_deficiency .&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0009061&MedGen:C0010674&OMIM:219700&Orphanet:ORPHA586&SNOMED_CT:190905008&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MedGen:CN169374&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other Pathogenic(7)&Uncertain_significance(1) 440500:Pathogenic&440501:Pathogenic&626305:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 17 17580 15 30 50.0 -14 94380925 T A A missense_variant MODERATE SERPINA1 5265 Transcript NM_001002235.3 protein_coding 3/5 1046 863 288 E/V gAa/gTa -1 EntrezGene T T 0 0.996 32 4.522644 32 0.99125751745836055 4.060671 0.356045358213489 0.58872 5.370522 0.540959054635916 0.69532 -2.54&-2.54&-2.54&-2.54&-2.54&.&-2.54&-2.54&-2.54&-2.54 5.18 0.999999999804063 0.000193 0.2843 -0.1620 3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615 0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935 -6.8&-6.8&-6.8&-6.8&-6.8&.&-6.8&-6.8&-6.8&-6.71 14.3171 0.277&0.272&0.27&0.276&0.281&.&0.281&0.28&0.271&0.394 0.98481 0.553676 1.000000 0.029000 6.461000 1.138000 5.98 rs17580 4138 143306 2.88753e-02 8.93919e-03 9.37748e-03 8.42464e-03 1.18889e-01 8.93617e-02 1.51163e-01 5.55230e-02 5.29790e-02 5.74638e-02 1.02348e-02 1.02157e-02 1.02564e-02 0.00000e+00 0.00000e+00 0.00000e+00 3.00322e-02 7.82293e-03 8.77893e-03 7.52257e-03 2.76450e-02 4.15931e-02 4.19988e-02 4.10349e-02 4.46097e-02 3.64299e-02 5.31309e-02 2.89223e-02 0.00000e+00 0.00000e+00 0.00000e+00 17969 0.02007 0.01957 33008 PI_S&Chronic_obstructive_pulmonary_disease&Cystic_fibrosis&Alpha-1-antitrypsin_deficiency¬_specified¬_provided Alpha-1-antitrypsin_deficiency .&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0009061&MedGen:C0010674&OMIM:219700&Orphanet:ORPHA586&SNOMED_CT:190905008&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MedGen:CN169374&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other Pathogenic(7)&Uncertain_significance(1) 440500:Pathogenic&440501:Pathogenic&626305:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 17 17580 15 30 50.0 -14 94380925 T A A missense_variant MODERATE SERPINA1 5265 Transcript NM_001002236.3 protein_coding 5/7 1360 863 288 E/V gAa/gTa -1 EntrezGene T T 0 0.996 32 4.522644 32 0.99125751745836055 4.060671 0.356045358213489 0.58872 5.370522 0.540959054635916 0.69532 -2.54&-2.54&-2.54&-2.54&-2.54&.&-2.54&-2.54&-2.54&-2.54 5.18 0.999999999804063 0.000193 0.2843 -0.1620 3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615 0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935 -6.8&-6.8&-6.8&-6.8&-6.8&.&-6.8&-6.8&-6.8&-6.71 14.3171 0.277&0.272&0.27&0.276&0.281&.&0.281&0.28&0.271&0.394 0.98481 0.553676 1.000000 0.029000 6.461000 1.138000 5.98 rs17580 4138 143306 2.88753e-02 8.93919e-03 9.37748e-03 8.42464e-03 1.18889e-01 8.93617e-02 1.51163e-01 5.55230e-02 5.29790e-02 5.74638e-02 1.02348e-02 1.02157e-02 1.02564e-02 0.00000e+00 0.00000e+00 0.00000e+00 3.00322e-02 7.82293e-03 8.77893e-03 7.52257e-03 2.76450e-02 4.15931e-02 4.19988e-02 4.10349e-02 4.46097e-02 3.64299e-02 5.31309e-02 2.89223e-02 0.00000e+00 0.00000e+00 0.00000e+00 17969 0.02007 0.01957 33008 PI_S&Chronic_obstructive_pulmonary_disease&Cystic_fibrosis&Alpha-1-antitrypsin_deficiency¬_specified¬_provided Alpha-1-antitrypsin_deficiency .&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0009061&MedGen:C0010674&OMIM:219700&Orphanet:ORPHA586&SNOMED_CT:190905008&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MedGen:CN169374&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other Pathogenic(7)&Uncertain_significance(1) 440500:Pathogenic&440501:Pathogenic&626305:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 17 17580 15 30 50.0 -14 94380925 T A A missense_variant MODERATE SERPINA1 5265 Transcript NM_001127700.2 protein_coding 3/5 1083 863 288 E/V gAa/gTa -1 EntrezGene T T 0 0.996 32 4.522644 32 0.99125751745836055 4.060671 0.356045358213489 0.58872 5.370522 0.540959054635916 0.69532 -2.54&-2.54&-2.54&-2.54&-2.54&.&-2.54&-2.54&-2.54&-2.54 5.18 0.999999999804063 0.000193 0.2843 -0.1620 3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615 0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935 -6.8&-6.8&-6.8&-6.8&-6.8&.&-6.8&-6.8&-6.8&-6.71 14.3171 0.277&0.272&0.27&0.276&0.281&.&0.281&0.28&0.271&0.394 0.98481 0.553676 1.000000 0.029000 6.461000 1.138000 5.98 rs17580 4138 143306 2.88753e-02 8.93919e-03 9.37748e-03 8.42464e-03 1.18889e-01 8.93617e-02 1.51163e-01 5.55230e-02 5.29790e-02 5.74638e-02 1.02348e-02 1.02157e-02 1.02564e-02 0.00000e+00 0.00000e+00 0.00000e+00 3.00322e-02 7.82293e-03 8.77893e-03 7.52257e-03 2.76450e-02 4.15931e-02 4.19988e-02 4.10349e-02 4.46097e-02 3.64299e-02 5.31309e-02 2.89223e-02 0.00000e+00 0.00000e+00 0.00000e+00 17969 0.02007 0.01957 33008 PI_S&Chronic_obstructive_pulmonary_disease&Cystic_fibrosis&Alpha-1-antitrypsin_deficiency¬_specified¬_provided Alpha-1-antitrypsin_deficiency .&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0009061&MedGen:C0010674&OMIM:219700&Orphanet:ORPHA586&SNOMED_CT:190905008&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MedGen:CN169374&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other Pathogenic(7)&Uncertain_significance(1) 440500:Pathogenic&440501:Pathogenic&626305:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 17 17580 15 30 50.0 -14 94380925 T A A missense_variant MODERATE SERPINA1 5265 Transcript NM_001127701.2 protein_coding 5/7 1379 863 288 E/V gAa/gTa -1 EntrezGene T T 0 0.996 32 4.522644 32 0.99125751745836055 4.060671 0.356045358213489 0.58872 5.370522 0.540959054635916 0.69532 -2.54&-2.54&-2.54&-2.54&-2.54&.&-2.54&-2.54&-2.54&-2.54 5.18 0.999999999804063 0.000193 0.2843 -0.1620 3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615 0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935 -6.8&-6.8&-6.8&-6.8&-6.8&.&-6.8&-6.8&-6.8&-6.71 14.3171 0.277&0.272&0.27&0.276&0.281&.&0.281&0.28&0.271&0.394 0.98481 0.553676 1.000000 0.029000 6.461000 1.138000 5.98 rs17580 4138 143306 2.88753e-02 8.93919e-03 9.37748e-03 8.42464e-03 1.18889e-01 8.93617e-02 1.51163e-01 5.55230e-02 5.29790e-02 5.74638e-02 1.02348e-02 1.02157e-02 1.02564e-02 0.00000e+00 0.00000e+00 0.00000e+00 3.00322e-02 7.82293e-03 8.77893e-03 7.52257e-03 2.76450e-02 4.15931e-02 4.19988e-02 4.10349e-02 4.46097e-02 3.64299e-02 5.31309e-02 2.89223e-02 0.00000e+00 0.00000e+00 0.00000e+00 17969 0.02007 0.01957 33008 PI_S&Chronic_obstructive_pulmonary_disease&Cystic_fibrosis&Alpha-1-antitrypsin_deficiency¬_specified¬_provided Alpha-1-antitrypsin_deficiency .&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0009061&MedGen:C0010674&OMIM:219700&Orphanet:ORPHA586&SNOMED_CT:190905008&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MedGen:CN169374&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other Pathogenic(7)&Uncertain_significance(1) 440500:Pathogenic&440501:Pathogenic&626305:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 17 17580 15 30 50.0 -14 94380925 T A A missense_variant MODERATE SERPINA1 5265 Transcript NM_001127702.2 protein_coding 4/6 1187 863 288 E/V gAa/gTa -1 EntrezGene T T 0 0.996 32 4.522644 32 0.99125751745836055 4.060671 0.356045358213489 0.58872 5.370522 0.540959054635916 0.69532 -2.54&-2.54&-2.54&-2.54&-2.54&.&-2.54&-2.54&-2.54&-2.54 5.18 0.999999999804063 0.000193 0.2843 -0.1620 3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615 0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935 -6.8&-6.8&-6.8&-6.8&-6.8&.&-6.8&-6.8&-6.8&-6.71 14.3171 0.277&0.272&0.27&0.276&0.281&.&0.281&0.28&0.271&0.394 0.98481 0.553676 1.000000 0.029000 6.461000 1.138000 5.98 rs17580 4138 143306 2.88753e-02 8.93919e-03 9.37748e-03 8.42464e-03 1.18889e-01 8.93617e-02 1.51163e-01 5.55230e-02 5.29790e-02 5.74638e-02 1.02348e-02 1.02157e-02 1.02564e-02 0.00000e+00 0.00000e+00 0.00000e+00 3.00322e-02 7.82293e-03 8.77893e-03 7.52257e-03 2.76450e-02 4.15931e-02 4.19988e-02 4.10349e-02 4.46097e-02 3.64299e-02 5.31309e-02 2.89223e-02 0.00000e+00 0.00000e+00 0.00000e+00 17969 0.02007 0.01957 33008 PI_S&Chronic_obstructive_pulmonary_disease&Cystic_fibrosis&Alpha-1-antitrypsin_deficiency¬_specified¬_provided Alpha-1-antitrypsin_deficiency .&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0009061&MedGen:C0010674&OMIM:219700&Orphanet:ORPHA586&SNOMED_CT:190905008&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MedGen:CN169374&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other Pathogenic(7)&Uncertain_significance(1) 440500:Pathogenic&440501:Pathogenic&626305:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 17 17580 15 30 50.0 -14 94380925 T A A missense_variant MODERATE SERPINA1 5265 Transcript NM_001127703.2 protein_coding 5/7 1342 863 288 E/V gAa/gTa -1 EntrezGene T T 0 0.996 32 4.522644 32 0.99125751745836055 4.060671 0.356045358213489 0.58872 5.370522 0.540959054635916 0.69532 -2.54&-2.54&-2.54&-2.54&-2.54&.&-2.54&-2.54&-2.54&-2.54 5.18 0.999999999804063 0.000193 0.2843 -0.1620 3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615 0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935 -6.8&-6.8&-6.8&-6.8&-6.8&.&-6.8&-6.8&-6.8&-6.71 14.3171 0.277&0.272&0.27&0.276&0.281&.&0.281&0.28&0.271&0.394 0.98481 0.553676 1.000000 0.029000 6.461000 1.138000 5.98 rs17580 4138 143306 2.88753e-02 8.93919e-03 9.37748e-03 8.42464e-03 1.18889e-01 8.93617e-02 1.51163e-01 5.55230e-02 5.29790e-02 5.74638e-02 1.02348e-02 1.02157e-02 1.02564e-02 0.00000e+00 0.00000e+00 0.00000e+00 3.00322e-02 7.82293e-03 8.77893e-03 7.52257e-03 2.76450e-02 4.15931e-02 4.19988e-02 4.10349e-02 4.46097e-02 3.64299e-02 5.31309e-02 2.89223e-02 0.00000e+00 0.00000e+00 0.00000e+00 17969 0.02007 0.01957 33008 PI_S&Chronic_obstructive_pulmonary_disease&Cystic_fibrosis&Alpha-1-antitrypsin_deficiency¬_specified¬_provided Alpha-1-antitrypsin_deficiency .&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0009061&MedGen:C0010674&OMIM:219700&Orphanet:ORPHA586&SNOMED_CT:190905008&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MedGen:CN169374&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other Pathogenic(7)&Uncertain_significance(1) 440500:Pathogenic&440501:Pathogenic&626305:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 17 17580 15 30 50.0 -14 94380925 T A A missense_variant MODERATE SERPINA1 5265 Transcript NM_001127704.2 protein_coding 5/7 1339 863 288 E/V gAa/gTa -1 EntrezGene T T 0 0.996 32 4.522644 32 0.99125751745836055 4.060671 0.356045358213489 0.58872 5.370522 0.540959054635916 0.69532 -2.54&-2.54&-2.54&-2.54&-2.54&.&-2.54&-2.54&-2.54&-2.54 5.18 0.999999999804063 0.000193 0.2843 -0.1620 3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615 0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935 -6.8&-6.8&-6.8&-6.8&-6.8&.&-6.8&-6.8&-6.8&-6.71 14.3171 0.277&0.272&0.27&0.276&0.281&.&0.281&0.28&0.271&0.394 0.98481 0.553676 1.000000 0.029000 6.461000 1.138000 5.98 rs17580 4138 143306 2.88753e-02 8.93919e-03 9.37748e-03 8.42464e-03 1.18889e-01 8.93617e-02 1.51163e-01 5.55230e-02 5.29790e-02 5.74638e-02 1.02348e-02 1.02157e-02 1.02564e-02 0.00000e+00 0.00000e+00 0.00000e+00 3.00322e-02 7.82293e-03 8.77893e-03 7.52257e-03 2.76450e-02 4.15931e-02 4.19988e-02 4.10349e-02 4.46097e-02 3.64299e-02 5.31309e-02 2.89223e-02 0.00000e+00 0.00000e+00 0.00000e+00 17969 0.02007 0.01957 33008 PI_S&Chronic_obstructive_pulmonary_disease&Cystic_fibrosis&Alpha-1-antitrypsin_deficiency¬_specified¬_provided Alpha-1-antitrypsin_deficiency .&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0009061&MedGen:C0010674&OMIM:219700&Orphanet:ORPHA586&SNOMED_CT:190905008&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MedGen:CN169374&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other Pathogenic(7)&Uncertain_significance(1) 440500:Pathogenic&440501:Pathogenic&626305:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 17 17580 15 30 50.0 -14 94380925 T A A missense_variant MODERATE SERPINA1 5265 Transcript NM_001127705.2 protein_coding 5/7 1357 863 288 E/V gAa/gTa -1 EntrezGene T T 0 0.996 32 4.522644 32 0.99125751745836055 4.060671 0.356045358213489 0.58872 5.370522 0.540959054635916 0.69532 -2.54&-2.54&-2.54&-2.54&-2.54&.&-2.54&-2.54&-2.54&-2.54 5.18 0.999999999804063 0.000193 0.2843 -0.1620 3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615 0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935 -6.8&-6.8&-6.8&-6.8&-6.8&.&-6.8&-6.8&-6.8&-6.71 14.3171 0.277&0.272&0.27&0.276&0.281&.&0.281&0.28&0.271&0.394 0.98481 0.553676 1.000000 0.029000 6.461000 1.138000 5.98 rs17580 4138 143306 2.88753e-02 8.93919e-03 9.37748e-03 8.42464e-03 1.18889e-01 8.93617e-02 1.51163e-01 5.55230e-02 5.29790e-02 5.74638e-02 1.02348e-02 1.02157e-02 1.02564e-02 0.00000e+00 0.00000e+00 0.00000e+00 3.00322e-02 7.82293e-03 8.77893e-03 7.52257e-03 2.76450e-02 4.15931e-02 4.19988e-02 4.10349e-02 4.46097e-02 3.64299e-02 5.31309e-02 2.89223e-02 0.00000e+00 0.00000e+00 0.00000e+00 17969 0.02007 0.01957 33008 PI_S&Chronic_obstructive_pulmonary_disease&Cystic_fibrosis&Alpha-1-antitrypsin_deficiency¬_specified¬_provided Alpha-1-antitrypsin_deficiency .&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0009061&MedGen:C0010674&OMIM:219700&Orphanet:ORPHA586&SNOMED_CT:190905008&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MedGen:CN169374&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other Pathogenic(7)&Uncertain_significance(1) 440500:Pathogenic&440501:Pathogenic&626305:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 17 17580 15 30 50.0 -14 94380925 T A A missense_variant MODERATE SERPINA1 5265 Transcript NM_001127706.2 protein_coding 4/6 1150 863 288 E/V gAa/gTa -1 EntrezGene T T 0 0.996 32 4.522644 32 0.99125751745836055 4.060671 0.356045358213489 0.58872 5.370522 0.540959054635916 0.69532 -2.54&-2.54&-2.54&-2.54&-2.54&.&-2.54&-2.54&-2.54&-2.54 5.18 0.999999999804063 0.000193 0.2843 -0.1620 3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615 0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935 -6.8&-6.8&-6.8&-6.8&-6.8&.&-6.8&-6.8&-6.8&-6.71 14.3171 0.277&0.272&0.27&0.276&0.281&.&0.281&0.28&0.271&0.394 0.98481 0.553676 1.000000 0.029000 6.461000 1.138000 5.98 rs17580 4138 143306 2.88753e-02 8.93919e-03 9.37748e-03 8.42464e-03 1.18889e-01 8.93617e-02 1.51163e-01 5.55230e-02 5.29790e-02 5.74638e-02 1.02348e-02 1.02157e-02 1.02564e-02 0.00000e+00 0.00000e+00 0.00000e+00 3.00322e-02 7.82293e-03 8.77893e-03 7.52257e-03 2.76450e-02 4.15931e-02 4.19988e-02 4.10349e-02 4.46097e-02 3.64299e-02 5.31309e-02 2.89223e-02 0.00000e+00 0.00000e+00 0.00000e+00 17969 0.02007 0.01957 33008 PI_S&Chronic_obstructive_pulmonary_disease&Cystic_fibrosis&Alpha-1-antitrypsin_deficiency¬_specified¬_provided Alpha-1-antitrypsin_deficiency .&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0009061&MedGen:C0010674&OMIM:219700&Orphanet:ORPHA586&SNOMED_CT:190905008&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MedGen:CN169374&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other Pathogenic(7)&Uncertain_significance(1) 440500:Pathogenic&440501:Pathogenic&626305:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 17 17580 15 30 50.0 -14 94380925 T A A missense_variant MODERATE SERPINA1 5265 Transcript NM_001127707.2 protein_coding 4/6 1147 863 288 E/V gAa/gTa -1 EntrezGene T T 0 0.996 32 4.522644 32 0.99125751745836055 4.060671 0.356045358213489 0.58872 5.370522 0.540959054635916 0.69532 -2.54&-2.54&-2.54&-2.54&-2.54&.&-2.54&-2.54&-2.54&-2.54 5.18 0.999999999804063 0.000193 0.2843 -0.1620 3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615 0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935 -6.8&-6.8&-6.8&-6.8&-6.8&.&-6.8&-6.8&-6.8&-6.71 14.3171 0.277&0.272&0.27&0.276&0.281&.&0.281&0.28&0.271&0.394 0.98481 0.553676 1.000000 0.029000 6.461000 1.138000 5.98 rs17580 4138 143306 2.88753e-02 8.93919e-03 9.37748e-03 8.42464e-03 1.18889e-01 8.93617e-02 1.51163e-01 5.55230e-02 5.29790e-02 5.74638e-02 1.02348e-02 1.02157e-02 1.02564e-02 0.00000e+00 0.00000e+00 0.00000e+00 3.00322e-02 7.82293e-03 8.77893e-03 7.52257e-03 2.76450e-02 4.15931e-02 4.19988e-02 4.10349e-02 4.46097e-02 3.64299e-02 5.31309e-02 2.89223e-02 0.00000e+00 0.00000e+00 0.00000e+00 17969 0.02007 0.01957 33008 PI_S&Chronic_obstructive_pulmonary_disease&Cystic_fibrosis&Alpha-1-antitrypsin_deficiency¬_specified¬_provided Alpha-1-antitrypsin_deficiency .&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0009061&MedGen:C0010674&OMIM:219700&Orphanet:ORPHA586&SNOMED_CT:190905008&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MedGen:CN169374&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other Pathogenic(7)&Uncertain_significance(1) 440500:Pathogenic&440501:Pathogenic&626305:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 17 17580 15 30 50.0 -14 94380925 T A A missense_variant MODERATE SERPINA1 5265 Transcript XM_017021370.1 protein_coding 4/6 1304 863 288 E/V gAa/gTa -1 EntrezGene T T 0 0.996 32 4.522644 32 0.99125751745836055 4.060671 0.356045358213489 0.58872 5.370522 0.540959054635916 0.69532 -2.54&-2.54&-2.54&-2.54&-2.54&.&-2.54&-2.54&-2.54&-2.54 5.18 0.999999999804063 0.000193 0.2843 -0.1620 3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615&3.615 0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935&0.999935 -6.8&-6.8&-6.8&-6.8&-6.8&.&-6.8&-6.8&-6.8&-6.71 14.3171 0.277&0.272&0.27&0.276&0.281&.&0.281&0.28&0.271&0.394 0.98481 0.553676 1.000000 0.029000 6.461000 1.138000 5.98 rs17580 4138 143306 2.88753e-02 8.93919e-03 9.37748e-03 8.42464e-03 1.18889e-01 8.93617e-02 1.51163e-01 5.55230e-02 5.29790e-02 5.74638e-02 1.02348e-02 1.02157e-02 1.02564e-02 0.00000e+00 0.00000e+00 0.00000e+00 3.00322e-02 7.82293e-03 8.77893e-03 7.52257e-03 2.76450e-02 4.15931e-02 4.19988e-02 4.10349e-02 4.46097e-02 3.64299e-02 5.31309e-02 2.89223e-02 0.00000e+00 0.00000e+00 0.00000e+00 17969 0.02007 0.01957 33008 PI_S&Chronic_obstructive_pulmonary_disease&Cystic_fibrosis&Alpha-1-antitrypsin_deficiency¬_specified¬_provided Alpha-1-antitrypsin_deficiency .&Human_Phenotype_Ontology:HP:0006510&MedGen:C0024117&OMIM:606963&MONDO:MONDO:0009061&MedGen:C0010674&OMIM:219700&Orphanet:ORPHA586&SNOMED_CT:190905008&MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007&MedGen:CN169374&MedGen:CN517202 MONDO:MONDO:0013282&MedGen:C0221757&OMIM:613490&Orphanet:ORPHA60&SNOMED_CT:30188007 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other Pathogenic(7)&Uncertain_significance(1) 440500:Pathogenic&440501:Pathogenic&626305:Pathogenic single_nucleotide_variant SERPINA1:5265 SO:0001583&missense_variant 17 17580 15 30 50.0 -15 31038109 CAT C - frameshift_variant HIGH TRPM1 4308 Transcript NM_001252020.1 protein_coding 18/27 2737-2738 2423-2424 808 Y/X tAT/t -1 EntrezGene AT AT OK -5.25&0.329 15 30 50.0 -15 31038109 CAT C - frameshift_variant HIGH TRPM1 4308 Transcript NM_001252024.2 protein_coding 19/28 2524-2525 2372-2373 791 Y/X tAT/t -1 EntrezGene AT AT -5.25&0.329 15 30 50.0 -15 31038109 CAT C - frameshift_variant HIGH TRPM1 4308 Transcript NM_002420.6 protein_coding 18/27 2438-2439 2306-2307 769 Y/X tAT/t -1 EntrezGene AT AT -5.25&0.329 15 30 50.0 -15 31038109 CAT C - downstream_gene_variant MODIFIER LOC105370752 105370752 Transcript XR_001751769.1 lncRNA 963 1 EntrezGene AT AT -5.25&0.329 15 30 50.0 -15 31038109 CAT C - downstream_gene_variant MODIFIER LOC105370752 105370752 Transcript XR_932055.1 lncRNA 963 1 EntrezGene AT AT -5.25&0.329 15 30 50.0 -15 31038109 CAT C - downstream_gene_variant MODIFIER LOC105370752 105370752 Transcript XR_932056.1 lncRNA 963 1 EntrezGene AT AT -5.25&0.329 15 30 50.0 -15 31038109 CAT C - downstream_gene_variant MODIFIER LOC105370752 105370752 Transcript XR_932057.1 lncRNA 629 1 EntrezGene AT AT -5.25&0.329 15 30 50.0 -15 45101227 TGAAC T - frameshift_variant HIGH DUOX2 50506 Transcript NM_001363711.2 protein_coding 22/34 3109-3112 2895-2898 965-966 SF/X tcGTTC/tc -1 EntrezGene GAAC GAAC 28.8 4.212852 4.63&6.47&5.36&-1.36 rs530719719 378 143280 2.63819e-03 8.09061e-04 6.60909e-04 9.83030e-04 3.34076e-03 2.12766e-03 4.67290e-03 1.75670e-03 1.69033e-03 1.80738e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.42396e-03 9.92177e-03 9.57702e-03 1.00301e-02 2.86603e-03 3.19093e-03 3.39681e-03 2.90762e-03 1.39405e-03 9.10747e-04 1.89753e-03 2.64344e-03 1.31234e-03 1.77305e-03 1.20773e-03 189229 187125 Nongoitrous_Euthyroid_Hyperthyrotropinemia&Congenital_hypothyroidism&Thyroid_dyshormonogenesis_6&Inborn_genetic_diseases&Familial_thyroid_dyshormonogenesis¬_provided .&Human_Phenotype_Ontology:HP:0000851&MONDO:MONDO:0018612&MedGen:C0010308&Orphanet:ORPHA442&SNOMED_CT:190268003&MONDO:MONDO:0011792&MedGen:C1846632&OMIM:607200&MeSH:D030342&MedGen:C0950123&MedGen:C4273748&Orphanet:ORPHA95716&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic Deletion DUOX2:50506 SO:0001589&frameshift_variant 1 530719719 15 30 50.0 -15 45101227 TGAAC T - frameshift_variant HIGH DUOX2 50506 Transcript NM_014080.4 protein_coding 22/34 3098-3101 2895-2898 965-966 SF/X tcGTTC/tc -1 EntrezGene GAAC GAAC OK 28.8 4.212852 4.63&6.47&5.36&-1.36 rs530719719 378 143280 2.63819e-03 8.09061e-04 6.60909e-04 9.83030e-04 3.34076e-03 2.12766e-03 4.67290e-03 1.75670e-03 1.69033e-03 1.80738e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.42396e-03 9.92177e-03 9.57702e-03 1.00301e-02 2.86603e-03 3.19093e-03 3.39681e-03 2.90762e-03 1.39405e-03 9.10747e-04 1.89753e-03 2.64344e-03 1.31234e-03 1.77305e-03 1.20773e-03 189229 187125 Nongoitrous_Euthyroid_Hyperthyrotropinemia&Congenital_hypothyroidism&Thyroid_dyshormonogenesis_6&Inborn_genetic_diseases&Familial_thyroid_dyshormonogenesis¬_provided .&Human_Phenotype_Ontology:HP:0000851&MONDO:MONDO:0018612&MedGen:C0010308&Orphanet:ORPHA442&SNOMED_CT:190268003&MONDO:MONDO:0011792&MedGen:C1846632&OMIM:607200&MeSH:D030342&MedGen:C0950123&MedGen:C4273748&Orphanet:ORPHA95716&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic Deletion DUOX2:50506 SO:0001589&frameshift_variant 1 530719719 15 30 50.0 -15 53215578 G A A intron_variant MODIFIER LOC107983981 107983981 Transcript XM_017022785.1 protein_coding 5/5 1 EntrezGene G G 0.039 -0.723161 -7.23 rs4774651 60841 142928 4.25676e-01 1.48866e-01 1.52891e-01 1.44140e-01 7.42188e-01 7.36170e-01 7.48826e-01 4.40343e-01 4.24499e-01 4.52418e-01 5.73494e-01 5.81911e-01 5.64020e-01 2.49520e-01 2.58669e-01 2.41667e-01 4.28482e-01 5.20204e-01 5.11218e-01 5.23044e-01 4.22689e-01 5.81374e-01 5.84806e-01 5.76654e-01 4.46878e-01 4.33394e-01 4.60952e-01 4.25474e-01 4.59211e-01 4.98227e-01 4.50323e-01 15 30 50.0 -15 53215578 G A A intron_variant&non_coding_transcript_variant MODIFIER LOC107983981 107983981 Transcript XR_932257.2 misc_RNA 6/6 1 EntrezGene G G 0.039 -0.723161 -7.23 rs4774651 60841 142928 4.25676e-01 1.48866e-01 1.52891e-01 1.44140e-01 7.42188e-01 7.36170e-01 7.48826e-01 4.40343e-01 4.24499e-01 4.52418e-01 5.73494e-01 5.81911e-01 5.64020e-01 2.49520e-01 2.58669e-01 2.41667e-01 4.28482e-01 5.20204e-01 5.11218e-01 5.23044e-01 4.22689e-01 5.81374e-01 5.84806e-01 5.76654e-01 4.46878e-01 4.33394e-01 4.60952e-01 4.25474e-01 4.59211e-01 4.98227e-01 4.50323e-01 15 30 50.0 -15 72346234 C G G splice_donor_variant HIGH HEXA 3073 Transcript NM_000520.6 protein_coding 12/13 -1 EntrezGene C C 34 5.784520 6.29 rs147324677 3 143266 2.09401e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 1.13507e-03 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.70841e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44047e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09240e-05 0.00000e+00 0.00000e+00 0.00000e+00 3890 0.00023 0.00009 18929 Tay-Sachs_disease¬_provided MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant HEXA:3073 SO:0001575&splice_donor_variant 1 147324677 15 30 50.0 -15 72346234 C G G splice_donor_variant HIGH HEXA 3073 Transcript NM_001318825.2 protein_coding 12/13 -1 EntrezGene C C 34 5.784520 6.29 rs147324677 3 143266 2.09401e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 1.13507e-03 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.70841e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44047e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09240e-05 0.00000e+00 0.00000e+00 0.00000e+00 3890 0.00023 0.00009 18929 Tay-Sachs_disease¬_provided MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant HEXA:3073 SO:0001575&splice_donor_variant 1 147324677 15 30 50.0 -15 72346234 C G G splice_donor_variant&non_coding_transcript_variant HIGH HEXA 3073 Transcript NR_134869.2 misc_RNA 10/10 -1 EntrezGene C C 34 5.784520 6.29 rs147324677 3 143266 2.09401e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.03070e-04 1.13507e-03 6.41026e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.70841e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44047e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09240e-05 0.00000e+00 0.00000e+00 0.00000e+00 3890 0.00023 0.00009 18929 Tay-Sachs_disease¬_provided MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant HEXA:3073 SO:0001575&splice_donor_variant 1 147324677 15 30 50.0 -15 72346580 G GATAG ATAG frameshift_variant HIGH HEXA 3073 Transcript NM_000520.6 protein_coding 11/14 1318-1319 1276-1277 426 S/SIX tcc/tCTATcc -1 EntrezGene -1.35&-2.59 15 30 50.0 -15 72346580 G GATAG ATAG frameshift_variant HIGH HEXA 3073 Transcript NM_001318825.2 protein_coding 11/14 1351-1352 1309-1310 437 S/SIX tcc/tCTATcc -1 EntrezGene -1.35&-2.59 15 30 50.0 -15 72346580 G GATAG ATAG intron_variant&non_coding_transcript_variant MODIFIER HEXA 3073 Transcript NR_134869.2 misc_RNA 9/10 -1 EntrezGene -1.35&-2.59 15 30 50.0 -15 72348047 C T T splice_donor_variant HIGH HEXA 3073 Transcript NM_000520.6 protein_coding 9/13 -1 EntrezGene C C 34 5.338247 6.08 chr15:72348047-72348047 30 143258 2.09412e-04 9.51520e-05 1.32194e-04 5.16956e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16714e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.01648e-04 4.02863e-04 3.47761e-04 4.78716e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.09281e-04 0.00000e+00 0.00000e+00 0.00000e+00 3920 0.00031 0.00023 0.00040 18959 Tay-Sachs_disease¬_provided MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant HEXA:3073 SO:0001575&splice_donor_variant 1 76173977 15 30 50.0 -15 72348047 C T T splice_donor_variant HIGH HEXA 3073 Transcript NM_001318825.2 protein_coding 9/13 -1 EntrezGene C C 34 5.338247 6.08 chr15:72348047-72348047 30 143258 2.09412e-04 9.51520e-05 1.32194e-04 5.16956e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16714e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.01648e-04 4.02863e-04 3.47761e-04 4.78716e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.09281e-04 0.00000e+00 0.00000e+00 0.00000e+00 3920 0.00031 0.00023 0.00040 18959 Tay-Sachs_disease¬_provided MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant HEXA:3073 SO:0001575&splice_donor_variant 1 76173977 15 30 50.0 -15 72348047 C T T splice_donor_variant&non_coding_transcript_variant HIGH HEXA 3073 Transcript NR_134869.2 misc_RNA 9/10 -1 EntrezGene C C 34 5.338247 6.08 chr15:72348047-72348047 30 143258 2.09412e-04 9.51520e-05 1.32194e-04 5.16956e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.16714e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.01648e-04 4.02863e-04 3.47761e-04 4.78716e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.09281e-04 0.00000e+00 0.00000e+00 0.00000e+00 3920 0.00031 0.00023 0.00040 18959 Tay-Sachs_disease¬_provided MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant HEXA:3073 SO:0001575&splice_donor_variant 1 76173977 15 30 50.0 -15 72350517 C G G splice_donor_variant HIGH HEXA 3073 Transcript NM_000520.6 protein_coding 7/13 -1 EntrezGene C C 34 5.388084 6.29 chr15:72350517-72350517 3 143322 2.09319e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35395e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87919e-05 3.09684e-05 2.67423e-05 3.67809e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.09231e-05 3.27869e-04 0.00000e+00 4.02576e-04 375358 362122 Tay-Sachs_disease MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant HEXA:3073 SO:0001575&splice_donor_variant 1 121907980 15 30 50.0 -15 72350517 C G G splice_donor_variant HIGH HEXA 3073 Transcript NM_001318825.2 protein_coding 7/13 -1 EntrezGene C C 34 5.388084 6.29 chr15:72350517-72350517 3 143322 2.09319e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35395e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87919e-05 3.09684e-05 2.67423e-05 3.67809e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.09231e-05 3.27869e-04 0.00000e+00 4.02576e-04 375358 362122 Tay-Sachs_disease MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant HEXA:3073 SO:0001575&splice_donor_variant 1 121907980 15 30 50.0 -15 72350517 C G G splice_donor_variant&non_coding_transcript_variant HIGH HEXA 3073 Transcript NR_134869.2 misc_RNA 7/10 -1 EntrezGene C C 34 5.388084 6.29 chr15:72350517-72350517 3 143322 2.09319e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35395e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.87919e-05 3.09684e-05 2.67423e-05 3.67809e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.09231e-05 3.27869e-04 0.00000e+00 4.02576e-04 375358 362122 Tay-Sachs_disease MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant HEXA:3073 SO:0001575&splice_donor_variant 1 121907980 15 30 50.0 -15 72350517 C T T splice_donor_variant HIGH HEXA 3073 Transcript NM_000520.6 protein_coding 7/13 -1 EntrezGene C C 34 5.410626 6.29 3938 18977 Gm2-gangliosidosis&_late_onset&Tay-Sachs_disease .&MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant HEXA:3073 SO:0001575&splice_donor_variant 1 121907980 15 30 50.0 -15 72350517 C T T splice_donor_variant HIGH HEXA 3073 Transcript NM_001318825.2 protein_coding 7/13 -1 EntrezGene C C 34 5.410626 6.29 3938 18977 Gm2-gangliosidosis&_late_onset&Tay-Sachs_disease .&MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant HEXA:3073 SO:0001575&splice_donor_variant 1 121907980 15 30 50.0 -15 72350517 C T T splice_donor_variant&non_coding_transcript_variant HIGH HEXA 3073 Transcript NR_134869.2 misc_RNA 7/10 -1 EntrezGene C C 34 5.410626 6.29 3938 18977 Gm2-gangliosidosis&_late_onset&Tay-Sachs_disease .&MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant HEXA:3073 SO:0001575&splice_donor_variant 1 121907980 15 30 50.0 -15 72350518 C G G missense_variant&splice_region_variant MODERATE HEXA 3073 Transcript NM_000520.6 protein_coding 7/14 847 805 269 G/R Ggt/Cgt -1 EntrezGene C C 0 0.999 34 5.475361 34 0.99930744067020871 17.46993 0.976484595031704 0.98118 15.10131 1.03629901609162 0.96756 -3.72&-3.72&-3.72 5.87 0.999999999998453 0.000000 0.356914 0.9513 1.0815 3.575&.&. 1&1&1&1&1 -7.56&-7.56&-7.56 20.2227 0.943&0.945&0.923 0.99821 0.732398 1.000000 1.000000 7.813000 1.026000 6.29 375357 362123 Tay-Sachs_disease MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000 criteria_provided&_single_submitter Likely_pathogenic single_nucleotide_variant HEXA:3073 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 121907954 15 30 50.0 -15 72350518 C G G missense_variant&splice_region_variant MODERATE HEXA 3073 Transcript NM_001318825.2 protein_coding 7/14 880 838 280 G/R Ggt/Cgt -1 EntrezGene C C 0 1 34 5.475361 34 0.99930744067020871 17.46993 0.976484595031704 0.98118 15.10131 1.03629901609162 0.96756 -3.72&-3.72&-3.72 5.87 0.999999999998453 0.000000 0.356914 0.9513 1.0815 3.575&.&. 1&1&1&1&1 -7.56&-7.56&-7.56 20.2227 0.943&0.945&0.923 0.99821 0.732398 1.000000 1.000000 7.813000 1.026000 6.29 375357 362123 Tay-Sachs_disease MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000 criteria_provided&_single_submitter Likely_pathogenic single_nucleotide_variant HEXA:3073 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 121907954 15 30 50.0 -15 72350518 C G G splice_region_variant&non_coding_transcript_exon_variant LOW HEXA 3073 Transcript NR_134869.2 misc_RNA 7/11 847 -1 EntrezGene C C 34 5.475361 6.29 375357 362123 Tay-Sachs_disease MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000 criteria_provided&_single_submitter Likely_pathogenic single_nucleotide_variant HEXA:3073 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 121907954 15 30 50.0 -15 72350518 C T T missense_variant&splice_region_variant MODERATE HEXA 3073 Transcript NM_000520.6 protein_coding 7/14 847 805 269 G/S Ggt/Agt -1 EntrezGene C C 0.1 0.942 33 4.916203 33 0.99838664019906287 10.30386 0.806007047990702 0.90207 8.38043 0.77885255669036 0.84767 -3.58&-3.58&-3.58 5.87 0.999999999998453 0.000000 0.329188 0.8903 0.8636 0.7&.&. 1&1&1&1&1 -5.51&-5.51&-5.51 20.2227 0.737&0.747&0.731 0.99795 0.732398 1.000000 1.000000 7.813000 1.026000 6.29 chr15:72350518-72350518 21 143300 1.46546e-04 2.37914e-05 0.00000e+00 5.17331e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.00842e-04 5.67537e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.89538e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.00812e-04 2.94245e-04 3.47594e-04 2.20816e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.46468e-04 0.00000e+00 0.00000e+00 0.00000e+00 3898 0.00010 18937 Tay-Sachs_disease&Gm2-gangliosidosis&_adult¬_provided MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000&MedGen:C2874270&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant HEXA:3073 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 121907954 15 30 50.0 -15 72350518 C T T missense_variant&splice_region_variant MODERATE HEXA 3073 Transcript NM_001318825.2 protein_coding 7/14 880 838 280 G/S Ggt/Agt -1 EntrezGene C C 0.11 1 33 4.916203 33 0.99838664019906287 10.30386 0.806007047990702 0.90207 8.38043 0.77885255669036 0.84767 -3.58&-3.58&-3.58 5.87 0.999999999998453 0.000000 0.329188 0.8903 0.8636 0.7&.&. 1&1&1&1&1 -5.51&-5.51&-5.51 20.2227 0.737&0.747&0.731 0.99795 0.732398 1.000000 1.000000 7.813000 1.026000 6.29 chr15:72350518-72350518 21 143300 1.46546e-04 2.37914e-05 0.00000e+00 5.17331e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.00842e-04 5.67537e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.89538e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.00812e-04 2.94245e-04 3.47594e-04 2.20816e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.46468e-04 0.00000e+00 0.00000e+00 0.00000e+00 3898 0.00010 18937 Tay-Sachs_disease&Gm2-gangliosidosis&_adult¬_provided MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000&MedGen:C2874270&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant HEXA:3073 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 121907954 15 30 50.0 -15 72350518 C T T splice_region_variant&non_coding_transcript_exon_variant LOW HEXA 3073 Transcript NR_134869.2 misc_RNA 7/11 847 -1 EntrezGene C C 33 4.916203 6.29 chr15:72350518-72350518 21 143300 1.46546e-04 2.37914e-05 0.00000e+00 5.17331e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.00842e-04 5.67537e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.89538e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.00812e-04 2.94245e-04 3.47594e-04 2.20816e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.46468e-04 0.00000e+00 0.00000e+00 0.00000e+00 3898 0.00010 18937 Tay-Sachs_disease&Gm2-gangliosidosis&_adult¬_provided MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000&MedGen:C2874270&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant HEXA:3073 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 121907954 15 30 50.0 -15 72350578 G A A missense_variant MODERATE HEXA 3073 Transcript NM_000520.6 protein_coding 7/14 787 745 249 R/W Cgg/Tgg -1 EntrezGene G G 0.01 0.993 25.1 3.627127 25.1 0.99909779808172783 5.609457 0.546273898777318 0.71138 6.442853 0.647397520111713 0.76201 -4.53&-4.53&-4.53 4.96 0.347259325416156 0.000009 0.25397 0.9737 1.0652 3.895&.&. 0.999999&1&0.999999&0.999998&0.999998 -7.78&-7.78&-7.78 14.1356 0.88&0.888&0.88 0.94140 0.732398 0.994000 0.998000 2.108000 0.213000 6.29 rs138058578 26 143278 1.81465e-04 1.18940e-04 1.32194e-04 1.03391e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.91449e-03 1.38122e-03 2.37248e-03 1.89625e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.72791e-04 2.32356e-04 2.40783e-04 2.20767e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.95304e-04 0.00000e+00 0.00000e+00 0.00000e+00 126510 0.00038 0.00023 132034 Tay-Sachs_disease¬_provided MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Benign&_other single_nucleotide_variant HEXA:3073 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 138058578 15 30 50.0 -15 72350578 G A A missense_variant MODERATE HEXA 3073 Transcript NM_001318825.2 protein_coding 7/14 820 778 260 R/W Cgg/Tgg -1 EntrezGene G G 0.01 0.329 25.1 3.627127 25.1 0.99909779808172783 5.609457 0.546273898777318 0.71138 6.442853 0.647397520111713 0.76201 -4.53&-4.53&-4.53 4.96 0.347259325416156 0.000009 0.25397 0.9737 1.0652 3.895&.&. 0.999999&1&0.999999&0.999998&0.999998 -7.78&-7.78&-7.78 14.1356 0.88&0.888&0.88 0.94140 0.732398 0.994000 0.998000 2.108000 0.213000 6.29 rs138058578 26 143278 1.81465e-04 1.18940e-04 1.32194e-04 1.03391e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.91449e-03 1.38122e-03 2.37248e-03 1.89625e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.72791e-04 2.32356e-04 2.40783e-04 2.20767e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.95304e-04 0.00000e+00 0.00000e+00 0.00000e+00 126510 0.00038 0.00023 132034 Tay-Sachs_disease¬_provided MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Benign&_other single_nucleotide_variant HEXA:3073 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 138058578 15 30 50.0 -15 72350578 G A A non_coding_transcript_exon_variant MODIFIER HEXA 3073 Transcript NR_134869.2 misc_RNA 7/11 787 -1 EntrezGene G G 25.1 3.627127 6.29 rs138058578 26 143278 1.81465e-04 1.18940e-04 1.32194e-04 1.03391e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.91449e-03 1.38122e-03 2.37248e-03 1.89625e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.72791e-04 2.32356e-04 2.40783e-04 2.20767e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.95304e-04 0.00000e+00 0.00000e+00 0.00000e+00 126510 0.00038 0.00023 132034 Tay-Sachs_disease¬_provided MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Benign&_other single_nucleotide_variant HEXA:3073 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 138058578 15 30 50.0 -15 72350584 G A A missense_variant MODERATE HEXA 3073 Transcript NM_000520.6 protein_coding 7/14 781 739 247 R/W Cgg/Tgg -1 EntrezGene G G 0 1 25.1 3.626515 25.1 0.99924681093539947 5.92587 0.575158713206192 0.73164 7.321044 0.714133744759648 0.80558 -3.9&-3.9&-3.9 3.72 0.32610680752086 0.000000 0.2064 0.9530 1.1036 3.95&.&. 0.999999&0.999999&1&0.999999&1 -7.8&-7.8&-7.8 13.6895 0.911&0.909&0.91 0.91343 0.732398 0.647000 0.978000 0.906000 0.161000 3.4 rs121907970 267 143296 1.86328e-03 7.61361e-04 7.93021e-04 7.24188e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.46370e-04 1.69205e-04 1.28966e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.11256e-03 1.90876e-04 0.00000e+00 2.50878e-04 1.59823e-03 3.56169e-03 3.63695e-03 3.45817e-03 4.65549e-04 9.12409e-04 0.00000e+00 1.86231e-03 0.00000e+00 0.00000e+00 0.00000e+00 3922 0.00164 0.00040 18961 Tay-Sachs_disease&Beta-hexosaminidase_a&_pseudodeficiency_of¬_specified¬_provided MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000&MedGen:CN068777&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other Benign(2)&Uncertain_significance(1) single_nucleotide_variant HEXA:3073 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 121907970 15 30 50.0 -15 72350584 G A A missense_variant MODERATE HEXA 3073 Transcript NM_001318825.2 protein_coding 7/14 814 772 258 R/W Cgg/Tgg -1 EntrezGene G G 0 0.999 25.1 3.626515 25.1 0.99924681093539947 5.92587 0.575158713206192 0.73164 7.321044 0.714133744759648 0.80558 -3.9&-3.9&-3.9 3.72 0.32610680752086 0.000000 0.2064 0.9530 1.1036 3.95&.&. 0.999999&0.999999&1&0.999999&1 -7.8&-7.8&-7.8 13.6895 0.911&0.909&0.91 0.91343 0.732398 0.647000 0.978000 0.906000 0.161000 3.4 rs121907970 267 143296 1.86328e-03 7.61361e-04 7.93021e-04 7.24188e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.46370e-04 1.69205e-04 1.28966e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.11256e-03 1.90876e-04 0.00000e+00 2.50878e-04 1.59823e-03 3.56169e-03 3.63695e-03 3.45817e-03 4.65549e-04 9.12409e-04 0.00000e+00 1.86231e-03 0.00000e+00 0.00000e+00 0.00000e+00 3922 0.00164 0.00040 18961 Tay-Sachs_disease&Beta-hexosaminidase_a&_pseudodeficiency_of¬_specified¬_provided MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000&MedGen:CN068777&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other Benign(2)&Uncertain_significance(1) single_nucleotide_variant HEXA:3073 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 121907970 15 30 50.0 -15 72350584 G A A non_coding_transcript_exon_variant MODIFIER HEXA 3073 Transcript NR_134869.2 misc_RNA 7/11 781 -1 EntrezGene G G 25.1 3.626515 3.4 rs121907970 267 143296 1.86328e-03 7.61361e-04 7.93021e-04 7.24188e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.46370e-04 1.69205e-04 1.28966e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.11256e-03 1.90876e-04 0.00000e+00 2.50878e-04 1.59823e-03 3.56169e-03 3.63695e-03 3.45817e-03 4.65549e-04 9.12409e-04 0.00000e+00 1.86231e-03 0.00000e+00 0.00000e+00 0.00000e+00 3922 0.00164 0.00040 18961 Tay-Sachs_disease&Beta-hexosaminidase_a&_pseudodeficiency_of¬_specified¬_provided MONDO:MONDO:0010100&MedGen:C0039373&OMIM:272800&Orphanet:ORPHA845&SNOMED_CT:111385000&MedGen:CN068777&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity&_other Benign(2)&Uncertain_significance(1) single_nucleotide_variant HEXA:3073 SO:0001583&missense_variant&SO:0001619&non-coding_transcript_variant 1 121907970 15 30 50.0 -15 90766923 ATCTGA TAGATTC TAGATTC frameshift_variant HIGH BLM 641 Transcript NM_000057.4 protein_coding 10/22 2306-2311 2207-2212 736-738 YLT/LDSX tATCTGAca/tTAGATTCca 1 EntrezGene ATCTGA ATCTGA 6.36&2.94&4.49&6.36&4.5&6.36 5454 20493 Bloom_syndrome&Hereditary_cancer-predisposing_syndrome¬_provided MONDO:MONDO:0008876&MedGen:C0005859&OMIM:210900&Orphanet:ORPHA125&SNOMED_CT:4434006&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Indel BLM:641 SO:0001589&frameshift_variant 1 113993962 15 30 50.0 -15 90766923 ATCTGA TAGATTC TAGATTC frameshift_variant HIGH BLM 641 Transcript NM_001287246.2 protein_coding 11/23 2416-2421 2207-2212 736-738 YLT/LDSX tATCTGAca/tTAGATTCca 1 EntrezGene ATCTGA ATCTGA 6.36&2.94&4.49&6.36&4.5&6.36 5454 20493 Bloom_syndrome&Hereditary_cancer-predisposing_syndrome¬_provided MONDO:MONDO:0008876&MedGen:C0005859&OMIM:210900&Orphanet:ORPHA125&SNOMED_CT:4434006&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Indel BLM:641 SO:0001589&frameshift_variant 1 113993962 15 30 50.0 -15 90766923 ATCTGA TAGATTC TAGATTC frameshift_variant HIGH BLM 641 Transcript NM_001287247.2 protein_coding 10/20 2306-2311 2207-2212 736-738 YLT/LDSX tATCTGAca/tTAGATTCca 1 EntrezGene ATCTGA ATCTGA 6.36&2.94&4.49&6.36&4.5&6.36 5454 20493 Bloom_syndrome&Hereditary_cancer-predisposing_syndrome¬_provided MONDO:MONDO:0008876&MedGen:C0005859&OMIM:210900&Orphanet:ORPHA125&SNOMED_CT:4434006&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Indel BLM:641 SO:0001589&frameshift_variant 1 113993962 15 30 50.0 -15 90766923 ATCTGA TAGATTC TAGATTC frameshift_variant HIGH BLM 641 Transcript NM_001287248.2 protein_coding 10/22 2472-2477 1082-1087 361-363 YLT/LDSX tATCTGAca/tTAGATTCca 1 EntrezGene ATCTGA ATCTGA 6.36&2.94&4.49&6.36&4.5&6.36 5454 20493 Bloom_syndrome&Hereditary_cancer-predisposing_syndrome¬_provided MONDO:MONDO:0008876&MedGen:C0005859&OMIM:210900&Orphanet:ORPHA125&SNOMED_CT:4434006&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Indel BLM:641 SO:0001589&frameshift_variant 1 113993962 15 30 50.0 -15 90766923 ATCTGA TAGATTC TAGATTC frameshift_variant HIGH BLM 641 Transcript XM_006720632.2 protein_coding 4/16 384-389 245-250 82-84 YLT/LDSX tATCTGAca/tTAGATTCca 1 EntrezGene ATCTGA ATCTGA 6.36&2.94&4.49&6.36&4.5&6.36 5454 20493 Bloom_syndrome&Hereditary_cancer-predisposing_syndrome¬_provided MONDO:MONDO:0008876&MedGen:C0005859&OMIM:210900&Orphanet:ORPHA125&SNOMED_CT:4434006&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Indel BLM:641 SO:0001589&frameshift_variant 1 113993962 15 30 50.0 -15 90766923 ATCTGA TAGATTC TAGATTC frameshift_variant HIGH BLM 641 Transcript XM_011521881.2 protein_coding 7/19 1360-1365 893-898 298-300 YLT/LDSX tATCTGAca/tTAGATTCca 1 EntrezGene ATCTGA ATCTGA 6.36&2.94&4.49&6.36&4.5&6.36 5454 20493 Bloom_syndrome&Hereditary_cancer-predisposing_syndrome¬_provided MONDO:MONDO:0008876&MedGen:C0005859&OMIM:210900&Orphanet:ORPHA125&SNOMED_CT:4434006&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Indel BLM:641 SO:0001589&frameshift_variant 1 113993962 15 30 50.0 -15 90766923 ATCTGA TAGATTC TAGATTC frameshift_variant HIGH BLM 641 Transcript XM_011521882.3 protein_coding 10/13 2254-2259 2207-2212 736-738 YLT/LDSX tATCTGAca/tTAGATTCca 1 EntrezGene ATCTGA ATCTGA 6.36&2.94&4.49&6.36&4.5&6.36 5454 20493 Bloom_syndrome&Hereditary_cancer-predisposing_syndrome¬_provided MONDO:MONDO:0008876&MedGen:C0005859&OMIM:210900&Orphanet:ORPHA125&SNOMED_CT:4434006&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Indel BLM:641 SO:0001589&frameshift_variant 1 113993962 15 30 50.0 -16 23199643 T TTTTTC TTTTC intron_variant MODIFIER SCNN1G 6340 Transcript NM_001039.4 protein_coding 6/12 1 EntrezGene 1.807 0.066433 -0.0218&-0.219 chr16:23199644-23199644 241 140996 1.70927e-03 4.57459e-03 4.57925e-03 4.56912e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.24093e-04 8.63558e-04 7.93861e-04 6.69507e-03 7.46269e-03 5.82902e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.81458e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.59702e-03 2.95472e-04 3.22027e-04 2.58876e-04 4.69043e-04 0.00000e+00 9.65251e-04 2.78143e-03 3.35121e-04 1.80505e-03 0.00000e+00 15 30 50.0 -16 31096368 C T T upstream_gene_variant MODIFIER VKORC1 79001 Transcript NM_001311311.1 protein_coding 1369 -1 EntrezGene C C OK 1.603 0.039574 -0.835 chr16:31096368-31096368 44172 143090 3.08701e-01 1.02006e-01 1.01695e-01 1.02370e-01 4.39597e-01 4.01709e-01 4.81221e-01 3.90822e-01 4.04988e-01 3.80016e-01 4.98195e-01 5.02838e-01 4.92958e-01 8.94182e-01 8.87119e-01 9.00238e-01 3.06138e-01 3.91396e-01 3.87600e-01 3.92595e-01 3.11427e-01 3.76367e-01 3.75515e-01 3.77541e-01 3.80000e-01 3.88889e-01 3.70722e-01 3.08673e-01 1.76741e-01 1.84397e-01 1.75000e-01 2211 0.35563 17250 Warfarin_response¬_specified¬_provided MONDO:MONDO:0007390&MedGen:C0750384&OMIM:122700&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_benign&_other single_nucleotide_variant VKORC1:79001 1 9923231 15 30 50.0 -16 31096368 C T T upstream_gene_variant MODIFIER VKORC1 79001 Transcript NM_024006.6 protein_coding 1571 -1 EntrezGene C C 1.603 0.039574 -0.835 chr16:31096368-31096368 44172 143090 3.08701e-01 1.02006e-01 1.01695e-01 1.02370e-01 4.39597e-01 4.01709e-01 4.81221e-01 3.90822e-01 4.04988e-01 3.80016e-01 4.98195e-01 5.02838e-01 4.92958e-01 8.94182e-01 8.87119e-01 9.00238e-01 3.06138e-01 3.91396e-01 3.87600e-01 3.92595e-01 3.11427e-01 3.76367e-01 3.75515e-01 3.77541e-01 3.80000e-01 3.88889e-01 3.70722e-01 3.08673e-01 1.76741e-01 1.84397e-01 1.75000e-01 2211 0.35563 17250 Warfarin_response¬_specified¬_provided MONDO:MONDO:0007390&MedGen:C0750384&OMIM:122700&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_benign&_other single_nucleotide_variant VKORC1:79001 1 9923231 15 30 50.0 -16 31096368 C T T upstream_gene_variant MODIFIER VKORC1 79001 Transcript NM_206824.2 protein_coding 1369 -1 EntrezGene C C OK 1.603 0.039574 -0.835 chr16:31096368-31096368 44172 143090 3.08701e-01 1.02006e-01 1.01695e-01 1.02370e-01 4.39597e-01 4.01709e-01 4.81221e-01 3.90822e-01 4.04988e-01 3.80016e-01 4.98195e-01 5.02838e-01 4.92958e-01 8.94182e-01 8.87119e-01 9.00238e-01 3.06138e-01 3.91396e-01 3.87600e-01 3.92595e-01 3.11427e-01 3.76367e-01 3.75515e-01 3.77541e-01 3.80000e-01 3.88889e-01 3.70722e-01 3.08673e-01 1.76741e-01 1.84397e-01 1.75000e-01 2211 0.35563 17250 Warfarin_response¬_specified¬_provided MONDO:MONDO:0007390&MedGen:C0750384&OMIM:122700&MedGen:CN169374&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Likely_benign&_other single_nucleotide_variant VKORC1:79001 1 9923231 15 30 50.0 -16 54316844 C G G intergenic_variant MODIFIER 20.9 2.203343 6.54 rs17255375 15972 143248 1.11499e-01 7.36582e-02 7.58390e-02 7.10959e-02 1.02679e-01 1.02128e-01 1.03286e-01 1.31136e-01 1.31989e-01 1.30485e-01 1.33353e-01 1.32955e-01 1.33803e-01 9.84655e-02 9.12863e-02 1.04637e-01 1.09802e-01 1.47109e-01 1.34984e-01 1.50932e-01 1.13304e-01 1.23842e-01 1.24271e-01 1.23252e-01 1.18384e-01 1.09091e-01 1.28083e-01 1.11630e-01 1.49146e-01 1.57143e-01 1.47343e-01 15 30 50.0 -16 89546737 C T T missense_variant MODERATE SPG7 6687 Transcript NM_001363850.1 protein_coding 11/18 1559 1529 510 A/V gCa/gTa 1 EntrezGene C C 0 0.996 25.4 3.689045 25.4 0.99924371284409541 6.537899 0.624057380328121 0.76692 7.051021 0.695095080564955 0.79312 .&.&.&.&.&.&.&.&. 5.42 0.999999999999994 0.000000 0.563757 0.9271 1.0570 .&.&2.53&.&.&.&.&.&. 1 .&.&.&.&.&.&.&.&. 19.2181 .&.&.&.&.&.&.&.&. 0.96780 0.706548 1.000000 0.992000 4.972000 1.000000 4.43 rs61755320 508 143294 3.54516e-03 1.56978e-03 1.58590e-03 1.55087e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.78144e-03 3.04363e-03 2.58131e-03 1.50421e-03 2.83768e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.95417e-03 8.59435e-04 3.99680e-04 1.00376e-03 3.11024e-03 5.93081e-03 6.12365e-03 5.66551e-03 2.32558e-03 1.82482e-03 2.84630e-03 3.54996e-03 6.59196e-04 1.77936e-03 4.04531e-04 42016 0.00346 0.00252 0.00220 51184 Optic_nerve_hypoplasia&Intellectual_disability&Dysarthria&Gait_ataxia&Cerebral_cortical_atrophy&Spastic_paraparesis&Spastic_ataxia&Sensorimotor_neuropathy&Hereditary_spastic_paraplegia_7&Hereditary_spastic_paraplegia&Inborn_genetic_diseases&Spastic_Paraplegia&_Recessive¬_provided Hereditary_spastic_paraplegia_7 Human_Phenotype_Ontology:HP:0000609&Human_Phenotype_Ontology:HP:0007273&MedGen:C0338502&Orphanet:ORPHA137902&Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&Human_Phenotype_Ontology:HP:0001260&Human_Phenotype_Ontology:HP:0002327&MedGen:C0013362&Human_Phenotype_Ontology:HP:0002066&Human_Phenotype_Ontology:HP:0002379&MedGen:C0751837&Human_Phenotype_Ontology:HP:0002120&Human_Phenotype_Ontology:HP:0006823&Human_Phenotype_Ontology:HP:0006835&MedGen:C4551583&Human_Phenotype_Ontology:HP:0002313&Human_Phenotype_Ontology:HP:0007191&MedGen:C0037771&Human_Phenotype_Ontology:HP:0002497&MedGen:C1849156&OMIM:270500&OMIM:PS108600&Orphanet:ORPHA316226&Human_Phenotype_Ontology:HP:0007055&Human_Phenotype_Ontology:HP:0007141&Human_Phenotype_Ontology:HP:0007237&MedGen:C1112256&MONDO:MONDO:0011803&MedGen:C1846564&OMIM:607259&Orphanet:ORPHA99013&MONDO:MONDO:0019064&MedGen:C0037773&OMIM:PS303350&Orphanet:ORPHA685&SNOMED_CT:39912006&MeSH:D030342&MedGen:C0950123&MedGen:CN239433&MedGen:CN517202 MONDO:MONDO:0011803&MedGen:C1846564&OMIM:607259&Orphanet:ORPHA99013 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(6)&Pathogenic(16)&Uncertain_significance(1) 424800:Likely_pathogenic single_nucleotide_variant SPG7:6687 SO:0001583&missense_variant 29 61755320 15 30 50.0 -16 89546737 C T T missense_variant MODERATE SPG7 6687 Transcript NM_003119.4 protein_coding 11/17 1544 1529 510 A/V gCa/gTa 1 EntrezGene C C 0 0.994 25.4 3.689045 25.4 0.99924371284409541 6.537899 0.624057380328121 0.76692 7.051021 0.695095080564955 0.79312 .&.&.&.&.&.&.&.&. 5.42 0.999999999999994 0.000000 0.563757 0.9271 1.0570 .&.&2.53&.&.&.&.&.&. 1 .&.&.&.&.&.&.&.&. 19.2181 .&.&.&.&.&.&.&.&. 0.96780 0.706548 1.000000 0.992000 4.972000 1.000000 4.43 rs61755320 508 143294 3.54516e-03 1.56978e-03 1.58590e-03 1.55087e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.78144e-03 3.04363e-03 2.58131e-03 1.50421e-03 2.83768e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.95417e-03 8.59435e-04 3.99680e-04 1.00376e-03 3.11024e-03 5.93081e-03 6.12365e-03 5.66551e-03 2.32558e-03 1.82482e-03 2.84630e-03 3.54996e-03 6.59196e-04 1.77936e-03 4.04531e-04 42016 0.00346 0.00252 0.00220 51184 Optic_nerve_hypoplasia&Intellectual_disability&Dysarthria&Gait_ataxia&Cerebral_cortical_atrophy&Spastic_paraparesis&Spastic_ataxia&Sensorimotor_neuropathy&Hereditary_spastic_paraplegia_7&Hereditary_spastic_paraplegia&Inborn_genetic_diseases&Spastic_Paraplegia&_Recessive¬_provided Hereditary_spastic_paraplegia_7 Human_Phenotype_Ontology:HP:0000609&Human_Phenotype_Ontology:HP:0007273&MedGen:C0338502&Orphanet:ORPHA137902&Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&Human_Phenotype_Ontology:HP:0001260&Human_Phenotype_Ontology:HP:0002327&MedGen:C0013362&Human_Phenotype_Ontology:HP:0002066&Human_Phenotype_Ontology:HP:0002379&MedGen:C0751837&Human_Phenotype_Ontology:HP:0002120&Human_Phenotype_Ontology:HP:0006823&Human_Phenotype_Ontology:HP:0006835&MedGen:C4551583&Human_Phenotype_Ontology:HP:0002313&Human_Phenotype_Ontology:HP:0007191&MedGen:C0037771&Human_Phenotype_Ontology:HP:0002497&MedGen:C1849156&OMIM:270500&OMIM:PS108600&Orphanet:ORPHA316226&Human_Phenotype_Ontology:HP:0007055&Human_Phenotype_Ontology:HP:0007141&Human_Phenotype_Ontology:HP:0007237&MedGen:C1112256&MONDO:MONDO:0011803&MedGen:C1846564&OMIM:607259&Orphanet:ORPHA99013&MONDO:MONDO:0019064&MedGen:C0037773&OMIM:PS303350&Orphanet:ORPHA685&SNOMED_CT:39912006&MeSH:D030342&MedGen:C0950123&MedGen:CN239433&MedGen:CN517202 MONDO:MONDO:0011803&MedGen:C1846564&OMIM:607259&Orphanet:ORPHA99013 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(6)&Pathogenic(16)&Uncertain_significance(1) 424800:Likely_pathogenic single_nucleotide_variant SPG7:6687 SO:0001583&missense_variant 29 61755320 15 30 50.0 -16 89546737 C T T downstream_gene_variant MODIFIER SPG7 6687 Transcript XM_005256321.4 protein_coding 4458 1 EntrezGene C C 25.4 3.689045 4.43 rs61755320 508 143294 3.54516e-03 1.56978e-03 1.58590e-03 1.55087e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.78144e-03 3.04363e-03 2.58131e-03 1.50421e-03 2.83768e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.95417e-03 8.59435e-04 3.99680e-04 1.00376e-03 3.11024e-03 5.93081e-03 6.12365e-03 5.66551e-03 2.32558e-03 1.82482e-03 2.84630e-03 3.54996e-03 6.59196e-04 1.77936e-03 4.04531e-04 42016 0.00346 0.00252 0.00220 51184 Optic_nerve_hypoplasia&Intellectual_disability&Dysarthria&Gait_ataxia&Cerebral_cortical_atrophy&Spastic_paraparesis&Spastic_ataxia&Sensorimotor_neuropathy&Hereditary_spastic_paraplegia_7&Hereditary_spastic_paraplegia&Inborn_genetic_diseases&Spastic_Paraplegia&_Recessive¬_provided Hereditary_spastic_paraplegia_7 Human_Phenotype_Ontology:HP:0000609&Human_Phenotype_Ontology:HP:0007273&MedGen:C0338502&Orphanet:ORPHA137902&Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&Human_Phenotype_Ontology:HP:0001260&Human_Phenotype_Ontology:HP:0002327&MedGen:C0013362&Human_Phenotype_Ontology:HP:0002066&Human_Phenotype_Ontology:HP:0002379&MedGen:C0751837&Human_Phenotype_Ontology:HP:0002120&Human_Phenotype_Ontology:HP:0006823&Human_Phenotype_Ontology:HP:0006835&MedGen:C4551583&Human_Phenotype_Ontology:HP:0002313&Human_Phenotype_Ontology:HP:0007191&MedGen:C0037771&Human_Phenotype_Ontology:HP:0002497&MedGen:C1849156&OMIM:270500&OMIM:PS108600&Orphanet:ORPHA316226&Human_Phenotype_Ontology:HP:0007055&Human_Phenotype_Ontology:HP:0007141&Human_Phenotype_Ontology:HP:0007237&MedGen:C1112256&MONDO:MONDO:0011803&MedGen:C1846564&OMIM:607259&Orphanet:ORPHA99013&MONDO:MONDO:0019064&MedGen:C0037773&OMIM:PS303350&Orphanet:ORPHA685&SNOMED_CT:39912006&MeSH:D030342&MedGen:C0950123&MedGen:CN239433&MedGen:CN517202 MONDO:MONDO:0011803&MedGen:C1846564&OMIM:607259&Orphanet:ORPHA99013 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(6)&Pathogenic(16)&Uncertain_significance(1) 424800:Likely_pathogenic single_nucleotide_variant SPG7:6687 SO:0001583&missense_variant 29 61755320 15 30 50.0 -16 89546737 C T T missense_variant MODERATE SPG7 6687 Transcript XM_017023597.1 protein_coding 11/13 1568 1529 510 A/V gCa/gTa 1 EntrezGene C C 25.4 3.689045 25.4 0.99924371284409541 6.537899 0.624057380328121 0.76692 7.051021 0.695095080564955 0.79312 .&.&.&.&.&.&.&.&. 5.42 0.999999999999994 0.000000 0.563757 0.9271 1.0570 .&.&2.53&.&.&.&.&.&. 1 .&.&.&.&.&.&.&.&. 19.2181 .&.&.&.&.&.&.&.&. 0.96780 0.706548 1.000000 0.992000 4.972000 1.000000 4.43 rs61755320 508 143294 3.54516e-03 1.56978e-03 1.58590e-03 1.55087e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.78144e-03 3.04363e-03 2.58131e-03 1.50421e-03 2.83768e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.95417e-03 8.59435e-04 3.99680e-04 1.00376e-03 3.11024e-03 5.93081e-03 6.12365e-03 5.66551e-03 2.32558e-03 1.82482e-03 2.84630e-03 3.54996e-03 6.59196e-04 1.77936e-03 4.04531e-04 42016 0.00346 0.00252 0.00220 51184 Optic_nerve_hypoplasia&Intellectual_disability&Dysarthria&Gait_ataxia&Cerebral_cortical_atrophy&Spastic_paraparesis&Spastic_ataxia&Sensorimotor_neuropathy&Hereditary_spastic_paraplegia_7&Hereditary_spastic_paraplegia&Inborn_genetic_diseases&Spastic_Paraplegia&_Recessive¬_provided Hereditary_spastic_paraplegia_7 Human_Phenotype_Ontology:HP:0000609&Human_Phenotype_Ontology:HP:0007273&MedGen:C0338502&Orphanet:ORPHA137902&Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&Human_Phenotype_Ontology:HP:0001260&Human_Phenotype_Ontology:HP:0002327&MedGen:C0013362&Human_Phenotype_Ontology:HP:0002066&Human_Phenotype_Ontology:HP:0002379&MedGen:C0751837&Human_Phenotype_Ontology:HP:0002120&Human_Phenotype_Ontology:HP:0006823&Human_Phenotype_Ontology:HP:0006835&MedGen:C4551583&Human_Phenotype_Ontology:HP:0002313&Human_Phenotype_Ontology:HP:0007191&MedGen:C0037771&Human_Phenotype_Ontology:HP:0002497&MedGen:C1849156&OMIM:270500&OMIM:PS108600&Orphanet:ORPHA316226&Human_Phenotype_Ontology:HP:0007055&Human_Phenotype_Ontology:HP:0007141&Human_Phenotype_Ontology:HP:0007237&MedGen:C1112256&MONDO:MONDO:0011803&MedGen:C1846564&OMIM:607259&Orphanet:ORPHA99013&MONDO:MONDO:0019064&MedGen:C0037773&OMIM:PS303350&Orphanet:ORPHA685&SNOMED_CT:39912006&MeSH:D030342&MedGen:C0950123&MedGen:CN239433&MedGen:CN517202 MONDO:MONDO:0011803&MedGen:C1846564&OMIM:607259&Orphanet:ORPHA99013 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(6)&Pathogenic(16)&Uncertain_significance(1) 424800:Likely_pathogenic single_nucleotide_variant SPG7:6687 SO:0001583&missense_variant 29 61755320 15 30 50.0 -16 89546737 C T T missense_variant MODERATE SPG7 6687 Transcript XM_017023598.1 protein_coding 11/13 1568 1529 510 A/V gCa/gTa 1 EntrezGene C C 25.4 3.689045 25.4 0.99924371284409541 6.537899 0.624057380328121 0.76692 7.051021 0.695095080564955 0.79312 .&.&.&.&.&.&.&.&. 5.42 0.999999999999994 0.000000 0.563757 0.9271 1.0570 .&.&2.53&.&.&.&.&.&. 1 .&.&.&.&.&.&.&.&. 19.2181 .&.&.&.&.&.&.&.&. 0.96780 0.706548 1.000000 0.992000 4.972000 1.000000 4.43 rs61755320 508 143294 3.54516e-03 1.56978e-03 1.58590e-03 1.55087e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.78144e-03 3.04363e-03 2.58131e-03 1.50421e-03 2.83768e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.95417e-03 8.59435e-04 3.99680e-04 1.00376e-03 3.11024e-03 5.93081e-03 6.12365e-03 5.66551e-03 2.32558e-03 1.82482e-03 2.84630e-03 3.54996e-03 6.59196e-04 1.77936e-03 4.04531e-04 42016 0.00346 0.00252 0.00220 51184 Optic_nerve_hypoplasia&Intellectual_disability&Dysarthria&Gait_ataxia&Cerebral_cortical_atrophy&Spastic_paraparesis&Spastic_ataxia&Sensorimotor_neuropathy&Hereditary_spastic_paraplegia_7&Hereditary_spastic_paraplegia&Inborn_genetic_diseases&Spastic_Paraplegia&_Recessive¬_provided Hereditary_spastic_paraplegia_7 Human_Phenotype_Ontology:HP:0000609&Human_Phenotype_Ontology:HP:0007273&MedGen:C0338502&Orphanet:ORPHA137902&Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&Human_Phenotype_Ontology:HP:0001260&Human_Phenotype_Ontology:HP:0002327&MedGen:C0013362&Human_Phenotype_Ontology:HP:0002066&Human_Phenotype_Ontology:HP:0002379&MedGen:C0751837&Human_Phenotype_Ontology:HP:0002120&Human_Phenotype_Ontology:HP:0006823&Human_Phenotype_Ontology:HP:0006835&MedGen:C4551583&Human_Phenotype_Ontology:HP:0002313&Human_Phenotype_Ontology:HP:0007191&MedGen:C0037771&Human_Phenotype_Ontology:HP:0002497&MedGen:C1849156&OMIM:270500&OMIM:PS108600&Orphanet:ORPHA316226&Human_Phenotype_Ontology:HP:0007055&Human_Phenotype_Ontology:HP:0007141&Human_Phenotype_Ontology:HP:0007237&MedGen:C1112256&MONDO:MONDO:0011803&MedGen:C1846564&OMIM:607259&Orphanet:ORPHA99013&MONDO:MONDO:0019064&MedGen:C0037773&OMIM:PS303350&Orphanet:ORPHA685&SNOMED_CT:39912006&MeSH:D030342&MedGen:C0950123&MedGen:CN239433&MedGen:CN517202 MONDO:MONDO:0011803&MedGen:C1846564&OMIM:607259&Orphanet:ORPHA99013 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(6)&Pathogenic(16)&Uncertain_significance(1) 424800:Likely_pathogenic single_nucleotide_variant SPG7:6687 SO:0001583&missense_variant 29 61755320 15 30 50.0 -16 89546737 C T T non_coding_transcript_exon_variant MODIFIER SPG7 6687 Transcript XR_001751971.2 misc_RNA 12/18 1878 1 EntrezGene C C 25.4 3.689045 4.43 rs61755320 508 143294 3.54516e-03 1.56978e-03 1.58590e-03 1.55087e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.78144e-03 3.04363e-03 2.58131e-03 1.50421e-03 2.83768e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.95417e-03 8.59435e-04 3.99680e-04 1.00376e-03 3.11024e-03 5.93081e-03 6.12365e-03 5.66551e-03 2.32558e-03 1.82482e-03 2.84630e-03 3.54996e-03 6.59196e-04 1.77936e-03 4.04531e-04 42016 0.00346 0.00252 0.00220 51184 Optic_nerve_hypoplasia&Intellectual_disability&Dysarthria&Gait_ataxia&Cerebral_cortical_atrophy&Spastic_paraparesis&Spastic_ataxia&Sensorimotor_neuropathy&Hereditary_spastic_paraplegia_7&Hereditary_spastic_paraplegia&Inborn_genetic_diseases&Spastic_Paraplegia&_Recessive¬_provided Hereditary_spastic_paraplegia_7 Human_Phenotype_Ontology:HP:0000609&Human_Phenotype_Ontology:HP:0007273&MedGen:C0338502&Orphanet:ORPHA137902&Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&Human_Phenotype_Ontology:HP:0001260&Human_Phenotype_Ontology:HP:0002327&MedGen:C0013362&Human_Phenotype_Ontology:HP:0002066&Human_Phenotype_Ontology:HP:0002379&MedGen:C0751837&Human_Phenotype_Ontology:HP:0002120&Human_Phenotype_Ontology:HP:0006823&Human_Phenotype_Ontology:HP:0006835&MedGen:C4551583&Human_Phenotype_Ontology:HP:0002313&Human_Phenotype_Ontology:HP:0007191&MedGen:C0037771&Human_Phenotype_Ontology:HP:0002497&MedGen:C1849156&OMIM:270500&OMIM:PS108600&Orphanet:ORPHA316226&Human_Phenotype_Ontology:HP:0007055&Human_Phenotype_Ontology:HP:0007141&Human_Phenotype_Ontology:HP:0007237&MedGen:C1112256&MONDO:MONDO:0011803&MedGen:C1846564&OMIM:607259&Orphanet:ORPHA99013&MONDO:MONDO:0019064&MedGen:C0037773&OMIM:PS303350&Orphanet:ORPHA685&SNOMED_CT:39912006&MeSH:D030342&MedGen:C0950123&MedGen:CN239433&MedGen:CN517202 MONDO:MONDO:0011803&MedGen:C1846564&OMIM:607259&Orphanet:ORPHA99013 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(6)&Pathogenic(16)&Uncertain_significance(1) 424800:Likely_pathogenic single_nucleotide_variant SPG7:6687 SO:0001583&missense_variant 29 61755320 15 30 50.0 -16 89546737 C T T non_coding_transcript_exon_variant MODIFIER SPG7 6687 Transcript XR_001751972.2 misc_RNA 12/19 1878 1 EntrezGene C C 25.4 3.689045 4.43 rs61755320 508 143294 3.54516e-03 1.56978e-03 1.58590e-03 1.55087e-03 0.00000e+00 0.00000e+00 0.00000e+00 2.78144e-03 3.04363e-03 2.58131e-03 1.50421e-03 2.83768e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.95417e-03 8.59435e-04 3.99680e-04 1.00376e-03 3.11024e-03 5.93081e-03 6.12365e-03 5.66551e-03 2.32558e-03 1.82482e-03 2.84630e-03 3.54996e-03 6.59196e-04 1.77936e-03 4.04531e-04 42016 0.00346 0.00252 0.00220 51184 Optic_nerve_hypoplasia&Intellectual_disability&Dysarthria&Gait_ataxia&Cerebral_cortical_atrophy&Spastic_paraparesis&Spastic_ataxia&Sensorimotor_neuropathy&Hereditary_spastic_paraplegia_7&Hereditary_spastic_paraplegia&Inborn_genetic_diseases&Spastic_Paraplegia&_Recessive¬_provided Hereditary_spastic_paraplegia_7 Human_Phenotype_Ontology:HP:0000609&Human_Phenotype_Ontology:HP:0007273&MedGen:C0338502&Orphanet:ORPHA137902&Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&Human_Phenotype_Ontology:HP:0001260&Human_Phenotype_Ontology:HP:0002327&MedGen:C0013362&Human_Phenotype_Ontology:HP:0002066&Human_Phenotype_Ontology:HP:0002379&MedGen:C0751837&Human_Phenotype_Ontology:HP:0002120&Human_Phenotype_Ontology:HP:0006823&Human_Phenotype_Ontology:HP:0006835&MedGen:C4551583&Human_Phenotype_Ontology:HP:0002313&Human_Phenotype_Ontology:HP:0007191&MedGen:C0037771&Human_Phenotype_Ontology:HP:0002497&MedGen:C1849156&OMIM:270500&OMIM:PS108600&Orphanet:ORPHA316226&Human_Phenotype_Ontology:HP:0007055&Human_Phenotype_Ontology:HP:0007141&Human_Phenotype_Ontology:HP:0007237&MedGen:C1112256&MONDO:MONDO:0011803&MedGen:C1846564&OMIM:607259&Orphanet:ORPHA99013&MONDO:MONDO:0019064&MedGen:C0037773&OMIM:PS303350&Orphanet:ORPHA685&SNOMED_CT:39912006&MeSH:D030342&MedGen:C0950123&MedGen:CN239433&MedGen:CN517202 MONDO:MONDO:0011803&MedGen:C1846564&OMIM:607259&Orphanet:ORPHA99013 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(6)&Pathogenic(16)&Uncertain_significance(1) 424800:Likely_pathogenic single_nucleotide_variant SPG7:6687 SO:0001583&missense_variant 29 61755320 15 30 50.0 -17 1933854 G T T downstream_gene_variant MODIFIER RTN4RL1 146760 Transcript NM_178568.4 protein_coding 823 -1 EntrezGene G G 18.53 1.905931 4.46 15 30 50.0 -17 3483497 A G G splice_acceptor_variant HIGH ASPA 443 Transcript NM_000049.4 protein_coding 2/5 1 EntrezGene A A 33 5.110669 6.31 40118 48619 not_provided MedGen:CN517202 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001574&splice_acceptor_variant&SO:0001627&intron_variant 1 63751297 15 30 50.0 -17 3483497 A G G splice_acceptor_variant HIGH ASPA 443 Transcript NM_001128085.1 protein_coding 3/6 1 EntrezGene A A OK 33 5.110669 6.31 40118 48619 not_provided MedGen:CN517202 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001574&splice_acceptor_variant&SO:0001627&intron_variant 1 63751297 15 30 50.0 -17 3483497 A G G intron_variant MODIFIER SPATA22 84690 Transcript NM_001321336.1 protein_coding 1/8 -1 EntrezGene A A OK 33 5.110669 6.31 40118 48619 not_provided MedGen:CN517202 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001574&splice_acceptor_variant&SO:0001627&intron_variant 1 63751297 15 30 50.0 -17 3483497 A G G intron_variant MODIFIER SPATA22 84690 Transcript NM_001321337.1 protein_coding 1/8 -1 EntrezGene A A OK 33 5.110669 6.31 40118 48619 not_provided MedGen:CN517202 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001574&splice_acceptor_variant&SO:0001627&intron_variant 1 63751297 15 30 50.0 -17 3483497 A G G splice_acceptor_variant HIGH ASPA 443 Transcript XM_017024661.1 protein_coding 4/7 1 EntrezGene A A 33 5.110669 6.31 40118 48619 not_provided MedGen:CN517202 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001574&splice_acceptor_variant&SO:0001627&intron_variant 1 63751297 15 30 50.0 -17 3483497 A G G splice_acceptor_variant HIGH ASPA 443 Transcript XM_024450764.1 protein_coding 3/6 1 EntrezGene A A 33 5.110669 6.31 40118 48619 not_provided MedGen:CN517202 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001574&splice_acceptor_variant&SO:0001627&intron_variant 1 63751297 15 30 50.0 -17 3483497 A G G splice_acceptor_variant&non_coding_transcript_variant HIGH ASPA 443 Transcript XR_934026.2 misc_RNA 2/6 1 EntrezGene A A 33 5.110669 6.31 40118 48619 not_provided MedGen:CN517202 criteria_provided&_single_submitter Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001574&splice_acceptor_variant&SO:0001627&intron_variant 1 63751297 15 30 50.0 -17 3494408 C A A stop_gained HIGH ASPA 443 Transcript NM_000049.4 protein_coding 5/6 856 693 231 Y/* taC/taA 1 EntrezGene C C 35 6.335791 35 0.9965790558729839 2.463282 0.0295894096427818 0.41096 3.205675 0.177637014533917 0.50125 .&. 3.51 1.06485198564774E-5 0.000003 .&. 1&1&1 .&. 8.2842 0.951&0.951 0.80999 0.487112 0.998000 0.984000 0.453000 -0.557000 3.28 chr17:3494408-3494408 3 143082 2.09670e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.02410e-04 5.68828e-04 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.88567e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.65549e-04 0.00000e+00 9.52381e-04 2.09301e-05 0.00000e+00 0.00000e+00 0.00000e+00 2609 17648 Spongy_degeneration_of_central_nervous_system&Canavan_Disease&_Familial_Form¬_provided MONDO:MONDO:0010079&MedGen:C0206307&OMIM:271900&Orphanet:ORPHA141&SNOMED_CT:80544005&MedGen:C0751663&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001587&nonsense&SO:0001627&intron_variant 1 12948217 15 30 50.0 -17 3494408 C A A stop_gained HIGH ASPA 443 Transcript NM_001128085.1 protein_coding 6/7 784 693 231 Y/* taC/taA 1 EntrezGene C C OK 35 6.335791 35 0.9965790558729839 2.463282 0.0295894096427818 0.41096 3.205675 0.177637014533917 0.50125 .&. 3.51 1.06485198564774E-5 0.000003 .&. 1&1&1 .&. 8.2842 0.951&0.951 0.80999 0.487112 0.998000 0.984000 0.453000 -0.557000 3.28 chr17:3494408-3494408 3 143082 2.09670e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.02410e-04 5.68828e-04 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.88567e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.65549e-04 0.00000e+00 9.52381e-04 2.09301e-05 0.00000e+00 0.00000e+00 0.00000e+00 2609 17648 Spongy_degeneration_of_central_nervous_system&Canavan_Disease&_Familial_Form¬_provided MONDO:MONDO:0010079&MedGen:C0206307&OMIM:271900&Orphanet:ORPHA141&SNOMED_CT:80544005&MedGen:C0751663&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001587&nonsense&SO:0001627&intron_variant 1 12948217 15 30 50.0 -17 3494408 C A A intron_variant MODIFIER SPATA22 84690 Transcript NM_001321336.1 protein_coding 1/8 -1 EntrezGene C C OK 35 6.335791 3.28 chr17:3494408-3494408 3 143082 2.09670e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.02410e-04 5.68828e-04 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.88567e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.65549e-04 0.00000e+00 9.52381e-04 2.09301e-05 0.00000e+00 0.00000e+00 0.00000e+00 2609 17648 Spongy_degeneration_of_central_nervous_system&Canavan_Disease&_Familial_Form¬_provided MONDO:MONDO:0010079&MedGen:C0206307&OMIM:271900&Orphanet:ORPHA141&SNOMED_CT:80544005&MedGen:C0751663&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001587&nonsense&SO:0001627&intron_variant 1 12948217 15 30 50.0 -17 3494408 C A A intron_variant MODIFIER SPATA22 84690 Transcript NM_001321337.1 protein_coding 1/8 -1 EntrezGene C C OK 35 6.335791 3.28 chr17:3494408-3494408 3 143082 2.09670e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.02410e-04 5.68828e-04 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.88567e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.65549e-04 0.00000e+00 9.52381e-04 2.09301e-05 0.00000e+00 0.00000e+00 0.00000e+00 2609 17648 Spongy_degeneration_of_central_nervous_system&Canavan_Disease&_Familial_Form¬_provided MONDO:MONDO:0010079&MedGen:C0206307&OMIM:271900&Orphanet:ORPHA141&SNOMED_CT:80544005&MedGen:C0751663&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001587&nonsense&SO:0001627&intron_variant 1 12948217 15 30 50.0 -17 3494408 C A A stop_gained HIGH ASPA 443 Transcript XM_017024661.1 protein_coding 7/8 1000 693 231 Y/* taC/taA 1 EntrezGene C C 35 6.335791 35 0.9965790558729839 2.463282 0.0295894096427818 0.41096 3.205675 0.177637014533917 0.50125 .&. 3.51 1.06485198564774E-5 0.000003 .&. 1&1&1 .&. 8.2842 0.951&0.951 0.80999 0.487112 0.998000 0.984000 0.453000 -0.557000 3.28 chr17:3494408-3494408 3 143082 2.09670e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.02410e-04 5.68828e-04 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.88567e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.65549e-04 0.00000e+00 9.52381e-04 2.09301e-05 0.00000e+00 0.00000e+00 0.00000e+00 2609 17648 Spongy_degeneration_of_central_nervous_system&Canavan_Disease&_Familial_Form¬_provided MONDO:MONDO:0010079&MedGen:C0206307&OMIM:271900&Orphanet:ORPHA141&SNOMED_CT:80544005&MedGen:C0751663&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001587&nonsense&SO:0001627&intron_variant 1 12948217 15 30 50.0 -17 3494408 C A A stop_gained HIGH ASPA 443 Transcript XM_024450764.1 protein_coding 6/7 735 693 231 Y/* taC/taA 1 EntrezGene C C 35 6.335791 35 0.9965790558729839 2.463282 0.0295894096427818 0.41096 3.205675 0.177637014533917 0.50125 .&. 3.51 1.06485198564774E-5 0.000003 .&. 1&1&1 .&. 8.2842 0.951&0.951 0.80999 0.487112 0.998000 0.984000 0.453000 -0.557000 3.28 chr17:3494408-3494408 3 143082 2.09670e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.02410e-04 5.68828e-04 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.88567e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.65549e-04 0.00000e+00 9.52381e-04 2.09301e-05 0.00000e+00 0.00000e+00 0.00000e+00 2609 17648 Spongy_degeneration_of_central_nervous_system&Canavan_Disease&_Familial_Form¬_provided MONDO:MONDO:0010079&MedGen:C0206307&OMIM:271900&Orphanet:ORPHA141&SNOMED_CT:80544005&MedGen:C0751663&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001587&nonsense&SO:0001627&intron_variant 1 12948217 15 30 50.0 -17 3494408 C A A non_coding_transcript_exon_variant MODIFIER ASPA 443 Transcript XR_934026.2 misc_RNA 5/7 868 1 EntrezGene C C 35 6.335791 3.28 chr17:3494408-3494408 3 143082 2.09670e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.02410e-04 5.68828e-04 6.40205e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.35549e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.88567e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.65549e-04 0.00000e+00 9.52381e-04 2.09301e-05 0.00000e+00 0.00000e+00 0.00000e+00 2609 17648 Spongy_degeneration_of_central_nervous_system&Canavan_Disease&_Familial_Form¬_provided MONDO:MONDO:0010079&MedGen:C0206307&OMIM:271900&Orphanet:ORPHA141&SNOMED_CT:80544005&MedGen:C0751663&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001587&nonsense&SO:0001627&intron_variant 1 12948217 15 30 50.0 -17 3499000 A C C missense_variant MODERATE ASPA 443 Transcript NM_000049.4 protein_coding 6/6 1017 854 285 E/A gAg/gCg 1 EntrezGene A A 0 1 24.8 3.521903 24.8 0.99548204871533663 5.981255 0.579952107781817 0.73504 7.091101 0.698009439276086 0.79502 -4.66&-4.66 4.68 0.0186590843862637 0.000000 0.38468 0.9594 1.1430 3.445&3.445 1&0.999999&0.999999 -5.64&-5.64 11.1952 0.821&0.805 0.98380 0.553676 1.000000 0.859000 8.939000 1.312000 6.52 rs28940279 39 143312 2.72134e-04 2.37846e-05 0.00000e+00 5.17117e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.31636e-05 1.69033e-04 0.00000e+00 9.63855e-03 9.09091e-03 1.02564e-02 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.73578e-04 7.74329e-05 8.02311e-05 7.35835e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.71993e-04 0.00000e+00 0.00000e+00 0.00000e+00 2605 0.00036 17644 Spongy_degeneration_of_central_nervous_system&Canavan_Disease&_Familial_Form¬_provided MONDO:MONDO:0010079&MedGen:C0206307&OMIM:271900&Orphanet:ORPHA141&SNOMED_CT:80544005&MedGen:C0751663&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001583&missense_variant&SO:0001627&intron_variant 17 28940279 15 30 50.0 -17 3499000 A C C missense_variant MODERATE ASPA 443 Transcript NM_001128085.1 protein_coding 7/7 945 854 285 E/A gAg/gCg 1 EntrezGene A A OK 0 1 24.8 3.521903 24.8 0.99548204871533663 5.981255 0.579952107781817 0.73504 7.091101 0.698009439276086 0.79502 -4.66&-4.66 4.68 0.0186590843862637 0.000000 0.38468 0.9594 1.1430 3.445&3.445 1&0.999999&0.999999 -5.64&-5.64 11.1952 0.821&0.805 0.98380 0.553676 1.000000 0.859000 8.939000 1.312000 6.52 rs28940279 39 143312 2.72134e-04 2.37846e-05 0.00000e+00 5.17117e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.31636e-05 1.69033e-04 0.00000e+00 9.63855e-03 9.09091e-03 1.02564e-02 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.73578e-04 7.74329e-05 8.02311e-05 7.35835e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.71993e-04 0.00000e+00 0.00000e+00 0.00000e+00 2605 0.00036 17644 Spongy_degeneration_of_central_nervous_system&Canavan_Disease&_Familial_Form¬_provided MONDO:MONDO:0010079&MedGen:C0206307&OMIM:271900&Orphanet:ORPHA141&SNOMED_CT:80544005&MedGen:C0751663&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001583&missense_variant&SO:0001627&intron_variant 17 28940279 15 30 50.0 -17 3499000 A C C intron_variant MODIFIER SPATA22 84690 Transcript NM_001321336.1 protein_coding 1/8 -1 EntrezGene A A OK 24.8 3.521903 6.52 rs28940279 39 143312 2.72134e-04 2.37846e-05 0.00000e+00 5.17117e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.31636e-05 1.69033e-04 0.00000e+00 9.63855e-03 9.09091e-03 1.02564e-02 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.73578e-04 7.74329e-05 8.02311e-05 7.35835e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.71993e-04 0.00000e+00 0.00000e+00 0.00000e+00 2605 0.00036 17644 Spongy_degeneration_of_central_nervous_system&Canavan_Disease&_Familial_Form¬_provided MONDO:MONDO:0010079&MedGen:C0206307&OMIM:271900&Orphanet:ORPHA141&SNOMED_CT:80544005&MedGen:C0751663&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001583&missense_variant&SO:0001627&intron_variant 17 28940279 15 30 50.0 -17 3499000 A C C intron_variant MODIFIER SPATA22 84690 Transcript NM_001321337.1 protein_coding 1/8 -1 EntrezGene A A OK 24.8 3.521903 6.52 rs28940279 39 143312 2.72134e-04 2.37846e-05 0.00000e+00 5.17117e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.31636e-05 1.69033e-04 0.00000e+00 9.63855e-03 9.09091e-03 1.02564e-02 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.73578e-04 7.74329e-05 8.02311e-05 7.35835e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.71993e-04 0.00000e+00 0.00000e+00 0.00000e+00 2605 0.00036 17644 Spongy_degeneration_of_central_nervous_system&Canavan_Disease&_Familial_Form¬_provided MONDO:MONDO:0010079&MedGen:C0206307&OMIM:271900&Orphanet:ORPHA141&SNOMED_CT:80544005&MedGen:C0751663&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001583&missense_variant&SO:0001627&intron_variant 17 28940279 15 30 50.0 -17 3499000 A C C missense_variant MODERATE ASPA 443 Transcript XM_017024661.1 protein_coding 8/8 1161 854 285 E/A gAg/gCg 1 EntrezGene A A 0 1 24.8 3.521903 24.8 0.99548204871533663 5.981255 0.579952107781817 0.73504 7.091101 0.698009439276086 0.79502 -4.66&-4.66 4.68 0.0186590843862637 0.000000 0.38468 0.9594 1.1430 3.445&3.445 1&0.999999&0.999999 -5.64&-5.64 11.1952 0.821&0.805 0.98380 0.553676 1.000000 0.859000 8.939000 1.312000 6.52 rs28940279 39 143312 2.72134e-04 2.37846e-05 0.00000e+00 5.17117e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.31636e-05 1.69033e-04 0.00000e+00 9.63855e-03 9.09091e-03 1.02564e-02 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.73578e-04 7.74329e-05 8.02311e-05 7.35835e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.71993e-04 0.00000e+00 0.00000e+00 0.00000e+00 2605 0.00036 17644 Spongy_degeneration_of_central_nervous_system&Canavan_Disease&_Familial_Form¬_provided MONDO:MONDO:0010079&MedGen:C0206307&OMIM:271900&Orphanet:ORPHA141&SNOMED_CT:80544005&MedGen:C0751663&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001583&missense_variant&SO:0001627&intron_variant 17 28940279 15 30 50.0 -17 3499000 A C C missense_variant MODERATE ASPA 443 Transcript XM_024450764.1 protein_coding 7/7 896 854 285 E/A gAg/gCg 1 EntrezGene A A 0 1 24.8 3.521903 24.8 0.99548204871533663 5.981255 0.579952107781817 0.73504 7.091101 0.698009439276086 0.79502 -4.66&-4.66 4.68 0.0186590843862637 0.000000 0.38468 0.9594 1.1430 3.445&3.445 1&0.999999&0.999999 -5.64&-5.64 11.1952 0.821&0.805 0.98380 0.553676 1.000000 0.859000 8.939000 1.312000 6.52 rs28940279 39 143312 2.72134e-04 2.37846e-05 0.00000e+00 5.17117e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.31636e-05 1.69033e-04 0.00000e+00 9.63855e-03 9.09091e-03 1.02564e-02 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.73578e-04 7.74329e-05 8.02311e-05 7.35835e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.71993e-04 0.00000e+00 0.00000e+00 0.00000e+00 2605 0.00036 17644 Spongy_degeneration_of_central_nervous_system&Canavan_Disease&_Familial_Form¬_provided MONDO:MONDO:0010079&MedGen:C0206307&OMIM:271900&Orphanet:ORPHA141&SNOMED_CT:80544005&MedGen:C0751663&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001583&missense_variant&SO:0001627&intron_variant 17 28940279 15 30 50.0 -17 3499000 A C C non_coding_transcript_exon_variant MODIFIER ASPA 443 Transcript XR_934026.2 misc_RNA 7/7 1121 1 EntrezGene A A 24.8 3.521903 6.52 rs28940279 39 143312 2.72134e-04 2.37846e-05 0.00000e+00 5.17117e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.31636e-05 1.69033e-04 0.00000e+00 9.63855e-03 9.09091e-03 1.02564e-02 0.00000e+00 0.00000e+00 0.00000e+00 2.70775e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.73578e-04 7.74329e-05 8.02311e-05 7.35835e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.71993e-04 0.00000e+00 0.00000e+00 0.00000e+00 2605 0.00036 17644 Spongy_degeneration_of_central_nervous_system&Canavan_Disease&_Familial_Form¬_provided MONDO:MONDO:0010079&MedGen:C0206307&OMIM:271900&Orphanet:ORPHA141&SNOMED_CT:80544005&MedGen:C0751663&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001583&missense_variant&SO:0001627&intron_variant 17 28940279 15 30 50.0 -17 3499060 C A A missense_variant MODERATE ASPA 443 Transcript NM_000049.4 protein_coding 6/6 1077 914 305 A/E gCa/gAa 1 EntrezGene C C 0.03 0.999 23.4 2.974108 23.4 0.99444877549186605 5.417047 0.527286641535837 0.69833 5.282761 0.530635691563827 0.68912 -4.6&-4.6 5.76 0.999963793543663 0.000000 0.219803 0.9288 1.0605 1.63&1.63 1&1&1 -2.04&-2.04 19.3276 0.501&0.34 0.94510 0.553676 1.000000 0.059000 2.827000 1.026000 6.52 chr17:3499060-3499060 21 143184 1.46664e-04 4.76281e-05 4.41034e-05 5.17652e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.32493e-05 1.69262e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.03274e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.64653e-05 2.78802e-04 3.47742e-04 1.83972e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.46497e-04 0.00000e+00 0.00000e+00 0.00000e+00 2607 0.00023 0.00040 17646 Spongy_degeneration_of_central_nervous_system&Canavan_Disease&_Familial_Form¬_provided MONDO:MONDO:0010079&MedGen:C0206307&OMIM:271900&Orphanet:ORPHA141&SNOMED_CT:80544005&MedGen:C0751663&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001583&missense_variant&SO:0001627&intron_variant 1 28940574 15 30 50.0 -17 3499060 C A A missense_variant MODERATE ASPA 443 Transcript NM_001128085.1 protein_coding 7/7 1005 914 305 A/E gCa/gAa 1 EntrezGene C C OK 0.03 0.999 23.4 2.974108 23.4 0.99444877549186605 5.417047 0.527286641535837 0.69833 5.282761 0.530635691563827 0.68912 -4.6&-4.6 5.76 0.999963793543663 0.000000 0.219803 0.9288 1.0605 1.63&1.63 1&1&1 -2.04&-2.04 19.3276 0.501&0.34 0.94510 0.553676 1.000000 0.059000 2.827000 1.026000 6.52 chr17:3499060-3499060 21 143184 1.46664e-04 4.76281e-05 4.41034e-05 5.17652e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.32493e-05 1.69262e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.03274e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.64653e-05 2.78802e-04 3.47742e-04 1.83972e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.46497e-04 0.00000e+00 0.00000e+00 0.00000e+00 2607 0.00023 0.00040 17646 Spongy_degeneration_of_central_nervous_system&Canavan_Disease&_Familial_Form¬_provided MONDO:MONDO:0010079&MedGen:C0206307&OMIM:271900&Orphanet:ORPHA141&SNOMED_CT:80544005&MedGen:C0751663&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001583&missense_variant&SO:0001627&intron_variant 1 28940574 15 30 50.0 -17 3499060 C A A intron_variant MODIFIER SPATA22 84690 Transcript NM_001321336.1 protein_coding 1/8 -1 EntrezGene C C OK 23.4 2.974108 6.52 chr17:3499060-3499060 21 143184 1.46664e-04 4.76281e-05 4.41034e-05 5.17652e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.32493e-05 1.69262e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.03274e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.64653e-05 2.78802e-04 3.47742e-04 1.83972e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.46497e-04 0.00000e+00 0.00000e+00 0.00000e+00 2607 0.00023 0.00040 17646 Spongy_degeneration_of_central_nervous_system&Canavan_Disease&_Familial_Form¬_provided MONDO:MONDO:0010079&MedGen:C0206307&OMIM:271900&Orphanet:ORPHA141&SNOMED_CT:80544005&MedGen:C0751663&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001583&missense_variant&SO:0001627&intron_variant 1 28940574 15 30 50.0 -17 3499060 C A A intron_variant MODIFIER SPATA22 84690 Transcript NM_001321337.1 protein_coding 1/8 -1 EntrezGene C C OK 23.4 2.974108 6.52 chr17:3499060-3499060 21 143184 1.46664e-04 4.76281e-05 4.41034e-05 5.17652e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.32493e-05 1.69262e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.03274e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.64653e-05 2.78802e-04 3.47742e-04 1.83972e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.46497e-04 0.00000e+00 0.00000e+00 0.00000e+00 2607 0.00023 0.00040 17646 Spongy_degeneration_of_central_nervous_system&Canavan_Disease&_Familial_Form¬_provided MONDO:MONDO:0010079&MedGen:C0206307&OMIM:271900&Orphanet:ORPHA141&SNOMED_CT:80544005&MedGen:C0751663&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001583&missense_variant&SO:0001627&intron_variant 1 28940574 15 30 50.0 -17 3499060 C A A missense_variant MODERATE ASPA 443 Transcript XM_017024661.1 protein_coding 8/8 1221 914 305 A/E gCa/gAa 1 EntrezGene C C 0.03 0.999 23.4 2.974108 23.4 0.99444877549186605 5.417047 0.527286641535837 0.69833 5.282761 0.530635691563827 0.68912 -4.6&-4.6 5.76 0.999963793543663 0.000000 0.219803 0.9288 1.0605 1.63&1.63 1&1&1 -2.04&-2.04 19.3276 0.501&0.34 0.94510 0.553676 1.000000 0.059000 2.827000 1.026000 6.52 chr17:3499060-3499060 21 143184 1.46664e-04 4.76281e-05 4.41034e-05 5.17652e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.32493e-05 1.69262e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.03274e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.64653e-05 2.78802e-04 3.47742e-04 1.83972e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.46497e-04 0.00000e+00 0.00000e+00 0.00000e+00 2607 0.00023 0.00040 17646 Spongy_degeneration_of_central_nervous_system&Canavan_Disease&_Familial_Form¬_provided MONDO:MONDO:0010079&MedGen:C0206307&OMIM:271900&Orphanet:ORPHA141&SNOMED_CT:80544005&MedGen:C0751663&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001583&missense_variant&SO:0001627&intron_variant 1 28940574 15 30 50.0 -17 3499060 C A A missense_variant MODERATE ASPA 443 Transcript XM_024450764.1 protein_coding 7/7 956 914 305 A/E gCa/gAa 1 EntrezGene C C 0.03 0.999 23.4 2.974108 23.4 0.99444877549186605 5.417047 0.527286641535837 0.69833 5.282761 0.530635691563827 0.68912 -4.6&-4.6 5.76 0.999963793543663 0.000000 0.219803 0.9288 1.0605 1.63&1.63 1&1&1 -2.04&-2.04 19.3276 0.501&0.34 0.94510 0.553676 1.000000 0.059000 2.827000 1.026000 6.52 chr17:3499060-3499060 21 143184 1.46664e-04 4.76281e-05 4.41034e-05 5.17652e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.32493e-05 1.69262e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.03274e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.64653e-05 2.78802e-04 3.47742e-04 1.83972e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.46497e-04 0.00000e+00 0.00000e+00 0.00000e+00 2607 0.00023 0.00040 17646 Spongy_degeneration_of_central_nervous_system&Canavan_Disease&_Familial_Form¬_provided MONDO:MONDO:0010079&MedGen:C0206307&OMIM:271900&Orphanet:ORPHA141&SNOMED_CT:80544005&MedGen:C0751663&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001583&missense_variant&SO:0001627&intron_variant 1 28940574 15 30 50.0 -17 3499060 C A A non_coding_transcript_exon_variant MODIFIER ASPA 443 Transcript XR_934026.2 misc_RNA 7/7 1181 1 EntrezGene C C 23.4 2.974108 6.52 chr17:3499060-3499060 21 143184 1.46664e-04 4.76281e-05 4.41034e-05 5.17652e-05 0.00000e+00 0.00000e+00 0.00000e+00 7.32493e-05 1.69262e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.03274e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.64653e-05 2.78802e-04 3.47742e-04 1.83972e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.46497e-04 0.00000e+00 0.00000e+00 0.00000e+00 2607 0.00023 0.00040 17646 Spongy_degeneration_of_central_nervous_system&Canavan_Disease&_Familial_Form¬_provided MONDO:MONDO:0010079&MedGen:C0206307&OMIM:271900&Orphanet:ORPHA141&SNOMED_CT:80544005&MedGen:C0751663&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant ASPA:443&SPATA22:84690 SO:0001583&missense_variant&SO:0001627&intron_variant 1 28940574 15 30 50.0 -17 6425550 GGGTGGCTCTGCA G - inframe_deletion MODERATE AIPL1 23746 Transcript NM_001033054.3 protein_coding 5/5 881-892 864-875 288-292 PAEPP/P ccTGCAGAGCCACCc/ccc -1 EntrezGene GGTGGCTCTGCA GGTGGCTCTGCA 5.431 0.400398 0.819&-3.64&-1.8&1.17&2.24&-0.187&-3.34&1.28&-3.91&-0.342&-2.28&-2.85 rs281865195 93 143106 6.49868e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.40335e-04 6.79579e-04 2.58398e-04 1.08368e-02 1.19183e-02 9.61538e-03 0.00000e+00 0.00000e+00 0.00000e+00 7.32124e-04 1.91278e-04 3.99361e-04 1.25755e-04 5.62381e-04 7.28569e-04 7.22698e-04 7.36648e-04 9.30233e-04 9.12409e-04 9.48767e-04 6.48843e-04 0.00000e+00 0.00000e+00 0.00000e+00 5568 20607 Juvenile_retinitis_pigmentosa&_AIPL1-related&CONE-ROD_DYSTROPHY&_AIPL1-RELATED¬_provided MedGen:C2751763&MedGen:C2751764&MedGen:CN517202 no_assertion_criteria_provided Conflicting_interpretations_of_pathogenicity Pathogenic(2)&Uncertain_significance(1) Deletion AIPL1:23746 SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 281865195 15 30 50.0 -17 6425550 GGGTGGCTCTGCA G - inframe_deletion MODERATE AIPL1 23746 Transcript NM_001033055.3 protein_coding 5/5 890-901 873-884 291-295 PAEPP/P ccTGCAGAGCCACCc/ccc -1 EntrezGene GGTGGCTCTGCA GGTGGCTCTGCA 5.431 0.400398 0.819&-3.64&-1.8&1.17&2.24&-0.187&-3.34&1.28&-3.91&-0.342&-2.28&-2.85 rs281865195 93 143106 6.49868e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.40335e-04 6.79579e-04 2.58398e-04 1.08368e-02 1.19183e-02 9.61538e-03 0.00000e+00 0.00000e+00 0.00000e+00 7.32124e-04 1.91278e-04 3.99361e-04 1.25755e-04 5.62381e-04 7.28569e-04 7.22698e-04 7.36648e-04 9.30233e-04 9.12409e-04 9.48767e-04 6.48843e-04 0.00000e+00 0.00000e+00 0.00000e+00 5568 20607 Juvenile_retinitis_pigmentosa&_AIPL1-related&CONE-ROD_DYSTROPHY&_AIPL1-RELATED¬_provided MedGen:C2751763&MedGen:C2751764&MedGen:CN517202 no_assertion_criteria_provided Conflicting_interpretations_of_pathogenicity Pathogenic(2)&Uncertain_significance(1) Deletion AIPL1:23746 SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 281865195 15 30 50.0 -17 6425550 GGGTGGCTCTGCA G - inframe_deletion MODERATE AIPL1 23746 Transcript NM_001285399.3 protein_coding 6/6 1034-1045 1017-1028 339-343 PAEPP/P ccTGCAGAGCCACCc/ccc -1 EntrezGene GGTGGCTCTGCA GGTGGCTCTGCA 5.431 0.400398 0.819&-3.64&-1.8&1.17&2.24&-0.187&-3.34&1.28&-3.91&-0.342&-2.28&-2.85 rs281865195 93 143106 6.49868e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.40335e-04 6.79579e-04 2.58398e-04 1.08368e-02 1.19183e-02 9.61538e-03 0.00000e+00 0.00000e+00 0.00000e+00 7.32124e-04 1.91278e-04 3.99361e-04 1.25755e-04 5.62381e-04 7.28569e-04 7.22698e-04 7.36648e-04 9.30233e-04 9.12409e-04 9.48767e-04 6.48843e-04 0.00000e+00 0.00000e+00 0.00000e+00 5568 20607 Juvenile_retinitis_pigmentosa&_AIPL1-related&CONE-ROD_DYSTROPHY&_AIPL1-RELATED¬_provided MedGen:C2751763&MedGen:C2751764&MedGen:CN517202 no_assertion_criteria_provided Conflicting_interpretations_of_pathogenicity Pathogenic(2)&Uncertain_significance(1) Deletion AIPL1:23746 SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 281865195 15 30 50.0 -17 6425550 GGGTGGCTCTGCA G - inframe_deletion MODERATE AIPL1 23746 Transcript NM_001285400.3 protein_coding 6/6 1004-1015 987-998 329-333 PAEPP/P ccTGCAGAGCCACCc/ccc -1 EntrezGene GGTGGCTCTGCA GGTGGCTCTGCA 5.431 0.400398 0.819&-3.64&-1.8&1.17&2.24&-0.187&-3.34&1.28&-3.91&-0.342&-2.28&-2.85 rs281865195 93 143106 6.49868e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.40335e-04 6.79579e-04 2.58398e-04 1.08368e-02 1.19183e-02 9.61538e-03 0.00000e+00 0.00000e+00 0.00000e+00 7.32124e-04 1.91278e-04 3.99361e-04 1.25755e-04 5.62381e-04 7.28569e-04 7.22698e-04 7.36648e-04 9.30233e-04 9.12409e-04 9.48767e-04 6.48843e-04 0.00000e+00 0.00000e+00 0.00000e+00 5568 20607 Juvenile_retinitis_pigmentosa&_AIPL1-related&CONE-ROD_DYSTROPHY&_AIPL1-RELATED¬_provided MedGen:C2751763&MedGen:C2751764&MedGen:CN517202 no_assertion_criteria_provided Conflicting_interpretations_of_pathogenicity Pathogenic(2)&Uncertain_significance(1) Deletion AIPL1:23746 SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 281865195 15 30 50.0 -17 6425550 GGGTGGCTCTGCA G - inframe_deletion MODERATE AIPL1 23746 Transcript NM_001285401.3 protein_coding 6/6 998-1009 981-992 327-331 PAEPP/P ccTGCAGAGCCACCc/ccc -1 EntrezGene GGTGGCTCTGCA GGTGGCTCTGCA 5.431 0.400398 0.819&-3.64&-1.8&1.17&2.24&-0.187&-3.34&1.28&-3.91&-0.342&-2.28&-2.85 rs281865195 93 143106 6.49868e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.40335e-04 6.79579e-04 2.58398e-04 1.08368e-02 1.19183e-02 9.61538e-03 0.00000e+00 0.00000e+00 0.00000e+00 7.32124e-04 1.91278e-04 3.99361e-04 1.25755e-04 5.62381e-04 7.28569e-04 7.22698e-04 7.36648e-04 9.30233e-04 9.12409e-04 9.48767e-04 6.48843e-04 0.00000e+00 0.00000e+00 0.00000e+00 5568 20607 Juvenile_retinitis_pigmentosa&_AIPL1-related&CONE-ROD_DYSTROPHY&_AIPL1-RELATED¬_provided MedGen:C2751763&MedGen:C2751764&MedGen:CN517202 no_assertion_criteria_provided Conflicting_interpretations_of_pathogenicity Pathogenic(2)&Uncertain_significance(1) Deletion AIPL1:23746 SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 281865195 15 30 50.0 -17 6425550 GGGTGGCTCTGCA G - inframe_deletion MODERATE AIPL1 23746 Transcript NM_001285402.2 protein_coding 6/6 1128-1139 936-947 312-316 PAEPP/P ccTGCAGAGCCACCc/ccc -1 EntrezGene GGTGGCTCTGCA GGTGGCTCTGCA 5.431 0.400398 0.819&-3.64&-1.8&1.17&2.24&-0.187&-3.34&1.28&-3.91&-0.342&-2.28&-2.85 rs281865195 93 143106 6.49868e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.40335e-04 6.79579e-04 2.58398e-04 1.08368e-02 1.19183e-02 9.61538e-03 0.00000e+00 0.00000e+00 0.00000e+00 7.32124e-04 1.91278e-04 3.99361e-04 1.25755e-04 5.62381e-04 7.28569e-04 7.22698e-04 7.36648e-04 9.30233e-04 9.12409e-04 9.48767e-04 6.48843e-04 0.00000e+00 0.00000e+00 0.00000e+00 5568 20607 Juvenile_retinitis_pigmentosa&_AIPL1-related&CONE-ROD_DYSTROPHY&_AIPL1-RELATED¬_provided MedGen:C2751763&MedGen:C2751764&MedGen:CN517202 no_assertion_criteria_provided Conflicting_interpretations_of_pathogenicity Pathogenic(2)&Uncertain_significance(1) Deletion AIPL1:23746 SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 281865195 15 30 50.0 -17 6425550 GGGTGGCTCTGCA G - 3_prime_UTR_variant MODIFIER AIPL1 23746 Transcript NM_001285403.3 protein_coding 5/5 1830-1841 -1 EntrezGene GGTGGCTCTGCA GGTGGCTCTGCA 5.431 0.400398 0.819&-3.64&-1.8&1.17&2.24&-0.187&-3.34&1.28&-3.91&-0.342&-2.28&-2.85 rs281865195 93 143106 6.49868e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.40335e-04 6.79579e-04 2.58398e-04 1.08368e-02 1.19183e-02 9.61538e-03 0.00000e+00 0.00000e+00 0.00000e+00 7.32124e-04 1.91278e-04 3.99361e-04 1.25755e-04 5.62381e-04 7.28569e-04 7.22698e-04 7.36648e-04 9.30233e-04 9.12409e-04 9.48767e-04 6.48843e-04 0.00000e+00 0.00000e+00 0.00000e+00 5568 20607 Juvenile_retinitis_pigmentosa&_AIPL1-related&CONE-ROD_DYSTROPHY&_AIPL1-RELATED¬_provided MedGen:C2751763&MedGen:C2751764&MedGen:CN517202 no_assertion_criteria_provided Conflicting_interpretations_of_pathogenicity Pathogenic(2)&Uncertain_significance(1) Deletion AIPL1:23746 SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 281865195 15 30 50.0 -17 6425550 GGGTGGCTCTGCA G - inframe_deletion MODERATE AIPL1 23746 Transcript NM_014336.5 protein_coding 6/6 1070-1081 1053-1064 351-355 PAEPP/P ccTGCAGAGCCACCc/ccc -1 EntrezGene GGTGGCTCTGCA GGTGGCTCTGCA 5.431 0.400398 0.819&-3.64&-1.8&1.17&2.24&-0.187&-3.34&1.28&-3.91&-0.342&-2.28&-2.85 rs281865195 93 143106 6.49868e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.40335e-04 6.79579e-04 2.58398e-04 1.08368e-02 1.19183e-02 9.61538e-03 0.00000e+00 0.00000e+00 0.00000e+00 7.32124e-04 1.91278e-04 3.99361e-04 1.25755e-04 5.62381e-04 7.28569e-04 7.22698e-04 7.36648e-04 9.30233e-04 9.12409e-04 9.48767e-04 6.48843e-04 0.00000e+00 0.00000e+00 0.00000e+00 5568 20607 Juvenile_retinitis_pigmentosa&_AIPL1-related&CONE-ROD_DYSTROPHY&_AIPL1-RELATED¬_provided MedGen:C2751763&MedGen:C2751764&MedGen:CN517202 no_assertion_criteria_provided Conflicting_interpretations_of_pathogenicity Pathogenic(2)&Uncertain_significance(1) Deletion AIPL1:23746 SO:0001822&inframe_deletion&SO:0001624&3_prime_UTR_variant 1 281865195 15 30 50.0 -17 42900952 TC T - frameshift_variant HIGH G6PC 2538 Transcript NM_000151.4 protein_coding 1/5 155 77 26 S/X tCc/tc 1 EntrezGene C C 24.8 3.527039 5.5 rs1251265849 17 143266 1.18660e-04 4.75692e-05 4.40490e-05 5.17010e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48967e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.64254e-05 2.32335e-04 2.67465e-04 1.83999e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.18568e-04 0.00000e+00 0.00000e+00 0.00000e+00 21062 33914 Glycogen_storage_disease_due_to_glucose-6-phosphatase_deficiency_type_IA¬_provided MONDO:MONDO:0009287&MedGen:C2919796&OMIM:232200&Orphanet:ORPHA79258&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion G6PC1:2538 SO:0001589&frameshift_variant 1 80356479 15 30 50.0 -17 42900952 TC T - frameshift_variant HIGH G6PC 2538 Transcript NM_001270397.2 protein_coding 1/5 155 77 26 S/X tCc/tc 1 EntrezGene C C 24.8 3.527039 5.5 rs1251265849 17 143266 1.18660e-04 4.75692e-05 4.40490e-05 5.17010e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48967e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.64254e-05 2.32335e-04 2.67465e-04 1.83999e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.18568e-04 0.00000e+00 0.00000e+00 0.00000e+00 21062 33914 Glycogen_storage_disease_due_to_glucose-6-phosphatase_deficiency_type_IA¬_provided MONDO:MONDO:0009287&MedGen:C2919796&OMIM:232200&Orphanet:ORPHA79258&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion G6PC1:2538 SO:0001589&frameshift_variant 1 80356479 15 30 50.0 -17 42900952 TC T - upstream_gene_variant MODIFIER LINC00671 388387 Transcript NR_027254.1 lncRNA 2219 -1 EntrezGene C C 24.8 3.527039 5.5 rs1251265849 17 143266 1.18660e-04 4.75692e-05 4.40490e-05 5.17010e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.48967e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.64254e-05 2.32335e-04 2.67465e-04 1.83999e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.18568e-04 0.00000e+00 0.00000e+00 0.00000e+00 21062 33914 Glycogen_storage_disease_due_to_glucose-6-phosphatase_deficiency_type_IA¬_provided MONDO:MONDO:0009287&MedGen:C2919796&OMIM:232200&Orphanet:ORPHA79258&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion G6PC1:2538 SO:0001589&frameshift_variant 1 80356479 15 30 50.0 -17 42903947 C T T missense_variant MODERATE G6PC 2538 Transcript NM_000151.4 protein_coding 2/5 325 247 83 R/C Cgt/Tgt 1 EntrezGene C C 0.03 0.999 25.6 3.746062 25.6 0.99935487578864102 8.635508 0.742835872937734 0.85621 10.23433 0.867073051357471 0.90059 -3.98&-2.19&-3.33 4.93 0.99409054497724 0.000000 0.452186 0.8613 0.9979 4.54&4.54&. 1&1&1 .&-6.9&. 11.2363 0.969&0.981&0.986 0.86120 0.497415 1.000000 0.995000 2.666000 1.026000 6.41 rs1801175 48 143234 3.35116e-04 2.37891e-05 0.00000e+00 5.17010e-05 0.00000e+00 0.00000e+00 0.00000e+00 8.07162e-04 1.01626e-03 6.47333e-04 4.21687e-03 3.41297e-03 5.12164e-03 0.00000e+00 0.00000e+00 0.00000e+00 3.79280e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.88143e-04 3.40747e-04 4.27991e-04 2.20751e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.34803e-04 0.00000e+00 0.00000e+00 0.00000e+00 11998 0.00053 27037 Short_stature&Hypoglycemia&Glycogen_storage_disease&Glycogen_storage_disease_due_to_glucose-6-phosphatase_deficiency_type_IA¬_provided Human_Phenotype_Ontology:HP:0001509&Human_Phenotype_Ontology:HP:0003501&Human_Phenotype_Ontology:HP:0003507&Human_Phenotype_Ontology:HP:0003512&Human_Phenotype_Ontology:HP:0003518&Human_Phenotype_Ontology:HP:0003519&Human_Phenotype_Ontology:HP:0004322&Human_Phenotype_Ontology:HP:0008871&Human_Phenotype_Ontology:HP:0008882&Human_Phenotype_Ontology:HP:0008888&Human_Phenotype_Ontology:HP:0008913&MedGen:C0349588&Human_Phenotype_Ontology:HP:0001943&Human_Phenotype_Ontology:HP:0003356&MONDO:MONDO:0004946&MedGen:C0020615&MONDO:MONDO:0002412&MedGen:C0017919&Orphanet:ORPHA79201&SNOMED_CT:29633007&MONDO:MONDO:0009287&MedGen:C2919796&OMIM:232200&Orphanet:ORPHA79258&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant G6PC1:2538 SO:0001583&missense_variant 5 1801175 15 30 50.0 -17 42903947 C T T missense_variant MODERATE G6PC 2538 Transcript NM_001270397.2 protein_coding 2/5 325 247 83 R/C Cgt/Tgt 1 EntrezGene C C 0.01 0.999 25.6 3.746062 25.6 0.99935487578864102 8.635508 0.742835872937734 0.85621 10.23433 0.867073051357471 0.90059 -3.98&-2.19&-3.33 4.93 0.99409054497724 0.000000 0.452186 0.8613 0.9979 4.54&4.54&. 1&1&1 .&-6.9&. 11.2363 0.969&0.981&0.986 0.86120 0.497415 1.000000 0.995000 2.666000 1.026000 6.41 rs1801175 48 143234 3.35116e-04 2.37891e-05 0.00000e+00 5.17010e-05 0.00000e+00 0.00000e+00 0.00000e+00 8.07162e-04 1.01626e-03 6.47333e-04 4.21687e-03 3.41297e-03 5.12164e-03 0.00000e+00 0.00000e+00 0.00000e+00 3.79280e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.88143e-04 3.40747e-04 4.27991e-04 2.20751e-04 0.00000e+00 0.00000e+00 0.00000e+00 3.34803e-04 0.00000e+00 0.00000e+00 0.00000e+00 11998 0.00053 27037 Short_stature&Hypoglycemia&Glycogen_storage_disease&Glycogen_storage_disease_due_to_glucose-6-phosphatase_deficiency_type_IA¬_provided Human_Phenotype_Ontology:HP:0001509&Human_Phenotype_Ontology:HP:0003501&Human_Phenotype_Ontology:HP:0003507&Human_Phenotype_Ontology:HP:0003512&Human_Phenotype_Ontology:HP:0003518&Human_Phenotype_Ontology:HP:0003519&Human_Phenotype_Ontology:HP:0004322&Human_Phenotype_Ontology:HP:0008871&Human_Phenotype_Ontology:HP:0008882&Human_Phenotype_Ontology:HP:0008888&Human_Phenotype_Ontology:HP:0008913&MedGen:C0349588&Human_Phenotype_Ontology:HP:0001943&Human_Phenotype_Ontology:HP:0003356&MONDO:MONDO:0004946&MedGen:C0020615&MONDO:MONDO:0002412&MedGen:C0017919&Orphanet:ORPHA79201&SNOMED_CT:29633007&MONDO:MONDO:0009287&MedGen:C2919796&OMIM:232200&Orphanet:ORPHA79258&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant G6PC1:2538 SO:0001583&missense_variant 5 1801175 15 30 50.0 -17 42903948 G A A missense_variant MODERATE G6PC 2538 Transcript NM_000151.4 protein_coding 2/5 326 248 83 R/H cGt/cAt 1 EntrezGene G G 0.01 0.999 26.3 3.895569 26.3 0.99929406035269008 16.44719 0.957376194265814 0.97619 16.47545 1.07457034698156 0.97634 -3.97&-2.2&-3.33 4.93 0.9999999999793 0.000000 0.604425 0.8829 1.0412 4.54&4.54&. 1&1&1 .&-4.4&. 18.3324 0.961&0.976&0.99 0.97778 0.497415 1.000000 0.992000 9.940000 1.176000 6.41 rs1801176 2 143216 1.39649e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.19285e-04 0.00000e+00 5.93120e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.88201e-05 1.54871e-05 0.00000e+00 3.67863e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.39507e-05 0.00000e+00 0.00000e+00 0.00000e+00 38300 0.00002 27048 Glycogen_storage_disease_due_to_glucose-6-phosphatase_deficiency_type_IA MONDO:MONDO:0009287&MedGen:C2919796&OMIM:232200&Orphanet:ORPHA79258 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant G6PC1:2538 SO:0001583&missense_variant 1 1801176 15 30 50.0 -17 42903948 G A A missense_variant MODERATE G6PC 2538 Transcript NM_001270397.2 protein_coding 2/5 326 248 83 R/H cGt/cAt 1 EntrezGene G G 0.01 0.999 26.3 3.895569 26.3 0.99929406035269008 16.44719 0.957376194265814 0.97619 16.47545 1.07457034698156 0.97634 -3.97&-2.2&-3.33 4.93 0.9999999999793 0.000000 0.604425 0.8829 1.0412 4.54&4.54&. 1&1&1 .&-4.4&. 18.3324 0.961&0.976&0.99 0.97778 0.497415 1.000000 0.992000 9.940000 1.176000 6.41 rs1801176 2 143216 1.39649e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.19285e-04 0.00000e+00 5.93120e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.88201e-05 1.54871e-05 0.00000e+00 3.67863e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.39507e-05 0.00000e+00 0.00000e+00 0.00000e+00 38300 0.00002 27048 Glycogen_storage_disease_due_to_glucose-6-phosphatase_deficiency_type_IA MONDO:MONDO:0009287&MedGen:C2919796&OMIM:232200&Orphanet:ORPHA79258 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant G6PC1:2538 SO:0001583&missense_variant 1 1801176 15 30 50.0 -17 42907558 G GTA TA frameshift_variant HIGH G6PC 2538 Transcript NM_000151.4 protein_coding 3/5 454-455 376-377 126 V/VX gta/gTAta 1 EntrezGene 31 4.433133 6.41 rs1302922732 3 143274 2.09389e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.19748e-04 3.38409e-04 1.29166e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70812e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44047e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09234e-05 0.00000e+00 0.00000e+00 0.00000e+00 11997 27036 Glycogen_storage_disease_due_to_glucose-6-phosphatase_deficiency_type_IA¬_provided MONDO:MONDO:0009287&MedGen:C2919796&OMIM:232200&Orphanet:ORPHA79258&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite G6PC1:2538 SO:0001589&frameshift_variant&SO:0001627&intron_variant 1 80356488 15 30 50.0 -17 42907558 G GTA TA intron_variant MODIFIER G6PC 2538 Transcript NM_001270397.2 protein_coding 2/4 1 EntrezGene 31 4.433133 6.41 rs1302922732 3 143274 2.09389e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.19748e-04 3.38409e-04 1.29166e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.70812e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44047e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.09234e-05 0.00000e+00 0.00000e+00 0.00000e+00 11997 27036 Glycogen_storage_disease_due_to_glucose-6-phosphatase_deficiency_type_IA¬_provided MONDO:MONDO:0009287&MedGen:C2919796&OMIM:232200&Orphanet:ORPHA79258&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite G6PC1:2538 SO:0001589&frameshift_variant&SO:0001627&intron_variant 1 80356488 15 30 50.0 -17 42909418 G C C missense_variant&splice_region_variant MODERATE G6PC 2538 Transcript NM_000151.4 protein_coding 4/5 640 562 188 G/R Ggc/Cgc 1 EntrezGene G G 0.02 1 35 6.345237 35 0.99922627742213288 13.15557 0.885760135232964 0.94920 14.40025 1.01500325288899 0.96188 -1.79 5.62 0.999999999871279 0.000000 0.684713 0.8455 0.9325 1&1&1 -6.92 19.8538 0.976 0.99796 0.497415 1.000000 0.980000 8.952000 1.163000 6.08 chr17:42909418-42909418 4 143226 2.79279e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.06350e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44096e-05 6.19444e-05 8.02396e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79041e-05 0.00000e+00 0.00000e+00 0.00000e+00 12008 27047 Glycogen_storage_disease_due_to_glucose-6-phosphatase_deficiency_type_IA MONDO:MONDO:0009287&MedGen:C2919796&OMIM:232200&Orphanet:ORPHA79258 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant G6PC1:2538 SO:0001583&missense_variant 1 80356482 15 30 50.0 -17 42909418 G C C missense_variant&splice_region_variant MODERATE G6PC 2538 Transcript NM_001270397.2 protein_coding 4/5 563 485 162 R/T aGg/aCg 1 EntrezGene G G 0 0.93 35 6.345237 35 0.99922627742213288 13.15557 0.885760135232964 0.94920 14.40025 1.01500325288899 0.96188 -1.79 5.62 0.999999999871279 0.000000 0.684713 0.8455 0.9325 1&1&1 -6.92 19.8538 0.976 0.99796 0.497415 1.000000 0.980000 8.952000 1.163000 6.08 chr17:42909418-42909418 4 143226 2.79279e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.06350e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.44096e-05 6.19444e-05 8.02396e-05 3.67836e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79041e-05 0.00000e+00 0.00000e+00 0.00000e+00 12008 27047 Glycogen_storage_disease_due_to_glucose-6-phosphatase_deficiency_type_IA MONDO:MONDO:0009287&MedGen:C2919796&OMIM:232200&Orphanet:ORPHA79258 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant G6PC1:2538 SO:0001583&missense_variant 1 80356482 15 30 50.0 -17 42911076 C T T stop_gained HIGH G6PC 2538 Transcript NM_000151.4 protein_coding 5/5 802 724 242 Q/* Cag/Tag 1 EntrezGene C C 35 6.095339 35 0.9859648681280907 2.244644 -0.0345769361493179 0.38164 3.233888 0.184169357992818 0.50440 2.72 4.2402046007805E-4 0.000531 1&1&1 12.8768 0.585 0.78263 0.446893 1.000000 0.980000 1.985000 0.251000 3.08 rs80356485 2 142028 1.40817e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.36747e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.45138e-05 3.11662e-05 2.69397e-05 3.69658e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.39991e-05 0.00000e+00 0.00000e+00 0.00000e+00 21061 0.00002 33913 Glycogen_storage_disease_due_to_glucose-6-phosphatase_deficiency_type_IA MONDO:MONDO:0009287&MedGen:C2919796&OMIM:232200&Orphanet:ORPHA79258 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant G6PC1:2538 SO:0001587&nonsense&SO:0001624&3_prime_UTR_variant 1 80356485 15 30 50.0 -17 42911076 C T T 3_prime_UTR_variant MODIFIER G6PC 2538 Transcript NM_001270397.2 protein_coding 5/5 725 1 EntrezGene C C 35 6.095339 3.08 rs80356485 2 142028 1.40817e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.36747e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.45138e-05 3.11662e-05 2.69397e-05 3.69658e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.39991e-05 0.00000e+00 0.00000e+00 0.00000e+00 21061 0.00002 33913 Glycogen_storage_disease_due_to_glucose-6-phosphatase_deficiency_type_IA MONDO:MONDO:0009287&MedGen:C2919796&OMIM:232200&Orphanet:ORPHA79258 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant G6PC1:2538 SO:0001587&nonsense&SO:0001624&3_prime_UTR_variant 1 80356485 15 30 50.0 -17 42911161 G T T missense_variant MODERATE G6PC 2538 Transcript NM_000151.4 protein_coding 5/5 887 809 270 G/V gGc/gTc 1 EntrezGene G G 0 0.999 28.0 4.125298 28.0 0.99821109648730122 11.15104 0.83233270415507 0.91943 10.77281 0.889125472706734 0.91216 -1.41 4.94 0.99999997131518 0.000000 0.454822 0.7040 0.5508 2.9 1&1&1 -7.59 18.3588 0.969 0.98515 0.446893 1.000000 0.998000 9.881000 1.166000 6.39 rs80356483 3 143206 2.09488e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35465e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.88243e-05 4.64742e-05 2.67537e-05 7.35998e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.09269e-05 0.00000e+00 0.00000e+00 0.00000e+00 21063 0.00002 33915 Glycogen_storage_disease_due_to_glucose-6-phosphatase_deficiency_type_IA MONDO:MONDO:0009287&MedGen:C2919796&OMIM:232200&Orphanet:ORPHA79258 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant G6PC1:2538 SO:0001583&missense_variant&SO:0001624&3_prime_UTR_variant 1 80356483 15 30 50.0 -17 42911161 G T T 3_prime_UTR_variant MODIFIER G6PC 2538 Transcript NM_001270397.2 protein_coding 5/5 810 1 EntrezGene G G 28.0 4.125298 6.39 rs80356483 3 143206 2.09488e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35465e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.88243e-05 4.64742e-05 2.67537e-05 7.35998e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.09269e-05 0.00000e+00 0.00000e+00 0.00000e+00 21063 0.00002 33915 Glycogen_storage_disease_due_to_glucose-6-phosphatase_deficiency_type_IA MONDO:MONDO:0009287&MedGen:C2919796&OMIM:232200&Orphanet:ORPHA79258 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic/Likely_pathogenic single_nucleotide_variant G6PC1:2538 SO:0001583&missense_variant&SO:0001624&3_prime_UTR_variant 1 80356483 15 30 50.0 -17 42911330 CTTC C - inframe_deletion MODERATE G6PC 2538 Transcript NM_000151.4 protein_coding 5/5 1057-1059 979-981 327 F/- TTC/- 1 EntrezGene TTC TTC 6.39&5.43 15 30 50.0 -17 42911330 CTTC C - 3_prime_UTR_variant MODIFIER G6PC 2538 Transcript NM_001270397.2 protein_coding 5/5 980-982 1 EntrezGene TTC TTC 6.39&5.43 15 30 50.0 -17 42911391 C T T stop_gained HIGH G6PC 2538 Transcript NM_000151.4 protein_coding 5/5 1117 1039 347 Q/* Cag/Tag 1 EntrezGene C C 33 5.262392 33 0.90645940438543238 1.212305 -0.493586492552717 0.22329 1.971593 -0.166764722865897 0.34524 -1.8 5.61940808120144E-4 0.433154 0.999868&0.0227142 10.4266 0.534 0.20620 0.446893 0.001000 0.971000 0.194000 -0.244000 -1.56 chr17:42911391-42911391 36 143264 2.51284e-04 7.13878e-05 1.32182e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.19845e-04 3.38753e-04 1.29166e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.52113e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44042e-04 4.64641e-04 5.61678e-04 3.31150e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.51064e-04 0.00000e+00 0.00000e+00 0.00000e+00 12000 27039 Glycogen_storage_disease&Glycogen_storage_disease_due_to_glucose-6-phosphatase_deficiency_type_IA¬_provided MONDO:MONDO:0002412&MedGen:C0017919&Orphanet:ORPHA79201&SNOMED_CT:29633007&MONDO:MONDO:0009287&MedGen:C2919796&OMIM:232200&Orphanet:ORPHA79258&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant G6PC1:2538 SO:0001587&nonsense&SO:0001624&3_prime_UTR_variant 1 80356487 15 30 50.0 -17 42911391 C T T 3_prime_UTR_variant MODIFIER G6PC 2538 Transcript NM_001270397.2 protein_coding 5/5 1040 1 EntrezGene C C 33 5.262392 -1.56 chr17:42911391-42911391 36 143264 2.51284e-04 7.13878e-05 1.32182e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.19845e-04 3.38753e-04 1.29166e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.52113e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44042e-04 4.64641e-04 5.61678e-04 3.31150e-04 0.00000e+00 0.00000e+00 0.00000e+00 2.51064e-04 0.00000e+00 0.00000e+00 0.00000e+00 12000 27039 Glycogen_storage_disease&Glycogen_storage_disease_due_to_glucose-6-phosphatase_deficiency_type_IA¬_provided MONDO:MONDO:0002412&MedGen:C0017919&Orphanet:ORPHA79201&SNOMED_CT:29633007&MONDO:MONDO:0009287&MedGen:C2919796&OMIM:232200&Orphanet:ORPHA79258&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant G6PC1:2538 SO:0001587&nonsense&SO:0001624&3_prime_UTR_variant 1 80356487 15 30 50.0 -17 43057062 T TG G frameshift_variant HIGH BRCA1 672 Transcript NM_007294.4 protein_coding 19/23 5379-5380 5266-5267 1756 Q/PX cag/cCag -1 EntrezGene 33 4.995436 -1.67&4.55 rs1217805587 9 143278 6.28149e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.04704e-04 1.70843e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.08342e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44013e-05 7.74257e-05 1.06975e-04 3.67836e-05 4.64684e-04 9.10747e-04 0.00000e+00 6.27729e-05 0.00000e+00 0.00000e+00 0.00000e+00 17677 32716 Pancreatic_cancer&_susceptibility_to&Breast_neoplasm&Neoplasm_of_ovary&Hereditary_breast_and_ovarian_cancer_syndrome&Punctate_palmoplantar_keratoderma_type_2&Breast-ovarian_cancer&_familial_1&Hereditary_cancer-predisposing_syndrome&Familial_cancer_of_breast¬_specified&Breast_and/or_ovarian_cancer¬_provided .&Human_Phenotype_Ontology:HP:0010623&Human_Phenotype_Ontology:HP:0100013&MONDO:MONDO:0021100&MeSH:D001943&MedGen:C1458155&SNOMED_CT:126926005&Human_Phenotype_Ontology:HP:0100615&MONDO:MONDO:0021068&MeSH:D010051&MedGen:C0919267&OMIM:167000&SNOMED_CT:123843001&MONDO:MONDO:0003582&MeSH:D061325&MedGen:C0677776&OMIM:PS604370&Orphanet:ORPHA145&MONDO:MONDO:0008292&MedGen:C1867982&OMIM:175860&Orphanet:ORPHA79502&MONDO:MONDO:0011450&MedGen:C2676676&OMIM:604370&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0016419&MedGen:C0006142&OMIM:114480&Orphanet:ORPHA227535&SNOMED_CT:254843006&MedGen:CN169374&MedGen:CN221562&MedGen:CN517202 reviewed_by_expert_panel Pathogenic Duplication BRCA1:672 1 80357906 15 30 50.0 -17 43057062 T TG G frameshift_variant HIGH BRCA1 672 Transcript NM_007297.4 protein_coding 18/22 5319-5320 5125-5126 1709 Q/PX cag/cCag -1 EntrezGene 33 4.995436 -1.67&4.55 rs1217805587 9 143278 6.28149e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.04704e-04 1.70843e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.08342e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44013e-05 7.74257e-05 1.06975e-04 3.67836e-05 4.64684e-04 9.10747e-04 0.00000e+00 6.27729e-05 0.00000e+00 0.00000e+00 0.00000e+00 17677 32716 Pancreatic_cancer&_susceptibility_to&Breast_neoplasm&Neoplasm_of_ovary&Hereditary_breast_and_ovarian_cancer_syndrome&Punctate_palmoplantar_keratoderma_type_2&Breast-ovarian_cancer&_familial_1&Hereditary_cancer-predisposing_syndrome&Familial_cancer_of_breast¬_specified&Breast_and/or_ovarian_cancer¬_provided .&Human_Phenotype_Ontology:HP:0010623&Human_Phenotype_Ontology:HP:0100013&MONDO:MONDO:0021100&MeSH:D001943&MedGen:C1458155&SNOMED_CT:126926005&Human_Phenotype_Ontology:HP:0100615&MONDO:MONDO:0021068&MeSH:D010051&MedGen:C0919267&OMIM:167000&SNOMED_CT:123843001&MONDO:MONDO:0003582&MeSH:D061325&MedGen:C0677776&OMIM:PS604370&Orphanet:ORPHA145&MONDO:MONDO:0008292&MedGen:C1867982&OMIM:175860&Orphanet:ORPHA79502&MONDO:MONDO:0011450&MedGen:C2676676&OMIM:604370&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0016419&MedGen:C0006142&OMIM:114480&Orphanet:ORPHA227535&SNOMED_CT:254843006&MedGen:CN169374&MedGen:CN221562&MedGen:CN517202 reviewed_by_expert_panel Pathogenic Duplication BRCA1:672 1 80357906 15 30 50.0 -17 43057062 T TG G frameshift_variant HIGH BRCA1 672 Transcript NM_007298.3 protein_coding 18/22 1973-1974 1954-1955 652 Q/PX cag/cCag -1 EntrezGene OK 33 4.995436 -1.67&4.55 rs1217805587 9 143278 6.28149e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.04704e-04 1.70843e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.08342e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44013e-05 7.74257e-05 1.06975e-04 3.67836e-05 4.64684e-04 9.10747e-04 0.00000e+00 6.27729e-05 0.00000e+00 0.00000e+00 0.00000e+00 17677 32716 Pancreatic_cancer&_susceptibility_to&Breast_neoplasm&Neoplasm_of_ovary&Hereditary_breast_and_ovarian_cancer_syndrome&Punctate_palmoplantar_keratoderma_type_2&Breast-ovarian_cancer&_familial_1&Hereditary_cancer-predisposing_syndrome&Familial_cancer_of_breast¬_specified&Breast_and/or_ovarian_cancer¬_provided .&Human_Phenotype_Ontology:HP:0010623&Human_Phenotype_Ontology:HP:0100013&MONDO:MONDO:0021100&MeSH:D001943&MedGen:C1458155&SNOMED_CT:126926005&Human_Phenotype_Ontology:HP:0100615&MONDO:MONDO:0021068&MeSH:D010051&MedGen:C0919267&OMIM:167000&SNOMED_CT:123843001&MONDO:MONDO:0003582&MeSH:D061325&MedGen:C0677776&OMIM:PS604370&Orphanet:ORPHA145&MONDO:MONDO:0008292&MedGen:C1867982&OMIM:175860&Orphanet:ORPHA79502&MONDO:MONDO:0011450&MedGen:C2676676&OMIM:604370&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0016419&MedGen:C0006142&OMIM:114480&Orphanet:ORPHA227535&SNOMED_CT:254843006&MedGen:CN169374&MedGen:CN221562&MedGen:CN517202 reviewed_by_expert_panel Pathogenic Duplication BRCA1:672 1 80357906 15 30 50.0 -17 43057062 T TG G frameshift_variant HIGH BRCA1 672 Transcript NM_007299.4 protein_coding 19/22 2061-2062 1954-1955 652 Q/PX cag/cCag -1 EntrezGene 33 4.995436 -1.67&4.55 rs1217805587 9 143278 6.28149e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.04704e-04 1.70843e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.08342e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44013e-05 7.74257e-05 1.06975e-04 3.67836e-05 4.64684e-04 9.10747e-04 0.00000e+00 6.27729e-05 0.00000e+00 0.00000e+00 0.00000e+00 17677 32716 Pancreatic_cancer&_susceptibility_to&Breast_neoplasm&Neoplasm_of_ovary&Hereditary_breast_and_ovarian_cancer_syndrome&Punctate_palmoplantar_keratoderma_type_2&Breast-ovarian_cancer&_familial_1&Hereditary_cancer-predisposing_syndrome&Familial_cancer_of_breast¬_specified&Breast_and/or_ovarian_cancer¬_provided .&Human_Phenotype_Ontology:HP:0010623&Human_Phenotype_Ontology:HP:0100013&MONDO:MONDO:0021100&MeSH:D001943&MedGen:C1458155&SNOMED_CT:126926005&Human_Phenotype_Ontology:HP:0100615&MONDO:MONDO:0021068&MeSH:D010051&MedGen:C0919267&OMIM:167000&SNOMED_CT:123843001&MONDO:MONDO:0003582&MeSH:D061325&MedGen:C0677776&OMIM:PS604370&Orphanet:ORPHA145&MONDO:MONDO:0008292&MedGen:C1867982&OMIM:175860&Orphanet:ORPHA79502&MONDO:MONDO:0011450&MedGen:C2676676&OMIM:604370&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0016419&MedGen:C0006142&OMIM:114480&Orphanet:ORPHA227535&SNOMED_CT:254843006&MedGen:CN169374&MedGen:CN221562&MedGen:CN517202 reviewed_by_expert_panel Pathogenic Duplication BRCA1:672 1 80357906 15 30 50.0 -17 43057062 T TG G frameshift_variant HIGH BRCA1 672 Transcript NM_007300.4 protein_coding 20/24 5442-5443 5329-5330 1777 Q/PX cag/cCag -1 EntrezGene 33 4.995436 -1.67&4.55 rs1217805587 9 143278 6.28149e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.04704e-04 1.70843e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.08342e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44013e-05 7.74257e-05 1.06975e-04 3.67836e-05 4.64684e-04 9.10747e-04 0.00000e+00 6.27729e-05 0.00000e+00 0.00000e+00 0.00000e+00 17677 32716 Pancreatic_cancer&_susceptibility_to&Breast_neoplasm&Neoplasm_of_ovary&Hereditary_breast_and_ovarian_cancer_syndrome&Punctate_palmoplantar_keratoderma_type_2&Breast-ovarian_cancer&_familial_1&Hereditary_cancer-predisposing_syndrome&Familial_cancer_of_breast¬_specified&Breast_and/or_ovarian_cancer¬_provided .&Human_Phenotype_Ontology:HP:0010623&Human_Phenotype_Ontology:HP:0100013&MONDO:MONDO:0021100&MeSH:D001943&MedGen:C1458155&SNOMED_CT:126926005&Human_Phenotype_Ontology:HP:0100615&MONDO:MONDO:0021068&MeSH:D010051&MedGen:C0919267&OMIM:167000&SNOMED_CT:123843001&MONDO:MONDO:0003582&MeSH:D061325&MedGen:C0677776&OMIM:PS604370&Orphanet:ORPHA145&MONDO:MONDO:0008292&MedGen:C1867982&OMIM:175860&Orphanet:ORPHA79502&MONDO:MONDO:0011450&MedGen:C2676676&OMIM:604370&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0016419&MedGen:C0006142&OMIM:114480&Orphanet:ORPHA227535&SNOMED_CT:254843006&MedGen:CN169374&MedGen:CN221562&MedGen:CN517202 reviewed_by_expert_panel Pathogenic Duplication BRCA1:672 1 80357906 15 30 50.0 -17 43057062 T TG G non_coding_transcript_exon_variant MODIFIER BRCA1 672 Transcript NR_027676.2 misc_RNA 19/23 5443-5444 -1 EntrezGene 33 4.995436 -1.67&4.55 rs1217805587 9 143278 6.28149e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.04704e-04 1.70843e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.08342e-04 0.00000e+00 0.00000e+00 0.00000e+00 1.44013e-05 7.74257e-05 1.06975e-04 3.67836e-05 4.64684e-04 9.10747e-04 0.00000e+00 6.27729e-05 0.00000e+00 0.00000e+00 0.00000e+00 17677 32716 Pancreatic_cancer&_susceptibility_to&Breast_neoplasm&Neoplasm_of_ovary&Hereditary_breast_and_ovarian_cancer_syndrome&Punctate_palmoplantar_keratoderma_type_2&Breast-ovarian_cancer&_familial_1&Hereditary_cancer-predisposing_syndrome&Familial_cancer_of_breast¬_specified&Breast_and/or_ovarian_cancer¬_provided .&Human_Phenotype_Ontology:HP:0010623&Human_Phenotype_Ontology:HP:0100013&MONDO:MONDO:0021100&MeSH:D001943&MedGen:C1458155&SNOMED_CT:126926005&Human_Phenotype_Ontology:HP:0100615&MONDO:MONDO:0021068&MeSH:D010051&MedGen:C0919267&OMIM:167000&SNOMED_CT:123843001&MONDO:MONDO:0003582&MeSH:D061325&MedGen:C0677776&OMIM:PS604370&Orphanet:ORPHA145&MONDO:MONDO:0008292&MedGen:C1867982&OMIM:175860&Orphanet:ORPHA79502&MONDO:MONDO:0011450&MedGen:C2676676&OMIM:604370&MONDO:MONDO:0015356&MedGen:C0027672&Orphanet:ORPHA140162&SNOMED_CT:699346009&MONDO:MONDO:0016419&MedGen:C0006142&OMIM:114480&Orphanet:ORPHA227535&SNOMED_CT:254843006&MedGen:CN169374&MedGen:CN221562&MedGen:CN517202 reviewed_by_expert_panel Pathogenic Duplication BRCA1:672 1 80357906 15 30 50.0 -17 43063322 CAAGTACTTACCTCATTCAGCATTTTTCTTTCTTTAATAGACTGGGTCACCCCTAAAGAGATCATAGAAAAGACAGGTTACATACAGCAGAAGAACGTGCTCTTTTCACGGAGATAGAGAGGTCAGCGATTCACAAAAGAGCACAGGAAGAATGACAGAGGAGAGGTCCTTCCCTCTAAAGCCACAGCCCTTTAATAAGGCTTGTAGCAGCAGTTTCCTTCTGGAGACAGAGTTGATGTTTAATTTAAACATTATAAGTTTGCCTGCTGCACATGGATTCCTGCCGACTATTAAATAAATCCCTAGCTCATATGCTAACATTGCTAGGAGCAGATTAGGTCCTATTAGTTATAAAAGAGACCCATTTTCCCAGCATCACCAGCTTATCTGAACAAAGTGATATTAAAGATAAAAGTAGTTTAGTATTACAATTAAAGACCTTTTGGTAACTCAGACTCAGCATCAGCAAAAACCTTAGGTGTTAAACGTTAGGTGTAAAAATGCAATTCTGAGGTGTTAAAGGGAGGAGGGGAGAAATAGTATTATACTTACAGAAATAGCTAACTACCCATTTTCCTCCCGCAATTCCTAGAAAATATTTCAGTGTCCGTTCACACACAAACTCAGCATCTGCAGAATGAAAAACACT C - splice_donor_variant&splice_acceptor_variant&coding_sequence_variant&intron_variant HIGH BRCA1 672 Transcript NM_007294.4 protein_coding 17-18/23 16-18/22 -1 EntrezGene AAGTACTTACCTCATTCAGCATTTTTCTTTCTTTAATAGACTGGGTCACCCCTAAAGAGATCATAGAAAAGACAGGTTACATACAGCAGAAGAACGTGCTCTTTTCACGGAGATAGAGAGGTCAGCGATTCACAAAAGAGCACAGGAAGAATGACAGAGGAGAGGTCCTTCCCTCTAAAGCCACAGCCCTTTAATAAGGCTTGTAGCAGCAGTTTCCTTCTGGAGACAGAGTTGATGTTTAATTTAAACATTATAAGTTTGCCTGCTGCACATGGATTCCTGCCGACTATTAAATAAATCCCTAGCTCATATGCTAACATTGCTAGGAGCAGATTAGGTCCTATTAGTTATAAAAGAGACCCATTTTCCCAGCATCACCAGCTTATCTGAACAAAGTGATATTAAAGATAAAAGTAGTTTAGTATTACAATTAAAGACCTTTTGGTAACTCAGACTCAGCATCAGCAAAAACCTTAGGTGTTAAACGTTAGGTGTAAAAATGCAATTCTGAGGTGTTAAAGGGAGGAGGGGAGAAATAGTATTATACTTACAGAAATAGCTAACTACCCATTTTCCTCCCGCAATTCCTAGAAAATATTTCAGTGTCCGTTCACACACAAACTCAGCATCTGCAGAATGAAAAACACT AAGTACTTACCTCATTCAGCATTTTTCTTTCTTTAATAGACTGGGTCACCCCTAAAGAGATCATAGAAAAGACAGGTTACATACAGCAGAAGAACGTGCTCTTTTCACGGAGATAGAGAGGTCAGCGATTCACAAAAGAGCACAGGAAGAATGACAGAGGAGAGGTCCTTCCCTCTAAAGCCACAGCCCTTTAATAAGGCTTGTAGCAGCAGTTTCCTTCTGGAGACAGAGTTGATGTTTAATTTAAACATTATAAGTTTGCCTGCTGCACATGGATTCCTGCCGACTATTAAATAAATCCCTAGCTCATATGCTAACATTGCTAGGAGCAGATTAGGTCCTATTAGTTATAAAAGAGACCCATTTTCCCAGCATCACCAGCTTATCTGAACAAAGTGATATTAAAGATAAAAGTAGTTTAGTATTACAATTAAAGACCTTTTGGTAACTCAGACTCAGCATCAGCAAAAACCTTAGGTGTTAAACGTTAGGTGTAAAAATGCAATTCTGAGGTGTTAAAGGGAGGAGGGGAGAAATAGTATTATACTTACAGAAATAGCTAACTACCCATTTTCCTCCCGCAATTCCTAGAAAATATTTCAGTGTCCGTTCACACACAAACTCAGCATCTGCAGAATGAAAAACACT 1.27&3.63&0.83&2.92&5.48&6.54&-2.15&3.24&6.54&4.36&6.54&3.27&4.38&2.32&1.1&6.54&5.57&-7.42&5.46&1.83&3.39&3.28&6.54&1.95&2.52&-0.275&4.38&6.54&3.31&5.46&4.36&0.438&6.54&3.14&-3.52&4.58&6.54&3.26&4.51&-0.121&1.34&4.38&1.22&6.54&0.855&-3.04&3.22&3.58&6.54&5.58&6.54&-5.83&0.121&5.48&1.1&5.22&4.3&4.15&5.22&3.15&5.35&-4.61&0.451&-4.04&2.49&-3.04&5.57&6.54&0.14&5.48&3.4&0.297&-11.9&-0.432&-1.59&0.526&3.62&4.46&3.19&0.191&4.59&1.15&0.287&-2.69&-0.0401&-7.93&-5.61&-4.08&1.19&-2.41&1.03&1.7&1.18&-0.388&2.42&1.01&-0.389&-1.92&-2.68&2.63&0.495&2.38&-1.77&-6.21&1.27&-3.16&4.87&2.59&-2.71&-2.26&3.12&-2.7&1.31&-5.99&1.74&2.1&-12.3&-10.7&2.49&-1.14&1.61&0.963&0.379&-1.95&-8.89&-4.58&-6.64&2.58&0.716&3.75&-4.13&-3.11&-0.786&-8.2&-6.89&0.263&2.7&-0.249&-1.47&-2.67&3.27&2.06&-1.92&-3.36&0.535&0.757&1.31&-0.737&3.4&1.62&-5.88&2.35&-1.93&1.63&0.835&-4.09&-4.05&-3.82&-4.55&-4.79&-0.321&2.1&-6.26&0.325&-3.14&-4.68&-4.35&3.48&1.18&3.47&-2.16&2.41&-0.478&0.364&-11.1&0.248&2.93&-2.82&2.78&3.04&-0.0401&4.22&5.32&1&1.96&-0.583&-3.25&0.0723&0.594&0.258&-2.88&-3.91&-0.134&1.29&1.31&1.26&4.12&-3.9&2.8&-3.59&0.189&0.513&1.21&-3.39&4.33&3.7&3.87&-0.773&3.75&3.98&1.89&0.0629&-2.27&4.59&5.48&0.634&3.19&-2.83&3.32&-3.74&-4.9&1.98&-1.7&-7.03&0.119&-0.524&1.92&-1.23&-6.83&2.87&4.09&1.15&3.95&2.46&0.988&1.49&4.65&4.66&3.6&3.51&4.42&5.47&3.6&-4.48&1.22&0.7&4.2&4.13&4.25&-4.63&0.734&0.291&-0.396&0.144&3.17&4.58&2.52&5.48&2.55&4.56&3.23&3.44&1.76&4.06&2.15&4.31&1.83&5.23&2.97&2.67&5.46&2.95&2.22&1.22&3.45&-4.75&0.418&1.05&0.524&4.25&1.46&3.95&6.42&4.26&1.76&1.71&2.83&4.23&-1.77&-0.454&1.41&1.42&-6.08&2.07&3.28&1.26&0.808&3.25&2.27&-4.87&2.06&0.803&3.12&3.32&3.36&0.538&4.38&0.677&4.59&2.03&4.4&1.85&5.36&2.49&2.29&3.35&1.97&-1.35&4.11&2&-0.203&4.4&4.54&5.48&2.1&2.07&6.54&2.47&5.33&3.03&-2.39&1.05&3.29&2.06&4.34&4.38&3.47&5.18&6.24&-1.13&-0.944&1.52&5&5.02&4.73&4.07&5.22&3.97&-2.14&3.18&4.32&5.2&4.1&6.27&6.38&-4.96&0.178&1.06&4.77&3.6&0.603&5.11&3.92&-2.35&1.77&-1.17&-1.84&2.7&6.08&4.13&3.97&3.67&0.966&6.26&4.08&2.21&1.42&-0.22&6.18&3.94&3.96&3.97&4.27&-5.62&-1.73&4.07&4.05&4.12&1.72&4.05&0.385&2.85&2.9&5.29&6.26&4.08&6.15&2.85&2.87&1.14&3.33&1.83&4.17&5.39&2.65&6.38&-2.33&3.06&3.38&5.3&3.42&-1.89&1.9&6.38&5.08&0.726&-1.2&-2.68&6.08&4.9&5.31&5.33&4.39&3.08&4.42&5.42&5.19&0.784&4.23&-2.94&-0.143&4.15&5.33&5.24&1.66&3.07&4.23&2.37&6.38&3.41&5.19&-3.28&4.2&5.4&4.09&2.17&4.4&6.38&3.02&1.2&5.32&4.42&3.28&5.3&1.97&5.33&6.38&5.31&-1.5&2.08&1.78&-2.53&2.73&4.03&4&3.9&3.07&3.89&3.63&3.26&1.19&4.74&-1.69&4.84&4.9&4.89&4.99&3.97&5.96&3.89&2.08&3.92&2.8&3.83&3.87&4.79&4.85&3.02&6.08&1.49&3.91&3.95&5.11&0.174&4.12&5.18&-0.484&6.26&2.91&4.1&4.12&1.82&3.74&3.85&5.04&3.24&5.33&-1.13&2.3&3.04&0.0505&-0.382&-0.47&4.12&3.92&-0.116&4.1&1.7&4.01&-2.92&-3.61&0.733&-4.66&1.73&-5.89&1.9&2.28&1.92&6.38&3.04&6.38&-3.05&4.18&4.24&5.32&6.38&5.42&6.38&4.26&6.38&2.27&6.38&1.82&4.25&5.32&3.07&6.38&4.17&6.38&5.42&1.83&6.38&2.99&6.38&0.492&6.38&3.36&5.32&5.42&3.14&6.38&1.76&6.38&-1.72&6.38&5.42&6.38&4.4&-2.1&6.38&5.42&6.38&1.95&6.38&5.42&3.03&6.38&1.07&6.38&4.22&6.38&1.87&-2.61&6.38&5.4&-1.72&5.42&-1.14&0.352&6.38&-0.704&-1.59&1.95&1.2&0.72&4.25&-1.14&3.41&5.33&4.22&4.24&1.73&4.24&1.26&5.3&-4.94&-2.7 15 30 50.0 -17 43063322 CAAGTACTTACCTCATTCAGCATTTTTCTTTCTTTAATAGACTGGGTCACCCCTAAAGAGATCATAGAAAAGACAGGTTACATACAGCAGAAGAACGTGCTCTTTTCACGGAGATAGAGAGGTCAGCGATTCACAAAAGAGCACAGGAAGAATGACAGAGGAGAGGTCCTTCCCTCTAAAGCCACAGCCCTTTAATAAGGCTTGTAGCAGCAGTTTCCTTCTGGAGACAGAGTTGATGTTTAATTTAAACATTATAAGTTTGCCTGCTGCACATGGATTCCTGCCGACTATTAAATAAATCCCTAGCTCATATGCTAACATTGCTAGGAGCAGATTAGGTCCTATTAGTTATAAAAGAGACCCATTTTCCCAGCATCACCAGCTTATCTGAACAAAGTGATATTAAAGATAAAAGTAGTTTAGTATTACAATTAAAGACCTTTTGGTAACTCAGACTCAGCATCAGCAAAAACCTTAGGTGTTAAACGTTAGGTGTAAAAATGCAATTCTGAGGTGTTAAAGGGAGGAGGGGAGAAATAGTATTATACTTACAGAAATAGCTAACTACCCATTTTCCTCCCGCAATTCCTAGAAAATATTTCAGTGTCCGTTCACACACAAACTCAGCATCTGCAGAATGAAAAACACT C - splice_donor_variant&splice_acceptor_variant&coding_sequence_variant&intron_variant HIGH BRCA1 672 Transcript NM_007297.4 protein_coding 16-17/22 15-17/21 -1 EntrezGene AAGTACTTACCTCATTCAGCATTTTTCTTTCTTTAATAGACTGGGTCACCCCTAAAGAGATCATAGAAAAGACAGGTTACATACAGCAGAAGAACGTGCTCTTTTCACGGAGATAGAGAGGTCAGCGATTCACAAAAGAGCACAGGAAGAATGACAGAGGAGAGGTCCTTCCCTCTAAAGCCACAGCCCTTTAATAAGGCTTGTAGCAGCAGTTTCCTTCTGGAGACAGAGTTGATGTTTAATTTAAACATTATAAGTTTGCCTGCTGCACATGGATTCCTGCCGACTATTAAATAAATCCCTAGCTCATATGCTAACATTGCTAGGAGCAGATTAGGTCCTATTAGTTATAAAAGAGACCCATTTTCCCAGCATCACCAGCTTATCTGAACAAAGTGATATTAAAGATAAAAGTAGTTTAGTATTACAATTAAAGACCTTTTGGTAACTCAGACTCAGCATCAGCAAAAACCTTAGGTGTTAAACGTTAGGTGTAAAAATGCAATTCTGAGGTGTTAAAGGGAGGAGGGGAGAAATAGTATTATACTTACAGAAATAGCTAACTACCCATTTTCCTCCCGCAATTCCTAGAAAATATTTCAGTGTCCGTTCACACACAAACTCAGCATCTGCAGAATGAAAAACACT AAGTACTTACCTCATTCAGCATTTTTCTTTCTTTAATAGACTGGGTCACCCCTAAAGAGATCATAGAAAAGACAGGTTACATACAGCAGAAGAACGTGCTCTTTTCACGGAGATAGAGAGGTCAGCGATTCACAAAAGAGCACAGGAAGAATGACAGAGGAGAGGTCCTTCCCTCTAAAGCCACAGCCCTTTAATAAGGCTTGTAGCAGCAGTTTCCTTCTGGAGACAGAGTTGATGTTTAATTTAAACATTATAAGTTTGCCTGCTGCACATGGATTCCTGCCGACTATTAAATAAATCCCTAGCTCATATGCTAACATTGCTAGGAGCAGATTAGGTCCTATTAGTTATAAAAGAGACCCATTTTCCCAGCATCACCAGCTTATCTGAACAAAGTGATATTAAAGATAAAAGTAGTTTAGTATTACAATTAAAGACCTTTTGGTAACTCAGACTCAGCATCAGCAAAAACCTTAGGTGTTAAACGTTAGGTGTAAAAATGCAATTCTGAGGTGTTAAAGGGAGGAGGGGAGAAATAGTATTATACTTACAGAAATAGCTAACTACCCATTTTCCTCCCGCAATTCCTAGAAAATATTTCAGTGTCCGTTCACACACAAACTCAGCATCTGCAGAATGAAAAACACT 1.27&3.63&0.83&2.92&5.48&6.54&-2.15&3.24&6.54&4.36&6.54&3.27&4.38&2.32&1.1&6.54&5.57&-7.42&5.46&1.83&3.39&3.28&6.54&1.95&2.52&-0.275&4.38&6.54&3.31&5.46&4.36&0.438&6.54&3.14&-3.52&4.58&6.54&3.26&4.51&-0.121&1.34&4.38&1.22&6.54&0.855&-3.04&3.22&3.58&6.54&5.58&6.54&-5.83&0.121&5.48&1.1&5.22&4.3&4.15&5.22&3.15&5.35&-4.61&0.451&-4.04&2.49&-3.04&5.57&6.54&0.14&5.48&3.4&0.297&-11.9&-0.432&-1.59&0.526&3.62&4.46&3.19&0.191&4.59&1.15&0.287&-2.69&-0.0401&-7.93&-5.61&-4.08&1.19&-2.41&1.03&1.7&1.18&-0.388&2.42&1.01&-0.389&-1.92&-2.68&2.63&0.495&2.38&-1.77&-6.21&1.27&-3.16&4.87&2.59&-2.71&-2.26&3.12&-2.7&1.31&-5.99&1.74&2.1&-12.3&-10.7&2.49&-1.14&1.61&0.963&0.379&-1.95&-8.89&-4.58&-6.64&2.58&0.716&3.75&-4.13&-3.11&-0.786&-8.2&-6.89&0.263&2.7&-0.249&-1.47&-2.67&3.27&2.06&-1.92&-3.36&0.535&0.757&1.31&-0.737&3.4&1.62&-5.88&2.35&-1.93&1.63&0.835&-4.09&-4.05&-3.82&-4.55&-4.79&-0.321&2.1&-6.26&0.325&-3.14&-4.68&-4.35&3.48&1.18&3.47&-2.16&2.41&-0.478&0.364&-11.1&0.248&2.93&-2.82&2.78&3.04&-0.0401&4.22&5.32&1&1.96&-0.583&-3.25&0.0723&0.594&0.258&-2.88&-3.91&-0.134&1.29&1.31&1.26&4.12&-3.9&2.8&-3.59&0.189&0.513&1.21&-3.39&4.33&3.7&3.87&-0.773&3.75&3.98&1.89&0.0629&-2.27&4.59&5.48&0.634&3.19&-2.83&3.32&-3.74&-4.9&1.98&-1.7&-7.03&0.119&-0.524&1.92&-1.23&-6.83&2.87&4.09&1.15&3.95&2.46&0.988&1.49&4.65&4.66&3.6&3.51&4.42&5.47&3.6&-4.48&1.22&0.7&4.2&4.13&4.25&-4.63&0.734&0.291&-0.396&0.144&3.17&4.58&2.52&5.48&2.55&4.56&3.23&3.44&1.76&4.06&2.15&4.31&1.83&5.23&2.97&2.67&5.46&2.95&2.22&1.22&3.45&-4.75&0.418&1.05&0.524&4.25&1.46&3.95&6.42&4.26&1.76&1.71&2.83&4.23&-1.77&-0.454&1.41&1.42&-6.08&2.07&3.28&1.26&0.808&3.25&2.27&-4.87&2.06&0.803&3.12&3.32&3.36&0.538&4.38&0.677&4.59&2.03&4.4&1.85&5.36&2.49&2.29&3.35&1.97&-1.35&4.11&2&-0.203&4.4&4.54&5.48&2.1&2.07&6.54&2.47&5.33&3.03&-2.39&1.05&3.29&2.06&4.34&4.38&3.47&5.18&6.24&-1.13&-0.944&1.52&5&5.02&4.73&4.07&5.22&3.97&-2.14&3.18&4.32&5.2&4.1&6.27&6.38&-4.96&0.178&1.06&4.77&3.6&0.603&5.11&3.92&-2.35&1.77&-1.17&-1.84&2.7&6.08&4.13&3.97&3.67&0.966&6.26&4.08&2.21&1.42&-0.22&6.18&3.94&3.96&3.97&4.27&-5.62&-1.73&4.07&4.05&4.12&1.72&4.05&0.385&2.85&2.9&5.29&6.26&4.08&6.15&2.85&2.87&1.14&3.33&1.83&4.17&5.39&2.65&6.38&-2.33&3.06&3.38&5.3&3.42&-1.89&1.9&6.38&5.08&0.726&-1.2&-2.68&6.08&4.9&5.31&5.33&4.39&3.08&4.42&5.42&5.19&0.784&4.23&-2.94&-0.143&4.15&5.33&5.24&1.66&3.07&4.23&2.37&6.38&3.41&5.19&-3.28&4.2&5.4&4.09&2.17&4.4&6.38&3.02&1.2&5.32&4.42&3.28&5.3&1.97&5.33&6.38&5.31&-1.5&2.08&1.78&-2.53&2.73&4.03&4&3.9&3.07&3.89&3.63&3.26&1.19&4.74&-1.69&4.84&4.9&4.89&4.99&3.97&5.96&3.89&2.08&3.92&2.8&3.83&3.87&4.79&4.85&3.02&6.08&1.49&3.91&3.95&5.11&0.174&4.12&5.18&-0.484&6.26&2.91&4.1&4.12&1.82&3.74&3.85&5.04&3.24&5.33&-1.13&2.3&3.04&0.0505&-0.382&-0.47&4.12&3.92&-0.116&4.1&1.7&4.01&-2.92&-3.61&0.733&-4.66&1.73&-5.89&1.9&2.28&1.92&6.38&3.04&6.38&-3.05&4.18&4.24&5.32&6.38&5.42&6.38&4.26&6.38&2.27&6.38&1.82&4.25&5.32&3.07&6.38&4.17&6.38&5.42&1.83&6.38&2.99&6.38&0.492&6.38&3.36&5.32&5.42&3.14&6.38&1.76&6.38&-1.72&6.38&5.42&6.38&4.4&-2.1&6.38&5.42&6.38&1.95&6.38&5.42&3.03&6.38&1.07&6.38&4.22&6.38&1.87&-2.61&6.38&5.4&-1.72&5.42&-1.14&0.352&6.38&-0.704&-1.59&1.95&1.2&0.72&4.25&-1.14&3.41&5.33&4.22&4.24&1.73&4.24&1.26&5.3&-4.94&-2.7 15 30 50.0 -17 43063322 CAAGTACTTACCTCATTCAGCATTTTTCTTTCTTTAATAGACTGGGTCACCCCTAAAGAGATCATAGAAAAGACAGGTTACATACAGCAGAAGAACGTGCTCTTTTCACGGAGATAGAGAGGTCAGCGATTCACAAAAGAGCACAGGAAGAATGACAGAGGAGAGGTCCTTCCCTCTAAAGCCACAGCCCTTTAATAAGGCTTGTAGCAGCAGTTTCCTTCTGGAGACAGAGTTGATGTTTAATTTAAACATTATAAGTTTGCCTGCTGCACATGGATTCCTGCCGACTATTAAATAAATCCCTAGCTCATATGCTAACATTGCTAGGAGCAGATTAGGTCCTATTAGTTATAAAAGAGACCCATTTTCCCAGCATCACCAGCTTATCTGAACAAAGTGATATTAAAGATAAAAGTAGTTTAGTATTACAATTAAAGACCTTTTGGTAACTCAGACTCAGCATCAGCAAAAACCTTAGGTGTTAAACGTTAGGTGTAAAAATGCAATTCTGAGGTGTTAAAGGGAGGAGGGGAGAAATAGTATTATACTTACAGAAATAGCTAACTACCCATTTTCCTCCCGCAATTCCTAGAAAATATTTCAGTGTCCGTTCACACACAAACTCAGCATCTGCAGAATGAAAAACACT C - splice_donor_variant&splice_acceptor_variant&coding_sequence_variant&intron_variant HIGH BRCA1 672 Transcript NM_007298.3 protein_coding 16-17/22 15-17/21 -1 EntrezGene AAGTACTTACCTCATTCAGCATTTTTCTTTCTTTAATAGACTGGGTCACCCCTAAAGAGATCATAGAAAAGACAGGTTACATACAGCAGAAGAACGTGCTCTTTTCACGGAGATAGAGAGGTCAGCGATTCACAAAAGAGCACAGGAAGAATGACAGAGGAGAGGTCCTTCCCTCTAAAGCCACAGCCCTTTAATAAGGCTTGTAGCAGCAGTTTCCTTCTGGAGACAGAGTTGATGTTTAATTTAAACATTATAAGTTTGCCTGCTGCACATGGATTCCTGCCGACTATTAAATAAATCCCTAGCTCATATGCTAACATTGCTAGGAGCAGATTAGGTCCTATTAGTTATAAAAGAGACCCATTTTCCCAGCATCACCAGCTTATCTGAACAAAGTGATATTAAAGATAAAAGTAGTTTAGTATTACAATTAAAGACCTTTTGGTAACTCAGACTCAGCATCAGCAAAAACCTTAGGTGTTAAACGTTAGGTGTAAAAATGCAATTCTGAGGTGTTAAAGGGAGGAGGGGAGAAATAGTATTATACTTACAGAAATAGCTAACTACCCATTTTCCTCCCGCAATTCCTAGAAAATATTTCAGTGTCCGTTCACACACAAACTCAGCATCTGCAGAATGAAAAACACT AAGTACTTACCTCATTCAGCATTTTTCTTTCTTTAATAGACTGGGTCACCCCTAAAGAGATCATAGAAAAGACAGGTTACATACAGCAGAAGAACGTGCTCTTTTCACGGAGATAGAGAGGTCAGCGATTCACAAAAGAGCACAGGAAGAATGACAGAGGAGAGGTCCTTCCCTCTAAAGCCACAGCCCTTTAATAAGGCTTGTAGCAGCAGTTTCCTTCTGGAGACAGAGTTGATGTTTAATTTAAACATTATAAGTTTGCCTGCTGCACATGGATTCCTGCCGACTATTAAATAAATCCCTAGCTCATATGCTAACATTGCTAGGAGCAGATTAGGTCCTATTAGTTATAAAAGAGACCCATTTTCCCAGCATCACCAGCTTATCTGAACAAAGTGATATTAAAGATAAAAGTAGTTTAGTATTACAATTAAAGACCTTTTGGTAACTCAGACTCAGCATCAGCAAAAACCTTAGGTGTTAAACGTTAGGTGTAAAAATGCAATTCTGAGGTGTTAAAGGGAGGAGGGGAGAAATAGTATTATACTTACAGAAATAGCTAACTACCCATTTTCCTCCCGCAATTCCTAGAAAATATTTCAGTGTCCGTTCACACACAAACTCAGCATCTGCAGAATGAAAAACACT OK 1.27&3.63&0.83&2.92&5.48&6.54&-2.15&3.24&6.54&4.36&6.54&3.27&4.38&2.32&1.1&6.54&5.57&-7.42&5.46&1.83&3.39&3.28&6.54&1.95&2.52&-0.275&4.38&6.54&3.31&5.46&4.36&0.438&6.54&3.14&-3.52&4.58&6.54&3.26&4.51&-0.121&1.34&4.38&1.22&6.54&0.855&-3.04&3.22&3.58&6.54&5.58&6.54&-5.83&0.121&5.48&1.1&5.22&4.3&4.15&5.22&3.15&5.35&-4.61&0.451&-4.04&2.49&-3.04&5.57&6.54&0.14&5.48&3.4&0.297&-11.9&-0.432&-1.59&0.526&3.62&4.46&3.19&0.191&4.59&1.15&0.287&-2.69&-0.0401&-7.93&-5.61&-4.08&1.19&-2.41&1.03&1.7&1.18&-0.388&2.42&1.01&-0.389&-1.92&-2.68&2.63&0.495&2.38&-1.77&-6.21&1.27&-3.16&4.87&2.59&-2.71&-2.26&3.12&-2.7&1.31&-5.99&1.74&2.1&-12.3&-10.7&2.49&-1.14&1.61&0.963&0.379&-1.95&-8.89&-4.58&-6.64&2.58&0.716&3.75&-4.13&-3.11&-0.786&-8.2&-6.89&0.263&2.7&-0.249&-1.47&-2.67&3.27&2.06&-1.92&-3.36&0.535&0.757&1.31&-0.737&3.4&1.62&-5.88&2.35&-1.93&1.63&0.835&-4.09&-4.05&-3.82&-4.55&-4.79&-0.321&2.1&-6.26&0.325&-3.14&-4.68&-4.35&3.48&1.18&3.47&-2.16&2.41&-0.478&0.364&-11.1&0.248&2.93&-2.82&2.78&3.04&-0.0401&4.22&5.32&1&1.96&-0.583&-3.25&0.0723&0.594&0.258&-2.88&-3.91&-0.134&1.29&1.31&1.26&4.12&-3.9&2.8&-3.59&0.189&0.513&1.21&-3.39&4.33&3.7&3.87&-0.773&3.75&3.98&1.89&0.0629&-2.27&4.59&5.48&0.634&3.19&-2.83&3.32&-3.74&-4.9&1.98&-1.7&-7.03&0.119&-0.524&1.92&-1.23&-6.83&2.87&4.09&1.15&3.95&2.46&0.988&1.49&4.65&4.66&3.6&3.51&4.42&5.47&3.6&-4.48&1.22&0.7&4.2&4.13&4.25&-4.63&0.734&0.291&-0.396&0.144&3.17&4.58&2.52&5.48&2.55&4.56&3.23&3.44&1.76&4.06&2.15&4.31&1.83&5.23&2.97&2.67&5.46&2.95&2.22&1.22&3.45&-4.75&0.418&1.05&0.524&4.25&1.46&3.95&6.42&4.26&1.76&1.71&2.83&4.23&-1.77&-0.454&1.41&1.42&-6.08&2.07&3.28&1.26&0.808&3.25&2.27&-4.87&2.06&0.803&3.12&3.32&3.36&0.538&4.38&0.677&4.59&2.03&4.4&1.85&5.36&2.49&2.29&3.35&1.97&-1.35&4.11&2&-0.203&4.4&4.54&5.48&2.1&2.07&6.54&2.47&5.33&3.03&-2.39&1.05&3.29&2.06&4.34&4.38&3.47&5.18&6.24&-1.13&-0.944&1.52&5&5.02&4.73&4.07&5.22&3.97&-2.14&3.18&4.32&5.2&4.1&6.27&6.38&-4.96&0.178&1.06&4.77&3.6&0.603&5.11&3.92&-2.35&1.77&-1.17&-1.84&2.7&6.08&4.13&3.97&3.67&0.966&6.26&4.08&2.21&1.42&-0.22&6.18&3.94&3.96&3.97&4.27&-5.62&-1.73&4.07&4.05&4.12&1.72&4.05&0.385&2.85&2.9&5.29&6.26&4.08&6.15&2.85&2.87&1.14&3.33&1.83&4.17&5.39&2.65&6.38&-2.33&3.06&3.38&5.3&3.42&-1.89&1.9&6.38&5.08&0.726&-1.2&-2.68&6.08&4.9&5.31&5.33&4.39&3.08&4.42&5.42&5.19&0.784&4.23&-2.94&-0.143&4.15&5.33&5.24&1.66&3.07&4.23&2.37&6.38&3.41&5.19&-3.28&4.2&5.4&4.09&2.17&4.4&6.38&3.02&1.2&5.32&4.42&3.28&5.3&1.97&5.33&6.38&5.31&-1.5&2.08&1.78&-2.53&2.73&4.03&4&3.9&3.07&3.89&3.63&3.26&1.19&4.74&-1.69&4.84&4.9&4.89&4.99&3.97&5.96&3.89&2.08&3.92&2.8&3.83&3.87&4.79&4.85&3.02&6.08&1.49&3.91&3.95&5.11&0.174&4.12&5.18&-0.484&6.26&2.91&4.1&4.12&1.82&3.74&3.85&5.04&3.24&5.33&-1.13&2.3&3.04&0.0505&-0.382&-0.47&4.12&3.92&-0.116&4.1&1.7&4.01&-2.92&-3.61&0.733&-4.66&1.73&-5.89&1.9&2.28&1.92&6.38&3.04&6.38&-3.05&4.18&4.24&5.32&6.38&5.42&6.38&4.26&6.38&2.27&6.38&1.82&4.25&5.32&3.07&6.38&4.17&6.38&5.42&1.83&6.38&2.99&6.38&0.492&6.38&3.36&5.32&5.42&3.14&6.38&1.76&6.38&-1.72&6.38&5.42&6.38&4.4&-2.1&6.38&5.42&6.38&1.95&6.38&5.42&3.03&6.38&1.07&6.38&4.22&6.38&1.87&-2.61&6.38&5.4&-1.72&5.42&-1.14&0.352&6.38&-0.704&-1.59&1.95&1.2&0.72&4.25&-1.14&3.41&5.33&4.22&4.24&1.73&4.24&1.26&5.3&-4.94&-2.7 15 30 50.0 -17 43063322 CAAGTACTTACCTCATTCAGCATTTTTCTTTCTTTAATAGACTGGGTCACCCCTAAAGAGATCATAGAAAAGACAGGTTACATACAGCAGAAGAACGTGCTCTTTTCACGGAGATAGAGAGGTCAGCGATTCACAAAAGAGCACAGGAAGAATGACAGAGGAGAGGTCCTTCCCTCTAAAGCCACAGCCCTTTAATAAGGCTTGTAGCAGCAGTTTCCTTCTGGAGACAGAGTTGATGTTTAATTTAAACATTATAAGTTTGCCTGCTGCACATGGATTCCTGCCGACTATTAAATAAATCCCTAGCTCATATGCTAACATTGCTAGGAGCAGATTAGGTCCTATTAGTTATAAAAGAGACCCATTTTCCCAGCATCACCAGCTTATCTGAACAAAGTGATATTAAAGATAAAAGTAGTTTAGTATTACAATTAAAGACCTTTTGGTAACTCAGACTCAGCATCAGCAAAAACCTTAGGTGTTAAACGTTAGGTGTAAAAATGCAATTCTGAGGTGTTAAAGGGAGGAGGGGAGAAATAGTATTATACTTACAGAAATAGCTAACTACCCATTTTCCTCCCGCAATTCCTAGAAAATATTTCAGTGTCCGTTCACACACAAACTCAGCATCTGCAGAATGAAAAACACT C - splice_donor_variant&splice_acceptor_variant&coding_sequence_variant&intron_variant HIGH BRCA1 672 Transcript NM_007299.4 protein_coding 17-18/22 16-18/21 -1 EntrezGene AAGTACTTACCTCATTCAGCATTTTTCTTTCTTTAATAGACTGGGTCACCCCTAAAGAGATCATAGAAAAGACAGGTTACATACAGCAGAAGAACGTGCTCTTTTCACGGAGATAGAGAGGTCAGCGATTCACAAAAGAGCACAGGAAGAATGACAGAGGAGAGGTCCTTCCCTCTAAAGCCACAGCCCTTTAATAAGGCTTGTAGCAGCAGTTTCCTTCTGGAGACAGAGTTGATGTTTAATTTAAACATTATAAGTTTGCCTGCTGCACATGGATTCCTGCCGACTATTAAATAAATCCCTAGCTCATATGCTAACATTGCTAGGAGCAGATTAGGTCCTATTAGTTATAAAAGAGACCCATTTTCCCAGCATCACCAGCTTATCTGAACAAAGTGATATTAAAGATAAAAGTAGTTTAGTATTACAATTAAAGACCTTTTGGTAACTCAGACTCAGCATCAGCAAAAACCTTAGGTGTTAAACGTTAGGTGTAAAAATGCAATTCTGAGGTGTTAAAGGGAGGAGGGGAGAAATAGTATTATACTTACAGAAATAGCTAACTACCCATTTTCCTCCCGCAATTCCTAGAAAATATTTCAGTGTCCGTTCACACACAAACTCAGCATCTGCAGAATGAAAAACACT AAGTACTTACCTCATTCAGCATTTTTCTTTCTTTAATAGACTGGGTCACCCCTAAAGAGATCATAGAAAAGACAGGTTACATACAGCAGAAGAACGTGCTCTTTTCACGGAGATAGAGAGGTCAGCGATTCACAAAAGAGCACAGGAAGAATGACAGAGGAGAGGTCCTTCCCTCTAAAGCCACAGCCCTTTAATAAGGCTTGTAGCAGCAGTTTCCTTCTGGAGACAGAGTTGATGTTTAATTTAAACATTATAAGTTTGCCTGCTGCACATGGATTCCTGCCGACTATTAAATAAATCCCTAGCTCATATGCTAACATTGCTAGGAGCAGATTAGGTCCTATTAGTTATAAAAGAGACCCATTTTCCCAGCATCACCAGCTTATCTGAACAAAGTGATATTAAAGATAAAAGTAGTTTAGTATTACAATTAAAGACCTTTTGGTAACTCAGACTCAGCATCAGCAAAAACCTTAGGTGTTAAACGTTAGGTGTAAAAATGCAATTCTGAGGTGTTAAAGGGAGGAGGGGAGAAATAGTATTATACTTACAGAAATAGCTAACTACCCATTTTCCTCCCGCAATTCCTAGAAAATATTTCAGTGTCCGTTCACACACAAACTCAGCATCTGCAGAATGAAAAACACT 1.27&3.63&0.83&2.92&5.48&6.54&-2.15&3.24&6.54&4.36&6.54&3.27&4.38&2.32&1.1&6.54&5.57&-7.42&5.46&1.83&3.39&3.28&6.54&1.95&2.52&-0.275&4.38&6.54&3.31&5.46&4.36&0.438&6.54&3.14&-3.52&4.58&6.54&3.26&4.51&-0.121&1.34&4.38&1.22&6.54&0.855&-3.04&3.22&3.58&6.54&5.58&6.54&-5.83&0.121&5.48&1.1&5.22&4.3&4.15&5.22&3.15&5.35&-4.61&0.451&-4.04&2.49&-3.04&5.57&6.54&0.14&5.48&3.4&0.297&-11.9&-0.432&-1.59&0.526&3.62&4.46&3.19&0.191&4.59&1.15&0.287&-2.69&-0.0401&-7.93&-5.61&-4.08&1.19&-2.41&1.03&1.7&1.18&-0.388&2.42&1.01&-0.389&-1.92&-2.68&2.63&0.495&2.38&-1.77&-6.21&1.27&-3.16&4.87&2.59&-2.71&-2.26&3.12&-2.7&1.31&-5.99&1.74&2.1&-12.3&-10.7&2.49&-1.14&1.61&0.963&0.379&-1.95&-8.89&-4.58&-6.64&2.58&0.716&3.75&-4.13&-3.11&-0.786&-8.2&-6.89&0.263&2.7&-0.249&-1.47&-2.67&3.27&2.06&-1.92&-3.36&0.535&0.757&1.31&-0.737&3.4&1.62&-5.88&2.35&-1.93&1.63&0.835&-4.09&-4.05&-3.82&-4.55&-4.79&-0.321&2.1&-6.26&0.325&-3.14&-4.68&-4.35&3.48&1.18&3.47&-2.16&2.41&-0.478&0.364&-11.1&0.248&2.93&-2.82&2.78&3.04&-0.0401&4.22&5.32&1&1.96&-0.583&-3.25&0.0723&0.594&0.258&-2.88&-3.91&-0.134&1.29&1.31&1.26&4.12&-3.9&2.8&-3.59&0.189&0.513&1.21&-3.39&4.33&3.7&3.87&-0.773&3.75&3.98&1.89&0.0629&-2.27&4.59&5.48&0.634&3.19&-2.83&3.32&-3.74&-4.9&1.98&-1.7&-7.03&0.119&-0.524&1.92&-1.23&-6.83&2.87&4.09&1.15&3.95&2.46&0.988&1.49&4.65&4.66&3.6&3.51&4.42&5.47&3.6&-4.48&1.22&0.7&4.2&4.13&4.25&-4.63&0.734&0.291&-0.396&0.144&3.17&4.58&2.52&5.48&2.55&4.56&3.23&3.44&1.76&4.06&2.15&4.31&1.83&5.23&2.97&2.67&5.46&2.95&2.22&1.22&3.45&-4.75&0.418&1.05&0.524&4.25&1.46&3.95&6.42&4.26&1.76&1.71&2.83&4.23&-1.77&-0.454&1.41&1.42&-6.08&2.07&3.28&1.26&0.808&3.25&2.27&-4.87&2.06&0.803&3.12&3.32&3.36&0.538&4.38&0.677&4.59&2.03&4.4&1.85&5.36&2.49&2.29&3.35&1.97&-1.35&4.11&2&-0.203&4.4&4.54&5.48&2.1&2.07&6.54&2.47&5.33&3.03&-2.39&1.05&3.29&2.06&4.34&4.38&3.47&5.18&6.24&-1.13&-0.944&1.52&5&5.02&4.73&4.07&5.22&3.97&-2.14&3.18&4.32&5.2&4.1&6.27&6.38&-4.96&0.178&1.06&4.77&3.6&0.603&5.11&3.92&-2.35&1.77&-1.17&-1.84&2.7&6.08&4.13&3.97&3.67&0.966&6.26&4.08&2.21&1.42&-0.22&6.18&3.94&3.96&3.97&4.27&-5.62&-1.73&4.07&4.05&4.12&1.72&4.05&0.385&2.85&2.9&5.29&6.26&4.08&6.15&2.85&2.87&1.14&3.33&1.83&4.17&5.39&2.65&6.38&-2.33&3.06&3.38&5.3&3.42&-1.89&1.9&6.38&5.08&0.726&-1.2&-2.68&6.08&4.9&5.31&5.33&4.39&3.08&4.42&5.42&5.19&0.784&4.23&-2.94&-0.143&4.15&5.33&5.24&1.66&3.07&4.23&2.37&6.38&3.41&5.19&-3.28&4.2&5.4&4.09&2.17&4.4&6.38&3.02&1.2&5.32&4.42&3.28&5.3&1.97&5.33&6.38&5.31&-1.5&2.08&1.78&-2.53&2.73&4.03&4&3.9&3.07&3.89&3.63&3.26&1.19&4.74&-1.69&4.84&4.9&4.89&4.99&3.97&5.96&3.89&2.08&3.92&2.8&3.83&3.87&4.79&4.85&3.02&6.08&1.49&3.91&3.95&5.11&0.174&4.12&5.18&-0.484&6.26&2.91&4.1&4.12&1.82&3.74&3.85&5.04&3.24&5.33&-1.13&2.3&3.04&0.0505&-0.382&-0.47&4.12&3.92&-0.116&4.1&1.7&4.01&-2.92&-3.61&0.733&-4.66&1.73&-5.89&1.9&2.28&1.92&6.38&3.04&6.38&-3.05&4.18&4.24&5.32&6.38&5.42&6.38&4.26&6.38&2.27&6.38&1.82&4.25&5.32&3.07&6.38&4.17&6.38&5.42&1.83&6.38&2.99&6.38&0.492&6.38&3.36&5.32&5.42&3.14&6.38&1.76&6.38&-1.72&6.38&5.42&6.38&4.4&-2.1&6.38&5.42&6.38&1.95&6.38&5.42&3.03&6.38&1.07&6.38&4.22&6.38&1.87&-2.61&6.38&5.4&-1.72&5.42&-1.14&0.352&6.38&-0.704&-1.59&1.95&1.2&0.72&4.25&-1.14&3.41&5.33&4.22&4.24&1.73&4.24&1.26&5.3&-4.94&-2.7 15 30 50.0 -17 43063322 CAAGTACTTACCTCATTCAGCATTTTTCTTTCTTTAATAGACTGGGTCACCCCTAAAGAGATCATAGAAAAGACAGGTTACATACAGCAGAAGAACGTGCTCTTTTCACGGAGATAGAGAGGTCAGCGATTCACAAAAGAGCACAGGAAGAATGACAGAGGAGAGGTCCTTCCCTCTAAAGCCACAGCCCTTTAATAAGGCTTGTAGCAGCAGTTTCCTTCTGGAGACAGAGTTGATGTTTAATTTAAACATTATAAGTTTGCCTGCTGCACATGGATTCCTGCCGACTATTAAATAAATCCCTAGCTCATATGCTAACATTGCTAGGAGCAGATTAGGTCCTATTAGTTATAAAAGAGACCCATTTTCCCAGCATCACCAGCTTATCTGAACAAAGTGATATTAAAGATAAAAGTAGTTTAGTATTACAATTAAAGACCTTTTGGTAACTCAGACTCAGCATCAGCAAAAACCTTAGGTGTTAAACGTTAGGTGTAAAAATGCAATTCTGAGGTGTTAAAGGGAGGAGGGGAGAAATAGTATTATACTTACAGAAATAGCTAACTACCCATTTTCCTCCCGCAATTCCTAGAAAATATTTCAGTGTCCGTTCACACACAAACTCAGCATCTGCAGAATGAAAAACACT C - splice_donor_variant&splice_acceptor_variant&coding_sequence_variant&intron_variant HIGH BRCA1 672 Transcript NM_007300.4 protein_coding 18-19/24 17-19/23 -1 EntrezGene AAGTACTTACCTCATTCAGCATTTTTCTTTCTTTAATAGACTGGGTCACCCCTAAAGAGATCATAGAAAAGACAGGTTACATACAGCAGAAGAACGTGCTCTTTTCACGGAGATAGAGAGGTCAGCGATTCACAAAAGAGCACAGGAAGAATGACAGAGGAGAGGTCCTTCCCTCTAAAGCCACAGCCCTTTAATAAGGCTTGTAGCAGCAGTTTCCTTCTGGAGACAGAGTTGATGTTTAATTTAAACATTATAAGTTTGCCTGCTGCACATGGATTCCTGCCGACTATTAAATAAATCCCTAGCTCATATGCTAACATTGCTAGGAGCAGATTAGGTCCTATTAGTTATAAAAGAGACCCATTTTCCCAGCATCACCAGCTTATCTGAACAAAGTGATATTAAAGATAAAAGTAGTTTAGTATTACAATTAAAGACCTTTTGGTAACTCAGACTCAGCATCAGCAAAAACCTTAGGTGTTAAACGTTAGGTGTAAAAATGCAATTCTGAGGTGTTAAAGGGAGGAGGGGAGAAATAGTATTATACTTACAGAAATAGCTAACTACCCATTTTCCTCCCGCAATTCCTAGAAAATATTTCAGTGTCCGTTCACACACAAACTCAGCATCTGCAGAATGAAAAACACT AAGTACTTACCTCATTCAGCATTTTTCTTTCTTTAATAGACTGGGTCACCCCTAAAGAGATCATAGAAAAGACAGGTTACATACAGCAGAAGAACGTGCTCTTTTCACGGAGATAGAGAGGTCAGCGATTCACAAAAGAGCACAGGAAGAATGACAGAGGAGAGGTCCTTCCCTCTAAAGCCACAGCCCTTTAATAAGGCTTGTAGCAGCAGTTTCCTTCTGGAGACAGAGTTGATGTTTAATTTAAACATTATAAGTTTGCCTGCTGCACATGGATTCCTGCCGACTATTAAATAAATCCCTAGCTCATATGCTAACATTGCTAGGAGCAGATTAGGTCCTATTAGTTATAAAAGAGACCCATTTTCCCAGCATCACCAGCTTATCTGAACAAAGTGATATTAAAGATAAAAGTAGTTTAGTATTACAATTAAAGACCTTTTGGTAACTCAGACTCAGCATCAGCAAAAACCTTAGGTGTTAAACGTTAGGTGTAAAAATGCAATTCTGAGGTGTTAAAGGGAGGAGGGGAGAAATAGTATTATACTTACAGAAATAGCTAACTACCCATTTTCCTCCCGCAATTCCTAGAAAATATTTCAGTGTCCGTTCACACACAAACTCAGCATCTGCAGAATGAAAAACACT 1.27&3.63&0.83&2.92&5.48&6.54&-2.15&3.24&6.54&4.36&6.54&3.27&4.38&2.32&1.1&6.54&5.57&-7.42&5.46&1.83&3.39&3.28&6.54&1.95&2.52&-0.275&4.38&6.54&3.31&5.46&4.36&0.438&6.54&3.14&-3.52&4.58&6.54&3.26&4.51&-0.121&1.34&4.38&1.22&6.54&0.855&-3.04&3.22&3.58&6.54&5.58&6.54&-5.83&0.121&5.48&1.1&5.22&4.3&4.15&5.22&3.15&5.35&-4.61&0.451&-4.04&2.49&-3.04&5.57&6.54&0.14&5.48&3.4&0.297&-11.9&-0.432&-1.59&0.526&3.62&4.46&3.19&0.191&4.59&1.15&0.287&-2.69&-0.0401&-7.93&-5.61&-4.08&1.19&-2.41&1.03&1.7&1.18&-0.388&2.42&1.01&-0.389&-1.92&-2.68&2.63&0.495&2.38&-1.77&-6.21&1.27&-3.16&4.87&2.59&-2.71&-2.26&3.12&-2.7&1.31&-5.99&1.74&2.1&-12.3&-10.7&2.49&-1.14&1.61&0.963&0.379&-1.95&-8.89&-4.58&-6.64&2.58&0.716&3.75&-4.13&-3.11&-0.786&-8.2&-6.89&0.263&2.7&-0.249&-1.47&-2.67&3.27&2.06&-1.92&-3.36&0.535&0.757&1.31&-0.737&3.4&1.62&-5.88&2.35&-1.93&1.63&0.835&-4.09&-4.05&-3.82&-4.55&-4.79&-0.321&2.1&-6.26&0.325&-3.14&-4.68&-4.35&3.48&1.18&3.47&-2.16&2.41&-0.478&0.364&-11.1&0.248&2.93&-2.82&2.78&3.04&-0.0401&4.22&5.32&1&1.96&-0.583&-3.25&0.0723&0.594&0.258&-2.88&-3.91&-0.134&1.29&1.31&1.26&4.12&-3.9&2.8&-3.59&0.189&0.513&1.21&-3.39&4.33&3.7&3.87&-0.773&3.75&3.98&1.89&0.0629&-2.27&4.59&5.48&0.634&3.19&-2.83&3.32&-3.74&-4.9&1.98&-1.7&-7.03&0.119&-0.524&1.92&-1.23&-6.83&2.87&4.09&1.15&3.95&2.46&0.988&1.49&4.65&4.66&3.6&3.51&4.42&5.47&3.6&-4.48&1.22&0.7&4.2&4.13&4.25&-4.63&0.734&0.291&-0.396&0.144&3.17&4.58&2.52&5.48&2.55&4.56&3.23&3.44&1.76&4.06&2.15&4.31&1.83&5.23&2.97&2.67&5.46&2.95&2.22&1.22&3.45&-4.75&0.418&1.05&0.524&4.25&1.46&3.95&6.42&4.26&1.76&1.71&2.83&4.23&-1.77&-0.454&1.41&1.42&-6.08&2.07&3.28&1.26&0.808&3.25&2.27&-4.87&2.06&0.803&3.12&3.32&3.36&0.538&4.38&0.677&4.59&2.03&4.4&1.85&5.36&2.49&2.29&3.35&1.97&-1.35&4.11&2&-0.203&4.4&4.54&5.48&2.1&2.07&6.54&2.47&5.33&3.03&-2.39&1.05&3.29&2.06&4.34&4.38&3.47&5.18&6.24&-1.13&-0.944&1.52&5&5.02&4.73&4.07&5.22&3.97&-2.14&3.18&4.32&5.2&4.1&6.27&6.38&-4.96&0.178&1.06&4.77&3.6&0.603&5.11&3.92&-2.35&1.77&-1.17&-1.84&2.7&6.08&4.13&3.97&3.67&0.966&6.26&4.08&2.21&1.42&-0.22&6.18&3.94&3.96&3.97&4.27&-5.62&-1.73&4.07&4.05&4.12&1.72&4.05&0.385&2.85&2.9&5.29&6.26&4.08&6.15&2.85&2.87&1.14&3.33&1.83&4.17&5.39&2.65&6.38&-2.33&3.06&3.38&5.3&3.42&-1.89&1.9&6.38&5.08&0.726&-1.2&-2.68&6.08&4.9&5.31&5.33&4.39&3.08&4.42&5.42&5.19&0.784&4.23&-2.94&-0.143&4.15&5.33&5.24&1.66&3.07&4.23&2.37&6.38&3.41&5.19&-3.28&4.2&5.4&4.09&2.17&4.4&6.38&3.02&1.2&5.32&4.42&3.28&5.3&1.97&5.33&6.38&5.31&-1.5&2.08&1.78&-2.53&2.73&4.03&4&3.9&3.07&3.89&3.63&3.26&1.19&4.74&-1.69&4.84&4.9&4.89&4.99&3.97&5.96&3.89&2.08&3.92&2.8&3.83&3.87&4.79&4.85&3.02&6.08&1.49&3.91&3.95&5.11&0.174&4.12&5.18&-0.484&6.26&2.91&4.1&4.12&1.82&3.74&3.85&5.04&3.24&5.33&-1.13&2.3&3.04&0.0505&-0.382&-0.47&4.12&3.92&-0.116&4.1&1.7&4.01&-2.92&-3.61&0.733&-4.66&1.73&-5.89&1.9&2.28&1.92&6.38&3.04&6.38&-3.05&4.18&4.24&5.32&6.38&5.42&6.38&4.26&6.38&2.27&6.38&1.82&4.25&5.32&3.07&6.38&4.17&6.38&5.42&1.83&6.38&2.99&6.38&0.492&6.38&3.36&5.32&5.42&3.14&6.38&1.76&6.38&-1.72&6.38&5.42&6.38&4.4&-2.1&6.38&5.42&6.38&1.95&6.38&5.42&3.03&6.38&1.07&6.38&4.22&6.38&1.87&-2.61&6.38&5.4&-1.72&5.42&-1.14&0.352&6.38&-0.704&-1.59&1.95&1.2&0.72&4.25&-1.14&3.41&5.33&4.22&4.24&1.73&4.24&1.26&5.3&-4.94&-2.7 15 30 50.0 -17 43063322 CAAGTACTTACCTCATTCAGCATTTTTCTTTCTTTAATAGACTGGGTCACCCCTAAAGAGATCATAGAAAAGACAGGTTACATACAGCAGAAGAACGTGCTCTTTTCACGGAGATAGAGAGGTCAGCGATTCACAAAAGAGCACAGGAAGAATGACAGAGGAGAGGTCCTTCCCTCTAAAGCCACAGCCCTTTAATAAGGCTTGTAGCAGCAGTTTCCTTCTGGAGACAGAGTTGATGTTTAATTTAAACATTATAAGTTTGCCTGCTGCACATGGATTCCTGCCGACTATTAAATAAATCCCTAGCTCATATGCTAACATTGCTAGGAGCAGATTAGGTCCTATTAGTTATAAAAGAGACCCATTTTCCCAGCATCACCAGCTTATCTGAACAAAGTGATATTAAAGATAAAAGTAGTTTAGTATTACAATTAAAGACCTTTTGGTAACTCAGACTCAGCATCAGCAAAAACCTTAGGTGTTAAACGTTAGGTGTAAAAATGCAATTCTGAGGTGTTAAAGGGAGGAGGGGAGAAATAGTATTATACTTACAGAAATAGCTAACTACCCATTTTCCTCCCGCAATTCCTAGAAAATATTTCAGTGTCCGTTCACACACAAACTCAGCATCTGCAGAATGAAAAACACT C - splice_donor_variant&splice_acceptor_variant&non_coding_transcript_exon_variant&intron_variant HIGH BRCA1 672 Transcript NR_027676.2 misc_RNA 17-18/23 16-18/22 -1 EntrezGene AAGTACTTACCTCATTCAGCATTTTTCTTTCTTTAATAGACTGGGTCACCCCTAAAGAGATCATAGAAAAGACAGGTTACATACAGCAGAAGAACGTGCTCTTTTCACGGAGATAGAGAGGTCAGCGATTCACAAAAGAGCACAGGAAGAATGACAGAGGAGAGGTCCTTCCCTCTAAAGCCACAGCCCTTTAATAAGGCTTGTAGCAGCAGTTTCCTTCTGGAGACAGAGTTGATGTTTAATTTAAACATTATAAGTTTGCCTGCTGCACATGGATTCCTGCCGACTATTAAATAAATCCCTAGCTCATATGCTAACATTGCTAGGAGCAGATTAGGTCCTATTAGTTATAAAAGAGACCCATTTTCCCAGCATCACCAGCTTATCTGAACAAAGTGATATTAAAGATAAAAGTAGTTTAGTATTACAATTAAAGACCTTTTGGTAACTCAGACTCAGCATCAGCAAAAACCTTAGGTGTTAAACGTTAGGTGTAAAAATGCAATTCTGAGGTGTTAAAGGGAGGAGGGGAGAAATAGTATTATACTTACAGAAATAGCTAACTACCCATTTTCCTCCCGCAATTCCTAGAAAATATTTCAGTGTCCGTTCACACACAAACTCAGCATCTGCAGAATGAAAAACACT AAGTACTTACCTCATTCAGCATTTTTCTTTCTTTAATAGACTGGGTCACCCCTAAAGAGATCATAGAAAAGACAGGTTACATACAGCAGAAGAACGTGCTCTTTTCACGGAGATAGAGAGGTCAGCGATTCACAAAAGAGCACAGGAAGAATGACAGAGGAGAGGTCCTTCCCTCTAAAGCCACAGCCCTTTAATAAGGCTTGTAGCAGCAGTTTCCTTCTGGAGACAGAGTTGATGTTTAATTTAAACATTATAAGTTTGCCTGCTGCACATGGATTCCTGCCGACTATTAAATAAATCCCTAGCTCATATGCTAACATTGCTAGGAGCAGATTAGGTCCTATTAGTTATAAAAGAGACCCATTTTCCCAGCATCACCAGCTTATCTGAACAAAGTGATATTAAAGATAAAAGTAGTTTAGTATTACAATTAAAGACCTTTTGGTAACTCAGACTCAGCATCAGCAAAAACCTTAGGTGTTAAACGTTAGGTGTAAAAATGCAATTCTGAGGTGTTAAAGGGAGGAGGGGAGAAATAGTATTATACTTACAGAAATAGCTAACTACCCATTTTCCTCCCGCAATTCCTAGAAAATATTTCAGTGTCCGTTCACACACAAACTCAGCATCTGCAGAATGAAAAACACT 1.27&3.63&0.83&2.92&5.48&6.54&-2.15&3.24&6.54&4.36&6.54&3.27&4.38&2.32&1.1&6.54&5.57&-7.42&5.46&1.83&3.39&3.28&6.54&1.95&2.52&-0.275&4.38&6.54&3.31&5.46&4.36&0.438&6.54&3.14&-3.52&4.58&6.54&3.26&4.51&-0.121&1.34&4.38&1.22&6.54&0.855&-3.04&3.22&3.58&6.54&5.58&6.54&-5.83&0.121&5.48&1.1&5.22&4.3&4.15&5.22&3.15&5.35&-4.61&0.451&-4.04&2.49&-3.04&5.57&6.54&0.14&5.48&3.4&0.297&-11.9&-0.432&-1.59&0.526&3.62&4.46&3.19&0.191&4.59&1.15&0.287&-2.69&-0.0401&-7.93&-5.61&-4.08&1.19&-2.41&1.03&1.7&1.18&-0.388&2.42&1.01&-0.389&-1.92&-2.68&2.63&0.495&2.38&-1.77&-6.21&1.27&-3.16&4.87&2.59&-2.71&-2.26&3.12&-2.7&1.31&-5.99&1.74&2.1&-12.3&-10.7&2.49&-1.14&1.61&0.963&0.379&-1.95&-8.89&-4.58&-6.64&2.58&0.716&3.75&-4.13&-3.11&-0.786&-8.2&-6.89&0.263&2.7&-0.249&-1.47&-2.67&3.27&2.06&-1.92&-3.36&0.535&0.757&1.31&-0.737&3.4&1.62&-5.88&2.35&-1.93&1.63&0.835&-4.09&-4.05&-3.82&-4.55&-4.79&-0.321&2.1&-6.26&0.325&-3.14&-4.68&-4.35&3.48&1.18&3.47&-2.16&2.41&-0.478&0.364&-11.1&0.248&2.93&-2.82&2.78&3.04&-0.0401&4.22&5.32&1&1.96&-0.583&-3.25&0.0723&0.594&0.258&-2.88&-3.91&-0.134&1.29&1.31&1.26&4.12&-3.9&2.8&-3.59&0.189&0.513&1.21&-3.39&4.33&3.7&3.87&-0.773&3.75&3.98&1.89&0.0629&-2.27&4.59&5.48&0.634&3.19&-2.83&3.32&-3.74&-4.9&1.98&-1.7&-7.03&0.119&-0.524&1.92&-1.23&-6.83&2.87&4.09&1.15&3.95&2.46&0.988&1.49&4.65&4.66&3.6&3.51&4.42&5.47&3.6&-4.48&1.22&0.7&4.2&4.13&4.25&-4.63&0.734&0.291&-0.396&0.144&3.17&4.58&2.52&5.48&2.55&4.56&3.23&3.44&1.76&4.06&2.15&4.31&1.83&5.23&2.97&2.67&5.46&2.95&2.22&1.22&3.45&-4.75&0.418&1.05&0.524&4.25&1.46&3.95&6.42&4.26&1.76&1.71&2.83&4.23&-1.77&-0.454&1.41&1.42&-6.08&2.07&3.28&1.26&0.808&3.25&2.27&-4.87&2.06&0.803&3.12&3.32&3.36&0.538&4.38&0.677&4.59&2.03&4.4&1.85&5.36&2.49&2.29&3.35&1.97&-1.35&4.11&2&-0.203&4.4&4.54&5.48&2.1&2.07&6.54&2.47&5.33&3.03&-2.39&1.05&3.29&2.06&4.34&4.38&3.47&5.18&6.24&-1.13&-0.944&1.52&5&5.02&4.73&4.07&5.22&3.97&-2.14&3.18&4.32&5.2&4.1&6.27&6.38&-4.96&0.178&1.06&4.77&3.6&0.603&5.11&3.92&-2.35&1.77&-1.17&-1.84&2.7&6.08&4.13&3.97&3.67&0.966&6.26&4.08&2.21&1.42&-0.22&6.18&3.94&3.96&3.97&4.27&-5.62&-1.73&4.07&4.05&4.12&1.72&4.05&0.385&2.85&2.9&5.29&6.26&4.08&6.15&2.85&2.87&1.14&3.33&1.83&4.17&5.39&2.65&6.38&-2.33&3.06&3.38&5.3&3.42&-1.89&1.9&6.38&5.08&0.726&-1.2&-2.68&6.08&4.9&5.31&5.33&4.39&3.08&4.42&5.42&5.19&0.784&4.23&-2.94&-0.143&4.15&5.33&5.24&1.66&3.07&4.23&2.37&6.38&3.41&5.19&-3.28&4.2&5.4&4.09&2.17&4.4&6.38&3.02&1.2&5.32&4.42&3.28&5.3&1.97&5.33&6.38&5.31&-1.5&2.08&1.78&-2.53&2.73&4.03&4&3.9&3.07&3.89&3.63&3.26&1.19&4.74&-1.69&4.84&4.9&4.89&4.99&3.97&5.96&3.89&2.08&3.92&2.8&3.83&3.87&4.79&4.85&3.02&6.08&1.49&3.91&3.95&5.11&0.174&4.12&5.18&-0.484&6.26&2.91&4.1&4.12&1.82&3.74&3.85&5.04&3.24&5.33&-1.13&2.3&3.04&0.0505&-0.382&-0.47&4.12&3.92&-0.116&4.1&1.7&4.01&-2.92&-3.61&0.733&-4.66&1.73&-5.89&1.9&2.28&1.92&6.38&3.04&6.38&-3.05&4.18&4.24&5.32&6.38&5.42&6.38&4.26&6.38&2.27&6.38&1.82&4.25&5.32&3.07&6.38&4.17&6.38&5.42&1.83&6.38&2.99&6.38&0.492&6.38&3.36&5.32&5.42&3.14&6.38&1.76&6.38&-1.72&6.38&5.42&6.38&4.4&-2.1&6.38&5.42&6.38&1.95&6.38&5.42&3.03&6.38&1.07&6.38&4.22&6.38&1.87&-2.61&6.38&5.4&-1.72&5.42&-1.14&0.352&6.38&-0.704&-1.59&1.95&1.2&0.72&4.25&-1.14&3.41&5.33&4.22&4.24&1.73&4.24&1.26&5.3&-4.94&-2.7 15 30 50.0 -17 75750112 TGAACTGGACCG T - frameshift_variant&splice_region_variant HIGH ITGB4 3691 Transcript NM_000213.5 protein_coding 28/40 3483-3493 3319-3329 1107-1110 ELDR/X GAACTGGACCGg/g 1 EntrezGene GAACTGGACCG GAACTGGACCG 32 4.663505 4.55&6.54&-0.574&-12.6&3.26&4.51&6.54&2.54&-1.51&-6.3 chr17:75750113-75750124 1 143190 6.98373e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 1.69262e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35483e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97603e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -17 75750112 TGAACTGGACCG T - frameshift_variant&splice_region_variant HIGH ITGB4 3691 Transcript NM_001005619.1 protein_coding 27/39 3327-3337 3319-3329 1107-1110 ELDR/X GAACTGGACCGg/g 1 EntrezGene GAACTGGACCG GAACTGGACCG OK 32 4.663505 4.55&6.54&-0.574&-12.6&3.26&4.51&6.54&2.54&-1.51&-6.3 chr17:75750113-75750124 1 143190 6.98373e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 1.69262e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35483e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97603e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -17 75750112 TGAACTGGACCG T - frameshift_variant&splice_region_variant HIGH ITGB4 3691 Transcript NM_001005731.3 protein_coding 28/39 3483-3493 3319-3329 1107-1110 ELDR/X GAACTGGACCGg/g 1 EntrezGene GAACTGGACCG GAACTGGACCG 32 4.663505 4.55&6.54&-0.574&-12.6&3.26&4.51&6.54&2.54&-1.51&-6.3 chr17:75750113-75750124 1 143190 6.98373e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 1.69262e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35483e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97603e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -17 75750112 TGAACTGGACCG T - frameshift_variant&splice_region_variant HIGH ITGB4 3691 Transcript NM_001321123.2 protein_coding 28/39 3434-3444 3319-3329 1107-1110 ELDR/X GAACTGGACCGg/g 1 EntrezGene GAACTGGACCG GAACTGGACCG 32 4.663505 4.55&6.54&-0.574&-12.6&3.26&4.51&6.54&2.54&-1.51&-6.3 chr17:75750113-75750124 1 143190 6.98373e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 1.69262e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35483e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97603e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -17 75750112 TGAACTGGACCG T - downstream_gene_variant MODIFIER GALK1 2584 Transcript NM_001381985.1 protein_coding 1346 -1 EntrezGene GAACTGGACCG GAACTGGACCG 32 4.663505 4.55&6.54&-0.574&-12.6&3.26&4.51&6.54&2.54&-1.51&-6.3 chr17:75750113-75750124 1 143190 6.98373e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 1.69262e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35483e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97603e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -17 75750112 TGAACTGGACCG T - frameshift_variant&splice_region_variant HIGH ITGB4 3691 Transcript XM_005257309.2 protein_coding 28/41 3384-3394 3319-3329 1107-1110 ELDR/X GAACTGGACCGg/g 1 EntrezGene GAACTGGACCG GAACTGGACCG 32 4.663505 4.55&6.54&-0.574&-12.6&3.26&4.51&6.54&2.54&-1.51&-6.3 chr17:75750113-75750124 1 143190 6.98373e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 1.69262e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35483e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97603e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -17 75750112 TGAACTGGACCG T - frameshift_variant&splice_region_variant HIGH ITGB4 3691 Transcript XM_005257311.4 protein_coding 28/41 3603-3613 3319-3329 1107-1110 ELDR/X GAACTGGACCGg/g 1 EntrezGene GAACTGGACCG GAACTGGACCG 32 4.663505 4.55&6.54&-0.574&-12.6&3.26&4.51&6.54&2.54&-1.51&-6.3 chr17:75750113-75750124 1 143190 6.98373e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 1.69262e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35483e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97603e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -17 75750112 TGAACTGGACCG T - frameshift_variant&splice_region_variant HIGH ITGB4 3691 Transcript XM_006721866.3 protein_coding 28/41 3530-3540 3424-3434 1142-1145 ELDR/X GAACTGGACCGg/g 1 EntrezGene GAACTGGACCG GAACTGGACCG 32 4.663505 4.55&6.54&-0.574&-12.6&3.26&4.51&6.54&2.54&-1.51&-6.3 chr17:75750113-75750124 1 143190 6.98373e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 1.69262e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35483e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97603e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -17 75750112 TGAACTGGACCG T - frameshift_variant&splice_region_variant HIGH ITGB4 3691 Transcript XM_006721867.3 protein_coding 28/40 3527-3537 3424-3434 1142-1145 ELDR/X GAACTGGACCGg/g 1 EntrezGene GAACTGGACCG GAACTGGACCG 32 4.663505 4.55&6.54&-0.574&-12.6&3.26&4.51&6.54&2.54&-1.51&-6.3 chr17:75750113-75750124 1 143190 6.98373e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 1.69262e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35483e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97603e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -17 75750112 TGAACTGGACCG T - frameshift_variant&splice_region_variant HIGH ITGB4 3691 Transcript XM_006721868.3 protein_coding 28/40 3524-3534 3424-3434 1142-1145 ELDR/X GAACTGGACCGg/g 1 EntrezGene GAACTGGACCG GAACTGGACCG 32 4.663505 4.55&6.54&-0.574&-12.6&3.26&4.51&6.54&2.54&-1.51&-6.3 chr17:75750113-75750124 1 143190 6.98373e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 1.69262e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35483e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97603e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -17 75750112 TGAACTGGACCG T - frameshift_variant&splice_region_variant HIGH ITGB4 3691 Transcript XM_006721870.3 protein_coding 28/39 3520-3530 3424-3434 1142-1145 ELDR/X GAACTGGACCGg/g 1 EntrezGene GAACTGGACCG GAACTGGACCG 32 4.663505 4.55&6.54&-0.574&-12.6&3.26&4.51&6.54&2.54&-1.51&-6.3 chr17:75750113-75750124 1 143190 6.98373e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 1.69262e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35483e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97603e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -17 75750112 TGAACTGGACCG T - frameshift_variant&splice_region_variant HIGH ITGB4 3691 Transcript XM_011524751.2 protein_coding 28/39 3528-3538 3424-3434 1142-1145 ELDR/X GAACTGGACCGg/g 1 EntrezGene GAACTGGACCG GAACTGGACCG 32 4.663505 4.55&6.54&-0.574&-12.6&3.26&4.51&6.54&2.54&-1.51&-6.3 chr17:75750113-75750124 1 143190 6.98373e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 1.69262e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35483e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97603e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -17 75750112 TGAACTGGACCG T - frameshift_variant&splice_region_variant HIGH ITGB4 3691 Transcript XM_011524752.2 protein_coding 13/26 1813-1823 1264-1274 422-425 ELDR/X GAACTGGACCGg/g 1 EntrezGene GAACTGGACCG GAACTGGACCG 32 4.663505 4.55&6.54&-0.574&-12.6&3.26&4.51&6.54&2.54&-1.51&-6.3 chr17:75750113-75750124 1 143190 6.98373e-06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 7.32279e-05 1.69262e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35483e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97603e-06 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -17 75834092 ACTCC A - frameshift_variant HIGH UNC13D 201294 Transcript NM_199242.3 protein_coding 24/32 2413-2416 2346-2349 782-783 RE/X agGGAG/ag -1 EntrezGene CTCC CTCC 26.3 3.893613 1&0.448&5.39&4.33 rs764196809 10 143104 6.98792e-05 2.38368e-05 4.41540e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 9.49075e-05 0.00000e+00 0.00000e+00 0.00000e+00 4.32601e-05 1.39427e-04 1.60505e-04 1.10424e-04 0.00000e+00 0.00000e+00 0.00000e+00 6.97691e-05 0.00000e+00 0.00000e+00 0.00000e+00 420155 410246 Familial_hemophagocytic_lymphohistiocytosis_3¬_provided MONDO:MONDO:0012146&MedGen:C1837174&OMIM:608898&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion UNC13D:201294 SO:0001589&frameshift_variant 1 764196809 15 30 50.0 -17 77606958 ATCATTCAT A - intergenic_variant MODIFIER 2.539 0.146437 0.106&0.0977&1.46 rs59669437 29179 141240 2.06592e-01 2.56535e-01 2.54709e-01 2.58683e-01 1.95946e-01 2.25322e-01 1.63507e-01 2.57149e-01 2.59635e-01 2.55252e-01 2.58745e-01 2.54556e-01 2.63462e-01 3.56173e-01 3.59135e-01 3.53614e-01 2.07071e-01 1.10517e-01 1.02356e-01 1.13148e-01 2.06079e-01 1.69980e-01 1.68875e-01 1.71504e-01 2.04717e-01 2.16790e-01 1.92085e-01 2.07189e-01 1.91582e-01 1.85921e-01 1.92881e-01 15 30 50.0 -18 10671602 ATCT A - inframe_deletion MODERATE PIEZO2 63895 Transcript NM_001378183.1 protein_coding 56/56 9501-9503 8520-8522 2840-2841 ED/D gaAGAt/gat -1 EntrezGene TCT TCT 19.04 1.971586 6.45&1.79 rs587777077 0 143308 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97399e-06 0.00000e+00 0.00000e+00 0.00000e+00 235839 237493 Distal_arthrogryposis&Arthrogryposis-_oculomotor_limitation-electroretinal_anomalies_syndrome&Gordon's_syndrome¬_provided Human_Phenotype_Ontology:HP:0005684&MONDO:MONDO:0019942&MedGen:C0265213&OMIM:PS108120&Orphanet:ORPHA97120&MONDO:MONDO:0007158&MedGen:C1862472&OMIM:108145&Orphanet:ORPHA1154&MONDO:MONDO:0007252&MedGen:C0220666&OMIM:114300&Orphanet:ORPHA376&SNOMED_CT:237850008&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite PIEZO2:63895 SO:0001822&inframe_deletion 33 1555621138 15 30 50.0 -18 10671602 ATCT A - inframe_deletion MODERATE PIEZO2 63895 Transcript NM_022068.4 protein_coding 52/52 9162-9164 8181-8183 2727-2728 ED/D gaAGAt/gat -1 EntrezGene TCT TCT 19.04 1.971586 6.45&1.79 rs587777077 0 143308 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97399e-06 0.00000e+00 0.00000e+00 0.00000e+00 235839 237493 Distal_arthrogryposis&Arthrogryposis-_oculomotor_limitation-electroretinal_anomalies_syndrome&Gordon's_syndrome¬_provided Human_Phenotype_Ontology:HP:0005684&MONDO:MONDO:0019942&MedGen:C0265213&OMIM:PS108120&Orphanet:ORPHA97120&MONDO:MONDO:0007158&MedGen:C1862472&OMIM:108145&Orphanet:ORPHA1154&MONDO:MONDO:0007252&MedGen:C0220666&OMIM:114300&Orphanet:ORPHA376&SNOMED_CT:237850008&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite PIEZO2:63895 SO:0001822&inframe_deletion 33 1555621138 15 30 50.0 -18 10671602 ATCT A - downstream_gene_variant MODIFIER LOC101927410 101927410 Transcript NR_110777.1 lncRNA 4716 1 EntrezGene TCT TCT 19.04 1.971586 6.45&1.79 rs587777077 0 143308 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97399e-06 0.00000e+00 0.00000e+00 0.00000e+00 235839 237493 Distal_arthrogryposis&Arthrogryposis-_oculomotor_limitation-electroretinal_anomalies_syndrome&Gordon's_syndrome¬_provided Human_Phenotype_Ontology:HP:0005684&MONDO:MONDO:0019942&MedGen:C0265213&OMIM:PS108120&Orphanet:ORPHA97120&MONDO:MONDO:0007158&MedGen:C1862472&OMIM:108145&Orphanet:ORPHA1154&MONDO:MONDO:0007252&MedGen:C0220666&OMIM:114300&Orphanet:ORPHA376&SNOMED_CT:237850008&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite PIEZO2:63895 SO:0001822&inframe_deletion 33 1555621138 15 30 50.0 -18 10671602 ATCT A - inframe_deletion MODERATE PIEZO2 63895 Transcript XM_011525723.3 protein_coding 55/55 9310-9312 8313-8315 2771-2772 ED/D gaAGAt/gat -1 EntrezGene TCT TCT 19.04 1.971586 6.45&1.79 rs587777077 0 143308 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97399e-06 0.00000e+00 0.00000e+00 0.00000e+00 235839 237493 Distal_arthrogryposis&Arthrogryposis-_oculomotor_limitation-electroretinal_anomalies_syndrome&Gordon's_syndrome¬_provided Human_Phenotype_Ontology:HP:0005684&MONDO:MONDO:0019942&MedGen:C0265213&OMIM:PS108120&Orphanet:ORPHA97120&MONDO:MONDO:0007158&MedGen:C1862472&OMIM:108145&Orphanet:ORPHA1154&MONDO:MONDO:0007252&MedGen:C0220666&OMIM:114300&Orphanet:ORPHA376&SNOMED_CT:237850008&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite PIEZO2:63895 SO:0001822&inframe_deletion 33 1555621138 15 30 50.0 -18 10671602 ATCT A - inframe_deletion MODERATE PIEZO2 63895 Transcript XM_011525724.3 protein_coding 54/54 9253-9255 8256-8258 2752-2753 ED/D gaAGAt/gat -1 EntrezGene TCT TCT 19.04 1.971586 6.45&1.79 rs587777077 0 143308 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97399e-06 0.00000e+00 0.00000e+00 0.00000e+00 235839 237493 Distal_arthrogryposis&Arthrogryposis-_oculomotor_limitation-electroretinal_anomalies_syndrome&Gordon's_syndrome¬_provided Human_Phenotype_Ontology:HP:0005684&MONDO:MONDO:0019942&MedGen:C0265213&OMIM:PS108120&Orphanet:ORPHA97120&MONDO:MONDO:0007158&MedGen:C1862472&OMIM:108145&Orphanet:ORPHA1154&MONDO:MONDO:0007252&MedGen:C0220666&OMIM:114300&Orphanet:ORPHA376&SNOMED_CT:237850008&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite PIEZO2:63895 SO:0001822&inframe_deletion 33 1555621138 15 30 50.0 -18 10671602 ATCT A - inframe_deletion MODERATE PIEZO2 63895 Transcript XM_011525725.3 protein_coding 53/53 9220-9222 8223-8225 2741-2742 ED/D gaAGAt/gat -1 EntrezGene TCT TCT 19.04 1.971586 6.45&1.79 rs587777077 0 143308 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97399e-06 0.00000e+00 0.00000e+00 0.00000e+00 235839 237493 Distal_arthrogryposis&Arthrogryposis-_oculomotor_limitation-electroretinal_anomalies_syndrome&Gordon's_syndrome¬_provided Human_Phenotype_Ontology:HP:0005684&MONDO:MONDO:0019942&MedGen:C0265213&OMIM:PS108120&Orphanet:ORPHA97120&MONDO:MONDO:0007158&MedGen:C1862472&OMIM:108145&Orphanet:ORPHA1154&MONDO:MONDO:0007252&MedGen:C0220666&OMIM:114300&Orphanet:ORPHA376&SNOMED_CT:237850008&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite PIEZO2:63895 SO:0001822&inframe_deletion 33 1555621138 15 30 50.0 -18 10671602 ATCT A - inframe_deletion MODERATE PIEZO2 63895 Transcript XM_011525726.3 protein_coding 54/54 9127-9129 8130-8132 2710-2711 ED/D gaAGAt/gat -1 EntrezGene TCT TCT 19.04 1.971586 6.45&1.79 rs587777077 0 143308 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97399e-06 0.00000e+00 0.00000e+00 0.00000e+00 235839 237493 Distal_arthrogryposis&Arthrogryposis-_oculomotor_limitation-electroretinal_anomalies_syndrome&Gordon's_syndrome¬_provided Human_Phenotype_Ontology:HP:0005684&MONDO:MONDO:0019942&MedGen:C0265213&OMIM:PS108120&Orphanet:ORPHA97120&MONDO:MONDO:0007158&MedGen:C1862472&OMIM:108145&Orphanet:ORPHA1154&MONDO:MONDO:0007252&MedGen:C0220666&OMIM:114300&Orphanet:ORPHA376&SNOMED_CT:237850008&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite PIEZO2:63895 SO:0001822&inframe_deletion 33 1555621138 15 30 50.0 -18 10671602 ATCT A - inframe_deletion MODERATE PIEZO2 63895 Transcript XM_017025918.2 protein_coding 54/54 9271-9273 8274-8276 2758-2759 ED/D gaAGAt/gat -1 EntrezGene TCT TCT 19.04 1.971586 6.45&1.79 rs587777077 0 143308 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97399e-06 0.00000e+00 0.00000e+00 0.00000e+00 235839 237493 Distal_arthrogryposis&Arthrogryposis-_oculomotor_limitation-electroretinal_anomalies_syndrome&Gordon's_syndrome¬_provided Human_Phenotype_Ontology:HP:0005684&MONDO:MONDO:0019942&MedGen:C0265213&OMIM:PS108120&Orphanet:ORPHA97120&MONDO:MONDO:0007158&MedGen:C1862472&OMIM:108145&Orphanet:ORPHA1154&MONDO:MONDO:0007252&MedGen:C0220666&OMIM:114300&Orphanet:ORPHA376&SNOMED_CT:237850008&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite PIEZO2:63895 SO:0001822&inframe_deletion 33 1555621138 15 30 50.0 -18 10671602 ATCT A - downstream_gene_variant MODIFIER PIEZO2 63895 Transcript XR_001753259.2 misc_RNA 3604 -1 EntrezGene TCT TCT 19.04 1.971586 6.45&1.79 rs587777077 0 143308 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 6.97399e-06 0.00000e+00 0.00000e+00 0.00000e+00 235839 237493 Distal_arthrogryposis&Arthrogryposis-_oculomotor_limitation-electroretinal_anomalies_syndrome&Gordon's_syndrome¬_provided Human_Phenotype_Ontology:HP:0005684&MONDO:MONDO:0019942&MedGen:C0265213&OMIM:PS108120&Orphanet:ORPHA97120&MONDO:MONDO:0007158&MedGen:C1862472&OMIM:108145&Orphanet:ORPHA1154&MONDO:MONDO:0007252&MedGen:C0220666&OMIM:114300&Orphanet:ORPHA376&SNOMED_CT:237850008&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Microsatellite PIEZO2:63895 SO:0001822&inframe_deletion 33 1555621138 15 30 50.0 -18 24143127 AAGCCCAGACTTGTCG A - intron_variant MODIFIER CABYR 26256 Transcript NM_001308231.1 protein_coding 1/2 1 EntrezGene AGCCCAGACTTGTCG AGCCCAGACTTGTCG 4.19&4.77&3.82&6.52&5.66&6.52&5.65&6.52&5.38&5.66&5.38&-0.863&1.94 15 30 50.0 -18 24143127 AAGCCCAGACTTGTCG A - inframe_deletion MODERATE CABYR 26256 Transcript NM_012189.4 protein_coding 2/6 95-109 14-28 5-10 KPRLVV/I aAGCCCAGACTTGTCGta/ata 1 EntrezGene AGCCCAGACTTGTCG AGCCCAGACTTGTCG 4.19&4.77&3.82&6.52&5.66&6.52&5.65&6.52&5.38&5.66&5.38&-0.863&1.94 15 30 50.0 -18 24143127 AAGCCCAGACTTGTCG A - intron_variant MODIFIER CABYR 26256 Transcript NM_138643.3 protein_coding 1/3 1 EntrezGene AGCCCAGACTTGTCG AGCCCAGACTTGTCG 4.19&4.77&3.82&6.52&5.66&6.52&5.65&6.52&5.38&5.66&5.38&-0.863&1.94 15 30 50.0 -18 24143127 AAGCCCAGACTTGTCG A - inframe_deletion MODERATE CABYR 26256 Transcript NM_138644.2 protein_coding 2/6 74-88 14-28 5-10 KPRLVV/I aAGCCCAGACTTGTCGta/ata 1 EntrezGene AGCCCAGACTTGTCG AGCCCAGACTTGTCG OK 4.19&4.77&3.82&6.52&5.66&6.52&5.65&6.52&5.38&5.66&5.38&-0.863&1.94 15 30 50.0 -18 24143127 AAGCCCAGACTTGTCG A - inframe_deletion MODERATE CABYR 26256 Transcript NM_153768.3 protein_coding 2/5 95-109 14-28 5-10 KPRLVV/I aAGCCCAGACTTGTCGta/ata 1 EntrezGene AGCCCAGACTTGTCG AGCCCAGACTTGTCG 4.19&4.77&3.82&6.52&5.66&6.52&5.65&6.52&5.38&5.66&5.38&-0.863&1.94 15 30 50.0 -18 24143127 AAGCCCAGACTTGTCG A - inframe_deletion MODERATE CABYR 26256 Transcript NM_153769.3 protein_coding 2/6 95-109 14-28 5-10 KPRLVV/I aAGCCCAGACTTGTCGta/ata 1 EntrezGene AGCCCAGACTTGTCG AGCCCAGACTTGTCG 4.19&4.77&3.82&6.52&5.66&6.52&5.65&6.52&5.38&5.66&5.38&-0.863&1.94 15 30 50.0 -18 24143127 AAGCCCAGACTTGTCG A - inframe_deletion MODERATE CABYR 26256 Transcript NM_153770.3 protein_coding 2/6 95-109 14-28 5-10 KPRLVV/I aAGCCCAGACTTGTCGta/ata 1 EntrezGene AGCCCAGACTTGTCG AGCCCAGACTTGTCG 4.19&4.77&3.82&6.52&5.66&6.52&5.65&6.52&5.38&5.66&5.38&-0.863&1.94 15 30 50.0 -18 24143127 AAGCCCAGACTTGTCG A - inframe_deletion MODERATE CABYR 26256 Transcript XM_005258247.2 protein_coding 2/5 95-109 14-28 5-10 KPRLVV/I aAGCCCAGACTTGTCGta/ata 1 EntrezGene AGCCCAGACTTGTCG AGCCCAGACTTGTCG 4.19&4.77&3.82&6.52&5.66&6.52&5.65&6.52&5.38&5.66&5.38&-0.863&1.94 15 30 50.0 -18 24143127 AAGCCCAGACTTGTCG A - inframe_deletion MODERATE CABYR 26256 Transcript XM_024451159.1 protein_coding 2/6 170-184 14-28 5-10 KPRLVV/I aAGCCCAGACTTGTCGta/ata 1 EntrezGene AGCCCAGACTTGTCG AGCCCAGACTTGTCG 4.19&4.77&3.82&6.52&5.66&6.52&5.65&6.52&5.38&5.66&5.38&-0.863&1.94 15 30 50.0 -18 24143127 AAGCCCAGACTTGTCG A - inframe_deletion MODERATE CABYR 26256 Transcript XM_024451160.1 protein_coding 2/6 123-137 14-28 5-10 KPRLVV/I aAGCCCAGACTTGTCGta/ata 1 EntrezGene AGCCCAGACTTGTCG AGCCCAGACTTGTCG 4.19&4.77&3.82&6.52&5.66&6.52&5.65&6.52&5.38&5.66&5.38&-0.863&1.94 15 30 50.0 -18 24143127 AAGCCCAGACTTGTCG A - inframe_deletion MODERATE CABYR 26256 Transcript XM_024451161.1 protein_coding 2/5 140-154 14-28 5-10 KPRLVV/I aAGCCCAGACTTGTCGta/ata 1 EntrezGene AGCCCAGACTTGTCG AGCCCAGACTTGTCG 4.19&4.77&3.82&6.52&5.66&6.52&5.65&6.52&5.38&5.66&5.38&-0.863&1.94 15 30 50.0 -18 24143127 AAGCCCAGACTTGTCG A - 5_prime_UTR_variant MODIFIER CABYR 26256 Transcript XM_024451162.1 protein_coding 2/7 95-109 1 EntrezGene AGCCCAGACTTGTCG AGCCCAGACTTGTCG 4.19&4.77&3.82&6.52&5.66&6.52&5.65&6.52&5.38&5.66&5.38&-0.863&1.94 15 30 50.0 -18 24143127 AAGCCCAGACTTGTCG A - intron_variant MODIFIER CABYR 26256 Transcript XM_024451163.1 protein_coding 1/3 1 EntrezGene AGCCCAGACTTGTCG AGCCCAGACTTGTCG 4.19&4.77&3.82&6.52&5.66&6.52&5.65&6.52&5.38&5.66&5.38&-0.863&1.94 15 30 50.0 -18 24143127 AAGCCCAGACTTGTCG A - upstream_gene_variant MODIFIER LOC112268213 112268213 Transcript XR_002958214.1 lncRNA 4274 -1 EntrezGene AGCCCAGACTTGTCG AGCCCAGACTTGTCG 4.19&4.77&3.82&6.52&5.66&6.52&5.65&6.52&5.38&5.66&5.38&-0.863&1.94 15 30 50.0 -18 34303248 G A A intergenic_variant MODIFIER 3.270 0.212773 0.237 rs8087015 87265 142912 6.10621e-01 3.53808e-01 3.54673e-01 3.52792e-01 7.34444e-01 7.34043e-01 7.34884e-01 6.51206e-01 6.49474e-01 6.52529e-01 6.98556e-01 7.11691e-01 6.83739e-01 6.79949e-01 6.86981e-01 6.73861e-01 6.09596e-01 6.99386e-01 6.91907e-01 7.01741e-01 6.11712e-01 7.41377e-01 7.41073e-01 7.41796e-01 6.35601e-01 6.33212e-01 6.38095e-01 6.10564e-01 6.68754e-01 6.76786e-01 6.66936e-01 15 30 50.0 -19 7526759 A G G splice_acceptor_variant HIGH MCOLN1 57192 Transcript NM_020533.3 protein_coding 3/13 1 EntrezGene A A 32 4.670150 6.54 rs104886461 12 143294 8.37439e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.31325e-03 2.84414e-03 3.84123e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.12348e-05 0.00000e+00 0.00000e+00 0.00000e+00 8.64130e-05 1.54885e-05 2.67437e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.36960e-05 0.00000e+00 0.00000e+00 0.00000e+00 5131 0.00018 20170 Mucolipidosis_type_IV&Mucolipidosis¬_provided MONDO:MONDO:0009653&MedGen:C0238286&OMIM:252650&Orphanet:ORPHA578&SNOMED_CT:111384001&MONDO:MONDO:0019248&MedGen:C0026697&Orphanet:ORPHA79212&SNOMED_CT:70528007&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant MCOLN1:57192 SO:0001574&splice_acceptor_variant 1 104886461 15 30 50.0 -19 7526759 A G G upstream_gene_variant MODIFIER LOC105372261 105372261 Transcript XR_936293.2 lncRNA 1779 -1 EntrezGene A A 32 4.670150 6.54 rs104886461 12 143294 8.37439e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.31325e-03 2.84414e-03 3.84123e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.12348e-05 0.00000e+00 0.00000e+00 0.00000e+00 8.64130e-05 1.54885e-05 2.67437e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.36960e-05 0.00000e+00 0.00000e+00 0.00000e+00 5131 0.00018 20170 Mucolipidosis_type_IV&Mucolipidosis¬_provided MONDO:MONDO:0009653&MedGen:C0238286&OMIM:252650&Orphanet:ORPHA578&SNOMED_CT:111384001&MONDO:MONDO:0019248&MedGen:C0026697&Orphanet:ORPHA79212&SNOMED_CT:70528007&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant MCOLN1:57192 SO:0001574&splice_acceptor_variant 1 104886461 15 30 50.0 -19 7526759 A G G upstream_gene_variant MODIFIER LOC105372261 105372261 Transcript XR_936294.2 lncRNA 1779 -1 EntrezGene A A 32 4.670150 6.54 rs104886461 12 143294 8.37439e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.31325e-03 2.84414e-03 3.84123e-03 0.00000e+00 0.00000e+00 0.00000e+00 8.12348e-05 0.00000e+00 0.00000e+00 0.00000e+00 8.64130e-05 1.54885e-05 2.67437e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 8.36960e-05 0.00000e+00 0.00000e+00 0.00000e+00 5131 0.00018 20170 Mucolipidosis_type_IV&Mucolipidosis¬_provided MONDO:MONDO:0009653&MedGen:C0238286&OMIM:252650&Orphanet:ORPHA578&SNOMED_CT:111384001&MONDO:MONDO:0019248&MedGen:C0026697&Orphanet:ORPHA79212&SNOMED_CT:70528007&MedGen:CN517202 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic single_nucleotide_variant MCOLN1:57192 SO:0001574&splice_acceptor_variant 1 104886461 15 30 50.0 -19 11447528 GGAG G - inframe_deletion MODERATE PRKCSH 5589 Transcript NM_001001329.2 protein_coding 11/18 1467-1469 940-942 314 E/- GAG/- 1 EntrezGene GAG GAG OK 4.76&5.8&-3.05 15 30 50.0 -19 11447528 GGAG G - inframe_deletion MODERATE PRKCSH 5589 Transcript NM_001289102.1 protein_coding 10/17 1318-1320 940-942 314 E/- GAG/- 1 EntrezGene GAG GAG OK 4.76&5.8&-3.05 15 30 50.0 -19 11447528 GGAG G - inframe_deletion MODERATE PRKCSH 5589 Transcript NM_001289103.1 protein_coding 11/18 1471-1473 940-942 314 E/- GAG/- 1 EntrezGene GAG GAG OK 4.76&5.8&-3.05 15 30 50.0 -19 11447528 GGAG G - inframe_deletion MODERATE PRKCSH 5589 Transcript NM_001289104.2 protein_coding 11/18 1089-1091 940-942 314 E/- GAG/- 1 EntrezGene GAG GAG 4.76&5.8&-3.05 15 30 50.0 -19 11447528 GGAG G - inframe_deletion MODERATE PRKCSH 5589 Transcript NM_001379608.1 protein_coding 11/18 1093-1095 940-942 314 E/- GAG/- 1 EntrezGene GAG GAG 4.76&5.8&-3.05 15 30 50.0 -19 11447528 GGAG G - inframe_deletion MODERATE PRKCSH 5589 Transcript NM_001379609.1 protein_coding 11/18 1093-1095 940-942 314 E/- GAG/- 1 EntrezGene GAG GAG 4.76&5.8&-3.05 15 30 50.0 -19 11447528 GGAG G - downstream_gene_variant MODIFIER ELAVL3 1995 Transcript NM_001420.4 protein_coding 3795 -1 EntrezGene GAG GAG 4.76&5.8&-3.05 15 30 50.0 -19 11447528 GGAG G - inframe_deletion MODERATE PRKCSH 5589 Transcript NM_002743.3 protein_coding 11/18 1079-1081 940-942 314 E/- GAG/- 1 EntrezGene GAG GAG OK 4.76&5.8&-3.05 15 30 50.0 -19 11447528 GGAG G - downstream_gene_variant MODIFIER ELAVL3 1995 Transcript NM_032281.3 protein_coding 3795 -1 EntrezGene GAG GAG 4.76&5.8&-3.05 15 30 50.0 -19 11447528 GGAG G - downstream_gene_variant MODIFIER ELAVL3 1995 Transcript XM_011527778.3 protein_coding 3795 -1 EntrezGene GAG GAG 4.76&5.8&-3.05 15 30 50.0 -19 11447528 GGAG G - inframe_deletion MODERATE PRKCSH 5589 Transcript XM_011528130.1 protein_coding 12/19 1233-1235 940-942 314 E/- GAG/- 1 EntrezGene GAG GAG 4.76&5.8&-3.05 15 30 50.0 -19 11447528 GGAG G - inframe_deletion MODERATE PRKCSH 5589 Transcript XM_011528131.1 protein_coding 12/19 1233-1235 940-942 314 E/- GAG/- 1 EntrezGene GAG GAG 4.76&5.8&-3.05 15 30 50.0 -19 11447528 GGAG G - inframe_deletion MODERATE PRKCSH 5589 Transcript XM_011528132.1 protein_coding 12/19 1233-1235 940-942 314 E/- GAG/- 1 EntrezGene GAG GAG 4.76&5.8&-3.05 15 30 50.0 -19 11447528 GGAG G - downstream_gene_variant MODIFIER ELAVL3 1995 Transcript XM_024451410.1 protein_coding 3795 -1 EntrezGene GAG GAG 4.76&5.8&-3.05 15 30 50.0 -19 11447528 GGAG G - downstream_gene_variant MODIFIER ELAVL3 1995 Transcript XM_024451411.1 protein_coding 3795 -1 EntrezGene GAG GAG 4.76&5.8&-3.05 15 30 50.0 -19 11447528 GGAG G - downstream_gene_variant MODIFIER ELAVL3 1995 Transcript XM_024451412.1 protein_coding 3795 -1 EntrezGene GAG GAG 4.76&5.8&-3.05 15 30 50.0 -19 11447528 GGAG G - downstream_gene_variant MODIFIER ELAVL3 1995 Transcript XM_024451413.1 protein_coding 3795 -1 EntrezGene GAG GAG 4.76&5.8&-3.05 15 30 50.0 -19 29702971 GCCC G - inframe_deletion MODERATE C19orf12 83636 Transcript NM_001031726.3 protein_coding 3/3 324-326 197-199 66-67 GA/A gGGGct/gct -1 EntrezGene CCC CCC 16.34 1.594598 2.95&-12.3&6.3 rs398122409 4 142756 2.80198e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.71702e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.89243e-05 6.20906e-05 5.35906e-05 7.37953e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79271e-05 0.00000e+00 0.00000e+00 0.00000e+00 88866 94435 Neurodegeneration_with_brain_iron_accumulation_4 MONDO:MONDO:0013674&MedGen:C3280371&OMIM:614298&Orphanet:ORPHA289560 no_assertion_criteria_provided Pathogenic Deletion C19orf12:83636 1 398122409 15 30 50.0 -19 29702971 GCCC G - inframe_deletion MODERATE C19orf12 83636 Transcript NM_001256046.2 protein_coding 3/4 311-313 164-166 55-56 GA/A gGGGct/gct -1 EntrezGene CCC CCC 16.34 1.594598 2.95&-12.3&6.3 rs398122409 4 142756 2.80198e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.71702e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.89243e-05 6.20906e-05 5.35906e-05 7.37953e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79271e-05 0.00000e+00 0.00000e+00 0.00000e+00 88866 94435 Neurodegeneration_with_brain_iron_accumulation_4 MONDO:MONDO:0013674&MedGen:C3280371&OMIM:614298&Orphanet:ORPHA289560 no_assertion_criteria_provided Pathogenic Deletion C19orf12:83636 1 398122409 15 30 50.0 -19 29702971 GCCC G - inframe_deletion MODERATE C19orf12 83636 Transcript NM_001256047.1 protein_coding 3/3 401-403 164-166 55-56 GA/A gGGGct/gct -1 EntrezGene CCC CCC 16.34 1.594598 2.95&-12.3&6.3 rs398122409 4 142756 2.80198e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.71702e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.89243e-05 6.20906e-05 5.35906e-05 7.37953e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79271e-05 0.00000e+00 0.00000e+00 0.00000e+00 88866 94435 Neurodegeneration_with_brain_iron_accumulation_4 MONDO:MONDO:0013674&MedGen:C3280371&OMIM:614298&Orphanet:ORPHA289560 no_assertion_criteria_provided Pathogenic Deletion C19orf12:83636 1 398122409 15 30 50.0 -19 29702971 GCCC G - 5_prime_UTR_variant MODIFIER C19orf12 83636 Transcript NM_001282929.1 protein_coding 2/2 154-156 -1 EntrezGene CCC CCC 16.34 1.594598 2.95&-12.3&6.3 rs398122409 4 142756 2.80198e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.71702e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.89243e-05 6.20906e-05 5.35906e-05 7.37953e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79271e-05 0.00000e+00 0.00000e+00 0.00000e+00 88866 94435 Neurodegeneration_with_brain_iron_accumulation_4 MONDO:MONDO:0013674&MedGen:C3280371&OMIM:614298&Orphanet:ORPHA289560 no_assertion_criteria_provided Pathogenic Deletion C19orf12:83636 1 398122409 15 30 50.0 -19 29702971 GCCC G - 5_prime_UTR_variant MODIFIER C19orf12 83636 Transcript NM_001282930.2 protein_coding 2/2 141-143 -1 EntrezGene CCC CCC 16.34 1.594598 2.95&-12.3&6.3 rs398122409 4 142756 2.80198e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.71702e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.89243e-05 6.20906e-05 5.35906e-05 7.37953e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79271e-05 0.00000e+00 0.00000e+00 0.00000e+00 88866 94435 Neurodegeneration_with_brain_iron_accumulation_4 MONDO:MONDO:0013674&MedGen:C3280371&OMIM:614298&Orphanet:ORPHA289560 no_assertion_criteria_provided Pathogenic Deletion C19orf12:83636 1 398122409 15 30 50.0 -19 29702971 GCCC G - 5_prime_UTR_variant MODIFIER C19orf12 83636 Transcript NM_001282931.2 protein_coding 4/4 432-434 -1 EntrezGene CCC CCC 16.34 1.594598 2.95&-12.3&6.3 rs398122409 4 142756 2.80198e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.71702e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.89243e-05 6.20906e-05 5.35906e-05 7.37953e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79271e-05 0.00000e+00 0.00000e+00 0.00000e+00 88866 94435 Neurodegeneration_with_brain_iron_accumulation_4 MONDO:MONDO:0013674&MedGen:C3280371&OMIM:614298&Orphanet:ORPHA289560 no_assertion_criteria_provided Pathogenic Deletion C19orf12:83636 1 398122409 15 30 50.0 -19 29702971 GCCC G - inframe_deletion MODERATE C19orf12 83636 Transcript NM_031448.6 protein_coding 3/3 311-313 164-166 55-56 GA/A gGGGct/gct -1 EntrezGene CCC CCC 16.34 1.594598 2.95&-12.3&6.3 rs398122409 4 142756 2.80198e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.71702e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.89243e-05 6.20906e-05 5.35906e-05 7.37953e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79271e-05 0.00000e+00 0.00000e+00 0.00000e+00 88866 94435 Neurodegeneration_with_brain_iron_accumulation_4 MONDO:MONDO:0013674&MedGen:C3280371&OMIM:614298&Orphanet:ORPHA289560 no_assertion_criteria_provided Pathogenic Deletion C19orf12:83636 1 398122409 15 30 50.0 -19 29702971 GCCC G - inframe_deletion MODERATE C19orf12 83636 Transcript XM_024451734.1 protein_coding 3/3 1067-1069 326-328 109-110 GA/A gGGGct/gct -1 EntrezGene CCC CCC 16.34 1.594598 2.95&-12.3&6.3 rs398122409 4 142756 2.80198e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.71702e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.89243e-05 6.20906e-05 5.35906e-05 7.37953e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79271e-05 0.00000e+00 0.00000e+00 0.00000e+00 88866 94435 Neurodegeneration_with_brain_iron_accumulation_4 MONDO:MONDO:0013674&MedGen:C3280371&OMIM:614298&Orphanet:ORPHA289560 no_assertion_criteria_provided Pathogenic Deletion C19orf12:83636 1 398122409 15 30 50.0 -19 29702971 GCCC G - inframe_deletion MODERATE C19orf12 83636 Transcript XM_024451735.1 protein_coding 3/3 1401-1403 164-166 55-56 GA/A gGGGct/gct -1 EntrezGene CCC CCC 16.34 1.594598 2.95&-12.3&6.3 rs398122409 4 142756 2.80198e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.71702e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.89243e-05 6.20906e-05 5.35906e-05 7.37953e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79271e-05 0.00000e+00 0.00000e+00 0.00000e+00 88866 94435 Neurodegeneration_with_brain_iron_accumulation_4 MONDO:MONDO:0013674&MedGen:C3280371&OMIM:614298&Orphanet:ORPHA289560 no_assertion_criteria_provided Pathogenic Deletion C19orf12:83636 1 398122409 15 30 50.0 -19 29702971 GCCC G - inframe_deletion MODERATE C19orf12 83636 Transcript XM_024451736.1 protein_coding 4/4 1246-1248 164-166 55-56 GA/A gGGGct/gct -1 EntrezGene CCC CCC 16.34 1.594598 2.95&-12.3&6.3 rs398122409 4 142756 2.80198e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.71702e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.89243e-05 6.20906e-05 5.35906e-05 7.37953e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79271e-05 0.00000e+00 0.00000e+00 0.00000e+00 88866 94435 Neurodegeneration_with_brain_iron_accumulation_4 MONDO:MONDO:0013674&MedGen:C3280371&OMIM:614298&Orphanet:ORPHA289560 no_assertion_criteria_provided Pathogenic Deletion C19orf12:83636 1 398122409 15 30 50.0 -19 29702971 GCCC G - inframe_deletion MODERATE C19orf12 83636 Transcript XM_024451737.1 protein_coding 4/4 648-650 164-166 55-56 GA/A gGGGct/gct -1 EntrezGene CCC CCC 16.34 1.594598 2.95&-12.3&6.3 rs398122409 4 142756 2.80198e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.71702e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.89243e-05 6.20906e-05 5.35906e-05 7.37953e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79271e-05 0.00000e+00 0.00000e+00 0.00000e+00 88866 94435 Neurodegeneration_with_brain_iron_accumulation_4 MONDO:MONDO:0013674&MedGen:C3280371&OMIM:614298&Orphanet:ORPHA289560 no_assertion_criteria_provided Pathogenic Deletion C19orf12:83636 1 398122409 15 30 50.0 -19 29702971 GCCC G - inframe_deletion MODERATE C19orf12 83636 Transcript XM_024451738.1 protein_coding 4/4 340-342 164-166 55-56 GA/A gGGGct/gct -1 EntrezGene CCC CCC 16.34 1.594598 2.95&-12.3&6.3 rs398122409 4 142756 2.80198e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.71702e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.89243e-05 6.20906e-05 5.35906e-05 7.37953e-05 0.00000e+00 0.00000e+00 0.00000e+00 2.79271e-05 0.00000e+00 0.00000e+00 0.00000e+00 88866 94435 Neurodegeneration_with_brain_iron_accumulation_4 MONDO:MONDO:0013674&MedGen:C3280371&OMIM:614298&Orphanet:ORPHA289560 no_assertion_criteria_provided Pathogenic Deletion C19orf12:83636 1 398122409 15 30 50.0 -19 39248147 C T T upstream_gene_variant MODIFIER IFNL3 282617 Transcript NM_001346937.1 protein_coding 3070 -1 EntrezGene C C 0.230 -0.363149 -9.27 rs12979860 56366 143124 3.93826e-01 5.99509e-01 5.98129e-01 6.01128e-01 3.61486e-01 3.77155e-01 3.44340e-01 3.72142e-01 3.78301e-01 3.67442e-01 3.91697e-01 3.84222e-01 4.00128e-01 6.55371e-02 7.06371e-02 6.11639e-02 4.02098e-01 2.40914e-01 2.49002e-01 2.38365e-01 3.85028e-01 3.15002e-01 3.14675e-01 3.15452e-01 3.61860e-01 3.74317e-01 3.48859e-01 3.93987e-01 2.21274e-01 2.24382e-01 2.20565e-01 225949 0.35583 227801 not_specified&peginterferon_alfa-2a&_peginterferon_alfa-2b&_and_ribavirin_response_-_Efficacy&peginterferon_alfa-2a&_peginterferon_alfa-2b&_ribavirin&_and_telaprevir_response_-_Efficacy&peginterferon_alfa-2b_response_-_Efficacy&boceprevir_response_-_Efficacy&ribavirin_response_-_Efficacy MedGen:CN169374&MedGen:CN236450&MedGen:CN236451&MedGen:CN240584&MedGen:CN240591&MedGen:CN240603 reviewed_by_expert_panel drug_response single_nucleotide_variant IFNL3:282617&IFNL4:101180976 1 12979860 15 30 50.0 -19 39248147 C T T upstream_gene_variant MODIFIER IFNL3 282617 Transcript NM_172139.4 protein_coding 3141 -1 EntrezGene C C 0.230 -0.363149 -9.27 rs12979860 56366 143124 3.93826e-01 5.99509e-01 5.98129e-01 6.01128e-01 3.61486e-01 3.77155e-01 3.44340e-01 3.72142e-01 3.78301e-01 3.67442e-01 3.91697e-01 3.84222e-01 4.00128e-01 6.55371e-02 7.06371e-02 6.11639e-02 4.02098e-01 2.40914e-01 2.49002e-01 2.38365e-01 3.85028e-01 3.15002e-01 3.14675e-01 3.15452e-01 3.61860e-01 3.74317e-01 3.48859e-01 3.93987e-01 2.21274e-01 2.24382e-01 2.20565e-01 225949 0.35583 227801 not_specified&peginterferon_alfa-2a&_peginterferon_alfa-2b&_and_ribavirin_response_-_Efficacy&peginterferon_alfa-2a&_peginterferon_alfa-2b&_ribavirin&_and_telaprevir_response_-_Efficacy&peginterferon_alfa-2b_response_-_Efficacy&boceprevir_response_-_Efficacy&ribavirin_response_-_Efficacy MedGen:CN169374&MedGen:CN236450&MedGen:CN236451&MedGen:CN240584&MedGen:CN240591&MedGen:CN240603 reviewed_by_expert_panel drug_response single_nucleotide_variant IFNL3:282617&IFNL4:101180976 1 12979860 15 30 50.0 -19 39248147 C T T intron_variant&non_coding_transcript_variant MODIFIER IFNL4 101180976 Transcript NR_074079.1 transcribed_pseudogene 1/4 -1 EntrezGene C C 0.230 -0.363149 -9.27 rs12979860 56366 143124 3.93826e-01 5.99509e-01 5.98129e-01 6.01128e-01 3.61486e-01 3.77155e-01 3.44340e-01 3.72142e-01 3.78301e-01 3.67442e-01 3.91697e-01 3.84222e-01 4.00128e-01 6.55371e-02 7.06371e-02 6.11639e-02 4.02098e-01 2.40914e-01 2.49002e-01 2.38365e-01 3.85028e-01 3.15002e-01 3.14675e-01 3.15452e-01 3.61860e-01 3.74317e-01 3.48859e-01 3.93987e-01 2.21274e-01 2.24382e-01 2.20565e-01 225949 0.35583 227801 not_specified&peginterferon_alfa-2a&_peginterferon_alfa-2b&_and_ribavirin_response_-_Efficacy&peginterferon_alfa-2a&_peginterferon_alfa-2b&_ribavirin&_and_telaprevir_response_-_Efficacy&peginterferon_alfa-2b_response_-_Efficacy&boceprevir_response_-_Efficacy&ribavirin_response_-_Efficacy MedGen:CN169374&MedGen:CN236450&MedGen:CN236451&MedGen:CN240584&MedGen:CN240591&MedGen:CN240603 reviewed_by_expert_panel drug_response single_nucleotide_variant IFNL3:282617&IFNL4:101180976 1 12979860 15 30 50.0 -19 44907777 GAGCA G - frameshift_variant HIGH APOE 348 Transcript NM_000041.4 protein_coding 3/4 131-134 62-65 21-22 EQ/X gAGCAa/ga 1 EntrezGene AGCA AGCA 4.41&3.38&0.947&0.271 15 30 50.0 -19 44907777 GAGCA G - downstream_gene_variant MODIFIER TOMM40 10452 Transcript NM_001128916.1 protein_coding 4089 1 EntrezGene AGCA AGCA OK 4.41&3.38&0.947&0.271 15 30 50.0 -19 44907777 GAGCA G - downstream_gene_variant MODIFIER TOMM40 10452 Transcript NM_001128917.2 protein_coding 4089 1 EntrezGene AGCA AGCA 4.41&3.38&0.947&0.271 15 30 50.0 -19 44907777 GAGCA G - frameshift_variant HIGH APOE 348 Transcript NM_001302688.2 protein_coding 3/4 213-216 140-143 47-48 EQ/X gAGCAa/ga 1 EntrezGene AGCA AGCA 4.41&3.38&0.947&0.271 15 30 50.0 -19 44907777 GAGCA G - frameshift_variant HIGH APOE 348 Transcript NM_001302689.2 protein_coding 3/4 109-112 62-65 21-22 EQ/X gAGCAa/ga 1 EntrezGene AGCA AGCA 4.41&3.38&0.947&0.271 15 30 50.0 -19 44907777 GAGCA G - frameshift_variant HIGH APOE 348 Transcript NM_001302690.1 protein_coding 3/4 209-212 62-65 21-22 EQ/X gAGCAa/ga 1 EntrezGene AGCA AGCA OK 4.41&3.38&0.947&0.271 15 30 50.0 -19 44907777 GAGCA G - frameshift_variant HIGH APOE 348 Transcript NM_001302691.2 protein_coding 3/4 146-149 62-65 21-22 EQ/X gAGCAa/ga 1 EntrezGene AGCA AGCA 4.41&3.38&0.947&0.271 15 30 50.0 -19 44907777 GAGCA G - downstream_gene_variant MODIFIER TOMM40 10452 Transcript NM_006114.3 protein_coding 4089 1 EntrezGene AGCA AGCA 4.41&3.38&0.947&0.271 15 30 50.0 -19 51731546 C T T intergenic_variant MODIFIER 0.964 -0.068420 -0.0478 rs11667678 47200 142710 3.30741e-01 3.23414e-01 3.24909e-01 3.21659e-01 1.30290e-01 1.38298e-01 1.21495e-01 4.12717e-01 4.12224e-01 4.13094e-01 3.04702e-01 3.10934e-01 2.97695e-01 7.32558e-01 7.29749e-01 7.34976e-01 3.27454e-01 3.16431e-01 3.17054e-01 3.16233e-01 3.34246e-01 3.01629e-01 3.02319e-01 3.00679e-01 3.35814e-01 3.46364e-01 3.24762e-01 3.31200e-01 4.05859e-01 4.04594e-01 4.06149e-01 15 30 50.0 -19 53823971 C CTCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT TCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT frameshift_variant HIGH NLRP12 91662 Transcript NM_001277126.2 protein_coding 1/10 343-344 203-204 68 E/EAWRRPVPWKWPSCSSPTSGQRX gag/gaAGCATGGAGAAGGCCGGTCCCCTGGAAATGGCCCAGCTGCTCATCACCCACTTCGGGCCAGAGGAg -1 EntrezGene -0.0834&0.354 chr19:53823972-53823972 7 143286 4.88533e-05 1.66524e-04 1.32182e-04 2.06825e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.06240e-05 0.00000e+00 0.00000e+00 0.00000e+00 5.76053e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 5.57935e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -19 53823971 C CTCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT TCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT frameshift_variant HIGH NLRP12 91662 Transcript NM_001277129.1 protein_coding 1/9 432-433 203-204 68 E/EAWRRPVPWKWPSCSSPTSGQRX gag/gaAGCATGGAGAAGGCCGGTCCCCTGGAAATGGCCCAGCTGCTCATCACCCACTTCGGGCCAGAGGAg -1 EntrezGene OK -0.0834&0.354 chr19:53823972-53823972 7 143286 4.88533e-05 1.66524e-04 1.32182e-04 2.06825e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.06240e-05 0.00000e+00 0.00000e+00 0.00000e+00 5.76053e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 5.57935e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -19 53823971 C CTCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT TCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT frameshift_variant HIGH NLRP12 91662 Transcript NM_144687.4 protein_coding 1/10 343-344 203-204 68 E/EAWRRPVPWKWPSCSSPTSGQRX gag/gaAGCATGGAGAAGGCCGGTCCCCTGGAAATGGCCCAGCTGCTCATCACCCACTTCGGGCCAGAGGAg -1 EntrezGene -0.0834&0.354 chr19:53823972-53823972 7 143286 4.88533e-05 1.66524e-04 1.32182e-04 2.06825e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.06240e-05 0.00000e+00 0.00000e+00 0.00000e+00 5.76053e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 5.57935e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -19 53823971 C CTCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT TCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT frameshift_variant HIGH NLRP12 91662 Transcript XM_011527479.1 protein_coding 1/9 432-433 203-204 68 E/EAWRRPVPWKWPSCSSPTSGQRX gag/gaAGCATGGAGAAGGCCGGTCCCCTGGAAATGGCCCAGCTGCTCATCACCCACTTCGGGCCAGAGGAg -1 EntrezGene -0.0834&0.354 chr19:53823972-53823972 7 143286 4.88533e-05 1.66524e-04 1.32182e-04 2.06825e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.06240e-05 0.00000e+00 0.00000e+00 0.00000e+00 5.76053e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 5.57935e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -19 53823971 C CTCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT TCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT frameshift_variant HIGH NLRP12 91662 Transcript XM_011527480.1 protein_coding 1/9 432-433 203-204 68 E/EAWRRPVPWKWPSCSSPTSGQRX gag/gaAGCATGGAGAAGGCCGGTCCCCTGGAAATGGCCCAGCTGCTCATCACCCACTTCGGGCCAGAGGAg -1 EntrezGene -0.0834&0.354 chr19:53823972-53823972 7 143286 4.88533e-05 1.66524e-04 1.32182e-04 2.06825e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.06240e-05 0.00000e+00 0.00000e+00 0.00000e+00 5.76053e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 5.57935e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -19 53823971 C CTCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT TCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT frameshift_variant HIGH NLRP12 91662 Transcript XM_011527482.1 protein_coding 1/8 432-433 203-204 68 E/EAWRRPVPWKWPSCSSPTSGQRX gag/gaAGCATGGAGAAGGCCGGTCCCCTGGAAATGGCCCAGCTGCTCATCACCCACTTCGGGCCAGAGGAg -1 EntrezGene -0.0834&0.354 chr19:53823972-53823972 7 143286 4.88533e-05 1.66524e-04 1.32182e-04 2.06825e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.06240e-05 0.00000e+00 0.00000e+00 0.00000e+00 5.76053e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 5.57935e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -19 53823971 C CTCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT TCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT frameshift_variant HIGH NLRP12 91662 Transcript XM_017027460.1 protein_coding 1/10 432-433 203-204 68 E/EAWRRPVPWKWPSCSSPTSGQRX gag/gaAGCATGGAGAAGGCCGGTCCCCTGGAAATGGCCCAGCTGCTCATCACCCACTTCGGGCCAGAGGAg -1 EntrezGene -0.0834&0.354 chr19:53823972-53823972 7 143286 4.88533e-05 1.66524e-04 1.32182e-04 2.06825e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.06240e-05 0.00000e+00 0.00000e+00 0.00000e+00 5.76053e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 5.57935e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -19 53823971 C CTCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT TCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT frameshift_variant HIGH NLRP12 91662 Transcript XM_017027461.1 protein_coding 1/9 432-433 203-204 68 E/EAWRRPVPWKWPSCSSPTSGQRX gag/gaAGCATGGAGAAGGCCGGTCCCCTGGAAATGGCCCAGCTGCTCATCACCCACTTCGGGCCAGAGGAg -1 EntrezGene -0.0834&0.354 chr19:53823972-53823972 7 143286 4.88533e-05 1.66524e-04 1.32182e-04 2.06825e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.06240e-05 0.00000e+00 0.00000e+00 0.00000e+00 5.76053e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 5.57935e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -19 53823971 C CTCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT TCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT frameshift_variant HIGH NLRP12 91662 Transcript XM_017027462.1 protein_coding 1/9 432-433 203-204 68 E/EAWRRPVPWKWPSCSSPTSGQRX gag/gaAGCATGGAGAAGGCCGGTCCCCTGGAAATGGCCCAGCTGCTCATCACCCACTTCGGGCCAGAGGAg -1 EntrezGene -0.0834&0.354 chr19:53823972-53823972 7 143286 4.88533e-05 1.66524e-04 1.32182e-04 2.06825e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.06240e-05 0.00000e+00 0.00000e+00 0.00000e+00 5.76053e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 5.57935e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -19 53823971 C CTCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT TCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT upstream_gene_variant MODIFIER NLRP12 91662 Transcript XM_017027463.1 protein_coding 4 -1 EntrezGene -0.0834&0.354 chr19:53823972-53823972 7 143286 4.88533e-05 1.66524e-04 1.32182e-04 2.06825e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.06240e-05 0.00000e+00 0.00000e+00 0.00000e+00 5.76053e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 5.57935e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -19 53823971 C CTCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT TCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT 5_prime_UTR_variant MODIFIER NLRP12 91662 Transcript XM_017027464.1 protein_coding 1/10 10-11 -1 EntrezGene -0.0834&0.354 chr19:53823972-53823972 7 143286 4.88533e-05 1.66524e-04 1.32182e-04 2.06825e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.06240e-05 0.00000e+00 0.00000e+00 0.00000e+00 5.76053e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 5.57935e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -19 53823971 C CTCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT TCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT 5_prime_UTR_variant MODIFIER NLRP12 91662 Transcript XM_017027465.1 protein_coding 1/11 11-12 -1 EntrezGene -0.0834&0.354 chr19:53823972-53823972 7 143286 4.88533e-05 1.66524e-04 1.32182e-04 2.06825e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.06240e-05 0.00000e+00 0.00000e+00 0.00000e+00 5.76053e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 5.57935e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -19 53823971 C CTCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT TCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT upstream_gene_variant MODIFIER NLRP12 91662 Transcript XM_017027466.1 protein_coding 104 -1 EntrezGene -0.0834&0.354 chr19:53823972-53823972 7 143286 4.88533e-05 1.66524e-04 1.32182e-04 2.06825e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.06240e-05 0.00000e+00 0.00000e+00 0.00000e+00 5.76053e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 5.57935e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -19 53823971 C CTCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT TCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT upstream_gene_variant MODIFIER NLRP12 91662 Transcript XM_017027467.1 protein_coding 100 -1 EntrezGene -0.0834&0.354 chr19:53823972-53823972 7 143286 4.88533e-05 1.66524e-04 1.32182e-04 2.06825e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.06240e-05 0.00000e+00 0.00000e+00 0.00000e+00 5.76053e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 5.57935e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -20 4511688 C CA A intergenic_variant MODIFIER 2.9&4.83 15 30 50.0 -20 50945838 ACCACTTACATG A - splice_donor_variant&coding_sequence_variant&intron_variant HIGH DPM1 8813 Transcript NM_001317034.1 protein_coding 4/10 4/9 411-? 370-? 124-? -1 EntrezGene CCACTTACATG CCACTTACATG OK -2.34&1.06&0.199&6.54&5.46&1.97&6.54&1.97&6.54 15 30 50.0 -20 50945838 ACCACTTACATG A - splice_donor_variant&coding_sequence_variant&intron_variant HIGH DPM1 8813 Transcript NM_001317035.1 protein_coding 4/10 4/9 411-? 370-? 124-? -1 EntrezGene CCACTTACATG CCACTTACATG OK -2.34&1.06&0.199&6.54&5.46&1.97&6.54&1.97&6.54 15 30 50.0 -20 50945838 ACCACTTACATG A - splice_donor_variant&coding_sequence_variant&intron_variant HIGH DPM1 8813 Transcript NM_001317036.1 protein_coding 4/8 4/7 411-? 370-? 124-? -1 EntrezGene CCACTTACATG CCACTTACATG OK -2.34&1.06&0.199&6.54&5.46&1.97&6.54&1.97&6.54 15 30 50.0 -20 50945838 ACCACTTACATG A - splice_donor_variant&coding_sequence_variant&intron_variant HIGH DPM1 8813 Transcript NM_003859.3 protein_coding 4/9 4/8 379-? 370-? 124-? -1 EntrezGene CCACTTACATG CCACTTACATG -2.34&1.06&0.199&6.54&5.46&1.97&6.54&1.97&6.54 15 30 50.0 -20 50945838 ACCACTTACATG A - downstream_gene_variant MODIFIER ADNP-AS1 101927631 Transcript NR_110007.1 lncRNA 705 1 EntrezGene CCACTTACATG CCACTTACATG OK -2.34&1.06&0.199&6.54&5.46&1.97&6.54&1.97&6.54 15 30 50.0 -20 50945838 ACCACTTACATG A - downstream_gene_variant MODIFIER ADNP-AS1 101927631 Transcript NR_110008.1 lncRNA 705 1 EntrezGene CCACTTACATG CCACTTACATG OK -2.34&1.06&0.199&6.54&5.46&1.97&6.54&1.97&6.54 15 30 50.0 -20 50945838 ACCACTTACATG A - downstream_gene_variant MODIFIER ADNP-AS1 101927631 Transcript NR_110009.1 lncRNA 705 1 EntrezGene CCACTTACATG CCACTTACATG OK -2.34&1.06&0.199&6.54&5.46&1.97&6.54&1.97&6.54 15 30 50.0 -20 50945838 ACCACTTACATG A - splice_donor_variant&non_coding_transcript_exon_variant&intron_variant HIGH DPM1 8813 Transcript NR_133648.1 misc_RNA 4/9 4/8 411-? -1 EntrezGene CCACTTACATG CCACTTACATG OK -2.34&1.06&0.199&6.54&5.46&1.97&6.54&1.97&6.54 15 30 50.0 -20 50945838 ACCACTTACATG A - splice_donor_variant&non_coding_transcript_exon_variant&intron_variant HIGH DPM1 8813 Transcript XR_002958550.1 misc_RNA 4/9 4/8 408-? -1 EntrezGene CCACTTACATG CCACTTACATG -2.34&1.06&0.199&6.54&5.46&1.97&6.54&1.97&6.54 15 30 50.0 -20 50945838 ACCACTTACATG A - splice_donor_variant&non_coding_transcript_exon_variant&intron_variant HIGH DPM1 8813 Transcript XR_002958551.1 misc_RNA 4/7 4/6 409-? -1 EntrezGene CCACTTACATG CCACTTACATG -2.34&1.06&0.199&6.54&5.46&1.97&6.54&1.97&6.54 15 30 50.0 -20 50945875 ATGATGTAGTTTCC A - frameshift_variant HIGH DPM1 8813 Transcript NM_001317034.1 protein_coding 4/10 372-384 331-343 111-115 GNYII/X GGAAACTACATCAtt/tt -1 EntrezGene TGATGTAGTTTCC TGATGTAGTTTCC OK 32 4.500006 3.19&-4.39&6.54&3.2&-3.36&5.44&6.54&4.63&6.54&0.654&6.54 rs1272097668 2 143318 1.39550e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35380e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.43984e-05 3.09722e-05 2.67451e-05 3.67863e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.39482e-05 0.00000e+00 0.00000e+00 0.00000e+00 570864 571306 Congenital_disorder_of_glycosylation_type_1E MONDO:MONDO:0012123&MedGen:C1837396&OMIM:608799&Orphanet:ORPHA79322 criteria_provided&_single_submitter Pathogenic Deletion DPM1:8813 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 1272097668 15 30 50.0 -20 50945875 ATGATGTAGTTTCC A - frameshift_variant HIGH DPM1 8813 Transcript NM_001317035.1 protein_coding 4/10 372-384 331-343 111-115 GNYII/X GGAAACTACATCAtt/tt -1 EntrezGene TGATGTAGTTTCC TGATGTAGTTTCC OK 32 4.500006 3.19&-4.39&6.54&3.2&-3.36&5.44&6.54&4.63&6.54&0.654&6.54 rs1272097668 2 143318 1.39550e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35380e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.43984e-05 3.09722e-05 2.67451e-05 3.67863e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.39482e-05 0.00000e+00 0.00000e+00 0.00000e+00 570864 571306 Congenital_disorder_of_glycosylation_type_1E MONDO:MONDO:0012123&MedGen:C1837396&OMIM:608799&Orphanet:ORPHA79322 criteria_provided&_single_submitter Pathogenic Deletion DPM1:8813 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 1272097668 15 30 50.0 -20 50945875 ATGATGTAGTTTCC A - frameshift_variant HIGH DPM1 8813 Transcript NM_001317036.1 protein_coding 4/8 372-384 331-343 111-115 GNYII/X GGAAACTACATCAtt/tt -1 EntrezGene TGATGTAGTTTCC TGATGTAGTTTCC OK 32 4.500006 3.19&-4.39&6.54&3.2&-3.36&5.44&6.54&4.63&6.54&0.654&6.54 rs1272097668 2 143318 1.39550e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35380e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.43984e-05 3.09722e-05 2.67451e-05 3.67863e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.39482e-05 0.00000e+00 0.00000e+00 0.00000e+00 570864 571306 Congenital_disorder_of_glycosylation_type_1E MONDO:MONDO:0012123&MedGen:C1837396&OMIM:608799&Orphanet:ORPHA79322 criteria_provided&_single_submitter Pathogenic Deletion DPM1:8813 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 1272097668 15 30 50.0 -20 50945875 ATGATGTAGTTTCC A - frameshift_variant HIGH DPM1 8813 Transcript NM_003859.3 protein_coding 4/9 340-352 331-343 111-115 GNYII/X GGAAACTACATCAtt/tt -1 EntrezGene TGATGTAGTTTCC TGATGTAGTTTCC 32 4.500006 3.19&-4.39&6.54&3.2&-3.36&5.44&6.54&4.63&6.54&0.654&6.54 rs1272097668 2 143318 1.39550e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35380e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.43984e-05 3.09722e-05 2.67451e-05 3.67863e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.39482e-05 0.00000e+00 0.00000e+00 0.00000e+00 570864 571306 Congenital_disorder_of_glycosylation_type_1E MONDO:MONDO:0012123&MedGen:C1837396&OMIM:608799&Orphanet:ORPHA79322 criteria_provided&_single_submitter Pathogenic Deletion DPM1:8813 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 1272097668 15 30 50.0 -20 50945875 ATGATGTAGTTTCC A - downstream_gene_variant MODIFIER ADNP-AS1 101927631 Transcript NR_110007.1 lncRNA 742 1 EntrezGene TGATGTAGTTTCC TGATGTAGTTTCC OK 32 4.500006 3.19&-4.39&6.54&3.2&-3.36&5.44&6.54&4.63&6.54&0.654&6.54 rs1272097668 2 143318 1.39550e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35380e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.43984e-05 3.09722e-05 2.67451e-05 3.67863e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.39482e-05 0.00000e+00 0.00000e+00 0.00000e+00 570864 571306 Congenital_disorder_of_glycosylation_type_1E MONDO:MONDO:0012123&MedGen:C1837396&OMIM:608799&Orphanet:ORPHA79322 criteria_provided&_single_submitter Pathogenic Deletion DPM1:8813 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 1272097668 15 30 50.0 -20 50945875 ATGATGTAGTTTCC A - downstream_gene_variant MODIFIER ADNP-AS1 101927631 Transcript NR_110008.1 lncRNA 742 1 EntrezGene TGATGTAGTTTCC TGATGTAGTTTCC OK 32 4.500006 3.19&-4.39&6.54&3.2&-3.36&5.44&6.54&4.63&6.54&0.654&6.54 rs1272097668 2 143318 1.39550e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35380e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.43984e-05 3.09722e-05 2.67451e-05 3.67863e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.39482e-05 0.00000e+00 0.00000e+00 0.00000e+00 570864 571306 Congenital_disorder_of_glycosylation_type_1E MONDO:MONDO:0012123&MedGen:C1837396&OMIM:608799&Orphanet:ORPHA79322 criteria_provided&_single_submitter Pathogenic Deletion DPM1:8813 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 1272097668 15 30 50.0 -20 50945875 ATGATGTAGTTTCC A - downstream_gene_variant MODIFIER ADNP-AS1 101927631 Transcript NR_110009.1 lncRNA 742 1 EntrezGene TGATGTAGTTTCC TGATGTAGTTTCC OK 32 4.500006 3.19&-4.39&6.54&3.2&-3.36&5.44&6.54&4.63&6.54&0.654&6.54 rs1272097668 2 143318 1.39550e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35380e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.43984e-05 3.09722e-05 2.67451e-05 3.67863e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.39482e-05 0.00000e+00 0.00000e+00 0.00000e+00 570864 571306 Congenital_disorder_of_glycosylation_type_1E MONDO:MONDO:0012123&MedGen:C1837396&OMIM:608799&Orphanet:ORPHA79322 criteria_provided&_single_submitter Pathogenic Deletion DPM1:8813 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 1272097668 15 30 50.0 -20 50945875 ATGATGTAGTTTCC A - non_coding_transcript_exon_variant MODIFIER DPM1 8813 Transcript NR_133648.1 misc_RNA 4/9 372-384 -1 EntrezGene TGATGTAGTTTCC TGATGTAGTTTCC OK 32 4.500006 3.19&-4.39&6.54&3.2&-3.36&5.44&6.54&4.63&6.54&0.654&6.54 rs1272097668 2 143318 1.39550e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35380e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.43984e-05 3.09722e-05 2.67451e-05 3.67863e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.39482e-05 0.00000e+00 0.00000e+00 0.00000e+00 570864 571306 Congenital_disorder_of_glycosylation_type_1E MONDO:MONDO:0012123&MedGen:C1837396&OMIM:608799&Orphanet:ORPHA79322 criteria_provided&_single_submitter Pathogenic Deletion DPM1:8813 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 1272097668 15 30 50.0 -20 50945875 ATGATGTAGTTTCC A - non_coding_transcript_exon_variant MODIFIER DPM1 8813 Transcript XR_002958550.1 misc_RNA 4/9 369-381 -1 EntrezGene TGATGTAGTTTCC TGATGTAGTTTCC 32 4.500006 3.19&-4.39&6.54&3.2&-3.36&5.44&6.54&4.63&6.54&0.654&6.54 rs1272097668 2 143318 1.39550e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35380e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.43984e-05 3.09722e-05 2.67451e-05 3.67863e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.39482e-05 0.00000e+00 0.00000e+00 0.00000e+00 570864 571306 Congenital_disorder_of_glycosylation_type_1E MONDO:MONDO:0012123&MedGen:C1837396&OMIM:608799&Orphanet:ORPHA79322 criteria_provided&_single_submitter Pathogenic Deletion DPM1:8813 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 1272097668 15 30 50.0 -20 50945875 ATGATGTAGTTTCC A - non_coding_transcript_exon_variant MODIFIER DPM1 8813 Transcript XR_002958551.1 misc_RNA 4/7 370-382 -1 EntrezGene TGATGTAGTTTCC TGATGTAGTTTCC 32 4.500006 3.19&-4.39&6.54&3.2&-3.36&5.44&6.54&4.63&6.54&0.654&6.54 rs1272097668 2 143318 1.39550e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.35380e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.43984e-05 3.09722e-05 2.67451e-05 3.67863e-05 0.00000e+00 0.00000e+00 0.00000e+00 1.39482e-05 0.00000e+00 0.00000e+00 0.00000e+00 570864 571306 Congenital_disorder_of_glycosylation_type_1E MONDO:MONDO:0012123&MedGen:C1837396&OMIM:608799&Orphanet:ORPHA79322 criteria_provided&_single_submitter Pathogenic Deletion DPM1:8813 SO:0001589&frameshift_variant&SO:0001619&non-coding_transcript_variant 1 1272097668 15 30 50.0 -21 10469529 C T T intron_variant MODIFIER BAGE2 85319 Transcript NM_182482.2 protein_coding 5/9 1 EntrezGene C C 2.794 0.170491 -1.8 chr21:10469529-10469529 2096 112020 1.87109e-02 3.70651e-02 3.46547e-02 3.98805e-02 1.90616e-02 2.01149e-02 1.79641e-02 1.46686e-02 1.56380e-02 1.39245e-02 1.33432e-02 1.39470e-02 1.26582e-02 9.26641e-03 7.56303e-03 1.07143e-02 1.84496e-02 1.36209e-02 1.79961e-02 1.22890e-02 1.89866e-02 1.13688e-02 1.14939e-02 1.11967e-02 1.96897e-02 1.61290e-02 2.35149e-02 1.07278e-01 1.18367e-02 8.62069e-03 1.25881e-02 15 30 50.0 -21 10469529 C T T intron_variant MODIFIER LOC105378260 105378260 Transcript XM_017028520.1 protein_coding 2/7 1 EntrezGene C C 2.794 0.170491 -1.8 chr21:10469529-10469529 2096 112020 1.87109e-02 3.70651e-02 3.46547e-02 3.98805e-02 1.90616e-02 2.01149e-02 1.79641e-02 1.46686e-02 1.56380e-02 1.39245e-02 1.33432e-02 1.39470e-02 1.26582e-02 9.26641e-03 7.56303e-03 1.07143e-02 1.84496e-02 1.36209e-02 1.79961e-02 1.22890e-02 1.89866e-02 1.13688e-02 1.14939e-02 1.11967e-02 1.96897e-02 1.61290e-02 2.35149e-02 1.07278e-01 1.18367e-02 8.62069e-03 1.25881e-02 15 30 50.0 -21 10469529 C T T intron_variant MODIFIER LOC105378260 105378260 Transcript XM_017028521.1 protein_coding 2/8 1 EntrezGene C C 2.794 0.170491 -1.8 chr21:10469529-10469529 2096 112020 1.87109e-02 3.70651e-02 3.46547e-02 3.98805e-02 1.90616e-02 2.01149e-02 1.79641e-02 1.46686e-02 1.56380e-02 1.39245e-02 1.33432e-02 1.39470e-02 1.26582e-02 9.26641e-03 7.56303e-03 1.07143e-02 1.84496e-02 1.36209e-02 1.79961e-02 1.22890e-02 1.89866e-02 1.13688e-02 1.14939e-02 1.11967e-02 1.96897e-02 1.61290e-02 2.35149e-02 1.07278e-01 1.18367e-02 8.62069e-03 1.25881e-02 15 30 50.0 -21 10469529 C T T intron_variant&non_coding_transcript_variant MODIFIER LOC105378260 105378260 Transcript XR_001754956.1 misc_RNA 2/10 1 EntrezGene C C 2.794 0.170491 -1.8 chr21:10469529-10469529 2096 112020 1.87109e-02 3.70651e-02 3.46547e-02 3.98805e-02 1.90616e-02 2.01149e-02 1.79641e-02 1.46686e-02 1.56380e-02 1.39245e-02 1.33432e-02 1.39470e-02 1.26582e-02 9.26641e-03 7.56303e-03 1.07143e-02 1.84496e-02 1.36209e-02 1.79961e-02 1.22890e-02 1.89866e-02 1.13688e-02 1.14939e-02 1.11967e-02 1.96897e-02 1.61290e-02 2.35149e-02 1.07278e-01 1.18367e-02 8.62069e-03 1.25881e-02 15 30 50.0 -21 12972580 T G G intergenic_variant MODIFIER 5.372 0.395095 0 rs75261179 3 142350 2.10748e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.08697e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.68677e-05 8.07885e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.80143e-05 0.00000e+00 0.00000e+00 0.00000e+00 15 30 50.0 -21 46145925 G GC C frameshift_variant HIGH FTCD 10841 Transcript NM_001320412.2 protein_coding 9/15 1046-1047 990-991 330-331 -/X -/G -1 EntrezGene 21.7 2.314098 0.637&2.33 rs398124234 543 140616 3.86158e-03 1.04827e-03 1.04072e-03 1.05708e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.62915e-03 6.87049e-04 2.34314e-03 9.07441e-04 1.71038e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.83459e-03 7.83392e-03 1.15037e-02 6.68552e-03 3.89031e-03 6.05308e-03 5.93496e-03 6.21630e-03 1.42315e-03 0.00000e+00 2.91829e-03 3.92012e-03 2.39562e-03 1.85874e-03 2.51678e-03 4019 19058 GLUTAMATE_FORMIMINOTRANSFERASE_DEFICIENCY¬_provided MONDO:MONDO:0009240&MedGen:C0268609&OMIM:229100&Orphanet:ORPHA51208&SNOMED_CT:59761008&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(2)&Pathogenic(2)&Uncertain_significance(1) Duplication FTCD:10841 SO:0001589&frameshift_variant 17 398124234 15 30 50.0 -21 46145925 G GC C frameshift_variant HIGH FTCD 10841 Transcript NM_006657.3 protein_coding 9/15 1046-1047 990-991 330-331 -/X -/G -1 EntrezGene 21.7 2.314098 0.637&2.33 rs398124234 543 140616 3.86158e-03 1.04827e-03 1.04072e-03 1.05708e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.62915e-03 6.87049e-04 2.34314e-03 9.07441e-04 1.71038e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.83459e-03 7.83392e-03 1.15037e-02 6.68552e-03 3.89031e-03 6.05308e-03 5.93496e-03 6.21630e-03 1.42315e-03 0.00000e+00 2.91829e-03 3.92012e-03 2.39562e-03 1.85874e-03 2.51678e-03 4019 19058 GLUTAMATE_FORMIMINOTRANSFERASE_DEFICIENCY¬_provided MONDO:MONDO:0009240&MedGen:C0268609&OMIM:229100&Orphanet:ORPHA51208&SNOMED_CT:59761008&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(2)&Pathogenic(2)&Uncertain_significance(1) Duplication FTCD:10841 SO:0001589&frameshift_variant 17 398124234 15 30 50.0 -21 46145925 G GC C frameshift_variant HIGH FTCD 10841 Transcript NM_206965.2 protein_coding 9/14 1046-1047 990-991 330-331 -/X -/G -1 EntrezGene 21.7 2.314098 0.637&2.33 rs398124234 543 140616 3.86158e-03 1.04827e-03 1.04072e-03 1.05708e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.62915e-03 6.87049e-04 2.34314e-03 9.07441e-04 1.71038e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.83459e-03 7.83392e-03 1.15037e-02 6.68552e-03 3.89031e-03 6.05308e-03 5.93496e-03 6.21630e-03 1.42315e-03 0.00000e+00 2.91829e-03 3.92012e-03 2.39562e-03 1.85874e-03 2.51678e-03 4019 19058 GLUTAMATE_FORMIMINOTRANSFERASE_DEFICIENCY¬_provided MONDO:MONDO:0009240&MedGen:C0268609&OMIM:229100&Orphanet:ORPHA51208&SNOMED_CT:59761008&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(2)&Pathogenic(2)&Uncertain_significance(1) Duplication FTCD:10841 SO:0001589&frameshift_variant 17 398124234 15 30 50.0 -21 46145925 G GC C frameshift_variant HIGH FTCD 10841 Transcript XM_006723961.4 protein_coding 9/14 1327-1328 1110-1111 370-371 -/X -/G -1 EntrezGene 21.7 2.314098 0.637&2.33 rs398124234 543 140616 3.86158e-03 1.04827e-03 1.04072e-03 1.05708e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.62915e-03 6.87049e-04 2.34314e-03 9.07441e-04 1.71038e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.83459e-03 7.83392e-03 1.15037e-02 6.68552e-03 3.89031e-03 6.05308e-03 5.93496e-03 6.21630e-03 1.42315e-03 0.00000e+00 2.91829e-03 3.92012e-03 2.39562e-03 1.85874e-03 2.51678e-03 4019 19058 GLUTAMATE_FORMIMINOTRANSFERASE_DEFICIENCY¬_provided MONDO:MONDO:0009240&MedGen:C0268609&OMIM:229100&Orphanet:ORPHA51208&SNOMED_CT:59761008&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(2)&Pathogenic(2)&Uncertain_significance(1) Duplication FTCD:10841 SO:0001589&frameshift_variant 17 398124234 15 30 50.0 -21 46145925 G GC C frameshift_variant HIGH FTCD 10841 Transcript XM_006723962.4 protein_coding 9/14 1936-1937 1110-1111 370-371 -/X -/G -1 EntrezGene 21.7 2.314098 0.637&2.33 rs398124234 543 140616 3.86158e-03 1.04827e-03 1.04072e-03 1.05708e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.62915e-03 6.87049e-04 2.34314e-03 9.07441e-04 1.71038e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.83459e-03 7.83392e-03 1.15037e-02 6.68552e-03 3.89031e-03 6.05308e-03 5.93496e-03 6.21630e-03 1.42315e-03 0.00000e+00 2.91829e-03 3.92012e-03 2.39562e-03 1.85874e-03 2.51678e-03 4019 19058 GLUTAMATE_FORMIMINOTRANSFERASE_DEFICIENCY¬_provided MONDO:MONDO:0009240&MedGen:C0268609&OMIM:229100&Orphanet:ORPHA51208&SNOMED_CT:59761008&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(2)&Pathogenic(2)&Uncertain_significance(1) Duplication FTCD:10841 SO:0001589&frameshift_variant 17 398124234 15 30 50.0 -21 46145925 G GC C frameshift_variant HIGH FTCD 10841 Transcript XM_011529434.3 protein_coding 9/15 1949-1950 1110-1111 370-371 -/X -/G -1 EntrezGene 21.7 2.314098 0.637&2.33 rs398124234 543 140616 3.86158e-03 1.04827e-03 1.04072e-03 1.05708e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.62915e-03 6.87049e-04 2.34314e-03 9.07441e-04 1.71038e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.83459e-03 7.83392e-03 1.15037e-02 6.68552e-03 3.89031e-03 6.05308e-03 5.93496e-03 6.21630e-03 1.42315e-03 0.00000e+00 2.91829e-03 3.92012e-03 2.39562e-03 1.85874e-03 2.51678e-03 4019 19058 GLUTAMATE_FORMIMINOTRANSFERASE_DEFICIENCY¬_provided MONDO:MONDO:0009240&MedGen:C0268609&OMIM:229100&Orphanet:ORPHA51208&SNOMED_CT:59761008&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(2)&Pathogenic(2)&Uncertain_significance(1) Duplication FTCD:10841 SO:0001589&frameshift_variant 17 398124234 15 30 50.0 -21 46145925 G GC C frameshift_variant HIGH FTCD 10841 Transcript XM_011529435.3 protein_coding 9/15 1333-1334 1110-1111 370-371 -/X -/G -1 EntrezGene 21.7 2.314098 0.637&2.33 rs398124234 543 140616 3.86158e-03 1.04827e-03 1.04072e-03 1.05708e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.62915e-03 6.87049e-04 2.34314e-03 9.07441e-04 1.71038e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.83459e-03 7.83392e-03 1.15037e-02 6.68552e-03 3.89031e-03 6.05308e-03 5.93496e-03 6.21630e-03 1.42315e-03 0.00000e+00 2.91829e-03 3.92012e-03 2.39562e-03 1.85874e-03 2.51678e-03 4019 19058 GLUTAMATE_FORMIMINOTRANSFERASE_DEFICIENCY¬_provided MONDO:MONDO:0009240&MedGen:C0268609&OMIM:229100&Orphanet:ORPHA51208&SNOMED_CT:59761008&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(2)&Pathogenic(2)&Uncertain_significance(1) Duplication FTCD:10841 SO:0001589&frameshift_variant 17 398124234 15 30 50.0 -21 46145925 G GC C frameshift_variant HIGH FTCD 10841 Transcript XM_011529436.3 protein_coding 9/15 1936-1937 1110-1111 370-371 -/X -/G -1 EntrezGene 21.7 2.314098 0.637&2.33 rs398124234 543 140616 3.86158e-03 1.04827e-03 1.04072e-03 1.05708e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.62915e-03 6.87049e-04 2.34314e-03 9.07441e-04 1.71038e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.83459e-03 7.83392e-03 1.15037e-02 6.68552e-03 3.89031e-03 6.05308e-03 5.93496e-03 6.21630e-03 1.42315e-03 0.00000e+00 2.91829e-03 3.92012e-03 2.39562e-03 1.85874e-03 2.51678e-03 4019 19058 GLUTAMATE_FORMIMINOTRANSFERASE_DEFICIENCY¬_provided MONDO:MONDO:0009240&MedGen:C0268609&OMIM:229100&Orphanet:ORPHA51208&SNOMED_CT:59761008&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(2)&Pathogenic(2)&Uncertain_significance(1) Duplication FTCD:10841 SO:0001589&frameshift_variant 17 398124234 15 30 50.0 -21 46145925 G GC C frameshift_variant HIGH FTCD 10841 Transcript XM_011529437.3 protein_coding 9/15 1328-1329 1110-1111 370-371 -/X -/G -1 EntrezGene 21.7 2.314098 0.637&2.33 rs398124234 543 140616 3.86158e-03 1.04827e-03 1.04072e-03 1.05708e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.62915e-03 6.87049e-04 2.34314e-03 9.07441e-04 1.71038e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.83459e-03 7.83392e-03 1.15037e-02 6.68552e-03 3.89031e-03 6.05308e-03 5.93496e-03 6.21630e-03 1.42315e-03 0.00000e+00 2.91829e-03 3.92012e-03 2.39562e-03 1.85874e-03 2.51678e-03 4019 19058 GLUTAMATE_FORMIMINOTRANSFERASE_DEFICIENCY¬_provided MONDO:MONDO:0009240&MedGen:C0268609&OMIM:229100&Orphanet:ORPHA51208&SNOMED_CT:59761008&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(2)&Pathogenic(2)&Uncertain_significance(1) Duplication FTCD:10841 SO:0001589&frameshift_variant 17 398124234 15 30 50.0 -21 46145925 G GC C frameshift_variant HIGH FTCD 10841 Transcript XM_011529439.2 protein_coding 7/13 737-738 597-598 199-200 -/X -/G -1 EntrezGene 21.7 2.314098 0.637&2.33 rs398124234 543 140616 3.86158e-03 1.04827e-03 1.04072e-03 1.05708e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.62915e-03 6.87049e-04 2.34314e-03 9.07441e-04 1.71038e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.83459e-03 7.83392e-03 1.15037e-02 6.68552e-03 3.89031e-03 6.05308e-03 5.93496e-03 6.21630e-03 1.42315e-03 0.00000e+00 2.91829e-03 3.92012e-03 2.39562e-03 1.85874e-03 2.51678e-03 4019 19058 GLUTAMATE_FORMIMINOTRANSFERASE_DEFICIENCY¬_provided MONDO:MONDO:0009240&MedGen:C0268609&OMIM:229100&Orphanet:ORPHA51208&SNOMED_CT:59761008&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(2)&Pathogenic(2)&Uncertain_significance(1) Duplication FTCD:10841 SO:0001589&frameshift_variant 17 398124234 15 30 50.0 -21 46145925 G GC C frameshift_variant HIGH FTCD 10841 Transcript XM_011529440.3 protein_coding 9/11 1321-1322 1110-1111 370-371 -/X -/G -1 EntrezGene 21.7 2.314098 0.637&2.33 rs398124234 543 140616 3.86158e-03 1.04827e-03 1.04072e-03 1.05708e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.62915e-03 6.87049e-04 2.34314e-03 9.07441e-04 1.71038e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.83459e-03 7.83392e-03 1.15037e-02 6.68552e-03 3.89031e-03 6.05308e-03 5.93496e-03 6.21630e-03 1.42315e-03 0.00000e+00 2.91829e-03 3.92012e-03 2.39562e-03 1.85874e-03 2.51678e-03 4019 19058 GLUTAMATE_FORMIMINOTRANSFERASE_DEFICIENCY¬_provided MONDO:MONDO:0009240&MedGen:C0268609&OMIM:229100&Orphanet:ORPHA51208&SNOMED_CT:59761008&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(2)&Pathogenic(2)&Uncertain_significance(1) Duplication FTCD:10841 SO:0001589&frameshift_variant 17 398124234 15 30 50.0 -21 46145925 G GC C non_coding_transcript_exon_variant MODIFIER FTCD 10841 Transcript XR_937433.3 misc_RNA 9/15 1327-1328 -1 EntrezGene 21.7 2.314098 0.637&2.33 rs398124234 543 140616 3.86158e-03 1.04827e-03 1.04072e-03 1.05708e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.62915e-03 6.87049e-04 2.34314e-03 9.07441e-04 1.71038e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.83459e-03 7.83392e-03 1.15037e-02 6.68552e-03 3.89031e-03 6.05308e-03 5.93496e-03 6.21630e-03 1.42315e-03 0.00000e+00 2.91829e-03 3.92012e-03 2.39562e-03 1.85874e-03 2.51678e-03 4019 19058 GLUTAMATE_FORMIMINOTRANSFERASE_DEFICIENCY¬_provided MONDO:MONDO:0009240&MedGen:C0268609&OMIM:229100&Orphanet:ORPHA51208&SNOMED_CT:59761008&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(2)&Pathogenic(2)&Uncertain_significance(1) Duplication FTCD:10841 SO:0001589&frameshift_variant 17 398124234 15 30 50.0 -22 26933109 G GA A intergenic_variant MODIFIER 18.68 1.924282 3.55&4.6 rs397766839 95714 142900 6.69797e-01 4.66090e-01 4.66498e-01 4.65609e-01 7.79510e-01 7.75641e-01 7.83721e-01 7.67278e-01 7.64925e-01 7.69072e-01 7.60529e-01 7.62770e-01 7.58003e-01 9.33419e-01 9.36288e-01 9.30952e-01 6.64486e-01 7.38237e-01 7.34375e-01 7.39454e-01 6.75451e-01 7.42505e-01 7.43630e-01 7.40956e-01 6.97479e-01 6.96527e-01 6.98473e-01 6.69681e-01 8.35306e-01 8.36879e-01 8.34948e-01 15 30 50.0 -22 42128945 C T T splice_acceptor_variant HIGH CYP2D6 1565 Transcript NM_000106.6 protein_coding 3/8 -1 EntrezGene C C 33 5.331363 5.81 rs3892097 20150 141228 1.42677e-01 7.76491e-02 7.81995e-02 7.70005e-02 2.38149e-01 2.32759e-01 2.44076e-01 1.31190e-01 1.27434e-01 1.34054e-01 1.81956e-01 1.62271e-01 2.04134e-01 6.72646e-03 9.00277e-03 4.76758e-03 1.48477e-01 9.62757e-02 1.15169e-01 9.03356e-02 1.36497e-01 1.99351e-01 2.00541e-01 1.97707e-01 1.47472e-01 1.57221e-01 1.37236e-01 1.46247e-01 1.09218e-01 1.00000e-01 1.11339e-01 16889 0.15105 0.17076 0.09305 31928 Debrisoquine&_poor_metabolism_of&Tamoxifen_response&Tramadol_response¬_specified&amitriptyline_response_-_Dosage&_Toxicity/ADR&antidepressants_response_-_Dosage&_Toxicity/ADR&clomipramine_response_-_Dosage&_Toxicity/ADR&desipramine_response_-_Dosage&_Toxicity/ADR&doxepin_response_-_Dosage&_Toxicity/ADR&imipramine_response_-_Dosage&_Toxicity/ADR&nortriptyline_response_-_Dosage&_Toxicity/ADR&trimipramine_response_-_Dosage&_Toxicity/ADR&tamoxifen_response_-_Efficacy&_Toxicity/ADR&Deutetrabenazine_response¬_provided MedGen:C1837156&MedGen:CN078013&MedGen:CN078023&MedGen:CN169374&MedGen:CN236466&MedGen:CN236477&MedGen:CN236505&MedGen:CN236523&MedGen:CN236529&MedGen:CN236532&MedGen:CN236538&MedGen:CN236546&MedGen:CN236584&MedGen:CN258189&MedGen:CN517202 reviewed_by_expert_panel drug_response single_nucleotide_variant CYP2D6:1565 SO:0001574&splice_acceptor_variant 3 3892097 15 30 50.0 -22 42128945 C T T splice_acceptor_variant HIGH CYP2D6 1565 Transcript NM_001025161.3 protein_coding 2/7 -1 EntrezGene C C 33 5.331363 5.81 rs3892097 20150 141228 1.42677e-01 7.76491e-02 7.81995e-02 7.70005e-02 2.38149e-01 2.32759e-01 2.44076e-01 1.31190e-01 1.27434e-01 1.34054e-01 1.81956e-01 1.62271e-01 2.04134e-01 6.72646e-03 9.00277e-03 4.76758e-03 1.48477e-01 9.62757e-02 1.15169e-01 9.03356e-02 1.36497e-01 1.99351e-01 2.00541e-01 1.97707e-01 1.47472e-01 1.57221e-01 1.37236e-01 1.46247e-01 1.09218e-01 1.00000e-01 1.11339e-01 16889 0.15105 0.17076 0.09305 31928 Debrisoquine&_poor_metabolism_of&Tamoxifen_response&Tramadol_response¬_specified&amitriptyline_response_-_Dosage&_Toxicity/ADR&antidepressants_response_-_Dosage&_Toxicity/ADR&clomipramine_response_-_Dosage&_Toxicity/ADR&desipramine_response_-_Dosage&_Toxicity/ADR&doxepin_response_-_Dosage&_Toxicity/ADR&imipramine_response_-_Dosage&_Toxicity/ADR&nortriptyline_response_-_Dosage&_Toxicity/ADR&trimipramine_response_-_Dosage&_Toxicity/ADR&tamoxifen_response_-_Efficacy&_Toxicity/ADR&Deutetrabenazine_response¬_provided MedGen:C1837156&MedGen:CN078013&MedGen:CN078023&MedGen:CN169374&MedGen:CN236466&MedGen:CN236477&MedGen:CN236505&MedGen:CN236523&MedGen:CN236529&MedGen:CN236532&MedGen:CN236538&MedGen:CN236546&MedGen:CN236584&MedGen:CN258189&MedGen:CN517202 reviewed_by_expert_panel drug_response single_nucleotide_variant CYP2D6:1565 SO:0001574&splice_acceptor_variant 3 3892097 15 30 50.0 -22 42128945 C T T downstream_gene_variant MODIFIER NDUFA6-AS1 100132273 Transcript NR_034118.2 lncRNA 3596 1 EntrezGene C C OK 33 5.331363 5.81 rs3892097 20150 141228 1.42677e-01 7.76491e-02 7.81995e-02 7.70005e-02 2.38149e-01 2.32759e-01 2.44076e-01 1.31190e-01 1.27434e-01 1.34054e-01 1.81956e-01 1.62271e-01 2.04134e-01 6.72646e-03 9.00277e-03 4.76758e-03 1.48477e-01 9.62757e-02 1.15169e-01 9.03356e-02 1.36497e-01 1.99351e-01 2.00541e-01 1.97707e-01 1.47472e-01 1.57221e-01 1.37236e-01 1.46247e-01 1.09218e-01 1.00000e-01 1.11339e-01 16889 0.15105 0.17076 0.09305 31928 Debrisoquine&_poor_metabolism_of&Tamoxifen_response&Tramadol_response¬_specified&amitriptyline_response_-_Dosage&_Toxicity/ADR&antidepressants_response_-_Dosage&_Toxicity/ADR&clomipramine_response_-_Dosage&_Toxicity/ADR&desipramine_response_-_Dosage&_Toxicity/ADR&doxepin_response_-_Dosage&_Toxicity/ADR&imipramine_response_-_Dosage&_Toxicity/ADR&nortriptyline_response_-_Dosage&_Toxicity/ADR&trimipramine_response_-_Dosage&_Toxicity/ADR&tamoxifen_response_-_Efficacy&_Toxicity/ADR&Deutetrabenazine_response¬_provided MedGen:C1837156&MedGen:CN078013&MedGen:CN078023&MedGen:CN169374&MedGen:CN236466&MedGen:CN236477&MedGen:CN236505&MedGen:CN236523&MedGen:CN236529&MedGen:CN236532&MedGen:CN236538&MedGen:CN236546&MedGen:CN236584&MedGen:CN258189&MedGen:CN517202 reviewed_by_expert_panel drug_response single_nucleotide_variant CYP2D6:1565 SO:0001574&splice_acceptor_variant 3 3892097 15 30 50.0 -22 42128945 C T T upstream_gene_variant MODIFIER LOC112268294 112268294 Transcript XR_002958749.1 lncRNA 1126 1 EntrezGene C C 33 5.331363 5.81 rs3892097 20150 141228 1.42677e-01 7.76491e-02 7.81995e-02 7.70005e-02 2.38149e-01 2.32759e-01 2.44076e-01 1.31190e-01 1.27434e-01 1.34054e-01 1.81956e-01 1.62271e-01 2.04134e-01 6.72646e-03 9.00277e-03 4.76758e-03 1.48477e-01 9.62757e-02 1.15169e-01 9.03356e-02 1.36497e-01 1.99351e-01 2.00541e-01 1.97707e-01 1.47472e-01 1.57221e-01 1.37236e-01 1.46247e-01 1.09218e-01 1.00000e-01 1.11339e-01 16889 0.15105 0.17076 0.09305 31928 Debrisoquine&_poor_metabolism_of&Tamoxifen_response&Tramadol_response¬_specified&amitriptyline_response_-_Dosage&_Toxicity/ADR&antidepressants_response_-_Dosage&_Toxicity/ADR&clomipramine_response_-_Dosage&_Toxicity/ADR&desipramine_response_-_Dosage&_Toxicity/ADR&doxepin_response_-_Dosage&_Toxicity/ADR&imipramine_response_-_Dosage&_Toxicity/ADR&nortriptyline_response_-_Dosage&_Toxicity/ADR&trimipramine_response_-_Dosage&_Toxicity/ADR&tamoxifen_response_-_Efficacy&_Toxicity/ADR&Deutetrabenazine_response¬_provided MedGen:C1837156&MedGen:CN078013&MedGen:CN078023&MedGen:CN169374&MedGen:CN236466&MedGen:CN236477&MedGen:CN236505&MedGen:CN236523&MedGen:CN236529&MedGen:CN236532&MedGen:CN236538&MedGen:CN236546&MedGen:CN236584&MedGen:CN258189&MedGen:CN517202 reviewed_by_expert_panel drug_response single_nucleotide_variant CYP2D6:1565 SO:0001574&splice_acceptor_variant 3 3892097 15 30 50.0 -22 50524395 C CTGAGTCACTGCTGCATGCT TGAGTCACTGCTGCATGCT downstream_gene_variant MODIFIER TYMP 1890 Transcript NM_001113755.3 protein_coding 1356 -1 EntrezGene 21.2 2.241712 -6.5&2.84 rs749838192 127 143248 8.86574e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.65755e-04 1.08862e-02 8.38658e-03 1.16717e-02 1.44034e-03 1.85920e-04 1.60505e-04 2.20897e-04 4.64684e-04 0.00000e+00 9.48767e-04 9.06631e-04 0.00000e+00 0.00000e+00 0.00000e+00 222816 0.00080 224577 Primary_dilated_cardiomyopathy EFO:EFO_0000407&Human_Phenotype_Ontology:HP:0001644&Human_Phenotype_Ontology:HP:0001725&Human_Phenotype_Ontology:HP:0005159&Human_Phenotype_Ontology:HP:0200130&MONDO:MONDO:0005021&MedGen:C0007193&Orphanet:ORPHA217604&SNOMED_CT:195021004&SNOMED_CT:399020009 criteria_provided&_single_submitter Likely_pathogenic Insertion SCO2:9997&NCAPH2:29781 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 749838192 15 30 50.0 -22 50524395 C CTGAGTCACTGCTGCATGCT TGAGTCACTGCTGCATGCT downstream_gene_variant MODIFIER TYMP 1890 Transcript NM_001113756.3 protein_coding 1356 -1 EntrezGene 21.2 2.241712 -6.5&2.84 rs749838192 127 143248 8.86574e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.65755e-04 1.08862e-02 8.38658e-03 1.16717e-02 1.44034e-03 1.85920e-04 1.60505e-04 2.20897e-04 4.64684e-04 0.00000e+00 9.48767e-04 9.06631e-04 0.00000e+00 0.00000e+00 0.00000e+00 222816 0.00080 224577 Primary_dilated_cardiomyopathy EFO:EFO_0000407&Human_Phenotype_Ontology:HP:0001644&Human_Phenotype_Ontology:HP:0001725&Human_Phenotype_Ontology:HP:0005159&Human_Phenotype_Ontology:HP:0200130&MONDO:MONDO:0005021&MedGen:C0007193&Orphanet:ORPHA217604&SNOMED_CT:195021004&SNOMED_CT:399020009 criteria_provided&_single_submitter Likely_pathogenic Insertion SCO2:9997&NCAPH2:29781 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 749838192 15 30 50.0 -22 50524395 C CTGAGTCACTGCTGCATGCT TGAGTCACTGCTGCATGCT frameshift_variant HIGH SCO2 9997 Transcript NM_001169109.1 protein_coding 2/2 223-224 16-17 6 R/QHAAVTQX cgg/cAGCATGCAGCAGTGACTCAgg -1 EntrezGene 21.2 2.241712 -6.5&2.84 rs749838192 127 143248 8.86574e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.65755e-04 1.08862e-02 8.38658e-03 1.16717e-02 1.44034e-03 1.85920e-04 1.60505e-04 2.20897e-04 4.64684e-04 0.00000e+00 9.48767e-04 9.06631e-04 0.00000e+00 0.00000e+00 0.00000e+00 222816 0.00080 224577 Primary_dilated_cardiomyopathy EFO:EFO_0000407&Human_Phenotype_Ontology:HP:0001644&Human_Phenotype_Ontology:HP:0001725&Human_Phenotype_Ontology:HP:0005159&Human_Phenotype_Ontology:HP:0200130&MONDO:MONDO:0005021&MedGen:C0007193&Orphanet:ORPHA217604&SNOMED_CT:195021004&SNOMED_CT:399020009 criteria_provided&_single_submitter Likely_pathogenic Insertion SCO2:9997&NCAPH2:29781 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 749838192 15 30 50.0 -22 50524395 C CTGAGTCACTGCTGCATGCT TGAGTCACTGCTGCATGCT frameshift_variant HIGH SCO2 9997 Transcript NM_001169110.1 protein_coding 2/2 174-175 16-17 6 R/QHAAVTQX cgg/cAGCATGCAGCAGTGACTCAgg -1 EntrezGene 21.2 2.241712 -6.5&2.84 rs749838192 127 143248 8.86574e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.65755e-04 1.08862e-02 8.38658e-03 1.16717e-02 1.44034e-03 1.85920e-04 1.60505e-04 2.20897e-04 4.64684e-04 0.00000e+00 9.48767e-04 9.06631e-04 0.00000e+00 0.00000e+00 0.00000e+00 222816 0.00080 224577 Primary_dilated_cardiomyopathy EFO:EFO_0000407&Human_Phenotype_Ontology:HP:0001644&Human_Phenotype_Ontology:HP:0001725&Human_Phenotype_Ontology:HP:0005159&Human_Phenotype_Ontology:HP:0200130&MONDO:MONDO:0005021&MedGen:C0007193&Orphanet:ORPHA217604&SNOMED_CT:195021004&SNOMED_CT:399020009 criteria_provided&_single_submitter Likely_pathogenic Insertion SCO2:9997&NCAPH2:29781 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 749838192 15 30 50.0 -22 50524395 C CTGAGTCACTGCTGCATGCT TGAGTCACTGCTGCATGCT frameshift_variant HIGH SCO2 9997 Transcript NM_001169111.1 protein_coding 2/2 192-193 16-17 6 R/QHAAVTQX cgg/cAGCATGCAGCAGTGACTCAgg -1 EntrezGene 21.2 2.241712 -6.5&2.84 rs749838192 127 143248 8.86574e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.65755e-04 1.08862e-02 8.38658e-03 1.16717e-02 1.44034e-03 1.85920e-04 1.60505e-04 2.20897e-04 4.64684e-04 0.00000e+00 9.48767e-04 9.06631e-04 0.00000e+00 0.00000e+00 0.00000e+00 222816 0.00080 224577 Primary_dilated_cardiomyopathy EFO:EFO_0000407&Human_Phenotype_Ontology:HP:0001644&Human_Phenotype_Ontology:HP:0001725&Human_Phenotype_Ontology:HP:0005159&Human_Phenotype_Ontology:HP:0200130&MONDO:MONDO:0005021&MedGen:C0007193&Orphanet:ORPHA217604&SNOMED_CT:195021004&SNOMED_CT:399020009 criteria_provided&_single_submitter Likely_pathogenic Insertion SCO2:9997&NCAPH2:29781 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 749838192 15 30 50.0 -22 50524395 C CTGAGTCACTGCTGCATGCT TGAGTCACTGCTGCATGCT 3_prime_UTR_variant MODIFIER NCAPH2 29781 Transcript NM_001185011.2 protein_coding 20/20 2955-2956 1 EntrezGene 21.2 2.241712 -6.5&2.84 rs749838192 127 143248 8.86574e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.65755e-04 1.08862e-02 8.38658e-03 1.16717e-02 1.44034e-03 1.85920e-04 1.60505e-04 2.20897e-04 4.64684e-04 0.00000e+00 9.48767e-04 9.06631e-04 0.00000e+00 0.00000e+00 0.00000e+00 222816 0.00080 224577 Primary_dilated_cardiomyopathy EFO:EFO_0000407&Human_Phenotype_Ontology:HP:0001644&Human_Phenotype_Ontology:HP:0001725&Human_Phenotype_Ontology:HP:0005159&Human_Phenotype_Ontology:HP:0200130&MONDO:MONDO:0005021&MedGen:C0007193&Orphanet:ORPHA217604&SNOMED_CT:195021004&SNOMED_CT:399020009 criteria_provided&_single_submitter Likely_pathogenic Insertion SCO2:9997&NCAPH2:29781 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 749838192 15 30 50.0 -22 50524395 C CTGAGTCACTGCTGCATGCT TGAGTCACTGCTGCATGCT downstream_gene_variant MODIFIER TYMP 1890 Transcript NM_001257988.1 protein_coding 1356 -1 EntrezGene OK 21.2 2.241712 -6.5&2.84 rs749838192 127 143248 8.86574e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.65755e-04 1.08862e-02 8.38658e-03 1.16717e-02 1.44034e-03 1.85920e-04 1.60505e-04 2.20897e-04 4.64684e-04 0.00000e+00 9.48767e-04 9.06631e-04 0.00000e+00 0.00000e+00 0.00000e+00 222816 0.00080 224577 Primary_dilated_cardiomyopathy EFO:EFO_0000407&Human_Phenotype_Ontology:HP:0001644&Human_Phenotype_Ontology:HP:0001725&Human_Phenotype_Ontology:HP:0005159&Human_Phenotype_Ontology:HP:0200130&MONDO:MONDO:0005021&MedGen:C0007193&Orphanet:ORPHA217604&SNOMED_CT:195021004&SNOMED_CT:399020009 criteria_provided&_single_submitter Likely_pathogenic Insertion SCO2:9997&NCAPH2:29781 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 749838192 15 30 50.0 -22 50524395 C CTGAGTCACTGCTGCATGCT TGAGTCACTGCTGCATGCT downstream_gene_variant MODIFIER TYMP 1890 Transcript NM_001257989.1 protein_coding 1356 -1 EntrezGene OK 21.2 2.241712 -6.5&2.84 rs749838192 127 143248 8.86574e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.65755e-04 1.08862e-02 8.38658e-03 1.16717e-02 1.44034e-03 1.85920e-04 1.60505e-04 2.20897e-04 4.64684e-04 0.00000e+00 9.48767e-04 9.06631e-04 0.00000e+00 0.00000e+00 0.00000e+00 222816 0.00080 224577 Primary_dilated_cardiomyopathy EFO:EFO_0000407&Human_Phenotype_Ontology:HP:0001644&Human_Phenotype_Ontology:HP:0001725&Human_Phenotype_Ontology:HP:0005159&Human_Phenotype_Ontology:HP:0200130&MONDO:MONDO:0005021&MedGen:C0007193&Orphanet:ORPHA217604&SNOMED_CT:195021004&SNOMED_CT:399020009 criteria_provided&_single_submitter Likely_pathogenic Insertion SCO2:9997&NCAPH2:29781 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 749838192 15 30 50.0 -22 50524395 C CTGAGTCACTGCTGCATGCT TGAGTCACTGCTGCATGCT downstream_gene_variant MODIFIER TYMP 1890 Transcript NM_001953.5 protein_coding 1356 -1 EntrezGene 21.2 2.241712 -6.5&2.84 rs749838192 127 143248 8.86574e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.65755e-04 1.08862e-02 8.38658e-03 1.16717e-02 1.44034e-03 1.85920e-04 1.60505e-04 2.20897e-04 4.64684e-04 0.00000e+00 9.48767e-04 9.06631e-04 0.00000e+00 0.00000e+00 0.00000e+00 222816 0.00080 224577 Primary_dilated_cardiomyopathy EFO:EFO_0000407&Human_Phenotype_Ontology:HP:0001644&Human_Phenotype_Ontology:HP:0001725&Human_Phenotype_Ontology:HP:0005159&Human_Phenotype_Ontology:HP:0200130&MONDO:MONDO:0005021&MedGen:C0007193&Orphanet:ORPHA217604&SNOMED_CT:195021004&SNOMED_CT:399020009 criteria_provided&_single_submitter Likely_pathogenic Insertion SCO2:9997&NCAPH2:29781 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 749838192 15 30 50.0 -22 50524395 C CTGAGTCACTGCTGCATGCT TGAGTCACTGCTGCATGCT frameshift_variant HIGH SCO2 9997 Transcript NM_005138.3 protein_coding 2/2 156-157 16-17 6 R/QHAAVTQX cgg/cAGCATGCAGCAGTGACTCAgg -1 EntrezGene 21.2 2.241712 -6.5&2.84 rs749838192 127 143248 8.86574e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.65755e-04 1.08862e-02 8.38658e-03 1.16717e-02 1.44034e-03 1.85920e-04 1.60505e-04 2.20897e-04 4.64684e-04 0.00000e+00 9.48767e-04 9.06631e-04 0.00000e+00 0.00000e+00 0.00000e+00 222816 0.00080 224577 Primary_dilated_cardiomyopathy EFO:EFO_0000407&Human_Phenotype_Ontology:HP:0001644&Human_Phenotype_Ontology:HP:0001725&Human_Phenotype_Ontology:HP:0005159&Human_Phenotype_Ontology:HP:0200130&MONDO:MONDO:0005021&MedGen:C0007193&Orphanet:ORPHA217604&SNOMED_CT:195021004&SNOMED_CT:399020009 criteria_provided&_single_submitter Likely_pathogenic Insertion SCO2:9997&NCAPH2:29781 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 749838192 15 30 50.0 -22 50524395 C CTGAGTCACTGCTGCATGCT TGAGTCACTGCTGCATGCT downstream_gene_variant MODIFIER NCAPH2 29781 Transcript NM_014551.4 protein_coding 4633 1 EntrezGene OK 21.2 2.241712 -6.5&2.84 rs749838192 127 143248 8.86574e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.65755e-04 1.08862e-02 8.38658e-03 1.16717e-02 1.44034e-03 1.85920e-04 1.60505e-04 2.20897e-04 4.64684e-04 0.00000e+00 9.48767e-04 9.06631e-04 0.00000e+00 0.00000e+00 0.00000e+00 222816 0.00080 224577 Primary_dilated_cardiomyopathy EFO:EFO_0000407&Human_Phenotype_Ontology:HP:0001644&Human_Phenotype_Ontology:HP:0001725&Human_Phenotype_Ontology:HP:0005159&Human_Phenotype_Ontology:HP:0200130&MONDO:MONDO:0005021&MedGen:C0007193&Orphanet:ORPHA217604&SNOMED_CT:195021004&SNOMED_CT:399020009 criteria_provided&_single_submitter Likely_pathogenic Insertion SCO2:9997&NCAPH2:29781 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 749838192 15 30 50.0 -22 50524395 C CTGAGTCACTGCTGCATGCT TGAGTCACTGCTGCATGCT 3_prime_UTR_variant MODIFIER NCAPH2 29781 Transcript NM_152299.4 protein_coding 20/20 2952-2953 1 EntrezGene 21.2 2.241712 -6.5&2.84 rs749838192 127 143248 8.86574e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.65755e-04 1.08862e-02 8.38658e-03 1.16717e-02 1.44034e-03 1.85920e-04 1.60505e-04 2.20897e-04 4.64684e-04 0.00000e+00 9.48767e-04 9.06631e-04 0.00000e+00 0.00000e+00 0.00000e+00 222816 0.00080 224577 Primary_dilated_cardiomyopathy EFO:EFO_0000407&Human_Phenotype_Ontology:HP:0001644&Human_Phenotype_Ontology:HP:0001725&Human_Phenotype_Ontology:HP:0005159&Human_Phenotype_Ontology:HP:0200130&MONDO:MONDO:0005021&MedGen:C0007193&Orphanet:ORPHA217604&SNOMED_CT:195021004&SNOMED_CT:399020009 criteria_provided&_single_submitter Likely_pathogenic Insertion SCO2:9997&NCAPH2:29781 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 749838192 15 30 50.0 -22 50524395 C CTGAGTCACTGCTGCATGCT TGAGTCACTGCTGCATGCT downstream_gene_variant MODIFIER NCAPH2 29781 Transcript XM_005261912.4 protein_coding 922 1 EntrezGene 21.2 2.241712 -6.5&2.84 rs749838192 127 143248 8.86574e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.65755e-04 1.08862e-02 8.38658e-03 1.16717e-02 1.44034e-03 1.85920e-04 1.60505e-04 2.20897e-04 4.64684e-04 0.00000e+00 9.48767e-04 9.06631e-04 0.00000e+00 0.00000e+00 0.00000e+00 222816 0.00080 224577 Primary_dilated_cardiomyopathy EFO:EFO_0000407&Human_Phenotype_Ontology:HP:0001644&Human_Phenotype_Ontology:HP:0001725&Human_Phenotype_Ontology:HP:0005159&Human_Phenotype_Ontology:HP:0200130&MONDO:MONDO:0005021&MedGen:C0007193&Orphanet:ORPHA217604&SNOMED_CT:195021004&SNOMED_CT:399020009 criteria_provided&_single_submitter Likely_pathogenic Insertion SCO2:9997&NCAPH2:29781 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 749838192 15 30 50.0 -22 50524395 C CTGAGTCACTGCTGCATGCT TGAGTCACTGCTGCATGCT downstream_gene_variant MODIFIER NCAPH2 29781 Transcript XM_011530685.2 protein_coding 922 1 EntrezGene 21.2 2.241712 -6.5&2.84 rs749838192 127 143248 8.86574e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.65755e-04 1.08862e-02 8.38658e-03 1.16717e-02 1.44034e-03 1.85920e-04 1.60505e-04 2.20897e-04 4.64684e-04 0.00000e+00 9.48767e-04 9.06631e-04 0.00000e+00 0.00000e+00 0.00000e+00 222816 0.00080 224577 Primary_dilated_cardiomyopathy EFO:EFO_0000407&Human_Phenotype_Ontology:HP:0001644&Human_Phenotype_Ontology:HP:0001725&Human_Phenotype_Ontology:HP:0005159&Human_Phenotype_Ontology:HP:0200130&MONDO:MONDO:0005021&MedGen:C0007193&Orphanet:ORPHA217604&SNOMED_CT:195021004&SNOMED_CT:399020009 criteria_provided&_single_submitter Likely_pathogenic Insertion SCO2:9997&NCAPH2:29781 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 749838192 15 30 50.0 -22 50524395 C CTGAGTCACTGCTGCATGCT TGAGTCACTGCTGCATGCT downstream_gene_variant MODIFIER NCAPH2 29781 Transcript XM_017028793.2 protein_coding 921 1 EntrezGene 21.2 2.241712 -6.5&2.84 rs749838192 127 143248 8.86574e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.65755e-04 1.08862e-02 8.38658e-03 1.16717e-02 1.44034e-03 1.85920e-04 1.60505e-04 2.20897e-04 4.64684e-04 0.00000e+00 9.48767e-04 9.06631e-04 0.00000e+00 0.00000e+00 0.00000e+00 222816 0.00080 224577 Primary_dilated_cardiomyopathy EFO:EFO_0000407&Human_Phenotype_Ontology:HP:0001644&Human_Phenotype_Ontology:HP:0001725&Human_Phenotype_Ontology:HP:0005159&Human_Phenotype_Ontology:HP:0200130&MONDO:MONDO:0005021&MedGen:C0007193&Orphanet:ORPHA217604&SNOMED_CT:195021004&SNOMED_CT:399020009 criteria_provided&_single_submitter Likely_pathogenic Insertion SCO2:9997&NCAPH2:29781 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 749838192 15 30 50.0 -22 50524395 C CTGAGTCACTGCTGCATGCT TGAGTCACTGCTGCATGCT non_coding_transcript_exon_variant MODIFIER NCAPH2 29781 Transcript XR_001755232.1 misc_RNA 20/20 3048-3049 1 EntrezGene 21.2 2.241712 -6.5&2.84 rs749838192 127 143248 8.86574e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.65755e-04 1.08862e-02 8.38658e-03 1.16717e-02 1.44034e-03 1.85920e-04 1.60505e-04 2.20897e-04 4.64684e-04 0.00000e+00 9.48767e-04 9.06631e-04 0.00000e+00 0.00000e+00 0.00000e+00 222816 0.00080 224577 Primary_dilated_cardiomyopathy EFO:EFO_0000407&Human_Phenotype_Ontology:HP:0001644&Human_Phenotype_Ontology:HP:0001725&Human_Phenotype_Ontology:HP:0005159&Human_Phenotype_Ontology:HP:0200130&MONDO:MONDO:0005021&MedGen:C0007193&Orphanet:ORPHA217604&SNOMED_CT:195021004&SNOMED_CT:399020009 criteria_provided&_single_submitter Likely_pathogenic Insertion SCO2:9997&NCAPH2:29781 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 749838192 15 30 50.0 -22 50524395 C CTGAGTCACTGCTGCATGCT TGAGTCACTGCTGCATGCT downstream_gene_variant MODIFIER NCAPH2 29781 Transcript XR_001755233.1 misc_RNA 1061 1 EntrezGene 21.2 2.241712 -6.5&2.84 rs749838192 127 143248 8.86574e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.65755e-04 1.08862e-02 8.38658e-03 1.16717e-02 1.44034e-03 1.85920e-04 1.60505e-04 2.20897e-04 4.64684e-04 0.00000e+00 9.48767e-04 9.06631e-04 0.00000e+00 0.00000e+00 0.00000e+00 222816 0.00080 224577 Primary_dilated_cardiomyopathy EFO:EFO_0000407&Human_Phenotype_Ontology:HP:0001644&Human_Phenotype_Ontology:HP:0001725&Human_Phenotype_Ontology:HP:0005159&Human_Phenotype_Ontology:HP:0200130&MONDO:MONDO:0005021&MedGen:C0007193&Orphanet:ORPHA217604&SNOMED_CT:195021004&SNOMED_CT:399020009 criteria_provided&_single_submitter Likely_pathogenic Insertion SCO2:9997&NCAPH2:29781 SO:0001589&frameshift_variant&SO:0001624&3_prime_UTR_variant 1 749838192 15 30 50.0 -22 50526478 TGCGG T - downstream_gene_variant MODIFIER ODF3B 440836 Transcript NM_001014440.4 protein_coding 3944 -1 EntrezGene GCGG GCGG 14.24 1.280372 2.09&0.764&-2.72&0.694 rs201685922 1811 143148 1.26512e-02 2.69099e-03 2.64597e-03 2.74384e-03 6.66667e-03 8.51064e-03 4.65116e-03 1.20076e-02 1.14981e-02 1.23967e-02 1.56627e-02 1.64960e-02 1.47247e-02 6.38570e-04 1.38122e-03 0.00000e+00 1.15882e-02 4.06776e-02 4.36699e-02 3.97384e-02 1.37820e-02 1.55199e-02 1.52042e-02 1.59543e-02 6.98324e-03 5.47445e-03 8.55513e-03 1.26767e-02 1.08126e-02 1.59011e-02 9.65406e-03 215324 211976 Mitochondrial_complex_IV_deficiency&Mitochondrial_DNA_depletion_syndrome_1_(MNGIE_type)&Mitochondrial_neurogastrointestinal_encephalomyopathy¬_specified&Fatal_Infantile_Cardioencephalomyopathy¬_provided MONDO:MONDO:0009068&MedGen:C0268237&OMIM:220110&Orphanet:ORPHA254905&SNOMED_CT:67434000&MONDO:MONDO:0011283&MedGen:C4551995&OMIM:603041&MONDO:MONDO:0017575&MedGen:CN918676&Orphanet:ORPHA298&MedGen:CN169374&MedGen:CN239235&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(6)&Likely_benign(4)&Uncertain_significance(1) Microsatellite TYMP:1890&SCO2:9997 SO:0001627&intron_variant 1 201685922 15 30 50.0 -22 50526478 TGCGG T - splice_region_variant&intron_variant LOW TYMP 1890 Transcript NM_001113755.3 protein_coding 7/9 -1 EntrezGene GCGG GCGG 14.24 1.280372 2.09&0.764&-2.72&0.694 rs201685922 1811 143148 1.26512e-02 2.69099e-03 2.64597e-03 2.74384e-03 6.66667e-03 8.51064e-03 4.65116e-03 1.20076e-02 1.14981e-02 1.23967e-02 1.56627e-02 1.64960e-02 1.47247e-02 6.38570e-04 1.38122e-03 0.00000e+00 1.15882e-02 4.06776e-02 4.36699e-02 3.97384e-02 1.37820e-02 1.55199e-02 1.52042e-02 1.59543e-02 6.98324e-03 5.47445e-03 8.55513e-03 1.26767e-02 1.08126e-02 1.59011e-02 9.65406e-03 215324 211976 Mitochondrial_complex_IV_deficiency&Mitochondrial_DNA_depletion_syndrome_1_(MNGIE_type)&Mitochondrial_neurogastrointestinal_encephalomyopathy¬_specified&Fatal_Infantile_Cardioencephalomyopathy¬_provided MONDO:MONDO:0009068&MedGen:C0268237&OMIM:220110&Orphanet:ORPHA254905&SNOMED_CT:67434000&MONDO:MONDO:0011283&MedGen:C4551995&OMIM:603041&MONDO:MONDO:0017575&MedGen:CN918676&Orphanet:ORPHA298&MedGen:CN169374&MedGen:CN239235&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(6)&Likely_benign(4)&Uncertain_significance(1) Microsatellite TYMP:1890&SCO2:9997 SO:0001627&intron_variant 1 201685922 15 30 50.0 -22 50526478 TGCGG T - splice_region_variant&intron_variant LOW TYMP 1890 Transcript NM_001113756.3 protein_coding 6/8 -1 EntrezGene GCGG GCGG 14.24 1.280372 2.09&0.764&-2.72&0.694 rs201685922 1811 143148 1.26512e-02 2.69099e-03 2.64597e-03 2.74384e-03 6.66667e-03 8.51064e-03 4.65116e-03 1.20076e-02 1.14981e-02 1.23967e-02 1.56627e-02 1.64960e-02 1.47247e-02 6.38570e-04 1.38122e-03 0.00000e+00 1.15882e-02 4.06776e-02 4.36699e-02 3.97384e-02 1.37820e-02 1.55199e-02 1.52042e-02 1.59543e-02 6.98324e-03 5.47445e-03 8.55513e-03 1.26767e-02 1.08126e-02 1.59011e-02 9.65406e-03 215324 211976 Mitochondrial_complex_IV_deficiency&Mitochondrial_DNA_depletion_syndrome_1_(MNGIE_type)&Mitochondrial_neurogastrointestinal_encephalomyopathy¬_specified&Fatal_Infantile_Cardioencephalomyopathy¬_provided MONDO:MONDO:0009068&MedGen:C0268237&OMIM:220110&Orphanet:ORPHA254905&SNOMED_CT:67434000&MONDO:MONDO:0011283&MedGen:C4551995&OMIM:603041&MONDO:MONDO:0017575&MedGen:CN918676&Orphanet:ORPHA298&MedGen:CN169374&MedGen:CN239235&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(6)&Likely_benign(4)&Uncertain_significance(1) Microsatellite TYMP:1890&SCO2:9997 SO:0001627&intron_variant 1 201685922 15 30 50.0 -22 50526478 TGCGG T - upstream_gene_variant MODIFIER SCO2 9997 Transcript NM_001169109.1 protein_coding 40 -1 EntrezGene GCGG GCGG 14.24 1.280372 2.09&0.764&-2.72&0.694 rs201685922 1811 143148 1.26512e-02 2.69099e-03 2.64597e-03 2.74384e-03 6.66667e-03 8.51064e-03 4.65116e-03 1.20076e-02 1.14981e-02 1.23967e-02 1.56627e-02 1.64960e-02 1.47247e-02 6.38570e-04 1.38122e-03 0.00000e+00 1.15882e-02 4.06776e-02 4.36699e-02 3.97384e-02 1.37820e-02 1.55199e-02 1.52042e-02 1.59543e-02 6.98324e-03 5.47445e-03 8.55513e-03 1.26767e-02 1.08126e-02 1.59011e-02 9.65406e-03 215324 211976 Mitochondrial_complex_IV_deficiency&Mitochondrial_DNA_depletion_syndrome_1_(MNGIE_type)&Mitochondrial_neurogastrointestinal_encephalomyopathy¬_specified&Fatal_Infantile_Cardioencephalomyopathy¬_provided MONDO:MONDO:0009068&MedGen:C0268237&OMIM:220110&Orphanet:ORPHA254905&SNOMED_CT:67434000&MONDO:MONDO:0011283&MedGen:C4551995&OMIM:603041&MONDO:MONDO:0017575&MedGen:CN918676&Orphanet:ORPHA298&MedGen:CN169374&MedGen:CN239235&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(6)&Likely_benign(4)&Uncertain_significance(1) Microsatellite TYMP:1890&SCO2:9997 SO:0001627&intron_variant 1 201685922 15 30 50.0 -22 50526478 TGCGG T - upstream_gene_variant MODIFIER SCO2 9997 Transcript NM_001169110.1 protein_coding 334 -1 EntrezGene GCGG GCGG 14.24 1.280372 2.09&0.764&-2.72&0.694 rs201685922 1811 143148 1.26512e-02 2.69099e-03 2.64597e-03 2.74384e-03 6.66667e-03 8.51064e-03 4.65116e-03 1.20076e-02 1.14981e-02 1.23967e-02 1.56627e-02 1.64960e-02 1.47247e-02 6.38570e-04 1.38122e-03 0.00000e+00 1.15882e-02 4.06776e-02 4.36699e-02 3.97384e-02 1.37820e-02 1.55199e-02 1.52042e-02 1.59543e-02 6.98324e-03 5.47445e-03 8.55513e-03 1.26767e-02 1.08126e-02 1.59011e-02 9.65406e-03 215324 211976 Mitochondrial_complex_IV_deficiency&Mitochondrial_DNA_depletion_syndrome_1_(MNGIE_type)&Mitochondrial_neurogastrointestinal_encephalomyopathy¬_specified&Fatal_Infantile_Cardioencephalomyopathy¬_provided MONDO:MONDO:0009068&MedGen:C0268237&OMIM:220110&Orphanet:ORPHA254905&SNOMED_CT:67434000&MONDO:MONDO:0011283&MedGen:C4551995&OMIM:603041&MONDO:MONDO:0017575&MedGen:CN918676&Orphanet:ORPHA298&MedGen:CN169374&MedGen:CN239235&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(6)&Likely_benign(4)&Uncertain_significance(1) Microsatellite TYMP:1890&SCO2:9997 SO:0001627&intron_variant 1 201685922 15 30 50.0 -22 50526478 TGCGG T - upstream_gene_variant MODIFIER SCO2 9997 Transcript NM_001169111.1 protein_coding 875 -1 EntrezGene GCGG GCGG 14.24 1.280372 2.09&0.764&-2.72&0.694 rs201685922 1811 143148 1.26512e-02 2.69099e-03 2.64597e-03 2.74384e-03 6.66667e-03 8.51064e-03 4.65116e-03 1.20076e-02 1.14981e-02 1.23967e-02 1.56627e-02 1.64960e-02 1.47247e-02 6.38570e-04 1.38122e-03 0.00000e+00 1.15882e-02 4.06776e-02 4.36699e-02 3.97384e-02 1.37820e-02 1.55199e-02 1.52042e-02 1.59543e-02 6.98324e-03 5.47445e-03 8.55513e-03 1.26767e-02 1.08126e-02 1.59011e-02 9.65406e-03 215324 211976 Mitochondrial_complex_IV_deficiency&Mitochondrial_DNA_depletion_syndrome_1_(MNGIE_type)&Mitochondrial_neurogastrointestinal_encephalomyopathy¬_specified&Fatal_Infantile_Cardioencephalomyopathy¬_provided MONDO:MONDO:0009068&MedGen:C0268237&OMIM:220110&Orphanet:ORPHA254905&SNOMED_CT:67434000&MONDO:MONDO:0011283&MedGen:C4551995&OMIM:603041&MONDO:MONDO:0017575&MedGen:CN918676&Orphanet:ORPHA298&MedGen:CN169374&MedGen:CN239235&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(6)&Likely_benign(4)&Uncertain_significance(1) Microsatellite TYMP:1890&SCO2:9997 SO:0001627&intron_variant 1 201685922 15 30 50.0 -22 50526478 TGCGG T - downstream_gene_variant MODIFIER NCAPH2 29781 Transcript NM_001185011.2 protein_coding 1699 1 EntrezGene GCGG GCGG 14.24 1.280372 2.09&0.764&-2.72&0.694 rs201685922 1811 143148 1.26512e-02 2.69099e-03 2.64597e-03 2.74384e-03 6.66667e-03 8.51064e-03 4.65116e-03 1.20076e-02 1.14981e-02 1.23967e-02 1.56627e-02 1.64960e-02 1.47247e-02 6.38570e-04 1.38122e-03 0.00000e+00 1.15882e-02 4.06776e-02 4.36699e-02 3.97384e-02 1.37820e-02 1.55199e-02 1.52042e-02 1.59543e-02 6.98324e-03 5.47445e-03 8.55513e-03 1.26767e-02 1.08126e-02 1.59011e-02 9.65406e-03 215324 211976 Mitochondrial_complex_IV_deficiency&Mitochondrial_DNA_depletion_syndrome_1_(MNGIE_type)&Mitochondrial_neurogastrointestinal_encephalomyopathy¬_specified&Fatal_Infantile_Cardioencephalomyopathy¬_provided MONDO:MONDO:0009068&MedGen:C0268237&OMIM:220110&Orphanet:ORPHA254905&SNOMED_CT:67434000&MONDO:MONDO:0011283&MedGen:C4551995&OMIM:603041&MONDO:MONDO:0017575&MedGen:CN918676&Orphanet:ORPHA298&MedGen:CN169374&MedGen:CN239235&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(6)&Likely_benign(4)&Uncertain_significance(1) Microsatellite TYMP:1890&SCO2:9997 SO:0001627&intron_variant 1 201685922 15 30 50.0 -22 50526478 TGCGG T - splice_region_variant&intron_variant LOW TYMP 1890 Transcript NM_001257988.1 protein_coding 7/9 -1 EntrezGene GCGG GCGG OK 14.24 1.280372 2.09&0.764&-2.72&0.694 rs201685922 1811 143148 1.26512e-02 2.69099e-03 2.64597e-03 2.74384e-03 6.66667e-03 8.51064e-03 4.65116e-03 1.20076e-02 1.14981e-02 1.23967e-02 1.56627e-02 1.64960e-02 1.47247e-02 6.38570e-04 1.38122e-03 0.00000e+00 1.15882e-02 4.06776e-02 4.36699e-02 3.97384e-02 1.37820e-02 1.55199e-02 1.52042e-02 1.59543e-02 6.98324e-03 5.47445e-03 8.55513e-03 1.26767e-02 1.08126e-02 1.59011e-02 9.65406e-03 215324 211976 Mitochondrial_complex_IV_deficiency&Mitochondrial_DNA_depletion_syndrome_1_(MNGIE_type)&Mitochondrial_neurogastrointestinal_encephalomyopathy¬_specified&Fatal_Infantile_Cardioencephalomyopathy¬_provided MONDO:MONDO:0009068&MedGen:C0268237&OMIM:220110&Orphanet:ORPHA254905&SNOMED_CT:67434000&MONDO:MONDO:0011283&MedGen:C4551995&OMIM:603041&MONDO:MONDO:0017575&MedGen:CN918676&Orphanet:ORPHA298&MedGen:CN169374&MedGen:CN239235&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(6)&Likely_benign(4)&Uncertain_significance(1) Microsatellite TYMP:1890&SCO2:9997 SO:0001627&intron_variant 1 201685922 15 30 50.0 -22 50526478 TGCGG T - splice_region_variant&intron_variant LOW TYMP 1890 Transcript NM_001257989.1 protein_coding 7/9 -1 EntrezGene GCGG GCGG OK 14.24 1.280372 2.09&0.764&-2.72&0.694 rs201685922 1811 143148 1.26512e-02 2.69099e-03 2.64597e-03 2.74384e-03 6.66667e-03 8.51064e-03 4.65116e-03 1.20076e-02 1.14981e-02 1.23967e-02 1.56627e-02 1.64960e-02 1.47247e-02 6.38570e-04 1.38122e-03 0.00000e+00 1.15882e-02 4.06776e-02 4.36699e-02 3.97384e-02 1.37820e-02 1.55199e-02 1.52042e-02 1.59543e-02 6.98324e-03 5.47445e-03 8.55513e-03 1.26767e-02 1.08126e-02 1.59011e-02 9.65406e-03 215324 211976 Mitochondrial_complex_IV_deficiency&Mitochondrial_DNA_depletion_syndrome_1_(MNGIE_type)&Mitochondrial_neurogastrointestinal_encephalomyopathy¬_specified&Fatal_Infantile_Cardioencephalomyopathy¬_provided MONDO:MONDO:0009068&MedGen:C0268237&OMIM:220110&Orphanet:ORPHA254905&SNOMED_CT:67434000&MONDO:MONDO:0011283&MedGen:C4551995&OMIM:603041&MONDO:MONDO:0017575&MedGen:CN918676&Orphanet:ORPHA298&MedGen:CN169374&MedGen:CN239235&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(6)&Likely_benign(4)&Uncertain_significance(1) Microsatellite TYMP:1890&SCO2:9997 SO:0001627&intron_variant 1 201685922 15 30 50.0 -22 50526478 TGCGG T - downstream_gene_variant MODIFIER ODF3B 440836 Transcript NM_001382807.1 protein_coding 3944 -1 EntrezGene GCGG GCGG 14.24 1.280372 2.09&0.764&-2.72&0.694 rs201685922 1811 143148 1.26512e-02 2.69099e-03 2.64597e-03 2.74384e-03 6.66667e-03 8.51064e-03 4.65116e-03 1.20076e-02 1.14981e-02 1.23967e-02 1.56627e-02 1.64960e-02 1.47247e-02 6.38570e-04 1.38122e-03 0.00000e+00 1.15882e-02 4.06776e-02 4.36699e-02 3.97384e-02 1.37820e-02 1.55199e-02 1.52042e-02 1.59543e-02 6.98324e-03 5.47445e-03 8.55513e-03 1.26767e-02 1.08126e-02 1.59011e-02 9.65406e-03 215324 211976 Mitochondrial_complex_IV_deficiency&Mitochondrial_DNA_depletion_syndrome_1_(MNGIE_type)&Mitochondrial_neurogastrointestinal_encephalomyopathy¬_specified&Fatal_Infantile_Cardioencephalomyopathy¬_provided MONDO:MONDO:0009068&MedGen:C0268237&OMIM:220110&Orphanet:ORPHA254905&SNOMED_CT:67434000&MONDO:MONDO:0011283&MedGen:C4551995&OMIM:603041&MONDO:MONDO:0017575&MedGen:CN918676&Orphanet:ORPHA298&MedGen:CN169374&MedGen:CN239235&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(6)&Likely_benign(4)&Uncertain_significance(1) Microsatellite TYMP:1890&SCO2:9997 SO:0001627&intron_variant 1 201685922 15 30 50.0 -22 50526478 TGCGG T - downstream_gene_variant MODIFIER ODF3B 440836 Transcript NM_001382808.1 protein_coding 3944 -1 EntrezGene GCGG GCGG 14.24 1.280372 2.09&0.764&-2.72&0.694 rs201685922 1811 143148 1.26512e-02 2.69099e-03 2.64597e-03 2.74384e-03 6.66667e-03 8.51064e-03 4.65116e-03 1.20076e-02 1.14981e-02 1.23967e-02 1.56627e-02 1.64960e-02 1.47247e-02 6.38570e-04 1.38122e-03 0.00000e+00 1.15882e-02 4.06776e-02 4.36699e-02 3.97384e-02 1.37820e-02 1.55199e-02 1.52042e-02 1.59543e-02 6.98324e-03 5.47445e-03 8.55513e-03 1.26767e-02 1.08126e-02 1.59011e-02 9.65406e-03 215324 211976 Mitochondrial_complex_IV_deficiency&Mitochondrial_DNA_depletion_syndrome_1_(MNGIE_type)&Mitochondrial_neurogastrointestinal_encephalomyopathy¬_specified&Fatal_Infantile_Cardioencephalomyopathy¬_provided MONDO:MONDO:0009068&MedGen:C0268237&OMIM:220110&Orphanet:ORPHA254905&SNOMED_CT:67434000&MONDO:MONDO:0011283&MedGen:C4551995&OMIM:603041&MONDO:MONDO:0017575&MedGen:CN918676&Orphanet:ORPHA298&MedGen:CN169374&MedGen:CN239235&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(6)&Likely_benign(4)&Uncertain_significance(1) Microsatellite TYMP:1890&SCO2:9997 SO:0001627&intron_variant 1 201685922 15 30 50.0 -22 50526478 TGCGG T - splice_region_variant&intron_variant LOW TYMP 1890 Transcript NM_001953.5 protein_coding 7/9 -1 EntrezGene GCGG GCGG 14.24 1.280372 2.09&0.764&-2.72&0.694 rs201685922 1811 143148 1.26512e-02 2.69099e-03 2.64597e-03 2.74384e-03 6.66667e-03 8.51064e-03 4.65116e-03 1.20076e-02 1.14981e-02 1.23967e-02 1.56627e-02 1.64960e-02 1.47247e-02 6.38570e-04 1.38122e-03 0.00000e+00 1.15882e-02 4.06776e-02 4.36699e-02 3.97384e-02 1.37820e-02 1.55199e-02 1.52042e-02 1.59543e-02 6.98324e-03 5.47445e-03 8.55513e-03 1.26767e-02 1.08126e-02 1.59011e-02 9.65406e-03 215324 211976 Mitochondrial_complex_IV_deficiency&Mitochondrial_DNA_depletion_syndrome_1_(MNGIE_type)&Mitochondrial_neurogastrointestinal_encephalomyopathy¬_specified&Fatal_Infantile_Cardioencephalomyopathy¬_provided MONDO:MONDO:0009068&MedGen:C0268237&OMIM:220110&Orphanet:ORPHA254905&SNOMED_CT:67434000&MONDO:MONDO:0011283&MedGen:C4551995&OMIM:603041&MONDO:MONDO:0017575&MedGen:CN918676&Orphanet:ORPHA298&MedGen:CN169374&MedGen:CN239235&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(6)&Likely_benign(4)&Uncertain_significance(1) Microsatellite TYMP:1890&SCO2:9997 SO:0001627&intron_variant 1 201685922 15 30 50.0 -22 50526478 TGCGG T - upstream_gene_variant MODIFIER SCO2 9997 Transcript NM_005138.3 protein_coding 881 -1 EntrezGene GCGG GCGG 14.24 1.280372 2.09&0.764&-2.72&0.694 rs201685922 1811 143148 1.26512e-02 2.69099e-03 2.64597e-03 2.74384e-03 6.66667e-03 8.51064e-03 4.65116e-03 1.20076e-02 1.14981e-02 1.23967e-02 1.56627e-02 1.64960e-02 1.47247e-02 6.38570e-04 1.38122e-03 0.00000e+00 1.15882e-02 4.06776e-02 4.36699e-02 3.97384e-02 1.37820e-02 1.55199e-02 1.52042e-02 1.59543e-02 6.98324e-03 5.47445e-03 8.55513e-03 1.26767e-02 1.08126e-02 1.59011e-02 9.65406e-03 215324 211976 Mitochondrial_complex_IV_deficiency&Mitochondrial_DNA_depletion_syndrome_1_(MNGIE_type)&Mitochondrial_neurogastrointestinal_encephalomyopathy¬_specified&Fatal_Infantile_Cardioencephalomyopathy¬_provided MONDO:MONDO:0009068&MedGen:C0268237&OMIM:220110&Orphanet:ORPHA254905&SNOMED_CT:67434000&MONDO:MONDO:0011283&MedGen:C4551995&OMIM:603041&MONDO:MONDO:0017575&MedGen:CN918676&Orphanet:ORPHA298&MedGen:CN169374&MedGen:CN239235&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(6)&Likely_benign(4)&Uncertain_significance(1) Microsatellite TYMP:1890&SCO2:9997 SO:0001627&intron_variant 1 201685922 15 30 50.0 -22 50526478 TGCGG T - downstream_gene_variant MODIFIER NCAPH2 29781 Transcript NM_152299.4 protein_coding 1699 1 EntrezGene GCGG GCGG 14.24 1.280372 2.09&0.764&-2.72&0.694 rs201685922 1811 143148 1.26512e-02 2.69099e-03 2.64597e-03 2.74384e-03 6.66667e-03 8.51064e-03 4.65116e-03 1.20076e-02 1.14981e-02 1.23967e-02 1.56627e-02 1.64960e-02 1.47247e-02 6.38570e-04 1.38122e-03 0.00000e+00 1.15882e-02 4.06776e-02 4.36699e-02 3.97384e-02 1.37820e-02 1.55199e-02 1.52042e-02 1.59543e-02 6.98324e-03 5.47445e-03 8.55513e-03 1.26767e-02 1.08126e-02 1.59011e-02 9.65406e-03 215324 211976 Mitochondrial_complex_IV_deficiency&Mitochondrial_DNA_depletion_syndrome_1_(MNGIE_type)&Mitochondrial_neurogastrointestinal_encephalomyopathy¬_specified&Fatal_Infantile_Cardioencephalomyopathy¬_provided MONDO:MONDO:0009068&MedGen:C0268237&OMIM:220110&Orphanet:ORPHA254905&SNOMED_CT:67434000&MONDO:MONDO:0011283&MedGen:C4551995&OMIM:603041&MONDO:MONDO:0017575&MedGen:CN918676&Orphanet:ORPHA298&MedGen:CN169374&MedGen:CN239235&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(6)&Likely_benign(4)&Uncertain_significance(1) Microsatellite TYMP:1890&SCO2:9997 SO:0001627&intron_variant 1 201685922 15 30 50.0 -22 50526478 TGCGG T - downstream_gene_variant MODIFIER NCAPH2 29781 Transcript XM_005261912.4 protein_coding 3006 1 EntrezGene GCGG GCGG 14.24 1.280372 2.09&0.764&-2.72&0.694 rs201685922 1811 143148 1.26512e-02 2.69099e-03 2.64597e-03 2.74384e-03 6.66667e-03 8.51064e-03 4.65116e-03 1.20076e-02 1.14981e-02 1.23967e-02 1.56627e-02 1.64960e-02 1.47247e-02 6.38570e-04 1.38122e-03 0.00000e+00 1.15882e-02 4.06776e-02 4.36699e-02 3.97384e-02 1.37820e-02 1.55199e-02 1.52042e-02 1.59543e-02 6.98324e-03 5.47445e-03 8.55513e-03 1.26767e-02 1.08126e-02 1.59011e-02 9.65406e-03 215324 211976 Mitochondrial_complex_IV_deficiency&Mitochondrial_DNA_depletion_syndrome_1_(MNGIE_type)&Mitochondrial_neurogastrointestinal_encephalomyopathy¬_specified&Fatal_Infantile_Cardioencephalomyopathy¬_provided MONDO:MONDO:0009068&MedGen:C0268237&OMIM:220110&Orphanet:ORPHA254905&SNOMED_CT:67434000&MONDO:MONDO:0011283&MedGen:C4551995&OMIM:603041&MONDO:MONDO:0017575&MedGen:CN918676&Orphanet:ORPHA298&MedGen:CN169374&MedGen:CN239235&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(6)&Likely_benign(4)&Uncertain_significance(1) Microsatellite TYMP:1890&SCO2:9997 SO:0001627&intron_variant 1 201685922 15 30 50.0 -22 50526478 TGCGG T - downstream_gene_variant MODIFIER NCAPH2 29781 Transcript XM_011530685.2 protein_coding 3006 1 EntrezGene GCGG GCGG 14.24 1.280372 2.09&0.764&-2.72&0.694 rs201685922 1811 143148 1.26512e-02 2.69099e-03 2.64597e-03 2.74384e-03 6.66667e-03 8.51064e-03 4.65116e-03 1.20076e-02 1.14981e-02 1.23967e-02 1.56627e-02 1.64960e-02 1.47247e-02 6.38570e-04 1.38122e-03 0.00000e+00 1.15882e-02 4.06776e-02 4.36699e-02 3.97384e-02 1.37820e-02 1.55199e-02 1.52042e-02 1.59543e-02 6.98324e-03 5.47445e-03 8.55513e-03 1.26767e-02 1.08126e-02 1.59011e-02 9.65406e-03 215324 211976 Mitochondrial_complex_IV_deficiency&Mitochondrial_DNA_depletion_syndrome_1_(MNGIE_type)&Mitochondrial_neurogastrointestinal_encephalomyopathy¬_specified&Fatal_Infantile_Cardioencephalomyopathy¬_provided MONDO:MONDO:0009068&MedGen:C0268237&OMIM:220110&Orphanet:ORPHA254905&SNOMED_CT:67434000&MONDO:MONDO:0011283&MedGen:C4551995&OMIM:603041&MONDO:MONDO:0017575&MedGen:CN918676&Orphanet:ORPHA298&MedGen:CN169374&MedGen:CN239235&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(6)&Likely_benign(4)&Uncertain_significance(1) Microsatellite TYMP:1890&SCO2:9997 SO:0001627&intron_variant 1 201685922 15 30 50.0 -22 50526478 TGCGG T - downstream_gene_variant MODIFIER NCAPH2 29781 Transcript XM_017028793.2 protein_coding 3005 1 EntrezGene GCGG GCGG 14.24 1.280372 2.09&0.764&-2.72&0.694 rs201685922 1811 143148 1.26512e-02 2.69099e-03 2.64597e-03 2.74384e-03 6.66667e-03 8.51064e-03 4.65116e-03 1.20076e-02 1.14981e-02 1.23967e-02 1.56627e-02 1.64960e-02 1.47247e-02 6.38570e-04 1.38122e-03 0.00000e+00 1.15882e-02 4.06776e-02 4.36699e-02 3.97384e-02 1.37820e-02 1.55199e-02 1.52042e-02 1.59543e-02 6.98324e-03 5.47445e-03 8.55513e-03 1.26767e-02 1.08126e-02 1.59011e-02 9.65406e-03 215324 211976 Mitochondrial_complex_IV_deficiency&Mitochondrial_DNA_depletion_syndrome_1_(MNGIE_type)&Mitochondrial_neurogastrointestinal_encephalomyopathy¬_specified&Fatal_Infantile_Cardioencephalomyopathy¬_provided MONDO:MONDO:0009068&MedGen:C0268237&OMIM:220110&Orphanet:ORPHA254905&SNOMED_CT:67434000&MONDO:MONDO:0011283&MedGen:C4551995&OMIM:603041&MONDO:MONDO:0017575&MedGen:CN918676&Orphanet:ORPHA298&MedGen:CN169374&MedGen:CN239235&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(6)&Likely_benign(4)&Uncertain_significance(1) Microsatellite TYMP:1890&SCO2:9997 SO:0001627&intron_variant 1 201685922 15 30 50.0 -22 50526478 TGCGG T - downstream_gene_variant MODIFIER NCAPH2 29781 Transcript XR_001755232.1 misc_RNA 1701 1 EntrezGene GCGG GCGG 14.24 1.280372 2.09&0.764&-2.72&0.694 rs201685922 1811 143148 1.26512e-02 2.69099e-03 2.64597e-03 2.74384e-03 6.66667e-03 8.51064e-03 4.65116e-03 1.20076e-02 1.14981e-02 1.23967e-02 1.56627e-02 1.64960e-02 1.47247e-02 6.38570e-04 1.38122e-03 0.00000e+00 1.15882e-02 4.06776e-02 4.36699e-02 3.97384e-02 1.37820e-02 1.55199e-02 1.52042e-02 1.59543e-02 6.98324e-03 5.47445e-03 8.55513e-03 1.26767e-02 1.08126e-02 1.59011e-02 9.65406e-03 215324 211976 Mitochondrial_complex_IV_deficiency&Mitochondrial_DNA_depletion_syndrome_1_(MNGIE_type)&Mitochondrial_neurogastrointestinal_encephalomyopathy¬_specified&Fatal_Infantile_Cardioencephalomyopathy¬_provided MONDO:MONDO:0009068&MedGen:C0268237&OMIM:220110&Orphanet:ORPHA254905&SNOMED_CT:67434000&MONDO:MONDO:0011283&MedGen:C4551995&OMIM:603041&MONDO:MONDO:0017575&MedGen:CN918676&Orphanet:ORPHA298&MedGen:CN169374&MedGen:CN239235&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(6)&Likely_benign(4)&Uncertain_significance(1) Microsatellite TYMP:1890&SCO2:9997 SO:0001627&intron_variant 1 201685922 15 30 50.0 -22 50526478 TGCGG T - downstream_gene_variant MODIFIER NCAPH2 29781 Transcript XR_001755233.1 misc_RNA 3145 1 EntrezGene GCGG GCGG 14.24 1.280372 2.09&0.764&-2.72&0.694 rs201685922 1811 143148 1.26512e-02 2.69099e-03 2.64597e-03 2.74384e-03 6.66667e-03 8.51064e-03 4.65116e-03 1.20076e-02 1.14981e-02 1.23967e-02 1.56627e-02 1.64960e-02 1.47247e-02 6.38570e-04 1.38122e-03 0.00000e+00 1.15882e-02 4.06776e-02 4.36699e-02 3.97384e-02 1.37820e-02 1.55199e-02 1.52042e-02 1.59543e-02 6.98324e-03 5.47445e-03 8.55513e-03 1.26767e-02 1.08126e-02 1.59011e-02 9.65406e-03 215324 211976 Mitochondrial_complex_IV_deficiency&Mitochondrial_DNA_depletion_syndrome_1_(MNGIE_type)&Mitochondrial_neurogastrointestinal_encephalomyopathy¬_specified&Fatal_Infantile_Cardioencephalomyopathy¬_provided MONDO:MONDO:0009068&MedGen:C0268237&OMIM:220110&Orphanet:ORPHA254905&SNOMED_CT:67434000&MONDO:MONDO:0011283&MedGen:C4551995&OMIM:603041&MONDO:MONDO:0017575&MedGen:CN918676&Orphanet:ORPHA298&MedGen:CN169374&MedGen:CN239235&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(6)&Likely_benign(4)&Uncertain_significance(1) Microsatellite TYMP:1890&SCO2:9997 SO:0001627&intron_variant 1 201685922 15 30 50.0 -22 50721897 CTG C - frameshift_variant HIGH SHANK3 85358 Transcript NM_001372044.2 protein_coding 24/25 4621-4622 4251-4252 1417-1418 SG/SX tcTGgg/tcgg 1 EntrezGene TG TG -11.2&6.14 978857 967017 Intellectual_disability&22q13.3_deletion_syndrome Human_Phenotype_Ontology:HP:0000730&Human_Phenotype_Ontology:HP:0001249&Human_Phenotype_Ontology:HP:0001267&Human_Phenotype_Ontology:HP:0001286&Human_Phenotype_Ontology:HP:0002122&Human_Phenotype_Ontology:HP:0002192&Human_Phenotype_Ontology:HP:0002316&Human_Phenotype_Ontology:HP:0002382&Human_Phenotype_Ontology:HP:0002386&Human_Phenotype_Ontology:HP:0002402&Human_Phenotype_Ontology:HP:0002458&Human_Phenotype_Ontology:HP:0002482&Human_Phenotype_Ontology:HP:0002499&Human_Phenotype_Ontology:HP:0002543&Human_Phenotype_Ontology:HP:0003767&Human_Phenotype_Ontology:HP:0006833&Human_Phenotype_Ontology:HP:0007154&Human_Phenotype_Ontology:HP:0007176&Human_Phenotype_Ontology:HP:0007180&Human_Phenotype_Ontology:HP:HP:0001249&MONDO:MONDO:0001071&MedGen:C3714756&SNOMED_CT:228156007&MONDO:MONDO:0011652&MedGen:C1853490&OMIM:606232&Orphanet:ORPHA48652 criteria_provided&_multiple_submitters&_no_conflicts Pathogenic Deletion SHANK3:85358 SO:0001589&frameshift_variant 32 15 30 50.0 -X 1553205 G A A intergenic_variant MODIFIER 3.138 0.201250 0.0377 chrX:1553205-1553205 104190 142842 7.29407e-01 7.33841e-01 7.34108e-01 7.33527e-01 7.03786e-01 6.90171e-01 7.18605e-01 7.53567e-01 7.53394e-01 7.53698e-01 7.80892e-01 7.65643e-01 7.98077e-01 8.11418e-01 8.25243e-01 7.99523e-01 7.29738e-01 7.53605e-01 7.45607e-01 7.56141e-01 7.29055e-01 7.16329e-01 7.17920e-01 7.14138e-01 7.48835e-01 7.60512e-01 7.36692e-01 7.29088e-01 6.07356e-01 6.35714e-01 6.00895e-01 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - 3_prime_UTR_variant MODIFIER DMD 1756 Transcript NM_000109.4 protein_coding 79/79 11180-11192 -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - 3_prime_UTR_variant MODIFIER DMD 1756 Transcript NM_004006.3 protein_coding 79/79 11318-11330 -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - 3_prime_UTR_variant MODIFIER DMD 1756 Transcript NM_004009.3 protein_coding 79/79 11332-11344 -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA OK 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - 3_prime_UTR_variant MODIFIER DMD 1756 Transcript NM_004010.3 protein_coding 79/79 11415-11427 -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA OK 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - 3_prime_UTR_variant MODIFIER DMD 1756 Transcript NM_004011.4 protein_coding 51/51 7077-7089 -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - 3_prime_UTR_variant MODIFIER DMD 1756 Transcript NM_004012.4 protein_coding 51/51 7220-7232 -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - 3_prime_UTR_variant MODIFIER DMD 1756 Transcript NM_004013.2 protein_coding 36/36 4742-4754 -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA OK 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - 3_prime_UTR_variant MODIFIER DMD 1756 Transcript NM_004014.2 protein_coding 25/25 2955-2967 -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA OK 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - 3_prime_UTR_variant MODIFIER DMD 1756 Transcript NM_004015.3 protein_coding 18/18 2002-2014 -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - frameshift_variant HIGH DMD 1756 Transcript NM_004016.3 protein_coding 17/17 1970-1982 1845-1857 615-619 DDLGR/X gaTGATTTGGGCAGA/ga -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - 3_prime_UTR_variant MODIFIER DMD 1756 Transcript NM_004017.3 protein_coding 17/17 1963-1975 -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - frameshift_variant HIGH DMD 1756 Transcript NM_004018.3 protein_coding 16/16 1931-1943 1806-1818 602-606 DDLGR/X gaTGATTTGGGCAGA/ga -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - 3_prime_UTR_variant MODIFIER DMD 1756 Transcript NM_004020.3 protein_coding 32/32 4412-4424 -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA OK 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - frameshift_variant HIGH DMD 1756 Transcript NM_004021.3 protein_coding 35/35 4710-4722 3669-3681 1223-1227 DDLGR/X gaTGATTTGGGCAGA/ga -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - frameshift_variant HIGH DMD 1756 Transcript NM_004022.2 protein_coding 34/34 4671-4683 3630-3642 1210-1214 DDLGR/X gaTGATTTGGGCAGA/ga -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA OK 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - frameshift_variant HIGH DMD 1756 Transcript NM_004023.3 protein_coding 31/31 4380-4392 3339-3351 1113-1117 DDLGR/X gaTGATTTGGGCAGA/ga -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - frameshift_variant HIGH DMD 1756 Transcript XM_006724468.2 protein_coding 78/78 11246-11258 11049-11061 3683-3687 DDLGR/X gaTGATTTGGGCAGA/ga -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - frameshift_variant HIGH DMD 1756 Transcript XM_006724469.3 protein_coding 78/78 11220-11232 11025-11037 3675-3679 DDLGR/X gaTGATTTGGGCAGA/ga -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - frameshift_variant HIGH DMD 1756 Transcript XM_006724470.3 protein_coding 77/77 11207-11219 11010-11022 3670-3674 DDLGR/X gaTGATTTGGGCAGA/ga -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - frameshift_variant HIGH DMD 1756 Transcript XM_006724473.2 protein_coding 77/77 11108-11120 10911-10923 3637-3641 DDLGR/X gaTGATTTGGGCAGA/ga -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - frameshift_variant HIGH DMD 1756 Transcript XM_006724474.3 protein_coding 74/74 10916-10928 10719-10731 3573-3577 DDLGR/X gaTGATTTGGGCAGA/ga -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - 3_prime_UTR_variant MODIFIER DMD 1756 Transcript XM_006724475.2 protein_coding 75/75 10948-10960 -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - frameshift_variant HIGH DMD 1756 Transcript XM_011545467.1 protein_coding 77/77 11123-11135 10926-10938 3642-3646 DDLGR/X gaTGATTTGGGCAGA/ga -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - 3_prime_UTR_variant MODIFIER DMD 1756 Transcript XM_017029328.1 protein_coding 78/78 11239-11251 -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 31121883 CTCTGCCCAAATCA C - frameshift_variant HIGH DMD 1756 Transcript XM_017029331.1 protein_coding 38/38 5822-5834 5223-5235 1741-1745 DDLGR/X gaTGATTTGGGCAGA/ga -1 EntrezGene TCTGCCCAAATCA TCTGCCCAAATCA 13.07 1.132141 5.22&4.08&3.46&2.57&2.53&5.22&4.07&5.22 rs752332058 153 106031 1.44297e-03 0.00000e+00 0.00000e+00 0.00000e+00 1.69184e-01 1.70213e-01 1.66667e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.54455e-03 4.96114e-04 1.19904e-03 0.00000e+00 1.21032e-03 6.92699e-04 7.49024e-04 5.32522e-04 1.91083e-03 2.73224e-03 0.00000e+00 1.43808e-03 0.00000e+00 0.00000e+00 0.00000e+00 201754 198562 Dilated_cardiomyopathy_1B¬_specified¬_provided MedGen:C0340427&OMIM:600884&OMIM:PS115200&Orphanet:ORPHA217607&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Benign(1)&Likely_benign(1)&Uncertain_significance(3) Deletion DMD:1756 1 752332058 15 30 50.0 -X 38286537 TCCTCTACTTCCCCTC T - intron_variant MODIFIER RPGR 6103 Transcript NM_000328.3 protein_coding 15/18 -1 EntrezGene CCTCTACTTCCCCTC CCTCTACTTCCCCTC 3.977 0.273251 1.56&-5.33&1.83&-0.273&-5.44&0.878&-5.44&-1.37&-1.7&-6.36&-1.33&2.37&-7.36&-3.82 rs777850798 56 30650 1.82708e-03 1.81865e-03 1.89753e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39535e-03 1.49701e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.94134e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.12427e-03 2.24859e-03 0.00000e+00 4.44444e-03 4.67290e-03 0.00000e+00 2.42631e-03 6.57895e-03 6.94444e-03 0.00000e+00 414015 404573 Primary_ciliary_dyskinesia Human_Phenotype_Ontology:HP:0012265&MONDO:MONDO:0016575&MedGen:C0008780&OMIM:PS244400&Orphanet:ORPHA244 criteria_provided&_single_submitter Likely_benign Microsatellite RPGR:6103 SO:0001822&inframe_deletion&SO:0001627&intron_variant 1 777850798 15 30 50.0 -X 38286537 TCCTCTACTTCCCCTC T - inframe_deletion MODERATE RPGR 6103 Transcript NM_001034853.2 protein_coding 15/15 2589-2603 2447-2461 816-821 GGEVEE/E gGAGGGGAAGTAGAGGag/gag -1 EntrezGene CCTCTACTTCCCCTC CCTCTACTTCCCCTC 3.977 0.273251 1.56&-5.33&1.83&-0.273&-5.44&0.878&-5.44&-1.37&-1.7&-6.36&-1.33&2.37&-7.36&-3.82 rs777850798 56 30650 1.82708e-03 1.81865e-03 1.89753e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39535e-03 1.49701e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.94134e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.12427e-03 2.24859e-03 0.00000e+00 4.44444e-03 4.67290e-03 0.00000e+00 2.42631e-03 6.57895e-03 6.94444e-03 0.00000e+00 414015 404573 Primary_ciliary_dyskinesia Human_Phenotype_Ontology:HP:0012265&MONDO:MONDO:0016575&MedGen:C0008780&OMIM:PS244400&Orphanet:ORPHA244 criteria_provided&_single_submitter Likely_benign Microsatellite RPGR:6103 SO:0001822&inframe_deletion&SO:0001627&intron_variant 1 777850798 15 30 50.0 -X 38286537 TCCTCTACTTCCCCTC T - intron_variant MODIFIER RPGR 6103 Transcript NM_001367245.1 protein_coding 15/18 -1 EntrezGene CCTCTACTTCCCCTC CCTCTACTTCCCCTC 3.977 0.273251 1.56&-5.33&1.83&-0.273&-5.44&0.878&-5.44&-1.37&-1.7&-6.36&-1.33&2.37&-7.36&-3.82 rs777850798 56 30650 1.82708e-03 1.81865e-03 1.89753e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39535e-03 1.49701e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.94134e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.12427e-03 2.24859e-03 0.00000e+00 4.44444e-03 4.67290e-03 0.00000e+00 2.42631e-03 6.57895e-03 6.94444e-03 0.00000e+00 414015 404573 Primary_ciliary_dyskinesia Human_Phenotype_Ontology:HP:0012265&MONDO:MONDO:0016575&MedGen:C0008780&OMIM:PS244400&Orphanet:ORPHA244 criteria_provided&_single_submitter Likely_benign Microsatellite RPGR:6103 SO:0001822&inframe_deletion&SO:0001627&intron_variant 1 777850798 15 30 50.0 -X 38286537 TCCTCTACTTCCCCTC T - intron_variant MODIFIER RPGR 6103 Transcript NM_001367246.1 protein_coding 14/17 -1 EntrezGene CCTCTACTTCCCCTC CCTCTACTTCCCCTC 3.977 0.273251 1.56&-5.33&1.83&-0.273&-5.44&0.878&-5.44&-1.37&-1.7&-6.36&-1.33&2.37&-7.36&-3.82 rs777850798 56 30650 1.82708e-03 1.81865e-03 1.89753e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39535e-03 1.49701e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.94134e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.12427e-03 2.24859e-03 0.00000e+00 4.44444e-03 4.67290e-03 0.00000e+00 2.42631e-03 6.57895e-03 6.94444e-03 0.00000e+00 414015 404573 Primary_ciliary_dyskinesia Human_Phenotype_Ontology:HP:0012265&MONDO:MONDO:0016575&MedGen:C0008780&OMIM:PS244400&Orphanet:ORPHA244 criteria_provided&_single_submitter Likely_benign Microsatellite RPGR:6103 SO:0001822&inframe_deletion&SO:0001627&intron_variant 1 777850798 15 30 50.0 -X 38286537 TCCTCTACTTCCCCTC T - intron_variant MODIFIER RPGR 6103 Transcript NM_001367247.1 protein_coding 13/16 -1 EntrezGene CCTCTACTTCCCCTC CCTCTACTTCCCCTC 3.977 0.273251 1.56&-5.33&1.83&-0.273&-5.44&0.878&-5.44&-1.37&-1.7&-6.36&-1.33&2.37&-7.36&-3.82 rs777850798 56 30650 1.82708e-03 1.81865e-03 1.89753e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39535e-03 1.49701e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.94134e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.12427e-03 2.24859e-03 0.00000e+00 4.44444e-03 4.67290e-03 0.00000e+00 2.42631e-03 6.57895e-03 6.94444e-03 0.00000e+00 414015 404573 Primary_ciliary_dyskinesia Human_Phenotype_Ontology:HP:0012265&MONDO:MONDO:0016575&MedGen:C0008780&OMIM:PS244400&Orphanet:ORPHA244 criteria_provided&_single_submitter Likely_benign Microsatellite RPGR:6103 SO:0001822&inframe_deletion&SO:0001627&intron_variant 1 777850798 15 30 50.0 -X 38286537 TCCTCTACTTCCCCTC T - intron_variant MODIFIER RPGR 6103 Transcript NM_001367248.1 protein_coding 13/16 -1 EntrezGene CCTCTACTTCCCCTC CCTCTACTTCCCCTC 3.977 0.273251 1.56&-5.33&1.83&-0.273&-5.44&0.878&-5.44&-1.37&-1.7&-6.36&-1.33&2.37&-7.36&-3.82 rs777850798 56 30650 1.82708e-03 1.81865e-03 1.89753e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39535e-03 1.49701e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.94134e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.12427e-03 2.24859e-03 0.00000e+00 4.44444e-03 4.67290e-03 0.00000e+00 2.42631e-03 6.57895e-03 6.94444e-03 0.00000e+00 414015 404573 Primary_ciliary_dyskinesia Human_Phenotype_Ontology:HP:0012265&MONDO:MONDO:0016575&MedGen:C0008780&OMIM:PS244400&Orphanet:ORPHA244 criteria_provided&_single_submitter Likely_benign Microsatellite RPGR:6103 SO:0001822&inframe_deletion&SO:0001627&intron_variant 1 777850798 15 30 50.0 -X 38286537 TCCTCTACTTCCCCTC T - intron_variant MODIFIER RPGR 6103 Transcript NM_001367249.1 protein_coding 13/16 -1 EntrezGene CCTCTACTTCCCCTC CCTCTACTTCCCCTC 3.977 0.273251 1.56&-5.33&1.83&-0.273&-5.44&0.878&-5.44&-1.37&-1.7&-6.36&-1.33&2.37&-7.36&-3.82 rs777850798 56 30650 1.82708e-03 1.81865e-03 1.89753e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39535e-03 1.49701e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.94134e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.12427e-03 2.24859e-03 0.00000e+00 4.44444e-03 4.67290e-03 0.00000e+00 2.42631e-03 6.57895e-03 6.94444e-03 0.00000e+00 414015 404573 Primary_ciliary_dyskinesia Human_Phenotype_Ontology:HP:0012265&MONDO:MONDO:0016575&MedGen:C0008780&OMIM:PS244400&Orphanet:ORPHA244 criteria_provided&_single_submitter Likely_benign Microsatellite RPGR:6103 SO:0001822&inframe_deletion&SO:0001627&intron_variant 1 777850798 15 30 50.0 -X 38286537 TCCTCTACTTCCCCTC T - intron_variant MODIFIER RPGR 6103 Transcript NM_001367250.1 protein_coding 13/16 -1 EntrezGene CCTCTACTTCCCCTC CCTCTACTTCCCCTC 3.977 0.273251 1.56&-5.33&1.83&-0.273&-5.44&0.878&-5.44&-1.37&-1.7&-6.36&-1.33&2.37&-7.36&-3.82 rs777850798 56 30650 1.82708e-03 1.81865e-03 1.89753e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39535e-03 1.49701e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.94134e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.12427e-03 2.24859e-03 0.00000e+00 4.44444e-03 4.67290e-03 0.00000e+00 2.42631e-03 6.57895e-03 6.94444e-03 0.00000e+00 414015 404573 Primary_ciliary_dyskinesia Human_Phenotype_Ontology:HP:0012265&MONDO:MONDO:0016575&MedGen:C0008780&OMIM:PS244400&Orphanet:ORPHA244 criteria_provided&_single_submitter Likely_benign Microsatellite RPGR:6103 SO:0001822&inframe_deletion&SO:0001627&intron_variant 1 777850798 15 30 50.0 -X 38286537 TCCTCTACTTCCCCTC T - intron_variant MODIFIER RPGR 6103 Transcript NM_001367251.1 protein_coding 12/15 -1 EntrezGene CCTCTACTTCCCCTC CCTCTACTTCCCCTC 3.977 0.273251 1.56&-5.33&1.83&-0.273&-5.44&0.878&-5.44&-1.37&-1.7&-6.36&-1.33&2.37&-7.36&-3.82 rs777850798 56 30650 1.82708e-03 1.81865e-03 1.89753e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39535e-03 1.49701e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.94134e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.12427e-03 2.24859e-03 0.00000e+00 4.44444e-03 4.67290e-03 0.00000e+00 2.42631e-03 6.57895e-03 6.94444e-03 0.00000e+00 414015 404573 Primary_ciliary_dyskinesia Human_Phenotype_Ontology:HP:0012265&MONDO:MONDO:0016575&MedGen:C0008780&OMIM:PS244400&Orphanet:ORPHA244 criteria_provided&_single_submitter Likely_benign Microsatellite RPGR:6103 SO:0001822&inframe_deletion&SO:0001627&intron_variant 1 777850798 15 30 50.0 -X 38286537 TCCTCTACTTCCCCTC T - intron_variant&non_coding_transcript_variant MODIFIER RPGR 6103 Transcript NR_159803.1 misc_RNA 17/20 -1 EntrezGene CCTCTACTTCCCCTC CCTCTACTTCCCCTC 3.977 0.273251 1.56&-5.33&1.83&-0.273&-5.44&0.878&-5.44&-1.37&-1.7&-6.36&-1.33&2.37&-7.36&-3.82 rs777850798 56 30650 1.82708e-03 1.81865e-03 1.89753e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39535e-03 1.49701e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.94134e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.12427e-03 2.24859e-03 0.00000e+00 4.44444e-03 4.67290e-03 0.00000e+00 2.42631e-03 6.57895e-03 6.94444e-03 0.00000e+00 414015 404573 Primary_ciliary_dyskinesia Human_Phenotype_Ontology:HP:0012265&MONDO:MONDO:0016575&MedGen:C0008780&OMIM:PS244400&Orphanet:ORPHA244 criteria_provided&_single_submitter Likely_benign Microsatellite RPGR:6103 SO:0001822&inframe_deletion&SO:0001627&intron_variant 1 777850798 15 30 50.0 -X 38286537 TCCTCTACTTCCCCTC T - intron_variant&non_coding_transcript_variant MODIFIER RPGR 6103 Transcript NR_159804.1 misc_RNA 13/16 -1 EntrezGene CCTCTACTTCCCCTC CCTCTACTTCCCCTC 3.977 0.273251 1.56&-5.33&1.83&-0.273&-5.44&0.878&-5.44&-1.37&-1.7&-6.36&-1.33&2.37&-7.36&-3.82 rs777850798 56 30650 1.82708e-03 1.81865e-03 1.89753e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39535e-03 1.49701e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.94134e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.12427e-03 2.24859e-03 0.00000e+00 4.44444e-03 4.67290e-03 0.00000e+00 2.42631e-03 6.57895e-03 6.94444e-03 0.00000e+00 414015 404573 Primary_ciliary_dyskinesia Human_Phenotype_Ontology:HP:0012265&MONDO:MONDO:0016575&MedGen:C0008780&OMIM:PS244400&Orphanet:ORPHA244 criteria_provided&_single_submitter Likely_benign Microsatellite RPGR:6103 SO:0001822&inframe_deletion&SO:0001627&intron_variant 1 777850798 15 30 50.0 -X 38286537 TCCTCTACTTCCCCTC T - intron_variant&non_coding_transcript_variant MODIFIER RPGR 6103 Transcript NR_159805.1 misc_RNA 13/17 -1 EntrezGene CCTCTACTTCCCCTC CCTCTACTTCCCCTC 3.977 0.273251 1.56&-5.33&1.83&-0.273&-5.44&0.878&-5.44&-1.37&-1.7&-6.36&-1.33&2.37&-7.36&-3.82 rs777850798 56 30650 1.82708e-03 1.81865e-03 1.89753e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39535e-03 1.49701e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.94134e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.12427e-03 2.24859e-03 0.00000e+00 4.44444e-03 4.67290e-03 0.00000e+00 2.42631e-03 6.57895e-03 6.94444e-03 0.00000e+00 414015 404573 Primary_ciliary_dyskinesia Human_Phenotype_Ontology:HP:0012265&MONDO:MONDO:0016575&MedGen:C0008780&OMIM:PS244400&Orphanet:ORPHA244 criteria_provided&_single_submitter Likely_benign Microsatellite RPGR:6103 SO:0001822&inframe_deletion&SO:0001627&intron_variant 1 777850798 15 30 50.0 -X 38286537 TCCTCTACTTCCCCTC T - intron_variant&non_coding_transcript_variant MODIFIER RPGR 6103 Transcript NR_159806.1 misc_RNA 14/17 -1 EntrezGene CCTCTACTTCCCCTC CCTCTACTTCCCCTC 3.977 0.273251 1.56&-5.33&1.83&-0.273&-5.44&0.878&-5.44&-1.37&-1.7&-6.36&-1.33&2.37&-7.36&-3.82 rs777850798 56 30650 1.82708e-03 1.81865e-03 1.89753e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39535e-03 1.49701e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.94134e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.12427e-03 2.24859e-03 0.00000e+00 4.44444e-03 4.67290e-03 0.00000e+00 2.42631e-03 6.57895e-03 6.94444e-03 0.00000e+00 414015 404573 Primary_ciliary_dyskinesia Human_Phenotype_Ontology:HP:0012265&MONDO:MONDO:0016575&MedGen:C0008780&OMIM:PS244400&Orphanet:ORPHA244 criteria_provided&_single_submitter Likely_benign Microsatellite RPGR:6103 SO:0001822&inframe_deletion&SO:0001627&intron_variant 1 777850798 15 30 50.0 -X 38286537 TCCTCTACTTCCCCTC T - intron_variant&non_coding_transcript_variant MODIFIER RPGR 6103 Transcript NR_159807.1 misc_RNA 12/15 -1 EntrezGene CCTCTACTTCCCCTC CCTCTACTTCCCCTC 3.977 0.273251 1.56&-5.33&1.83&-0.273&-5.44&0.878&-5.44&-1.37&-1.7&-6.36&-1.33&2.37&-7.36&-3.82 rs777850798 56 30650 1.82708e-03 1.81865e-03 1.89753e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39535e-03 1.49701e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.94134e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.12427e-03 2.24859e-03 0.00000e+00 4.44444e-03 4.67290e-03 0.00000e+00 2.42631e-03 6.57895e-03 6.94444e-03 0.00000e+00 414015 404573 Primary_ciliary_dyskinesia Human_Phenotype_Ontology:HP:0012265&MONDO:MONDO:0016575&MedGen:C0008780&OMIM:PS244400&Orphanet:ORPHA244 criteria_provided&_single_submitter Likely_benign Microsatellite RPGR:6103 SO:0001822&inframe_deletion&SO:0001627&intron_variant 1 777850798 15 30 50.0 -X 38286537 TCCTCTACTTCCCCTC T - intron_variant&non_coding_transcript_variant MODIFIER RPGR 6103 Transcript NR_159808.1 misc_RNA 13/17 -1 EntrezGene CCTCTACTTCCCCTC CCTCTACTTCCCCTC 3.977 0.273251 1.56&-5.33&1.83&-0.273&-5.44&0.878&-5.44&-1.37&-1.7&-6.36&-1.33&2.37&-7.36&-3.82 rs777850798 56 30650 1.82708e-03 1.81865e-03 1.89753e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.39535e-03 1.49701e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 1.94134e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 2.12427e-03 2.24859e-03 0.00000e+00 4.44444e-03 4.67290e-03 0.00000e+00 2.42631e-03 6.57895e-03 6.94444e-03 0.00000e+00 414015 404573 Primary_ciliary_dyskinesia Human_Phenotype_Ontology:HP:0012265&MONDO:MONDO:0016575&MedGen:C0008780&OMIM:PS244400&Orphanet:ORPHA244 criteria_provided&_single_submitter Likely_benign Microsatellite RPGR:6103 SO:0001822&inframe_deletion&SO:0001627&intron_variant 1 777850798 15 30 50.0 -X 124322939 T A A intergenic_variant MODIFIER 3.684 0.248292 1.02 15 30 50.0 -X 154534419 G A A missense_variant MODERATE G6PD 2539 Transcript NM_000402.4 protein_coding 6/13 801 653 218 S/F tCc/tTc -1 EntrezGene G G OK 0 0.018 23.6 3.101813 23.6 0.99560193252273865 .&.&-4.87&-4.87&-4.87&-4.87&-4.87 5.65 0.999999999999995 0.000101 0.9334 0.9565 3.42&3.42&3.42&3.42&.&.&. 1&1&1&1 .&.&-4.22&-4.22&-4.12&-4.12&-4.23 15.9286 0.562&.&0.564&0.702&.&.&. 0.94225 1.000000 0.993000 5.542000 1.172000 5.22 rs5030868 51 105509 4.83371e-04 1.27861e-04 1.32287e-04 1.16198e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.75813e-03 3.98180e-03 6.54450e-03 0.00000e+00 0.00000e+00 0.00000e+00 3.38854e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.19388e-04 2.38873e-04 1.60608e-04 4.65911e-04 6.36537e-04 9.10747e-04 0.00000e+00 5.44255e-04 1.43509e-02 1.41343e-02 1.44778e-02 100057 0.00265 0.00079 25407 G6PD_CAGLIARI&G6PD_MEDITERRANEAN&G6PD_SASSARI&G6PD_deficient_hemolytic_anemia&Hemolytic_anemia&_G6PD_deficient_(favism)&Glucose_6_phosphate_dehydrogenase_deficiency&Angioedema_induced_by_ACE_inhibitors&_susceptibility_to&Susceptibility_to_malaria&Inborn_genetic_diseases&Anemia&_nonspherocytic_hemolytic&_due_to_G6PD_deficiency¬_specified¬_provided .&.&.&.&.&MONDO:MONDO:0005775&MedGen:C2939465&Orphanet:ORPHA362&MONDO:MONDO:0015057&MedGen:C3806711&OMIM:300909&Orphanet:ORPHA100057&MONDO:MONDO:0021024&MedGen:C1970028&OMIM:611162&MeSH:D030342&MedGen:C0950123&MedGen:C2720289&OMIM:300908&Orphanet:ORPHA466026&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(17)&Uncertain_significance(1) single_nucleotide_variant G6PD:2539 SO:0001583&missense_variant 9 5030868 15 30 50.0 -X 154534419 G A A missense_variant MODERATE G6PD 2539 Transcript NM_001042351.3 protein_coding 6/13 673 563 188 S/F tCc/tTc -1 EntrezGene G G 0 0.03 23.6 3.101813 23.6 0.99560193252273865 .&.&-4.87&-4.87&-4.87&-4.87&-4.87 5.65 0.999999999999995 0.000101 0.9334 0.9565 3.42&3.42&3.42&3.42&.&.&. 1&1&1&1 .&.&-4.22&-4.22&-4.12&-4.12&-4.23 15.9286 0.562&.&0.564&0.702&.&.&. 0.94225 1.000000 0.993000 5.542000 1.172000 5.22 rs5030868 51 105509 4.83371e-04 1.27861e-04 1.32287e-04 1.16198e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.75813e-03 3.98180e-03 6.54450e-03 0.00000e+00 0.00000e+00 0.00000e+00 3.38854e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.19388e-04 2.38873e-04 1.60608e-04 4.65911e-04 6.36537e-04 9.10747e-04 0.00000e+00 5.44255e-04 1.43509e-02 1.41343e-02 1.44778e-02 100057 0.00265 0.00079 25407 G6PD_CAGLIARI&G6PD_MEDITERRANEAN&G6PD_SASSARI&G6PD_deficient_hemolytic_anemia&Hemolytic_anemia&_G6PD_deficient_(favism)&Glucose_6_phosphate_dehydrogenase_deficiency&Angioedema_induced_by_ACE_inhibitors&_susceptibility_to&Susceptibility_to_malaria&Inborn_genetic_diseases&Anemia&_nonspherocytic_hemolytic&_due_to_G6PD_deficiency¬_specified¬_provided .&.&.&.&.&MONDO:MONDO:0005775&MedGen:C2939465&Orphanet:ORPHA362&MONDO:MONDO:0015057&MedGen:C3806711&OMIM:300909&Orphanet:ORPHA100057&MONDO:MONDO:0021024&MedGen:C1970028&OMIM:611162&MeSH:D030342&MedGen:C0950123&MedGen:C2720289&OMIM:300908&Orphanet:ORPHA466026&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(17)&Uncertain_significance(1) single_nucleotide_variant G6PD:2539 SO:0001583&missense_variant 9 5030868 15 30 50.0 -X 154534419 G A A missense_variant MODERATE G6PD 2539 Transcript NM_001360016.2 protein_coding 6/13 629 563 188 S/F tCc/tTc -1 EntrezGene G G 0 0.03 23.6 3.101813 23.6 0.99560193252273865 .&.&-4.87&-4.87&-4.87&-4.87&-4.87 5.65 0.999999999999995 0.000101 0.9334 0.9565 3.42&3.42&3.42&3.42&.&.&. 1&1&1&1 .&.&-4.22&-4.22&-4.12&-4.12&-4.23 15.9286 0.562&.&0.564&0.702&.&.&. 0.94225 1.000000 0.993000 5.542000 1.172000 5.22 rs5030868 51 105509 4.83371e-04 1.27861e-04 1.32287e-04 1.16198e-04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 4.75813e-03 3.98180e-03 6.54450e-03 0.00000e+00 0.00000e+00 0.00000e+00 3.38854e-04 0.00000e+00 0.00000e+00 0.00000e+00 8.19388e-04 2.38873e-04 1.60608e-04 4.65911e-04 6.36537e-04 9.10747e-04 0.00000e+00 5.44255e-04 1.43509e-02 1.41343e-02 1.44778e-02 100057 0.00265 0.00079 25407 G6PD_CAGLIARI&G6PD_MEDITERRANEAN&G6PD_SASSARI&G6PD_deficient_hemolytic_anemia&Hemolytic_anemia&_G6PD_deficient_(favism)&Glucose_6_phosphate_dehydrogenase_deficiency&Angioedema_induced_by_ACE_inhibitors&_susceptibility_to&Susceptibility_to_malaria&Inborn_genetic_diseases&Anemia&_nonspherocytic_hemolytic&_due_to_G6PD_deficiency¬_specified¬_provided .&.&.&.&.&MONDO:MONDO:0005775&MedGen:C2939465&Orphanet:ORPHA362&MONDO:MONDO:0015057&MedGen:C3806711&OMIM:300909&Orphanet:ORPHA100057&MONDO:MONDO:0021024&MedGen:C1970028&OMIM:611162&MeSH:D030342&MedGen:C0950123&MedGen:C2720289&OMIM:300908&Orphanet:ORPHA466026&MedGen:CN169374&MedGen:CN517202 criteria_provided&_conflicting_interpretations Conflicting_interpretations_of_pathogenicity Likely_pathogenic(1)&Pathogenic(17)&Uncertain_significance(1) single_nucleotide_variant G6PD:2539 SO:0001583&missense_variant 9 5030868 15 30 50.0 -X 154536002 C T T missense_variant MODERATE G6PD 2539 Transcript NM_000402.4 protein_coding 4/13 440 292 98 V/M Gtg/Atg -1 EntrezGene C C OK 0.02 0.926 23.9 3.205689 23.9 0.99890082302514294 .&.&-6.26&-6.26&-6.26&-6.26&-6.26 5.25 0.999999999999875 0.000000 0.5767 -0.9649 3.365&3.365&3.365&3.365&.&.&. 2.74215e-07&2.03781e-07&2.74215e-07&2.74215e-07 .&.&-1.56&-1.56&-1.6&-1.6&-1.95 15.2054 0.427&.&0.427&0.16&.&.&. 0.87568 0.998000 0.997000 3.752000 1.022000 5.22 rs1050828 3768 105756 3.56292e-02 1.15231e-01 1.17943e-01 1.08092e-01 0.00000e+00 0.00000e+00 0.00000e+00 1.03115e-02 1.05014e-02 9.99144e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.77571e-02 0.00000e+00 0.00000e+00 0.00000e+00 3.07119e-02 2.57706e-04 2.67437e-04 2.29832e-04 3.38226e-02 3.73406e-02 2.55864e-02 3.66404e-02 0.00000e+00 0.00000e+00 0.00000e+00 37123 0.01151 0.03762 25400 G6PD_ASAHI&Glucose_6_phosphate_dehydrogenase_deficiency&Parkinsonism_with_spasticity&_X-linked&Inborn_genetic_diseases&Anemia&_nonspherocytic_hemolytic&_due_to_G6PD_deficiency¬_specified&chlorproguanil_and_dapsone_response_-_Toxicity/ADR¬_provided Glucose_6_phosphate_dehydrogenase_deficiency .&MONDO:MONDO:0005775&MedGen:C2939465&Orphanet:ORPHA362&MONDO:MONDO:0010482&MedGen:C3806722&OMIM:300911&Orphanet:ORPHA363654&MeSH:D030342&MedGen:C0950123&MedGen:C2720289&OMIM:300908&Orphanet:ORPHA466026&MedGen:CN169374&MedGen:CN236472&MedGen:CN517202 MONDO:MONDO:0005775&MedGen:C2939465&Orphanet:ORPHA362 reviewed_by_expert_panel drug_response 10361:Pathogenic single_nucleotide_variant G6PD:2539 SO:0001583&missense_variant 1 1050828 15 30 50.0 -X 154536002 C T T missense_variant MODERATE G6PD 2539 Transcript NM_001042351.3 protein_coding 4/13 312 202 68 V/M Gtg/Atg -1 EntrezGene C C 0.02 0.944 23.9 3.205689 23.9 0.99890082302514294 .&.&-6.26&-6.26&-6.26&-6.26&-6.26 5.25 0.999999999999875 0.000000 0.5767 -0.9649 3.365&3.365&3.365&3.365&.&.&. 2.74215e-07&2.03781e-07&2.74215e-07&2.74215e-07 .&.&-1.56&-1.56&-1.6&-1.6&-1.95 15.2054 0.427&.&0.427&0.16&.&.&. 0.87568 0.998000 0.997000 3.752000 1.022000 5.22 rs1050828 3768 105756 3.56292e-02 1.15231e-01 1.17943e-01 1.08092e-01 0.00000e+00 0.00000e+00 0.00000e+00 1.03115e-02 1.05014e-02 9.99144e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.77571e-02 0.00000e+00 0.00000e+00 0.00000e+00 3.07119e-02 2.57706e-04 2.67437e-04 2.29832e-04 3.38226e-02 3.73406e-02 2.55864e-02 3.66404e-02 0.00000e+00 0.00000e+00 0.00000e+00 37123 0.01151 0.03762 25400 G6PD_ASAHI&Glucose_6_phosphate_dehydrogenase_deficiency&Parkinsonism_with_spasticity&_X-linked&Inborn_genetic_diseases&Anemia&_nonspherocytic_hemolytic&_due_to_G6PD_deficiency¬_specified&chlorproguanil_and_dapsone_response_-_Toxicity/ADR¬_provided Glucose_6_phosphate_dehydrogenase_deficiency .&MONDO:MONDO:0005775&MedGen:C2939465&Orphanet:ORPHA362&MONDO:MONDO:0010482&MedGen:C3806722&OMIM:300911&Orphanet:ORPHA363654&MeSH:D030342&MedGen:C0950123&MedGen:C2720289&OMIM:300908&Orphanet:ORPHA466026&MedGen:CN169374&MedGen:CN236472&MedGen:CN517202 MONDO:MONDO:0005775&MedGen:C2939465&Orphanet:ORPHA362 reviewed_by_expert_panel drug_response 10361:Pathogenic single_nucleotide_variant G6PD:2539 SO:0001583&missense_variant 1 1050828 15 30 50.0 -X 154536002 C T T missense_variant MODERATE G6PD 2539 Transcript NM_001360016.2 protein_coding 4/13 268 202 68 V/M Gtg/Atg -1 EntrezGene C C 0.02 0.944 23.9 3.205689 23.9 0.99890082302514294 .&.&-6.26&-6.26&-6.26&-6.26&-6.26 5.25 0.999999999999875 0.000000 0.5767 -0.9649 3.365&3.365&3.365&3.365&.&.&. 2.74215e-07&2.03781e-07&2.74215e-07&2.74215e-07 .&.&-1.56&-1.56&-1.6&-1.6&-1.95 15.2054 0.427&.&0.427&0.16&.&.&. 0.87568 0.998000 0.997000 3.752000 1.022000 5.22 rs1050828 3768 105756 3.56292e-02 1.15231e-01 1.17943e-01 1.08092e-01 0.00000e+00 0.00000e+00 0.00000e+00 1.03115e-02 1.05014e-02 9.99144e-03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 3.77571e-02 0.00000e+00 0.00000e+00 0.00000e+00 3.07119e-02 2.57706e-04 2.67437e-04 2.29832e-04 3.38226e-02 3.73406e-02 2.55864e-02 3.66404e-02 0.00000e+00 0.00000e+00 0.00000e+00 37123 0.01151 0.03762 25400 G6PD_ASAHI&Glucose_6_phosphate_dehydrogenase_deficiency&Parkinsonism_with_spasticity&_X-linked&Inborn_genetic_diseases&Anemia&_nonspherocytic_hemolytic&_due_to_G6PD_deficiency¬_specified&chlorproguanil_and_dapsone_response_-_Toxicity/ADR¬_provided Glucose_6_phosphate_dehydrogenase_deficiency .&MONDO:MONDO:0005775&MedGen:C2939465&Orphanet:ORPHA362&MONDO:MONDO:0010482&MedGen:C3806722&OMIM:300911&Orphanet:ORPHA363654&MeSH:D030342&MedGen:C0950123&MedGen:C2720289&OMIM:300908&Orphanet:ORPHA466026&MedGen:CN169374&MedGen:CN236472&MedGen:CN517202 MONDO:MONDO:0005775&MedGen:C2939465&Orphanet:ORPHA362 reviewed_by_expert_panel drug_response 10361:Pathogenic single_nucleotide_variant G6PD:2539 SO:0001583&missense_variant 1 1050828 15 30 50.0 -Y 10123227 G A A intergenic_variant MODIFIER 2.616 0.153877 0.202 15 30 50.0 -Y 12720687 G T T missense_variant MODERATE USP9Y 8287 Transcript NM_004654.4 protein_coding 4/46 1140 195 65 E/D gaG/gaT 1 EntrezGene G G 0.15 0.02 33 5.188906 33 0.90648539318642507 3.96 -4.54 0.99996101735451 1.67 -1.29 0.072 0.51272 0.995000 0.053000 0.323000 -3.480000 -11.3 rs7067496 7148 30704 2.32804e-01 7.05904e-01 7.05904e-01 0.00000e+00 0.00000e+00 1.30116e-01 1.30116e-01 2.38806e-01 2.38806e-01 7.58355e-02 7.58355e-02 1.78998e-03 1.78998e-03 2.32804e-01 4.57358e-02 4.57358e-02 1.76211e-01 1.76211e-01 2.46114e-01 4.39791e-02 4.39791e-02 15 30 50.0 -Y 12720687 G T T missense_variant MODERATE USP9Y 8287 Transcript XM_017030078.2 protein_coding 3/45 280 195 65 E/D gaG/gaT 1 EntrezGene G G 33 5.188906 33 0.90648539318642507 3.96 -4.54 0.99996101735451 1.67 -1.29 0.072 0.51272 0.995000 0.053000 0.323000 -3.480000 -11.3 rs7067496 7148 30704 2.32804e-01 7.05904e-01 7.05904e-01 0.00000e+00 0.00000e+00 1.30116e-01 1.30116e-01 2.38806e-01 2.38806e-01 7.58355e-02 7.58355e-02 1.78998e-03 1.78998e-03 2.32804e-01 4.57358e-02 4.57358e-02 1.76211e-01 1.76211e-01 2.46114e-01 4.39791e-02 4.39791e-02 15 30 50.0 diff --git a/configs/cluster_config.json b/configs/cluster_config.json deleted file mode 100644 index 12fec63..0000000 --- a/configs/cluster_config.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "__default__": { - "ntasks": 1, - "partition": "express", - "cpus-per-task": "{threads}", - "mem-per-cpu": "4G", - "output": "logs/rule_logs/{rule}-%j.log" - }, - "ditto_filter": { - "partition": "largemem", - "mem-per-cpu": "200G" - }, - "combine_scores": { - "mem-per-cpu": "50G" - } -} diff --git a/configs/col_config.yaml b/configs/col_config.yaml deleted file mode 100644 index a60b8c8..0000000 --- a/configs/col_config.yaml +++ /dev/null @@ -1,997 +0,0 @@ -# columns to be needed in dataset -columns: - - '#chr' - - pos(1-based) - - ref - - alt - - aaref - - aaalt - - genename - - Ensembl_geneid - - Ensembl_transcriptid - - Ensembl_proteinid - - Uniprot_acc - - cds_strand - - SIFT_score - - SIFT4G_score - - Polyphen2_HDIV_score - - Polyphen2_HVAR_score - - LRT_score - - LRT_Omega - - MutationTaster_converted_rankscore - - MutationAssessor_score - - FATHMM_score - - PROVEAN_score - - PROVEAN_converted_rankscore - - VEST4_score - - MetaSVM_score - - MetaLR_score - - Reliability_index - - MetaRNN_score - - M-CAP_score - - REVEL_score - - MutPred_score - - MVP_score - - MPC_score - - PrimateAI_score - - DEOGEN2_score - - BayesDel_addAF_score - - BayesDel_noAF_score - - ClinPred_score - - LIST-S2_score - - CADD_raw - - CADD_phred - - DANN_score - - fathmm-MKL_coding_score - - fathmm-XF_coding_score - - Eigen-raw_coding - - Eigen-phred_coding - - Eigen-PC-raw_coding - - Eigen-PC-phred_coding - - GenoCanyon_score - - integrated_fitCons_score - - integrated_confidence_value - - GM12878_fitCons_score - - H1-hESC_fitCons_score - - HUVEC_fitCons_score - - LINSIGHT - - GERP++_NR - - GERP++_RS - - phyloP100way_vertebrate - - phyloP30way_mammalian - - phyloP17way_primate - - phastCons100way_vertebrate - - phastCons30way_mammalian - - phastCons17way_primate - - SiPhy_29way_logOdds - - bStatistic - - gnomAD_genomes_AF - - gnomAD_genomes_AFR_AF - - gnomAD_genomes_AMI_AF - - gnomAD_genomes_AMR_AF - - gnomAD_genomes_ASJ_AF - - gnomAD_genomes_EAS_AF - - gnomAD_genomes_FIN_AF - - gnomAD_genomes_MID_AF - - gnomAD_genomes_NFE_AF - - gnomAD_genomes_SAS_AF - - clinvar_clnsig - - clinvar_review - - Interpro_domain - -ClinicalSignificance: - - Uncertain_significance - - Pathogenic - - Likely_pathogenic - - Benign - - Likely_benign - - Benign/Likely_benign - - Pathogenic/Likely_pathogenic - -BenchmarkSignificance: - - Pathogenic - - Likely_pathogenic - - Likely_benign - - Benign - - Benign/Likely_benign - - Pathogenic/Likely_pathogenic - - Pathogenic,_other - - Pathogenic/Likely_pathogenic,_other - - Pathogenic,_drug_response - -Clinsig_train: - - Benign - - Pathogenic - - Likely_benign - -Clinsig_test: - - Pathogenic/Likely_pathogenic - - Likely_pathogenic - - Benign/Likely_benign - - -CLNREVSTAT: #https://www.ncbi.nlm.nih.gov/clinvar/docs/review_status/ - - practice_guideline - - reviewed_by_expert_panel - - criteria_provided,_multiple_submitters,_no_conflicts - - criteria_provided,_single_submitter - -var: - - '#chr' - - pos(1-based) - - ref - - alt - - cds_strand - - genename - - Ensembl_geneid - - Ensembl_transcriptid - - Ensembl_proteinid - - Uniprot_acc - - clinvar_clnsig - - clinvar_review - - Interpro_domain - -ditto_info: - - '#chr' - - pos(1-based) - - ref - - alt - - cds_strand - - genename - - Ensembl_geneid - - Ensembl_transcriptid - - Ensembl_proteinid - - Uniprot_acc - - clinvar_clnsig - - clinvar_review - - Interpro_domain - - aapos - - aaref - - aaalt - - CADD_phred - - gnomAD_genomes_AF - - HGVSc_VEP - - HGVSp_VEP - -allele_freq_columns: - - gnomAD_genomes_AF - - gnomAD_genomes_AFR_AF - - gnomAD_genomes_AMI_AF - - gnomAD_genomes_AMR_AF - - gnomAD_genomes_ASJ_AF - - gnomAD_genomes_EAS_AF - - gnomAD_genomes_FIN_AF - - gnomAD_genomes_MID_AF - - gnomAD_genomes_NFE_AF - - gnomAD_genomes_SAS_AF - -nssnv_columns: - - '#chr' - - pos(1-based) - - ref - - alt - - cds_strand - - genename - - Ensembl_geneid - - Ensembl_transcriptid - - Ensembl_proteinid - - Uniprot_acc - - clinvar_clnsig - - clinvar_review - - Interpro_domain - - SIFT_score - - SIFT4G_score - - Polyphen2_HDIV_score - - Polyphen2_HVAR_score - - LRT_score - - LRT_Omega - - MutationTaster_converted_rankscore - - MutationAssessor_score - - FATHMM_score - - PROVEAN_score - - PROVEAN_converted_rankscore - - VEST4_score - - MetaSVM_score - - MetaLR_score - - Reliability_index - - MetaRNN_score - - M-CAP_score - - REVEL_score - - MutPred_score - - MVP_score - - MPC_score - - PrimateAI_score - - DEOGEN2_score - - BayesDel_addAF_score - - BayesDel_noAF_score - - ClinPred_score - - LIST-S2_score - - CADD_raw - - CADD_phred - - DANN_score - - fathmm-MKL_coding_score - - fathmm-XF_coding_score - - Eigen-raw_coding - - Eigen-phred_coding - - Eigen-PC-raw_coding - - Eigen-PC-phred_coding - - GenoCanyon_score - - integrated_fitCons_score - - integrated_confidence_value - - GM12878_fitCons_score - - H1-hESC_fitCons_score - - HUVEC_fitCons_score - - LINSIGHT - - GERP++_NR - - GERP++_RS - - phyloP100way_vertebrate - - phyloP30way_mammalian - - phyloP17way_primate - - phastCons100way_vertebrate - - phastCons30way_mammalian - - phastCons17way_primate - - SiPhy_29way_logOdds - - bStatistic - - gnomAD_genomes_AF - - gnomAD_genomes_AFR_AF - - gnomAD_genomes_AMI_AF - - gnomAD_genomes_AMR_AF - - gnomAD_genomes_ASJ_AF - - gnomAD_genomes_EAS_AF - - gnomAD_genomes_FIN_AF - - gnomAD_genomes_MID_AF - - gnomAD_genomes_NFE_AF - - gnomAD_genomes_SAS_AF - - aaref_A - - aaref_C - - aaref_D - - aaref_E - - aaref_F - - aaref_G - - aaref_H - - aaref_I - - aaref_K - - aaref_L - - aaref_M - - aaref_N - - aaref_P - - aaref_Q - - aaref_R - - aaref_S - - aaref_T - - aaref_U - - aaref_V - - aaref_W - - aaref_X - - aaref_Y - - aaalt_A - - aaalt_C - - aaalt_D - - aaalt_E - - aaalt_F - - aaalt_G - - aaalt_H - - aaalt_I - - aaalt_K - - aaalt_L - - aaalt_M - - aaalt_N - - aaalt_P - - aaalt_Q - - aaalt_R - - aaalt_S - - aaalt_T - - aaalt_V - - aaalt_W - - aaalt_X - - aaalt_Y - -nssnv_median: - SIFT_score: 0.06199999898672104 - SIFT4G_score: 0.1080000028014183 - Polyphen2_HDIV_score: 0.48500001430511475 - Polyphen2_HVAR_score: 0.16699999570846558 - LRT_score: 0.00011200000153621659 - LRT_Omega: 0.1196729987859726 - MutationTaster_converted_rankscore: 0.8100100159645081 - MutationAssessor_score: 1.6100000143051147 - FATHMM_score: -0.2199999988079071 - PROVEAN_score: -1.5800000429153442 - PROVEAN_converted_rankscore: 0.4390600025653839 - VEST4_score: 0.5450000166893005 - MetaSVM_score: -0.7731999754905701 - MetaLR_score: 0.19979999959468842 - Reliability_index: 10.0 - MetaRNN_score: 0.02568286657333374 - M-CAP_score: 0.08201000094413757 - REVEL_score: 0.21199999749660492 - MutPred_score: 0.5960000157356262 - MVP_score: 0.6482067108154297 - MPC_score: 0.36203378438949585 - PrimateAI_score: 0.5189425349235535 - DEOGEN2_score: 0.1603820025920868 - BayesDel_addAF_score: 0.1817370057106018 - BayesDel_noAF_score: 0.1416580080986023 - ClinPred_score: 0.07361292839050293 - LIST-S2_score: 0.8526149988174438 - CADD_raw: 3.801330089569092 - CADD_phred: 25.799999237060547 - DANN_score: 0.9942993521690369 - fathmm-MKL_coding_score: 0.9268699884414673 - fathmm-XF_coding_score: 0.2985199987888336 - Eigen-raw_coding: 0.4470433294773102 - Eigen-phred_coding: 4.64929723739624 - Eigen-PC-raw_coding: 0.3914338946342468 - Eigen-PC-phred_coding: 4.298270225524902 - GenoCanyon_score: 0.9999961853027344 - integrated_fitCons_score: 0.6717699766159058 - integrated_confidence_value: 0.0 - GM12878_fitCons_score: 0.6159480214118958 - H1-hESC_fitCons_score: 0.6589829921722412 - HUVEC_fitCons_score: 0.6355509757995605 - LINSIGHT: 0.9758660197257996 - GERP++_NR: 5.340000152587891 - GERP++_RS: 4.579999923706055 - phyloP100way_vertebrate: 3.869999885559082 - phyloP30way_mammalian: 1.0260000228881836 - phyloP17way_primate: 0.5989999771118164 - phastCons100way_vertebrate: 1.0 - phastCons30way_mammalian: 0.9879999756813049 - phastCons17way_primate: 0.9629999995231628 - SiPhy_29way_logOdds: 13.411700248718262 - bStatistic: 707.0 - gnomAD_genomes_AF: 0.0005719619803130627 - gnomAD_genomes_AFR_AF: 0.00024128900258801877 - gnomAD_genomes_AMI_AF: 0.0 - gnomAD_genomes_AMR_AF: 0.0002617459977045655 - gnomAD_genomes_ASJ_AF: 0.0 - gnomAD_genomes_EAS_AF: 0.0 - gnomAD_genomes_FIN_AF: 0.0 - gnomAD_genomes_MID_AF: 0.0 - gnomAD_genomes_NFE_AF: 5.6453049182891846e-05 - gnomAD_genomes_SAS_AF: 0.0 - -raw_cols: - - '#chr' - - pos(1-based) - - ref - - alt - - aaref - - aaalt - - rs_dbSNP - - hg19_chr - - hg19_pos(1-based) - - hg18_chr - - hg18_pos(1-based) - - aapos - - genename - - Ensembl_geneid - - Ensembl_transcriptid - - Ensembl_proteinid - - Uniprot_acc - - Uniprot_entry - - HGVSc_ANNOVAR - - HGVSp_ANNOVAR - - HGVSc_snpEff - - HGVSp_snpEff - - HGVSc_VEP - - HGVSp_VEP - - APPRIS - - GENCODE_basic - - TSL - - VEP_canonical - - cds_strand - - refcodon - - codonpos - - codon_degeneracy - - Ancestral_allele - - AltaiNeandertal - - Denisova - - VindijiaNeandertal - - ChagyrskayaNeandertal - - SIFT_score - - SIFT_converted_rankscore - - SIFT_pred - - SIFT4G_score - - SIFT4G_converted_rankscore - - SIFT4G_pred - - Polyphen2_HDIV_score - - Polyphen2_HDIV_rankscore - - Polyphen2_HDIV_pred - - Polyphen2_HVAR_score - - Polyphen2_HVAR_rankscore - - Polyphen2_HVAR_pred - - LRT_score - - LRT_converted_rankscore - - LRT_pred - - LRT_Omega - - MutationTaster_score - - MutationTaster_converted_rankscore - - MutationTaster_pred - - MutationTaster_model - - MutationTaster_AAE - - MutationAssessor_score - - MutationAssessor_rankscore - - MutationAssessor_pred - - FATHMM_score - - FATHMM_converted_rankscore - - FATHMM_pred - - PROVEAN_score - - PROVEAN_converted_rankscore - - PROVEAN_pred - - VEST4_score - - VEST4_rankscore - - MetaSVM_score - - MetaSVM_rankscore - - MetaSVM_pred - - MetaLR_score - - MetaLR_rankscore - - MetaLR_pred - - Reliability_index - - MetaRNN_score - - MetaRNN_rankscore - - MetaRNN_pred - - M-CAP_score - - M-CAP_rankscore - - M-CAP_pred - - REVEL_score - - REVEL_rankscore - - MutPred_score - - MutPred_rankscore - - MutPred_protID - - MutPred_AAchange - - MutPred_Top5features - - MVP_score - - MVP_rankscore - - MPC_score - - MPC_rankscore - - PrimateAI_score - - PrimateAI_rankscore - - PrimateAI_pred - - DEOGEN2_score - - DEOGEN2_rankscore - - DEOGEN2_pred - - BayesDel_addAF_score - - BayesDel_addAF_rankscore - - BayesDel_addAF_pred - - BayesDel_noAF_score - - BayesDel_noAF_rankscore - - BayesDel_noAF_pred - - ClinPred_score - - ClinPred_rankscore - - ClinPred_pred - - LIST-S2_score - - LIST-S2_rankscore - - LIST-S2_pred - - Aloft_Fraction_transcripts_affected - - Aloft_prob_Tolerant - - Aloft_prob_Recessive - - Aloft_prob_Dominant - - Aloft_pred - - Aloft_Confidence - - CADD_raw - - CADD_raw_rankscore - - CADD_phred - - CADD_raw_hg19 - - CADD_raw_rankscore_hg19 - - CADD_phred_hg19 - - DANN_score - - DANN_rankscore - - fathmm-MKL_coding_score - - fathmm-MKL_coding_rankscore - - fathmm-MKL_coding_pred - - fathmm-MKL_coding_group - - fathmm-XF_coding_score - - fathmm-XF_coding_rankscore - - fathmm-XF_coding_pred - - Eigen-raw_coding - - Eigen-raw_coding_rankscore - - Eigen-phred_coding - - Eigen-PC-raw_coding - - Eigen-PC-raw_coding_rankscore - - Eigen-PC-phred_coding - - GenoCanyon_score - - GenoCanyon_rankscore - - integrated_fitCons_score - - integrated_fitCons_rankscore - - integrated_confidence_value - - GM12878_fitCons_score - - GM12878_fitCons_rankscore - - GM12878_confidence_value - - H1-hESC_fitCons_score - - H1-hESC_fitCons_rankscore - - H1-hESC_confidence_value - - HUVEC_fitCons_score - - HUVEC_fitCons_rankscore - - HUVEC_confidence_value - - LINSIGHT - - LINSIGHT_rankscore - - GERP++_NR - - GERP++_RS - - GERP++_RS_rankscore - - phyloP100way_vertebrate - - phyloP100way_vertebrate_rankscore - - phyloP30way_mammalian - - phyloP30way_mammalian_rankscore - - phyloP17way_primate - - phyloP17way_primate_rankscore - - phastCons100way_vertebrate - - phastCons100way_vertebrate_rankscore - - phastCons30way_mammalian - - phastCons30way_mammalian_rankscore - - phastCons17way_primate - - phastCons17way_primate_rankscore - - SiPhy_29way_pi - - SiPhy_29way_logOdds - - SiPhy_29way_logOdds_rankscore - - bStatistic - - bStatistic_converted_rankscore - - 1000Gp3_AC - - 1000Gp3_AF - - 1000Gp3_AFR_AC - - 1000Gp3_AFR_AF - - 1000Gp3_EUR_AC - - 1000Gp3_EUR_AF - - 1000Gp3_AMR_AC - - 1000Gp3_AMR_AF - - 1000Gp3_EAS_AC - - 1000Gp3_EAS_AF - - 1000Gp3_SAS_AC - - 1000Gp3_SAS_AF - - TWINSUK_AC - - TWINSUK_AF - - ALSPAC_AC - - ALSPAC_AF - - UK10K_AC - - UK10K_AF - - ESP6500_AA_AC - - ESP6500_AA_AF - - ESP6500_EA_AC - - ESP6500_EA_AF - - ExAC_AC - - ExAC_AF - - ExAC_Adj_AC - - ExAC_Adj_AF - - ExAC_AFR_AC - - ExAC_AFR_AF - - ExAC_AMR_AC - - ExAC_AMR_AF - - ExAC_EAS_AC - - ExAC_EAS_AF - - ExAC_FIN_AC - - ExAC_FIN_AF - - ExAC_NFE_AC - - ExAC_NFE_AF - - ExAC_SAS_AC - - ExAC_SAS_AF - - ExAC_nonTCGA_AC - - ExAC_nonTCGA_AF - - ExAC_nonTCGA_Adj_AC - - ExAC_nonTCGA_Adj_AF - - ExAC_nonTCGA_AFR_AC - - ExAC_nonTCGA_AFR_AF - - ExAC_nonTCGA_AMR_AC - - ExAC_nonTCGA_AMR_AF - - ExAC_nonTCGA_EAS_AC - - ExAC_nonTCGA_EAS_AF - - ExAC_nonTCGA_FIN_AC - - ExAC_nonTCGA_FIN_AF - - ExAC_nonTCGA_NFE_AC - - ExAC_nonTCGA_NFE_AF - - ExAC_nonTCGA_SAS_AC - - ExAC_nonTCGA_SAS_AF - - ExAC_nonpsych_AC - - ExAC_nonpsych_AF - - ExAC_nonpsych_Adj_AC - - ExAC_nonpsych_Adj_AF - - ExAC_nonpsych_AFR_AC - - ExAC_nonpsych_AFR_AF - - ExAC_nonpsych_AMR_AC - - ExAC_nonpsych_AMR_AF - - ExAC_nonpsych_EAS_AC - - ExAC_nonpsych_EAS_AF - - ExAC_nonpsych_FIN_AC - - ExAC_nonpsych_FIN_AF - - ExAC_nonpsych_NFE_AC - - ExAC_nonpsych_NFE_AF - - ExAC_nonpsych_SAS_AC - - ExAC_nonpsych_SAS_AF - - gnomAD_exomes_flag - - gnomAD_exomes_AC - - gnomAD_exomes_AN - - gnomAD_exomes_AF - - gnomAD_exomes_nhomalt - - gnomAD_exomes_AFR_AC - - gnomAD_exomes_AFR_AN - - gnomAD_exomes_AFR_AF - - gnomAD_exomes_AFR_nhomalt - - gnomAD_exomes_AMR_AC - - gnomAD_exomes_AMR_AN - - gnomAD_exomes_AMR_AF - - gnomAD_exomes_AMR_nhomalt - - gnomAD_exomes_ASJ_AC - - gnomAD_exomes_ASJ_AN - - gnomAD_exomes_ASJ_AF - - gnomAD_exomes_ASJ_nhomalt - - gnomAD_exomes_EAS_AC - - gnomAD_exomes_EAS_AN - - gnomAD_exomes_EAS_AF - - gnomAD_exomes_EAS_nhomalt - - gnomAD_exomes_FIN_AC - - gnomAD_exomes_FIN_AN - - gnomAD_exomes_FIN_AF - - gnomAD_exomes_FIN_nhomalt - - gnomAD_exomes_NFE_AC - - gnomAD_exomes_NFE_AN - - gnomAD_exomes_NFE_AF - - gnomAD_exomes_NFE_nhomalt - - gnomAD_exomes_SAS_AC - - gnomAD_exomes_SAS_AN - - gnomAD_exomes_SAS_AF - - gnomAD_exomes_SAS_nhomalt - - gnomAD_exomes_POPMAX_AC - - gnomAD_exomes_POPMAX_AN - - gnomAD_exomes_POPMAX_AF - - gnomAD_exomes_POPMAX_nhomalt - - gnomAD_exomes_controls_AC - - gnomAD_exomes_controls_AN - - gnomAD_exomes_controls_AF - - gnomAD_exomes_controls_nhomalt - - gnomAD_exomes_non_neuro_AC - - gnomAD_exomes_non_neuro_AN - - gnomAD_exomes_non_neuro_AF - - gnomAD_exomes_non_neuro_nhomalt - - gnomAD_exomes_non_cancer_AC - - gnomAD_exomes_non_cancer_AN - - gnomAD_exomes_non_cancer_AF - - gnomAD_exomes_non_cancer_nhomalt - - gnomAD_exomes_non_topmed_AC - - gnomAD_exomes_non_topmed_AN - - gnomAD_exomes_non_topmed_AF - - gnomAD_exomes_non_topmed_nhomalt - - gnomAD_exomes_controls_AFR_AC - - gnomAD_exomes_controls_AFR_AN - - gnomAD_exomes_controls_AFR_AF - - gnomAD_exomes_controls_AFR_nhomalt - - gnomAD_exomes_controls_AMR_AC - - gnomAD_exomes_controls_AMR_AN - - gnomAD_exomes_controls_AMR_AF - - gnomAD_exomes_controls_AMR_nhomalt - - gnomAD_exomes_controls_ASJ_AC - - gnomAD_exomes_controls_ASJ_AN - - gnomAD_exomes_controls_ASJ_AF - - gnomAD_exomes_controls_ASJ_nhomalt - - gnomAD_exomes_controls_EAS_AC - - gnomAD_exomes_controls_EAS_AN - - gnomAD_exomes_controls_EAS_AF - - gnomAD_exomes_controls_EAS_nhomalt - - gnomAD_exomes_controls_FIN_AC - - gnomAD_exomes_controls_FIN_AN - - gnomAD_exomes_controls_FIN_AF - - gnomAD_exomes_controls_FIN_nhomalt - - gnomAD_exomes_controls_NFE_AC - - gnomAD_exomes_controls_NFE_AN - - gnomAD_exomes_controls_NFE_AF - - gnomAD_exomes_controls_NFE_nhomalt - - gnomAD_exomes_controls_SAS_AC - - gnomAD_exomes_controls_SAS_AN - - gnomAD_exomes_controls_SAS_AF - - gnomAD_exomes_controls_SAS_nhomalt - - gnomAD_exomes_controls_POPMAX_AC - - gnomAD_exomes_controls_POPMAX_AN - - gnomAD_exomes_controls_POPMAX_AF - - gnomAD_exomes_controls_POPMAX_nhomalt - - gnomAD_exomes_non_neuro_AFR_AC - - gnomAD_exomes_non_neuro_AFR_AN - - gnomAD_exomes_non_neuro_AFR_AF - - gnomAD_exomes_non_neuro_AFR_nhomalt - - gnomAD_exomes_non_neuro_AMR_AC - - gnomAD_exomes_non_neuro_AMR_AN - - gnomAD_exomes_non_neuro_AMR_AF - - gnomAD_exomes_non_neuro_AMR_nhomalt - - gnomAD_exomes_non_neuro_ASJ_AC - - gnomAD_exomes_non_neuro_ASJ_AN - - gnomAD_exomes_non_neuro_ASJ_AF - - gnomAD_exomes_non_neuro_ASJ_nhomalt - - gnomAD_exomes_non_neuro_EAS_AC - - gnomAD_exomes_non_neuro_EAS_AN - - gnomAD_exomes_non_neuro_EAS_AF - - gnomAD_exomes_non_neuro_EAS_nhomalt - - gnomAD_exomes_non_neuro_FIN_AC - - gnomAD_exomes_non_neuro_FIN_AN - - gnomAD_exomes_non_neuro_FIN_AF - - gnomAD_exomes_non_neuro_FIN_nhomalt - - gnomAD_exomes_non_neuro_NFE_AC - - gnomAD_exomes_non_neuro_NFE_AN - - gnomAD_exomes_non_neuro_NFE_AF - - gnomAD_exomes_non_neuro_NFE_nhomalt - - gnomAD_exomes_non_neuro_SAS_AC - - gnomAD_exomes_non_neuro_SAS_AN - - gnomAD_exomes_non_neuro_SAS_AF - - gnomAD_exomes_non_neuro_SAS_nhomalt - - gnomAD_exomes_non_neuro_POPMAX_AC - - gnomAD_exomes_non_neuro_POPMAX_AN - - gnomAD_exomes_non_neuro_POPMAX_AF - - gnomAD_exomes_non_neuro_POPMAX_nhomalt - - gnomAD_exomes_non_cancer_AFR_AC - - gnomAD_exomes_non_cancer_AFR_AN - - gnomAD_exomes_non_cancer_AFR_AF - - gnomAD_exomes_non_cancer_AFR_nhomalt - - gnomAD_exomes_non_cancer_AMR_AC - - gnomAD_exomes_non_cancer_AMR_AN - - gnomAD_exomes_non_cancer_AMR_AF - - gnomAD_exomes_non_cancer_AMR_nhomalt - - gnomAD_exomes_non_cancer_ASJ_AC - - gnomAD_exomes_non_cancer_ASJ_AN - - gnomAD_exomes_non_cancer_ASJ_AF - - gnomAD_exomes_non_cancer_ASJ_nhomalt - - gnomAD_exomes_non_cancer_EAS_AC - - gnomAD_exomes_non_cancer_EAS_AN - - gnomAD_exomes_non_cancer_EAS_AF - - gnomAD_exomes_non_cancer_EAS_nhomalt - - gnomAD_exomes_non_cancer_FIN_AC - - gnomAD_exomes_non_cancer_FIN_AN - - gnomAD_exomes_non_cancer_FIN_AF - - gnomAD_exomes_non_cancer_FIN_nhomalt - - gnomAD_exomes_non_cancer_NFE_AC - - gnomAD_exomes_non_cancer_NFE_AN - - gnomAD_exomes_non_cancer_NFE_AF - - gnomAD_exomes_non_cancer_NFE_nhomalt - - gnomAD_exomes_non_cancer_SAS_AC - - gnomAD_exomes_non_cancer_SAS_AN - - gnomAD_exomes_non_cancer_SAS_AF - - gnomAD_exomes_non_cancer_SAS_nhomalt - - gnomAD_exomes_non_cancer_POPMAX_AC - - gnomAD_exomes_non_cancer_POPMAX_AN - - gnomAD_exomes_non_cancer_POPMAX_AF - - gnomAD_exomes_non_cancer_POPMAX_nhomalt - - gnomAD_exomes_non_topmed_AFR_AC - - gnomAD_exomes_non_topmed_AFR_AN - - gnomAD_exomes_non_topmed_AFR_AF - - gnomAD_exomes_non_topmed_AFR_nhomalt - - gnomAD_exomes_non_topmed_AMR_AC - - gnomAD_exomes_non_topmed_AMR_AN - - gnomAD_exomes_non_topmed_AMR_AF - - gnomAD_exomes_non_topmed_AMR_nhomalt - - gnomAD_exomes_non_topmed_ASJ_AC - - gnomAD_exomes_non_topmed_ASJ_AN - - gnomAD_exomes_non_topmed_ASJ_AF - - gnomAD_exomes_non_topmed_ASJ_nhomalt - - gnomAD_exomes_non_topmed_EAS_AC - - gnomAD_exomes_non_topmed_EAS_AN - - gnomAD_exomes_non_topmed_EAS_AF - - gnomAD_exomes_non_topmed_EAS_nhomalt - - gnomAD_exomes_non_topmed_FIN_AC - - gnomAD_exomes_non_topmed_FIN_AN - - gnomAD_exomes_non_topmed_FIN_AF - - gnomAD_exomes_non_topmed_FIN_nhomalt - - gnomAD_exomes_non_topmed_NFE_AC - - gnomAD_exomes_non_topmed_NFE_AN - - gnomAD_exomes_non_topmed_NFE_AF - - gnomAD_exomes_non_topmed_NFE_nhomalt - - gnomAD_exomes_non_topmed_SAS_AC - - gnomAD_exomes_non_topmed_SAS_AN - - gnomAD_exomes_non_topmed_SAS_AF - - gnomAD_exomes_non_topmed_SAS_nhomalt - - gnomAD_exomes_non_topmed_POPMAX_AC - - gnomAD_exomes_non_topmed_POPMAX_AN - - gnomAD_exomes_non_topmed_POPMAX_AF - - gnomAD_exomes_non_topmed_POPMAX_nhomalt - - gnomAD_genomes_flag - - gnomAD_genomes_AC - - gnomAD_genomes_AN - - gnomAD_genomes_AF - - gnomAD_genomes_nhomalt - - gnomAD_genomes_POPMAX_AC - - gnomAD_genomes_POPMAX_AN - - gnomAD_genomes_POPMAX_AF - - gnomAD_genomes_POPMAX_nhomalt - - gnomAD_genomes_AFR_AC - - gnomAD_genomes_AFR_AN - - gnomAD_genomes_AFR_AF - - gnomAD_genomes_AFR_nhomalt - - gnomAD_genomes_AMI_AC - - gnomAD_genomes_AMI_AN - - gnomAD_genomes_AMI_AF - - gnomAD_genomes_AMI_nhomalt - - gnomAD_genomes_AMR_AC - - gnomAD_genomes_AMR_AN - - gnomAD_genomes_AMR_AF - - gnomAD_genomes_AMR_nhomalt - - gnomAD_genomes_ASJ_AC - - gnomAD_genomes_ASJ_AN - - gnomAD_genomes_ASJ_AF - - gnomAD_genomes_ASJ_nhomalt - - gnomAD_genomes_EAS_AC - - gnomAD_genomes_EAS_AN - - gnomAD_genomes_EAS_AF - - gnomAD_genomes_EAS_nhomalt - - gnomAD_genomes_FIN_AC - - gnomAD_genomes_FIN_AN - - gnomAD_genomes_FIN_AF - - gnomAD_genomes_FIN_nhomalt - - gnomAD_genomes_MID_AC - - gnomAD_genomes_MID_AN - - gnomAD_genomes_MID_AF - - gnomAD_genomes_MID_nhomalt - - gnomAD_genomes_NFE_AC - - gnomAD_genomes_NFE_AN - - gnomAD_genomes_NFE_AF - - gnomAD_genomes_NFE_nhomalt - - gnomAD_genomes_SAS_AC - - gnomAD_genomes_SAS_AN - - gnomAD_genomes_SAS_AF - - gnomAD_genomes_SAS_nhomalt - - gnomAD_genomes_controls_and_biobanks_AC - - gnomAD_genomes_controls_and_biobanks_AN - - gnomAD_genomes_controls_and_biobanks_AF - - gnomAD_genomes_controls_and_biobanks_nhomalt - - gnomAD_genomes_non_neuro_AC - - gnomAD_genomes_non_neuro_AN - - gnomAD_genomes_non_neuro_AF - - gnomAD_genomes_non_neuro_nhomalt - - gnomAD_genomes_non_cancer_AC - - gnomAD_genomes_non_cancer_AN - - gnomAD_genomes_non_cancer_AF - - gnomAD_genomes_non_cancer_nhomalt - - gnomAD_genomes_non_topmed_AC - - gnomAD_genomes_non_topmed_AN - - gnomAD_genomes_non_topmed_AF - - gnomAD_genomes_non_topmed_nhomalt - - gnomAD_genomes_controls_and_biobanks_AFR_AC - - gnomAD_genomes_controls_and_biobanks_AFR_AN - - gnomAD_genomes_controls_and_biobanks_AFR_AF - - gnomAD_genomes_controls_and_biobanks_AFR_nhomalt - - gnomAD_genomes_controls_and_biobanks_AMI_AC - - gnomAD_genomes_controls_and_biobanks_AMI_AN - - gnomAD_genomes_controls_and_biobanks_AMI_AF - - gnomAD_genomes_controls_and_biobanks_AMI_nhomalt - - gnomAD_genomes_controls_and_biobanks_AMR_AC - - gnomAD_genomes_controls_and_biobanks_AMR_AN - - gnomAD_genomes_controls_and_biobanks_AMR_AF - - gnomAD_genomes_controls_and_biobanks_AMR_nhomalt - - gnomAD_genomes_controls_and_biobanks_ASJ_AC - - gnomAD_genomes_controls_and_biobanks_ASJ_AN - - gnomAD_genomes_controls_and_biobanks_ASJ_AF - - gnomAD_genomes_controls_and_biobanks_ASJ_nhomalt - - gnomAD_genomes_controls_and_biobanks_EAS_AC - - gnomAD_genomes_controls_and_biobanks_EAS_AN - - gnomAD_genomes_controls_and_biobanks_EAS_AF - - gnomAD_genomes_controls_and_biobanks_EAS_nhomalt - - gnomAD_genomes_controls_and_biobanks_FIN_AC - - gnomAD_genomes_controls_and_biobanks_FIN_AN - - gnomAD_genomes_controls_and_biobanks_FIN_AF - - gnomAD_genomes_controls_and_biobanks_FIN_nhomalt - - gnomAD_genomes_controls_and_biobanks_MID_AC - - gnomAD_genomes_controls_and_biobanks_MID_AN - - gnomAD_genomes_controls_and_biobanks_MID_AF - - gnomAD_genomes_controls_and_biobanks_MID_nhomalt - - gnomAD_genomes_controls_and_biobanks_NFE_AC - - gnomAD_genomes_controls_and_biobanks_NFE_AN - - gnomAD_genomes_controls_and_biobanks_NFE_AF - - gnomAD_genomes_controls_and_biobanks_NFE_nhomalt - - gnomAD_genomes_controls_and_biobanks_SAS_AC - - gnomAD_genomes_controls_and_biobanks_SAS_AN - - gnomAD_genomes_controls_and_biobanks_SAS_AF - - gnomAD_genomes_controls_and_biobanks_SAS_nhomalt - - gnomAD_genomes_non_neuro_AFR_AC - - gnomAD_genomes_non_neuro_AFR_AN - - gnomAD_genomes_non_neuro_AFR_AF - - gnomAD_genomes_non_neuro_AFR_nhomalt - - gnomAD_genomes_non_neuro_AMI_AC - - gnomAD_genomes_non_neuro_AMI_AN - - gnomAD_genomes_non_neuro_AMI_AF - - gnomAD_genomes_non_neuro_AMI_nhomalt - - gnomAD_genomes_non_neuro_AMR_AC - - gnomAD_genomes_non_neuro_AMR_AN - - gnomAD_genomes_non_neuro_AMR_AF - - gnomAD_genomes_non_neuro_AMR_nhomalt - - gnomAD_genomes_non_neuro_ASJ_AC - - gnomAD_genomes_non_neuro_ASJ_AN - - gnomAD_genomes_non_neuro_ASJ_AF - - gnomAD_genomes_non_neuro_ASJ_nhomalt - - gnomAD_genomes_non_neuro_EAS_AC - - gnomAD_genomes_non_neuro_EAS_AN - - gnomAD_genomes_non_neuro_EAS_AF - - gnomAD_genomes_non_neuro_EAS_nhomalt - - gnomAD_genomes_non_neuro_FIN_AC - - gnomAD_genomes_non_neuro_FIN_AN - - gnomAD_genomes_non_neuro_FIN_AF - - gnomAD_genomes_non_neuro_FIN_nhomalt - - gnomAD_genomes_non_neuro_MID_AC - - gnomAD_genomes_non_neuro_MID_AN - - gnomAD_genomes_non_neuro_MID_AF - - gnomAD_genomes_non_neuro_MID_nhomalt - - gnomAD_genomes_non_neuro_NFE_AC - - gnomAD_genomes_non_neuro_NFE_AN - - gnomAD_genomes_non_neuro_NFE_AF - - gnomAD_genomes_non_neuro_NFE_nhomalt - - gnomAD_genomes_non_neuro_SAS_AC - - gnomAD_genomes_non_neuro_SAS_AN - - gnomAD_genomes_non_neuro_SAS_AF - - gnomAD_genomes_non_neuro_SAS_nhomalt - - gnomAD_genomes_non_cancer_AFR_AC - - gnomAD_genomes_non_cancer_AFR_AN - - gnomAD_genomes_non_cancer_AFR_AF - - gnomAD_genomes_non_cancer_AFR_nhomalt - - gnomAD_genomes_non_cancer_AMI_AC - - gnomAD_genomes_non_cancer_AMI_AN - - gnomAD_genomes_non_cancer_AMI_AF - - gnomAD_genomes_non_cancer_AMI_nhomalt - - gnomAD_genomes_non_cancer_AMR_AC - - gnomAD_genomes_non_cancer_AMR_AN - - gnomAD_genomes_non_cancer_AMR_AF - - gnomAD_genomes_non_cancer_AMR_nhomalt - - gnomAD_genomes_non_cancer_ASJ_AC - - gnomAD_genomes_non_cancer_ASJ_AN - - gnomAD_genomes_non_cancer_ASJ_AF - - gnomAD_genomes_non_cancer_ASJ_nhomalt - - gnomAD_genomes_non_cancer_EAS_AC - - gnomAD_genomes_non_cancer_EAS_AN - - gnomAD_genomes_non_cancer_EAS_AF - - gnomAD_genomes_non_cancer_EAS_nhomalt - - gnomAD_genomes_non_cancer_FIN_AC - - gnomAD_genomes_non_cancer_FIN_AN - - gnomAD_genomes_non_cancer_FIN_AF - - gnomAD_genomes_non_cancer_FIN_nhomalt - - gnomAD_genomes_non_cancer_MID_AC - - gnomAD_genomes_non_cancer_MID_AN - - gnomAD_genomes_non_cancer_MID_AF - - gnomAD_genomes_non_cancer_MID_nhomalt - - gnomAD_genomes_non_cancer_NFE_AC - - gnomAD_genomes_non_cancer_NFE_AN - - gnomAD_genomes_non_cancer_NFE_AF - - gnomAD_genomes_non_cancer_NFE_nhomalt - - gnomAD_genomes_non_cancer_SAS_AC - - gnomAD_genomes_non_cancer_SAS_AN - - gnomAD_genomes_non_cancer_SAS_AF - - gnomAD_genomes_non_cancer_SAS_nhomalt - - gnomAD_genomes_non_topmed_AFR_AC - - gnomAD_genomes_non_topmed_AFR_AN - - gnomAD_genomes_non_topmed_AFR_AF - - gnomAD_genomes_non_topmed_AFR_nhomalt - - gnomAD_genomes_non_topmed_AMI_AC - - gnomAD_genomes_non_topmed_AMI_AN - - gnomAD_genomes_non_topmed_AMI_AF - - gnomAD_genomes_non_topmed_AMI_nhomalt - - gnomAD_genomes_non_topmed_AMR_AC - - gnomAD_genomes_non_topmed_AMR_AN - - gnomAD_genomes_non_topmed_AMR_AF - - gnomAD_genomes_non_topmed_AMR_nhomalt - - gnomAD_genomes_non_topmed_ASJ_AC - - gnomAD_genomes_non_topmed_ASJ_AN - - gnomAD_genomes_non_topmed_ASJ_AF - - gnomAD_genomes_non_topmed_ASJ_nhomalt - - gnomAD_genomes_non_topmed_EAS_AC - - gnomAD_genomes_non_topmed_EAS_AN - - gnomAD_genomes_non_topmed_EAS_AF - - gnomAD_genomes_non_topmed_EAS_nhomalt - - gnomAD_genomes_non_topmed_FIN_AC - - gnomAD_genomes_non_topmed_FIN_AN - - gnomAD_genomes_non_topmed_FIN_AF - - gnomAD_genomes_non_topmed_FIN_nhomalt - - gnomAD_genomes_non_topmed_MID_AC - - gnomAD_genomes_non_topmed_MID_AN - - gnomAD_genomes_non_topmed_MID_AF - - gnomAD_genomes_non_topmed_MID_nhomalt - - gnomAD_genomes_non_topmed_NFE_AC - - gnomAD_genomes_non_topmed_NFE_AN - - gnomAD_genomes_non_topmed_NFE_AF - - gnomAD_genomes_non_topmed_NFE_nhomalt - - gnomAD_genomes_non_topmed_SAS_AC - - gnomAD_genomes_non_topmed_SAS_AN - - gnomAD_genomes_non_topmed_SAS_AF - - gnomAD_genomes_non_topmed_SAS_nhomalt - - clinvar_id - - clinvar_clnsig - - clinvar_trait - - clinvar_review - - clinvar_hgvs - - clinvar_var_source - - clinvar_MedGen_id - - clinvar_OMIM_id - - clinvar_Orphanet_id - - Interpro_domain - - GTEx_V8_gene - - GTEx_V8_tissue - - Geuvadis_eQTL_target_gene diff --git a/configs/columns_config.yaml b/configs/columns_config.yaml deleted file mode 100644 index d4ecc19..0000000 --- a/configs/columns_config.yaml +++ /dev/null @@ -1,295 +0,0 @@ -# columns to be needed in dataset -columns: - - Chromosome - - Position - - Reference Allele - - Alternate Allele - - Consequence - - IMPACT - - SYMBOL - - Feature - - BIOTYPE - - SIFT - - PolyPhen - - CADD_PHRED - - CADD_RAW - - DANN_score - - Eigen-PC-phred_coding - - Eigen-PC-raw_coding - - Eigen-PC-raw_coding_rankscore - - Eigen-phred_coding - - Eigen-raw_coding - - Eigen-raw_coding_rankscore - - FATHMM_score - - GERP++_RS - - GenoCanyon_score - - LRT_score - - M-CAP_score - - MetaLR_score - - MetaSVM_score - - MutationAssessor_score - - MutationTaster_score - - PROVEAN_score - - SiPhy_29way_logOdds - - VEST4_score - - fathmm-MKL_coding_score - - integrated_fitCons_score - - phastCons100way_vertebrate - - phastCons30way_mammalian - - phyloP100way_vertebrate - - phyloP30way_mammalian - - GERP - - gnomADv3_AF - - gnomADv3_AF_afr - - gnomADv3_AF_afr_female - - gnomADv3_AF_afr_male - - gnomADv3_AF_ami - - gnomADv3_AF_ami_female - - gnomADv3_AF_ami_male - - gnomADv3_AF_amr - - gnomADv3_AF_amr_female - - gnomADv3_AF_amr_male - - gnomADv3_AF_asj - - gnomADv3_AF_asj_female - - gnomADv3_AF_asj_male - - gnomADv3_AF_eas - - gnomADv3_AF_eas_female - - gnomADv3_AF_eas_male - - gnomADv3_AF_female - - gnomADv3_AF_fin - - gnomADv3_AF_fin_female - - gnomADv3_AF_fin_male - - gnomADv3_AF_male - - gnomADv3_AF_nfe - - gnomADv3_AF_nfe_female - - gnomADv3_AF_nfe_male - - gnomADv3_AF_oth - - gnomADv3_AF_oth_female - - gnomADv3_AF_oth_male - - gnomADv3_AF_raw - - gnomADv3_AF_sas - - gnomADv3_AF_sas_female - - gnomADv3_AF_sas_male - - clinvar_CLNREVSTAT - - clinvar_CLNSIG - - hgmd_class - -ClinicalSignificance: - - DP - - DFP - - FP - - DM? - - DM - - Benign - - Benign/Likely_benign - - Pathogenic/Likely_pathogenic - - Pathogenic - - Likely_pathogenic - - Likely_benign - -Clinsig_train: - - Pathogenic/Likely_pathogenic - - DM - - Benign - - Pathogenic - - Likely_benign - - Likely_pathogenic - -Clinsig_test: - - - DM? - - DP - - DFP - - FP - - Benign/Likely_benign - - -CLNREVSTAT: #https://www.ncbi.nlm.nih.gov/clinvar/docs/review_status/ - - practice_guideline - - reviewed_by_expert_panel - - criteria_provided,_multiple_submitters,_no_conflicts - - criteria_provided,_single_submitter - -col_conv: - - MutationTaster_score - - MutationAssessor_score - - PROVEAN_score - - VEST4_score - - FATHMM_score - - GERP - -ML_VAR: - - SYMBOL - - Feature - - Consequence - - clinvar_CLNREVSTAT - - clinvar_CLNSIG - - Chromosome - - Position - - Alternate Allele - - Reference Allele - - ID - -var: - - SYMBOL - - Feature - - Consequence - - clinvar_CLNREVSTAT - - clinvar_CLNSIG - - Chromosome - - Position - - Alternate Allele - - Reference Allele - -Consequence: - - missense_variant - - missense_variant&splice_region_variant - - stop_gained - - start_lost&NMD_transcript_variant - - start_lost - - stop_gained&splice_region_variant - - stop_gained&NMD_transcript_variant - - missense_variant&splice_region_variant&NMD_transcript_variant - - stop_gained&splice_region_variant&NMD_transcript_variant - - start_lost&splice_region_variant - - stop_lost&splice_region_variant - - stop_lost&splice_region_variant&NMD_transcript_variant - - splice_donor_variant&missense_variant - - start_lost&splice_region_variant&NMD_transcript_variant - -gnomad_columns: - - gnomADv3_AF - - gnomADv3_AF_afr - - gnomADv3_AF_afr_female - - gnomADv3_AF_afr_male - - gnomADv3_AF_ami - - gnomADv3_AF_ami_female - - gnomADv3_AF_ami_male - - gnomADv3_AF_amr - - gnomADv3_AF_amr_female - - gnomADv3_AF_amr_male - - gnomADv3_AF_asj - - gnomADv3_AF_asj_female - - gnomADv3_AF_asj_male - - gnomADv3_AF_eas - - gnomADv3_AF_eas_female - - gnomADv3_AF_eas_male - - gnomADv3_AF_female - - gnomADv3_AF_fin - - gnomADv3_AF_fin_female - - gnomADv3_AF_fin_male - - gnomADv3_AF_male - - gnomADv3_AF_nfe - - gnomADv3_AF_nfe_female - - gnomADv3_AF_nfe_male - - gnomADv3_AF_oth - - gnomADv3_AF_oth_female - - gnomADv3_AF_oth_male - - gnomADv3_AF_raw - - gnomADv3_AF_sas - - gnomADv3_AF_sas_female - - gnomADv3_AF_sas_male - -nssnv_columns: - SIFT: 0.5 - PolyPhen: 0.5 - CADD_PHRED: 20 - MetaSVM_score: 0.5 # range - -2 to 3 - FATHMM_score: 0 #weighted for human inherited- disease mutations with Disease Ontology (FATHMMori). Scores range from -18.09 to 11.0. This is for coding and MKL for non-coding variants - MutationAssessor_score: 0 #MutationAssessor functional impact combined score (MAori). The score ranges from -5.135 to 6.49 in dbNSFP. - PROVEAN_score: 0 # range - -14 to 13.57 - VEST4_score: 0.5 # range - 0 to 1 - GERP: 0 #ranges from -12.36 to 6.18 - MutationTaster_score: 0.5 - DANN_score: 0.5 - Eigen-PC-phred_coding: 20 - Eigen-PC-raw_coding: 0 #functional annotations. input range - -3.252 to 8.426. - Eigen-PC-raw_coding_rankscore: 0 - Eigen-phred_coding: 20 - Eigen-raw_coding: 0 - Eigen-raw_coding_rankscore: 0 - GERP++_RS: 0 - GenoCanyon_score: 0.5 - LRT_score: 0.5 #The score ranges from 0 to 1 and a larger score signifies that the codon is more constrained or a NS is more likely to be deleterious. - M-CAP_score: 0.5 # range - 0 to 1 - MetaLR_score: 0.5 # range - 0 to 1 - SiPhy_29way_logOdds: 15 # input range - 0 to 33.272. SiPhy score based on 29 mammals genomes. The larger the score, the more conserved the site. - fathmm-MKL_coding_score: 0.5 # range - 0 to 1. This is for non-coding variants - functional prediction tool - integrated_fitCons_score: 0.5 # range - 0 to 1 - phastCons100way_vertebrate: 0.5 - phastCons30way_mammalian: 0.5 - phyloP100way_vertebrate: 20 - phyloP30way_mammalian: 20 - IMPACT_HIGH: 0 - IMPACT_LOW: 0 - IMPACT_MODERATE: 0 - IMPACT_MODIFIER: 0 - BIOTYPE_RNase_MRP_RNA: 0 - BIOTYPE_RNase_P_RNA: 0 - BIOTYPE_antisense_RNA: 0 - BIOTYPE_guide_RNA: 0 - BIOTYPE_lncRNA: 0 - BIOTYPE_miRNA: 0 - BIOTYPE_misc_RNA: 0 - BIOTYPE_ncRNA_pseudogene: 0 - BIOTYPE_protein_coding: 0 - BIOTYPE_scRNA: 0 - BIOTYPE_snRNA: 0 - BIOTYPE_snoRNA: 0 - BIOTYPE_telomerase_RNA: 0 - BIOTYPE_transcribed_pseudogene: 0 - BIOTYPE_nonsense_mediated_decay: 0 - BIOTYPE_polymorphic_pseudogene: 0 - BIOTYPE_IG_C_gene: 0 - BIOTYPE_non_stop_decay: 0 - -non_nssnv_columns: - CADD_PHRED: 20 - GERP: 0 - IMPACT_HIGH: 0 - IMPACT_LOW: 0 - IMPACT_MODERATE: 0 - IMPACT_MODIFIER: 0 - BIOTYPE_RNase_MRP_RNA: 0 - BIOTYPE_RNase_P_RNA: 0 - BIOTYPE_antisense_RNA: 0 - BIOTYPE_guide_RNA: 0 - BIOTYPE_lncRNA: 0 - BIOTYPE_miRNA: 0 - BIOTYPE_misc_RNA: 0 - BIOTYPE_protein_coding: 0 - BIOTYPE_snRNA: 0 - BIOTYPE_snoRNA: 0 - BIOTYPE_transcribed_pseudogene: 0 - -nssnv_median_3_0_1: - gnomADv3_AF: 0 - SIFT: 0.02 - PolyPhen: 0.757 - CADD_PHRED: 25.3 - DANN_score: 0.995325924 - Eigen-PC-phred_coding: 4.859703 - Eigen-PC-raw_coding: 0.465540408 - FATHMM_score: -1.26 - GERP++_RS: 4.73 - GenoCanyon_score: 0.999998451 - LRT_score: 2.30E-05 - M-CAP_score: 0.226631 - MetaSVM_score: -0.1847 - MutationAssessor_score: 2.215 - MutationTaster_score: 1 - PROVEAN_score: -2.88 - SiPhy_29way_logOdds: 13.8644 - VEST4_score: 0.762 - fathmm-MKL_coding_score: 0.94206 - integrated_fitCons_score: 0.675202 - phastCons100way_vertebrate: 1 - phastCons30way_mammalian: 0.99 - phyloP100way_vertebrate: 4.564 - phyloP30way_mammalian: 1.026 - GERP: 5.67 - IMPACT_HIGH: 0 - BIOTYPE_IG_C_gene: 0 - BIOTYPE_non_stop_decay: 0 - BIOTYPE_polymorphic_pseudogene: 0 - BIOTYPE_protein_coding: 1 diff --git a/configs/dbnsfp_column_config.yaml b/configs/dbnsfp_column_config.yaml deleted file mode 100644 index b1f9660..0000000 --- a/configs/dbnsfp_column_config.yaml +++ /dev/null @@ -1,640 +0,0 @@ -# columns to be needed in dataset -columns: - - '#chr' - - pos(1-based) - - ref - - alt - - aaref - - aaalt - - genename - - Ensembl_geneid - - Ensembl_transcriptid - - Ensembl_proteinid - - Uniprot_acc - - cds_strand - - SIFT_score - - SIFT_converted_rankscore - - SIFT_pred - - SIFT4G_score - - SIFT4G_converted_rankscore - - SIFT4G_pred - - Polyphen2_HDIV_score - - Polyphen2_HDIV_rankscore - - Polyphen2_HDIV_pred - - Polyphen2_HVAR_score - - Polyphen2_HVAR_rankscore - - Polyphen2_HVAR_pred - - LRT_score - - LRT_converted_rankscore - - LRT_pred - - LRT_Omega - - MutationTaster_converted_rankscore - - MutationAssessor_score - - MutationAssessor_rankscore - - MutationAssessor_pred - - FATHMM_score - - FATHMM_converted_rankscore - - FATHMM_pred - - PROVEAN_score - - PROVEAN_converted_rankscore - - PROVEAN_pred - - VEST4_score - - VEST4_rankscore - - MetaSVM_score - - MetaSVM_rankscore - - MetaSVM_pred - - MetaLR_score - - MetaLR_rankscore - - MetaLR_pred - - Reliability_index - - MetaRNN_score - - MetaRNN_rankscore - - MetaRNN_pred - - M-CAP_score - - M-CAP_rankscore - - M-CAP_pred - - REVEL_score - - REVEL_rankscore - - MutPred_score - - MutPred_rankscore - - MVP_score - - MVP_rankscore - - MPC_score - - MPC_rankscore - - PrimateAI_score - - PrimateAI_rankscore - - PrimateAI_pred - - DEOGEN2_score - - DEOGEN2_rankscore - - DEOGEN2_pred - - BayesDel_addAF_score - - BayesDel_addAF_rankscore - - BayesDel_addAF_pred - - BayesDel_noAF_score - - BayesDel_noAF_rankscore - - BayesDel_noAF_pred - - ClinPred_score - - ClinPred_rankscore - - ClinPred_pred - - LIST-S2_score - - LIST-S2_rankscore - - LIST-S2_pred - - CADD_raw - - CADD_raw_rankscore - - CADD_phred - - CADD_raw_hg19 - - CADD_raw_rankscore_hg19 - - CADD_phred_hg19 - - DANN_score - - DANN_rankscore - - fathmm-MKL_coding_score - - fathmm-MKL_coding_rankscore - - fathmm-MKL_coding_pred - - fathmm-XF_coding_score - - fathmm-XF_coding_rankscore - - fathmm-XF_coding_pred - - Eigen-raw_coding - - Eigen-raw_coding_rankscore - - Eigen-phred_coding - - Eigen-PC-raw_coding - - Eigen-PC-raw_coding_rankscore - - Eigen-PC-phred_coding - - GenoCanyon_score - - GenoCanyon_rankscore - - integrated_fitCons_score - - integrated_fitCons_rankscore - - integrated_confidence_value - - GM12878_fitCons_score - - GM12878_fitCons_rankscore - - GM12878_confidence_value - - H1-hESC_fitCons_score - - H1-hESC_fitCons_rankscore - - H1-hESC_confidence_value - - HUVEC_fitCons_score - - HUVEC_fitCons_rankscore - - HUVEC_confidence_value - - LINSIGHT - - LINSIGHT_rankscore - - GERP++_NR - - GERP++_RS - - GERP++_RS_rankscore - - phyloP100way_vertebrate - - phyloP100way_vertebrate_rankscore - - phyloP30way_mammalian - - phyloP30way_mammalian_rankscore - - phyloP17way_primate - - phyloP17way_primate_rankscore - - phastCons100way_vertebrate - - phastCons100way_vertebrate_rankscore - - phastCons30way_mammalian - - phastCons30way_mammalian_rankscore - - phastCons17way_primate - - phastCons17way_primate_rankscore - - SiPhy_29way_logOdds - - SiPhy_29way_logOdds_rankscore - - bStatistic - - bStatistic_converted_rankscore - - 1000Gp3_AF - - 1000Gp3_AFR_AF - - 1000Gp3_EUR_AF - - 1000Gp3_AMR_AF - - 1000Gp3_EAS_AF - - 1000Gp3_SAS_AF - - TWINSUK_AF - - ALSPAC_AF - - UK10K_AF - - ESP6500_AA_AF - - ESP6500_EA_AF - - ExAC_AF - - ExAC_Adj_AF - - ExAC_AFR_AF - - ExAC_AMR_AF - - ExAC_EAS_AF - - ExAC_FIN_AF - - ExAC_NFE_AF - - ExAC_SAS_AF - - ExAC_nonTCGA_AF - - ExAC_nonTCGA_Adj_AF - - ExAC_nonTCGA_AFR_AF - - ExAC_nonTCGA_AMR_AF - - ExAC_nonTCGA_EAS_AF - - ExAC_nonTCGA_FIN_AF - - ExAC_nonTCGA_NFE_AF - - ExAC_nonTCGA_SAS_AF - - ExAC_nonpsych_AF - - ExAC_nonpsych_Adj_AF - - ExAC_nonpsych_AFR_AF - - ExAC_nonpsych_AMR_AF - - ExAC_nonpsych_EAS_AF - - ExAC_nonpsych_FIN_AF - - ExAC_nonpsych_NFE_AF - - ExAC_nonpsych_SAS_AF - - gnomAD_exomes_AF - - gnomAD_exomes_AFR_AF - - gnomAD_exomes_AMR_AF - - gnomAD_exomes_ASJ_AF - - gnomAD_exomes_EAS_AF - - gnomAD_exomes_FIN_AF - - gnomAD_exomes_NFE_AF - - gnomAD_exomes_SAS_AF - - gnomAD_exomes_POPMAX_AF - - gnomAD_exomes_controls_AF - - gnomAD_exomes_non_neuro_AF - - gnomAD_exomes_non_cancer_AF - - gnomAD_exomes_non_topmed_AF - - gnomAD_exomes_controls_AFR_AF - - gnomAD_exomes_controls_AMR_AF - - gnomAD_exomes_controls_ASJ_AF - - gnomAD_exomes_controls_EAS_AF - - gnomAD_exomes_controls_FIN_AF - - gnomAD_exomes_controls_NFE_AF - - gnomAD_exomes_controls_SAS_AF - - gnomAD_exomes_controls_POPMAX_AF - - gnomAD_exomes_non_neuro_AFR_AF - - gnomAD_exomes_non_neuro_AMR_AF - - gnomAD_exomes_non_neuro_ASJ_AF - - gnomAD_exomes_non_neuro_EAS_AF - - gnomAD_exomes_non_neuro_FIN_AF - - gnomAD_exomes_non_neuro_NFE_AF - - gnomAD_exomes_non_neuro_SAS_AF - - gnomAD_exomes_non_neuro_POPMAX_AF - - gnomAD_exomes_non_cancer_AFR_AF - - gnomAD_exomes_non_cancer_AMR_AF - - gnomAD_exomes_non_cancer_ASJ_AF - - gnomAD_exomes_non_cancer_EAS_AF - - gnomAD_exomes_non_cancer_FIN_AF - - gnomAD_exomes_non_cancer_NFE_AF - - gnomAD_exomes_non_cancer_SAS_AF - - gnomAD_exomes_non_cancer_POPMAX_AF - - gnomAD_exomes_non_topmed_AFR_AF - - gnomAD_exomes_non_topmed_AMR_AF - - gnomAD_exomes_non_topmed_ASJ_AF - - gnomAD_exomes_non_topmed_EAS_AF - - gnomAD_exomes_non_topmed_FIN_AF - - gnomAD_exomes_non_topmed_NFE_AF - - gnomAD_exomes_non_topmed_SAS_AF - - gnomAD_exomes_non_topmed_POPMAX_AF - - gnomAD_genomes_AF - - gnomAD_genomes_POPMAX_AF - - gnomAD_genomes_AFR_AF - - gnomAD_genomes_AMI_AF - - gnomAD_genomes_AMR_AF - - gnomAD_genomes_ASJ_AF - - gnomAD_genomes_EAS_AF - - gnomAD_genomes_FIN_AF - - gnomAD_genomes_MID_AF - - gnomAD_genomes_NFE_AF - - gnomAD_genomes_SAS_AF - - gnomAD_genomes_controls_and_biobanks_AF - - gnomAD_genomes_non_neuro_AF - - gnomAD_genomes_non_cancer_AF - - gnomAD_genomes_non_topmed_AF - - gnomAD_genomes_controls_and_biobanks_AFR_AF - - gnomAD_genomes_controls_and_biobanks_AMI_AF - - gnomAD_genomes_controls_and_biobanks_AMR_AF - - gnomAD_genomes_controls_and_biobanks_ASJ_AF - - gnomAD_genomes_controls_and_biobanks_EAS_AF - - gnomAD_genomes_controls_and_biobanks_FIN_AF - - gnomAD_genomes_controls_and_biobanks_MID_AF - - gnomAD_genomes_controls_and_biobanks_NFE_AF - - gnomAD_genomes_controls_and_biobanks_SAS_AF - - gnomAD_genomes_non_neuro_AFR_AF - - gnomAD_genomes_non_neuro_AMI_AF - - gnomAD_genomes_non_neuro_AMR_AF - - gnomAD_genomes_non_neuro_ASJ_AF - - gnomAD_genomes_non_neuro_EAS_AF - - gnomAD_genomes_non_neuro_FIN_AF - - gnomAD_genomes_non_neuro_MID_AF - - gnomAD_genomes_non_neuro_NFE_AF - - gnomAD_genomes_non_neuro_SAS_AF - - gnomAD_genomes_non_cancer_AFR_AF - - gnomAD_genomes_non_cancer_AMI_AF - - gnomAD_genomes_non_cancer_AMR_AF - - gnomAD_genomes_non_cancer_ASJ_AF - - gnomAD_genomes_non_cancer_EAS_AF - - gnomAD_genomes_non_cancer_FIN_AF - - gnomAD_genomes_non_cancer_MID_AF - - gnomAD_genomes_non_cancer_NFE_AF - - gnomAD_genomes_non_cancer_SAS_AF - - gnomAD_genomes_non_topmed_AFR_AF - - gnomAD_genomes_non_topmed_AMI_AF - - gnomAD_genomes_non_topmed_AMR_AF - - gnomAD_genomes_non_topmed_ASJ_AF - - gnomAD_genomes_non_topmed_EAS_AF - - gnomAD_genomes_non_topmed_FIN_AF - - gnomAD_genomes_non_topmed_MID_AF - - gnomAD_genomes_non_topmed_NFE_AF - - gnomAD_genomes_non_topmed_SAS_AF - - clinvar_clnsig - - clinvar_review - - Interpro_domain - -ClinicalSignificance: - - Uncertain_significance - - Pathogenic - - Likely_pathogenic - - Benign - - Likely_benign - - Benign/Likely_benign - - Pathogenic/Likely_pathogenic - -Clinsig_train: - - Benign - - Pathogenic - - Likely_pathogenic - - Likely_benign - -Clinsig_test: - - Pathogenic/Likely_pathogenic - - Benign/Likely_benign - - -CLNREVSTAT: #https://www.ncbi.nlm.nih.gov/clinvar/docs/review_status/ - - practice_guideline - - reviewed_by_expert_panel - - criteria_provided,_multiple_submitters,_no_conflicts - - criteria_provided,_single_submitter - -col_conv: - - MutationTaster_score - - MutationAssessor_score - - PROVEAN_score - - VEST4_score - - FATHMM_score - - GERP - -ML_VAR: - - '#chr' - - pos(1-based) - - ref - - alt - - aaref - - aaalt - - genename - - Ensembl_geneid - - Ensembl_transcriptid - - Ensembl_proteinid - - Uniprot_acc - - clinvar_review - - Interpro_domain - -var: - - '#chr' - - pos(1-based) - - ref - - alt - - aaref - - aaalt - - genename - - Ensembl_geneid - - Ensembl_transcriptid - - Ensembl_proteinid - - Uniprot_acc - - clinvar_review - - Interpro_domain - -Consequence: - - missense_variant - - missense_variant&splice_region_variant - - stop_gained - - start_lost&NMD_transcript_variant - - start_lost - - stop_gained&splice_region_variant - - stop_gained&NMD_transcript_variant - - missense_variant&splice_region_variant&NMD_transcript_variant - - stop_gained&splice_region_variant&NMD_transcript_variant - - start_lost&splice_region_variant - - stop_lost&splice_region_variant - - stop_lost&splice_region_variant&NMD_transcript_variant - - splice_donor_variant&missense_variant - - start_lost&splice_region_variant&NMD_transcript_variant - -allele_freq_columns: - - 1000Gp3_AF - - 1000Gp3_AFR_AF - - 1000Gp3_EUR_AF - - 1000Gp3_AMR_AF - - 1000Gp3_EAS_AF - - 1000Gp3_SAS_AF - - TWINSUK_AF - - ALSPAC_AF - - UK10K_AF - - ESP6500_AA_AF - - ESP6500_EA_AF - - ExAC_AF - - ExAC_Adj_AF - - ExAC_AFR_AF - - ExAC_AMR_AF - - ExAC_EAS_AF - - ExAC_FIN_AF - - ExAC_NFE_AF - - ExAC_SAS_AF - - ExAC_nonTCGA_AF - - ExAC_nonTCGA_Adj_AF - - ExAC_nonTCGA_AFR_AF - - ExAC_nonTCGA_AMR_AF - - ExAC_nonTCGA_EAS_AF - - ExAC_nonTCGA_FIN_AF - - ExAC_nonTCGA_NFE_AF - - ExAC_nonTCGA_SAS_AF - - ExAC_nonpsych_AF - - ExAC_nonpsych_Adj_AF - - ExAC_nonpsych_AFR_AF - - ExAC_nonpsych_AMR_AF - - ExAC_nonpsych_EAS_AF - - ExAC_nonpsych_FIN_AF - - ExAC_nonpsych_NFE_AF - - ExAC_nonpsych_SAS_AF - - gnomAD_exomes_AF - - gnomAD_exomes_AFR_AF - - gnomAD_exomes_AMR_AF - - gnomAD_exomes_ASJ_AF - - gnomAD_exomes_EAS_AF - - gnomAD_exomes_FIN_AF - - gnomAD_exomes_NFE_AF - - gnomAD_exomes_SAS_AF - - gnomAD_exomes_POPMAX_AF - - gnomAD_exomes_controls_AF - - gnomAD_exomes_non_neuro_AF - - gnomAD_exomes_non_cancer_AF - - gnomAD_exomes_non_topmed_AF - - gnomAD_exomes_controls_AFR_AF - - gnomAD_exomes_controls_AMR_AF - - gnomAD_exomes_controls_ASJ_AF - - gnomAD_exomes_controls_EAS_AF - - gnomAD_exomes_controls_FIN_AF - - gnomAD_exomes_controls_NFE_AF - - gnomAD_exomes_controls_SAS_AF - - gnomAD_exomes_controls_POPMAX_AF - - gnomAD_exomes_non_neuro_AFR_AF - - gnomAD_exomes_non_neuro_AMR_AF - - gnomAD_exomes_non_neuro_ASJ_AF - - gnomAD_exomes_non_neuro_EAS_AF - - gnomAD_exomes_non_neuro_FIN_AF - - gnomAD_exomes_non_neuro_NFE_AF - - gnomAD_exomes_non_neuro_SAS_AF - - gnomAD_exomes_non_neuro_POPMAX_AF - - gnomAD_exomes_non_cancer_AFR_AF - - gnomAD_exomes_non_cancer_AMR_AF - - gnomAD_exomes_non_cancer_ASJ_AF - - gnomAD_exomes_non_cancer_EAS_AF - - gnomAD_exomes_non_cancer_FIN_AF - - gnomAD_exomes_non_cancer_NFE_AF - - gnomAD_exomes_non_cancer_SAS_AF - - gnomAD_exomes_non_cancer_POPMAX_AF - - gnomAD_exomes_non_topmed_AFR_AF - - gnomAD_exomes_non_topmed_AMR_AF - - gnomAD_exomes_non_topmed_ASJ_AF - - gnomAD_exomes_non_topmed_EAS_AF - - gnomAD_exomes_non_topmed_FIN_AF - - gnomAD_exomes_non_topmed_NFE_AF - - gnomAD_exomes_non_topmed_SAS_AF - - gnomAD_exomes_non_topmed_POPMAX_AF - - gnomAD_genomes_AF - - gnomAD_genomes_POPMAX_AF - - gnomAD_genomes_AFR_AF - - gnomAD_genomes_AMI_AF - - gnomAD_genomes_AMR_AF - - gnomAD_genomes_ASJ_AF - - gnomAD_genomes_EAS_AF - - gnomAD_genomes_FIN_AF - - gnomAD_genomes_MID_AF - - gnomAD_genomes_NFE_AF - - gnomAD_genomes_SAS_AF - - gnomAD_genomes_controls_and_biobanks_AF - - gnomAD_genomes_non_neuro_AF - - gnomAD_genomes_non_cancer_AF - - gnomAD_genomes_non_topmed_AF - - gnomAD_genomes_controls_and_biobanks_AFR_AF - - gnomAD_genomes_controls_and_biobanks_AMI_AF - - gnomAD_genomes_controls_and_biobanks_AMR_AF - - gnomAD_genomes_controls_and_biobanks_ASJ_AF - - gnomAD_genomes_controls_and_biobanks_EAS_AF - - gnomAD_genomes_controls_and_biobanks_FIN_AF - - gnomAD_genomes_controls_and_biobanks_MID_AF - - gnomAD_genomes_controls_and_biobanks_NFE_AF - - gnomAD_genomes_controls_and_biobanks_SAS_AF - - gnomAD_genomes_non_neuro_AFR_AF - - gnomAD_genomes_non_neuro_AMI_AF - - gnomAD_genomes_non_neuro_AMR_AF - - gnomAD_genomes_non_neuro_ASJ_AF - - gnomAD_genomes_non_neuro_EAS_AF - - gnomAD_genomes_non_neuro_FIN_AF - - gnomAD_genomes_non_neuro_MID_AF - - gnomAD_genomes_non_neuro_NFE_AF - - gnomAD_genomes_non_neuro_SAS_AF - - gnomAD_genomes_non_cancer_AFR_AF - - gnomAD_genomes_non_cancer_AMI_AF - - gnomAD_genomes_non_cancer_AMR_AF - - gnomAD_genomes_non_cancer_ASJ_AF - - gnomAD_genomes_non_cancer_EAS_AF - - gnomAD_genomes_non_cancer_FIN_AF - - gnomAD_genomes_non_cancer_MID_AF - - gnomAD_genomes_non_cancer_NFE_AF - - gnomAD_genomes_non_cancer_SAS_AF - - gnomAD_genomes_non_topmed_AFR_AF - - gnomAD_genomes_non_topmed_AMI_AF - - gnomAD_genomes_non_topmed_AMR_AF - - gnomAD_genomes_non_topmed_ASJ_AF - - gnomAD_genomes_non_topmed_EAS_AF - - gnomAD_genomes_non_topmed_FIN_AF - - gnomAD_genomes_non_topmed_MID_AF - - gnomAD_genomes_non_topmed_NFE_AF - - gnomAD_genomes_non_topmed_SAS_AF - -nssnv_columns: - SIFT: 0.5 - PolyPhen: 0.5 - CADD_PHRED: 20 - MetaSVM_score: 0.5 # range - -2 to 3 - FATHMM_score: 0 #weighted for human inherited- disease mutations with Disease Ontology (FATHMMori). Scores range from -18.09 to 11.0. This is for coding and MKL for non-coding variants - MutationAssessor_score: 0 #MutationAssessor functional impact combined score (MAori). The score ranges from -5.135 to 6.49 in dbNSFP. - PROVEAN_score: 0 # range - -14 to 13.57 - VEST4_score: 0.5 # range - 0 to 1 - GERP: 0 #ranges from -12.36 to 6.18 - MutationTaster_score: 0.5 - DANN_score: 0.5 - Eigen-PC-phred_coding: 20 - Eigen-PC-raw_coding: 0 #functional annotations. input range - -3.252 to 8.426. - Eigen-PC-raw_coding_rankscore: 0 - Eigen-phred_coding: 20 - Eigen-raw_coding: 0 - Eigen-raw_coding_rankscore: 0 - GERP++_RS: 0 - GenoCanyon_score: 0.5 - LRT_score: 0.5 #The score ranges from 0 to 1 and a larger score signifies that the codon is more constrained or a NS is more likely to be deleterious. - M-CAP_score: 0.5 # range - 0 to 1 - MetaLR_score: 0.5 # range - 0 to 1 - SiPhy_29way_logOdds: 15 # input range - 0 to 33.272. SiPhy score based on 29 mammals genomes. The larger the score the more conserved the site. - fathmm-MKL_coding_score: 0.5 # range - 0 to 1. This is for non-coding variants - functional prediction tool - integrated_fitCons_score: 0.5 # range - 0 to 1 - phastCons100way_vertebrate: 0.5 - phastCons30way_mammalian: 0.5 - phyloP100way_vertebrate: 20 - phyloP30way_mammalian: 20 - IMPACT_HIGH: 0 - IMPACT_LOW: 0 - IMPACT_MODERATE: 0 - IMPACT_MODIFIER: 0 - BIOTYPE_RNase_MRP_RNA: 0 - BIOTYPE_RNase_P_RNA: 0 - BIOTYPE_antisense_RNA: 0 - BIOTYPE_guide_RNA: 0 - BIOTYPE_lncRNA: 0 - BIOTYPE_miRNA: 0 - BIOTYPE_misc_RNA: 0 - BIOTYPE_ncRNA_pseudogene: 0 - BIOTYPE_protein_coding: 0 - BIOTYPE_scRNA: 0 - BIOTYPE_snRNA: 0 - BIOTYPE_snoRNA: 0 - BIOTYPE_telomerase_RNA: 0 - BIOTYPE_transcribed_pseudogene: 0 - BIOTYPE_nonsense_mediated_decay: 0 - BIOTYPE_polymorphic_pseudogene: 0 - BIOTYPE_IG_C_gene: 0 - BIOTYPE_non_stop_decay: 0 - -nssnv_median_3_0_1: - SIFT_score: 0.020999999716877937 - SIFT_converted_rankscore: 0.5748000144958496 - SIFT4G_score: 0.04600000008940697 - SIFT4G_converted_rankscore: 0.5663999915122986 - Polyphen2_HDIV_score: 0.902999997138977 - Polyphen2_HDIV_rankscore: 0.5759900212287903 - Polyphen2_HVAR_score: 0.5410000085830688 - Polyphen2_HVAR_rankscore: 0.57014000415802 - LRT_score: 2.2000000171829015e-05 - LRT_converted_rankscore: 0.5587499737739563 - LRT_Omega: 0.0792670026421547 - MutationTaster_converted_rankscore: 0.8100100159645081 - MutationAssessor_score: 1.9850000143051147 - MutationAssessor_rankscore: 0.5646899938583374 - FATHMM_score: -0.9700000286102295 - FATHMM_converted_rankscore: 0.7842699885368347 - PROVEAN_score: -2.3399999141693115 - PROVEAN_converted_rankscore: 0.5808500051498413 - VEST4_score: 0.6420000195503235 - VEST4_rankscore: 0.7211999893188477 - MetaSVM_score: -0.3720000088214874 - MetaSVM_rankscore: 0.7289999723434448 - MetaLR_score: 0.37599998712539673 - MetaLR_rankscore: 0.7340099811553955 - Reliability_index: 10.0 - MetaRNN_score: 0.19602182507514954 - MetaRNN_rankscore: 0.35618001222610474 - M-CAP_score: 0.19680799543857574 - M-CAP_rankscore: 0.8650100231170654 - REVEL_score: 0.335999995470047 - REVEL_rankscore: 0.6969599723815918 - MutPred_score: 0.7049999833106995 - MutPred_rankscore: 0.8413800001144409 - MVP_score: 0.7887082695960999 - MVP_rankscore: 0.8119000196456909 - MPC_score: 0.48845353722572327 - MPC_rankscore: 0.5411499738693237 - PrimateAI_score: 0.607215166091919 - PrimateAI_rankscore: 0.5392299890518188 - DEOGEN2_score: 0.2666429877281189 - DEOGEN2_rankscore: 0.7485499978065491 - BayesDel_addAF_score: 0.17945000529289246 - BayesDel_addAF_rankscore: 0.7199100255966187 - BayesDel_noAF_score: 0.11684300005435944 - BayesDel_noAF_rankscore: 0.7802600264549255 - ClinPred_score: 0.3047238290309906 - ClinPred_rankscore: 0.2519800066947937 - LIST-S2_score: 0.8972100019454956 - LIST-S2_rankscore: 0.657829999923706 - CADD_raw: 3.644090414047241 - CADD_raw_rankscore: 0.6784899830818176 - CADD_phred: 25.200000762939453 - CADD_raw_hg19: 3.536958932876587 - CADD_raw_rankscore_hg19: 0.6806600093841553 - CADD_phred_hg19: 25.100000381469727 - DANN_score: 0.9947124123573303 - DANN_rankscore: 0.6636499762535095 - fathmm-MKL_coding_score: 0.9383599758148193 - fathmm-MKL_coding_rankscore: 0.5933099985122681 - fathmm-XF_coding_score: 0.47531598806381226 - fathmm-XF_coding_rankscore: 0.5192099809646606 - Eigen-raw_coding: 0.4773275852203369 - Eigen-raw_coding_rankscore: 0.6576700210571289 - Eigen-phred_coding: 4.864253044128418 - Eigen-PC-raw_coding: 0.4332945644855499 - Eigen-PC-raw_coding_rankscore: 0.6365000009536743 - Eigen-PC-phred_coding: 4.603184223175049 - GenoCanyon_score: 0.9999987483024597 - GenoCanyon_rankscore: 0.7476599812507629 - integrated_fitCons_score: 0.6752020120620728 - integrated_fitCons_rankscore: 0.5506500005722046 - integrated_confidence_value: 0.0 - GM12878_fitCons_score: 0.6271780133247375 - GM12878_fitCons_rankscore: 0.54093998670578 - GM12878_confidence_value: 0.0 - H1-hESC_fitCons_score: 0.6589829921722412 - H1-hESC_fitCons_rankscore: 0.5588099956512451 - H1-hESC_confidence_value: 0.0 - HUVEC_fitCons_score: 0.6355509757995605 - HUVEC_fitCons_rankscore: 0.5308799743652344 - HUVEC_confidence_value: 0.0 - LINSIGHT: 0.9745749831199646 - LINSIGHT_rankscore: 0.7726799845695496 - GERP++_NR: 5.329999923706055 - GERP++_RS: 4.659999847412109 - GERP++_RS_rankscore: 0.5785700082778931 - phyloP100way_vertebrate: 4.394999980926514 - phyloP100way_vertebrate_rankscore: 0.5943899750709534 - phyloP30way_mammalian: 1.0260000228881836 - phyloP30way_mammalian_rankscore: 0.45945999026298523 - phyloP17way_primate: 0.5989999771118164 - phyloP17way_primate_rankscore: 0.4025000035762787 - phastCons100way_vertebrate: 1.0 - phastCons100way_vertebrate_rankscore: 0.7163800001144409 - phastCons30way_mammalian: 0.9909999966621399 - phastCons30way_mammalian_rankscore: 0.5321999788284302 - phastCons17way_primate: 0.9679999947547913 - phastCons17way_primate_rankscore: 0.5372599959373474 - SiPhy_29way_logOdds: 13.720999717712402 - SiPhy_29way_logOdds_rankscore: 0.6220899820327759 - bStatistic: 715.0 - bStatistic_converted_rankscore: 0.5613700151443481 diff --git a/configs/envs/testing.yaml b/configs/envs/testing.yaml deleted file mode 100644 index 7e5100f..0000000 --- a/configs/envs/testing.yaml +++ /dev/null @@ -1,24 +0,0 @@ -name: testing - -channels: - - conda-forge - - anaconda - - bioconda - -dependencies: - - python=3.9.7 - - pandas=1.3.3 - - numpy=1.19.5 - - scikit-learn=0.24.2 - - imbalanced-learn=0.7.0 - - scipy=1.7.1 - - shap=0.39.0 - - bcftools=1.13 - - pip=21.2.4 - - bioconda::snakefmt==0.4.0 - - bioconda::snakemake==6.0.5 - - seaborn=0.11.2 - - black=20.8b1 - - pylint=2.11.1 - - lz4=3.1.3 - - gpy=1.10.0 diff --git a/configs/testing.yaml b/configs/testing.yaml deleted file mode 100644 index 175c398..0000000 --- a/configs/testing.yaml +++ /dev/null @@ -1,159 +0,0 @@ -# columns to be needed in dataset -columns: - - Chromosome - - Position - - Reference Allele - - Alternate Allele - - Consequence - - Gene - - HGNC_ID - - IMPACT - - SYMBOL - - Feature - - BIOTYPE - - SIFT - - PolyPhen - - CADD_PHRED - - CADD_RAW - - DANN_score - - Eigen-PC-phred_coding - - Eigen-PC-raw_coding - - Eigen-PC-raw_coding_rankscore - - Eigen-phred_coding - - Eigen-raw_coding - - Eigen-raw_coding_rankscore - - FATHMM_score - - GERP++_RS - - GenoCanyon_score - - LRT_score - - M-CAP_score - - MetaLR_score - - MetaSVM_score - - MutationAssessor_score - - MutationTaster_score - - PROVEAN_score - - SiPhy_29way_logOdds - - VEST4_score - - fathmm-MKL_coding_score - - integrated_fitCons_score - - phastCons100way_vertebrate - - phastCons30way_mammalian - - phyloP100way_vertebrate - - phyloP30way_mammalian - - GERP - - gnomADv3_AF - - gnomADv3_AF_afr - - gnomADv3_AF_afr_female - - gnomADv3_AF_afr_male - - gnomADv3_AF_ami - - gnomADv3_AF_ami_female - - gnomADv3_AF_ami_male - - gnomADv3_AF_amr - - gnomADv3_AF_amr_female - - gnomADv3_AF_amr_male - - gnomADv3_AF_asj - - gnomADv3_AF_asj_female - - gnomADv3_AF_asj_male - - gnomADv3_AF_eas - - gnomADv3_AF_eas_female - - gnomADv3_AF_eas_male - - gnomADv3_AF_female - - gnomADv3_AF_fin - - gnomADv3_AF_fin_female - - gnomADv3_AF_fin_male - - gnomADv3_AF_male - - gnomADv3_AF_nfe - - gnomADv3_AF_nfe_female - - gnomADv3_AF_nfe_male - - gnomADv3_AF_oth - - gnomADv3_AF_oth_female - - gnomADv3_AF_oth_male - - gnomADv3_AF_raw - - gnomADv3_AF_sas - - gnomADv3_AF_sas_female - - gnomADv3_AF_sas_male - - clinvar_CLNREVSTAT - - clinvar_CLNSIG - -col_conv: - - MutationTaster_score - - MutationAssessor_score - - PROVEAN_score - - VEST4_score - - FATHMM_score - - GERP - -ML_VAR: - - SYMBOL - - Feature - - Consequence - - Gene - - HGNC_ID - - clinvar_CLNREVSTAT - - clinvar_CLNSIG - - Chromosome - - Position - - Alternate Allele - - Reference Allele - #- ID - -var: - - SYMBOL - - Feature - - Consequence - - Gene - - HGNC_ID - - clinvar_CLNREVSTAT - - clinvar_CLNSIG - - Chromosome - - Position - - Alternate Allele - - Reference Allele - -Consequence: - - missense_variant - - missense_variant&splice_region_variant - - stop_gained - - start_lost&NMD_transcript_variant - - start_lost - - stop_gained&splice_region_variant - - stop_gained&NMD_transcript_variant - - missense_variant&splice_region_variant&NMD_transcript_variant - - stop_gained&splice_region_variant&NMD_transcript_variant - - start_lost&splice_region_variant - - stop_lost&splice_region_variant - - stop_lost&splice_region_variant&NMD_transcript_variant - - splice_donor_variant&missense_variant - - start_lost&splice_region_variant&NMD_transcript_variant - -nssnv_median_3_0_1: - gnomADv3_AF: 0 - SIFT: 0.02 - PolyPhen: 0.757 - CADD_PHRED: 25.3 - DANN_score: 0.995325924 - Eigen-PC-phred_coding: 4.859703 - Eigen-PC-raw_coding: 0.465540408 - FATHMM_score: -1.26 - GERP++_RS: 4.73 - GenoCanyon_score: 0.999998451 - LRT_score: 2.30E-05 - M-CAP_score: 0.226631 - MetaSVM_score: -0.1847 - MutationAssessor_score: 2.215 - MutationTaster_score: 1 - PROVEAN_score: -2.88 - SiPhy_29way_logOdds: 13.8644 - VEST4_score: 0.762 - fathmm-MKL_coding_score: 0.94206 - integrated_fitCons_score: 0.675202 - phastCons100way_vertebrate: 1 - phastCons30way_mammalian: 0.99 - phyloP100way_vertebrate: 4.564 - phyloP30way_mammalian: 1.026 - GERP: 5.67 - IMPACT_HIGH: 0 - BIOTYPE_IG_C_gene: 0 - BIOTYPE_non_stop_decay: 0 - BIOTYPE_polymorphic_pseudogene: 0 - BIOTYPE_protein_coding: 1 diff --git a/dag.png b/dag.png deleted file mode 100644 index 25841e4b614b67d86916489e2e7adac2dd1897cb..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 36929 zcmZsCWmHvN)b2jCbazR2mq>?%G)M_ZgMf4^-AFe|mvkfDEea9}NK1os_g%c-cmLdR zkux0oaL$gk=9+Upu_Dw|`3=fFM{Gc(tM+fp@gJWSzi&NTx64WT1z~ ze>v@65+R5hQjn3<^33?x?vX+MD|zsyOl8uki1AEDS%Y-lU!qo$7_FBkE0~=T0Zj_d zDo_{O2p-MX*HZ6uH9}phaZo%rPqk6hyJ}?~d4IM5XQ*&IiXkj(iZ`=p(#NlUrI@N7uA8K(dg`gNjCg zm^h~SwPV+-*hy&$Ym%J+Mm_{_v~TRBIPm(C82Kz{7J64{S+An#i%7q7r%3x#H(JY( z_q~I#nn#ya+o@K0ReA3#Za=3vl*egsNHHwBkPo!RNDtxGUw&|B??`!ghHqWFPRMHU zq?&qhN8DfV2CHk|V~8Cyp4yi%n070)cW=OI-?DA75jo@!xq*pcpkFOVx+9GQQ*0WB 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{cluster.partition} --cpus-per-task {cluster.cpus-per-task} --mem-per-cpu {cluster.mem-per-cpu} --output {cluster.output} --parsable' diff --git a/variant_annotation/.test/data/processed/vep/testing_variants_hg38_vep-annotated.vcf.gz b/variant_annotation/.test/data/processed/vep/testing_variants_hg38_vep-annotated.vcf.gz deleted file mode 100644 index e4c52554b8ad0b63be18944e2a3c6fa647cc62b3..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 118179 zcmZs>bBr%e@GUsFW6vGiwmo-j+qP}nwr$(CZR0bxx!+&jZrxAV7OW#aT9X*b{JLd#_3~batBktf505!gCPC4modhvm*@)Z2{JG z`0^S|BqKJxJ{^lU7sCrXUm-Av1P!lZvyP@5Bw?SYGv7ueX19A zsxTU9U7LGtP0yKnLx8+l-yQW!T*pUIcSyGZf z^RJ_Ak;E$Hb%ZuNM=eAia#G!4Giw?<<)40SmTo&`tR5#akheMfaWg{`I!;+fS&rHk zGeyAfq&JO1$+vI06b&Y}I`j2I0}O^XjP6=+WfHSqh#^D~GJ4 z7kneH!!}iRw6!YAoalkEV6qGBQFTj~{=tA5uU%`_Yv=iu-F9M3Za|4;m`~|xX0^oV zKs$S#D+EG@;h@9f)HdTm(5q)72iUDc_R4;b43gI?ic#byW$5@YM{R5SWh*VPd*AGe 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-##contig= -##contig= -##contig= -##contig= -##contig= -#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT testing_variants -1 7977659 . TG T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 26438228 . A G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 33010831 . A AGGATGT 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 37540757 . T TGCTTCACTTTGACTGTTGAGTGGTGAGGACTTCGGTTTCTCTTACTGCGAGG 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 70415987 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 74342872 . C CACTCCATGGG 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 75761161 . A G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 93028124 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 94031015 . G A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 97082391 . T A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 114679616 . T A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 155235002 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 155235252 . A G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 155235727 . C G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 155235749 . GGGACTGTCGACAAAGTTACGCACCCAATTGGGTCCTCCTTCGGGGTTCAGGGCAA G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 155235772 . C A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 155235843 . T G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 155240629 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 155240660 . G GC 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 193424454 . C CTA 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 236861122 . TAGGGCA T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 239642204 . T A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 240207719 . TCTTCCCGGAGCAGGAATACCTCCTCCACCCCCTCTACCCGGAGCGGGCATACCCCCTCCTCCCCCACTTCCCGGAGCGGGCATACCCCCTCCGCCCCCA T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 241497927 . A ATTT 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 247424041 . G A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -1 247425556 . C A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -2 4619138 . T C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -2 21006160 . G A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -2 26195184 . C G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -2 31370367 . A AAGGT 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -2 73926914 . G T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -2 73950650 . A G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -2 98396811 . C A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -2 205776516 . CGCA C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -2 218890289 . T A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -3 10046722 . TAGTA T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -3 49101380 . A ATAT 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -3 90449078 . A C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -3 95240551 . G C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -3 136283929 . CT T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -3 150972565 . A C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -4 33388675 . A AAT 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -4 70642685 . T TAG 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -4 110684608 . G C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -4 141883074 . T C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -5 92105771 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -5 95516411 . TAAAAA T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -5 135778811 . T C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -5 177994145 . ACT A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -6 6167589 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -6 13407909 . CCCAA C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -6 18130687 . T C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -6 18138997 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -6 26092913 . G A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -6 26893059 . C G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -6 32062442 . GTTCT G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -6 32491972 . A C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -6 35509302 . TTTGG T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -6 38931857 . TTTACA T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -6 80168945 . G C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -6 80201023 . G A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -6 80343739 . G T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -6 87545676 . CTTT C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -6 94330597 . CAC C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -6 135466360 . CTAATAGTG C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -7 2513251 . G GGATG 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -7 24142825 . GGT G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -7 107893264 . A TA 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -7 107893265 . C G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -7 107915506 . G T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -7 117559590 . ATCT A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -7 141649323 . A ATAAC 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -7 152247986 . G GT 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -7 117559590 . ATCT A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -8 29593089 . A AAAAAG 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -8 34004551 . G A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -8 63065942 . T TAA 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -8 86643780 . AG A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -8 96330228 . CTCTG C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -9 34648170 . A G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -9 35657753 . A G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -9 69221309 . GGCCT G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -9 94639191 . T TGTGCAG 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -9 95249224 . TC T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -9 104794512 . T TAA 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -9 108899816 . A G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -9 108900303 . C G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -9 131764714 . T TTCCTCCTCCTCCTCGTCCTCCTCCTCCTCG 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -9 133454548 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -9 136687357 . T TGGCCC 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -9 137007944 . CGTG GTCTGCGGTA 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -9 137028134 . AGC TTT 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -9 137879160 . G GACGACACGGAGCCCTATTTCATCGGGATCTTTTGCTTCGAGGCAGGGATCAAAATCATCGC 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -10 17071457 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -10 27034798 . CTCTT C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -10 54317413 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -10 54317414 . G A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -10 70598966 . T C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -10 87460988 . A C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -10 98435647 . ATACAGGAAGT A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -10 98490050 . CTT C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -10 102065939 . A AGCAGCCGCTT 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -10 104479773 . ACGGGAAG A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -11 5227002 . T A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -11 6391976 . T C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -11 6392055 . TC T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -11 6394203 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -11 6394204 . G A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -11 6394204 . G T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -11 6394536 . TGCC T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -11 17395887 . GAGA G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -11 17397055 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -11 17463457 . A T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -11 42403298 . A AT 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -11 46739505 . G A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -11 59842516 . GTTC G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -11 61393964 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -11 61393965 . G A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -11 61393965 . G T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -11 95882497 . CTAAAA C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -11 108249050 . G GTAAT 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -11 22221100 . C CA 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -12 6034812 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -12 6333477 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -12 20855146 . T TAATTG 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -12 75996946 . CAAGGATGATG C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -12 88049400 . CTTCT C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -12 102852875 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -12 109596515 . G A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -13 20189546 . AC A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -13 24246861 . CAA C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -13 36329415 . GT G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -13 38151605 . CTG C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -13 108209518 . ATCTTT A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -13 108209993 . CTCTTT C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -14 64081559 . TCACCATGCTAG T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -14 67729336 . CGCCCT C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -14 87219614 . T C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -14 94378547 . C CG 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -14 94378610 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -14 94380925 . T A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -15 31038109 . CAT C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -15 45101227 . TGAAC T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -15 53215578 . G A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -15 72346234 . C G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -15 72346580 . G GATAG 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -15 72348047 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -15 72350517 . C G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -15 72350517 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -15 72350518 . C G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -15 72350518 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -15 72350578 . G A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -15 72350584 . G A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -15 90766923 . ATCTGA TAGATTC 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -16 23199643 . T TTTTTC 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -16 31096368 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -16 54316844 . C G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -16 89546737 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -17 1933854 . G T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -17 3483497 . A G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -17 3494408 . C A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -17 3499000 . A C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -17 3499060 . C A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -17 6425550 . GGGTGGCTCTGCA G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -17 42900952 . TC T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -17 42903947 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -17 42903948 . G A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -17 42907558 . G GTA 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -17 42909418 . G C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -17 42911076 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -17 42911161 . G T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -17 42911330 . CTTC C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -17 42911391 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -17 43057062 . T TG 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -17 43063322 . CAAGTACTTACCTCATTCAGCATTTTTCTTTCTTTAATAGACTGGGTCACCCCTAAAGAGATCATAGAAAAGACAGGTTACATACAGCAGAAGAACGTGCTCTTTTCACGGAGATAGAGAGGTCAGCGATTCACAAAAGAGCACAGGAAGAATGACAGAGGAGAGGTCCTTCCCTCTAAAGCCACAGCCCTTTAATAAGGCTTGTAGCAGCAGTTTCCTTCTGGAGACAGAGTTGATGTTTAATTTAAACATTATAAGTTTGCCTGCTGCACATGGATTCCTGCCGACTATTAAATAAATCCCTAGCTCATATGCTAACATTGCTAGGAGCAGATTAGGTCCTATTAGTTATAAAAGAGACCCATTTTCCCAGCATCACCAGCTTATCTGAACAAAGTGATATTAAAGATAAAAGTAGTTTAGTATTACAATTAAAGACCTTTTGGTAACTCAGACTCAGCATCAGCAAAAACCTTAGGTGTTAAACGTTAGGTGTAAAAATGCAATTCTGAGGTGTTAAAGGGAGGAGGGGAGAAATAGTATTATACTTACAGAAATAGCTAACTACCCATTTTCCTCCCGCAATTCCTAGAAAATATTTCAGTGTCCGTTCACACACAAACTCAGCATCTGCAGAATGAAAAACACT C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -17 75750112 . TGAACTGGACCG T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -17 75834092 . ACTCC A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -17 77606958 . ATCATTCAT A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -18 10671602 . ATCT A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -18 24143127 . AAGCCCAGACTTGTCG A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -18 34303248 . G A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -19 7526759 . A G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -19 11447528 . GGAG G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -19 29702971 . GCCC G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -19 39248147 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -19 44907777 . GAGCA G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -19 51731546 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -19 53823971 . C CTCCTCTGGCCCGAAGTGGGTGATGAGCAGCTGGGCCATTTCCAGGGGACCGGCCTTCTCCATGCT 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -20 4511688 . C CA 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -20 50945838 . ACCACTTACATG A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -20 50945875 . ATGATGTAGTTTCC A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -21 10469529 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -21 12972580 . T G 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -21 46145925 . G GC 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -22 26933109 . G GA 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -22 42128945 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -22 50524395 . C CTGAGTCACTGCTGCATGCT 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -22 50526478 . TGCGG T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -22 50721897 . CTG C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -X 1553205 . G A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -X 31121883 . CTCTGCCCAAATCA C 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -X 38286537 . TCCTCTACTTCCCCTC T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -X 124322939 . T A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -X 154534419 . G A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -X 154536002 . C T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -Y 10123227 . G A 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 -Y 12720687 . G T 1000 PASS AC=2;DP=30 GT:AD:DP 0/1:15,15:30 diff --git a/variant_annotation/README.md b/variant_annotation/README.md deleted file mode 100644 index 105f9ad..0000000 --- a/variant_annotation/README.md +++ /dev/null @@ -1,73 +0,0 @@ -# Variant annotation - -Annotated variants in VCF using Variant Effect Predictor (VEP). - -Script [`src/run_pipeline.sh`](src/run_pipeline.sh) runs the snakemake workflow, which sets up VEP and then uses it for annotation. - -## Setup - -1. Download submodules - -```sh -git submodule update --init -``` - - -2. Create necessary directories to store log files - -```sh -cd variant_annotation -mkdir -p logs/rule_logs -``` - -3. Create dataset config YAML and populate with paths - -```sh -touch ~/.ditto_datasets.yaml -``` - -Enter path info into the YAML file in the following format - -```yml -cadd_snv: "/path/to/data/cadd/hg38/v1.6/whole_genome_SNVs.tsv.gz" -cadd_indel: "/path/to/data/cadd/raw/hg38/v1.6/gnomad.genomes.r3.0.indel.tsv.gz" -gerp: "/path/to/data/gerp/processed/hg38/v1.6/gerp_score_hg38.bg.gz" -gnomad_genomes: "/path/to/data/gnomad/v3.0/data/gnomad.genomes.r3.0.sites.vcf.bgz" -clinvar: "/path/to/data/clinvar/data/grch38/20210119/clinvar_20210119.vcf.gz" -dbNSFP: "/path/to/data/dbnsfp/processed/v4.1a_20200616/dbNSFP4.1a_variant.complete.bgz" -``` - -## Datasets in custom format - -Two of the datasets listed in the datasets YAML require custom formatting for use with the VEP annotator. The following -describes that formatting process that will need to be performed. - -**gerp:** - - - GERP is extracted from the annotation database distributed by CADD found [here](https://cadd.gs.washington.edu/download) - - Format GERP base-wise RS scores from extracted annotation file into final compressed BedGraph file - -**dbNSFP:** - - - dbNSFP data is extracted from dbNSFP zip found [here](https://sites.google.com/site/jpopgen/dbNSFP) - - per chromosome tab-seperated value files are extracted from the zip, sorted by GRCh38/hg38 coordinates, joined - into a single file, bgzipped and indexed. - -All other dataset files listed in the config file are in usable in the format provided by their originating source. - -## How to run - -- To run in current session (Note: only runs main Snakemake process in current session, Snakemake will still send jobs - to Slurm): - - ```sh - cd variant_annotation - ./src/run_pipeline.sh -v .test/data/raw/testing_variants_hg38.vcf -o .test/data/processed/vep -d ~/.ditto_datasets.yaml - ``` - -- To run it as slurm job: - - ```sh - cd variant_annotation - ./src/run_pipeline.sh -s -v .test/data/raw/testing_variants_hg38.vcf -o .test/data/processed/vep -d ~/.ditto_datasets.yaml - ``` diff --git a/variant_annotation/configs/cluster_config.json b/variant_annotation/configs/cluster_config.json deleted file mode 100644 index 764cf8d..0000000 --- a/variant_annotation/configs/cluster_config.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "__default__": { - "ntasks": 1, - "partition": "long", - "cpus-per-task": "{threads}", - "mem": "20G", - "output": "logs/rule_logs/{rule}-%j.log", - "error": "logs/rule_logs/{rule}-%j.err" - } -} diff --git a/variant_annotation/configs/env/vep.yaml b/variant_annotation/configs/env/vep.yaml deleted file mode 100644 index 5f3befe..0000000 --- a/variant_annotation/configs/env/vep.yaml +++ /dev/null @@ -1,6 +0,0 @@ -channels: - - bioconda - - conda-forge -dependencies: - - ensembl-vep =102 - - bcftools =1.10.2 diff --git a/variant_annotation/configs/snakemake_slurm_profile b/variant_annotation/configs/snakemake_slurm_profile deleted file mode 160000 index 4ecaf55..0000000 --- a/variant_annotation/configs/snakemake_slurm_profile +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 4ecaf55d398ebfdf8415dff50c26beea0237c34d diff --git a/variant_annotation/src/Snakefile b/variant_annotation/src/Snakefile deleted file mode 100644 index 1338e17..0000000 --- a/variant_annotation/src/Snakefile +++ /dev/null @@ -1,137 +0,0 @@ -""" -This pipeline annotates VCF using Variant Effect Predictor -1. Sets up VEP cache and plugins -2. Using cache, plugins and other locally available datasets, annoate variants in VCF -""" - -from pathlib import Path - -# datasets to use for annotations -configfile: config["datasets"] - - -#### VEP parameters #### -VEP_CACHE = 'homo_sapiens_merged' #'homo_sapiens_refseq' -SPECIES = 'homo_sapiens' -REF_BUILD = "GRCh38" -ENSEMBL_DATASET_VERSION = "102" -HGVS = False -STATS = False - -### I/O parameters -INPUT_VCF = config["vcf"] -PROCESSED_DIR = Path(config["outdir"]) -EXTERNAL_DIR = Path("data/external") - -if not (INPUT_VCF.endswith('vcf') or INPUT_VCF.endswith('vcf.gz') or INPUT_VCF.endswith('vcf.bgz')): - print (f"Error: Input file extension not in expected format: found {INPUT_VCF}, expecting *.vcf, *.vcf.gz or *.vcf.bgz") - raise SystemExit(1) - -INPUT_VCF = Path(INPUT_VCF) -OUTPUT_VCF = PROCESSED_DIR / ((INPUT_VCF.name).rstrip(".bgz").rstrip(".gz").rstrip(".vcf") + "_vep-annotated.vcf.gz") - - -rule all: - input: - OUTPUT_VCF - - -rule get_vep_cache: - output: - cache = directory(EXTERNAL_DIR / "vep" / "cache" / VEP_CACHE), - params: - species = VEP_CACHE, - build = REF_BUILD, - release = ENSEMBL_DATASET_VERSION, - plugins = "CADD" - message: - "Retrieves VEP cache data" - conda: - "../configs/env/vep.yaml" - shell: - r""" - vep_install --AUTO cfp \ - --SPECIES {params.species} \ - --ASSEMBLY {params.build} \ - --PLUGINS {params.plugins} \ - --CACHE_VERSION {params.release} \ - --CACHEDIR {output.cache} \ - --CONVERT \ - --NO_UPDATE - """ - - -rule get_vep_plugins: - output: - directory(EXTERNAL_DIR / "vep" / "plugins"), - message: - "Downloads VEP plugins" - params: - release = ENSEMBL_DATASET_VERSION - wrapper: - "0.59.2/bio/vep/plugins" - - -rule annotate_variants: - input: - calls = INPUT_VCF, - cache = EXTERNAL_DIR / "vep" / "cache" / VEP_CACHE, - plugins = EXTERNAL_DIR / "vep" / "plugins", - cadd_snv = config['cadd_snv'], - cadd_indel = config['cadd_indel'], - gerp = config['gerp'], - gnomad_genomes = config['gnomad_genomes'], - clinvar = config['clinvar'], - dbNSFP = config['dbNSFP'], - output: - calls = OUTPUT_VCF, - message: - "Annotated vcf using VEP with CADD, gnomad-exomes, gnomad-genomes and GERP. " - f"VEP cache used: {VEP_CACHE}, ref build: {REF_BUILD}, Ensemble version: {ENSEMBL_DATASET_VERSION}" - params: - release = ENSEMBL_DATASET_VERSION, - species = SPECIES, - build = REF_BUILD, - #refseq_flag = "--refseq" if 'refseq' in VEP_CACHE else "", - #refseq_flag = "--merged" if 'merged' in VEP_CACHE else "", - refseq_flag = "--gencode_basic" if 'gencode_basic' in VEP_CACHE else "", - hgvs_flag = "--hgvs" if HGVS else "", - stats_flag = lambda wildcards, output: f"--stats_file {output.stats}" if STATS else "--no_stats", - gnomad_fields = "AF,AF_afr,AF_afr_female,AF_afr_male,AF_ami,AF_ami_female,AF_ami_male,AF_amr,AF_amr_female,AF_amr_male,AF_asj,AF_asj_female," \ - "AF_asj_male,AF_eas,AF_eas_female,AF_eas_male,AF_female,AF_fin,AF_fin_female,AF_fin_male,AF_male,AF_nfe,AF_nfe_female,AF_nfe_male," \ - "AF_oth,AF_oth_female,AF_oth_male,AF_raw,AF_sas,AF_sas_female,AF_sas_male", - clinvar_fields = "AF_ESP,AF_EXAC,AF_TGP,ALLELEID,CLNDN,CLNDNINCL,CLNDISDB,CLNDISDBINCL,CLNREVSTAT,CLNSIG,CLNSIGCONF,CLNSIGINCL,CLNVC,GENEINFO,MC,ORIGIN,RS,SSR", - dbNSFP_fields = "Ensembl_transcriptid,LRT_score,MutationTaster_score,MutationAssessor_score,FATHMM_score,PROVEAN_score,VEST4_score,MetaSVM_score,MetaLR_score,M-CAP_score," \ - "CADD_phred,DANN_score,fathmm-MKL_coding_score,GenoCanyon_score,integrated_fitCons_score,GERP++_RS,phyloP100way_vertebrate,phyloP30way_mammalian," \ - "phastCons100way_vertebrate,phastCons30way_mammalian,SiPhy_29way_logOdds,Eigen-raw_coding,Eigen-raw_coding_rankscore,Eigen-phred_coding," \ - "Eigen-PC-raw_coding,Eigen-PC-raw_coding_rankscore,Eigen-PC-phred_coding", - #dbNSFP_fields = "ALL", - warnings_file = lambda wildcards, output: str(output.calls).replace('.vcf.gz', '_STDOUT_warnings.txt'), - threads: 8 - conda: - "../configs/env/vep.yaml" - shell: - r""" - # using bcftools view as it might catch vcf-related errors (https://stackoverflow.com/a/63371639/3998252) - bcftools view {input.calls} | \ - vep --fork {threads} \ - --format vcf \ - --vcf \ - --offline \ - --cache \ - --cache_version {params.release} \ - --species {params.species} \ - --assembly {params.build} \ - {params.refseq_flag} {params.hgvs_flag} \ - --sift s --polyphen s \ - --dir_cache {input.cache} \ - --dir_plugins {input.plugins} \ - --plugin CADD,{input.cadd_snv},{input.cadd_indel} \ - --custom {input.gerp},GERP,bed \ - --custom {input.gnomad_genomes},gnomADv3,vcf,exact,0,{params.gnomad_fields} \ - --custom {input.clinvar},clinvar,vcf,exact,0,{params.clinvar_fields} \ - {params.stats_flag} \ - --warning_file {params.warnings_file} \ - --compress_output bgzip \ - --output_file {output.calls} - """ diff --git a/variant_annotation/src/run_pipeline.sh b/variant_annotation/src/run_pipeline.sh deleted file mode 100755 index d1afcdf..0000000 --- a/variant_annotation/src/run_pipeline.sh +++ /dev/null @@ -1,80 +0,0 @@ -#!/usr/bin/env bash -#SBATCH --job-name=vep -#SBATCH --output=logs/vep-%j.log -#SBATCH --cpus-per-task=1 -#SBATCH --mem-per-cpu=4G -#SBATCH --partition=long - -set -eo pipefail - -usage() { - echo "usage: $0" - echo "" - echo "required:" - echo " -v | --vcf [file] path to VCF file to annotate" - echo " -o | --out [dir] path to directory to place annotated VCF file" - echo " -d | --datasets [file] path to datasets config YAML file" - echo "" - echo "options:" - echo " -s | --slurm flag to indicate execution should be done as Slurm job" - echo " -h | --help print usage info" -} - -while [ "$1" != "" ]; do - case $1 in - -v | --vcf) - shift - INPUT_VCF=$1 - ;; - -d | --datasets) - shift - DATASETS_CONFIG=$1 - ;; - -o | --out) - shift - OUT_DIR=$1 - ;; - -s | --slurm) - USE_SLURM="yes" - ;; - -h | --help) - usage - exit - ;; - *) - usage - exit 1 - ;; - esac - shift -done - -# ensure required info set either from CLI or as environement variables (when executed by slurm) -if [[ -z $INPUT_VCF || -z $DATASETS_CONFIG || -z $OUT_DIR ]]; then - echo "Missing required input, check usage" - usage - exit 1 -fi - -module reset - -if [[ -z $USE_SLURM ]]; then - # run in current environment - module load Anaconda3/2020.02 - module load snakemake/5.9.1-foss-2018b-Python-3.6.6 - snakemake \ - --snakefile "src/Snakefile" \ - --config vcf="${INPUT_VCF}" datasets="${DATASETS_CONFIG}" outdir="${OUT_DIR}" \ - --use-conda \ - --profile 'configs/snakemake_slurm_profile/{{cookiecutter.profile_name}}' \ - --cluster-config 'configs/cluster_config.json' \ - --cluster 'sbatch --ntasks {cluster.ntasks} --partition {cluster.partition} --cpus-per-task {cluster.cpus-per-task} --mem {cluster.mem} --output {cluster.output} --error {cluster.error} --parsable' -else - # execute as slurm job - module load gcc - module load slurm - sbatch --export=ALL,INPUT_VCF="${INPUT_VCF}",DATASETS_CONFIG="${DATASETS_CONFIG}",OUT_DIR="${OUT_DIR}" $0 -fi - - - From b597f5a8b6e49430ebe42ef0032c18f0a27d11af Mon Sep 17 00:00:00 2001 From: Brandon Wilk Date: Tue, 18 Apr 2023 10:38:20 -0500 Subject: [PATCH 02/13] cleaning up part 2 --- .gitmodules | 3 - annotation_parsing/README.md | 31 - src/Ditto/Exomiser_ranks.py | 72 - src/Ditto/combine_scores.py | 185 - src/Ditto/dbnsfp_prediction.py | 131 - src/Ditto/dbnsfp_predictions.py | 144 - src/Ditto/dittodb.py | 163 - src/Ditto/filter.py | 194 - src/Ditto/model.job | 32 - src/Ditto/predict_UDN.py | 68 - src/Ditto/ranks.py | 73 - src/Ditto/submission.py | 29 - .../parse_annotated_vars.py | 0 src/cohort/combine_scores.py | 173 - src/cohort/ranks.py | 81 - src/dbNSFP/Ditto_dbNSFP_filtering.ipynb | 9749 ----------------- src/dbNSFP/array_script.job | 22 - src/dbNSFP/extract_genes.py | 32 - src/dbNSFP/predictions.py | 118 - src/pkd/array_script.job | 22 - src/pkd/extract_pkd.py | 42 - src/pkd/merge.py | 40 - src/pkd/model.job | 28 - src/pkd/predictions.py | 135 - src/{Ditto => predict}/predict.py | 0 src/streamlit.py | 121 - src/training/data-prep/extract_class.py | 35 - .../extract_dbNSFP_clinvar_variants.py | 25 - src/training/data-prep/extract_gnomad_snv.py | 17 - src/training/data-prep/extract_variants.py | 67 - src/training/data-prep/filter.py | 380 - src/training/data-prep/parse_clinvar.py | 26 - src/training/data-prep/parse_dbNSFP.py | 79 - src/training/training/{Tuning => }/NN.py | 0 src/training/training/Tuning/ABC.py | 312 - src/training/training/Tuning/BRF.py | 329 - src/training/training/Tuning/DT.py | 325 - src/training/training/Tuning/ET.py | 328 - src/training/training/Tuning/GBC.py | 332 - src/training/training/Tuning/GNB.py | 294 - src/training/training/Tuning/LDA.py | 317 - src/training/training/Tuning/RF.py | 329 - src/training/training/Tuning/stacking.py | 625 -- src/training/training/stacking.py | 209 - src/training/training/stacking_LC.py | 196 - src/training/training/stacking_LC_error.py | 188 - src/training/training/temp_files/ABC.py | 191 - src/training/training/temp_files/BRF.py | 195 - src/training/training/temp_files/DT.py | 192 - src/training/training/temp_files/ET.py | 196 - .../training/temp_files/Explain.ipynb | 430 - src/training/training/temp_files/GBC.py | 195 - src/training/training/temp_files/GNB.py | 209 - .../training/temp_files/K-chooser.ipynb | 966 -- src/training/training/temp_files/LDA.py | 209 - src/training/training/temp_files/MLP.py | 332 - src/training/training/temp_files/RF.py | 195 - src/training/training/temp_files/Tune_RF.py | 310 - .../training/temp_files/Tune_RF_PB2.py | 306 - .../training/temp_files/Tune_RF_PBT.py | 241 - .../training/temp_files/Tune_hp_stacking.py | 617 -- .../training/temp_files/Tune_models.py | 327 - .../training/temp_files/Tune_models1.py | 363 - .../training/temp_files/Tune_models_copy.py | 250 - .../training/temp_files/Tune_stacking.py | 548 - src/training/training/temp_files/filter.py | 101 - src/training/training/temp_files/model1.job | 94 - .../training/temp_files/optuna-model.py | 179 - .../training/temp_files/optuna-tpe-2.ipy | 222 - .../optuna-tpe-stacking_results.ipy | 199 - .../optuna-tpe-stacking_training.ipy | 245 - src/training/training/temp_files/parse_vcf.py | 26 - src/training/training/temp_files/predict.py | 81 - .../temp_files/snakemake_template.smk | 38 - workflow/Snakefile | 140 - workflow/Snakefile1 | 50 - 76 files changed, 23748 deletions(-) delete mode 100644 annotation_parsing/README.md delete mode 100644 src/Ditto/Exomiser_ranks.py delete mode 100644 src/Ditto/combine_scores.py delete mode 100644 src/Ditto/dbnsfp_prediction.py delete mode 100644 src/Ditto/dbnsfp_predictions.py delete mode 100644 src/Ditto/dittodb.py delete mode 100644 src/Ditto/filter.py delete mode 100644 src/Ditto/model.job delete mode 100644 src/Ditto/predict_UDN.py delete mode 100644 src/Ditto/ranks.py delete mode 100644 src/Ditto/submission.py rename {annotation_parsing => src/annotation_parsing}/parse_annotated_vars.py (100%) delete mode 100644 src/cohort/combine_scores.py delete mode 100644 src/cohort/ranks.py delete mode 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src/training/training/Tuning/ABC.py delete mode 100644 src/training/training/Tuning/BRF.py delete mode 100644 src/training/training/Tuning/DT.py delete mode 100644 src/training/training/Tuning/ET.py delete mode 100644 src/training/training/Tuning/GBC.py delete mode 100644 src/training/training/Tuning/GNB.py delete mode 100644 src/training/training/Tuning/LDA.py delete mode 100644 src/training/training/Tuning/RF.py delete mode 100644 src/training/training/Tuning/stacking.py delete mode 100644 src/training/training/stacking.py delete mode 100644 src/training/training/stacking_LC.py delete mode 100644 src/training/training/stacking_LC_error.py delete mode 100644 src/training/training/temp_files/ABC.py delete mode 100644 src/training/training/temp_files/BRF.py delete mode 100644 src/training/training/temp_files/DT.py delete mode 100644 src/training/training/temp_files/ET.py delete mode 100644 src/training/training/temp_files/Explain.ipynb delete mode 100644 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src/training/training/temp_files/model1.job delete mode 100644 src/training/training/temp_files/optuna-model.py delete mode 100644 src/training/training/temp_files/optuna-tpe-2.ipy delete mode 100644 src/training/training/temp_files/optuna-tpe-stacking_results.ipy delete mode 100644 src/training/training/temp_files/optuna-tpe-stacking_training.ipy delete mode 100644 src/training/training/temp_files/parse_vcf.py delete mode 100644 src/training/training/temp_files/predict.py delete mode 100644 src/training/training/temp_files/snakemake_template.smk delete mode 100644 workflow/Snakefile delete mode 100644 workflow/Snakefile1 diff --git a/.gitmodules b/.gitmodules index 380238a..e69de29 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,3 +0,0 @@ -[submodule "variant_annotation/configs/snakemake_slurm_profile"] - path = variant_annotation/configs/snakemake_slurm_profile - url = git@gitlab.rc.uab.edu:center-for-computational-genomics-and-data-science/sciops/external-projects/snakemake_slurm_profile.git diff --git a/annotation_parsing/README.md b/annotation_parsing/README.md deleted file mode 100644 index b762304..0000000 --- a/annotation_parsing/README.md +++ /dev/null @@ -1,31 +0,0 @@ -# VEP annotated VCF to TSV parser - -This is a simple, no extra fluff parser for taking an annotated VCF produced via VEP and converting it -to a Tab Seperated Values (TSV) file. - -## Requirements - - - Python 3.7+ - - VEP annotated VCF file (text or gzipped) - -## Input and Output Format Info - - - The parser is agnostic to which VEP fields are present, it pulls the column headers and ordering from VCF info - - Works on VCF files with no sample info, with 1 sample, and with multiple samples - - Columns without annotated information for a variant are left blank (i.e. there can be multiple tabs without - data between them) - - Certain annotated fields from VEP have information in them seperated by characters like `&` and are **_NOT_** - parsed by this parser, that is up to downstream users of the parsed information - - Variants affecting more than 1 transcript will have their variant information duplicated and each transcript - of info will be printed on its own line (e.g. a variant affects 2 transcripts it will have two rows in the output - with the same variant info but each transcripts worth of info seperated onto one of those lines) - -## How to Run - -To parse the example provided with this repo: - -```sh -python parse_annotated_vars.py -i ../variant_annotation/.test/data/processed/vep/testing_variants_hg38_vep-annotated.vcf.gz -o .test/testing_variants_hg38_vep-annotated.tsv -``` - -Or run the parser without any arguments to get more help info. \ No newline at end of file diff --git a/src/Ditto/Exomiser_ranks.py b/src/Ditto/Exomiser_ranks.py deleted file mode 100644 index 67d668a..0000000 --- a/src/Ditto/Exomiser_ranks.py +++ /dev/null @@ -1,72 +0,0 @@ -import json -import pandas as pd -import glob -import os -import argparse - -parser = argparse.ArgumentParser() -parser.add_argument( - "--input-dir", - "-id", - type=str, - required=True, - help="Input raw annotated file with path.", -) -parser.add_argument( - "--json", type=str, required=True, help="Input raw annotated file with path." -) -parser.add_argument( - "--output", "-o", type=str, default="ranks.csv", help="Output csv filename only" -) -args = parser.parse_args() - -# json_file = json.load(open("/data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/metadata/train_test_metadata_original.json", 'r')) -json_file = json.load(open(args.json, "r")) -# -with open(f"{args.input_dir}/{args.output}", "w") as f: - f.write(f"PROBANDID,SYMBOL,Exomiser,Ditto,Combined,Rank\n") -rank_list = [] -for samples in json_file["train"].keys(): - if "PROBAND" in samples: - # print(samples) - for i in range(len(json_file["train"][samples]["solves"])): - gene = str(json_file["train"][samples]["solves"][i]["Gene"]) - # print(gene) - # print('Reading Exomiser scores...') - all_files = glob.glob( - os.path.join( - f"/data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/exomiser/hpo_original/train/{samples}", - "*.tsv", - ) - ) - exo_scores = pd.concat( - (pd.read_csv(f, sep="\t") for f in all_files), ignore_index=True - ) - exo_scores = exo_scores[ - ["#GENE_SYMBOL", "ENTREZ_GENE_ID", "EXOMISER_GENE_PHENO_SCORE"] - ] - exo_scores = exo_scores.sort_values( - "EXOMISER_GENE_PHENO_SCORE", ascending=False - ) - exo_scores = exo_scores.drop_duplicates( - subset=["#GENE_SYMBOL"], keep="first" - ).reset_index(drop=True) - rank = (exo_scores.loc[(exo_scores["#GENE_SYMBOL"] == gene)].index) + 1 - # #rank = ((genes.loc[(genes['CHROM'] == variants[0]) & (genes['POS'] == int(variants[1])) & (genes['ALT'] == variants[3]) & (genes['REF'] == variants[2])].index)+1) - rank_list = [*rank_list, *rank] # unpack both iterables in a list literal - with open(f"{args.input_dir}/{args.output}", "a") as f: - # f.write(f"{samples}, {variants}, {genes.loc[rank-1]['SYMBOL'].values}, {genes.loc[rank-1]['E'].values}, {genes.loc[rank-1]['D'].values}, {genes.loc[rank-1]['P'].values}, {rank.tolist()}\n") - f.write( - f"{samples},{gene},{exo_scores.loc[rank-1]['EXOMISER_GENE_PHENO_SCORE'].values},{rank.tolist()}\n" - ) - -with open(f"{args.input_dir}/{args.output}", "a") as f: - f.write(f"\nList,{rank_list}\n") - f.write(f"Rank-1,{sum(i < 2 for i in rank_list)}\n") - f.write(f"Rank-10,{sum(i < 11 for i in rank_list)}\n") - f.write(f"Rank-50,{sum(i < 51 for i in rank_list)}\n") - f.write(f"Rank-100,{sum(i < 101 for i in rank_list)}\n") - f.write(f"Rank-500,{sum(i < 501 for i in rank_list)}\n") - f.write(f"Rank-1000,{sum(i < 1001 for i in rank_list)}\n") - f.write(f"Rank-10000,{sum(i < 10001 for i in rank_list)}\n") - f.write(f"#Predictions,{len(rank_list)}\n") diff --git a/src/Ditto/combine_scores.py b/src/Ditto/combine_scores.py deleted file mode 100644 index 79a5bd0..0000000 --- a/src/Ditto/combine_scores.py +++ /dev/null @@ -1,185 +0,0 @@ -import pandas as pd -import warnings - -warnings.simplefilter("ignore") -import argparse -import os -import glob - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--raw", type=str, required=True, help="Input raw annotated file with path." - ) - parser.add_argument( - "--ditto", type=str, required=True, help="Input Ditto file with path." - ) - parser.add_argument( - "--exomiser", - "-ep", - type=str, - # default="predictions.csv", - help="Path to Exomiser output directory", - ) - parser.add_argument( - "--sample", - type=str, - # required=True, - help="Input sample name to showup in results", - ) - parser.add_argument( - "--output", - "-o", - type=str, - default="predictions_with_exomiser.csv", - help="Output csv file with path", - ) - parser.add_argument( - "--output100", - "-o100", - type=str, - default="predictions_with_exomiser_100.csv", - help="Output csv file with path for Top 100 variants", - ) - parser.add_argument( - "--output1000", - "-o1000", - type=str, - default="predictions_with_exomiser_1000.csv", - help="Output csv file with path for Top 1000 variants", - ) - args = parser.parse_args() - # print (args) - - ditto = pd.read_csv(args.ditto) - raw = pd.read_csv( - args.raw, - sep="\t", - usecols=[ - "SYMBOL", - "Chromosome", - "Position", - "Reference Allele", - "Alternate Allele", - "SYMBOL", - "Gene", - "Feature", - "HGNC_ID", - ], - ) - # raw = raw[['Chromosome','Position','Reference Allele','Alternate Allele','SYMBOL','Gene','Feature', 'HGNC_ID']] - print("Raw file loaded!") - - overall = pd.merge( - raw, - ditto, - how="left", - on=[ - "Chromosome", - "Position", - "Alternate Allele", - "Reference Allele", - "Feature", - ], - ) - # print(overall.columns.values.tolist()) - del raw, ditto - id_map = pd.read_csv( - "/data/project/worthey_lab/temp_datasets_central/tarun/HGNC/biomart_9_23_21.txt", - sep="\t", - ) - - if args.exomiser: - print("Reading Exomiser scores...") - all_files = glob.glob(os.path.join(args.exomiser, "*.tsv")) - exo_scores = pd.concat( - (pd.read_csv(f, sep="\t") for f in all_files), ignore_index=True - ) - exo_scores = exo_scores[ - ["#GENE_SYMBOL", "ENTREZ_GENE_ID", "EXOMISER_GENE_PHENO_SCORE"] - ] - id_map = id_map.merge( - exo_scores, left_on="NCBI gene ID", right_on="ENTREZ_GENE_ID" - ) - overall = overall.merge( - id_map, how="left", left_on="HGNC_ID_x", right_on="HGNC ID" - ) - del id_map, exo_scores - # overall = overall.sort_values(by = ['Ditto_Deleterious','EXOMISER_GENE_PHENO_SCORE'], axis=0, ascending=[False,False], kind='quicksort', ignore_index=True) - # overall['Exo_norm'] = (overall['EXOMISER_GENE_PHENO_SCORE'] - overall['EXOMISER_GENE_PHENO_SCORE'].min()) / (overall['EXOMISER_GENE_PHENO_SCORE'].max() - overall['EXOMISER_GENE_PHENO_SCORE'].min()) - overall["combined"] = ( - overall["EXOMISER_GENE_PHENO_SCORE"].fillna(0) - + overall["Ditto_Deleterious"].fillna(0) - ) / 2 - overall = overall[ - [ - "SYMBOL_x", - "Chromosome", - "Position", - "Reference Allele", - "Alternate Allele", - "EXOMISER_GENE_PHENO_SCORE", - "Ditto_Deleterious", - "combined", - "SD", - "C", - ] - ] - overall.insert(0, "PROBANDID", args.sample) - overall.columns = [ - "PROBANDID", - "SYMBOL", - "CHROM", - "POS", - "REF", - "ALT", - "E", - "D", - "P", - "SD", - "C", - ] - # genes = genes[genes['EXOMISER_GENE_PHENO_SCORE'] != 0] - - # overall.sort_values('pred_Benign', ascending=False).head(500).to_csv(args.output500, index=False) - else: - # overall = overall.sort_values('Ditto_Deleterious', ascending=False) - overall = overall[ - [ - "SYMBOL_x", - "Chromosome", - "Position", - "Reference Allele", - "Alternate Allele", - "Ditto_Deleterious", - "SD", - "C", - ] - ] - overall.insert(0, "PROBANDID", args.sample) - overall.columns = [ - "PROBANDID", - "SYMBOL", - "CHROM", - "POS", - "REF", - "ALT", - "P", - "SD", - "C", - ] - - overall = overall.sort_values("P", ascending=False) - overall = overall.reset_index(drop=True) - overall["SD"] = 0 - overall["C"] = "*" - overall.to_csv(args.output, index=False) - - overall = overall.drop_duplicates( - subset=["CHROM", "POS", "REF", "ALT"], keep="first" - ).reset_index(drop=True) - overall = overall[["PROBANDID", "CHROM", "POS", "REF", "ALT", "P", "SD", "C"]] - overall.head(100).to_csv(args.output100, index=False, sep=":") - overall.head(1000).to_csv(args.output1000, index=False, sep=":") - - # del genes, overall diff --git a/src/Ditto/dbnsfp_prediction.py b/src/Ditto/dbnsfp_prediction.py deleted file mode 100644 index 3375003..0000000 --- a/src/Ditto/dbnsfp_prediction.py +++ /dev/null @@ -1,131 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -#python slurm-launch.py --exp-name predictions --command "python Ditto/dbnsfp_prediction.py -i /data/project/worthey_lab/temp_datasets_central/tarun/dbNSFP/v4.3_20220319/dbNSFP4.3a_variant.complete.parsed.sorted.tsv.gz --filter /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/all_data_filter-dbnsfp.tsv.gz --ditto /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/Ditto/dbnsfp_only_ditto_predictions.tsv.gz" --partition long --mem 10G - -import pandas as pd -import yaml -import warnings -warnings.simplefilter("ignore") -from joblib import load -from tqdm import tqdm -import argparse -import numpy as np -import functools -print = functools.partial(print, flush=True) - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--input", - "-i", - type=str, - required=True, - help="Input csv file with path for filtering and predictions", - ) - parser.add_argument( - "--filter", - type=str, - default="filter.csv.gz", - help="Output file with path (default:filter.csv.gz)", - ) - parser.add_argument( - "--ditto", - type=str, - default="ditto_predictions.csv.gz", - help="Output file with path (default:ditto_predictions.csv.gz)", - ) - - - args = parser.parse_args() - - print("Loading data and Ditto model....") - - with open( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/configs/col_config.yaml" - ) as fh: - config_dict = yaml.safe_load(fh) - - - def parse_and_predict(dataframe, config_dict): - - dataframe.columns = config_dict["raw_cols"] - dataframe = dataframe[config_dict["columns"]] - # Drop variant info columns so we can perform one-hot encoding - var = dataframe[config_dict['var']] - dataframe = dataframe.drop(config_dict['var'], axis=1) - dataframe = dataframe.replace(['.','-'], np.nan) - - for key in tqdm(dataframe.columns): - try: - dataframe[key] = ( - dataframe[key] - .astype("float32") - ) - except: - dataframe[key] = dataframe[key] - - #Perform one-hot encoding - dataframe = pd.get_dummies(dataframe, prefix_sep='_') - dataframe[config_dict['allele_freq_columns']] = dataframe[config_dict['allele_freq_columns']].fillna(0) - - for key in tqdm(config_dict['nssnv_median'].keys()): - if key in dataframe.columns: - dataframe[key] = ( - dataframe[key] - .fillna(config_dict['nssnv_median'][key]) - .astype("float32") - ) - - df2 = pd.DataFrame() - for key in tqdm(config_dict['nssnv_columns']): - if key in dataframe.columns: - df2[key] = dataframe[key] - else: - df2[key] = 0 - - del dataframe - - - df2 = df2.drop(config_dict['var'], axis=1) - X_test = df2.values - y_score = clf.predict_proba(X_test) - del X_test - pred = pd.DataFrame(y_score, columns=["Ditto_Benign", "Ditto_Deleterious"]) - - ditto_scores = pd.concat([var, pred], axis=1) - df2 = pd.concat([var.reset_index(drop=True), df2.reset_index(drop=True)], axis=1) - - return df2, ditto_scores - - - with open( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/models_custom/dbnsfp/StackingClassifier_dbnsfp.joblib", - "rb", - ) as f: - clf = load(f) - - print('Processing data...') - - - df = pd.read_csv(args.input, sep='\t', header=None, chunksize=1000000) - - for i, df_chunk in enumerate(df): - df2, ditto_scores = parse_and_predict(df_chunk, config_dict) - # Set writing mode to append after first chunk - mode = 'w' if i == 0 else 'a' - - # Add header if it is the first chunk - header = i == 0 - #print('\nData shape (nsSNV) =', df2.shape) - # Write it to a file - df2.to_csv(args.filter, index=False, - header=header, sep='\t', - mode=mode, - compression='gzip') - ditto_scores.to_csv(args.ditto, index=False, - header=header,sep='\t', - mode=mode, - compression="gzip") - del df2, ditto_scores - diff --git a/src/Ditto/dbnsfp_predictions.py b/src/Ditto/dbnsfp_predictions.py deleted file mode 100644 index f50bb6a..0000000 --- a/src/Ditto/dbnsfp_predictions.py +++ /dev/null @@ -1,144 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -#python slurm-launch.py --exp-name predictions --command "python Ditto/dbnsfp_predictions.py -i /data/project/worthey_lab/temp_datasets_central/tarun/dbNSFP/v4.3_20220319/dbNSFP4.3a_variant.complete.parsed.sorted.tsv.gz --filter /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/all_data_custom-dbnsfp.csv.gz --ditto /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/Ditto/dbnsfp_ditto_predictions.csv.gz --shapley /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/Ditto/shapley.csv.gz" --partition short --mem 50G - -import pandas as pd -import yaml -import warnings -warnings.simplefilter("ignore") -from joblib import load -from tqdm import tqdm -import argparse -import shap -import numpy as np -import functools -print = functools.partial(print, flush=True) - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--input", - "-i", - type=str, - required=True, - help="Input csv file with path for filtering and predictions", - ) - parser.add_argument( - "--filter", - type=str, - default="filter.csv.gz", - help="Output file with path (default:filter.csv.gz)", - ) - parser.add_argument( - "--ditto", - type=str, - default="ditto_predictions.csv.gz", - help="Output file with path (default:ditto_predictions.csv.gz)", - ) - parser.add_argument( - "--shapley", - type=str, - default="shapley.csv.gz", - help="Output file with path (default:shapley.csv.gz)", - ) - - args = parser.parse_args() - - print("Loading data and Ditto model....") - - with open( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/configs/col_config.yaml" - ) as fh: - config_dict = yaml.safe_load(fh) - - - def parse_and_predict(dataframe, config_dict, explainer): - # Drop variant info columns so we can perform one-hot encoding - var = dataframe[config_dict['var']] - dataframe = dataframe.drop(config_dict['var'], axis=1) - dataframe = dataframe.replace(['.','-'], np.nan) - - for key in tqdm(dataframe.columns): - try: - dataframe[key] = ( - dataframe[key] - .astype("float32") - ) - except: - dataframe[key] = dataframe[key] - - #Perform one-hot encoding - dataframe = pd.get_dummies(dataframe, prefix_sep='_') - dataframe[config_dict['allele_freq_columns']] = dataframe[config_dict['allele_freq_columns']].fillna(0) - - for key in tqdm(config_dict['nssnv_median'].keys()): - if key in dataframe.columns: - dataframe[key] = ( - dataframe[key] - .fillna(config_dict['nssnv_median'][key]) - .astype("float32") - ) - - df2 = pd.DataFrame() - for key in tqdm(config_dict['nssnv_columns']): - if key in dataframe.columns: - df2[key] = dataframe[key] - else: - df2[key] = 0 - - del dataframe - - - df2 = df2.drop(config_dict['var'], axis=1) - shapley_values = explainer.shap_values(df2) - X_test = df2.values - y_score = clf.predict_proba(X_test) - del X_test - pred = pd.DataFrame(y_score, columns=["Ditto_Benign", "Ditto_Deleterious"]) - - ditto_scores = pd.concat([var, pred], axis=1) - df2 = pd.concat([var.reset_index(drop=True), df2.reset_index(drop=True)], axis=1) - - return df2, ditto_scores, shapley_values - - - with open( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/models_custom/dbnsfp/StackingClassifier_dbnsfp.joblib", - "rb", - ) as f: - clf = load(f) - - X_train = pd.read_csv('/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_custom_data-dbnsfp.csv') - X_train = X_train.drop(config_dict['var'], axis=1) - X_train = X_train.values - background = shap.kmeans(X_train, 10) - explainer = shap.KernelExplainer(clf.predict_proba, background) - del background, X_train - - - print('Processing data...') - df = pd.read_csv(args.input, sep='\t', usecols=config_dict["columns"], chunksize=1000000) - - for i, df_chunk in enumerate(df): - df2, ditto_scores, shapley_values = parse_and_predict(df_chunk, config_dict, explainer) - # Set writing mode to append after first chunk - mode = 'w' if i == 0 else 'a' - - # Add header if it is the first chunk - header = i == 0 - #print('\nData shape (nsSNV) =', df2.shape) - # Write it to a file - df2.to_csv(args.filter, index=False, - header=header, - mode=mode, - compression='gzip') - ditto_scores.to_csv(args.ditto, index=False, - header=header, - mode=mode, - compression="gzip") - shapley_values.to_csv(args.shapley, index=False, - mode=mode, - compression="gzip") - - del df2, ditto_scores, shapley_values diff --git a/src/Ditto/dittodb.py b/src/Ditto/dittodb.py deleted file mode 100644 index 48da951..0000000 --- a/src/Ditto/dittodb.py +++ /dev/null @@ -1,163 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -#python slurm-launch.py --exp-name Training --command "python Ditto/dittodb.py -i /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/all_data_custom-dbnsfp.csv -O /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/Ditto" --partition largemem --mem 150G - -import pandas as pd -import yaml -import warnings -warnings.simplefilter("ignore") -from joblib import load, dump -import argparse -import shap -import numpy as np -import matplotlib.pyplot as plt -import functools -print = functools.partial(print, flush=True) -from sklearn.preprocessing import label_binarize -from sklearn import metrics - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--input", - "-i", - type=str, - required=True, - help="Input csv file with path for predictions", - ) - parser.add_argument( - "--out_dir", - "-O", - type=str, - default=".", - help="Output directory path", - ) - - args = parser.parse_args() - - print("Loading data and Ditto model....") - - with open( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/configs/col_config.yaml" - ) as fh: - config_dict = yaml.safe_load(fh) - - ditto_db = {} - - X = pd.read_csv(args.input) - ditto_db['dbnsfp'] = X - X_test = X - var = X_test[config_dict["var"]] - X_test = X_test.drop(config_dict["var"], axis=1) - X_test = X_test.values - - with open( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/models_custom/dbnsfp/StackingClassifier_dbnsfp.joblib", - "rb", - ) as f: - clf = load(f) - - X_train = pd.read_csv(f"/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_custom_data-dbnsfp.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["var"], axis=1) - feature_names = X_train.columns.tolist() - ditto_db['feature_names'] = feature_names - #X_train = X_train.sample(frac=1).reset_index(drop=True) - X_train = X_train.values - background = shap.kmeans(X_train, 10) - explainer = shap.KernelExplainer(clf.predict, background) - ditto_db['explainer'] = explainer - - del X_train - shap_values = explainer.shap_values(X_test) - - ditto_db['shap_values'] = shap_values - - plt.figure() - shap.summary_plot(shap_values, background, feature_names, max_display = 50, show=False) - del background, shap_values, feature_names - # shap.plots.waterfall(shap_values[0], max_display=15) - plt.savefig( - f"{args.out_dir}/shap_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - - print('Ditto Loaded!\nRunning predictions.....') - - y_score = clf.predict_proba(X_test) - del X_test, clf - pred = pd.DataFrame(y_score, columns=["Ditto_Benign", "Ditto_Deleterious"]) - - overall = pd.concat([var, pred], axis=1) - ditto_db['ditto_predictions'] = overall - overall.to_csv(args.out_dir + "ditto_predictions.csv.gz", index=False, - compression="gzip") - del y_score, overall - - print('writing to database...') - with open( - f"{args.out_dir}/dittoDB.joblib", - "wb", - ) as f: - dump(ditto_db, f, compress="lz4") - print('Database storage complete!\nBenchmarking Ditto....') - - #Benchmarking - X = pd.concat([X, pred['Ditto_Deleterious']], axis=1) - del pred, ditto_db - - benchmark_columns = ['Ditto_Deleterious','SIFT_score','MutationAssessor_score','CADD_phred','DANN_score','DEOGEN2_score','LRT_score','M-CAP_score','MetaLR_score','MetaSVM_score','MetaRNN_score','ClinPred_score','MutPred_score','VEST4_score','PrimateAI_score','clinvar_clnsig'] - X = X[benchmark_columns] - X.columns = ['Ditto','SIFT','MutationAssessor','CADD','DANN','DEOGEN2','LRT','M-CAP','MetaLR','MetaSVM','MetaRNN','ClinPred','MutPred','VEST4','PrimateAI','clinvar'] - X = X.loc[X['clinvar'].isin(config_dict['BenchmarkSignificance'])] - X = X.dropna(axis=0, subset=['clinvar']).reset_index(drop=True) - Y_test = X['clinvar'].replace(r'Pathogenic/Likely_pathogenic,_other','1').replace(r'Pathogenic/Likely_pathogenic','1').str.replace(r'Likely_pathogenic','1').replace(r'Pathogenic,_other','1').replace(r'Pathogenic,_drug_response','1').replace(r'Pathogenic','1').replace(r'Benign/Likely_benign','0').replace(r'Likely_benign','0').replace(r'Benign','0').astype('int8') - - X = X.drop('clinvar', axis=1) - - plt.figure().clf() - #plt.suptitle("Benchmarking damage prediction tools", fontsize=10) - plt.xlabel("False Positive Rate") - plt.ylabel("True Positive Rate") - plt.title("Receiver Operating Characteristic (ROC) curves") - plt.grid(linestyle="--") - - for name in list(X.columns): - fpr, tpr, thresh = metrics.roc_curve(Y_test, X[name].fillna(0).values) - auc = metrics.roc_auc_score(Y_test, X[name].fillna(0).values) - plt.plot(fpr,tpr,label=str(name)+", auc="+str(auc)) - - - plt.legend() - plt.savefig( - f"{args.out_dir}/ROC.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - - plt.figure().clf() - #plt.suptitle("Benchmarking damage prediction tools", fontsize=10) - plt.xlabel("Recall") - plt.ylabel("Precision") - plt.title("Precision Recall (PRC) curves") - plt.grid(linestyle="--") - - for name in list(X.columns): - precision, recall, thresholds = metrics.precision_recall_curve(Y_test, X[name].fillna(0).values) - prc = metrics.average_precision_score(Y_test, X[name].fillna(0).values) - plt.plot(recall,precision,label=str(name)+", prc="+str(prc)) - - - plt.legend() - plt.savefig( - f"{args.out_dir}/PRC.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - - diff --git a/src/Ditto/filter.py b/src/Ditto/filter.py deleted file mode 100644 index 99a0f6d..0000000 --- a/src/Ditto/filter.py +++ /dev/null @@ -1,194 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -# python slurm-launch.py --exp-name testing --command "python Ditto/filter.py -i ../data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND_vep-annotated_filtered.tsv -O ../data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND" - -import pandas as pd - -pd.set_option("display.max_rows", None) -import numpy as np -from tqdm import tqdm -import seaborn as sns -import yaml -import os -import argparse -import matplotlib.pyplot as plt - -# from sklearn.linear_model import LinearRegression -# from sklearn.experimental import enable_iterative_imputer -# from sklearn.impute import IterativeImputer -# import pickle - - -def get_col_configs(config_f): - with open(config_f) as fh: - config_dict = yaml.safe_load(fh) - - # print(config_dict) - return config_dict - - -def extract_col(config_dict, df, stats): - print("Extracting columns and rows according to config file !....") - df = df[config_dict["columns"]] - if "non_snv" in stats: - # df= df.loc[df['hgmd_class'].isin(config_dict['Clinsig_train'])] - df = df[ - (df["Alternate Allele"].str.len() > 1) - | (df["Reference Allele"].str.len() > 1) - ] - print("\nData shape (non-snv) =", df.shape, file=open(stats, "a")) - else: - # df= df.loc[df['hgmd_class'].isin(config_dict['Clinsig_train'])] - df = df[ - (df["Alternate Allele"].str.len() < 2) - & (df["Reference Allele"].str.len() < 2) - ] - if "protein" in stats: - df = df[df["BIOTYPE"] == "protein_coding"] - else: - pass - print("\nData shape (snv) =", df.shape, file=open(stats, "a")) - df = df.loc[df["Consequence"].isin(config_dict["Consequence"])] - print("\nData shape (nsSNV) =", df.shape, file=open(stats, "a")) - - # print('\nhgmd_class:\n', df['hgmd_class'].value_counts(), file=open(stats, "a")) - print( - "\nclinvar_CLNSIG:\n", - df["clinvar_CLNSIG"].value_counts(), - file=open(stats, "a"), - ) - print( - "\nclinvar_CLNREVSTAT:\n", - df["clinvar_CLNREVSTAT"].value_counts(), - file=open(stats, "a"), - ) - print("\nConsequence:\n", df["Consequence"].value_counts(), file=open(stats, "a")) - print("\nIMPACT:\n", df["IMPACT"].value_counts(), file=open(stats, "a")) - print("\nBIOTYPE:\n", df["BIOTYPE"].value_counts(), file=open(stats, "a")) - # df = df.drop(['CLNVC','MC'], axis=1) - # CLNREVSTAT, CLNVC, MC - return df - - -def fill_na(df, config_dict, column_info, stats): # (config_dict,df): - - var = df[config_dict["var"]] - df = df.drop(config_dict["var"], axis=1) - print("parsing difficult columns......") - # df['GERP'] = [np.mean([float(item.replace('.', '0')) if item == '.' else float(item) for item in i]) if type(i) is list else i for i in df['GERP'].str.split('&')] - if "nssnv" in stats: - # df['MutationTaster_score'] = [np.mean([float(item.replace('.', '0')) if item == '.' else float(item) for item in i]) if type(i) is list else i for i in df['MutationTaster_score'].str.split('&')] - # df['MutationAssessor_score'] = [np.mean([float(item.replace('.', '0')) if item == '.' else float(item) for item in i]) if type(i) is list else i for i in df['MutationAssessor_score'].str.split('&')] - # df['PROVEAN_score'] = [np.mean([float(item.replace('.', '0')) if item == '.' else float(item) for item in i]) if type(i) is list else i for i in df['PROVEAN_score'].str.split('&')] - # df['VEST4_score'] = [np.mean([float(item.replace('.', '0')) if item == '.' else float(item) for item in i]) if type(i) is list else i for i in df['VEST4_score'].str.split('&')] - # df['FATHMM_score'] = [np.mean([float(item.replace('.', '0')) if item == '.' else float(item) for item in i]) if type(i) is list else i for i in df['FATHMM_score'].str.split('&')] - # else: - for col in tqdm(config_dict["col_conv"]): - df[col] = [ - np.mean( - [ - float(item.replace(".", "0")) if item == "." else float(item) - for item in i.split("&") - ] - ) - if "&" in str(i) - else i - for i in df[col] - ] - df[col] = df[col].astype("float64") - - print("One-hot encoding...") - df = pd.get_dummies(df, prefix_sep="_") - print(df.columns.values.tolist(), file=open(column_info, "w")) - - # lr = LinearRegression() - # imp= IterativeImputer(estimator=lr, verbose=2, max_iter=10, tol=1e-10, imputation_order='roman') - print("Filling NAs ....") - # df = imp.fit_transform(df) - # df = pd.DataFrame(df, columns = columns) - - df1 = pd.DataFrame() - - for key in tqdm(config_dict["nssnv_median_3_0_1"]): - if key in df.columns: - df1[key] = df[key].astype("float64") - else: - df1[key] = config_dict["nssnv_median_3_0_1"][key] - - #save data for SHAP - #df = df[config_dict["nssnv_median_3_0_1"]] - df1 = pd.concat([var.reset_index(drop=True), df1], axis=1) - df1.to_csv("pre_processed_data.csv", index=False) - df1 = df1.drop(config_dict["var"], axis=1) - - - for key in tqdm(config_dict["nssnv_median_3_0_1"]): - df1[key] = ( - df1[key] - .fillna(config_dict["nssnv_median_3_0_1"][key]) - .astype("float64") - ) - - df = df1 - # df = df.drop(df.std()[(df.std() == 0)].index, axis=1) - del df1 - df = df.reset_index(drop=True) - print(df.columns.values.tolist(), file=open(column_info, "a")) - - fig = plt.figure(figsize=(20, 15)) - sns.heatmap(df.corr(), fmt=".2g", cmap="coolwarm") # annot = True, - plt.savefig(f"correlation_plot.pdf", format="pdf", dpi=1000, bbox_inches="tight") - - # df.dropna(axis=1, how='all', inplace=True) - # df['ID'] = [f'var_{num}' for num in range(len(df))] - print("NAs filled!") - df = pd.concat([var.reset_index(drop=True), df], axis=1) - return df - - -def main(df, config_dict, stats, column_info, null_info): - - print("\nData shape (Before filtering) =", df.shape, file=open(stats, "w")) - df = extract_col(config_dict, df, stats) - print("Columns extracted! Extracting class info....") - df.isnull().sum(axis=0).to_csv(null_info) - # df.drop_duplicates() - df.dropna(axis=1, how="all", inplace=True) - df = fill_na(df, config_dict, column_info, stats) - return df - - -if __name__ == "__main__": - - parser = argparse.ArgumentParser() - parser.add_argument( - "--out-dir", "-O", type=str, required=True, help="File path to output directory" - ) - parser.add_argument( - "--input", "-i", type=str, required=True, help="Input file with path" - ) - - args = parser.parse_args() - - config_f = "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/configs/testing.yaml" - # read QA config file - config_dict = get_col_configs(config_f) - print("Config file loaded!") - - print("Loading data...") - var_f = pd.read_csv(args.input, sep="\t", usecols=config_dict["columns"]) - print("Data Loaded !....") - - if not os.path.exists(args.out_dir): - os.makedirs(args.out_dir) - os.chdir(args.out_dir) - stats = "stats_nssnv.csv" - # print("Filtering "+var+" variants with at-least 50 percent data for each variant...") - column_info = "columns.csv" - null_info = "Nulls.csv" - df = main(var_f, config_dict, stats, column_info, null_info) - - print("\nData shape (After filtering) =", df.shape, file=open(stats, "a")) - print("writing to csv...") - df.to_csv("processed_data.csv", index=False) - del df diff --git a/src/Ditto/model.job b/src/Ditto/model.job deleted file mode 100644 index eefdbe2..0000000 --- a/src/Ditto/model.job +++ /dev/null @@ -1,32 +0,0 @@ -#!/bin/bash -# -#SBATCH --job-name=Ditto_predict -#SBATCH --output=Ditto_predict.out -# -# Number of tasks needed for this job. Generally, used with MPI jobs -#SBATCH --ntasks=1 -#SBATCH --partition=express -# -# Number of CPUs allocated to each task. -#SBATCH --cpus-per-task=10 -# -# Mimimum memory required per allocated CPU in MegaBytes. -#SBATCH --mem=275G -# -# Send mail to the email address when the job fails -#SBATCH --mail-type=FAIL -#SBATCH --mail-user=tmamidi@uab.edu - -#Set your environment here -module load Anaconda3/2020.02 -source activate testing - -#Run your commands here -#for file in /data/project/worthey_lab/temp_datasets_central/tarun/UDN/splits/chr22/*.gz; do sbatch model.job $(cut -d'/' -f10 <<<"$file"); done -python predict_UDN.py -i $1 -#python ranks.py -id /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/debugged/filter_vcf_by_DP8_AB --json /data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/metadata/train_test_metadata_original.json -#python ranks.py -id /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/debugged/filter_vcf_by_DP6_AB --json /data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/metadata/train_test_metadata_original.json -#python ranks.py -id /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/debugged/filter_vcf_by_DP8_AB_hpo_removed --json /data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/metadata/train_test_metadata_original.json -#python ranks.py -id /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/debugged/filter_vcf_by_DP6_AB_hpo_removed --json /data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/metadata/train_test_metadata_original.json -#python ranks.py -id /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/debugged/annotated_vcf --json /data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/metadata/train_test_metadata_original.json -#python ranks.py -id /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/debugged/annotated_vcf_hpo_removed --json /data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/metadata/train_test_metadata_original.json diff --git a/src/Ditto/predict_UDN.py b/src/Ditto/predict_UDN.py deleted file mode 100644 index de10e1e..0000000 --- a/src/Ditto/predict_UDN.py +++ /dev/null @@ -1,68 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -#python src/Ditto/predict.py -i - -import pandas as pd -import warnings -warnings.simplefilter("ignore") -import argparse -import os -import io -import gzip -import functools -print = functools.partial(print, flush=True) - -os.chdir('/data/project/worthey_lab/temp_datasets_central/tarun/UDN') - -def main(args): - - print("Loading Ditto predictions....") - - ditto = pd.read_csv('../Ditto/dbnsfp_only_ditto_predictions.csv.gz') - ditto = ditto.dropna(subset=['pos(1-based)', 'Ditto_Deleterious']) - ditto = ditto.sort_values("Ditto_Deleterious", ascending=False) - ditto = ditto.drop_duplicates(subset=['#chr','pos(1-based)','ref','alt'], keep='first') - ditto['#chr'] = ditto['#chr'].astype(str) - ditto['pos(1-based)'] = ditto['pos(1-based)'].astype(int) - ditto['ref'] = ditto['ref'].astype(str) - ditto['alt'] = ditto['alt'].astype(str) - print('Ditto Loaded!\nRunning predictions.....') - - overall = pd.concat([read_vcf('./splits/'+f+'/'+ args.input) for f in os.listdir("./splits/")]) - overall = overall.drop([args.input.split('.')[0],'FILTER','INFO','FORMAT'], axis=1) - - - overall = overall.merge(ditto, left_on=['CHROM','POS','REF','ALT'], right_on = ['#chr','pos(1-based)','ref','alt'], how='left') - del ditto - overall.drop_duplicates(inplace=True) - overall = overall.sort_values("Ditto_Deleterious", ascending=False) - - overall.to_csv('./predictions/ditto/'+args.input, index=False, compression="gzip") - - overall.head(100).to_csv('./predictions/ditto/100_'+args.input, index=False, compression="gzip") - del overall - return None - -def read_vcf(path): - with gzip.open(path, 'rt') as f: - lines = [l for l in f if not l.startswith('##')] - return pd.read_csv( - io.StringIO(''.join(lines)), - dtype={'#CHROM': str, 'POS': int, 'REF': str, 'ALT': str}, - sep='\t' - ).rename(columns={'#CHROM': 'CHROM'}) - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--input", - "-i", - type=str, - required=True, - help="Input sample name for predictions", - ) - - args = parser.parse_args() - main(args) - diff --git a/src/Ditto/ranks.py b/src/Ditto/ranks.py deleted file mode 100644 index e652572..0000000 --- a/src/Ditto/ranks.py +++ /dev/null @@ -1,73 +0,0 @@ -import json -import pandas as pd -import argparse - -parser = argparse.ArgumentParser() -parser.add_argument( - "--input-dir", - "-id", - type=str, - required=True, - help="Input raw annotated file with path.", -) -parser.add_argument( - "--json", type=str, required=True, help="Input raw annotated file with path." -) -parser.add_argument( - "--output", "-o", type=str, default="ranks.csv", help="Output csv filename only" -) -args = parser.parse_args() - -# json_file = json.load(open("/data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/metadata/train_test_metadata_original.json", 'r')) -json_file = json.load(open(args.json, "r")) - -with open(f"{args.input_dir}/{args.output}", "w") as f: - f.write(f"PROBANDID,[CHROM,POS,REF,ALT],SYMBOL,Exomiser,Ditto,Combined,Rank\n") -rank_list = [] -for samples in json_file["train"].keys(): - if "PROBAND" in samples: - # genes = pd.read_csv(f"/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/debugged/filter_vcf_by_DP6_AB/train/{samples}/combined_predictions.csv")#, sep=':') - genes = pd.read_csv( - f"{args.input_dir}/train/{samples}/combined_predictions.csv" - ) # , sep=':') - # genes = genes.sort_values(by = ['E','P'], axis=0, ascending=[False,False], kind='quicksort', ignore_index=True) - # genes = genes.drop_duplicates(subset=['Chromosome','Position','Alternate Allele','Reference Allele'], keep='first').reset_index(drop=True) - genes = genes.drop_duplicates( - subset=["CHROM", "POS", "ALT", "REF"], keep="first" - ).reset_index(drop=True) - for i in range(len(json_file["train"][samples]["solves"])): - variants = str( - "chr" - + str(json_file["train"][samples]["solves"][i]["Chrom"]).split(".")[0] - + "," - + str(json_file["train"][samples]["solves"][i]["Pos"]) - + "," - + json_file["train"][samples]["solves"][i]["Ref"] - + "," - + json_file["train"][samples]["solves"][i]["Alt"] - ).split(",") - # rank = ((genes.loc[(genes['Chromosome'] == variants[0]) & (genes['Position'] == int(variants[1])) & (genes['Alternate Allele'] == variants[3]) & (genes['Reference Allele'] == variants[2])].index)+1) - rank = ( - genes.loc[ - (genes["CHROM"] == variants[0]) - & (genes["POS"] == int(variants[1])) - & (genes["ALT"] == variants[3]) - & (genes["REF"] == variants[2]) - ].index - ) + 1 - rank_list = [*rank_list, *rank] # unpack both iterables in a list literal - with open(f"{args.input_dir}/{args.output}", "a") as f: - f.write( - f"{samples}, {variants}, {genes.loc[rank-1]['SYMBOL'].values}, {genes.loc[rank-1]['E'].values}, {genes.loc[rank-1]['D'].values}, {genes.loc[rank-1]['P'].values}, {rank.tolist()}\n" - ) - # f.write(f"{samples}, {variants}, {genes.loc[rank-1]['SYMBOL'].values}, {genes.loc[rank-1]['Ditto_Deleterious'].values}, {rank.tolist()}\n") - -with open(f"{args.input_dir}/{args.output}", "a") as f: - # f.write(f"\nList,{rank_list}\n") - f.write(f"Rank-1,{sum(i < 2 for i in rank_list)}\n") - f.write(f"Rank-5,{sum(i < 6 for i in rank_list)}\n") - f.write(f"Rank-10,{sum(i < 11 for i in rank_list)}\n") - f.write(f"Rank-20,{sum(i < 21 for i in rank_list)}\n") - f.write(f"Rank-50,{sum(i < 51 for i in rank_list)}\n") - f.write(f"Rank-100,{sum(i < 101 for i in rank_list)}\n") - f.write(f"#Predictions,{len(rank_list)}\n") diff --git a/src/Ditto/submission.py b/src/Ditto/submission.py deleted file mode 100644 index f4cf87f..0000000 --- a/src/Ditto/submission.py +++ /dev/null @@ -1,29 +0,0 @@ -import json -import pandas as pd - -json_file = json.load( - open( - "/data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/metadata/train_test_metadata_original.json", - "r", - ) -) - -fnames = [] -for samples in json_file["test"].keys(): - # for train_test in json_file.keys(): - # if "TEST" in train_test: - # for samples in json_file[train_test].keys(): - if "PROBAND" in samples: - # fnames.append(train_test+samples) - fnames.append( - f"/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/debugged/annotated_vcf/test/{samples}/combined_predictions_100.csv" - ) # , sep=':') -# print(fnames) -model = pd.concat((pd.read_csv(f, sep=":") for f in fnames), ignore_index=True) -model["SD"] = 0 -model["C"] = "*" -model.to_csv( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/debugged/ditto_model_debugged_annotated_vcf.txt", - index=False, - sep=":", -) diff --git a/annotation_parsing/parse_annotated_vars.py b/src/annotation_parsing/parse_annotated_vars.py similarity index 100% rename from annotation_parsing/parse_annotated_vars.py rename to src/annotation_parsing/parse_annotated_vars.py diff --git a/src/cohort/combine_scores.py b/src/cohort/combine_scores.py deleted file mode 100644 index 1e65561..0000000 --- a/src/cohort/combine_scores.py +++ /dev/null @@ -1,173 +0,0 @@ -# python src/cohort/combine_scores.py --json /data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/metadata/train_test_metadata_original.json --ditto /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/debugged/annotated_vcf --hazel /data/project/worthey_lab/projects/experimental_pipelines/tarun/uab-meter/data/processed/CAGI6 - -import pandas as pd -import warnings -import json -warnings.simplefilter("ignore") -import argparse -import os -import sys - -def main(args): - - print("Loading Biomart file....") - id_map = pd.read_csv( - "/data/project/worthey_lab/temp_datasets_central/tarun/HGNC/biomart_9_23_21.txt", - sep="\t", - ) - - print("Loading Ditto file....") - ditto = pd.read_csv(args.ditto) - print("Loading Raw file....") - raw = pd.read_csv( - args.raw, - sep="\t", - usecols=[ - "SYMBOL", - "Chromosome", - "Position", - "Reference Allele", - "Alternate Allele", - "Gene", - "Feature", - "HGNC_ID", - ], - ) - print("Raw file loaded!") - overall = pd.merge( - raw, - ditto, - how="left", - on=[ - "Chromosome", - "Position", - "Alternate Allele", - "Reference Allele", - "Feature", - ], - ) - # print(overall.columns.values.tolist()) - del raw, ditto - - print("Reading Hazel scores...") - hazel = pd.read_csv(args.hazel) - - id_map = id_map.merge( - hazel, left_on="Approved symbol", right_on="Genes" - ) - - overall = overall.merge( - id_map, how="left", left_on="HGNC_ID_x", right_on="HGNC ID" - ) - - #print(overall.columns.values.tolist()) - - del id_map, hazel - - print("Combining Ditto and Hazel scores....") - overall["combined_cosine"] = ( - overall["cosine"].fillna(0) - + overall["Ditto_Deleterious"].fillna(0) - ) / 2 - overall["combined_projection"] = ( - overall["projection"].fillna(0) - + overall["Ditto_Deleterious"].fillna(0) - ) / 2 - overall["combined_jaccard"] = ( - overall["jaccard"].fillna(0) - + overall["Ditto_Deleterious"].fillna(0) - ) / 2 - overall = overall[ - [ - "SYMBOL_x", - "Chromosome", - "Position", - "Reference Allele", - "Alternate Allele", - "Ditto_Deleterious", - "cosine","projection","jaccard", - "combined_cosine", - "combined_projection", - "combined_jaccard", - ] - ] - overall.insert(0, "PROBANDID", args.sample) - overall.columns = [ - "PROBANDID", - "SYMBOL", - "CHROM", - "POS", - "REF", - "ALT", - "Ditto", - "cosine","projection","jaccard", - "combined_cosine", - "combined_projection", - "combined_jaccard", - ] - - overall = overall.sort_values("Ditto", ascending=False) - overall = overall.reset_index(drop=True) - - print("Writing 'Hazel_Ditto.csv' file to Ditto directory....") - overall.to_csv(args.output, index=False) - - overall = overall.drop_duplicates( - subset=["CHROM", "POS", "REF", "ALT"], keep="first" - ).reset_index(drop=True) - - overall = overall.sort_values("combined_cosine", ascending=False) - - overall.head(100).to_csv(args.output100, index=False) - - del overall - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--raw", type=str, required=True, help="Input raw annotated file with path." - ) - parser.add_argument( - "--ditto", type=str, required=True, help="Input Ditto file with path." - ) - parser.add_argument( - "--hazel", - type=str, - # default="predictions.csv", - help="Input hazel file with path", - ) - parser.add_argument( - "--sample", - type=str, - # required=True, - help="Input sample name to showup in results", - ) - parser.add_argument( - "--output", - "-o", - type=str, - default="Ditto_Hazel.csv", - help="Output csv file with path", - ) - parser.add_argument( - "--output100", - "-o100", - type=str, - default="Ditto_Hazel_100.csv", - help="Output csv file with path for Top 100 variants", - ) - args = parser.parse_args() - - # Validate paths exist - if not os.path.exists(args.raw): - print("Can't process because the raw file ", args.raw, " doesn't exist.") - sys.exit(1) - if not os.path.exists(args.hazel): - print("Can't process because Hazel file ", args.hazel, " doesn't exist.") - sys.exit(1) - if not os.path.exists(args.ditto): - print("Can't process because Ditto file ", args.ditto, " doesn't exist.") - sys.exit(1) - - main(args) diff --git a/src/cohort/ranks.py b/src/cohort/ranks.py deleted file mode 100644 index c2edc6c..0000000 --- a/src/cohort/ranks.py +++ /dev/null @@ -1,81 +0,0 @@ -# python src/cohort/ranks.py --json /data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/metadata/train_test_metadata_original.json -id /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/debugged/annotated_vcf -o cosine_ranks.csv - -import json -import pandas as pd -import argparse -import os - -def main(args): - - json_file = json.load(open(args.json, "r")) - - with open(f"{args.input_dir}/{args.output}", "w") as f: - - f.write(f"PROBANDID,[CHROM,POS,REF,ALT],SYMBOL,Ditto,cosine,projection,jaccard,combined_cosine,combined_projection,combined_jaccard,Rank['Ditto', 'cosine', 'jaccard', 'projection', 'combined_cosine', 'combined_jaccard', 'combined_projection']\n") - - #rank_list = [] - for samples in json_file["train"].keys(): - if "PROBAND" in samples: - - - genes = pd.read_csv( - f"{args.input_dir}/train/{samples}/Ditto_Hazel_fixed_no_bias.csv" - ) # , sep=':') - - for i in range(len(json_file["train"][samples]["solves"])): - variants = str( - "chr" - + str(json_file["train"][samples]["solves"][i]["Chrom"]).split(".")[0] - + "," - + str(json_file["train"][samples]["solves"][i]["Pos"]) - + "," - + json_file["train"][samples]["solves"][i]["Ref"] - + "," - + json_file["train"][samples]["solves"][i]["Alt"] - ).split(",") - - - var_rank = [] - for method in ['Ditto', 'cosine', 'jaccard', 'projection', 'combined_cosine', 'combined_jaccard', 'combined_projection']: - if method == 'Ditto': - genes = genes.sort_values(by = 'Ditto', axis=0, ascending=False).reset_index(drop=True) - else: - genes = genes.sort_values(by = [method,'Ditto'], axis=0, ascending=[False,False]).reset_index(drop=True) - genes = genes.drop_duplicates( - subset=["CHROM", "POS", "ALT", "REF"], keep="first" - ).reset_index(drop=True) - rank = ( - genes.loc[ - (genes["CHROM"] == variants[0]) - & (genes["POS"] == int(variants[1])) - & (genes["ALT"] == variants[3]) - & (genes["REF"] == variants[2]) - ].index - ) + 1 - var_rank.append(rank.tolist()[0]) - - #rank_list = [*rank_list, *rank] # unpack both iterables in a list literal - with open(f"{args.input_dir}/{args.output}", "a") as f: - f.write( - f"{samples}, {variants}, {genes.loc[rank-1]['SYMBOL'].values}, {genes.loc[rank-1]['Ditto'].values[0]}, {genes.loc[rank-1]['cosine'].values[0]}, {genes.loc[rank-1]['jaccard'].values[0]}, {genes.loc[rank-1]['projection'].values[0]}, {genes.loc[rank-1]['combined_cosine'].values[0]}, {genes.loc[rank-1]['combined_jaccard'].values[0]}, {genes.loc[rank-1]['combined_projection'].values[0]}, {var_rank}\n" - ) - del genes, rank, variants - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument( - "--input-dir", - "-id", - type=str, - required=True, - help="Input raw annotated file with path.", - ) - parser.add_argument( - "--json", type=str, required=True, help="Input raw annotated file with path." - ) - parser.add_argument( - "--output", "-o", type=str, default="ranks.csv", help="Output csv filename only" - ) - args = parser.parse_args() - - main(args) diff --git a/src/dbNSFP/Ditto_dbNSFP_filtering.ipynb b/src/dbNSFP/Ditto_dbNSFP_filtering.ipynb deleted file mode 100644 index 2f0778a..0000000 --- a/src/dbNSFP/Ditto_dbNSFP_filtering.ipynb +++ /dev/null @@ -1,9749 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Filtering annotated variants to extract nsSNVs for ML" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "pd.set_option('display.max_rows', None)\n", - "import numpy as np\n", - "from tqdm import tqdm \n", - "import yaml\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import os\n", - "os.chdir( '/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to open and load config file for filtering columns and rows\n", - "def get_col_configs(config_f):\n", - " with open(config_f) as fh:\n", - " config_dict = yaml.safe_load(fh)\n", - "\n", - " # print(config_dict)\n", - " return config_dict\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Load the config file as dictionary\n", - "config_f = \"../../configs/dbnsfp_column_config.yaml\"\n", - "config_dict = get_col_configs(config_f)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['#chr',\n", - " 'pos(1-based)',\n", - " 'ref',\n", - " 'alt',\n", - " 'aaref',\n", - " 'aaalt',\n", - " 'genename',\n", - " 'Ensembl_geneid',\n", - " 'Ensembl_transcriptid',\n", - " 'Ensembl_proteinid',\n", - " 'Uniprot_acc',\n", - " 'cds_strand',\n", - " 'SIFT_score',\n", - " 'SIFT_converted_rankscore',\n", - " 'SIFT_pred',\n", - " 'SIFT4G_score',\n", - " 'SIFT4G_converted_rankscore',\n", - " 'SIFT4G_pred',\n", - " 'Polyphen2_HDIV_score',\n", - " 'Polyphen2_HDIV_rankscore',\n", - " 'Polyphen2_HDIV_pred',\n", - " 'Polyphen2_HVAR_score',\n", - " 'Polyphen2_HVAR_rankscore',\n", - " 'Polyphen2_HVAR_pred',\n", - " 'LRT_score',\n", - " 'LRT_converted_rankscore',\n", - " 'LRT_pred',\n", - " 'LRT_Omega',\n", - " 'MutationTaster_converted_rankscore',\n", - " 'MutationAssessor_score',\n", - " 'MutationAssessor_rankscore',\n", - " 'MutationAssessor_pred',\n", - " 'FATHMM_score',\n", - " 'FATHMM_converted_rankscore',\n", - " 'FATHMM_pred',\n", - " 'PROVEAN_score',\n", - " 'PROVEAN_converted_rankscore',\n", - " 'PROVEAN_pred',\n", - " 'VEST4_score',\n", - " 'VEST4_rankscore',\n", - " 'MetaSVM_score',\n", - " 'MetaSVM_rankscore',\n", - " 'MetaSVM_pred',\n", - " 'MetaLR_score',\n", - " 'MetaLR_rankscore',\n", - " 'MetaLR_pred',\n", - " 'Reliability_index',\n", - " 'MetaRNN_score',\n", - " 'MetaRNN_rankscore',\n", - " 'MetaRNN_pred',\n", - " 'M-CAP_score',\n", - " 'M-CAP_rankscore',\n", - " 'M-CAP_pred',\n", - " 'REVEL_score',\n", - " 'REVEL_rankscore',\n", - " 'MutPred_score',\n", - " 'MutPred_rankscore',\n", - " 'MVP_score',\n", - " 'MVP_rankscore',\n", - " 'MPC_score',\n", - " 'MPC_rankscore',\n", - " 'PrimateAI_score',\n", - " 'PrimateAI_rankscore',\n", - " 'PrimateAI_pred',\n", - " 'DEOGEN2_score',\n", - " 'DEOGEN2_rankscore',\n", - " 'DEOGEN2_pred',\n", - " 'BayesDel_addAF_score',\n", - " 'BayesDel_addAF_rankscore',\n", - " 'BayesDel_addAF_pred',\n", - " 'BayesDel_noAF_score',\n", - " 'BayesDel_noAF_rankscore',\n", - " 'BayesDel_noAF_pred',\n", - " 'ClinPred_score',\n", - " 'ClinPred_rankscore',\n", - " 'ClinPred_pred',\n", - " 'LIST-S2_score',\n", - " 'LIST-S2_rankscore',\n", - " 'LIST-S2_pred',\n", - " 'CADD_raw',\n", - " 'CADD_raw_rankscore',\n", - " 'CADD_phred',\n", - " 'CADD_raw_hg19',\n", - " 'CADD_raw_rankscore_hg19',\n", - " 'CADD_phred_hg19',\n", - " 'DANN_score',\n", - " 'DANN_rankscore',\n", - " 'fathmm-MKL_coding_score',\n", - " 'fathmm-MKL_coding_rankscore',\n", - " 'fathmm-MKL_coding_pred',\n", - " 'fathmm-XF_coding_score',\n", - " 'fathmm-XF_coding_rankscore',\n", - " 'fathmm-XF_coding_pred',\n", - " 'Eigen-raw_coding',\n", - " 'Eigen-raw_coding_rankscore',\n", - " 'Eigen-phred_coding',\n", - " 'Eigen-PC-raw_coding',\n", - " 'Eigen-PC-raw_coding_rankscore',\n", - " 'Eigen-PC-phred_coding',\n", - " 'GenoCanyon_score',\n", - " 'GenoCanyon_rankscore',\n", - " 'integrated_fitCons_score',\n", - " 'integrated_fitCons_rankscore',\n", - " 'integrated_confidence_value',\n", - " 'GM12878_fitCons_score',\n", - " 'GM12878_fitCons_rankscore',\n", - " 'GM12878_confidence_value',\n", - " 'H1-hESC_fitCons_score',\n", - " 'H1-hESC_fitCons_rankscore',\n", - " 'H1-hESC_confidence_value',\n", - " 'HUVEC_fitCons_score',\n", - " 'HUVEC_fitCons_rankscore',\n", - " 'HUVEC_confidence_value',\n", - " 'LINSIGHT',\n", - " 'LINSIGHT_rankscore',\n", - " 'GERP++_NR',\n", - " 'GERP++_RS',\n", - " 'GERP++_RS_rankscore',\n", - " 'phyloP100way_vertebrate',\n", - " 'phyloP100way_vertebrate_rankscore',\n", - " 'phyloP30way_mammalian',\n", - " 'phyloP30way_mammalian_rankscore',\n", - " 'phyloP17way_primate',\n", - " 'phyloP17way_primate_rankscore',\n", - " 'phastCons100way_vertebrate',\n", - " 'phastCons100way_vertebrate_rankscore',\n", - " 'phastCons30way_mammalian',\n", - " 'phastCons30way_mammalian_rankscore',\n", - " 'phastCons17way_primate',\n", - " 'phastCons17way_primate_rankscore',\n", - " 'SiPhy_29way_logOdds',\n", - " 'SiPhy_29way_logOdds_rankscore',\n", - " 'bStatistic',\n", - " 'bStatistic_converted_rankscore',\n", - " '1000Gp3_AF',\n", - " '1000Gp3_AFR_AF',\n", - " '1000Gp3_EUR_AF',\n", - " '1000Gp3_AMR_AF',\n", - " '1000Gp3_EAS_AF',\n", - " '1000Gp3_SAS_AF',\n", - " 'TWINSUK_AF',\n", - " 'ALSPAC_AF',\n", - " 'UK10K_AF',\n", - " 'ESP6500_AA_AF',\n", - " 'ESP6500_EA_AF',\n", - " 'ExAC_AF',\n", - " 'ExAC_Adj_AF',\n", - " 'ExAC_AFR_AF',\n", - " 'ExAC_AMR_AF',\n", - " 'ExAC_EAS_AF',\n", - " 'ExAC_FIN_AF',\n", - " 'ExAC_NFE_AF',\n", - " 'ExAC_SAS_AF',\n", - " 'ExAC_nonTCGA_AF',\n", - " 'ExAC_nonTCGA_Adj_AF',\n", - " 'ExAC_nonTCGA_AFR_AF',\n", - " 'ExAC_nonTCGA_AMR_AF',\n", - " 'ExAC_nonTCGA_EAS_AF',\n", - " 'ExAC_nonTCGA_FIN_AF',\n", - " 'ExAC_nonTCGA_NFE_AF',\n", - " 'ExAC_nonTCGA_SAS_AF',\n", - " 'ExAC_nonpsych_AF',\n", - " 'ExAC_nonpsych_Adj_AF',\n", - " 'ExAC_nonpsych_AFR_AF',\n", - " 'ExAC_nonpsych_AMR_AF',\n", - " 'ExAC_nonpsych_EAS_AF',\n", - " 'ExAC_nonpsych_FIN_AF',\n", - " 'ExAC_nonpsych_NFE_AF',\n", - " 'ExAC_nonpsych_SAS_AF',\n", - " 'gnomAD_exomes_AF',\n", - " 'gnomAD_exomes_AFR_AF',\n", - " 'gnomAD_exomes_AMR_AF',\n", - " 'gnomAD_exomes_ASJ_AF',\n", - " 'gnomAD_exomes_EAS_AF',\n", - " 'gnomAD_exomes_FIN_AF',\n", - " 'gnomAD_exomes_NFE_AF',\n", - " 'gnomAD_exomes_SAS_AF',\n", - " 'gnomAD_exomes_POPMAX_AF',\n", - " 'gnomAD_exomes_controls_AF',\n", - " 'gnomAD_exomes_non_neuro_AF',\n", - " 'gnomAD_exomes_non_cancer_AF',\n", - " 'gnomAD_exomes_non_topmed_AF',\n", - " 'gnomAD_exomes_controls_AFR_AF',\n", - " 'gnomAD_exomes_controls_AMR_AF',\n", - " 'gnomAD_exomes_controls_ASJ_AF',\n", - " 'gnomAD_exomes_controls_EAS_AF',\n", - " 'gnomAD_exomes_controls_FIN_AF',\n", - " 'gnomAD_exomes_controls_NFE_AF',\n", - " 'gnomAD_exomes_controls_SAS_AF',\n", - " 'gnomAD_exomes_controls_POPMAX_AF',\n", - " 'gnomAD_exomes_non_neuro_AFR_AF',\n", - " 'gnomAD_exomes_non_neuro_AMR_AF',\n", - " 'gnomAD_exomes_non_neuro_ASJ_AF',\n", - " 'gnomAD_exomes_non_neuro_EAS_AF',\n", - " 'gnomAD_exomes_non_neuro_FIN_AF',\n", - " 'gnomAD_exomes_non_neuro_NFE_AF',\n", - " 'gnomAD_exomes_non_neuro_SAS_AF',\n", - " 'gnomAD_exomes_non_neuro_POPMAX_AF',\n", - " 'gnomAD_exomes_non_cancer_AFR_AF',\n", - " 'gnomAD_exomes_non_cancer_AMR_AF',\n", - " 'gnomAD_exomes_non_cancer_ASJ_AF',\n", - " 'gnomAD_exomes_non_cancer_EAS_AF',\n", - " 'gnomAD_exomes_non_cancer_FIN_AF',\n", - " 'gnomAD_exomes_non_cancer_NFE_AF',\n", - " 'gnomAD_exomes_non_cancer_SAS_AF',\n", - " 'gnomAD_exomes_non_cancer_POPMAX_AF',\n", - " 'gnomAD_exomes_non_topmed_AFR_AF',\n", - " 'gnomAD_exomes_non_topmed_AMR_AF',\n", - " 'gnomAD_exomes_non_topmed_ASJ_AF',\n", - " 'gnomAD_exomes_non_topmed_EAS_AF',\n", - " 'gnomAD_exomes_non_topmed_FIN_AF',\n", - " 'gnomAD_exomes_non_topmed_NFE_AF',\n", - " 'gnomAD_exomes_non_topmed_SAS_AF',\n", - " 'gnomAD_exomes_non_topmed_POPMAX_AF',\n", - " 'gnomAD_genomes_AF',\n", - " 'gnomAD_genomes_POPMAX_AF',\n", - " 'gnomAD_genomes_AFR_AF',\n", - " 'gnomAD_genomes_AMI_AF',\n", - " 'gnomAD_genomes_AMR_AF',\n", - " 'gnomAD_genomes_ASJ_AF',\n", - " 'gnomAD_genomes_EAS_AF',\n", - " 'gnomAD_genomes_FIN_AF',\n", - " 'gnomAD_genomes_MID_AF',\n", - " 'gnomAD_genomes_NFE_AF',\n", - " 'gnomAD_genomes_SAS_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_AF',\n", - " 'gnomAD_genomes_non_neuro_AF',\n", - " 'gnomAD_genomes_non_cancer_AF',\n", - " 'gnomAD_genomes_non_topmed_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_AFR_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_AMI_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_AMR_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_ASJ_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_EAS_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_FIN_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_MID_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_NFE_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_SAS_AF',\n", - " 'gnomAD_genomes_non_neuro_AFR_AF',\n", - " 'gnomAD_genomes_non_neuro_AMI_AF',\n", - " 'gnomAD_genomes_non_neuro_AMR_AF',\n", - " 'gnomAD_genomes_non_neuro_ASJ_AF',\n", - " 'gnomAD_genomes_non_neuro_EAS_AF',\n", - " 'gnomAD_genomes_non_neuro_FIN_AF',\n", - " 'gnomAD_genomes_non_neuro_MID_AF',\n", - " 'gnomAD_genomes_non_neuro_NFE_AF',\n", - " 'gnomAD_genomes_non_neuro_SAS_AF',\n", - " 'gnomAD_genomes_non_cancer_AFR_AF',\n", - " 'gnomAD_genomes_non_cancer_AMI_AF',\n", - " 'gnomAD_genomes_non_cancer_AMR_AF',\n", - " 'gnomAD_genomes_non_cancer_ASJ_AF',\n", - " 'gnomAD_genomes_non_cancer_EAS_AF',\n", - " 'gnomAD_genomes_non_cancer_FIN_AF',\n", - " 'gnomAD_genomes_non_cancer_MID_AF',\n", - " 'gnomAD_genomes_non_cancer_NFE_AF',\n", - " 'gnomAD_genomes_non_cancer_SAS_AF',\n", - " 'gnomAD_genomes_non_topmed_AFR_AF',\n", - " 'gnomAD_genomes_non_topmed_AMI_AF',\n", - " 'gnomAD_genomes_non_topmed_AMR_AF',\n", - " 'gnomAD_genomes_non_topmed_ASJ_AF',\n", - " 'gnomAD_genomes_non_topmed_EAS_AF',\n", - " 'gnomAD_genomes_non_topmed_FIN_AF',\n", - " 'gnomAD_genomes_non_topmed_MID_AF',\n", - " 'gnomAD_genomes_non_topmed_NFE_AF',\n", - " 'gnomAD_genomes_non_topmed_SAS_AF',\n", - " 'clinvar_clnsig',\n", - " 'clinvar_review',\n", - " 'Interpro_domain']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "config_dict[\"columns\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading data...\n", - "Data Loaded !....\n" - ] - } - ], - "source": [ - "print('Loading data...')\n", - "df = pd.read_csv(\"../interim/dbNSFP_clinvar_variants_parsed.tsv.gz\", sep='\\t', usecols=config_dict[\"columns\"], low_memory=False)\n", - "print('Data Loaded !....')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['#chr',\n", - " 'pos(1-based)',\n", - " 'ref',\n", - " 'alt',\n", - " 'aaref',\n", - " 'aaalt',\n", - " 'genename',\n", - " 'Ensembl_geneid',\n", - " 'Ensembl_transcriptid',\n", - " 'Ensembl_proteinid',\n", - " 'Uniprot_acc',\n", - " 'cds_strand',\n", - " 'SIFT_score',\n", - " 'SIFT_converted_rankscore',\n", - " 'SIFT_pred',\n", - " 'SIFT4G_score',\n", - " 'SIFT4G_converted_rankscore',\n", - " 'SIFT4G_pred',\n", - " 'Polyphen2_HDIV_score',\n", - " 'Polyphen2_HDIV_rankscore',\n", - " 'Polyphen2_HDIV_pred',\n", - " 'Polyphen2_HVAR_score',\n", - " 'Polyphen2_HVAR_rankscore',\n", - " 'Polyphen2_HVAR_pred',\n", - " 'LRT_score',\n", - " 'LRT_converted_rankscore',\n", - " 'LRT_pred',\n", - " 'LRT_Omega',\n", - " 'MutationTaster_converted_rankscore',\n", - " 'MutationAssessor_score',\n", - " 'MutationAssessor_rankscore',\n", - " 'MutationAssessor_pred',\n", - " 'FATHMM_score',\n", - " 'FATHMM_converted_rankscore',\n", - " 'FATHMM_pred',\n", - " 'PROVEAN_score',\n", - " 'PROVEAN_converted_rankscore',\n", - " 'PROVEAN_pred',\n", - " 'VEST4_score',\n", - " 'VEST4_rankscore',\n", - " 'MetaSVM_score',\n", - " 'MetaSVM_rankscore',\n", - " 'MetaSVM_pred',\n", - " 'MetaLR_score',\n", - " 'MetaLR_rankscore',\n", - " 'MetaLR_pred',\n", - " 'Reliability_index',\n", - " 'MetaRNN_score',\n", - " 'MetaRNN_rankscore',\n", - " 'MetaRNN_pred',\n", - " 'M-CAP_score',\n", - " 'M-CAP_rankscore',\n", - " 'M-CAP_pred',\n", - " 'REVEL_score',\n", - " 'REVEL_rankscore',\n", - " 'MutPred_score',\n", - " 'MutPred_rankscore',\n", - " 'MVP_score',\n", - " 'MVP_rankscore',\n", - " 'MPC_score',\n", - " 'MPC_rankscore',\n", - " 'PrimateAI_score',\n", - " 'PrimateAI_rankscore',\n", - " 'PrimateAI_pred',\n", - " 'DEOGEN2_score',\n", - " 'DEOGEN2_rankscore',\n", - " 'DEOGEN2_pred',\n", - " 'BayesDel_addAF_score',\n", - " 'BayesDel_addAF_rankscore',\n", - " 'BayesDel_addAF_pred',\n", - " 'BayesDel_noAF_score',\n", - " 'BayesDel_noAF_rankscore',\n", - " 'BayesDel_noAF_pred',\n", - " 'ClinPred_score',\n", - " 'ClinPred_rankscore',\n", - " 'ClinPred_pred',\n", - " 'LIST-S2_score',\n", - " 'LIST-S2_rankscore',\n", - " 'LIST-S2_pred',\n", - " 'CADD_raw',\n", - " 'CADD_raw_rankscore',\n", - " 'CADD_phred',\n", - " 'CADD_raw_hg19',\n", - " 'CADD_raw_rankscore_hg19',\n", - " 'CADD_phred_hg19',\n", - " 'DANN_score',\n", - " 'DANN_rankscore',\n", - " 'fathmm-MKL_coding_score',\n", - " 'fathmm-MKL_coding_rankscore',\n", - " 'fathmm-MKL_coding_pred',\n", - " 'fathmm-XF_coding_score',\n", - " 'fathmm-XF_coding_rankscore',\n", - " 'fathmm-XF_coding_pred',\n", - " 'Eigen-raw_coding',\n", - " 'Eigen-raw_coding_rankscore',\n", - " 'Eigen-phred_coding',\n", - " 'Eigen-PC-raw_coding',\n", - " 'Eigen-PC-raw_coding_rankscore',\n", - " 'Eigen-PC-phred_coding',\n", - " 'GenoCanyon_score',\n", - " 'GenoCanyon_rankscore',\n", - " 'integrated_fitCons_score',\n", - " 'integrated_fitCons_rankscore',\n", - " 'integrated_confidence_value',\n", - " 'GM12878_fitCons_score',\n", - " 'GM12878_fitCons_rankscore',\n", - " 'GM12878_confidence_value',\n", - " 'H1-hESC_fitCons_score',\n", - " 'H1-hESC_fitCons_rankscore',\n", - " 'H1-hESC_confidence_value',\n", - " 'HUVEC_fitCons_score',\n", - " 'HUVEC_fitCons_rankscore',\n", - " 'HUVEC_confidence_value',\n", - " 'LINSIGHT',\n", - " 'LINSIGHT_rankscore',\n", - " 'GERP++_NR',\n", - " 'GERP++_RS',\n", - " 'GERP++_RS_rankscore',\n", - " 'phyloP100way_vertebrate',\n", - " 'phyloP100way_vertebrate_rankscore',\n", - " 'phyloP30way_mammalian',\n", - " 'phyloP30way_mammalian_rankscore',\n", - " 'phyloP17way_primate',\n", - " 'phyloP17way_primate_rankscore',\n", - " 'phastCons100way_vertebrate',\n", - " 'phastCons100way_vertebrate_rankscore',\n", - " 'phastCons30way_mammalian',\n", - " 'phastCons30way_mammalian_rankscore',\n", - " 'phastCons17way_primate',\n", - " 'phastCons17way_primate_rankscore',\n", - " 'SiPhy_29way_logOdds',\n", - " 'SiPhy_29way_logOdds_rankscore',\n", - " 'bStatistic',\n", - " 'bStatistic_converted_rankscore',\n", - " '1000Gp3_AF',\n", - " '1000Gp3_AFR_AF',\n", - " '1000Gp3_EUR_AF',\n", - " '1000Gp3_AMR_AF',\n", - " '1000Gp3_EAS_AF',\n", - " '1000Gp3_SAS_AF',\n", - " 'TWINSUK_AF',\n", - " 'ALSPAC_AF',\n", - " 'UK10K_AF',\n", - " 'ESP6500_AA_AF',\n", - " 'ESP6500_EA_AF',\n", - " 'ExAC_AF',\n", - " 'ExAC_Adj_AF',\n", - " 'ExAC_AFR_AF',\n", - " 'ExAC_AMR_AF',\n", - " 'ExAC_EAS_AF',\n", - " 'ExAC_FIN_AF',\n", - " 'ExAC_NFE_AF',\n", - " 'ExAC_SAS_AF',\n", - " 'ExAC_nonTCGA_AF',\n", - " 'ExAC_nonTCGA_Adj_AF',\n", - " 'ExAC_nonTCGA_AFR_AF',\n", - " 'ExAC_nonTCGA_AMR_AF',\n", - " 'ExAC_nonTCGA_EAS_AF',\n", - " 'ExAC_nonTCGA_FIN_AF',\n", - " 'ExAC_nonTCGA_NFE_AF',\n", - " 'ExAC_nonTCGA_SAS_AF',\n", - " 'ExAC_nonpsych_AF',\n", - " 'ExAC_nonpsych_Adj_AF',\n", - " 'ExAC_nonpsych_AFR_AF',\n", - " 'ExAC_nonpsych_AMR_AF',\n", - " 'ExAC_nonpsych_EAS_AF',\n", - " 'ExAC_nonpsych_FIN_AF',\n", - " 'ExAC_nonpsych_NFE_AF',\n", - " 'ExAC_nonpsych_SAS_AF',\n", - " 'gnomAD_exomes_AF',\n", - " 'gnomAD_exomes_AFR_AF',\n", - " 'gnomAD_exomes_AMR_AF',\n", - " 'gnomAD_exomes_ASJ_AF',\n", - " 'gnomAD_exomes_EAS_AF',\n", - " 'gnomAD_exomes_FIN_AF',\n", - " 'gnomAD_exomes_NFE_AF',\n", - " 'gnomAD_exomes_SAS_AF',\n", - " 'gnomAD_exomes_POPMAX_AF',\n", - " 'gnomAD_exomes_controls_AF',\n", - " 'gnomAD_exomes_non_neuro_AF',\n", - " 'gnomAD_exomes_non_cancer_AF',\n", - " 'gnomAD_exomes_non_topmed_AF',\n", - " 'gnomAD_exomes_controls_AFR_AF',\n", - " 'gnomAD_exomes_controls_AMR_AF',\n", - " 'gnomAD_exomes_controls_ASJ_AF',\n", - " 'gnomAD_exomes_controls_EAS_AF',\n", - " 'gnomAD_exomes_controls_FIN_AF',\n", - " 'gnomAD_exomes_controls_NFE_AF',\n", - " 'gnomAD_exomes_controls_SAS_AF',\n", - " 'gnomAD_exomes_controls_POPMAX_AF',\n", - " 'gnomAD_exomes_non_neuro_AFR_AF',\n", - " 'gnomAD_exomes_non_neuro_AMR_AF',\n", - " 'gnomAD_exomes_non_neuro_ASJ_AF',\n", - " 'gnomAD_exomes_non_neuro_EAS_AF',\n", - " 'gnomAD_exomes_non_neuro_FIN_AF',\n", - " 'gnomAD_exomes_non_neuro_NFE_AF',\n", - " 'gnomAD_exomes_non_neuro_SAS_AF',\n", - " 'gnomAD_exomes_non_neuro_POPMAX_AF',\n", - " 'gnomAD_exomes_non_cancer_AFR_AF',\n", - " 'gnomAD_exomes_non_cancer_AMR_AF',\n", - " 'gnomAD_exomes_non_cancer_ASJ_AF',\n", - " 'gnomAD_exomes_non_cancer_EAS_AF',\n", - " 'gnomAD_exomes_non_cancer_FIN_AF',\n", - " 'gnomAD_exomes_non_cancer_NFE_AF',\n", - " 'gnomAD_exomes_non_cancer_SAS_AF',\n", - " 'gnomAD_exomes_non_cancer_POPMAX_AF',\n", - " 'gnomAD_exomes_non_topmed_AFR_AF',\n", - " 'gnomAD_exomes_non_topmed_AMR_AF',\n", - " 'gnomAD_exomes_non_topmed_ASJ_AF',\n", - " 'gnomAD_exomes_non_topmed_EAS_AF',\n", - " 'gnomAD_exomes_non_topmed_FIN_AF',\n", - " 'gnomAD_exomes_non_topmed_NFE_AF',\n", - " 'gnomAD_exomes_non_topmed_SAS_AF',\n", - " 'gnomAD_exomes_non_topmed_POPMAX_AF',\n", - " 'gnomAD_genomes_AF',\n", - " 'gnomAD_genomes_POPMAX_AF',\n", - " 'gnomAD_genomes_AFR_AF',\n", - " 'gnomAD_genomes_AMI_AF',\n", - " 'gnomAD_genomes_AMR_AF',\n", - " 'gnomAD_genomes_ASJ_AF',\n", - " 'gnomAD_genomes_EAS_AF',\n", - " 'gnomAD_genomes_FIN_AF',\n", - " 'gnomAD_genomes_MID_AF',\n", - " 'gnomAD_genomes_NFE_AF',\n", - " 'gnomAD_genomes_SAS_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_AF',\n", - " 'gnomAD_genomes_non_neuro_AF',\n", - " 'gnomAD_genomes_non_cancer_AF',\n", - " 'gnomAD_genomes_non_topmed_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_AFR_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_AMI_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_AMR_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_ASJ_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_EAS_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_FIN_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_MID_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_NFE_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_SAS_AF',\n", - " 'gnomAD_genomes_non_neuro_AFR_AF',\n", - " 'gnomAD_genomes_non_neuro_AMI_AF',\n", - " 'gnomAD_genomes_non_neuro_AMR_AF',\n", - " 'gnomAD_genomes_non_neuro_ASJ_AF',\n", - " 'gnomAD_genomes_non_neuro_EAS_AF',\n", - " 'gnomAD_genomes_non_neuro_FIN_AF',\n", - " 'gnomAD_genomes_non_neuro_MID_AF',\n", - " 'gnomAD_genomes_non_neuro_NFE_AF',\n", - " 'gnomAD_genomes_non_neuro_SAS_AF',\n", - " 'gnomAD_genomes_non_cancer_AFR_AF',\n", - " 'gnomAD_genomes_non_cancer_AMI_AF',\n", - " 'gnomAD_genomes_non_cancer_AMR_AF',\n", - " 'gnomAD_genomes_non_cancer_ASJ_AF',\n", - " 'gnomAD_genomes_non_cancer_EAS_AF',\n", - " 'gnomAD_genomes_non_cancer_FIN_AF',\n", - " 'gnomAD_genomes_non_cancer_MID_AF',\n", - " 'gnomAD_genomes_non_cancer_NFE_AF',\n", - " 'gnomAD_genomes_non_cancer_SAS_AF',\n", - " 'gnomAD_genomes_non_topmed_AFR_AF',\n", - " 'gnomAD_genomes_non_topmed_AMI_AF',\n", - " 'gnomAD_genomes_non_topmed_AMR_AF',\n", - " 'gnomAD_genomes_non_topmed_ASJ_AF',\n", - " 'gnomAD_genomes_non_topmed_EAS_AF',\n", - " 'gnomAD_genomes_non_topmed_FIN_AF',\n", - " 'gnomAD_genomes_non_topmed_MID_AF',\n", - " 'gnomAD_genomes_non_topmed_NFE_AF',\n", - " 'gnomAD_genomes_non_topmed_SAS_AF',\n", - " 'clinvar_clnsig',\n", - " 'clinvar_review',\n", - " 'Interpro_domain']" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.columns.to_list()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(1937625, 268)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "df = df.replace('.', np.nan)\n", - "df = df.replace('-', np.nan)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 268/268 [00:19<00:00, 13.55it/s]\n" - ] - } - ], - "source": [ - "for key in tqdm(df.columns):\n", - " try:\n", - " df[key] = (\n", - " df[key]\n", - " .astype(\"float32\")\n", - " )\n", - " except:\n", - " df[key] = df[key]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "#chr object\n", - "pos(1-based) float32\n", - "ref object\n", - "alt object\n", - "aaref object\n", - "aaalt object\n", - "genename object\n", - "Ensembl_geneid object\n", - "Ensembl_transcriptid object\n", - "Ensembl_proteinid object\n", - "Uniprot_acc object\n", - "cds_strand object\n", - "SIFT_score float32\n", - "SIFT_converted_rankscore float32\n", - "SIFT_pred object\n", - "SIFT4G_score float32\n", - "SIFT4G_converted_rankscore float32\n", - "SIFT4G_pred object\n", - "Polyphen2_HDIV_score float32\n", - "Polyphen2_HDIV_rankscore float32\n", - "Polyphen2_HDIV_pred object\n", - "Polyphen2_HVAR_score float32\n", - "Polyphen2_HVAR_rankscore float32\n", - "Polyphen2_HVAR_pred object\n", - "LRT_score float32\n", - "LRT_converted_rankscore float32\n", - "LRT_pred object\n", - "LRT_Omega float32\n", - "MutationTaster_converted_rankscore float32\n", - "MutationAssessor_score float32\n", - "MutationAssessor_rankscore float32\n", - "MutationAssessor_pred object\n", - "FATHMM_score float32\n", - "FATHMM_converted_rankscore float32\n", - "FATHMM_pred object\n", - "PROVEAN_score float32\n", - "PROVEAN_converted_rankscore float32\n", - "PROVEAN_pred object\n", - "VEST4_score float32\n", - "VEST4_rankscore float32\n", - "MetaSVM_score float32\n", - "MetaSVM_rankscore float32\n", - "MetaSVM_pred object\n", - "MetaLR_score float32\n", - "MetaLR_rankscore float32\n", - "MetaLR_pred object\n", - "Reliability_index float32\n", - "MetaRNN_score float32\n", - "MetaRNN_rankscore float32\n", - "MetaRNN_pred object\n", - "M-CAP_score float32\n", - "M-CAP_rankscore float32\n", - "M-CAP_pred object\n", - "REVEL_score float32\n", - "REVEL_rankscore float32\n", - "MutPred_score float32\n", - "MutPred_rankscore float32\n", - "MVP_score float32\n", - "MVP_rankscore float32\n", - "MPC_score float32\n", - "MPC_rankscore float32\n", - "PrimateAI_score float32\n", - "PrimateAI_rankscore float32\n", - "PrimateAI_pred object\n", - "DEOGEN2_score float32\n", - "DEOGEN2_rankscore float32\n", - "DEOGEN2_pred object\n", - "BayesDel_addAF_score float32\n", - "BayesDel_addAF_rankscore float32\n", - "BayesDel_addAF_pred object\n", - "BayesDel_noAF_score float32\n", - "BayesDel_noAF_rankscore float32\n", - "BayesDel_noAF_pred object\n", - "ClinPred_score float32\n", - "ClinPred_rankscore float32\n", - "ClinPred_pred object\n", - "LIST-S2_score float32\n", - "LIST-S2_rankscore float32\n", - "LIST-S2_pred object\n", - "CADD_raw float32\n", - "CADD_raw_rankscore float32\n", - "CADD_phred float32\n", - "CADD_raw_hg19 float32\n", - "CADD_raw_rankscore_hg19 float32\n", - "CADD_phred_hg19 float32\n", - "DANN_score float32\n", - "DANN_rankscore float32\n", - "fathmm-MKL_coding_score float32\n", - "fathmm-MKL_coding_rankscore float32\n", - "fathmm-MKL_coding_pred object\n", - "fathmm-XF_coding_score float32\n", - "fathmm-XF_coding_rankscore float32\n", - "fathmm-XF_coding_pred object\n", - "Eigen-raw_coding float32\n", - "Eigen-raw_coding_rankscore float32\n", - "Eigen-phred_coding float32\n", - "Eigen-PC-raw_coding float32\n", - "Eigen-PC-raw_coding_rankscore float32\n", - "Eigen-PC-phred_coding float32\n", - "GenoCanyon_score float32\n", - "GenoCanyon_rankscore float32\n", - "integrated_fitCons_score float32\n", - "integrated_fitCons_rankscore float32\n", - "integrated_confidence_value float32\n", - "GM12878_fitCons_score float32\n", - "GM12878_fitCons_rankscore float32\n", - "GM12878_confidence_value float32\n", - "H1-hESC_fitCons_score float32\n", - "H1-hESC_fitCons_rankscore float32\n", - "H1-hESC_confidence_value float32\n", - "HUVEC_fitCons_score float32\n", - "HUVEC_fitCons_rankscore float32\n", - "HUVEC_confidence_value float32\n", - "LINSIGHT float32\n", - "LINSIGHT_rankscore float32\n", - "GERP++_NR float32\n", - "GERP++_RS float32\n", - "GERP++_RS_rankscore float32\n", - "phyloP100way_vertebrate float32\n", - "phyloP100way_vertebrate_rankscore float32\n", - "phyloP30way_mammalian float32\n", - "phyloP30way_mammalian_rankscore float32\n", - "phyloP17way_primate float32\n", - "phyloP17way_primate_rankscore float32\n", - "phastCons100way_vertebrate float32\n", - "phastCons100way_vertebrate_rankscore float32\n", - "phastCons30way_mammalian float32\n", - "phastCons30way_mammalian_rankscore float32\n", - "phastCons17way_primate float32\n", - "phastCons17way_primate_rankscore float32\n", - "SiPhy_29way_logOdds float32\n", - "SiPhy_29way_logOdds_rankscore float32\n", - "bStatistic float32\n", - "bStatistic_converted_rankscore float32\n", - "1000Gp3_AF float32\n", - "1000Gp3_AFR_AF float32\n", - "1000Gp3_EUR_AF float32\n", - "1000Gp3_AMR_AF float32\n", - "1000Gp3_EAS_AF float32\n", - "1000Gp3_SAS_AF float32\n", - "TWINSUK_AF float32\n", - "ALSPAC_AF float32\n", - "UK10K_AF float32\n", - "ESP6500_AA_AF float32\n", - "ESP6500_EA_AF float32\n", - "ExAC_AF float32\n", - "ExAC_Adj_AF float32\n", - "ExAC_AFR_AF float32\n", - "ExAC_AMR_AF float32\n", - "ExAC_EAS_AF float32\n", - "ExAC_FIN_AF float32\n", - "ExAC_NFE_AF float32\n", - "ExAC_SAS_AF float32\n", - "ExAC_nonTCGA_AF float32\n", - "ExAC_nonTCGA_Adj_AF float32\n", - "ExAC_nonTCGA_AFR_AF float32\n", - "ExAC_nonTCGA_AMR_AF float32\n", - "ExAC_nonTCGA_EAS_AF float32\n", - "ExAC_nonTCGA_FIN_AF float32\n", - "ExAC_nonTCGA_NFE_AF float32\n", - "ExAC_nonTCGA_SAS_AF float32\n", - "ExAC_nonpsych_AF float32\n", - "ExAC_nonpsych_Adj_AF float32\n", - "ExAC_nonpsych_AFR_AF float32\n", - "ExAC_nonpsych_AMR_AF float32\n", - "ExAC_nonpsych_EAS_AF float32\n", - "ExAC_nonpsych_FIN_AF float32\n", - "ExAC_nonpsych_NFE_AF float32\n", - "ExAC_nonpsych_SAS_AF float32\n", - "gnomAD_exomes_AF float32\n", - "gnomAD_exomes_AFR_AF float32\n", - "gnomAD_exomes_AMR_AF float32\n", - "gnomAD_exomes_ASJ_AF float32\n", - "gnomAD_exomes_EAS_AF float32\n", - "gnomAD_exomes_FIN_AF float32\n", - "gnomAD_exomes_NFE_AF float32\n", - "gnomAD_exomes_SAS_AF float32\n", - "gnomAD_exomes_POPMAX_AF float32\n", - "gnomAD_exomes_controls_AF float32\n", - "gnomAD_exomes_non_neuro_AF float32\n", - "gnomAD_exomes_non_cancer_AF float32\n", - "gnomAD_exomes_non_topmed_AF float32\n", - "gnomAD_exomes_controls_AFR_AF float32\n", - "gnomAD_exomes_controls_AMR_AF float32\n", - "gnomAD_exomes_controls_ASJ_AF float32\n", - "gnomAD_exomes_controls_EAS_AF float32\n", - "gnomAD_exomes_controls_FIN_AF float32\n", - "gnomAD_exomes_controls_NFE_AF float32\n", - "gnomAD_exomes_controls_SAS_AF float32\n", - "gnomAD_exomes_controls_POPMAX_AF float32\n", - "gnomAD_exomes_non_neuro_AFR_AF float32\n", - "gnomAD_exomes_non_neuro_AMR_AF float32\n", - "gnomAD_exomes_non_neuro_ASJ_AF float32\n", - "gnomAD_exomes_non_neuro_EAS_AF float32\n", - "gnomAD_exomes_non_neuro_FIN_AF float32\n", - "gnomAD_exomes_non_neuro_NFE_AF float32\n", - "gnomAD_exomes_non_neuro_SAS_AF float32\n", - "gnomAD_exomes_non_neuro_POPMAX_AF float32\n", - "gnomAD_exomes_non_cancer_AFR_AF float32\n", - "gnomAD_exomes_non_cancer_AMR_AF float32\n", - "gnomAD_exomes_non_cancer_ASJ_AF float32\n", - "gnomAD_exomes_non_cancer_EAS_AF float32\n", - "gnomAD_exomes_non_cancer_FIN_AF float32\n", - "gnomAD_exomes_non_cancer_NFE_AF float32\n", - "gnomAD_exomes_non_cancer_SAS_AF float32\n", - "gnomAD_exomes_non_cancer_POPMAX_AF float32\n", - "gnomAD_exomes_non_topmed_AFR_AF float32\n", - "gnomAD_exomes_non_topmed_AMR_AF float32\n", - "gnomAD_exomes_non_topmed_ASJ_AF float32\n", - "gnomAD_exomes_non_topmed_EAS_AF float32\n", - "gnomAD_exomes_non_topmed_FIN_AF float32\n", - "gnomAD_exomes_non_topmed_NFE_AF float32\n", - "gnomAD_exomes_non_topmed_SAS_AF float32\n", - "gnomAD_exomes_non_topmed_POPMAX_AF float32\n", - "gnomAD_genomes_AF float32\n", - "gnomAD_genomes_POPMAX_AF float32\n", - "gnomAD_genomes_AFR_AF float32\n", - "gnomAD_genomes_AMI_AF float32\n", - "gnomAD_genomes_AMR_AF float32\n", - "gnomAD_genomes_ASJ_AF float32\n", - "gnomAD_genomes_EAS_AF float32\n", - "gnomAD_genomes_FIN_AF float32\n", - "gnomAD_genomes_MID_AF float32\n", - "gnomAD_genomes_NFE_AF float32\n", - "gnomAD_genomes_SAS_AF float32\n", - "gnomAD_genomes_controls_and_biobanks_AF float32\n", - "gnomAD_genomes_non_neuro_AF float32\n", - "gnomAD_genomes_non_cancer_AF float32\n", - "gnomAD_genomes_non_topmed_AF float32\n", - "gnomAD_genomes_controls_and_biobanks_AFR_AF float32\n", - "gnomAD_genomes_controls_and_biobanks_AMI_AF float32\n", - "gnomAD_genomes_controls_and_biobanks_AMR_AF float32\n", - "gnomAD_genomes_controls_and_biobanks_ASJ_AF float32\n", - "gnomAD_genomes_controls_and_biobanks_EAS_AF float32\n", - "gnomAD_genomes_controls_and_biobanks_FIN_AF float32\n", - "gnomAD_genomes_controls_and_biobanks_MID_AF float32\n", - "gnomAD_genomes_controls_and_biobanks_NFE_AF float32\n", - "gnomAD_genomes_controls_and_biobanks_SAS_AF float32\n", - "gnomAD_genomes_non_neuro_AFR_AF float32\n", - "gnomAD_genomes_non_neuro_AMI_AF float32\n", - "gnomAD_genomes_non_neuro_AMR_AF float32\n", - "gnomAD_genomes_non_neuro_ASJ_AF float32\n", - "gnomAD_genomes_non_neuro_EAS_AF float32\n", - "gnomAD_genomes_non_neuro_FIN_AF float32\n", - "gnomAD_genomes_non_neuro_MID_AF float32\n", - "gnomAD_genomes_non_neuro_NFE_AF float32\n", - "gnomAD_genomes_non_neuro_SAS_AF float32\n", - "gnomAD_genomes_non_cancer_AFR_AF float32\n", - "gnomAD_genomes_non_cancer_AMI_AF float32\n", - "gnomAD_genomes_non_cancer_AMR_AF float32\n", - "gnomAD_genomes_non_cancer_ASJ_AF float32\n", - "gnomAD_genomes_non_cancer_EAS_AF float32\n", - "gnomAD_genomes_non_cancer_FIN_AF float32\n", - "gnomAD_genomes_non_cancer_MID_AF float32\n", - "gnomAD_genomes_non_cancer_NFE_AF float32\n", - "gnomAD_genomes_non_cancer_SAS_AF float32\n", - "gnomAD_genomes_non_topmed_AFR_AF float32\n", - "gnomAD_genomes_non_topmed_AMI_AF float32\n", - "gnomAD_genomes_non_topmed_AMR_AF float32\n", - "gnomAD_genomes_non_topmed_ASJ_AF float32\n", - "gnomAD_genomes_non_topmed_EAS_AF float32\n", - "gnomAD_genomes_non_topmed_FIN_AF float32\n", - "gnomAD_genomes_non_topmed_MID_AF float32\n", - "gnomAD_genomes_non_topmed_NFE_AF float32\n", - "gnomAD_genomes_non_topmed_SAS_AF float32\n", - "clinvar_clnsig object\n", - "clinvar_review object\n", - "Interpro_domain object\n", - "dtype: object" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.dtypes" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dropping empty columns and rows along with duplicate rows...\n", - "\n", - "Data shape (nsSNV) = (1774323, 268)\n", - "\n", - "clinvar_CLNSIG:\n", - " Uncertain_significance 1216719\n", - "Conflicting_interpretations_of_pathogenicity 115346\n", - "Pathogenic 110685\n", - "Likely_pathogenic 88317\n", - "Likely_benign 76815\n", - "Benign 74636\n", - "not_provided 33035\n", - "Benign/Likely_benign 27175\n", - "Pathogenic/Likely_pathogenic 24944\n", - "other 1887\n", - "drug_response 1690\n", - "risk_factor 967\n", - "association 469\n", - "Affects 356\n", - "Pathogenic,_other 232\n", - "Pathogenic/Likely_pathogenic,_other 170\n", - "Conflicting_interpretations_of_pathogenicity,_other 124\n", - "Pathogenic,_drug_response 79\n", - "protective 75\n", - "Pathogenic,_risk_factor 55\n", - "Benign,_other 52\n", - "Likely_pathogenic,_other 46\n", - "Pathogenic/Likely_pathogenic,_drug_response 40\n", - "Uncertain_significance,_drug_response 38\n", - "Conflicting_interpretations_of_pathogenicity,_risk_factor 36\n", - "Likely_benign,_other 33\n", - "Likely_pathogenic,_drug_response 32\n", - "Benign/Likely_benign,_other 26\n", - "Conflicting_interpretations_of_pathogenicity,_other,_risk_factor 22\n", - "Pathogenic/Likely_pathogenic,_risk_factor 20\n", - "Benign,_risk_factor 18\n", - "Pathogenic,_Affects 17\n", - "Likely_benign,_drug_response,_other 13\n", - "Likely_pathogenic,_risk_factor 12\n", - "Uncertain_significance,_risk_factor 12\n", - "Benign/Likely_benign,_risk_factor 11\n", - "confers_sensitivity 11\n", - "Pathogenic,_association 11\n", - "Likely_pathogenic,_Affects 11\n", - "protective,_risk_factor 7\n", - "drug_response,_risk_factor 7\n", - "Benign/Likely_benign,_other,_risk_factor 6\n", - "Conflicting_interpretations_of_pathogenicity,_drug_response 6\n", - "Conflicting_interpretations_of_pathogenicity,_association 6\n", - "Uncertain_significance,_Affects 5\n", - "Pathogenic,_protective 5\n", - "Pathogenic,_confers_sensitivity 5\n", - "Pathogenic,_drug_response,_other 4\n", - "Conflicting_interpretations_of_pathogenicity,_association,_risk_factor 4\n", - "Affects,_association 4\n", - "Uncertain_significance,_other 4\n", - "Benign,_drug_response 3\n", - "Benign/Likely_benign,_drug_response 3\n", - "Benign,_association,_confers_sensitivity 3\n", - "Benign,_confers_sensitivity 3\n", - "Benign,_protective 3\n", - "Uncertain_significance,_association 3\n", - "Conflicting_interpretations_of_pathogenicity,_Affects 2\n", - "drug_response,_other 1\n", - "Affects,_risk_factor 1\n", - "Benign/Likely_benign,_drug_response,_other 1\n", - "Name: clinvar_clnsig, dtype: int64\n", - "\n", - "clinvar_review:\n", - " criteria_provided,_single_submitter 1171954\n", - "criteria_provided,_multiple_submitters,_no_conflicts 337122\n", - "criteria_provided,_conflicting_interpretations 114647\n", - "no_assertion_criteria_provided 104857\n", - "no_assertion_provided 33035\n", - "reviewed_by_expert_panel 12598\n", - "practice_guideline 110\n", - "Name: clinvar_review, dtype: int64\n", - "\n", - "Interpro_domain:\n", - " Ion transport domain 31049\n", - "Fibronectin type III|Fibronectin type III|Fibronectin type III|Fibronectin type III 14247\n", - "Immunoglobulin I-set|Immunoglobulin-like domain|Immunoglobulin subtype 2|Immunoglobulin subtype 9830\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain 8866\n", - "p53, DNA-binding domain|p53, DNA-binding domain 7149\n", - "Cadherin-like|Cadherin-like 6477\n", - "BRCT domain|BRCT domain|BRCT domain|BRCT domain 6103\n", - "Tuberin, N-terminal 5985\n", - "Tuberin-type domain 5279\n", - "Laminin G domain|Laminin G domain|Laminin G domain 4660\n", - "Myosin tail 4075\n", - "Cadherin-like 3509\n", - "Intermediate filament, rod domain|Intermediate filament, rod domain|Intermediate filament, rod domain 3151\n", - "von Willebrand factor, type A|von Willebrand factor, type A|von Willebrand factor, type A 3033\n", - "Major facilitator superfamily domain|Major facilitator superfamily domain 2987\n", - "Rap GTPase activating protein domain|Rap GTPase activating protein domain 2974\n", - "Immunoglobulin I-set|Immunoglobulin-like domain|Immunoglobulin subtype 2967\n", - "Sodium ion transport-associated 2944\n", - "Neurotransmitter-gated ion-channel transmembrane domain 2922\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase domain|Tyrosine-protein kinase, catalytic domain 2817\n", - "DNA-directed DNA polymerase, family B, multifunctional domain 2761\n", - "Voltage-gated Na+ ion channel, cytoplasmic domain 2687\n", - "GPCR, rhodopsin-like, 7TM 2677\n", - "ABC transporter type 1, transmembrane domain|ABC transporter type 1, transmembrane domain 2594\n", - "DNA-directed DNA polymerase, family B, exonuclease domain 2568\n", - "DNA mismatch repair protein MutS, core|DNA mismatch repair protein MutS, core 2523\n", - "Neurotransmitter-gated ion-channel ligand-binding domain 2487\n", - "Helicase, C-terminal|Helicase, C-terminal|Helicase, C-terminal|Helicase, C-terminal 2465\n", - "DNA mismatch repair protein Mlh1, C-terminal 2441\n", - "Telomere-length maintenance and DNA damage repair|Telomere-length maintenance and DNA damage repair 2319\n", - "Aldehyde dehydrogenase domain 2318\n", - "Protein kinase domain|Protein kinase domain 2247\n", - "GPI ethanolamine phosphate transferase 1, C-terminal 2150\n", - "DNA mismatch repair protein MutS, C-terminal|DNA mismatch repair protein MutS, C-terminal 2107\n", - "Receptor, ligand binding region 2095\n", - "GPI ethanolamine phosphate transferase 1, N-terminal 2070\n", - "Serpin domain|Serpin domain 2023\n", - "Connexin, N-terminal 1983\n", - "Fibronectin type III|Fibronectin type III 1947\n", - "WD40-repeat-containing domain 1945\n", - "Ankyrin repeat-containing domain|Ankyrin repeat-containing domain 1888\n", - "Fibronectin type III 1868\n", - "Sulfatase, N-terminal 1855\n", - "ABC transporter-like|ABC transporter-like|AAA+ ATPase domain 1836\n", - "BRCA1, serine-rich domain 1834\n", - "DNA mismatch repair protein, S5 domain 2-like|DNA mismatch repair protein, S5 domain 2-like 1815\n", - "Zinc finger, C3HC4 RING-type|Zinc finger, RING-type|Zinc finger, RING-type 1797\n", - "DNA mismatch repair protein MutS, connector domain 1795\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain 1756\n", - "Laminin G domain 1744\n", - "Tetratricopeptide repeat-containing domain 1681\n", - "Dystroglycan-type cadherin-like 1670\n", - "Small GTP-binding protein domain 1661\n", - "Globin|Globin 1631\n", - "BRCT domain 1630\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase domain|Protein kinase domain 1615\n", - "Serine proteases, trypsin domain|Serine proteases, trypsin domain|Serine proteases, trypsin domain|Serine proteases, trypsin domain 1610\n", - "Wilm's tumour protein, N-terminal 1575\n", - "BRCT domain|BRCT domain|BRCT domain 1570\n", - "Ankyrin repeat-containing domain|Ankyrin repeat-containing domain|Ankyrin repeat-containing domain 1564\n", - "PIK-related kinase, FAT|PIK-related kinase 1557\n", - "DNA mismatch repair protein MutS-like, N-terminal 1533\n", - "Choline/carnitine acyltransferase domain 1491\n", - "SNF2-related, N-terminal domain|Helicase superfamily 1/2, ATP-binding domain|Helicase superfamily 1/2, ATP-binding domain 1449\n", - "DEAD/DEAH box helicase domain|Helicase superfamily 1/2, ATP-binding domain|Helicase superfamily 1/2, ATP-binding domain 1419\n", - "Major facilitator superfamily domain 1374\n", - "Fibronectin type III|Fibronectin type III|Fibronectin type III 1374\n", - "Nuclear hormone receptor, ligand-binding domain|Nuclear hormone receptor, ligand-binding domain|Nuclear hormone receptor, ligand-binding domain 1364\n", - "Immunoglobulin I-set|Immunoglobulin-like domain|Immunoglobulin V-set domain|Immunoglobulin subtype 2|Immunoglobulin subtype 1343\n", - "ATPase, AAA-type, core|AAA+ ATPase domain 1318\n", - "Dynein heavy chain domain 1293\n", - "HECT domain|HECT domain|HECT domain|HECT domain 1287\n", - "Calponin homology domain|Calponin homology domain|Calponin homology domain|Calponin homology domain 1287\n", - "Potassium channel, voltage dependent, KCNQ, C-terminal 1285\n", - "Dbl homology (DH) domain|Dbl homology (DH) domain|Dbl homology (DH) domain|Dbl homology (DH) domain 1268\n", - "HhH-GPD domain|HhH-GPD domain|HhH-GPD domain 1262\n", - "DNA recombination and repair protein Rad51-like, C-terminal|DNA recombination and repair protein RecA-like, ATP-binding domain 1242\n", - "PDZ domain|PDZ domain|PDZ domain 1240\n", - "Immunoglobulin-like domain 1221\n", - "Ezrin/radixin/moesin, C-terminal 1218\n", - "Sema domain|Sema domain|Sema domain 1209\n", - "Peptidase C12, ubiquitin carboxyl-terminal hydrolase 1199\n", - "EGF-like calcium-binding domain|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 1190\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase domain|Tyrosine-protein kinase, catalytic domain 1188\n", - "Forkhead-associated (FHA) domain|Forkhead-associated (FHA) domain|Forkhead-associated (FHA) domain|Forkhead-associated (FHA) domain 1187\n", - "Homeobox domain|Homeobox domain|Homeobox domain|Homeobox domain 1180\n", - "Immunoglobulin subtype 1165\n", - "Partner and localiser of BRCA2, WD40 domain 1160\n", - "BRCT domain|BRCT domain 1147\n", - "Adenomatous polyposis coli protein basic domain 1146\n", - "MutY, C-terminal|NUDIX hydrolase domain|MutY, C-terminal 1142\n", - "Calcineurin-like phosphoesterase domain, ApaH type 1119\n", - "Cadherin-like|Cadherin-like|Cadherin-like 1114\n", - "MutL, C-terminal, dimerisation|MutL, C-terminal, dimerisation 1088\n", - "Immunoglobulin I-set|Immunoglobulin subtype 2|Immunoglobulin subtype 1087\n", - "Fumarate lyase, N-terminal 1075\n", - "Pleckstrin homology domain|Pleckstrin homology domain|Pleckstrin homology domain 1066\n", - "GPCR family 3, C-terminal|GPCR family 3, C-terminal 1061\n", - "Zinc finger, LIM-type|Zinc finger, LIM-type|Zinc finger, LIM-type|Zinc finger, LIM-type 1059\n", - "Immunoglobulin I-set|Immunoglobulin subtype 1049\n", - "Helicase, C-terminal 1048\n", - "FAD-dependent oxidoreductase 2, FAD binding domain 1037\n", - "EGF-like domain 1022\n", - "Paired domain|Paired domain|Paired domain|Paired domain|Paired domain 1021\n", - "Domain of unknown function DUF1126|Uncharacterised domain DM10|Uncharacterised domain DM10 999\n", - "Zinc finger C2H2-type|Zinc finger C2H2-type|Zinc finger C2H2-type|Zinc finger C2H2-type 992\n", - "DNA mismatch repair protein MutS, clamp|DNA mismatch repair protein MutS, core|DNA mismatch repair protein MutS, core 984\n", - "Immunoglobulin-like domain|Immunoglobulin subtype 2|Immunoglobulin subtype 973\n", - "Rap GTPase activating protein domain 938\n", - "PIK-related kinase 887\n", - "Zinc finger, LIM-type|Zinc finger, LIM-type|Zinc finger, LIM-type 882\n", - "Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3-/4-kinase, catalytic domain 861\n", - "DNA polymerase epsilon, catalytic subunit A, C-terminal|DNA polymerase epsilon, catalytic subunit A, C-terminal 858\n", - "Glutamate [NMDA] receptor, epsilon subunit, C-terminal 850\n", - "Sushi/SCR/CCP domain|Sushi/SCR/CCP domain|Sushi/SCR/CCP domain|Sushi/SCR/CCP domain 849\n", - "Zinc finger C2H2-type|Zinc finger C2H2-type|Zinc finger C2H2-type 848\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase domain 845\n", - "Voltage-gated calcium channel subunit alpha, C-terminal 830\n", - "Hepatocyte nuclear factor 1, N-terminal 828\n", - "Adenosine/AMP deaminase domain 823\n", - "PLAT/LH2 domain|PLAT/LH2 domain|PLAT/LH2 domain 819\n", - "SMAD domain, Dwarfin-type|SMAD domain, Dwarfin-type|SMAD domain, Dwarfin-type 807\n", - "Acyl-CoA dehydrogenase/oxidase, N-terminal 805\n", - "Zinc finger C2H2-type 781\n", - "Immunoglobulin-like domain|Immunoglobulin subtype 765\n", - "PWWP domain|PWWP domain|PWWP domain 751\n", - "Dynein heavy chain, domain-1 748\n", - "Ribonuclease III domain|Ribonuclease III domain 736\n", - "Helicase/UvrB, N-terminal|Helicase superfamily 1/2, ATP-binding domain|Helicase superfamily 1/2, ATP-binding domain 722\n", - "DNA recombination and repair protein Rad51-like, C-terminal|DNA recombination and repair protein RecA-like, ATP-binding domain|Rad51/DMC1/RadA 722\n", - "RIH domain 713\n", - "Peptidase C2, calpain, catalytic domain|Peptidase C2, calpain, catalytic domain|Peptidase C2, calpain, catalytic domain|Peptidase C2, calpain, catalytic domain 712\n", - "Choloylglycine hydrolase/NAAA C-terminal 712\n", - "ABC transporter-like|AAA+ ATPase domain 709\n", - "Voltage-dependent calcium channel, alpha-2/delta subunit, conserved region 705\n", - "EGF-like calcium-binding domain|EGF-like calcium-binding, conserved site|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 702\n", - "Acetyl-CoA carboxylase|Acetyl-coenzyme A carboxyltransferase, N-terminal 701\n", - "Helicase superfamily 1/2, ATP-binding domain, DinG/Rad3-type|Helicase superfamily 1/2, ATP-binding domain|Helicase-like, DEXD box c2 type 700\n", - "Domain of unknown function DUF3498 696\n", - "Ribonuclease II/R|Ribonuclease II/R 693\n", - "Amino acid permease/ SLC12A domain 693\n", - "Cadherin prodomain|Cadherin prodomain 691\n", - "Zinc finger, C3HC4 RING-type|Zinc finger, RING-type, conserved site|Zinc finger, RING-type|Zinc finger, RING-type 690\n", - "Peptidase family A1 domain|Peptidase family A1 domain 689\n", - "Laminin EGF domain|Laminin EGF domain|Laminin EGF domain 688\n", - "Paired domain|Paired domain|Paired domain|Paired domain 685\n", - "TRAM/LAG1/CLN8 homology domain|TRAM/LAG1/CLN8 homology domain|TRAM/LAG1/CLN8 homology domain 684\n", - "Nuclear hormone receptor, ligand-binding domain 682\n", - "p53, DNA-binding domain 680\n", - "Connexin, N-terminal|Gap junction protein, cysteine-rich domain 678\n", - "Glycogen debranching enzyme, C-terminal 676\n", - "SNF2-related, N-terminal domain 674\n", - "FERM domain|Band 4.1 domain 673\n", - "Rab-GTPase-TBC domain|Rab-GTPase-TBC domain 668\n", - "Kinesin motor domain|Kinesin motor domain|Kinesin motor domain 667\n", - "EGF-like domain|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 663\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain|Serine/Threonine kinase LKB1, catalytic domain 658\n", - "EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 657\n", - "EF-hand domain 656\n", - "Immunoglobulin I-set|Immunoglobulin-like domain 650\n", - "Protein kinase domain 648\n", - "Homeobox domain|Homeobox, conserved site|Homeobox domain|Homeobox domain|Homeobox domain 639\n", - "Aminomethyltransferase, folate-binding domain 633\n", - "EYA domain|EYA domain 632\n", - "Ras GTPase-activating domain 627\n", - "Protein kinase domain|Protein kinase, ATP binding site|Protein kinase domain|Protein kinase domain 624\n", - "Bloom syndrome protein, N-terminal domain 620\n", - "Hexokinase, C-terminal 618\n", - "Peptidase M41 617\n", - "EGF-like domain|EGF-like domain 609\n", - "Acetyl-CoA carboxylase|Acetyl-coenzyme A carboxyltransferase, C-terminal 607\n", - "Cadherin, cytoplasmic domain 606\n", - "High mobility group box domain|High mobility group box domain|High mobility group box domain 606\n", - "AMP-dependent synthetase/ligase 599\n", - "EB-1 binding 598\n", - "CUB domain|CUB domain|CUB domain|CUB domain 596\n", - "Acyl-CoA dehydrogenase/oxidase C-terminal 595\n", - "Aromatic amino acid hydroxylase, C-terminal|Aromatic amino acid hydroxylase, C-terminal 595\n", - "Bromodomain 594\n", - "NACHT nucleoside triphosphatase|NACHT nucleoside triphosphatase 586\n", - "Dynamin-type guanine nucleotide-binding (G) domain|Dynamin, GTPase domain|Dynamin, GTPase domain 585\n", - "Doublecortin domain|Doublecortin domain|Doublecortin domain|Doublecortin domain 582\n", - "Myc-type, basic helix-loop-helix (bHLH) domain|Myc-type, basic helix-loop-helix (bHLH) domain|Myc-type, basic helix-loop-helix (bHLH) domain|Myc-type, basic helix-loop-helix (bHLH) domain 581\n", - "Ionotropic glutamate receptor|Ionotropic glutamate receptor 579\n", - "Aminotransferase class V domain 573\n", - "ATP-dependent helicase, C-terminal|ATP-dependent helicase, C-terminal 572\n", - "Dynein heavy chain, domain-2 572\n", - "Laminin, N-terminal|Laminin, N-terminal|Laminin, N-terminal 571\n", - "EGF-like calcium-binding domain|EGF-like, conserved site|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 571\n", - "Glycogen debranching enzyme, glucanotransferase domain|Glycogen debranching enzyme, glucanotransferase domain 568\n", - "Low-density lipoprotein (LDL) receptor class A, conserved site 563\n", - "Pyridoxal-phosphate dependent enzyme 562\n", - "Laminin alpha, domain I 558\n", - "Rad50/SbcC-type AAA domain 550\n", - "Ryanodine Receptor TM 4-6 539\n", - "SLC26A/SulP transporter domain 538\n", - "Ribonuclease III domain|Ribonuclease III domain|Ribonuclease III domain|Ribonuclease III domain 538\n", - "Aminoacyl-tRNA synthetase, class II 536\n", - "Ras GTPase-activating domain|Ras GTPase-activating domain|Ras GTPase-activating domain 536\n", - "DNA mismatch repair protein MutS, core 536\n", - "Laminin EGF domain|Laminin EGF domain|Laminin EGF domain|EGF-like domain 527\n", - "Bromodomain|Bromodomain|Bromodomain, conserved site|Bromodomain|Bromodomain 526\n", - "Cyclic nucleotide-binding domain|Cyclic nucleotide-binding domain|Cyclic nucleotide-binding domain|Cyclic nucleotide-binding domain 518\n", - "p53, tetramerisation domain 517\n", - "Formin, FH2 domain|Formin, FH2 domain|Formin, FH2 domain 515\n", - "Dynein heavy chain, coiled coil stalk 514\n", - "MATH/TRAF domain|MATH/TRAF domain|MATH/TRAF domain 511\n", - "FAD/NAD(P)-binding domain 507\n", - "Clathrin/coatomer adaptor, adaptin-like, N-terminal 506\n", - "DEAD2|Helicase superfamily 1/2, ATP-binding domain, DinG/Rad3-type|Helicase superfamily 1/2, ATP-binding domain|Helicase-like, DEXD box c2 type 506\n", - "Helicase, C-terminal|Helicase, C-terminal|Helicase, C-terminal 501\n", - "Pex, N-terminal 500\n", - "Ryanodine receptor Ryr 499\n", - "Methyl-CpG DNA binding|Methyl-CpG DNA binding|Methyl-CpG DNA binding 498\n", - "Laminin G domain|Laminin G domain 491\n", - "Forkhead-associated (FHA) domain 485\n", - "TGF-beta, propeptide 482\n", - "Axin-1/2, tankyrase-binding domain|Axin-1/2, tankyrase-binding domain 476\n", - "Tensin phosphatase, C2 domain|Tensin phosphatase, C2 domain|Tensin phosphatase, C2 domain 475\n", - "Receptor L-domain 473\n", - "FERM domain 473\n", - "von Willebrand factor, type D domain|von Willebrand factor, type D domain|von Willebrand factor, type D domain 469\n", - "Guanylate kinase/L-type calcium channel beta subunit|Guanylate kinase/L-type calcium channel beta subunit 467\n", - "EF-hand domain, type 1 465\n", - "GPCR, family 2-like 463\n", - "SLC12A transporter, C-terminal 460\n", - "TB domain|TB domain 459\n", - "DNA recombination and repair protein Rad51-like, C-terminal|DNA recombination and repair protein RecA-like, ATP-binding domain|AAA+ ATPase domain|Rad51/DMC1/RadA 459\n", - "C2 domain|C2 domain|C2 domain 456\n", - "Carboxylesterase, type B 455\n", - "Globin 453\n", - "Hepatocyte nuclear factor 1, beta isoform, C-terminal 450\n", - "ABC transporter-like 444\n", - "Fumarate reductase/succinate dehydrogenase flavoprotein-like, C-terminal 443\n", - "Transcription factor, MADS-box|Transcription factor, MADS-box|Transcription factor, MADS-box|Transcription factor, MADS-box|Transcription factor, MADS-box|MADS MEF2-like 442\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Class VII myosin, motor domain 442\n", - "MAD homology 1, Dwarfin-type|MAD homology, MH1|MAD homology 1, Dwarfin-type 437\n", - "Tubulin/FtsZ, GTPase domain|Tubulin/FtsZ, GTPase domain 437\n", - "Snf2, ATP coupling domain|Snf2, ATP coupling domain 436\n", - "EF-hand domain|EF-hand domain 434\n", - "Nibrin, second BRCT domain 434\n", - "MIR motif|MIR motif|MIR motif 432\n", - "Sedolisin domain|Sedolisin domain 429\n", - "Aromatic amino acid hydroxylase, C-terminal|Aromatic amino acid hydroxylase, C-terminal|Aromatic amino acid hydroxylase, C-terminal 428\n", - "FERM central domain|FERM domain|Band 4.1 domain|FERM central domain 428\n", - "Voltage-dependent calcium channel, alpha-1 subunit, IQ domain 425\n", - "IPT domain|IPT domain 425\n", - "Runt domain|Runt domain 422\n", - "DNA mismatch repair protein family, N-terminal 421\n", - "Lipid transport protein, N-terminal|Lipid transport protein, N-terminal|Lipid transport protein, N-terminal 421\n", - "BCS1, N-terminal|BCS1, N-terminal 420\n", - "Cation-transporting P-type ATPase, C-terminal 418\n", - "Fanconi anaemia group A protein, N-terminal domain 417\n", - "DNA mismatch repair protein family, N-terminal|Histidine kinase/HSP90-like ATPase 412\n", - "Voltage-dependent L-type calcium channel, IQ-associated domain 408\n", - "Helicase superfamily 1/2, ATP-binding domain 408\n", - "Dehydrogenase, E1 component 403\n", - "Alpha-crystallin, N-terminal 398\n", - "Connexin, N-terminal|Connexin, N-terminal 398\n", - "FERM, C-terminal PH-like domain|FERM domain|FERM, C-terminal PH-like domain 398\n", - "SH3 domain 393\n", - "Ankyrin repeat-containing domain 392\n", - "PLAT/LH2 domain|PLAT/LH2 domain 391\n", - "Zinc finger C2H2-type|Zinc finger C2H2-type 388\n", - "NACHT nucleoside triphosphatase 388\n", - "EGF-like, conserved site|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 388\n", - "Potassium channel tetramerisation-type BTB domain|BTB/POZ domain 387\n", - "PWWP domain 385\n", - "Dynamin-type guanine nucleotide-binding (G) domain 384\n", - "RecF/RecN/SMC, N-terminal 380\n", - "Fibrillar collagen, C-terminal|Fibrillar collagen, C-terminal|Fibrillar collagen, C-terminal 377\n", - "Fibrillar collagen, C-terminal|Fibrillar collagen, C-terminal|Fibrillar collagen, C-terminal|Fibrillar collagen, C-terminal 376\n", - "RGS domain|RGS domain|RGS domain|RGS domain 375\n", - "AAA+ ATPase domain 375\n", - "DNA mismatch repair protein, S5 domain 2-like 373\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase, ATP binding site|Protein kinase domain|Tyrosine-protein kinase, catalytic domain 373\n", - "GH15-like domain 372\n", - "BRK domain|BRK domain 372\n", - "Transthyretin/hydroxyisourate hydrolase domain|Transthyretin/hydroxyisourate hydrolase domain|Transthyretin/hydroxyisourate hydrolase domain 372\n", - "CRAL-TRIO lipid binding domain|CRAL-TRIO lipid binding domain|CRAL-TRIO lipid binding domain|CRAL-TRIO lipid binding domain 369\n", - "Glycosyl transferase family 39/83 369\n", - "ABC transporter type 1, transmembrane domain 368\n", - "Doublecortin domain|Doublecortin domain|Doublecortin domain 368\n", - "Lamin tail domain|Lamin tail domain 365\n", - "Furin-like cysteine-rich domain 365\n", - "Phosphofructokinase domain 365\n", - "Connexin, N-terminal|Connexin, conserved site|Gap junction protein, cysteine-rich domain 364\n", - "Forkhead-associated (FHA) domain|Forkhead-associated (FHA) domain 364\n", - "Glycoside hydrolase family 20, catalytic domain 359\n", - "Acyl-CoA oxidase/dehydrogenase, central domain 357\n", - "Peptidase C12, ubiquitin carboxyl-terminal hydrolase|Peptidase C12, ubiquitin carboxyl-terminal hydrolase 357\n", - "DNA mismatch repair protein, S5 domain 2-like|DNA mismatch repair protein, S5 domain 2-like|DNA mismatch repair protein family, N-terminal 356\n", - "PAZ domain|PAZ domain|PAZ domain 355\n", - "BAR domain|BAR domain|BAR domain 354\n", - "Aminoacyl-tRNA synthetase, class Ia 351\n", - "MAM domain|MAM domain|MAM domain 351\n", - "SET domain|SET domain|SET domain 350\n", - "von Hippel-Lindau disease tumour suppressor, beta domain|von Hippel-Lindau disease tumour suppressor, beta/alpha domain 348\n", - "MyTH4 domain|MyTH4 domain|MyTH4 domain 347\n", - "Helix-hairpin-helix motif|HhH-GPD domain|Endonuclease III-like, conserved site-2|HhH-GPD domain|HhH-GPD domain 346\n", - "Dedicator of cytokinesis, C-terminal|DHR-2 domain 345\n", - "Glycogen debranching enzyme, central domain 344\n", - "p53 transactivation domain 342\n", - "GAF domain|GAF domain 340\n", - "Guanylate kinase/L-type calcium channel beta subunit|Guanylate kinase-like domain|Guanylate kinase/L-type calcium channel beta subunit 339\n", - "Carbamoyl-phosphate synthetase large subunit-like, ATP-binding domain|ATP-grasp fold|Biotin carboxylation domain 338\n", - "Dynein heavy chain, ATP-binding dynein motor region D5 338\n", - "Telomere-length maintenance and DNA damage repair 338\n", - "Fork head domain|Fork head domain|Fork head domain|Fork head domain|Fork head domain 338\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain 337\n", - "STAT transcription factor, DNA-binding 335\n", - "Potassium channel domain 335\n", - "Connexin, N-terminal|Connexin, conserved site|Connexin, N-terminal 334\n", - "Dynamin central domain 333\n", - "Cyclic nucleotide-binding domain|Cyclic nucleotide-binding, conserved site|Cyclic nucleotide-binding domain|Cyclic nucleotide-binding domain|Cyclic nucleotide-binding domain 333\n", - "Tubulin/FtsZ, GTPase domain 328\n", - "SAC domain|SAC domain 328\n", - "Bloom syndrome protein, BDHCT-box associated domain 328\n", - "Immunoglobulin|Immunoglobulin-like domain|Immunoglobulin subtype 327\n", - "Ras GTPase-activating domain|Ras GTPase-activating domain 327\n", - "CBS domain|CBS domain|CBS domain 326\n", - "Fanconi anemia complex, subunit FancL, WD-repeat containing domain 323\n", - "Activin types I and II receptor domain 322\n", - "Peptidase C2, calpain, large subunit, domain III|Peptidase C2, calpain, domain III|Calpain subdomain III 321\n", - "cAMP-dependent protein kinase regulatory subunit, dimerization-anchoring domain|cAMP-dependent protein kinase regulatory subunit, dimerization-anchoring domain 319\n", - "Deoxynucleoside kinase domain 318\n", - "Isopropylmalate dehydrogenase-like domain|Isopropylmalate dehydrogenase-like domain 318\n", - "Serpin domain 318\n", - "Integrin alpha-2 316\n", - "C2 domain|C2 domain 316\n", - "LPS-induced tumour necrosis factor alpha factor|LPS-induced tumour necrosis factor alpha factor|LPS-induced tumour necrosis factor alpha factor 315\n", - "DNA-directed DNA polymerase, family A, palm domain|DNA-directed DNA polymerase, family A, palm domain 314\n", - "IQ motif, EF-hand binding site 314\n", - "Laminin IV|Laminin IV|Laminin IV 310\n", - "C4-type zinc-finger of DNA polymerase delta 307\n", - "Dynein heavy chain, AAA module D4 306\n", - "Growth factor receptor domain 4 305\n", - "IQ motif, EF-hand binding site|IQ motif, EF-hand binding site|IQ motif, EF-hand binding site 302\n", - "Bicarbonate transporter, C-terminal 301\n", - "Glutaminyl-tRNA synthetase, class Ib, non-specific RNA-binding domain, N-terminal 301\n", - "Hexokinase, N-terminal 301\n", - "Mre11, DNA-binding|Mre11, DNA-binding 300\n", - "Integrin beta subunit, VWA domain|Integrin beta subunit, VWA domain 300\n", - "Immunoglobulin V-set domain 300\n", - "MAD homology 1, Dwarfin-type|CTF transcription factor/nuclear factor 1, DNA-binding domain|MAD homology 1, Dwarfin-type 299\n", - "IPT domain 298\n", - "Myotubularin-like phosphatase domain|Myotubularin-like phosphatase domain 297\n", - "Folliculin, N-terminal|Folliculin/SMCR8, tripartite DENN domain 296\n", - "F-BAR domain 296\n", - "Dicer dimerisation domain|Dicer dimerisation domain 292\n", - "HAD-superfamily hydrolase,subfamily IIIA|Polynucleotide 3'-phosphatase 292\n", - "Tubulin/FtsZ, 2-layer sandwich domain|Tubulin/FtsZ, 2-layer sandwich domain 292\n", - "Pleckstrin homology domain|Pleckstrin homology domain 291\n", - "Paf1 complex subunit Cdc73, N-terminal domain 290\n", - "Zinc finger, nuclear hormone receptor-type|Zinc finger, nuclear hormone receptor-type|Zinc finger, nuclear hormone receptor-type|Zinc finger, nuclear hormone receptor-type|Zinc finger, nuclear hormone receptor-type 289\n", - "Alpha crystallin/Hsp20 domain|Alpha crystallin/Hsp20 domain 288\n", - "STAS domain|STAS domain 288\n", - "EF-hand domain|EF-Hand 1, calcium-binding site|EF-hand domain|EF-hand domain|EF-hand domain 288\n", - "Galactose mutarotase, N-terminal barrel 287\n", - "Endonuclease/exonuclease/phosphatase|Inositol polyphosphate-related phosphatase 287\n", - "Glucose-6-phosphate dehydrogenase, C-terminal 286\n", - "Cyclic nucleotide-binding domain|Cyclic nucleotide-binding domain|Cyclic nucleotide-binding domain 285\n", - "Serum albumin, N-terminal|Serum albumin, N-terminal|Serum albumin, N-terminal|Serum albumin, N-terminal 285\n", - "EGF-like domain|EGF-like, conserved site|EGF-like, conserved site|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 285\n", - "Helicase/SANT-associated domain|Helicase/SANT-associated domain|Helicase/SANT-associated domain 282\n", - "Phospholipid/glycerol acyltransferase|Phospholipid/glycerol acyltransferase 282\n", - "Treacle protein domain 282\n", - "HhH-GPD domain|HhH-GPD domain 281\n", - "von Willebrand factor, type A|von Willebrand factor, type A 279\n", - "BTB/POZ domain|BTB/POZ domain|BTB/POZ domain 279\n", - "Peptidase M13, N-terminal domain 279\n", - "Adenomatous polyposis coli, N-terminal dimerisation domain 279\n", - "EF-hand domain, type 2 278\n", - "DNA repair Nbs1, C-terminal|DNA repair Nbs1, C-terminal 276\n", - "Calponin homology domain|Actinin-type actin-binding domain, conserved site|Calponin homology domain|Calponin homology domain|Calponin homology domain 274\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain|Protein kinase B alpha, catalytic domain 273\n", - "Coagulation factor 5/8 C-terminal domain|Coagulation factor 5/8 C-terminal domain|Coagulation factor 5/8 C-terminal domain|Coagulation factor 5/8 C-terminal domain 270\n", - "Na-Ca exchanger/integrin-beta4|Na-Ca exchanger/integrin-beta4 268\n", - "FAD dependent oxidoreductase 267\n", - "GATA-type transcription activator, N-terminal 264\n", - "Guanylate-binding protein, N-terminal|GB1/RHD3-type guanine nucleotide-binding (G) domain 263\n", - "Paired domain|Paired domain|Paired domain|Paired domain|Paired domain|Paired domain 263\n", - "THIF-type NAD/FAD binding fold 262\n", - "PDZ domain 261\n", - "Arginine N-methyltransferase 2-like domain 260\n", - "Fumarylacetoacetase-like, C-terminal 258\n", - "3'5'-cyclic nucleotide phosphodiesterase, catalytic domain|3'5'-cyclic nucleotide phosphodiesterase, catalytic domain|HD/PDEase domain|HD/PDEase domain 257\n", - "Interferon regulatory factor DNA-binding domain 257\n", - "Laminin EGF domain|Laminin EGF domain|Laminin EGF domain|Laminin EGF domain|EGF-like domain 257\n", - "Peptidase S9A, N-terminal domain 256\n", - "ZU5 domain 256\n", - "Glycosyl hydrolase family 30, TIM-barrel domain 256\n", - "Kazal domain|Kazal domain|Kazal domain 256\n", - "PET domain|PET domain|PET prickle 255\n", - "Alanyl-tRNA synthetase, class IIc, N-terminal|Alanyl-tRNA synthetase, class IIc, core domain 254\n", - "Glucose-6-phosphate dehydrogenase, NAD-binding 254\n", - "Zinc finger, nuclear hormone receptor-type|Zinc finger, nuclear hormone receptor-type|Zinc finger, nuclear hormone receptor-type|Zinc finger, nuclear hormone receptor-type 253\n", - "Aminotransferase, class I/classII 252\n", - "SPRY domain|B30.2/SPRY domain|SPRY domain|Ryanodine receptor, SPRY domain 1 252\n", - "SAC domain 251\n", - "Interferon regulatory factor-3|Interferon regulatory factor DNA-binding domain|Interferon regulatory factor-3 251\n", - "Helicase superfamily 1/2, ATP-binding domain|Helicase superfamily 1/2, ATP-binding domain 250\n", - "Histone H2A/H2B/H3 250\n", - "Collagen IV, non-collagenous|Collagen IV, non-collagenous|Collagen IV, non-collagenous 249\n", - "Immunoglobulin V-set domain|Immunoglobulin-like domain|Immunoglobulin subtype 249\n", - "C2 domain 248\n", - "ACT domain|ACT domain 248\n", - "DEP domain|DEP domain|DEP domain 248\n", - "EF-hand domain|EF-hand domain|EF-hand domain 248\n", - "STAT transcription factor, all-alpha domain 246\n", - "Glutamyl/glutaminyl-tRNA synthetase, class Ib, catalytic domain 246\n", - "NUDIX hydrolase domain 245\n", - "Cadherin-like|Cadherin conserved site|Cadherin-like 245\n", - "DNA recombination and repair protein Rad51-like, C-terminal 244\n", - "Intermediate filament, rod domain|Intermediate filament, rod domain 242\n", - "MaoC-like dehydratase domain 240\n", - "Dual specificity phosphatase, catalytic domain|Dual specificity protein phosphatase domain|Dual specificity protein phosphatase domain 240\n", - "MyTH4 domain|MyTH4 domain 240\n", - "von Hippel-Lindau disease tumour suppressor, alpha domain|von Hippel-Lindau disease tumour suppressor, beta/alpha domain 238\n", - "Fibrinogen, alpha/beta/gamma chain, C-terminal globular domain|Fibrinogen, alpha/beta/gamma chain, C-terminal globular domain|Fibrinogen, alpha/beta/gamma chain, C-terminal globular domain|Fibrinogen, alpha/beta/gamma chain, C-terminal globular domain 237\n", - "RGS domain|RGS domain|RGS domain 237\n", - "Spatacsin, C-terminal domain 236\n", - "EF-hand domain|EF-hand domain|EF-hand domain|EF-hand domain 235\n", - "Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3-/4-kinase, catalytic domain 235\n", - "Heavy metal-associated domain, HMA|Heavy metal-associated domain, HMA|Heavy metal-associated domain, copper ion-binding|Heavy metal-associated domain, HMA 235\n", - "ABC transporter-like|ABC transporter, conserved site|ABC transporter-like|AAA+ ATPase domain 234\n", - "UbiB domain, ADCK3-like 234\n", - "Dystroglycan, C-terminal 234\n", - "Alpha/beta hydrolase fold-1 233\n", - "UDP-N-acetylglucosamine 2-epimerase domain|UDP-N-acetylglucosamine 2-epimerase domain 231\n", - "Homeobox protein Hox1A3 N-terminal 231\n", - "B30.2/SPRY domain 231\n", - "Glycosyltransferase 2-like 230\n", - "Mu homology domain|Mu homology domain 230\n", - "Ubiquitin-protein ligase E3A, N-terminal zinc-binding domain 230\n", - "Acid ceramidase, N-terminal 230\n", - "Biotin carboxylase, C-terminal|Biotin carboxylation domain|Biotin carboxylase, C-terminal 230\n", - "Heavy metal-associated domain, HMA|Heavy-metal-associated, conserved site|Heavy metal-associated domain, HMA|Heavy metal-associated domain, copper ion-binding|Heavy metal-associated domain, HMA 229\n", - "Protection of telomeres protein 1, ssDNA-binding domain 229\n", - "Cation/H+ exchanger 228\n", - "Glutamine-Leucine-Glutamine, QLQ|Glutamine-Leucine-Glutamine, QLQ|Glutamine-Leucine-Glutamine, QLQ 228\n", - "CARD domain|CARD domain 228\n", - "Uncharacterised domain, cysteine-rich|Uncharacterised domain, cysteine-rich 227\n", - "Pleckstrin homology domain|Pleckstrin homology domain|Pleckstrin homology domain|Protein Kinase B, pleckstrin homology domain 226\n", - "Alpha crystallin/Hsp20 domain|Alpha crystallin/Hsp20 domain|Alpha-crystallin B chain, ACD domain 224\n", - "Immunoglobulin|Immunoglobulin-like domain|Immunoglobulin subtype 2|Immunoglobulin subtype 224\n", - "SPRY domain|B30.2/SPRY domain|SPRY domain|Ryanodine receptor, SPRY domain 3 223\n", - "Inositol 1,4,5-trisphosphate/ryanodine receptor 223\n", - "Citron homology (CNH) domain 222\n", - "Domain of unknown function DUF4683 222\n", - "Thiolase, N-terminal 222\n", - "Polycystin cation channel, PKD1/PKD2 221\n", - "Immunoglobulin V-set domain|Immunoglobulin-like domain|Immunoglobulin V-set domain|Immunoglobulin subtype 221\n", - "Tensin-type phosphatase domain|Protein-tyrosine phosphatase, catalytic 220\n", - "IQ motif, EF-hand binding site|IQ motif, EF-hand binding site 220\n", - "Intermediate filament head, DNA-binding domain 218\n", - "Lipoxygenase, C-terminal|Lipoxygenase, C-terminal 218\n", - "Rho GTPase-activating protein domain|Rho GTPase-activating protein domain|Rho GTPase-activating protein domain 218\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase, ATP binding site|Protein kinase domain|Protein kinase domain 217\n", - "DNA recombination and repair protein Rad51-like, C-terminal|Rad51/DMC1/RadA 216\n", - "Zona pellucida domain 214\n", - "ATPase, AAA-type, core|ATPase, AAA-type, conserved site|AAA+ ATPase domain 212\n", - "Peptidase C19, ubiquitin carboxyl-terminal hydrolase|Ubiquitin specific protease domain 212\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tumor necrosis factor receptor 1A, N-terminal 210\n", - "WW domain|WW domain|WW domain|WW domain|WW domain 210\n", - "BEACH domain|BEACH domain|BEACH domain|BEACH domain 210\n", - "Ankyrin repeat-containing domain|Ankyrin repeat-containing domain|Ankyrin repeat-containing domain|Ankyrin repeat-containing domain 210\n", - "Sterile alpha motif domain|Sterile alpha motif domain|Sterile alpha motif domain 209\n", - "KRIT, N-terminal NPxY motif-rich region 209\n", - "Homeobox domain|Homeobox domain 207\n", - "CFTR regulator domain 206\n", - "Nucleotidyl transferase domain 206\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Class XV myosin, motor domain 206\n", - "SWI/SNF-like complex subunit BAF250, C-terminal 206\n", - "Transforming growth factor-beta, C-terminal|Transforming growth factor-beta, C-terminal|Transforming growth factor-beta, C-terminal 205\n", - "ATPase, dynein-related, AAA domain|AAA+ ATPase domain 205\n", - "EGF-like domain|EGF-like calcium-binding, conserved site|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 204\n", - "DEAD2|Helicase superfamily 1/2, ATP-binding domain, DinG/Rad3-type|Helicase-like, DEXD box c2 type 204\n", - "Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3/4-kinase, conserved site|Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3-/4-kinase, catalytic domain 203\n", - "FANCI solenoid 1 domain 202\n", - "Homeobox domain 202\n", - "Cell division control protein 73, C-terminal 202\n", - "Dual specificity phosphatase, catalytic domain|Dual specificity protein phosphatase domain|Tyrosine specific protein phosphatases domain|Dual specificity protein phosphatase domain 201\n", - "Lipase/vitellogenin|Lipase, N-terminal 200\n", - "ZU5 domain|ZU5 domain|ZU5 domain 200\n", - "SPRY domain|B30.2/SPRY domain|SPRY domain|Ryanodine receptor, SPRY domain 2 199\n", - "Phosphoinositide 3-kinase, accessory (PIK) domain|Phosphoinositide 3-kinase, accessory (PIK) domain|Phosphoinositide 3-kinase, accessory (PIK) domain 198\n", - "Transketolase-like, pyrimidine-binding domain|Transketolase-like, pyrimidine-binding domain 198\n", - "Zona pellucida domain|Zona pellucida domain|Zona pellucida domain 197\n", - "Glycosyl transferase, family 3 196\n", - "Ankyrin-G binding site 196\n", - "TMC domain 196\n", - "Inositol 1,4,5-trisphosphate/ryanodine receptor|MIR motif|MIR motif 196\n", - "Folate receptor-like 194\n", - "Pyrroline-5-carboxylate reductase, dimerisation domain 194\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase domain|Tyrosine-protein kinase, catalytic domain|Protein kinase domain 194\n", - "BARD1, Zinc finger, RING-type 194\n", - "Peptidase S9, prolyl oligopeptidase, catalytic domain 192\n", - "Ribonuclease III domain 192\n", - "SUN domain-containing protein 1, N-terminal 191\n", - "Pleckstrin homology domain 191\n", - "ABC transporter-like|ABC transporter-like 191\n", - "RQC domain|RQC domain 190\n", - "Helicase, C-terminal|Helicase, C-terminal 190\n", - "NADH-ubiquinone oxidoreductase 51kDa subunit, FMN-binding domain 190\n", - "DJ-1/PfpI 190\n", - "NAD(P)-binding domain 189\n", - "Dynein associated protein 189\n", - "Intermediate filament, rod domain|Intermediate filament protein, conserved site|Intermediate filament, rod domain|Intermediate filament, rod domain 188\n", - "Amine oxidase 188\n", - "Alpha carbonic anhydrase domain|Alpha carbonic anhydrase domain|Alpha carbonic anhydrase domain 188\n", - "DNA recombination and repair protein RecA-like, ATP-binding domain 187\n", - "Glycosyl hydrolase, family 13, catalytic domain|Glycosyl hydrolase, family 13, catalytic domain 187\n", - "Phospholipase C-beta, C-terminal domain 187\n", - "UDP-glycosyltransferase family, conserved site 185\n", - "EF-hand, Ca insensitive 185\n", - "Sema domain 185\n", - "Peptidase M13, C-terminal domain 185\n", - "Formin Homology 1 185\n", - "Kinesin motor domain|Kinesin motor domain|Kinesin motor domain|Kinesin motor domain 184\n", - "Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Diacylglycerol/phorbol-ester binding|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain 184\n", - "Gelsolin-like domain 184\n", - "Cep57 centrosome localisation domain 183\n", - "Sulfatase, N-terminal|Sulfatase, conserved site 183\n", - "Peptidase M12B, propeptide 182\n", - "TLDc domain|TLDc domain 182\n", - "3-beta hydroxysteroid dehydrogenase/isomerase 181\n", - "Interleukin-17 receptor C/E, N-terminal 181\n", - "Glycine cleavage T-protein, C-terminal barrel domain 181\n", - "RyR/IP3R Homology associated domain 180\n", - "DAPIN domain|DAPIN domain|DAPIN domain 180\n", - "Folliculin, C-terminal|Folliculin/SMCR8, tripartite DENN domain 179\n", - "Neurotransmitter-gated ion-channel ligand-binding domain|Neurotransmitter-gated ion-channel, conserved site 179\n", - "Fzo/mitofusin HR2 domain 178\n", - "PDZ domain|PDZ domain 178\n", - "Zinc finger, ZZ-type|Zinc finger, ZZ-type|Zinc finger, ZZ-type|Zinc finger, ZZ-type 178\n", - "Protein O-mannosyl-transferase, C-terminal four TM domain 177\n", - "Insulin-like|Insulin-like 177\n", - "Nuclear hormone receptor, ligand-binding domain|Nuclear hormone receptor, ligand-binding domain 177\n", - "DNA mismatch repair protein MutS, C-terminal|DNA mismatch repair protein MutS, C-terminal|DNA mismatch repair protein MutS, C-terminal 176\n", - "Drought induced 19 protein type, zinc-binding domain|Zinc finger C2H2-type|Zinc finger C2H2-type|Zinc finger C2H2-type 176\n", - "Keratin type II head 176\n", - "Glycoside hydrolase 35, catalytic domain 175\n", - "Domain of unknown function DUF1619 174\n", - "D-isomer specific 2-hydroxyacid dehydrogenase, catalytic domain 173\n", - "MPN domain 173\n", - "SPRY domain|B30.2/SPRY domain|SPRY domain|Heterogeneous nuclear ribonucleoprotein U, SPRY domain 172\n", - "DNA-directed DNA polymerase, family A, palm domain 172\n", - "ABC-2 type transporter 172\n", - "Porphobilinogen deaminase, N-terminal 171\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Class VI myosin, motor domain 171\n", - "Sterol-sensing domain 170\n", - "von Willebrand factor, type D domain 170\n", - "Ionotropic glutamate receptor 170\n", - "Dynein heavy chain, hydrolytic ATP-binding dynein motor region D1|AAA+ ATPase domain 169\n", - "Myosin, N-terminal, SH3-like|Myosin, N-terminal, SH3-like 169\n", - "Glutamyl/glutaminyl-tRNA synthetase, class Ib, anti-codon binding domain 169\n", - "Zinc finger, RING-type 169\n", - "Peptidase M41, FtsH extracellular 169\n", - "Formin, GTPase-binding domain|Rho GTPase-binding/formin homology 3 (GBD/FH3) domain|Formin, GTPase-binding domain 168\n", - "CBP/p300-type histone acetyltransferase domain 167\n", - "Membrane attack complex component/perforin (MACPF) domain|Membrane attack complex component/perforin (MACPF) domain|Membrane attack complex component/perforin (MACPF) domain 167\n", - "Kinesin-like 167\n", - "NUDIX hydrolase domain|MutY, C-terminal 167\n", - "Na-Ca exchanger/integrin-beta4 167\n", - "Interferon gamma receptor, poxvirus/mammal 167\n", - "Runx, C-terminal domain 166\n", - "Death effector domain|Death effector domain|Death effector domain 166\n", - "Sec23/Sec24, trunk domain 165\n", - "Anticodon-binding|Histidyl-anticodon-binding 165\n", - "SH2 domain|SH2 domain|SH2 domain 165\n", - "Adenosine/AMP deaminase N-terminal 164\n", - "Laminin EGF domain|Laminin EGF domain 164\n", - "AP complex, mu/sigma subunit 164\n", - "Patatin-like phospholipase domain|Patatin-like phospholipase domain 163\n", - "Succinate dehydogenase/fumarate reductase N-terminal|2Fe-2S ferredoxin-type iron-sulfur binding domain|2Fe-2S ferredoxin-type iron-sulfur binding domain 163\n", - "Endonuclease III, iron-sulphur binding site|Endonuclease III-like, iron-sulphur cluster loop motif 162\n", - "DIX domain|DIX domain|DIX domain 161\n", - "Sulfotransferase domain 161\n", - "ADD domain 161\n", - "Dynein assembly factor 3, C-terminal domain 160\n", - "Growth hormone/erythropoietin receptor, ligand binding 160\n", - "Cadherin conserved site|Cadherin-like 160\n", - "CUT domain|CUT domain|CUT domain 160\n", - "SH3 domain|SH3 domain|SH3 domain 160\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase domain|Integrin-linked protein kinase, pseudokinase domain 160\n", - "Proline dehydrogenase domain 160\n", - "GTP cyclohydrolase I domain 159\n", - "SET domain|SET domain 158\n", - "TLDc domain 157\n", - "Fox-1 C-terminal domain 157\n", - "Aspartate/ornithine carbamoyltransferase, carbamoyl-P binding 157\n", - "3-hydroxyacyl-CoA dehydrogenase, C-terminal 157\n", - "RING-type zinc-finger, LisH dimerisation motif 157\n", - "Carbon-nitrogen hydrolase|Carbon-nitrogen hydrolase 156\n", - "Methylmalonyl-CoA mutase, alpha/beta chain, catalytic|Methylmalonyl-CoA mutase, alpha chain, catalytic 156\n", - "Reverse transcriptase domain 156\n", - "Ubiquitin domain|Ubiquitin domain|Ubiquitin domain 156\n", - "Multicopper oxidase, type 3 155\n", - "Phosphatidylinositol-specific phospholipase C, X domain|Phosphatidylinositol-specific phospholipase C, X domain|Phosphatidylinositol-specific phospholipase C, X domain 155\n", - "Histone deacetylase domain 155\n", - "RPGR-interacting protein 1, first C2 domain 155\n", - "PTP type protein phosphatase|PTP type protein phosphatase|PTP type protein phosphatase 153\n", - "Suppressor of fused C-terminal 152\n", - "PSI domain 152\n", - "Zinc finger, GATA-type|Zinc finger, GATA-type|Zinc finger, GATA-type|Zinc finger, GATA-type|Zinc finger, GATA-type 152\n", - "Domain of unknown function DUF4749 152\n", - "SEA domain|SEA domain|SEA domain 151\n", - "Transcription factor, GTP-binding domain|Transcription factor, GTP-binding domain 151\n", - "Telomeric single stranded DNA binding POT1/Cdc13|Telomeric single stranded DNA binding POT1/Cdc13 150\n", - "Carbohydrate binding module family 20|Carbohydrate binding module family 20|Carbohydrate binding module family 20|Laforin, CBM20 domain 150\n", - "Bardet-Biedl syndrome 1, N-terminal 149\n", - "Retinoblastoma-associated protein, A-box|Retinoblastoma-associated protein, A-box 149\n", - "Glycosyl hydrolase family 63, C-terminal 148\n", - "FKBP-type peptidyl-prolyl cis-trans isomerase domain|FKBP-type peptidyl-prolyl cis-trans isomerase domain 148\n", - "VWA N-terminal 148\n", - "EGF-like, conserved site|EGF-like, conserved site|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 147\n", - "Succinate dehydogenase/fumarate reductase N-terminal|2Fe-2S ferredoxin-type iron-sulfur binding domain 147\n", - "Cytidyltransferase-like domain 147\n", - "DNA ligase, ATP-dependent, N-terminal 146\n", - "Biotin carboxylase-like, N-terminal domain|Biotin carboxylation domain 145\n", - "Cysteine-rich flanking region, C-terminal 145\n", - "Piezo non-specific cation channel, R-Ras-binding domain 145\n", - "MATH/TRAF domain|MATH/TRAF domain 145\n", - "EGF-like calcium-binding domain|EGF-like domain|EGF-like calcium-binding domain 144\n", - "Phosphoinositide-specific phospholipase C, EF-hand-like domain 143\n", - "3-hydroxyacyl-CoA dehydrogenase, NAD binding 143\n", - "Immunoglobulin V-set domain|Immunoglobulin subtype 143\n", - "Voltage-dependent calcium channel, alpha-1 subunit, IQ domain|Voltage-dependent calcium channel, alpha-1 subunit, IQ domain 142\n", - "TB domain 142\n", - "Type I cytokine receptor, cytokine-binding domain 142\n", - "Laminin domain II 142\n", - "BTB/Kelch-associated|BTB/Kelch-associated 141\n", - "G-protein, beta subunit|WD40-repeat-containing domain 141\n", - "ATP-dependent helicase, C-terminal 141\n", - "Mediator complex, subunit Med12, LCEWAV-domain 141\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Class I myosin, motor domain 141\n", - "SH2 domain 141\n", - "3'5'-cyclic nucleotide phosphodiesterase, catalytic domain|3'5'-cyclic nucleotide phosphodiesterase, catalytic domain 141\n", - "RPGR-interacting protein 1, first C2 domain|C2 domain 140\n", - "ATP-grasp fold, succinyl-CoA synthetase-type|ATP-grasp fold 140\n", - "Guanylate kinase/L-type calcium channel beta subunit 140\n", - "Aspartate/ornithine carbamoyltransferase, Asp/Orn-binding domain 139\n", - "Sodium/calcium exchanger membrane region 139\n", - "WD40 repeat, conserved site|WD40-repeat-containing domain 139\n", - "Glycosyl hydrolase, family 13, catalytic domain 139\n", - "Integrin beta subunit, VWA domain 139\n", - "Glutathione S-transferase, C-terminal-like 139\n", - "Glutaminyl-tRNA synthetase, class Ib, non-specific RNA-binding domain 2 139\n", - "Transglutaminase-like|Transglutaminase-like 139\n", - "EGF-like calcium-binding domain|EGF-like domain 138\n", - "Lipoyl synthase, N-terminal 138\n", - "SH3 domain|SH3 domain|SH3 domain|Rho guanine nucleotide exchange factor 9, SH3 domain 137\n", - "GHMP kinase N-terminal domain 137\n", - "HIT-like domain 137\n", - "Pyruvate carboxyltransferase|Pyruvate carboxyltransferase 137\n", - "OTU domain|OTU domain 137\n", - "3'5'-cyclic nucleotide phosphodiesterase, catalytic domain 136\n", - "Laminin EGF domain 136\n", - "Adaptor protein Cbl, N-terminal helical|Adaptor protein Cbl, PTB domain 135\n", - "Rrp44-like cold shock domain 135\n", - "Sterol-sensing domain|Sterol-sensing domain 135\n", - "Dynein heavy chain, AAA module D4|AAA+ ATPase domain 134\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Class III myosin, motor domain 134\n", - "Coagulation factor 5/8 C-terminal domain|Coagulation factor 5/8 C-terminal domain|Coagulation factor 5/8 C-terminal domain|Coagulation factor 5/8 C-terminal domain|Coagulation factor 5/8 C-terminal domain 134\n", - "N-terminal of MaoC-like dehydratase 134\n", - "Extended PHD (ePHD) domain 134\n", - "GHMP kinase, C-terminal domain 134\n", - "Sema domain|Sema domain 133\n", - "Zinc finger, nuclear hormone receptor-type|Zinc finger, nuclear hormone receptor-type|Zinc finger, nuclear hormone receptor-type 133\n", - "Laminin EGF domain|Laminin EGF domain|Laminin EGF domain|Laminin EGF domain 133\n", - "Membrane attack complex component/perforin (MACPF) domain 133\n", - "Granulin|Granulin 132\n", - "Dystroglycan, C-terminal|DG-type SEA domain 132\n", - "Myotubularin-like phosphatase domain|Myotubularin-like phosphatase domain|Protein-tyrosine phosphatase, catalytic 132\n", - "FERM, N-terminal|FERM domain|Band 4.1 domain 132\n", - "Radical SAM|Elp3/MiaB/NifB 132\n", - "Immunoglobulin subtype 2|Immunoglobulin subtype 132\n", - "Beta-hexosaminidase, eukaryotic type, N-terminal 131\n", - "Tail specific protease|Tail specific protease 131\n", - "Myosin head, motor domain|Myosin head, motor domain 131\n", - "Zinc finger, GATA-type|Zinc finger, GATA-type|Zinc finger, GATA-type|Zinc finger, GATA-type|Zinc finger, GATA-type|Zinc finger, GATA-type 130\n", - "RFX5, C-terminal|RFX5, C-terminal 130\n", - "Alpha-macroglobulin complement component 129\n", - "Double-stranded RNA-binding domain|Double-stranded RNA-binding domain|Double-stranded RNA-binding domain 129\n", - "MIR motif|MIR motif 129\n", - "GPI ethanolamine phosphate transferase 2, N-terminal 129\n", - "Zinc finger, LIM-type|Zinc finger, LIM-type 128\n", - "FATC domain|Phosphatidylinositol 3-/4-kinase, catalytic domain|FATC domain|FATC domain 127\n", - "D-isomer specific 2-hydroxyacid dehydrogenase, catalytic domain|D-isomer specific 2-hydroxyacid dehydrogenase, NAD-binding domain 127\n", - "Anoctamin, dimerisation domain 127\n", - "Fascin domain|Fascin domain 127\n", - "BTB/POZ domain|BTB/POZ domain 127\n", - "Dual specificity phosphatase, catalytic domain|Tensin-type phosphatase domain|Protein-tyrosine phosphatase, catalytic 126\n", - "CBS domain 126\n", - "Dynein heavy chain, hydrolytic ATP-binding dynein motor region D1 126\n", - "STAT transcription factor, protein interaction|STAT transcription factor, protein interaction 126\n", - "Domain of unknown function DUF4704 126\n", - "Phospholipase C, phosphatidylinositol-specific, Y domain|Phospholipase C, phosphatidylinositol-specific, Y domain|Phospholipase C, phosphatidylinositol-specific, Y domain 124\n", - "EGF-like, conserved site|EGF-like, conserved site|EGF-like domain|EGF-like domain 124\n", - "Eukaryotic glycogen debranching enzyme, N-terminal domain 124\n", - "Cation-transporting P-type ATPase, N-terminal|Cation-transporting P-type ATPase, N-terminal 124\n", - "Vinculin, conserved site 124\n", - "Ammonium transporter AmtB-like domain 124\n", - "Exonuclease, RNase T/DNA polymerase III 124\n", - "Aminoacyl-tRNA synthetase, class II (D/K/N)|Aminoacyl-tRNA synthetase, class II 123\n", - "Fork head domain|Fork head domain|Fork head domain|Fork head domain 123\n", - "Glutamyl/glutaminyl-tRNA synthetase, class Ib, catalytic domain|Glutamyl-tRNA synthetase 123\n", - "JmjC domain|JmjC domain|JmjC domain 123\n", - "3'-5' exonuclease domain|3'-5' exonuclease domain 123\n", - "PTHB1, N-terminal domain 123\n", - "Glycerol-3-phosphate dehydrogenase, NAD-dependent, N-terminal 122\n", - "GPCR family 3, C-terminal 122\n", - "Folliculin/SMCR8, tripartite DENN domain 122\n", - "HECT domain|HECT domain|HECT domain 122\n", - "Trypsin Inhibitor-like, cysteine rich domain 121\n", - "Transglutaminase, N-terminal 121\n", - "FYVE zinc finger|Zinc finger, FYVE-related|FYVE zinc finger 121\n", - "Rad51/DMC1/RadA 120\n", - "BTB/POZ domain 120\n", - "Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain 120\n", - "Marvel domain|Marvel domain 120\n", - "Cadherin-like|Cadherin-like|Cadherin conserved site|Cadherin-like 120\n", - "Ras-like guanine nucleotide exchange factor, N-terminal|Ras-like guanine nucleotide exchange factor, N-terminal|Ras-like guanine nucleotide exchange factor, N-terminal|Ras-like guanine nucleotide exchange factor, N-terminal 120\n", - "Death domain|Death domain|Death domain 120\n", - "Polyketide synthase, enoylreductase domain 119\n", - "SNF2-related, N-terminal domain|Helicase superfamily 1/2, ATP-binding domain 119\n", - "FKBP-type peptidyl-prolyl cis-trans isomerase domain 119\n", - "MD-2-related lipid-recognition domain|MD-2-related lipid-recognition domain|Npc2 like, ML domain 119\n", - "Radical SAM|Radical SAM|Elp3/MiaB/NifB 119\n", - "Aspartyl beta-hydroxylase/Triadin domain 119\n", - "LCCL domain|LCCL domain|LCCL domain 118\n", - "Glycosyl transferase, family 28, C-terminal 118\n", - "Ionotropic glutamate receptor, L-glutamate and glycine-binding domain|Ionotropic glutamate receptor|Ionotropic glutamate receptor, L-glutamate and glycine-binding domain 118\n", - "Tetrapyrrole biosynthesis, uroporphyrinogen III synthase|Tetrapyrrole biosynthesis, uroporphyrinogen III synthase 118\n", - "Adenosine deaminase/editase|Adenosine deaminase/editase|Adenosine deaminase/editase 118\n", - "Zinc finger, LIM-type 117\n", - "Phosphatidylinositol 3-kinase, C2 domain|Phosphatidylinositol 3-kinase, C2 domain 117\n", - "Endonuclease/exonuclease/phosphatase|Inositol polyphosphate-related phosphatase|OCRL1/INPP5B, INPP5c domain 116\n", - "DNA ligase, ATP-dependent, central 116\n", - "PKD/REJ-like domain|REJ domain|Polycystin cation channel 116\n", - "L27 domain, C-terminal|L27 domain|L27 domain 116\n", - "GRAM domain|GRAM domain 116\n", - "Acyl transferase|Polyketide synthase, acyl transferase domain 116\n", - "Immunoglobulin-like domain|Immunoglobulin-like domain|Immunoglobulin subtype 2|Immunoglobulin subtype 115\n", - "Moybdenum cofactor oxidoreductase, dimerisation 115\n", - "GMP phosphodiesterase, delta subunit 115\n", - "Link domain|Link domain|Link domain|Link domain 115\n", - "Helicase superfamily 1/2, ATP-binding domain, DinG/Rad3-type|Helicase-like, DEXD box c2 type 115\n", - "G-protein, beta subunit|WD40 repeat, conserved site|WD40-repeat-containing domain 115\n", - "Transketolase, C-terminal domain 114\n", - "SH3 domain|SH3 domain|CACNB2, SH3 domain 114\n", - "DNA-directed RNA polymerase, insert domain|DNA-directed RNA polymerase, RpoA/D/Rpb3-type|DNA-directed RNA polymerase, RpoA/D/Rpb3-type 114\n", - "Thioredoxin domain 114\n", - "RIH domain|B30.2/SPRY domain 114\n", - "Acyl-CoA dehydrogenase/oxidase C-terminal|Acyl-CoA dehydrogenase, conserved site 113\n", - "Dedicator of cytokinesis C/D, N-terminal 113\n", - "Retinoblastoma-associated protein, C-terminal|Retinoblastoma-associated protein, C-terminal 113\n", - "MIT 113\n", - "DNA ligase, ATP-dependent, C-terminal 113\n", - "Glycosyl transferase, family 1 113\n", - "Transglutaminase, C-terminal 113\n", - "Kinesin-associated 113\n", - "HECT domain 112\n", - "Enolase, N-terminal|Enolase, N-terminal 112\n", - "Cobalamin adenosyltransferase-like 112\n", - "Tubby, C-terminal 112\n", - "Peptidase C12, ubiquitin carboxyl-terminal hydrolase|Peptidase C12, ubiquitin carboxyl-terminal hydrolase|Peptidase C12, ubiquitin carboxyl-terminal hydrolase 112\n", - "PTP type protein phosphatase|PTP type protein phosphatase|PTP type protein phosphatase|Tyrosine specific protein phosphatases domain|PTP type protein phosphatase|Protein-tyrosine phosphatase, catalytic 112\n", - "PKD domain|PKD domain|PKD/Chitinase domain|Polycystin cation channel 112\n", - "Glycoside hydrolase family 31, N-terminal domain 112\n", - "PEHE domain|PEHE domain 111\n", - "GPCR, family 3, nine cysteines domain 111\n", - "SH2 domain|SH2 domain|STAT3, SH2 domain 111\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain 111\n", - "Acetyl-coenzyme A carboxyltransferase, N-terminal 111\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase domain|Tyrosine-protein kinase, catalytic domain|Fibroblast growth factor receptor 1, catalytic domain 111\n", - "Paired domain|Paired domain|Paired domain 111\n", - "Methyl-CpG DNA binding|Methyl-CpG DNA binding 111\n", - "Bromodomain|Bromodomain, conserved site|Bromodomain|Bromodomain 111\n", - "Galactose-1-phosphate uridyl transferase, N-terminal 110\n", - "VWFC domain|VWFC domain|VWFC domain|VWFC domain 110\n", - "Protein kinase domain|Protein kinase, ATP binding site|Protein kinase domain 110\n", - "Tumour necrosis factor domain|Tumour necrosis factor domain|Tumour necrosis factor domain 110\n", - "DNA helicase, DnaB-like, C-terminal 110\n", - "Ras GTPase-activating domain|Ras GTPase-activating protein, conserved site|Ras GTPase-activating domain|Ras GTPase-activating domain 110\n", - "ARID DNA-binding domain|ARID DNA-binding domain|ARID DNA-binding domain 110\n", - "Helicase superfamily 1/2, ATP-binding domain, DinG/Rad3-type 110\n", - "Hepatocyte nuclear factor 1, alpha isoform C-terminal 110\n", - "Fork head domain|Fork head domain|Fork head domain conserved site 2|Fork head domain|Fork head domain|Fork head domain 110\n", - "Peptidase S8/S53 domain|Proteinase K-like catalytic domain 109\n", - "Nucleoside phosphorylase domain 109\n", - "Chromo domain|Chromo/chromo shadow domain|Chromo/chromo shadow domain|Chromo/chromo shadow domain 109\n", - "Actin, conserved site 109\n", - "Sodium:dicarboxylate symporter, conserved site 108\n", - "CAP Gly-rich domain|CAP Gly-rich domain|CAP Gly-rich domain|CAP Gly-rich domain 108\n", - "PKD domain|PKD/Chitinase domain|Polycystin cation channel 108\n", - "Zinc finger, LIM-type|Zinc finger, LIM-type|Zinc finger, LIM-type|Zinc finger, LIM-type|LIM2 prickle 108\n", - "Laminin IV 108\n", - "NUDE domain 108\n", - "Ras guanine-nucleotide exchange factors catalytic domain|Ras guanine-nucleotide exchange factors catalytic domain|Ras guanine-nucleotide exchange factors catalytic domain|Ras guanine-nucleotide exchange factors catalytic domain 108\n", - "Formin, FH3 domain|Rho GTPase-binding/formin homology 3 (GBD/FH3) domain|Formin, FH3 domain 107\n", - "Beta/gamma crystallin|Beta/gamma crystallin|Beta/gamma crystallin 107\n", - "Zinc finger, GATA-type|Zinc finger, GATA-type|Zinc finger, GATA-type 106\n", - "Phosphoribosyltransferase domain|Phosphoribosyltransferase domain 106\n", - "Hemopexin-like domain 106\n", - "DnaJ domain|DnaJ domain|DnaJ domain|DnaJ domain|DnaJ domain 106\n", - "IMP dehydrogenase/GMP reductase|IMP dehydrogenase/GMP reductase 106\n", - "Carbon-nitrogen hydrolase 106\n", - "Calponin homology domain|Calponin homology domain 106\n", - "GNAT domain|GNAT domain 105\n", - "Oxidoreductase, molybdopterin-binding domain 105\n", - "Growth hormone-binding protein 105\n", - "Lebercilin domain 105\n", - "tRNA synthetases class I, catalytic domain|tRNA synthetases class I, catalytic domain 105\n", - "Laminin IV type B 105\n", - "Tubby, C-terminal|Tubby, C-terminal 104\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain|Myosin Light Chain Kinase 1, Kinase domain 104\n", - "Phosphatidic acid phosphatase type 2/haloperoxidase|Phosphatidic acid phosphatase type 2/haloperoxidase 104\n", - "LPS-induced tumour necrosis factor alpha factor|LPS-induced tumour necrosis factor alpha factor 104\n", - "Sushi/SCR/CCP domain|Sushi/SCR/CCP domain|Sushi/SCR/CCP domain 104\n", - "Ciliary BBSome complex subunit 2, C-terminal domain 104\n", - "ZU5 domain|ZU5 domain 104\n", - "DDHD domain|DDHD domain|DDHD domain 103\n", - "SPRY domain|B30.2/SPRY domain|SPRY domain 103\n", - "SMAD domain, Dwarfin-type 103\n", - "Phenylalanyl-tRNA synthetase|Aminoacyl-tRNA synthetase, class II 102\n", - "Phosphatidylinositol 3-kinase adaptor-binding (PI3K ABD) domain|Phosphatidylinositol 3-kinase adaptor-binding (PI3K ABD) domain|Phosphatidylinositol 3-kinase adaptor-binding (PI3K ABD) domain 102\n", - "Prolyl 4-hydroxylase, alpha subunit 102\n", - "HhH-GPD domain 102\n", - "Mediator complex, subunit Med25, synapsin 1 101\n", - "Alpha-N-acetylglucosaminidase, tim-barrel domain 101\n", - "Adenylyl cyclase class-3/4/guanylyl cyclase|Adenylyl cyclase class-3/4/guanylyl cyclase|Adenylyl cyclase class-3/4/guanylyl cyclase 101\n", - "Uncharacterised domain DM10|Uncharacterised domain DM10 100\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Class VII myosin, motor domain 100\n", - "Beta-ketoacyl synthase, N-terminal|Polyketide synthase, beta-ketoacyl synthase domain 100\n", - "Zinc finger, ZZ-type|Zinc finger, ZZ-type|Zinc finger, ZZ-type 100\n", - "FERM domain|Band 4.1 domain|FERM central domain 100\n", - "Complement Clr-like EGF domain|EGF-like, conserved site|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 100\n", - "Alpha-ketoglutarate-dependent dioxygenase FTO, catalytic domain|Alpha-ketoglutarate-dependent dioxygenase FTO, catalytic domain 100\n", - "Rel homology domain (RHD), DNA-binding domain|Rel homology domain (RHD), DNA-binding domain 100\n", - "Mediator complex, subunit Med25, NR box 100\n", - "Peptidase S53, activation domain|Peptidase S53, activation domain|Peptidase S53, activation domain 100\n", - "DNA-binding RFX-type winged-helix domain|DNA-binding RFX-type winged-helix domain 99\n", - "KASH domain|KASH domain|KASH domain 99\n", - "Frizzled domain|Frizzled domain 99\n", - "Mff-like domain 99\n", - "SH3 domain|SH3 domain|SH3 domain|Amphiphysin 2, SH3 domain 99\n", - "SPRY domain|Butyrophylin-like, SPRY domain|B30.2/SPRY domain|SPRY domain 99\n", - "NACHT-associated domain|NACHT-associated domain 99\n", - "Tumour necrosis factor domain|Tumour necrosis factor domain|Tumour necrosis factor domain|Tumour necrosis factor domain 99\n", - "FERM central domain|Band 4.1 domain|FERM domain|Band 4.1 domain|FERM central domain 98\n", - "TERF1-interacting nuclear factor 2, N-terminal domain 98\n", - "Lipocalin/cytosolic fatty-acid binding domain 98\n", - "SRCR domain|SRCR domain|SRCR-like domain 98\n", - "Peptidase M10, metallopeptidase|Peptidase, metallopeptidase|Peptidase M10A, catalytic domain 98\n", - "Band 7 domain|Band 7 domain 98\n", - "Sugar phosphate transporter domain 98\n", - "Peptidase M13, C-terminal domain|Peptidase M13, C-terminal domain 98\n", - "Sterol reductase, conserved site 98\n", - "LIS1 homology motif|LIS1 homology motif|LIS1 homology motif 98\n", - "Bromodomain|Bromodomain|Bromodomain 98\n", - "SH2 domain|SH2 domain|SH2 domain|SH2 domain 98\n", - "Cyclic nucleotide-binding domain|Cyclic nucleotide-binding domain 97\n", - "FERM conserved site|FERM domain|Band 4.1 domain 97\n", - "GNAT domain 97\n", - "Peroxin/Ferlin domain 96\n", - "Galactose-1-phosphate uridyl transferase, C-terminal 96\n", - "Vps4 oligomerisation, C-terminal 96\n", - "FOXP, coiled-coil domain 96\n", - "Ryanodine receptor Ryr|B30.2/SPRY domain 96\n", - "Transcription regulator Myc, N-terminal 96\n", - "EGF-like calcium-binding domain|EGF-like calcium-binding domain|EGF-like domain 96\n", - "Sushi/SCR/CCP domain 96\n", - "Senescence/spartin-associated 95\n", - "Amino acid transporter, transmembrane domain 95\n", - "Link domain|Link domain|Link domain 95\n", - "Immunoglobulin I-set 95\n", - "K Homology domain, type 1|K Homology domain 95\n", - "Sec7 domain|Sec7 domain|Sec7 domain|Sec7 domain 95\n", - "FAD-linked oxidase, C-terminal 95\n", - "RAD50, zinc hook|RAD50, zinc hook 94\n", - "Double-stranded RNA-binding domain|Double-stranded RNA-binding domain|Double-stranded RNA-binding domain|Double-stranded RNA-binding domain 94\n", - "Aspartic peptidase, N-terminal 94\n", - "Glycoside hydrolase family 2, catalytic domain 94\n", - "ADAM-TS Spacer 1 94\n", - "Histidine kinase/HSP90-like ATPase 93\n", - "EGF-like domain|EGF-like, conserved site|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 93\n", - "FAM65, N-terminal 93\n", - "Molybdopterin oxidoreductase 93\n", - "EGF-like domain|EGF-like calcium-binding domain 92\n", - "Sushi/SCR/CCP domain|Sushi/SCR/CCP domain 92\n", - "Alcohol dehydrogenase, C-terminal|Polyketide synthase, enoylreductase domain 92\n", - "DNA recombination and repair protein Rad51-like, C-terminal|AAA+ ATPase domain|Rad51/DMC1/RadA 92\n", - "SCP2 sterol-binding domain 92\n", - "SET domain 92\n", - "Cytochrome P450, conserved site 92\n", - "Phospholipid/glycerol acyltransferase 92\n", - "Sterile alpha motif domain 92\n", - "PIGA, GPI anchor biosynthesis 92\n", - "Fumarylacetoacetase, N-terminal 91\n", - "von Willebrand factor, type A 91\n", - "Kazal domain|Kazal domain 91\n", - "Glycosyl hydrolase family 38, C-terminal 91\n", - "Raf-like Ras-binding|Raf-like Ras-binding|Raf-like Ras-binding 90\n", - "Survival motor neuron 90\n", - "Caspase recruitment domain 90\n", - "EGF-like calcium-binding domain|EGF-like calcium-binding, conserved site|EGF-like domain|EGF-like calcium-binding domain 90\n", - "Pericentrin/AKAP-450 centrosomal targeting domain 90\n", - "GS domain|GS domain|GS domain 90\n", - "B-box, C-terminal 90\n", - "ATP-dependent DNA helicase RecQ, zinc-binding domain|Helicase, C-terminal 90\n", - "ATP-dependent DNA helicase RecQ, zinc-binding domain 90\n", - "Dedicator of cytokinesis, C-terminal 90\n", - "Fibronectin type III|Long hematopoietin receptor, single chain, conserved site|Fibronectin type III|Fibronectin type III 90\n", - "ALMS motif 89\n", - "Dynamin-type guanine nucleotide-binding (G) domain|Dynamin, GTPase domain 89\n", - "Aconitase/3-isopropylmalate dehydratase large subunit, alpha/beta/alpha domain 89\n", - "Zinc finger, LIM-type|Zinc finger, LIM-type|Zinc finger, LIM-type|LIM3 prickle 89\n", - "Death domain|Death domain 89\n", - "Pleckstrin homology domain|Pleckstrin homology domain|Pleckstrin homology domain|FGD1-4, C-terminal PH domain 89\n", - "Suppressor of fused-like domain 88\n", - "DNA-directed RNA polymerase, RpoA/D/Rpb3-type|DNA-directed RNA polymerase, RpoA/D/Rpb3-type 88\n", - "Peptidase M12B, ADAM/reprolysin|Peptidase M12B, ADAM/reprolysin 88\n", - "BARD1, Zinc finger, RING-type|Zinc finger, RING-type|BARD1, Zinc finger, RING-type 88\n", - "Rapsyn, myristoylation/linker region, N-terminal 88\n", - "SH3 domain|SH3 domain|SH3 domain|SH3 domain|Amphiphysin 2, SH3 domain 88\n", - "Bromo adjacent homology (BAH) domain|Bromo adjacent homology (BAH) domain|Bromo adjacent homology (BAH) domain 87\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase domain|Tyrosine-protein kinase, catalytic domain|Fibroblast growth factor receptor 1, catalytic domain 87\n", - "Adenylosuccinate lyase C-terminal|Adenylosuccinate lyase C-terminal 87\n", - "Toll/interleukin-1 receptor homology (TIR) domain|Toll/interleukin-1 receptor homology (TIR) domain|Toll/interleukin-1 receptor homology (TIR) domain 87\n", - "VWFC domain 87\n", - "PLC-beta, PH domain 87\n", - "Serpin domain|Serpin domain|Serpin H1 inhibitory domain 87\n", - "Formiminotransferase, N-terminal subdomain|Formiminotransferase, N-terminal subdomain|Formiminotransferase catalytic domain 87\n", - "Vacuolar protein sorting-associated protein 13, SHR-binding domain 87\n", - "Thiolase, C-terminal 86\n", - "Ferric reductase transmembrane component-like domain 86\n", - "ATPase, F1/V1/A1 complex, alpha/beta subunit, nucleotide-binding domain 86\n", - "2-oxoacid dehydrogenase acyltransferase, catalytic domain 86\n", - "Ribonuclease HII/HIII domain 86\n", - "Alpha crystallin/Hsp20 domain 86\n", - "Short-chain dehydrogenase/reductase, conserved site 86\n", - "Receptor, ligand binding region|GPCR, family 3, conserved site 86\n", - "PAZ domain|PAZ domain 85\n", - "Tyrosinase copper-binding domain 85\n", - "Alpha galactosidase A, C-terminal beta-sandwich domain 85\n", - "Oxoglutarate/iron-dependent dioxygenase|Oxoglutarate/iron-dependent dioxygenase|Prolyl 4-hydroxylase, alpha subunit 85\n", - "DHR-1 domain|DHR-1 domain 85\n", - "GPI ethanolamine phosphate transferase 3, N-terminal 85\n", - "ATP-grasp fold, succinyl-CoA synthetase-type 85\n", - "C1q domain|C1q domain|C1q domain|C1q domain 84\n", - "Nop domain|Nop domain 84\n", - "AAA+ ATPase domain|Helicase superfamily 1/2, ATP-binding domain 84\n", - "Ribose-phosphate pyrophosphokinase, N-terminal domain 84\n", - "MAD homology 1, Dwarfin-type|MAD homology, MH1 84\n", - "Phenylalanyl-tRNA synthetase 83\n", - "Lipoyl synthase, N-terminal|Elp3/MiaB/NifB 83\n", - "Transforming growth factor-beta, C-terminal 83\n", - "Zinc finger, RING-type|Zinc finger, RING-type|E3 ubiquitin-protein ligase CBL-B, RING finger, HC subclass 83\n", - "Inositol polyphosphate-related phosphatase 83\n", - "Ubiquitin domain 83\n", - "Dual oxidase, peroxidase domain 82\n", - "EXPERA domain 82\n", - "Transcription factor Otx, C-terminal 82\n", - "CC2D2A, N-terminal, C2 domain 82\n", - "Metallo-beta-lactamase 82\n", - "Prion/Doppel protein, beta-ribbon domain 82\n", - "GPCR, family 3, nine cysteines domain|GPCR, family 3, conserved site 82\n", - "SH3 domain|SH3 domain 82\n", - "Dbl homology (DH) domain|Dbl homology (DH) domain 82\n", - "Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain 82\n", - "Glycosyl hydrolases family 31, conserved site 82\n", - "Homeobox KN domain|Homeobox domain|Homeobox domain|Homeobox domain 81\n", - "Citrate transporter-like domain 81\n", - "NAD-dependent epimerase/dehydratase 81\n", - "Thiamin pyrophosphokinase, catalytic domain|Thiamin pyrophosphokinase, catalytic domain 81\n", - "Alpha-2-macroglobulin, N-terminal 2|Alpha-2-macroglobulin, N-terminal 2 81\n", - "CBS domain|CBS domain 80\n", - "Glycosyl transferase family 3, N-terminal domain 80\n", - "B-box-type zinc finger|B-box-type zinc finger|B-box-type zinc finger|B-box-type zinc finger 80\n", - "Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3-/4-kinase, catalytic domain|PI3Kdelta, catalytic domain 80\n", - "Phosphatidylinositol 3-kinase, C2 domain|Phosphatidylinositol 3-kinase, C2 domain|Phosphatidylinositol 3-kinase, C2 domain 80\n", - "Glutamine amidotransferase|Glutamine amidotransferase|CTP synthase GATase domain 80\n", - "Myotubularin-like phosphatase domain 80\n", - "CHD, C-terminal 2 80\n", - "BRICHOS domain|BRICHOS domain|BRICHOS domain 80\n", - "Ionotropic glutamate receptor, L-glutamate and glycine-binding domain|Ionotropic glutamate receptor 80\n", - "Retinoblastoma-associated protein, N-terminal|Retinoblastoma-associated protein, N-terminal 79\n", - "P-type trefoil domain|P-type trefoil domain|P-type trefoil domain|P-type trefoil domain 79\n", - "NADP-dependent oxidoreductase domain|NADP-dependent oxidoreductase domain 79\n", - "Fanconi anemia group M protein, MHF binding domain 79\n", - "WHEP-TRS domain|WHEP-TRS domain|WHEP-TRS domain|WHEP-TRS domain 79\n", - "Biotin/lipoyl attachment|Biotin/lipoyl attachment 79\n", - "Pyrroline-5-carboxylate reductase, catalytic, N-terminal 79\n", - "Sugar transporter, conserved site|Major facilitator superfamily domain|Major facilitator superfamily domain 79\n", - "Glycosyl hydrolase family 63, N-terminal 79\n", - "EGF-like domain|EGF-like domain|EGF-like domain 79\n", - "Ribosomal protein L10e/L16|Ribosomal protein L10e/L16 79\n", - "DNA ligase, ATP-dependent, central|DNA ligase, ATP-dependent, central 79\n", - "Rab-GTPase-TBC domain 79\n", - "Diacylglycerol kinase, catalytic domain|Diacylglycerol kinase, catalytic domain|Diacylglycerol kinase, catalytic domain 79\n", - "MAD homology, MH1 78\n", - "Ricin B, lectin domain|Ricin B, lectin domain|Ricin B, lectin domain|Ricin B, lectin domain 78\n", - "RBP-J/Cbf11/Cbf12, DNA binding|RBP-J/Cbf11/Cbf12, DNA binding 78\n", - "Elp3/MiaB/NifB 78\n", - "DNA mismatch repair protein MutS, C-terminal 78\n", - "P-type trefoil domain|P-type trefoil, conserved site|P-type trefoil domain|P-type trefoil domain|P-type trefoil domain 78\n", - "Frizzled domain|Frizzled domain|Frizzled domain 78\n", - "Biotinyl protein ligase (BPL) and lipoyl protein ligase (LPL), catalytic domain 78\n", - "Domain of unknown function DUF1088 78\n", - "Glycoside hydrolase family 38, N-terminal domain 77\n", - "Chromo domain|Chromo/chromo shadow domain|Chromo/chromo shadow domain 77\n", - "RNA helicase, DEAD-box type, Q motif 77\n", - "XPG N-terminal|XPG N-terminal 77\n", - "START domain|START domain|START domain 77\n", - "Basic leucine zipper domain, Maf-type|Basic-leucine zipper domain|Basic-leucine zipper domain 76\n", - "Superoxide dismutase, copper/zinc binding domain|Superoxide dismutase, copper/zinc binding domain|Superoxide dismutase, copper/zinc binding domain 76\n", - "4Fe-4S ferredoxin-type, iron-sulphur binding domain 76\n", - "Immunoglobulin C1-set|Immunoglobulin-like domain|Immunoglobulin C1-set 76\n", - "Actin/actin-like conserved site 76\n", - "TERF1-interacting nuclear factor 2, N-terminal domain|TERF1-interacting nuclear factor 2, N-terminal domain 76\n", - "Transforming growth factor beta receptor 2 ectodomain 76\n", - "Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A|Carboxypeptidase A, carboxypeptidase domain 76\n", - "WH1/EVH1 domain|WH1/EVH1 domain|WH1/EVH1 domain 76\n", - "AMP-binding enzyme, C-terminal domain 76\n", - "Serpin domain|Serpin domain|Antithrombin serpin domain 76\n", - "SEFIR domain|SEFIR domain 76\n", - "Band 4.1 domain 76\n", - "Mediator complex, subunit Med12, catenin-binding 75\n", - "Serum albumin, N-terminal|Serum albumin, N-terminal 75\n", - "DNA mismatch repair protein, S5 domain 2-like|DNA mismatch repair protein family, N-terminal 75\n", - "PTHB1, C-terminal domain 75\n", - "SEA domain|SEA domain 75\n", - "Exostosin , C-terminal 75\n", - "Kringle|Kringle|Kringle|Kringle 75\n", - "cDENN domain|Tripartite DENN domain|cDENN domain 75\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase domain 74\n", - "Sec23/Sec24, helical domain 74\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|FOX1, RNA recognition motif 74\n", - "Dynamin central domain|Dynamin-type guanine nucleotide-binding (G) domain|Dynamin, GTPase domain 74\n", - "Polyketide synthase, dehydratase domain|Polyketide synthase, dehydratase domain 74\n", - "HARP domain|HARP domain 74\n", - "Pleckstrin homology domain|SynGAP, PH domain 74\n", - "Lipase/vitellogenin 74\n", - "TOG domain 74\n", - "Glycoprotein hormone subunit beta|Cystine knot, C-terminal|Cystine knot, C-terminal 74\n", - "Cyclic nucleotide-gated channel, C-terminal leucine zipper domain 74\n", - "Holliday junction regulator protein family C-terminal 73\n", - "PLAT/LH2 domain 73\n", - "Transcription factor, T-box, conserved site 73\n", - "Zinc finger, RING-type|Zinc finger, RING-type|Zinc finger, RING-type 73\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Class V myosin, motor domain 73\n", - "Transcription factor, GTP-binding domain|Transcription factor, GTP-binding domain|Small GTP-binding protein domain 73\n", - "Calponin homology domain|Calponin homology domain|Calponin homology domain 73\n", - "E3 ubiquitin ligase, domain of unknown function DUF913 72\n", - "RAG1 importin-binding 72\n", - "Peptide chain release factor class I/class II 72\n", - "Myc-type, basic helix-loop-helix (bHLH) domain|Myc-type, basic helix-loop-helix (bHLH) domain|Myc-type, basic helix-loop-helix (bHLH) domain 72\n", - "FERM central domain|FERM conserved site|FERM domain|Band 4.1 domain|FERM central domain 72\n", - "Ribonuclease III domain|Ribonuclease III domain|Ribonuclease III domain 72\n", - "DnaJ domain 72\n", - "Ribonuclease III domain|Ribonuclease III domain|Ribonuclease III domain|Ribonuclease III domain|Ribonuclease III domain 72\n", - "Zinc finger, RING-type|Zinc finger, RING-type 72\n", - "Immunoglobulin C1-set|Immunoglobulin-like domain 72\n", - "Ribosomal protein L7Ae/L30e/S12e/Gadd45 72\n", - "Polycystin cation channel 71\n", - "RING-type zinc-finger, LisH dimerisation motif|Zinc finger, RING-type|Zinc finger, RING-type 71\n", - "Band 3 cytoplasmic domain 71\n", - "Peptidase C2, calpain, catalytic domain|Peptidase C2, calpain, catalytic domain 71\n", - "Zinc finger, GATA-type|Zinc finger, GATA-type 71\n", - "Granulin|Granulin|Granulin 71\n", - "Fatty acid hydroxylase 71\n", - "HSR domain|HSR domain 71\n", - "RIG-I-like receptor, C-terminal regulatory domain|RIG-I-like receptor, C-terminal regulatory domain 71\n", - "NF-kappa-B essential modulator NEMO, CC2-LZ domain 71\n", - "PET domain 70\n", - "P-type ATPase, C-terminal 70\n", - "Cytidyltransferase-like domain|Cytidyltransferase-like domain 70\n", - "Glutamine amidotransferase|Glutamine amidotransferase|Carbamoyl-phosphate synthase small subunit, GATase1 domain 70\n", - "Phosphatidylinositol 3-kinase Ras-binding (PI3K RBD) domain|Phosphatidylinositol 3-kinase Ras-binding (PI3K RBD) domain|Phosphatidylinositol 3-kinase Ras-binding (PI3K RBD) domain 70\n", - "Protein kinase, C-terminal|AGC-kinase, C-terminal|AGC-kinase, C-terminal|Protein kinase B alpha, catalytic domain 70\n", - "FIIND domain|FIIND domain 70\n", - "Nucleoside diphosphate kinase-like domain|Nucleoside diphosphate kinase-like domain 70\n", - "Aminotransferase, class I/classII|Tetrapyrrole biosynthesis, 5-aminolevulinic acid synthase 70\n", - "Zinc finger, PHD-finger|Zinc finger, PHD-finger|Zinc finger, PHD-type 69\n", - "Domain of unknown function DUF4430 69\n", - "Cullin, N-terminal 69\n", - "Pyridoxamine 5'-phosphate oxidase, putative 69\n", - "Carbamoyl-phosphate synthase small subunit, N-terminal domain|Carbamoyl-phosphate synthase small subunit, N-terminal domain 69\n", - "Tyrosine-protein kinase receptor NTRK, C2-Ig-like domain|Cysteine-rich flanking region, C-terminal 69\n", - "EGF-like, conserved site|EGF-like domain|EGF-like domain 69\n", - "MHC class I-like antigen recognition-like 69\n", - "Potassium channel, voltage dependent, Kv4, C-terminal 69\n", - "Ferric reductase, NAD binding domain 69\n", - "Electron transfer flavoprotein, alpha/beta-subunit, N-terminal|Electron transfer flavoprotein, alpha/beta-subunit, N-terminal|Electron transfer flavoprotein, alpha subunit, N-terminal 69\n", - "Pyridine nucleotide-disulphide oxidoreductase, dimerisation domain 69\n", - "Amino acid permease, N-terminal 69\n", - "RNA-polymerase II-associated protein 3-like, C-terminal domain 69\n", - "Peptidase S8/S53 domain|Tripeptidyl-peptidase II domain 68\n", - "HRDC domain|HRDC domain|HRDC domain 68\n", - "USP8 dimerisation domain 68\n", - "Beta/gamma crystallin|Beta/gamma crystallin|Beta/gamma crystallin|Beta/gamma crystallin 68\n", - "PAN/Apple domain|PAN/Apple domain|Apple domain|Apple domain 68\n", - "MAGE homology domain|MAGE homology domain|MAGE homology domain 68\n", - "GRAM domain 68\n", - "Polyketide synthase, ketoreductase domain 68\n", - "Tubby, N-terminal|Tubby, N-terminal 68\n", - "Transthyretin/hydroxyisourate hydrolase domain|Transthyretin, thyroxine binding site|Transthyretin/hydroxyisourate hydrolase domain|Transthyretin/hydroxyisourate hydrolase domain 68\n", - "Zinc finger, PHD-finger|Zinc finger, PHD-type 68\n", - "Fibrinogen, alpha/beta/gamma chain, coiled coil domain|Fibrinogen, alpha/beta/gamma chain, coiled coil domain 68\n", - "JmjC domain|JmjC domain 68\n", - "Doublecortin domain 67\n", - "Alpha-2-macroglobulin|Alpha-2-macroglobulin 67\n", - "Sterile alpha motif domain|Sterile alpha motif domain 67\n", - "Mediator complex, subunit Med25, PTOV domain 67\n", - "SMAD domain, Dwarfin-type|SMAD domain, Dwarfin-type 67\n", - "AGC-kinase, C-terminal|AGC-kinase, C-terminal 67\n", - "PAS domain 67\n", - "Myosin head, motor domain 67\n", - "BDHCT 66\n", - "Lipoxygenase, C-terminal 66\n", - "Right handed beta helix domain 66\n", - "Telomerase ribonucleoprotein complex - RNA-binding domain|Telomerase ribonucleoprotein complex - RNA-binding domain 66\n", - "Enolase, C-terminal TIM barrel domain|Enolase, C-terminal TIM barrel domain 66\n", - "DEAD/DEAH box helicase domain|Helicase superfamily 1/2, ATP-binding domain 66\n", - "FAD-binding 8|FAD-binding domain, ferredoxin reductase-type 66\n", - "NF-kappa-B essential modulator NEMO, N-terminal 66\n", - "Lactate/malate dehydrogenase, N-terminal 66\n", - "Myelin proteolipid protein PLP, conserved site 66\n", - "PH-BEACH domain|PH-BEACH domain|PH-BEACH domain 66\n", - "Pyridoxal-phosphate dependent enzyme|Cysteine synthase/cystathionine beta-synthase, pyridoxal-phosphate attachment site 65\n", - "Doublecortin domain|Doublecortin domain 65\n", - "PI3K p85 subunit, inter-SH2 domain 65\n", - "Death domain 65\n", - "Immunoglobulin-like domain|Immunoglobulin V-set domain|Immunoglobulin subtype 2|Immunoglobulin subtype 65\n", - "PIH1 domain 65\n", - "Transthyretin/hydroxyisourate hydrolase domain 65\n", - "SUN domain|SUN domain 65\n", - "Fork head domain|Fork head domain|Fork head domain conserved site1|Fork head domain|Fork head domain|Fork head domain 64\n", - "FANCI helical domain 2 64\n", - "Serine aminopeptidase, S33 64\n", - "Hedgehog, N-terminal signalling domain 64\n", - "E3 ubiquitin-protein ligase CBL-B, RING finger, HC subclass 64\n", - "RAD50, zinc hook 64\n", - "FCH domain|F-BAR domain|FCH domain 64\n", - "SH3 domain|SH3 domain|SH3 domain|SH3 domain 64\n", - "Domain of unknown function DUF1866|RNA recognition motif domain|Domain of unknown function DUF1866|Synaptojanin-1, RNA recognition motif 64\n", - "Growth arrest-specific protein 8 domain 64\n", - "WH1/EVH1 domain|WH1/EVH1 domain|WH1/EVH1 domain|WASP family, EVH1 domain 64\n", - "G8 domain|G8 domain|G8 domain 64\n", - "PAS domain|PAS domain|PAS domain|PAS domain 63\n", - "Zinc finger, LIM-type|Zinc finger, LIM-type|Zinc finger, LIM-type|LIM2 prickle 63\n", - "Ferlin B-domain|Ferlin B-domain 63\n", - "Lipid transport protein, N-terminal 63\n", - "PTP type protein phosphatase|PTP type protein phosphatase|Tyrosine specific protein phosphatases domain|PTP type protein phosphatase|Protein-tyrosine phosphatase, catalytic 63\n", - "Dynamin GTPase effector|GTPase effector domain|Dynamin GTPase effector 63\n", - "Uroporphyrinogen decarboxylase (URO-D) 63\n", - "DHR-1 domain|DHR-1 domain|Dedicator of cytokinesis C, C2 domain 63\n", - "Peptidase M24 63\n", - "Peptidase C1A, papain C-terminal|Peptidase C1A, papain C-terminal|Papain-like cysteine endopeptidase 63\n", - "Ras guanine-nucleotide exchange factors catalytic domain|Ras guanine-nucleotide exchange factors catalytic domain|Ras guanine-nucleotide exchange factors catalytic domain 63\n", - "SH3 domain|SH3 domain|SH3 domain|CASK, SH3 domain 63\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region 63\n", - "Transcription factor, MADS-box|Transcription factor, MADS-box 63\n", - "2OGFeDO, oxygenase domain|2OGFeDO, oxygenase domain 63\n", - "SAND domain|SAND domain|SAND domain 63\n", - "Reeler domain|Reeler domain|Reeler domain 62\n", - "PROCT domain 62\n", - "OB-fold nucleic acid binding domain, AA-tRNA synthetase-type 62\n", - "Helicase Helix-turn-helix domain 62\n", - "Fanconi Anaemia group E protein, C-terminal|Fanconi Anaemia group E protein, C-terminal 62\n", - "Gamma-carboxyglutamic acid-rich (GLA) domain 62\n", - "LRAT-like domain 62\n", - "Transcription factor AP-2, C-terminal 62\n", - "CAP Gly-rich domain|CAP Gly-rich domain 62\n", - "Mitochondrial apoptosis-inducing factor, C-terminal domain 62\n", - "Dedicator of cytokinesis, N-terminal domain 62\n", - "Zinc finger, PHD-type 62\n", - "Glucosidase 2 subunit beta-like 62\n", - "Dual specificity protein phosphatase domain|Dual specificity protein phosphatase domain 62\n", - "Peptidase C1A, propeptide 62\n", - "Target SNARE coiled-coil homology domain|Target SNARE coiled-coil homology domain 62\n", - "Arrestin-like, N-terminal 61\n", - "Gamma-carboxyglutamic acid-rich (GLA) domain|Gamma-carboxyglutamic acid-rich (GLA) domain|Gamma-carboxyglutamic acid-rich (GLA) domain|Gamma-carboxyglutamic acid-rich (GLA) domain|Gamma-carboxyglutamic acid-rich (GLA) domain 61\n", - "Glutamine amidotransferase type 2 domain|Asparagine synthase, N-terminal domain 61\n", - "Sec23/Sec24 beta-sandwich 61\n", - "Integrin beta subunit, tail|Integrin beta subunit, tail 61\n", - "Methionyl/Valyl/Leucyl/Isoleucyl-tRNA synthetase, anticodon-binding 61\n", - "ERCC4 domain|ERCC4 domain 61\n", - "ATPase, F1/V1/A1 complex, alpha/beta subunit, N-terminal domain 61\n", - "Endonuclease/exonuclease/phosphatase 61\n", - "C2 domain|C2 domain|SynGAP, PH domain 61\n", - "FANCI solenoid 1 cap 60\n", - "Dishevelled C-terminal 60\n", - "NECAP, PHear domain|NECAP, PHear domain 60\n", - "Integrin beta N-terminal|Integrin beta subunit, VWA domain|PSI domain 60\n", - "Thioesterase 60\n", - "Syntaxin, N-terminal domain|Syntaxin, N-terminal domain|Syntaxin, N-terminal domain 60\n", - "LPS-induced tumour necrosis factor alpha factor 60\n", - "Glycerol-3-phosphate dehydrogenase, NAD-dependent, C-terminal 60\n", - "Glutamine amidotransferase type 2 domain|Glutamine amidotransferase type 2 domain|Asparagine synthase, N-terminal domain 60\n", - "Biotinyl protein ligase (BPL) and lipoyl protein ligase (LPL), catalytic domain|Biotinyl protein ligase (BPL) and lipoyl protein ligase (LPL), catalytic domain 60\n", - "Transcription factor, MADS-box|Transcription factor, MADS-box|Transcription factor, MADS-box|Transcription factor, MADS-box|MADS MEF2-like 60\n", - "Transcription factor, MADS-box|Transcription factor, MADS-box|Transcription factor, MADS-box|Transcription factor, MADS-box|Transcription factor, MADS-box|Transcription factor, MADS-box|MADS MEF2-like 60\n", - "Lamin tail domain 60\n", - "Saposin-like type B, region 1|Saposin B type domain|Saposin B type domain 60\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain|Novel protein kinase C delta, catalytic domain 60\n", - "EGF-like, conserved site|EGF-like domain|Laminin EGF domain|EGF-like domain 60\n", - "FANCI solenoid 4 domain 60\n", - "Thiamin pyrophosphokinase, thiamin-binding domain|Thiamin pyrophosphokinase, thiamin-binding domain|Thiamin pyrophosphokinase, catalytic domain 60\n", - "EGF-like domain, extracellular|EGF-like domain 60\n", - "PB1 domain|PB1 domain|PB1 domain|TFG, PB1 domain 60\n", - "Calcineurin-like phosphoesterase domain, ApaH type|Serine/threonine-specific protein phosphatase/bis(5-nucleosyl)-tetraphosphatase|Serine/threonine-specific protein phosphatase/bis(5-nucleosyl)-tetraphosphatase 60\n", - "Metallo-beta-lactamase|Metallo-beta-lactamase 60\n", - "Serpin domain|Serpin, conserved site|Serpin domain 60\n", - "CRAL-TRIO lipid binding domain|CRAL-TRIO lipid binding domain|CRAL-TRIO lipid binding domain 60\n", - "C2 domain|Ferlin, sixth C2 domain 60\n", - "MADS MEF2-like 60\n", - "Paired-box protein 2 C-terminal 60\n", - "Domain of unknown function DUF4470 60\n", - "PTB/PI domain 59\n", - "Vacuolar protein sorting-associated protein 13, second N-terminal domain 59\n", - "Munc13 homology 1 59\n", - "Homeobox domain|Homeobox domain|Homeobox domain 59\n", - "Pyruvate kinase, barrel 59\n", - "LEM domain|LEM domain|LEM domain 59\n", - "Septin-type guanine nucleotide-binding (G) domain|Septin-type guanine nucleotide-binding (G) domain 59\n", - "Alpha-N-acetylglucosaminidase, C-terminal 59\n", - "Glutamine amidotransferase type 2 domain 59\n", - "Transthyretin/hydroxyisourate hydrolase domain|Transthyretin/hydroxyisourate hydrolase domain 58\n", - "EGF-like calcium-binding, conserved site|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 58\n", - "Short hematopoietin receptor, family 1, conserved site|Fibronectin type III|Fibronectin type III 58\n", - "Hermansky-Pudlak syndrome 3, central region 58\n", - "FANCI solenoid 3 domain 58\n", - "Serine proteases, trypsin domain 58\n", - "CTF transcription factor/nuclear factor 1, DNA-binding domain 58\n", - "C2 domain|Calpain C2 domain 58\n", - "Sulfatase-modifying factor enzyme 58\n", - "Thrombospondin, C-terminal|Thrombospondin, C-terminal 58\n", - "ATPase, AAA-type, core 58\n", - "Superoxide dismutase, copper/zinc binding domain|Superoxide dismutase, copper/zinc binding domain 58\n", - "C2 domain|C2 domain|C2 domain|Ferlin, fourth C2 domain 58\n", - "Carbamoyl-phosphate synthetase large subunit-like, ATP-binding domain|ATP-grasp fold 58\n", - "Endonuclease III-like, iron-sulphur cluster loop motif 57\n", - "MCM OB domain 57\n", - "GPR domain|Aldehyde dehydrogenase domain|GPR domain|GPR domain 57\n", - "MoaB/Mog domain|MoaB/Mog domain|MoaB/Mog domain 57\n", - "Myosin VI, cargo binding domain 57\n", - "EGF-like calcium-binding domain 57\n", - "IQ motif and SEC7 domain-containing protein, PH domain|Pleckstrin homology domain|IQ motif and SEC7 domain-containing protein, PH domain 57\n", - "Methionyl/Leucyl tRNA synthetase|Methioninyl-tRNA synthetase core domain 57\n", - "UbiB domain|UbiB domain, ADCK3-like|UbiB domain, ADCK3-like 57\n", - "Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A 57\n", - "Extended PHD (ePHD) domain|Zinc finger, PHD-type 57\n", - "Biotin carboxylation domain 57\n", - "Peptidase M28 57\n", - "Serine hydroxymethyltransferase-like domain 57\n", - "PROCN domain 57\n", - "Succinate dehydogenase/fumarate reductase N-terminal 57\n", - "Dipeptidylpeptidase IV, N-terminal domain 56\n", - "Peptidase C2, calpain, large subunit, domain III|Peptidase C2, calpain, domain III 56\n", - "Protein kinase B alpha, catalytic domain 56\n", - "MiT/TFE transcription factors, N-terminal 56\n", - "Glycoside hydrolase family 18, catalytic domain|Chitinase II 56\n", - "EF-hand domain|EF-Hand 1, calcium-binding site|EF-hand domain|EF-hand domain|EF-hand domain|EF-hand domain 56\n", - "UDP-glucose/GDP-mannose dehydrogenase, N-terminal 56\n", - "Phox homologous domain|Phox homologous domain|Phox homologous domain 56\n", - "Immunoglobulin-like domain|Immunoglobulin-like domain|Immunoglobulin subtype 56\n", - "Centrosomal protein of 290kDa, coiled-coil region 56\n", - "VPS13, repeated coiled region 56\n", - "Adaptor protein Cbl, EF hand-like|Adaptor protein Cbl, PTB domain 56\n", - "Protein kinase domain|Protein kinase, ATP binding site|Protein kinase domain|Protein kinase domain|Serine/Threonine kinase LKB1, catalytic domain 56\n", - "Saposin B type, region 2|Saposin B type domain|Saposin B type domain 56\n", - "SBF1/SBF2 domain 55\n", - "PI3Kdelta, catalytic domain 55\n", - "Myelin transcription factor 1 55\n", - "Sulfite reductase [NADPH] flavoprotein alpha-component-like, FAD-binding|FAD-binding domain, ferredoxin reductase-type 55\n", - "Methyl-CpG DNA binding 55\n", - "MIR motif 55\n", - "Tensin-type phosphatase domain 55\n", - "PWWP domain|PWWP domain 55\n", - "Argininosuccinate synthase, conserved site 55\n", - "Lipid-binding serum glycoprotein, C-terminal|Lipid-binding serum glycoprotein, C-terminal 55\n", - "Niemann-Pick C1, N-terminal 55\n", - "Lamina-associated polypeptide 2 alpha, C-terminal 55\n", - "BCL-6 corepressor, non-ankyrin-repeat domain 55\n", - "Dynamin, GTPase domain 55\n", - "Homeobox domain|Homeobox domain, metazoa|Homeobox, conserved site|Homeobox domain|Homeobox domain|Homeobox domain 55\n", - "Class I myosin tail homology domain|Class I myosin tail homology domain 55\n", - "DEAD/DEAH box helicase domain|ATP-dependent RNA helicase DEAD-box, conserved site|Helicase superfamily 1/2, ATP-binding domain|Helicase superfamily 1/2, ATP-binding domain 55\n", - "D-isomer specific 2-hydroxyacid dehydrogenase, catalytic domain|D-isomer specific 2-hydroxyacid dehydrogenase, NAD-binding domain|D-isomer specific 2-hydroxyacid dehydrogenase, NAD-binding domain conserved site 54\n", - "UbiB domain, ADCK3-like|UbiB domain, ADCK3-like 54\n", - "Branched-chain alpha-ketoacid dehydrogenase kinase/Pyruvate dehydrogenase kinase, N-terminal 54\n", - "IRAK4, Death domain 54\n", - "Zinc finger, LIM-type|Zinc finger, LIM-type|Zinc finger, LIM-type|LIM1 prickle 54\n", - "RGS domain|RGS domain 54\n", - "Immunoglobulin I-set|Immunoglobulin-like domain|Immunoglobulin V-set domain|Immunoglobulin subtype 54\n", - "Carnitine O-palmitoyltransferase, N-terminal 54\n", - "Phospholipase C-beta, conserved site 54\n", - "Carboxylase, conserved domain 54\n", - "PIH1 domain|PIH1 domain 53\n", - "Myosin, N-terminal, SH3-like 53\n", - "Cystatin domain|Cystatin domain|Cystatin domain 53\n", - "Transcription factor AP-2, C-terminal|Transcription factor AP-2, C-terminal 53\n", - "C2 domain|C2 domain|C2 domain|Ferlin, third C2 domain 53\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase, ATP binding site|Protein kinase domain 53\n", - "Phosphatidylinositol 3-/4-kinase, catalytic domain 53\n", - "Kazal domain|Factor I / membrane attack complex 53\n", - "Formin, FH2 domain 53\n", - "Succinate dehydrogenase, cytochrome b subunit, conserved site 53\n", - "RNA polymerase Rpb1, domain 5 53\n", - "Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain 53\n", - "Guanylate kinase/L-type calcium channel beta subunit|Guanylate kinase-like domain 53\n", - "Sec63 domain|Sec63 domain 53\n", - "Phosphoribosyltransferase domain 53\n", - "Exportin-1/Importin-beta-like 53\n", - "Alpha-D-phosphohexomutase, alpha/beta/alpha domain I 53\n", - "Uncharacterised domain, cysteine-rich|von Willebrand factor, type D domain|Uncharacterised domain, cysteine-rich 53\n", - "WW domain|WW domain|WW domain|WW domain 53\n", - "Domain of unknown function DUF3668 53\n", - "Malonyl-CoA decarboxylase, C-terminal 52\n", - "Olfactomedin-like domain|Olfactomedin-like domain|Olfactomedin-like domain 52\n", - "Lactate/malate dehydrogenase, C-terminal 52\n", - "Uracil-DNA glycosylase-like|Uracil-DNA glycosylase-like 52\n", - "Acyl-CoA oxidase/dehydrogenase, central domain|Acyl-CoA dehydrogenase, conserved site 52\n", - "Double-stranded RNA-specific adenosine deaminase (DRADA)|Double-stranded RNA-specific adenosine deaminase (DRADA)|Double-stranded RNA-specific adenosine deaminase (DRADA) 52\n", - "Ferlin, C-terminal domain 52\n", - "Recombination activating protein 2, PHD domain|Recombination activating protein 2, PHD domain 52\n", - "Pleckstrin homology domain|Pleckstrin homology domain|SynGAP, PH domain 52\n", - "C-type lectin-like|C-type lectin-like|C-type lectin-like|Aggrecan/versican, C-type lectin-like domain 52\n", - "Fructose-1-6-bisphosphatase class I, N-terminal 52\n", - "Axin beta-catenin binding 52\n", - "Pseudouridine synthase I, TruA, alpha/beta domain 52\n", - "GPCR, family 2, extracellular hormone receptor domain|GPCR, family 2, extracellular hormone receptor domain|GPCR, family 2, extracellular hormone receptor domain 52\n", - "GidA associated domain 3 52\n", - "FAD linked oxidase, N-terminal|FAD-binding domain, PCMH-type 51\n", - "Vitamin B12-dependent methionine synthase, activation domain|Vitamin B12-dependent methionine synthase, activation domain 51\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Class XV myosin, motor domain 51\n", - "Gamma-carboxyglutamic acid-rich (GLA) domain|Gamma-carboxyglutamic acid-rich (GLA) domain|Gamma-carboxyglutamic acid-rich (GLA) domain|Gamma-carboxyglutamic acid-rich (GLA) domain 51\n", - "Plectin/S10, N-terminal 51\n", - "Cadherin, C-terminal catenin-binding domain 51\n", - "SATB, ubiquitin-like oligomerisation domain|SATB, ubiquitin-like oligomerisation domain 51\n", - "Alpha-ketoglutarate-dependent dioxygenase FTO, C-terminal 51\n", - "Thyroglobulin type-1|Thyroglobulin type-1|Thyroglobulin type-1|Thyroglobulin type-1|Thyroglobulin type-1 51\n", - "Coagulation factor 5/8 C-terminal domain|Coagulation factor 5/8 C-terminal domain|Coagulation factor 5/8 C-terminal domain 51\n", - "Clp ATPase, C-terminal|Clp ATPase, C-terminal 51\n", - "E3 ubiquitin ligase, domain of unknown function DUF908 51\n", - "Kazal domain 51\n", - "Peptidase S16, Lon proteolytic domain|Peptidase S16, Lon proteolytic domain 51\n", - "Zinc finger, CXXC-type|Zinc finger, CXXC-type 51\n", - "Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A|Carboxypeptidase A6 51\n", - "Dihydroorotate dehydrogenase domain 51\n", - "Argininosuccinate lyase, C-terminal 51\n", - "DMAP1-binding domain|DMAP1-binding domain 51\n", - "LIS1 homology motif|LIS1 homology motif 50\n", - "Domain of unknown function DUF4208|Domain of unknown function DUF4208 50\n", - "CUB domain|CUB domain 50\n", - "CARD domain 50\n", - "Collagenase NC10/endostatin|Collagenase NC10/endostatin 50\n", - "Ketoacyl-synthetase, C-terminal extension 50\n", - "Gelsolin-like domain|Sec23, C-terminal 50\n", - "Peptidase S53, activation domain 50\n", - "Hexokinase, N-terminal|Hexokinase, binding site 50\n", - "Septin-type guanine nucleotide-binding (G) domain|Septin-type guanine nucleotide-binding (G) domain|Small GTP-binding protein domain 50\n", - "Foie gras liver health family 1 50\n", - "Polynucleotide 3'-phosphatase 50\n", - "RNA recognition motif domain|RNA recognition motif domain 50\n", - "Lon, substrate-binding domain|Lon, substrate-binding domain|Lon, substrate-binding domain 50\n", - "Fumarate lyase, N-terminal|Fumarate lyase, conserved site 50\n", - "C2 domain|C2 domain|C2 domain|Ferlin, second C2 domain 50\n", - "Peptidase M16, C-terminal 50\n", - "Pleckstrin homology domain|Pleckstrin homology domain|Pleckstrin homology domain|FGD4, N-terminal PH domain 50\n", - "Heparan sulphate-N-deacetylase 50\n", - "Pentacotripeptide-repeat region of PRORP 50\n", - "Aromatic amino acid beta-eliminating lyase/threonine aldolase 50\n", - "TILa domain|VWFC domain 49\n", - "Basic-leucine zipper domain|Basic-leucine zipper domain|Basic-leucine zipper domain 49\n", - "Peptidoglycan binding-like 49\n", - "Intermediate filament, rod domain 49\n", - "Nop domain 49\n", - "Paired domain|Paired domain 49\n", - "Multicopper oxidase, type 1 49\n", - "Ubiquitin-conjugating enzyme E2|Ubiquitin-conjugating enzyme E2|Ubiquitin-conjugating enzyme E2 49\n", - "THO complex subunit 2, N-terminal domain 49\n", - "Cystine knot, C-terminal|Cystine knot, C-terminal 49\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain|cGMP-dependent protein kinase, catalytic domain 49\n", - "Hedgehog protein, Hint domain|Hint domain N-terminal 48\n", - "TACI, cysteine-rich domain 48\n", - "THO complex, subunitTHOC2, C-terminal 48\n", - "Saposin B type domain|Saposin B type domain 48\n", - "Aromatic amino acid hydroxylase, C-terminal|Aromatic amino acid hydroxylase, iron/copper binding site|Aromatic amino acid hydroxylase, C-terminal 48\n", - "EGF-like calcium-binding domain|EGF-like calcium-binding domain 48\n", - "Kinesin-like KIF1-type 48\n", - "Asparagine synthase|Asparagine synthase 48\n", - "PAS domain|PAS-associated, C-terminal|PAS domain|PAS domain 48\n", - "Flavodoxin/nitric oxide synthase|Flavodoxin/nitric oxide synthase 48\n", - "Pyrimidine nucleoside phosphorylase, C-terminal|Pyrimidine nucleoside phosphorylase, C-terminal 48\n", - "ACT domain 48\n", - "Peptide N glycanase, PAW domain|Peptide N glycanase, PAW domain 48\n", - "Helical and beta-bridge domain 48\n", - "Tensin/EPS8 phosphotyrosine-binding domain|PTB/PI domain|Epidermal growth factor receptor kinase substrate, phosphotyrosine-binding domain 48\n", - "Alpha-D-phosphohexomutase, C-terminal 48\n", - "Glutathione synthase, substrate-binding domain 48\n", - "Pleckstrin homology domain, spectrin-type|Pleckstrin homology domain|Pleckstrin homology domain 48\n", - "Myotubularin-like phosphatase domain|Tyrosine specific protein phosphatases domain|Myotubularin-like phosphatase domain|Protein-tyrosine phosphatase, catalytic 48\n", - "Coagulation factor 5/8 C-terminal domain|Coagulation factor 5/8 C-terminal domain 48\n", - "Telomeric single stranded DNA binding POT1/Cdc13 48\n", - "B-box-type zinc finger 48\n", - "Ferrodoxin-fold anticodon-binding domain|Ferrodoxin-fold anticodon-binding domain|Ferrodoxin-fold anticodon-binding domain 48\n", - "CTF transcription factor/nuclear factor 1, N-terminal|CTF transcription factor/nuclear factor 1, conserved site|CTF transcription factor/nuclear factor 1, DNA-binding domain 48\n", - "SH2 domain|SH2 domain|SH2 domain|SH3BP2, SH2 domain 48\n", - "Neuregulin, C-terminal 48\n", - "EGF-like, conserved site|EGF-like calcium-binding, conserved site|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 47\n", - "Bromodomain|Bromodomain|Bromodomain|Bromodomain 47\n", - "WW domain|WW domain 47\n", - "Peptidase C14, p20 domain|Peptidase C14A, caspase catalytic domain|Peptidase C14A, caspase catalytic domain 47\n", - "Ribonuclease II/R|Ribonuclease II/R, conserved site|Ribonuclease II/R 47\n", - "C1q domain|C1q domain|C1q domain 47\n", - "Lipin, N-terminal 47\n", - "Phosphoenolpyruvate carboxykinase, GTP-utilising, C-terminal 47\n", - "Potassium channel tetramerisation-type BTB domain 47\n", - "Interferon/interleukin receptor domain|Fibronectin type III|Fibronectin type III 47\n", - "Vacuolar protein sorting-associated protein 13, C-terminal 47\n", - "C2 domain|C2 domain|C2 domain|Ferlin, fifth C2 domain 47\n", - "Phosphoenolpyruvate carboxykinase, GTP-utilising, N-terminal 46\n", - "DMAP1-binding domain 46\n", - "CDC48, domain 2|CDC48, domain 2 46\n", - "Peptidase C14, caspase non-catalytic subunit p10|Peptidase C14A, caspase catalytic domain|Peptidase C14A, caspase catalytic domain 46\n", - "ERCC4 domain 46\n", - "Low-density lipoprotein (LDL) receptor class A, conserved site|EGF-like domain 46\n", - "EF-hand domain|EF-Hand 1, calcium-binding site|EF-hand domain|EF-hand domain 46\n", - "Guanylate kinase-like domain 46\n", - "Alanyl-tRNA synthetase, class IIc, core domain 46\n", - "Cep57 centrosome microtubule-binding domain 46\n", - "PB1 domain|PB1 domain|PB1 domain|Sequestosome-1, PB1 domain 46\n", - "TRAM/LAG1/CLN8 homology domain 46\n", - "ATP-citrate lyase/succinyl-CoA ligase 46\n", - "HD domain|HD/PDEase domain|HD/PDEase domain|HD/PDEase domain 46\n", - "Aminoacyl-tRNA synthetase, class II|Glycyl-tRNA synthetase-like core domain 46\n", - "Transglutaminase-like 46\n", - "Alpha crystallin/Hsp20 domain|Alpha crystallin/Hsp20 domain|Heat shock protein beta-1, ACD domain 46\n", - "Anaphase-promoting complex subunit 4, WD40 domain|WD40-repeat-containing domain 46\n", - "CAP Gly-rich domain|CAP Gly-rich domain|CAP Gly-rich domain 45\n", - "Rab3GAP regulatory subunit, C-terminal 45\n", - "REJ domain 45\n", - "Formin, FH2 domain|Formin, FH2 domain 45\n", - "Histone acetyltransferase domain, MYST-type 45\n", - "Kinesin motor domain|Kinesin motor domain|Kinesin motor domain, conserved site|Kinesin motor domain|Kinesin motor domain 45\n", - "Adenylyl cyclase class-3/4/guanylyl cyclase|Adenylyl cyclase class-3/4/guanylyl cyclase 45\n", - "Calponin homology domain 45\n", - "BAAT/Acyl-CoA thioester hydrolase C-terminal 45\n", - "Peptidase S8 propeptide/proteinase inhibitor I9 45\n", - "PAS domain|PAS-associated, C-terminal 45\n", - "Reticulon|Reticulon 45\n", - "Reverse transcriptase domain|Reverse transcriptase domain 45\n", - "TRAM/LAG1/CLN8 homology domain|TRAM/LAG1/CLN8 homology domain 45\n", - "Asparagine synthase 45\n", - "PAS domain|PAS domain|PAS domain 44\n", - "Aldehyde oxidase/xanthine dehydrogenase, molybdopterin binding 44\n", - "Glycosyltransferase subfamily 4-like, N-terminal domain 44\n", - "CD80-like, immunoglobulin C2-set|Immunoglobulin-like domain|Immunoglobulin subtype 2|Immunoglobulin subtype 44\n", - "Glycyl-tRNA synthetase-like core domain 44\n", - "Sec39 domain 44\n", - "EGF-like calcium-binding domain|EGF-like, conserved site|EGF-like, conserved site|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 44\n", - "Rhodanese-like domain|Rhodanese-like domain|Rhodanese-like domain 44\n", - "Translation elongation factor EFTu-like, domain 2 44\n", - "Zinc finger, TRAF-type|Zinc finger, TRAF-type 44\n", - "Retinoblastoma-associated protein, B-box|Cyclin-like|Cyclin-like 44\n", - "SH3 domain|SH3 domain|SH3 domain|SH3 domain|PSTPIP1, SH3 domain 44\n", - "Aminoacyl-tRNA synthetase, class II (G/ P/ S/T)|Aminoacyl-tRNA synthetase, class II|Serine-tRNA ligase catalytic core domain 44\n", - "Beta-ketoacyl synthase, C-terminal|Polyketide synthase, beta-ketoacyl synthase domain 44\n", - "Sialidase 44\n", - "EF-Hand 1, calcium-binding site|EF-hand domain|EF-hand domain 44\n", - "CTP synthase, N-terminal 44\n", - "WWE domain|WWE domain 44\n", - "Translation initiation factor eIF-2B subunit epsilon, N-terminal 44\n", - "Guanylate-binding protein/Atlastin, C-terminal 44\n", - "Arginyl-tRNA synthetase, catalytic core domain|Arginyl-tRNA synthetase, catalytic core domain 44\n", - "Laminin IV|Laminin IV 44\n", - "Ras-like guanine nucleotide exchange factor, N-terminal|Ras-like guanine nucleotide exchange factor, N-terminal|Ras-like guanine nucleotide exchange factor, N-terminal 43\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Class II myosin, Myh3, motor domain 43\n", - "Surfactant protein C, N-terminal propeptide 43\n", - "Peptidase C14A, caspase catalytic domain|Peptidase C14, p20 domain|Peptidase C14A, caspase catalytic domain|Peptidase C14A, caspase catalytic domain 43\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain|Salt-Inducible kinase, catalytic domain 43\n", - "Rho GTPase-activating protein domain|Rho GTPase-activating protein domain 43\n", - "Rab-GTPase-TBC domain|Rab-GTPase-TBC domain|Rab-GTPase-TBC domain 43\n", - "DnaJ domain|DnaJ domain 43\n", - "FANCL C-terminal domain 43\n", - "Dbl homology (DH) domain 43\n", - "PBX 43\n", - "Long hematopoietin receptor, single chain, conserved site|Fibronectin type III|Fibronectin type III 43\n", - "THIF-type NAD/FAD binding fold|Ubiquitin-activating enzyme, catalytic cysteine domain 43\n", - "Alpha-macroglobulin, receptor-binding|Alpha-macroglobulin, receptor-binding 43\n", - "NUC194|NUC194 43\n", - "EGF-like domain|Laminin EGF domain|EGF-like domain 43\n", - "Transcription factor, GTP-binding domain|Transcription factor, GTP-binding domain|Transcription factor, GTP-binding domain 43\n", - "AGC-kinase, C-terminal|AGC-kinase, C-terminal|Protein kinase B alpha, catalytic domain 42\n", - "Adenylyl cyclase class-3/4/guanylyl cyclase 42\n", - "D-isomer specific 2-hydroxyacid dehydrogenase, catalytic domain|D-isomer specific 2-hydroxyacid dehydrogenase, NAD-binding domain|D-isomer specific 2-hydroxyacid dehydrogenase, NAD-binding domain conserved site 1 42\n", - "Cytosolic carboxypeptidase-like protein 5 catalytic domain 42\n", - "DNA-directed RNA polymerase, insert domain|DNA-directed RNA polymerase, RpoA/D/Rpb3-type|DNA-directed RNA polymerase, 30-40kDa subunit, conserved site|DNA-directed RNA polymerase, RpoA/D/Rpb3-type 42\n", - "STAT3, SH2 domain 42\n", - "HYR domain|HYR domain 42\n", - "Rel homology dimerisation domain|IPT domain|NFkappaB IPT domain 42\n", - "Sterile alpha motif domain|Sterile alpha motif domain|Sterile alpha motif domain|Stromal interaction molecule 1, SAM domain 42\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain|p21-activated kinase 3, catalytic domain 42\n", - "GPCR, family 2, extracellular hormone receptor domain 42\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Class XVIII myosin, motor domain 42\n", - "Guanylate kinase-like domain|Guanylate kinase/L-type calcium channel beta subunit 42\n", - "CDC48, N-terminal subdomain|CDC48, N-terminal subdomain 42\n", - "Dishevelled protein domain 42\n", - "DNA-directed RNA polymerase, subunit 2, hybrid-binding domain 42\n", - "Cyclic nucleotide-binding domain 42\n", - "Immunoglobulin V-set domain|Immunoglobulin V-set domain|Immunoglobulin subtype 42\n", - "Muniscin C-terminal|Mu homology domain 42\n", - "CTF transcription factor/nuclear factor 1, N-terminal|CTF transcription factor/nuclear factor 1, DNA-binding domain 42\n", - "Squalene cyclase, N-terminal 42\n", - "Transcription factor TFIIB, cyclin-like domain|Cyclin-like 41\n", - "GoLoco motif|GoLoco motif|GoLoco motif 41\n", - "Aldehyde dehydrogenase domain|Aldehyde dehydrogenase, glutamic acid active site 41\n", - "SLC26A/SulP transporter domain|Sulphate anion transporter, conserved site 41\n", - "Insulin-like|Insulin, conserved site|Insulin-like 41\n", - "3'5'-cyclic nucleotide phosphodiesterase, catalytic domain|3'5'-cyclic nucleotide phosphodiesterase, catalytic domain|HD/PDEase domain 41\n", - "Nerve growth factor-related|Nerve growth factor-related|Nerve growth factor-related|Nerve growth factor-related 41\n", - "DEP domain|DEP domain 41\n", - "Stimulator of interferon genes protein, C-terminal 41\n", - "Protein kinase A anchor protein, nuclear localisation signal domain 41\n", - "JAB1/MPN/MOV34 metalloenzyme domain|MPN domain|JAB1/MPN/MOV34 metalloenzyme domain 41\n", - "Biotin/lipoyl attachment|Biotin-binding site|Biotin/lipoyl attachment 41\n", - "COQ9 41\n", - "Vicinal oxygen chelate (VOC) domain 41\n", - "Peptidase C1A, papain C-terminal|Peptidase C1A, papain C-terminal|Cathepsin C 41\n", - "PAN/Apple domain|Apple domain|PAN/Apple domain|Apple domain|Apple domain 41\n", - "Glutathione S-transferase, N-terminal|Glutathione S-transferase, N-terminal 41\n", - "DAPIN domain 41\n", - "Glutamine synthetase, catalytic domain 41\n", - "Palmitoyltransferase, DHHC domain 40\n", - "Fumarase C, C-terminal 40\n", - "VWFC domain|VWFC domain|VWFC domain 40\n", - "Tubulin/FtsZ, GTPase domain|Tubulin, conserved site|Tubulin/FtsZ, GTPase domain 40\n", - "Cyclodeaminase/cyclohydrolase 40\n", - "Conserved oligomeric Golgi complex, subunit 4|Conserved oligomeric Golgi complex, subunit 4 40\n", - "Aspartate/glutamate/uridylate kinase 40\n", - "Myelin-PO, C-terminal 40\n", - "Lysine-specific demethylase-like domain 40\n", - "GPCR family 3, C-terminal|GPCR, family 3, conserved site|GPCR family 3, C-terminal 40\n", - "EGF domain|EGF-like, conserved site|EGF-like domain|EGF-like domain 40\n", - "FERM central domain|FERM domain|Band 4.1 domain 40\n", - "BRK domain 40\n", - "Laminin EGF domain|EGF-like domain 40\n", - "Domain of unknown function DUF1086|Domain of unknown function DUF1086 40\n", - "CS domain 40\n", - "Translation elongation factor EFG/EF2, domain IV|Translation elongation factor EFG/EF2, domain IV 40\n", - "DOMON domain|DOMON domain|DOMON domain 40\n", - "Peptide N glycanase, PAW domain|Peptide N glycanase, PAW domain|Peptide N glycanase, PAW domain 40\n", - "Alpha-L-arabinofuranosidase B, arabinose-binding domain 40\n", - "Sox developmental protein N-terminal 40\n", - "Dynamin central domain|Dynamin-type guanine nucleotide-binding (G) domain|Dynamin, GTPase domain|Dynamin, GTPase domain 40\n", - "Peptidase M12B, ADAM/reprolysin|ADAM10/ADAM17 catalytic domain 40\n", - "Glycosyl-hydrolase family 116, catalytic region 40\n", - "Ribonuclease A-domain|Ribonuclease A-domain 39\n", - "Anticodon-binding 39\n", - "WHEP-TRS domain 39\n", - "tRNA synthetases class I, catalytic domain 39\n", - "SAB domain 39\n", - "FERM central domain|Band 4.1 domain|FERM conserved site|FERM domain|Band 4.1 domain|FERM central domain 39\n", - "XPG-I domain|XPG-I domain 39\n", - "Rad21/Rec8-like protein, N-terminal 39\n", - "FAS1 domain|FAS1 domain|FAS1 domain 39\n", - "Adaptor protein Cbl, SH2-like domain|Adaptor protein Cbl, PTB domain|Adaptor protein Cbl, SH2-like domain 39\n", - "4Fe-4S ferredoxin, iron-sulphur binding, conserved site|4Fe-4S ferredoxin-type, iron-sulphur binding domain 39\n", - "WW domain|WW domain|WW domain 39\n", - "Dynein attachment factor, N-terminal 39\n", - "Laminin EGF domain|Laminin EGF domain|EGF-like domain 39\n", - "SANT/Myb domain 39\n", - "Amyloidogenic glycoprotein, amyloid-beta peptide|Amyloidogenic glycoprotein, amyloid-beta peptide 39\n", - "Ly-6 antigen/uPA receptor-like|Ly-6 antigen/uPA receptor-like 39\n", - "Glycoprotein hormone subunit beta|Cystine knot, C-terminal|Cystine knot, C-terminal|Cystine knot, C-terminal 39\n", - "Coatomer, WD associated region 39\n", - "Ly-6 antigen/uPA receptor-like 39\n", - "IBR domain 39\n", - "IMP dehydrogenase/GMP reductase|CBS domain|CBS domain|CBS domain|IMP dehydrogenase/GMP reductase 39\n", - "MAD homology, MH1|MAD homology 1, Dwarfin-type 39\n", - "Succinate dehydogenase/fumarate reductase N-terminal|2Fe-2S ferredoxin, iron-sulphur binding site|2Fe-2S ferredoxin-type iron-sulfur binding domain|2Fe-2S ferredoxin-type iron-sulfur binding domain 39\n", - "Phospholipid/glycerol acyltransferase|Phospholipid/glycerol acyltransferase|1-acyl-sn-glycerol-3-phosphate acyltransferase 38\n", - "Peroxisome proliferator-activated receptor gamma, N-terminal 38\n", - "C2 domain|C2 domain|C2 domain|Perforin-1, C2 domain 38\n", - "Ras-associating (RA) domain|Ras-associating (RA) domain|SNX27, RA domain 38\n", - "EGF domain|EGF-like domain|EGF-like domain 38\n", - "NIDO domain|NIDO domain|NIDO domain 38\n", - "MCM domain 38\n", - "Metallo-beta-lactamase|Metallo-beta-lactamase|Hydroxyacylglutathione hydrolase, MBL domain 38\n", - "Stromalin conservative domain 38\n", - "Acyl-CoA thioester hydrolase/bile acid-CoA amino acid N-acetyltransferase 38\n", - "Pyridoxine 5'-phosphate oxidase, dimerisation, C-terminal 38\n", - "Protein Lines, N-terminal 38\n", - "HRDC domain|HRDC domain 38\n", - "Glycosyl-hydrolase family 116, N-terminal 38\n", - "Homeobox protein SIX1, N-terminal SD domain 38\n", - "Hermansky-Pudlak syndrome 3 protein, C-terminal domain 38\n", - "Rab-binding domain 38\n", - "Transforming growth factor-beta, C-terminal|Transforming growth factor beta, conserved site|Transforming growth factor-beta, C-terminal|Transforming growth factor-beta, C-terminal 38\n", - "Serpin domain|Serpin domain|Pigment epithelium derived factor 38\n", - "S-adenosylmethionine synthetase, central domain 38\n", - "MATH/TRAF domain 38\n", - "Early growth response protein 38\n", - "Carbohydrate kinase PfkB 38\n", - "DnaJ domain|DnaJ domain|DnaJ domain, conserved site|DnaJ domain 38\n", - "DEAD/DEAH box helicase domain|RNA helicase, DEAD-box type, Q motif|Helicase superfamily 1/2, ATP-binding domain 38\n", - "Tripartite DENN domain 38\n", - "FERM, N-terminal|Band 4.1 domain|FERM domain|Band 4.1 domain 38\n", - "Protein kinase domain|Tyrosine-protein kinase, catalytic domain 38\n", - "Pre-SET domain|Pre-SET domain|Pre-SET domain 38\n", - "Prp31 C-terminal 37\n", - "FERM central domain|Band 4.1 domain|FERM conserved site|FERM domain|Band 4.1 domain 37\n", - "3-hydroxyacyl-CoA dehydrogenase, C-terminal|3-hydroxyacyl-CoA dehydrogenase, conserved site 37\n", - "IBR domain|IBR domain 37\n", - "Anaphase-promoting complex subunit 4, WD40 domain 37\n", - "SH2 domain|SH2 domain|SH2 domain|PI3K p85 subunit, N-terminal SH2 domain 37\n", - "Pleckstrin homology domain|Pleckstrin homology domain|Pleckstrin homology domain|Myosin phosphatase-RhoA interacting protein, PH domain 37\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Tyrosine-protein kinase, receptor class II, conserved site|Protein kinase domain|Tyrosine-protein kinase, catalytic domain 37\n", - "Coatomer, alpha subunit, C-terminal 37\n", - "AWS domain|AWS domain 37\n", - "TRPM, tetramerisation domain 37\n", - "Glycoprotein hormone subunit beta 37\n", - "Diacylglycerol kinase, catalytic domain|Diacylglycerol kinase, catalytic domain 37\n", - "Caveolin, conserved site 37\n", - "Long hematopoietin receptor, Gp130 family 2, conserved site 37\n", - "Adenylate cyclase, N-terminal 37\n", - "Kazal domain|Kazal domain|Factor I / membrane attack complex|Kazal domain 37\n", - "Peptidase M12B, ADAM/reprolysin 37\n", - "D-isomer specific 2-hydroxyacid dehydrogenase, NAD-binding domain 37\n", - "FERM central domain|FERM conserved site|FERM domain|Band 4.1 domain 36\n", - "FAM20, C-terminal 36\n", - "Thyroglobulin type-1|Thyroglobulin type-1|Thyroglobulin type-1 36\n", - "Peroxisome membrane anchor protein Pex14p, N-terminal 36\n", - "EF-hand, Ca insensitive|EF-hand domain 36\n", - "Syndecan/Neurexin domain 36\n", - "Biotin protein ligase, C-terminal 36\n", - "Helicase/UvrB, N-terminal|Helicase superfamily 1/2, ATP-binding domain 36\n", - "Adenylate kinase, active site lid domain 36\n", - "Alcohol dehydrogenase, N-terminal|Polyketide synthase, enoylreductase domain 36\n", - "RNA helicase, DEAD-box type, Q motif|Helicase superfamily 1/2, ATP-binding domain 36\n", - "Stromal interaction molecule, Orai1-activating region|Stromal interaction molecule, Orai1-activating region 36\n", - "Glycosyl hydrolase family 30, beta sandwich domain 36\n", - "Beta-adaptin appendage, C-terminal subdomain|Beta-adaptin appendage, C-terminal subdomain 36\n", - "DNA polymerase epsilon, catalytic subunit A, C-terminal 36\n", - "Rab3-GAP regulatory subunit, N-terminal 36\n", - "Ribosomal protein L5 eukaryotic/L18 archaeal, C-terminal 36\n", - "SNX27, atypical FERM-like domain 36\n", - "Piezo domain 36\n", - "Peptidase C14A, caspase catalytic domain|Peptidase C14A, caspase catalytic domain 36\n", - "CCR4-Not complex component, Not N-terminal domain 36\n", - "Laminin domain II|Laminin G domain 36\n", - "CARD domain|CARD domain|CARD domain 36\n", - "DNA replication factor Dna2, N-terminal 36\n", - "Sterile alpha motif domain|Sterile alpha motif domain|Tumour protein p63, SAM domain 36\n", - "ILEI/PANDER domain|Protein O-linked-mannose beta-1,2-N-acetylglucosaminyltransferase 1, PANDER-like domain 36\n", - "Glycogen debranching enzyme, glucanotransferase domain 36\n", - "Carbohydrate binding module family 20|Carbohydrate binding module family 20|Laforin, CBM20 domain 36\n", - "Thyroglobulin type-1|Thyroglobulin type-1 36\n", - "TNFR/NGFR cysteine-rich region 36\n", - "Tumour necrosis factor domain|Tumour necrosis factor domain 36\n", - "Transferrin receptor protein 1/2, PA domain 36\n", - "Alpha-amylase/branching enzyme, C-terminal all beta 36\n", - "PB1 domain|PB1 domain|PB1 domain|Neutrophil cytosol factor 2, PB1 domain 36\n", - "Survival motor neuron|Tudor domain|Tudor domain 36\n", - "STAG 35\n", - "MiT/TFE transcription factors, C-terminal 35\n", - "Connector enhancer of kinase suppressor of ras 2 35\n", - "DNA ligase IV domain 35\n", - "DNA mismatch repair, conserved site|DNA mismatch repair protein family, N-terminal|Histidine kinase/HSP90-like ATPase 35\n", - "Trypsin Inhibitor-like, cysteine rich domain|EGF-like domain 35\n", - "Exoribonuclease, phosphorolytic domain 1 35\n", - "Haem NO binding associated 35\n", - "Fibrinogen, alpha/beta/gamma chain, C-terminal globular domain 35\n", - "PTP type protein phosphatase|PTP type protein phosphatase 35\n", - "Collagenase NC10/endostatin 35\n", - "Patatin-like phospholipase domain 35\n", - "Integrin beta N-terminal|PSI domain 35\n", - "Peptidase M16, N-terminal 35\n", - "B30.2/SPRY domain|Ryanodine receptor, SPRY domain 1 35\n", - "NADH:ubiquinone oxidoreductase intermediate-associated protein 30 35\n", - "DNA-directed DNA polymerase, family A, palm domain|DNA-directed DNA polymerase, family A, conserved site|DNA-directed DNA polymerase, family A, palm domain 34\n", - "Integrin beta N-terminal 34\n", - "Leucine-rich repeat N-terminal domain|Leucine-rich repeat N-terminal domain 34\n", - "Right handed beta helix domain|Carbohydrate-binding/sugar hydrolysis domain 34\n", - "IMP dehydrogenase/GMP reductase 34\n", - "14-3-3 domain|14-3-3 domain 34\n", - "Poly A polymerase, head domain|Poly A polymerase, head domain 34\n", - "Zinc finger, nuclear hormone receptor-type 34\n", - "Acetyl-CoA carboxylase 34\n", - "Mad3/Bub1 homology region 1|Mad3/Bub1 homology region 1|Mad3/Bub1 homology region 1 34\n", - "REJ domain|Polycystin cation channel 34\n", - "RNA polymerase Rpb1, domain 5|DNA-directed RNA polymerase III subunit RPC1, C-terminal 34\n", - "Beta tubulin, autoregulation binding site 34\n", - "Fatty acid desaturase domain 34\n", - "Cobalamin (vitamin B12)-binding domain|Cobalamin (vitamin B12)-binding domain|Methylmalonyl-CoA mutase, C-terminal 34\n", - "Tubulin/FtsZ, 2-layer sandwich domain 34\n", - "MCM domain|MCM domain 34\n", - "ATPase family AAA domain-containing protein 3, domain of unknown function DUF3523 34\n", - "Zinc finger, TAZ-type|Zinc finger, TAZ-type|Zinc finger, TAZ-type 34\n", - "Phosphatidylinositol 3-kinase, C2 domain 34\n", - "DHHA1 domain 34\n", - "Vitellinogen, open beta-sheet|Vitellinogen, open beta-sheet 34\n", - "Single-minded, C-terminal|Single-minded, C-terminal 34\n", - "HhH-GPD domain|Endonuclease III-like, conserved site-2|HhH-GPD domain|HhH-GPD domain 33\n", - "Tubulin binding cofactor C-like domain 33\n", - "Biotin-protein ligase, N-terminal 33\n", - "Thyroglobulin type-1|Thyroglobulin type-1|Thyroglobulin type-1|Thyroglobulin type-1 33\n", - "Vertebrate-like NAGS Gcn5-related N-acetyltransferase (GNAT) domain|Vertebrate-like NAGS Gcn5-related N-acetyltransferase (GNAT) domain 33\n", - "Protein kinase, ATP binding site|Protein kinase domain|Protein kinase domain 33\n", - "IQ motif and SEC7 domain-containing protein, PH domain|IQ motif and SEC7 domain-containing protein, PH domain 33\n", - "CD80-like, immunoglobulin C2-set|Immunoglobulin-like domain|Immunoglobulin subtype 33\n", - "Myc-type, basic helix-loop-helix (bHLH) domain|Myc-type, basic helix-loop-helix (bHLH) domain 33\n", - "Galactosyltransferase, N-terminal 33\n", - "LamG-like jellyroll fold 33\n", - "Mediator complex, subunit Med13, N-terminal, metazoa/fungi 33\n", - "Domain of unknown function DUF155 33\n", - "Histone acetyltransferase domain, MYST-type|Histone acetyltransferase domain, MYST-type 33\n", - "RecF/RecN/SMC, N-terminal|SMCs flexible hinge|SMCs flexible hinge 33\n", - "CUT domain|CUT domain 33\n", - "Notch domain|Notch domain|Notch domain 33\n", - "Guanylate-binding protein, N-terminal 33\n", - "Sulfatase, N-terminal|GPI ethanolamine phosphate transferase 1, N-terminal 33\n", - "Peptidase S53, activation domain|Peptidase S53, activation domain 33\n", - "Saposin A-type domain|Saposin A-type domain|Saposin A-type domain 33\n", - "NADP transhydrogenase beta-like domain 33\n", - "AAA+ ATPase domain|AAA+ ATPase domain 33\n", - "Glucosidase II beta subunit, N-terminal 33\n", - "Beta-amyloid precursor protein C-terminal 33\n", - "BAR domain 33\n", - "tRNA methyltransferase TRMD/TRM10-type domain|tRNA methyltransferase TRM10-type domain 33\n", - "Aminoacyl-tRNA synthetase, class II (D/K/N) 33\n", - "Aconitase A/isopropylmalate dehydratase small subunit, swivel domain 33\n", - "Electron transfer flavoprotein, alpha/beta-subunit, N-terminal|Electron transfer flavoprotein, alpha/beta-subunit, N-terminal|Electron transfer flavoprotein, beta subunit, N-terminal 33\n", - "Domain of unknown function DUF4174 32\n", - "BARD1, Zinc finger, RING-type|Zinc finger, RING-type, conserved site|Zinc finger, RING-type|BARD1, Zinc finger, RING-type 32\n", - "Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3-/4-kinase, catalytic domain|DNA-dependent protein kinase catalytic subunit, catalytic domain 32\n", - "Adenylyl cyclase class-3/4/guanylyl cyclase|Adenylyl cyclase class-4/guanylyl cyclase, conserved site|Adenylyl cyclase class-3/4/guanylyl cyclase|Adenylyl cyclase class-3/4/guanylyl cyclase 32\n", - "Aminotransferase class V domain|Aminotransferase class-V, pyridoxal-phosphate binding site 32\n", - "Carbamoyl-phosphate synthetase, large subunit oligomerisation domain|Carbamoyl-phosphate synthetase, large subunit oligomerisation domain 32\n", - "Claudin, conserved site 32\n", - "Annexin repeat, conserved site 32\n", - "Domain of unknown function DUF1866|Domain of unknown function DUF1866 32\n", - "C-type lectin-like|C-type lectin-like|C-type lectin-like 32\n", - "Ciliary BBSome complex subunit 2, N-terminal 32\n", - "B-box-type zinc finger|B-box-type zinc finger|B-box-type zinc finger 32\n", - "Threonyl/alanyl tRNA synthetase, SAD|Alanyl-tRNA synthetase, class IIc, core domain|Threonyl/alanyl tRNA synthetase, SAD 32\n", - "FANCI solenoid 2 domain 32\n", - "Kazal domain|Kazal domain|Kazal domain|Kazal domain 32\n", - "Acyl-CoA-binding protein, ACBP|Acyl-CoA-binding protein, ACBP|Acyl-CoA-binding protein, ACBP|Acyl-CoA-binding protein, ACBP 32\n", - "Multicopper oxidase, type 2 32\n", - "Gap junction alpha-5 protein (Cx40), C-terminal 32\n", - "PTP type protein phosphatase|PTP type protein phosphatase|PTP type protein phosphatase|PTP type protein phosphatase|Protein-tyrosine phosphatase, catalytic 32\n", - "Transcription factor, GTP-binding domain|Transcription factor, GTP-binding domain|Transcription factor, GTP-binding domain|Small GTP-binding protein domain 32\n", - "BCL-6 corepressor, PCGF1 binding domain 32\n", - "Aminoacyl-tRNA synthetase, class II|Serine-tRNA ligase catalytic core domain 32\n", - "EF-hand domain|EF-Hand 1, calcium-binding site|EF-hand domain 32\n", - "DNA/RNA non-specific endonuclease|Extracellular Endonuclease, subunit A|DNA/RNA non-specific endonuclease 32\n", - "F-box domain|F-box domain 32\n", - "WASH complex subunit 7, N-terminal 32\n", - "Transcription factor COE, DNA-binding domain|Transcription factor COE, DNA-binding domain 32\n", - "Glycerate/sugar phosphate transporter, conserved site|Major facilitator superfamily domain|Major facilitator superfamily domain 32\n", - "Transferrin-like domain|Transferrin-like domain|Transferrin-like domain 32\n", - "Laminin EGF domain|EGF-like domain|Laminin EGF domain|EGF-like domain 32\n", - "Domain of unknown function DUF4472 32\n", - "SH3 domain|SH3 domain|SH3 domain|SH3 domain|Lck, SH3 domain 32\n", - "Lipin/Ned1/Smp2 (LNS2)|LNS2/PITP 32\n", - "Potassium channel, inwardly rectifying, Kir, N-terminal 31\n", - "Chromo/chromo shadow domain 31\n", - "Heavy metal-associated domain, HMA|Heavy metal-associated domain, copper ion-binding|Heavy metal-associated domain, HMA 31\n", - "Hydroxyacylglutathione hydrolase, C-terminal domain 31\n", - "Prolyl 4-hydroxylase alpha-subunit, N-terminal 31\n", - "FAD-binding domain 31\n", - "Sister chromatid cohesion C-terminal domain 31\n", - "B30.2/SPRY domain|SPRY domain|Heterogeneous nuclear ribonucleoprotein U, SPRY domain 31\n", - "Fibronectin type III|Short hematopoietin receptor, family 1, conserved site|Fibronectin type III|Fibronectin type III 31\n", - "Immunoglobulin C2-set-like, ligand-binding 31\n", - "Kinesin motor domain 31\n", - "Linker histone H1/H5, domain H15|Linker histone H1/H5, domain H15 31\n", - "Biotin/lipoyl attachment 31\n", - "Immunoglobulin|Immunoglobulin-like domain 31\n", - "Macro domain 31\n", - "Ran binding domain|Ran binding domain|Ran binding domain 31\n", - "PET domain|PET prickle 31\n", - "Zinc finger, PHD-type, conserved site|Zinc finger, PHD-finger|Zinc finger, PHD-type 31\n", - "PTP type protein phosphatase|PTP type protein phosphatase|PTP type protein phosphatase|Protein-tyrosine phosphatase, catalytic 31\n", - "AP-3 complex subunit beta, C-terminal domain|AP-3 complex subunit beta, C-terminal domain 31\n", - "Hexapeptide transferase, conserved site 31\n", - "RNA polymerase Rpb1, domain 3|DNA-directed RNA polymerase III subunit RPC1, N-terminal 31\n", - "Hydroxymethylglutaryl-coenzyme A synthase C-terminal domain 31\n", - "PAN/Apple domain|PAN/Apple domain|PAN/Apple domain 31\n", - "Zinc finger, GATA-type 31\n", - "CNNM, transmembrane domain|CNNM, transmembrane domain 31\n", - "Isopropylmalate dehydrogenase-like domain 31\n", - "Peptidase family A1 domain|Peptidase family A1 domain|Renin-like domain 30\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase, ATP binding site|Protein kinase domain|Tyrosine-protein kinase, catalytic domain|Fibroblast growth factor receptor 1, catalytic domain 30\n", - "Zinc finger, PHD-finger|Zinc finger, PHD-type, conserved site|Zinc finger, PHD-finger|Zinc finger, PHD-type 30\n", - "Patatin-like phospholipase domain|Patatin-like phospholipase domain|Patatin-like phospholipase domain-containing protein 2 30\n", - "Oxidoreductase, molybdopterin-binding domain|Oxidoreductase, molybdopterin binding site 30\n", - "Type I cytokine receptor, cytokine-binding domain|Short hematopoietin receptor, family 2, conserved site 30\n", - "Nop domain|NOSIC 30\n", - "MoaB/Mog domain|MoaB/Mog domain|MoaB/Mog domain|MoaB/Mog domain 30\n", - "PWWP domain|PWWP domain|PWWP domain|ZMYND8/11, PWWP domain 30\n", - "Frizzled/Smoothened, transmembrane domain|Frizzled/Smoothened, transmembrane domain|GPCR, family 2-like|Frizzled/Smoothened, transmembrane domain 30\n", - "Protein kinase, C-terminal|AGC-kinase, C-terminal|AGC-kinase, C-terminal 30\n", - "Zinc finger, RanBP2-type|Zinc finger, RanBP2-type|Zinc finger, RanBP2-type 30\n", - "Zona pellucida domain|Zona pellucida domain, conserved site|Zona pellucida domain|Zona pellucida domain 30\n", - "Peptidase C2, calpain, catalytic domain 30\n", - "Phosphoglucose isomerase, SIS domain 2 30\n", - "Lipid-binding serum glycoprotein, N-terminal|Lipid-binding serum glycoprotein, N-terminal 30\n", - "PPM-type phosphatase domain|PPM-type phosphatase domain|PPM-type phosphatase domain|PPM-type phosphatase domain 30\n", - "Cyclophilin-type peptidyl-prolyl cis-trans isomerase domain|Cyclophilin-type peptidyl-prolyl cis-trans isomerase domain 30\n", - "Amidohydrolase-related 30\n", - "Amyloidogenic glycoprotein, E2 domain 30\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Class VI myosin, motor domain 30\n", - "Sin3, C-terminal 30\n", - "Arrestin C-terminal-like domain|Arrestin C-terminal-like domain 30\n", - "Collagen IV, non-collagenous|Collagen IV, non-collagenous 30\n", - "Laminin EGF domain|EGF-like, conserved site|EGF-like domain|Laminin EGF domain|EGF-like domain 30\n", - "Granulin 30\n", - "Zinc finger, A20-type|Zinc finger, A20-type 30\n", - "Plk4, second cryptic polo-box domain 30\n", - "B30.2/SPRY domain|Ryanodine receptor, SPRY domain 2 30\n", - "Pleckstrin homology domain|Pleckstrin homology domain|Dok-7, PH domain 30\n", - "Alanine racemase, N-terminal 30\n", - "Pumilio homology domain|Pumilio, RNA binding domain 30\n", - "Peptidase M50 30\n", - "Origin recognition complex subunit 4, C-terminal 30\n", - "Pre-mRNA-splicing factor 3 30\n", - "G-protein gamma-like domain|G-protein gamma-like domain|G-protein gamma-like domain 30\n", - "SH2 domain|SH2 domain|SH2 domain|PLC-gamma, N-terminal SH2 domain 30\n", - "Retinoblastoma-associated protein, B-box|Cyclin-like 30\n", - "Alpha-crystallin, N-terminal|Alpha crystallin/Hsp20 domain 29\n", - "Gcp-like domain|Gcp-like domain 29\n", - "Transcription initiation factor TFIID subunit 1, domain of unknown function 29\n", - "Target SNARE coiled-coil homology domain 29\n", - "Sedolisin domain|Peptidase S53, activation domain|Sedolisin domain 29\n", - "Ets domain|Ets domain|Ets domain|Ets domain 29\n", - "ERV/ALR sulfhydryl oxidase domain|ERV/ALR sulfhydryl oxidase domain 29\n", - "DnaJ domain|DnaJ domain|DnaJ domain, conserved site|DnaJ domain|DnaJ domain|DnaJ domain 29\n", - "Pleckstrin homology domain|Pleckstrin homology domain|Protein Kinase B, pleckstrin homology domain 29\n", - "Tripartite DENN domain|dDENN domain 29\n", - "Dihydroorotate dehydrogenase domain|Dihydroorotate dehydrogenase domain 29\n", - "FCP1 homology domain|FCP1 homology domain|FCP1 homology domain 29\n", - "Band 7 domain 29\n", - "Interleukin-17 receptor, fibronectin-III-like domain 1 29\n", - "Ubiquitin-associated domain|Ubiquitin-associated domain|Ubiquitin-associated domain 29\n", - "Tubulin-specific chaperone D, C-terminal 29\n", - "Kringle|Kringle, conserved site|Kringle|Kringle|Kringle 29\n", - "Dynein regulatory complex protein 1/2, N-terminal 29\n", - "NEMO, Zinc finger 29\n", - "PB1 domain 29\n", - "Death domain|Death domain|Death domain|Fas receptor, death domain 29\n", - "Tumor necrosis factor receptor EDAR, N-terminal 29\n", - "RNA recognition motif domain 29\n", - "SH3 domain|SH3 domain|SH3 domain|Nebulin, SH3 domain 29\n", - "CUB domain|CUB domain|CUB domain 29\n", - "Heavy metal-associated domain, HMA 29\n", - "GAR domain|GAR domain|GAR domain 29\n", - "SRCR-like domain|SRCR domain|SRCR-like domain 29\n", - "Neurofascin/L1/NrCAM, C-terminal domain 28\n", - "Vps53-like, N-terminal 28\n", - "FKBP12-rapamycin binding domain 28\n", - "FERM central domain 28\n", - "Peptidase M20, dimerisation domain 28\n", - "PTP type protein phosphatase|PTP type protein phosphatase|PTP type protein phosphatase|PTP type protein phosphatase 28\n", - "SMARCC, N-terminal 28\n", - "Tubby, C-terminal|Tubby, C-terminal|Tubby, C-terminal, conserved site 28\n", - "Neurexin/syndecan/glycophorin C 28\n", - "Centrosomal protein Cep63/Deup1, N-terminal 28\n", - "Rhodanese-like domain|Rhodanese-like domain 28\n", - "ELK domain 28\n", - "SH2 domain|SH3BP2, SH2 domain 28\n", - "PRP8 domain IV core|PRP8 domain IV core 28\n", - "Ubiquitin domain|Ubiquitin domain 28\n", - "C2 domain|C2 domain|Ferlin, sixth C2 domain 28\n", - "Cullin, N-terminal|Cullin homology domain|Cullin homology domain 28\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|PUF60, RNA recognition motif 1 28\n", - "Domain of unknown function DUF1605 28\n", - "EGF-like calcium-binding, conserved site|EGF-like domain|EGF-like calcium-binding domain 28\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tumor necrosis factor receptor 1A, N-terminal 28\n", - "SH3 domain|SH3 domain|Rho guanine nucleotide exchange factor 9, SH3 domain 28\n", - "Domain of unknown function DUF3385, target of rapamycin protein|Domain of unknown function DUF3385, target of rapamycin protein 28\n", - "Helicase/UvrB, N-terminal 28\n", - "B30.2/SPRY domain|SPRY domain 28\n", - "Rhodopsin, N-terminal 28\n", - "Thioredoxin domain|Thioredoxin domain 28\n", - "LEM-like domain|LEM-like domain|LEM-like domain 28\n", - "GOLD domain 28\n", - "Transcriptional activator, Zfx / Zfy domain 28\n", - "Eukaryotic glycogen debranching enzyme, N-terminal domain|Glycogen debranching enzyme, glucanotransferase domain 28\n", - "FERM adjacent (FA)|FERM adjacent (FA) 28\n", - "VWFC domain|VWFC domain 28\n", - "Homeobox domain|POU domain|Homeobox domain|Homeobox domain|Homeobox domain 28\n", - "ArgE/DapE/ACY1/CPG2/YscS, conserved site 28\n", - "FERM central domain|Band 4.1 domain 28\n", - "Zinc finger, RING-type, conserved site|Zinc finger, RING-type|Zinc finger, RING-type|E3 ubiquitin-protein ligase CBL-B, RING finger, HC subclass 28\n", - "Flavoprotein pyridine nucleotide cytochrome reductase-like, FAD-binding domain|FAD-binding domain, ferredoxin reductase-type 28\n", - "Notch, NOD domain|Notch, NOD domain 28\n", - "Glyoxalase/fosfomycin resistance/dioxygenase domain|Vicinal oxygen chelate (VOC) domain 28\n", - "Aconitase/3-isopropylmalate dehydratase large subunit, alpha/beta/alpha domain|Aconitase/3-isopropylmalate dehydratase large subunit, alpha/beta/alpha domain 28\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain 28\n", - "Adenylate cyclase, conserved domain 28\n", - "S-adenosylmethionine synthetase, C-terminal 28\n", - "HARP domain 28\n", - "C-type lectin-like|C-type lectin-like|C-type lectin-like|Collectin, C-type lectin-like domain 28\n", - "Molybdenum cofactor synthesis C-terminal|Radical SAM 28\n", - "EGF-like calcium-binding domain|EGF-like calcium-binding, conserved site|EGF-like calcium-binding domain|EGF-like domain 28\n", - "Helicase XPB/Ssl2, N-terminal domain 28\n", - "PB1 domain|PB1 domain|Sequestosome-1, PB1 domain 28\n", - "Protein kinase domain|Protein kinase, ATP binding site|Protein kinase domain|Protein kinase domain|Protein kinase B alpha, catalytic domain 28\n", - "ER membrane protein complex subunit 1, C-terminal 28\n", - "Acyl-coenzyme A oxidase, N-terminal 27\n", - "Heat shock factor (HSF)-type, DNA-binding|Heat shock factor (HSF)-type, DNA-binding 27\n", - "RNA recognition motif domain|RNA recognition motif domain|Matrin-3, RNA recognition motif 2 27\n", - "NIF system FeS cluster assembly, NifU, C-terminal|NIF system FeS cluster assembly, NifU, C-terminal 27\n", - "PA14/GLEYA domain 27\n", - "Endothelin-like toxin|Endothelin-like toxin 27\n", - "EGF-like, conserved site|EGF-like, conserved site|EGF-like domain|Laminin EGF domain|EGF-like domain 27\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Class XVI myosin, motor domain 27\n", - "Formiminotransferase, C-terminal subdomain|Formiminotransferase, C-terminal subdomain|Formiminotransferase catalytic domain 27\n", - "116kDa U5 small nuclear ribonucleoprotein component, N-terminal 27\n", - "Zinc finger, PHD-finger 27\n", - "Hormone-sensitive lipase, N-terminal 27\n", - "DNA repair metallo-beta-lactamase 27\n", - "DNA ligase, ATP-dependent, central|DNA ligase, ATP-dependent, conserved site|DNA ligase, ATP-dependent, central 27\n", - "Endonuclease III, iron-sulphur binding site 27\n", - "Chromo domain|Chromo domain, conserved site|Chromo/chromo shadow domain|Chromo/chromo shadow domain|Chromo/chromo shadow domain 27\n", - "APOBEC-like, N-terminal|Cytidine and deoxycytidylate deaminase domain 27\n", - "Nitroreductase 27\n", - "EGF-like domain, extracellular 27\n", - "Notch ligand, N-terminal domain 27\n", - "SH3 domain|SH3 domain|SH3 domain|SH3 domain|Jouberin, SH3 domain 27\n", - "Immunoglobulin|Immunoglobulin-like domain|Immunoglobulin subtype 2 27\n", - "Pre-SET domain|Pre-SET domain 27\n", - "Pro-opiomelanocortin/corticotropin, ACTH, central region 27\n", - "Ferlin, third C2 domain 27\n", - "ASX homology domain 27\n", - "Neogenin, C-terminal 27\n", - "SH3 domain|SH3 domain|Phosphatidylinositol 3-kinase regulatory subunit beta, SH3 domain 27\n", - "Secreted phosphoprotein 24 27\n", - "PRO8NT domain|PRO8NT domain 27\n", - "UDP-N-acetylglucosamine 2-epimerase domain 27\n", - "Cathepsin propeptide inhibitor domain (I29)|Cathepsin propeptide inhibitor domain (I29) 27\n", - "Peptidase C1A, papain C-terminal|Peptidase C1A, papain C-terminal 27\n", - "EMI domain 27\n", - "Interleukin-1 conserved site 27\n", - "Aminoacyl-tRNA synthetase, class II (D/K/N)|Aminoacyl-tRNA synthetase, class II|Lysyl-tRNA synthetase, class II, C-terminal 27\n", - "Mu homology domain|Clathrin adaptor, mu subunit, conserved site|Mu homology domain 27\n", - "RUN domain|RUN domain 27\n", - "RNA recognition motif domain|RNA recognition motif domain|Matrin-3, RNA recognition motif 1 27\n", - "Domain of unknown function DUF1126 26\n", - "Ferlin, fourth C2 domain 26\n", - "B30.2/SPRY domain|Heterogeneous nuclear ribonucleoprotein U, SPRY domain 26\n", - "C2 domain|C2 domain|Ferlin, first C2 domain 26\n", - "SH2 domain|SH2 domain|SH2 domain|Ras GTPase-activating protein 1, C-terminal SH2 domain 26\n", - "Peptidase C2, calpain, domain III|Calpain subdomain III 26\n", - "Guanylate kinase/L-type calcium channel beta subunit|Guanylate kinase, conserved site|Guanylate kinase-like domain|Guanylate kinase/L-type calcium channel beta subunit 26\n", - "Transthyretin/hydroxyisourate hydrolase domain|Transthyretin, conserved site|Transthyretin/hydroxyisourate hydrolase domain|Transthyretin/hydroxyisourate hydrolase domain 26\n", - "Glycosyl hydrolases family 2, sugar binding domain 26\n", - "Zinc finger, PHD-type, conserved site|Zinc finger, PHD-type 26\n", - "RAG nonamer-binding domain 26\n", - "Superoxide dismutase, copper/zinc binding domain|Superoxide dismutase, copper/zinc binding domain|Superoxide dismutase, copper/zinc, binding site|Superoxide dismutase, copper/zinc binding domain 26\n", - "K Homology domain, type 1 26\n", - "Tyrosinase copper-binding domain|Tyrosinase copper-binding domain 26\n", - "WH2 domain|WH2 domain|WH2 domain 26\n", - "CP2 transcription factor 26\n", - "Phosphatidylinositol 3-kinase Ras-binding (PI3K RBD) domain|Phosphatidylinositol 3-kinase Ras-binding (PI3K RBD) domain 26\n", - "Polyketide synthase, dehydratase domain 26\n", - "Nitric oxide synthase, N-terminal 26\n", - "IRS-type PTB domain|IRS-type PTB domain|Dok-7, PTB domain 26\n", - "Aminoacyl-tRNA synthetase, class II (G/ P/ S/T)|Aminoacyl-tRNA synthetase, class II|Threonine-tRNA ligase catalytic core domain 26\n", - "THIF-type NAD/FAD binding fold|Ubiquitin-activating enzyme E1, four-helix bundle 26\n", - "RGS domain 26\n", - "Serpin domain|Serpin domain|Angiotensinogen serpin domain 26\n", - "Zinc finger, nuclear hormone receptor-type|Zinc finger, nuclear hormone receptor-type 26\n", - "Notch, NODP domain|Notch, NODP domain 26\n", - "Laminin, N-terminal|Laminin, N-terminal 26\n", - "Lipoxygenase, C-terminal|Lipoxygenase, C-terminal|Lipoxygenase, C-terminal 26\n", - "Squalene cyclase, C-terminal 26\n", - "Fork head domain 26\n", - "CHD, N-terminal 25\n", - "Domain of unknown function DUF3454, notch|Domain of unknown function DUF3454, notch 25\n", - "ELM2 domain 25\n", - "Syntaxin, N-terminal domain 25\n", - "Plk4, first cryptic polo-box domain 25\n", - "MCM N-terminal domain 25\n", - "Treacle protein domain|Treacle protein domain 25\n", - "Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3-/4-kinase, catalytic domain|PI3Kdelta, catalytic domain 25\n", - "FCP1-like phosphatase, C-terminal 25\n", - "Pancreatic trypsin inhibitor Kunitz domain|Pancreatic trypsin inhibitor Kunitz domain|Pancreatic trypsin inhibitor Kunitz domain|Pancreatic trypsin inhibitor Kunitz domain 25\n", - "Hedgehog protein, Hint domain|Hint domain C-terminal 25\n", - "MHC class I-like antigen recognition-like|MHC class I alpha chain, alpha1 alpha2 domains 25\n", - "Bromodomain|Bromodomain 25\n", - "Signal recognition particle, SRP54 subunit, GTPase domain|AAA+ ATPase domain|Signal recognition particle, SRP54 subunit, GTPase domain 25\n", - "Peroxin 13, N-terminal 25\n", - "PPM-type phosphatase domain|PPM-type phosphatase domain|PPM-type phosphatase domain 25\n", - "ADAM, cysteine-rich 25\n", - "GPR domain|GPR domain|GPR domain 25\n", - "Gap junction alpha-1 protein (Cx43), C-terminal 25\n", - "BAG domain|BAG domain|BAG domain 25\n", - "Domain of unknown function DUF4749|Zasp-like motif 25\n", - "Leucine-rich repeat N-terminal domain 25\n", - "Hermansky-Pudlak syndrome 3 protein, N-terminal domain 25\n", - "WH1/EVH1 domain|WH1/EVH1 domain 25\n", - "PTB/PI domain|PTB/PI domain|PTB/PI domain 25\n", - "Occludin homology domain 25\n", - "Formyl transferase, N-terminal 25\n", - "Proteinase K-like catalytic domain 25\n", - "FY-rich, C-terminal|FY-rich, C-terminal|FY-rich, C-terminal 25\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Tyrosine-protein kinase, receptor class III, conserved site|Protein kinase domain|Tyrosine-protein kinase, catalytic domain 25\n", - "Rad21/Rec8-like protein, C-terminal, eukaryotic 25\n", - "Zinc finger, PHD-finger|Zinc finger, RING-type|Zinc finger, PHD-type 25\n", - "UmuC domain|UmuC domain 25\n", - "Nucleoside phosphorylase domain|Purine phosphorylase, family 2, conserved site 24\n", - "Elongation factor G, domain III 24\n", - "EGF-like calcium-binding domain|EGF-like calcium-binding, conserved site 24\n", - "Polyribonucleotide 5'-hydroxyl-kinase Clp1, P-loop domain 24\n", - "F-box domain 24\n", - "FY-rich, N-terminal|FY-rich, N-terminal|FY-rich, N-terminal 24\n", - "MHCK/EF2 kinase|MHCK/EF2 kinase|MHCK/EF2 kinase 24\n", - "WASH complex subunit 7, central domain 24\n", - "Trafficking protein particle complex subunit 11, C-terminal 24\n", - "ATP-grasp fold 24\n", - "Cdc6, C-terminal|Cdc6, C-terminal|Cdc6, C-terminal 24\n", - "tRNA intron endonuclease, N-terminal 24\n", - "ELMO domain|ELMO domain 24\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Fas receptor, N-terminal 24\n", - "THAP-type zinc finger|THAP-type zinc finger|THAP-type zinc finger|THAP-type zinc finger 24\n", - "Peptidase M12B, ADAM/reprolysin|Peptidase M12B, ADAM/reprolysin|Reprolysin domain, adamalysin-type 24\n", - "Epoxide hydrolase, N-terminal 24\n", - "HECT domain|HECT domain 24\n", - "Dyskerin-like|Dyskerin-like 24\n", - "NADH-ubiquinone oxidoreductase 51kDa subunit, iron-sulphur binding domain|NADH-ubiquinone oxidoreductase 51kDa subunit, iron-sulphur binding domain 24\n", - "HB1/Asxl, restriction endonuclease HTH domain 24\n", - "Ion transport N-terminal 24\n", - "Single-minded, C-terminal 24\n", - "Disintegrin domain|Disintegrin domain|Disintegrin domain 24\n", - "Tudor domain 24\n", - "SATB, CUT1-like DNA-binding domain 24\n", - "Tetrapyrrole biosynthesis, uroporphyrinogen III synthase 24\n", - "BARD1, Zinc finger, RING-type|BARD1, Zinc finger, RING-type 24\n", - "Cytochrome c-like domain|Cytochrome c-like domain 24\n", - "SH2 domain|SH2 domain|SH2 domain|PLC-gamma, C-terminal SH2 domain 24\n", - "Biotin/lipoyl attachment|2-oxo acid dehydrogenase, lipoyl-binding site|Biotin/lipoyl attachment 24\n", - "Carboxypeptidase, activation peptide 24\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region 24\n", - "Domain of unknown function DUF4414 24\n", - "Sirtuin family, catalytic core domain 24\n", - "Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3-/4-kinase, catalytic domain|PI3Kalpha, catalytic domain 24\n", - "DNA ligase, ATP-dependent, central|DNA ligase, ATP-dependent, conserved site 24\n", - "Ubiquitin-activating enzyme E1, C-terminal|Ubiquitin-activating enzyme E1, C-terminal 24\n", - "Death domain|Death domain|Death domain|Tumor necrosis factor receptor 1A, death domain 24\n", - "Synapsin, ATP-binding domain 24\n", - "Actin-depolymerising factor homology domain|Actin-depolymerising factor homology domain|Actin-depolymerising factor homology domain 24\n", - "Peptidase C2, calpain, catalytic domain|Peptidase C2, calpain, catalytic domain|Peptidase C2, calpain, catalytic domain 24\n", - "GTP cyclohydrolase I domain|GTP cyclohydrolase I, conserved site 24\n", - "Domain of unknown function DUF4196 24\n", - "Ferlin, fifth C2 domain 24\n", - "2'-5'-oligoadenylate synthetase 1, domain 2/C-terminal 24\n", - "Serine proteases, trypsin domain|Serine proteases, trypsin domain|Serine proteases, trypsin domain 24\n", - "SKP1 component, POZ domain 24\n", - "SANT domain|SANT/Myb domain 24\n", - "Glutamine-Leucine-Glutamine, QLQ 24\n", - "Pyridoxine 5'-phosphate oxidase, dimerisation, C-terminal|Pyridoxamine 5'-phosphate oxidase, conserved site 24\n", - "Porphobilinogen deaminase, C-terminal 24\n", - "ATP-citrate lyase/succinyl-CoA ligase|Succinyl-CoA synthetase, beta subunit, conserved site 24\n", - "NADH:ubiquinone oxidoreductase-like, 20kDa subunit 24\n", - "Diaphanous autoregulatory (DAD) domain 24\n", - "Long hematopoietin receptor, Gp130 family 2, conserved site|Fibronectin type III|Fibronectin type III|Fibronectin type III 24\n", - "PB1 domain|PB1 domain 23\n", - "GAIN domain, N-terminal 23\n", - "Ets domain|Ets domain|Ets domain|Ets domain|Ets domain 23\n", - "Fibronectin type III|Long hematopoietin receptor, single chain, conserved site|Fibronectin type III|Fibronectin type III|Fibronectin type III 23\n", - "Oxidoreductase FAD/NAD(P)-binding 23\n", - "Apx/Shrm Domain 2|Apx/Shrm Domain 2 23\n", - "Glycoside hydrolase family 27/36, conserved site 23\n", - "Tyrosine hydroxylase, conserved site 23\n", - "Pro-opiomelanocortin/corticotropin, ACTH, central region|Pro-opiomelanocortin/corticotropin, ACTH, central region 23\n", - "Radical SAM 23\n", - "von Willebrand factor, VWA N-terminal domain 23\n", - "High mobility group box domain 23\n", - "Pyruvate kinase, C-terminal 23\n", - "Rho GTPase-activating protein domain 23\n", - "Hydroxymethylglutaryl-coenzyme A synthase, N-terminal 23\n", - "SH3 domain|Rho guanine nucleotide exchange factor 9, SH3 domain 23\n", - "Translation elongation factor, IF5A C-terminal|Translation elongation factor, IF5A C-terminal 23\n", - "CREB-binding protein/p300, atypical RING domain 23\n", - "DALR anticodon binding|DALR anticodon binding 23\n", - "von Willebrand factor, type D domain|von Willebrand factor, type D domain|VWFC domain|von Willebrand factor, type D domain 23\n", - "Fork head domain|Fork head domain 23\n", - "Long hematopoietin receptor, Gp130 family 2, conserved site|Fibronectin type III|Fibronectin type III 23\n", - "Galactosyltransferase, C-terminal 23\n", - "Regulatory factor X-associated protein, RFXANK-binding domain 23\n", - "Zinc finger, GATA-type|Zinc finger, GATA-type|Zinc finger, GATA-type|Zinc finger, GATA-type 23\n", - "4Fe-4S ferredoxin-type, iron-sulphur binding domain|4Fe-4S ferredoxin-type, iron-sulphur binding domain 23\n", - "Runt domain 23\n", - "Peptidase M48 22\n", - "Glutathione S-transferase, C-terminal|Glutathione S-transferase, C-terminal-like 22\n", - "C-type lectin-like|C-type lectin-like|C-type lectin-like|Polycystin cation channel 22\n", - "Krueppel-associated box|Krueppel-associated box|Krueppel-associated box|Krueppel-associated box 22\n", - "Cystatin domain|Proteinase inhibitor I25, cystatin, conserved site|Cystatin domain|Cystatin domain 22\n", - "POU-specific domain|POU domain|POU-specific domain|POU-specific domain|POU-specific domain|Cro/C1-type helix-turn-helix domain 22\n", - "Phox homologous domain|Phox homologous domain|Phox homologous domain|Neutrophil cytosol factor 4, PX domain 22\n", - "Complement Clr-like EGF domain|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 22\n", - "CHCH 22\n", - "Phospholipase A2 domain|Phospholipase A2 domain|Phospholipase A2 domain 22\n", - "Tubulin binding cofactor C-like domain|Tubulin binding cofactor C-like domain|C-CAP/cofactor C-like domain|CARP motif 22\n", - "BAR domain|BAR domain 22\n", - "CRIB domain 22\n", - "Roc domain|Small GTP-binding protein domain 22\n", - "Frizzled/Smoothened, transmembrane domain|GPCR, family 2-like|Frizzled/Smoothened, transmembrane domain 22\n", - "THIF-type NAD/FAD binding fold|Ubiquitin-activating enzyme E1, FCCH domain 22\n", - "SH3 domain|SH3 domain|Phosphatidylinositol 3-kinase regulatory subunit alpha, SH3 domain 22\n", - "Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain 22\n", - "PLAC|PLAC 22\n", - "IMP dehydrogenase/GMP reductase|CBS domain|CBS domain|IMP dehydrogenase/GMP reductase 22\n", - "EGF-like calcium-binding, conserved site|EGF-like calcium-binding domain|EGF-like domain 22\n", - "Translation elongation factor EFTs/EF1B, dimerisation 22\n", - "Stealth protein CR2, conserved region 2 22\n", - "Carbamoyl-phosphate synthase large subunit, CPSase domain 22\n", - "Dynamin central domain|Dynamin-type guanine nucleotide-binding (G) domain 22\n", - "Gamma-carboxyglutamic acid-rich (GLA) domain|Gamma-carboxyglutamic acid-rich (GLA) domain 22\n", - "GPS motif|GPS motif|GPS motif 22\n", - "Kinesin motor domain|Kinesin motor domain 22\n", - "TATA box binding protein associated factor (TAF)|TATA box binding protein associated factor (TAF) 22\n", - "B30.2/SPRY domain|Ryanodine receptor, SPRY domain 3 22\n", - "Toll/interleukin-1 receptor homology (TIR) domain|Toll/interleukin-1 receptor homology (TIR) domain 22\n", - "Alpha-2-macroglobulin, N-terminal 22\n", - "Protein kinase C-like, phorbol ester/diacylglycerol-binding domain 22\n", - "Glycoprotein hormone subunit beta|Gonadotropin, beta subunit, conserved site 22\n", - "Phostensin/Taperin PP1-binding domain 22\n", - "Homeobox domain|Homeobox domain, metazoa|Homeobox domain|Homeobox domain|Homeobox domain 22\n", - "Peptidase M3A/M3B catalytic domain 22\n", - "Peptidase S8A, tripeptidyl peptidase II 22\n", - "NADH-quinone oxidoreductase, subunit D 22\n", - "Aromatic amino acid hydroxylase, C-terminal 22\n", - "Serum albumin, N-terminal|Serum albumin, conserved site|Serum albumin, N-terminal|Serum albumin, N-terminal|Serum albumin, N-terminal 22\n", - "Synapsin, pre-ATP-grasp domain 22\n", - "Peptidase C19, ubiquitin carboxyl-terminal hydrolase|Ubiquitin specific protease, conserved site|Ubiquitin specific protease domain 21\n", - "MAM domain|MAM domain|MAM domain|MAM domain 21\n", - "Immunoglobulin I-set|Immunoglobulin-like domain|Immunoglobulin V-set domain 21\n", - "Cytochrome b5-like heme/steroid binding domain|Cytochrome b5-like heme/steroid binding domain|Cytochrome b5-like heme/steroid binding domain|Cytochrome b5-like heme/steroid binding domain 21\n", - "Zinc finger, Sec23/Sec24-type 21\n", - "PRO8NT domain 21\n", - "E3 ubiquitin-protein ligase HECW1/2, N-terminal 21\n", - "Phosphoglucose isomerase, SIS domain 1 21\n", - "Peptidase M14, carboxypeptidase A 21\n", - "tRNA-splicing endonuclease subunit Sen15 21\n", - "NECAP, PHear domain 21\n", - "Lsm14-like, N-terminal|Lsm14-like, N-terminal|Lsm16, N-terminal 21\n", - "Prion/Doppel protein, beta-ribbon domain|Prion/Doppel protein, beta-ribbon domain 21\n", - "Agenet-like domain 21\n", - "EGF-like, conserved site|EGF-like domain 21\n", - "Extended PHD (ePHD) domain|Zinc finger, RING-type|Zinc finger, PHD-type 21\n", - "tRNA intron endonuclease, catalytic domain-like 21\n", - "Axin-1/2, tankyrase-binding domain 21\n", - "Cytochrome b5-like heme/steroid binding domain|Cytochrome b5-like heme/steroid binding domain|Cytochrome b5-like heme/steroid binding domain 21\n", - "SH2 domain|SH2 domain|STAT1, SH2 domain 21\n", - "Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3-/4-kinase, catalytic domain|PI3Kalpha, catalytic domain 21\n", - "Domain of unknown function DUF4976 21\n", - "Cyclin, C-terminal domain 21\n", - "Immunoglobulin subtype 2 21\n", - "SKI/SNO/DAC domain 21\n", - "Lipin, middle domain 21\n", - "Proteasome component (PCI) domain 21\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain, eukaryote|TIA-1, RNA recognition motif 1 21\n", - "von Willebrand factor, type D domain|Uncharacterised domain, cysteine-rich 21\n", - "Zinc finger CCHC FOG-type|Zinc finger C2H2-type 21\n", - "DEAD/DEAH box helicase domain|DNA/RNA helicase, ATP-dependent, DEAH-box type, conserved site|Helicase superfamily 1/2, ATP-binding domain|Helicase superfamily 1/2, ATP-binding domain 21\n", - "Nuclear receptor coactivator, CREB-bp-like, interlocking 21\n", - "Cyclophilin-type peptidyl-prolyl cis-trans isomerase domain|Cyclophilin-type peptidyl-prolyl cis-trans isomerase domain|Cyclophilin-type peptidyl-prolyl cis-trans isomerase domain 21\n", - "Vacuolar protein sorting-associated protein 13, N-terminal domain 21\n", - "KAP family P-loop domain 21\n", - "DNA mismatch repair, conserved site|Histidine kinase/HSP90-like ATPase 21\n", - "Soluble ligand binding domain 20\n", - "Pro-opiomelanocortin N-terminal|Pro-opiomelanocortin N-terminal 20\n", - "T-complex protein 10, C-terminal domain 20\n", - "Diaphanous autoregulatory (DAD) domain|Formin, FH2 domain 20\n", - "Aminoacyl-tRNA synthetase, class II (G/ P/ S/T)|Aminoacyl-tRNA synthetase, class II|Glycyl-tRNA synthetase-like core domain 20\n", - "Zinc finger, MYM-type|TRASH domain 20\n", - "SPRY-associated|Butyrophylin-like, SPRY domain|B30.2/SPRY domain|SPRY-associated 20\n", - "Transcription factor, MADS-box|Transcription factor, MADS-box|Transcription factor, MADS-box|MADS MEF2-like 20\n", - "ADD domain|DNA (cytosine-5)-methyltransferase 3B, ADD domain 20\n", - "Heparinase II, N-terminal 20\n", - "Oxidoreductase FAD/NAD(P)-binding|Flavoprotein pyridine nucleotide cytochrome reductase 20\n", - "IMP dehydrogenase/GMP reductase|CBS domain|CBS domain|CBS domain 20\n", - "NIDO domain|NIDO domain 20\n", - "Visual pigments (opsins) retinal binding site|GPCR, rhodopsin-like, 7TM 20\n", - "Neurogenic differentiation factor, domain of unknown function 20\n", - "CD80-like, immunoglobulin C2-set 20\n", - "Cullin, N-terminal|Cullin homology domain 20\n", - "GS domain|GS domain 20\n", - "Thrombin light chain 20\n", - "Ketoacyl-synthetase, C-terminal extension|Polyketide synthase, beta-ketoacyl synthase domain 20\n", - "Fructose-bisphosphate aldolase class-I active site 20\n", - "Death domain|Death domain|Death domain|MyD88, death domain 20\n", - "CCDC144C-like, coiled-coil domain 20\n", - "Ubiquitin-activating enzyme, catalytic cysteine domain 20\n", - "Homocysteine-binding domain|Homocysteine-binding domain 20\n", - "Krueppel-associated box 20\n", - "Copper type II ascorbate-dependent monooxygenase, C-terminal 20\n", - "Ribosome maturation protein SBDS, C-terminal 20\n", - "Serpin domain|Serpin domain|Heparin cofactor II 20\n", - "Cystine knot, C-terminal|Cystine knot, C-terminal|Cystine knot, C-terminal 20\n", - "Pseudouridine synthase II, N-terminal 20\n", - "APC10/DOC domain|APC10/DOC domain|APC10/DOC domain 20\n", - "Patatin-like phospholipase domain-containing protein 2 20\n", - "Shal-type voltage-gated potassium channels, N-terminal 20\n", - "EGF-like calcium-binding domain|EGF-like, conserved site|EGF-like calcium-binding, conserved site|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 20\n", - "Protein kinase, ATP binding site|Protein kinase domain 20\n", - "Coactivator CBP, KIX domain|Coactivator CBP, KIX domain 20\n", - "tRNA-binding domain|tRNA-binding domain 20\n", - "Glycosyltransferase, DXD sugar-binding motif 20\n", - "Fragile X-related 1 protein, C-terminal core 20\n", - "Matrin/U1-C-like, C2H2-type zinc finger 20\n", - "c-SKI SMAD4-binding domain|c-SKI SMAD4-binding domain 20\n", - "Glutamine synthetase, catalytic domain|Glutamine synthetase, catalytic domain 20\n", - "F-BAR domain|FCH domain 20\n", - "C2 domain|Synaptotagmin|C2 domain|C2 domain 20\n", - "Syntaxin, N-terminal domain|Syntaxin/epimorphin, conserved site|Target SNARE coiled-coil homology domain|Target SNARE coiled-coil homology domain 20\n", - "Dilute domain|Myosin 5a, cargo-binding domain 20\n", - "FERM, N-terminal|FERM conserved site|FERM domain|Band 4.1 domain 20\n", - "Beta-galactosidase jelly roll domain 20\n", - "Ionotropic glutamate receptor|Ionotropic glutamate receptor, L-glutamate and glycine-binding domain 20\n", - "Elongation factor EFG, domain V-like|Elongation factor EFG, domain V-like|EFG, domain V 19\n", - "MOFRL-associated domain 19\n", - "Thyroglobulin type-1 19\n", - "uDENN domain|Tripartite DENN domain|uDENN domain 19\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase, ATP binding site|Protein kinase domain|Tyrosine-protein kinase, catalytic domain|Protein kinase domain 19\n", - "Multifunctional 2-oxoglutarate metabolism enzyme, C-terminal 19\n", - "Homeobox domain|POU domain|Homeobox, conserved site|Homeobox domain|Homeobox domain|Homeobox domain 19\n", - "Serine proteases, trypsin domain|Serine proteases, trypsin domain 19\n", - "Vps16, C-terminal 19\n", - "Pyrimidine nucleoside phosphorylase, C-terminal 19\n", - "Histone deacetylase interacting domain|Histone deacetylase interacting domain 19\n", - "EF-hand domain|EF-hand domain|EF-hand domain|EF-hand domain|EF-hand domain 19\n", - "POLO box domain|POLO box domain|Plk4, C-terminal polo-box domain 19\n", - "Cadherin prodomain 19\n", - "POU-specific domain|POU domain|POU-specific domain|POU-specific domain|POU-specific domain 19\n", - "Zinc finger, A20-type 19\n", - "CoA-binding|CoA-binding 19\n", - "Peripheral subunit-binding domain|Peripheral subunit-binding domain 19\n", - "Synaptobrevin|Synaptobrevin 19\n", - "SUN domain 19\n", - "Peroxidasin, peroxidase domain 19\n", - "SAM-dependent methyltransferase RsmB/NOP2-type|SAM-dependent methyltransferase RsmB/NOP2-type 19\n", - "EGF-like, conserved site|EGF-like, conserved site|EGF-like, conserved site|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 19\n", - "Peptidase C14, p20 domain 19\n", - "FAD-binding domain, PCMH-type 19\n", - "Heavy metal-associated domain, HMA|Heavy metal-associated domain, HMA|Heavy metal-associated domain, HMA 19\n", - "Lysosome-associated membrane glycoprotein, conserved site 19\n", - "Alpha/beta hydrolase fold-1|Alpha/beta hydrolase fold-1 19\n", - "Alpha-N-acetylglucosaminidase, N-terminal 19\n", - "Mad3/Bub1 homology region 1 19\n", - "Hydantoinase B/oxoprolinase 19\n", - "Ras-associating (RA) domain|Ras-associating (RA) domain|Ras-associating (RA) domain 19\n", - "Coagulation factor 5/8 C-terminal domain 19\n", - "Galanin message associated peptide (GMAP)|Galanin message associated peptide (GMAP) 19\n", - "PUA domain|PUA domain|PUA domain|Uncharacterised domain CHP00451 19\n", - "Toll/interleukin-1 receptor homology (TIR) domain 19\n", - "ILEI/PANDER domain 19\n", - "DBB domain|DBB domain|DBB domain 18\n", - "Trafficking kinesin-binding protein domain 18\n", - "Fibronectin, type I|Fibronectin, type I|Fibronectin, type I|Fibronectin, type I|Fibronectin, type I 18\n", - "Tail specific protease 18\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|CELF1/2, RNA recognition motif 3 18\n", - "Ferlin A-domain|Ferlin A-domain 18\n", - "Cysteine-rich flanking region, C-terminal|Cysteine-rich flanking region, C-terminal 18\n", - "Fibroblast growth factor receptor 1, catalytic domain 18\n", - "Zinc finger, double-stranded RNA binding|Matrin/U1-C-like, C2H2-type zinc finger 18\n", - "Kinesin-associated|Forkhead-associated (FHA) domain 18\n", - "Armadillo repeat-containing domain 18\n", - "SRCR-like domain 18\n", - "Copper type II, ascorbate-dependent monooxygenase, N-terminal 18\n", - "Glycosyl hydrolase family 59, central domain 18\n", - "DNA-directed RNA polymerase, RBP11-like dimerisation domain|DNA-directed RNA polymerase Rpb11, 13-16kDa subunit, conserved site 18\n", - "Nuclear receptor-interacting protein 1, repression domain 4 18\n", - "LCCL domain|LCCL domain 18\n", - "Cyclin, N-terminal|Cyclin-like|Cyclin-like 18\n", - "Fragile site-associated protein, C-terminal|Fragile site-associated protein, C-terminal 18\n", - "Peroxisome biogenesis factor 1, N-terminal, psi beta-barrel fold 18\n", - "Man1-Src1p-C-terminal domain 18\n", - "Electron transfer flavoprotein, alpha/beta-subunit, N-terminal 18\n", - "L27 domain|L27 domain 18\n", - "Insulin-like growth factor-binding protein, IGFBP|Insulin-like growth factor-binding protein, IGFBP|Insulin-like growth factor-binding protein, IGFBP 18\n", - "Long hematopoietin receptor, soluble alpha chain, conserved site|Fibronectin type III|Fibronectin type III|Fibronectin type III 18\n", - "Reeler domain 18\n", - "Myosin 5b, cargo-binding domain 18\n", - "GPS motif|GPS motif 18\n", - "Methyltransferase type 12 18\n", - "Dbl homology (DH) domain|Dbl homology (DH) domain|Dbl homology (DH) domain 18\n", - "Amidase signature domain 18\n", - "Cerebral cavernous malformations 2, harmonin-homology domain|Cerebral cavernous malformations 2, harmonin-homology domain 18\n", - "SH3 domain|CACNB2, SH3 domain 18\n", - "ERCC3/RAD25/XPB helicase, C-terminal domain 18\n", - "Ly-6 antigen/uPA receptor-like|CD59 antigen, conserved site|Ly-6 antigen/uPA receptor-like 18\n", - "Chaperone DnaJ, C-terminal 18\n", - "IQ motif, EF-hand binding site|IQ motif, EF-hand binding site|IQ motif, EF-hand binding site|IQ motif, EF-hand binding site 18\n", - "GRAM domain|GRAM domain|Myotubularin-related protein 13, PH-GRAM domain 18\n", - "EGF-like domain, extracellular|EGF-like domain|EGF-like domain 18\n", - "MoeA, N-terminal and linker domain 18\n", - "Protein-arginine deiminase, C-terminal 18\n", - "Calcineurin-like phosphoesterase domain, ApaH type|Serine/threonine-specific protein phosphatase/bis(5-nucleosyl)-tetraphosphatase 18\n", - "PUB domain|PUB domain 18\n", - "Zinc finger, RING-type, conserved site|Zinc finger, RING-type|Zinc finger, RING-type 18\n", - "Mammalian uncoordinated homology 13, subgroup, domain 2|Mammalian uncoordinated homology 13, domain 2 18\n", - "Polyamine biosynthesis domain 18\n", - "Methionyl/Valyl/Leucyl/Isoleucyl-tRNA synthetase, anticodon-binding|Isoleucyl tRNA synthetase type 2, anticodon-binding domain 18\n", - "Laminin domain II|Laminin G domain|Laminin G domain 18\n", - "Peroxisome biogenesis factor 1, N-terminal, alpha/beta 18\n", - "S-adenosylmethionine synthetase, N-terminal 18\n", - "C2 domain|C2 domain|C2 domain|Ferlin, first C2 domain 18\n", - "Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain|Interferon regulatory factor, conserved site|Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain 18\n", - "Testis expressed sequence 15 domain 18\n", - "CS domain|CS domain|Dynein axonemal assembly factor 4, CS domain 18\n", - "CTNNB1 binding, N-teminal 18\n", - "SPRY-associated|B30.2/SPRY domain 18\n", - "Immunoglobulin I-set|Immunoglobulin-like domain|Immunoglobulin subtype 2 18\n", - "Acyl-CoA oxidase, C-terminal 18\n", - "Protein O-linked-mannose beta-1,2-N-acetylglucosaminyltransferase 1, PANDER-like domain 18\n", - "SH2 domain|SH2 domain|SH2 domain|SH2 domain|PLC-gamma, C-terminal SH2 domain 18\n", - "PDGF/VEGF domain|PDGF/VEGF domain|PDGF/VEGF domain|PDGF/VEGF domain 18\n", - "COS domain|B-box, C-terminal 18\n", - "MD-2-related lipid-recognition domain|MD-2-related lipid-recognition domain 18\n", - "C-terminal of Roc (COR) domain 18\n", - "SRCR domain|SRCR domain|SRCR domain|SRCR-like domain 18\n", - "Acetyl-CoA carboxylase, central domain 17\n", - "Ferritin/DPS protein domain|Ferritin-like diiron domain 17\n", - "Archaeal primase DnaG/twinkle, TOPRIM domain 17\n", - "Lipid-binding serum glycoprotein, N-terminal 17\n", - "Domain of unknown function DUF3456 17\n", - "Histidine kinase/HSP90-like ATPase|Histidine kinase domain|Histidine kinase/HSP90-like ATPase|Histidine kinase/HSP90-like ATPase 17\n", - "Nup358/RanBP2 E3 ligase domain 17\n", - "Amyloidogenic glycoprotein, heparin-binding|Amyloidogenic glycoprotein, extracellular 17\n", - "AP-3 complex subunit beta 1, serine-rich domain 17\n", - "DAPIN domain|DAPIN domain 17\n", - "Immunoglobulin V-set domain|Immunoglobulin-like domain|Immunoglobulin subtype 2|Immunoglobulin subtype 17\n", - "PDZ domain|Pleckstrin homology domain|PDZ domain|PDZ domain|Pleckstrin homology domain 17\n", - "Enoyl-CoA hydratase/isomerase, conserved site 17\n", - "PKD/Chitinase domain|Polycystin cation channel 17\n", - "Serine/threonine-specific protein phosphatase/bis(5-nucleosyl)-tetraphosphatase|Serine/threonine-specific protein phosphatase/bis(5-nucleosyl)-tetraphosphatase 17\n", - "Hedgehog protein, Hint domain 17\n", - "Calsequestrin, conserved site 17\n", - "Immunoglobulin I-set|Immunoglobulin-like domain|Immunoglobulin subtype 2|Immunoglobulin subtype|Palladin, C-terminal immunoglobulin-like domain 17\n", - "Ferlin, first C2 domain 17\n", - "Rad4/PNGase transglutaminase-like fold 17\n", - "Peptidase M14, carboxypeptidase A|Cytosolic carboxypeptidase-like protein 5 catalytic domain 17\n", - "VPS9 domain|VPS9 domain|VPS9 domain 17\n", - "HORMA domain|HORMA domain 17\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Class IX myosin, motor domain 17\n", - "HAP1, N-terminal 17\n", - "Pterin-binding domain|Pterin-binding domain 17\n", - "Rhodanese-like domain 17\n", - "Piwi domain|Piwi domain|Piwi domain 17\n", - "CD3 gamma/delta subunit, Ig-like domain 17\n", - "Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Diacylglycerol/phorbol-ester binding|Diacylglycerol/phorbol-ester binding|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain 17\n", - "DNA endonuclease Ctp1, N-terminal 17\n", - "PAS fold|PAS domain|PAS domain|PAS domain 17\n", - "FAS1 domain|FAS1 domain 17\n", - "Sortilin, N-terminal|VPS10 17\n", - "3'5'-cyclic nucleotide phosphodiesterase, catalytic domain|3'5'-cyclic nucleotide phosphodiesterase, conserved site|3'5'-cyclic nucleotide phosphodiesterase, catalytic domain|HD/PDEase domain|HD/PDEase domain 17\n", - "Zinc finger, TRAF-type 17\n", - "Repulsive guidance molecule, C-terminal 17\n", - "Netrin domain 17\n", - "Sulphate adenylyltransferase catalytic domain|Sulphate adenylyltransferase|Sulphate adenylyltransferase 17\n", - "3-oxo-5-alpha-steroid 4-dehydrogenase, C-terminal|3-oxo-5-alpha-steroid 4-dehydrogenase, C-terminal 17\n", - "Copper amine oxidase, catalytic domain 17\n", - "Helicase/SANT-associated domain|Helicase/SANT-associated domain 16\n", - "SH2 domain|SH2 domain|SH3BP2, SH2 domain 16\n", - "Glycoside hydrolase, family 13, N-terminal 16\n", - "Homeobox domain|Helix-turn-helix motif|Homeobox, conserved site|Homeobox domain|Homeobox domain|Homeobox domain 16\n", - "Methyltransferase type 11 16\n", - "Zinc finger, LIM-type|Zinc finger, LIM-type|LIM2 prickle 16\n", - "Snf2, ATP coupling domain 16\n", - "SH3 domain|SH3 domain|SH3 domain|Nephrocystin-1, SH3 domain 16\n", - "Ciliary BBSome complex subunit 2, middle region 16\n", - "GPCR, family 2, extracellular hormone receptor domain|GPCR, family 2, secretin-like, conserved site|GPCR, family 2, extracellular hormone receptor domain|GPCR, family 2, extracellular hormone receptor domain 16\n", - "DNA endonuclease Ctp1, C-terminal 16\n", - "Synaptobrevin|Synaptobrevin|Synaptobrevin 16\n", - "DnaJ domain|DnaJ domain|DnaJ domain 16\n", - "Zona pellucida domain|Zona pellucida domain|Zona pellucida domain|Zona pellucida domain 16\n", - "Tyrosine specific protein phosphatases domain|Dual specificity protein phosphatase domain 16\n", - "ADAM, cysteine-rich|ADAM, cysteine-rich 16\n", - "Phox homologous domain 16\n", - "Modifier of rudimentary, Modr 16\n", - "SH2 domain|SH2 domain|SH2 domain|Tyrosine-protein kinase Lck, SH2 domain 16\n", - "O-GlcNAc transferase, C-terminal 16\n", - "Glutathione S-transferase, N-terminal 16\n", - "Electron transfer flavoprotein, alpha subunit, C-terminal 16\n", - "RUN domain 16\n", - "Proteasome beta-type subunit, conserved site 16\n", - "Mediator complex, subunit Med24, N-terminal 16\n", - "5-aminolevulinate synthase presequence 16\n", - "Membrane attack complex component/perforin (MACPF) domain|Membrane attack complex component/perforin domain, conserved site|Membrane attack complex component/perforin (MACPF) domain|Membrane attack complex component/perforin (MACPF) domain 16\n", - "Frataxin conserved site 16\n", - "Drought induced 19 protein type, zinc-binding domain|Zinc finger C2H2-type 16\n", - "Domain of unknown function DUF1866|Inositol polyphosphate-related phosphatase|Domain of unknown function DUF1866 16\n", - "Orotidine 5'-phosphate decarboxylase domain|Orotidine 5'-phosphate decarboxylase domain 16\n", - "Helical and beta-bridge domain|Helicase superfamily 1/2, ATP-binding domain, DinG/Rad3-type|Helicase-like, DEXD box c2 type 16\n", - "Somatotropin hormone, conserved site 16\n", - "c-Kit-binding domain 16\n", - "Saccharopine dehydrogenase, C-terminal 16\n", - "COG complex component, COG2, C-terminal 16\n", - "FAD-dependent oxidoreductase 2, FAD binding domain|Fumarate reductase/succinate dehydrogenase, FAD-binding site 16\n", - "MATH/TRAF domain|MATH/TRAF domain|TRAF3, MATH domain 16\n", - "DNA (cytosine-5)-methyltransferase 1, replication foci domain 16\n", - "Wnt protein, conserved site 16\n", - "Plexin, cytoplasmic RasGAP domain 16\n", - "DNA topoisomerase, type IIA, subunit A/C-terminal|DNA topoisomerase, type IIA, subunit A/C-terminal|DNA topoisomerase, type IIA, subunit A/C-terminal 16\n", - "Zinc finger, MYND-type|Zinc finger, MYND-type 16\n", - "Interferon regulatory factor DNA-binding domain|Interferon regulatory factor, conserved site|Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain 16\n", - "ELM2 domain|ELM2 domain|ELM2 domain 16\n", - "Polycomb protein, VEFS-Box 16\n", - "Maf transcription factor, N-terminal 16\n", - "PLC-gamma, C-terminal SH2 domain 16\n", - "SH2 domain|SH2 domain|SH2 domain|SYK/ZAP-70, N-terminal SH2 domain 16\n", - "Exosome complex component, N-terminal domain 16\n", - "Tyrosine-protein kinase, non-receptor, TYK2, N-terminal|FERM domain|Band 4.1 domain 16\n", - "Methionyl/Leucyl tRNA synthetase 16\n", - "Kinesin-associated microtubule-binding domain 16\n", - "Plasma membrane calcium transporting P-type ATPase, C-terminal 16\n", - "Lipoxygenase, C-terminal|Lipoxygenase, C-terminal|Lipoxygenase, iron binding site|Lipoxygenase, C-terminal 16\n", - "PB1 domain|PB1 domain|PB1 domain 16\n", - "DNA-directed RNA polymerase, RpoA/D/Rpb3-type|DNA-directed RNA polymerase, 30-40kDa subunit, conserved site|DNA-directed RNA polymerase, RpoA/D/Rpb3-type 16\n", - "Transmembrane protein family 132, middle domain 16\n", - "Ephrin receptor, transmembrane domain 16\n", - "GHMP kinase N-terminal domain|GHMP kinase, ATP-binding, conserved site 16\n", - "Methylglyoxal synthase-like domain|Methylglyoxal synthase-like domain 16\n", - "G2 nidogen/fibulin G2F|G2 nidogen/fibulin G2F|G2 nidogen/fibulin G2F|G2 nidogen/fibulin G2F 16\n", - "APOBEC-like, N-terminal 16\n", - "3-oxoacid CoA-transferase, subunit B 16\n", - "Thiolase, C-terminal|Thiolase, conserved site 16\n", - "Perforin-1, C2 domain 16\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain|Protein kinase B gamma, catalytic domain 16\n", - "Hemopexin, conserved site|Hemopexin-like domain 16\n", - "RUN domain|RUN domain|RUN domain 16\n", - "Fibronectin type II domain|Peptidase M10, metallopeptidase|Fibronectin type II domain|Fibronectin type II domain|Peptidase, metallopeptidase|Fibronectin type II domain|Peptidase M10A, catalytic domain 16\n", - "BRO1 domain|BRO1 domain|BRO1 domain 15\n", - "SH3 domain|SH3 domain|SH3 domain|SH3 domain|Alpha Spectrin, SH3 domain 15\n", - "Molybdopterin dehydrogenase, FAD-binding|FAD-binding domain, PCMH-type|Xanthine dehydrogenase, small subunit 15\n", - "Dual specificity/tyrosine protein phosphatase, N-terminal 15\n", - "NADH-quinone oxidoreductase, chain G, C-terminal 15\n", - "EGF-like calcium-binding domain|EGF-like, conserved site|EGF-like, conserved site|EGF-like calcium-binding, conserved site|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 15\n", - "Partial AB-hydrolase lipase domain 15\n", - "Transferrin-like domain|Transferrin-like domain|Transferrin-like domain|Transferrin-like domain 15\n", - "Serum albumin, N-terminal|Serum albumin, N-terminal|Serum albumin, N-terminal 15\n", - "Zinc finger, ZZ-type 15\n", - "TEA/ATTS domain 15\n", - "Acetyl-coenzyme A carboxyltransferase, C-terminal 15\n", - "L27 domain 15\n", - "cAMP-dependent protein kinase regulatory subunit, dimerization-anchoring domain 15\n", - "Sedolisin domain 15\n", - "Tetraspanin, conserved site 15\n", - "Domain of unknown function DUF4347 15\n", - "Cathepsin C exclusion 15\n", - "Glycosyl transferase, family 3|Pyrimidine-nucleoside phosphorylase, conserved site 15\n", - "3-oxo-5-alpha-steroid 4-dehydrogenase, C-terminal 15\n", - "Hydantoinase A/oxoprolinase 15\n", - "WASH complex subunit 7, C-terminal 15\n", - "Phosphorylase pyridoxal-phosphate attachment site 15\n", - "PAS-associated, C-terminal 15\n", - "Isopropylmalate dehydrogenase-like domain|Isocitrate/isopropylmalate dehydrogenase, conserved site|Isopropylmalate dehydrogenase-like domain 15\n", - "Zinc finger, AN1-type|Zinc finger, AN1-type|Zinc finger, AN1-type 15\n", - "Heat shock protein 70, conserved site 15\n", - "Vitamin K epoxide reductase|Vitamin K epoxide reductase 15\n", - "NADH-ubiquinone oxidoreductase 51kDa subunit, iron-sulphur binding domain|NADH:ubiquinone oxidoreductase, 51kDa subunit, conserved site|NADH-ubiquinone oxidoreductase 51kDa subunit, iron-sulphur binding domain 15\n", - "Gonadotropin hormone receptor, transmembrane domain 15\n", - "Neuroblastoma-amplified sequence, N-terminal 15\n", - "Domain of unknown function DUF959, collagen XVIII, N-terminal 15\n", - "Aldehyde dehydrogenase domain|Aldehyde dehydrogenase, cysteine active site 15\n", - "JNK-interacting protein, leucine zipper II 15\n", - "Immunoglobulin-like domain|Immunoglobulin V-set domain|Immunoglobulin subtype 15\n", - "SAM-dependent methyltransferase RsmB/NOP2-type 15\n", - "Ferritin/DPS protein domain|Ferritin, conserved site|Ferritin-like diiron domain 15\n", - "3-oxoacid CoA-transferase, subunit A 15\n", - "Myogenic basic muscle-specific protein|Myogenic basic muscle-specific protein 15\n", - "Glypican, conserved site 15\n", - "SH3 domain|SH3 domain|ZO-2, SH3 domain 15\n", - "Interferon regulatory factor DNA-binding domain|Interferon regulatory factor-3 15\n", - "Hydrolethalus syndrome protein 1, C-terminal domain 15\n", - "Interferon/interleukin receptor domain|Fibronectin type III 15\n", - "MoaB/Mog domain|Molybdenum cofactor biosynthesis, conserved site|MoaB/Mog domain|MoaB/Mog domain 15\n", - "Cell morphogenesis central region 15\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase, ATP binding site 15\n", - "ATPase, dynein-related, AAA domain 15\n", - "Argonaute hook domain 15\n", - "TNFR/NGFR cysteine-rich region|Tumor necrosis factor receptor 1A, N-terminal 15\n", - "Phosphatidylinositol 3-kinase adaptor-binding (PI3K ABD) domain|Phosphatidylinositol 3-kinase adaptor-binding (PI3K ABD) domain 15\n", - "THAP-type zinc finger|THAP-type zinc finger|THAP-type zinc finger 15\n", - "LEM domain|LEM domain|LEM domain|Emerin, LEM domain 15\n", - "CXC domain|Tesmin/TSO1-like CXC domain 15\n", - "Rel homology dimerisation domain|IPT domain 15\n", - "Domain of unknown function DUF1087|Domain of unknown function DUF1087 15\n", - "Notch domain|Notch domain|Notch domain|Notch domain 15\n", - "ATPsynthase alpha/beta subunit, N-terminal extension 15\n", - "Ribosomal RNA methyltransferase FtsJ domain 15\n", - "Disks large homologue 1, N-terminal PEST domain 15\n", - "Carbohydrate-binding WSC|Carbohydrate-binding WSC|Carbohydrate-binding WSC|Polycystin cation channel 14\n", - "Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain 14\n", - "Translation elongation factor EFTu/EF1A, C-terminal 14\n", - "IQ motif and SEC7 domain-containing protein, PH domain 14\n", - "CRIB domain|CRIB domain|p21 activated kinase binding domain 14\n", - "DnaJ domain|DnaJ domain|DnaJ domain, conserved site|DnaJ domain|DnaJ domain 14\n", - "Domain of unknown function DUF5009 14\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain, eukaryote|TIA-1, RNA recognition motif 3 14\n", - "D-isomer specific 2-hydroxyacid dehydrogenase, NAD-binding domain|D-isomer specific 2-hydroxyacid dehydrogenase, NAD-binding domain conserved site 14\n", - "AIG1-type guanine nucleotide-binding (G) domain|AIG1-type guanine nucleotide-binding (G) domain 14\n", - "CDT1 Geminin-binding domain-like|CDT1 Geminin-binding domain-like 14\n", - "SH3 domain|SH3 domain|SH3 domain|Neutrophil cytosol factor P40, SH3 domain 14\n", - "Peripheral subunit-binding domain 14\n", - "Zinc finger, RanBP2-type|Zinc finger, RanBP2-type|Zinc finger, RanBP2-type|Zinc finger, RanBP2-type 14\n", - "tRNase Z endonuclease 14\n", - "Calmodulin-binding domain C0, NMDA receptor, NR1 subunit 14\n", - "C2 domain|C2 domain|C2CD3, C2 domain 14\n", - "Immunoglobulin-like domain|Fibronectin type III 14\n", - "DML1/Misato, tubulin domain 14\n", - "BART domain 14\n", - "Helicase superfamily 1/2, ATP-binding domain, DinG/Rad3-type|Helicase superfamily 1/2, ATP-binding domain 14\n", - "RNA polymerase Rpb2, domain 2|RNA polymerase, beta subunit, protrusion 14\n", - "Zinc finger, MYND-type|Zinc finger, MYND-type|Zinc finger, MYND-type 14\n", - "Argonaute, linker 1 domain|Argonaute, linker 1 domain 14\n", - "Ribosomal protein L5, C-terminal 14\n", - "Phosphopantetheine binding ACP domain|Phosphopantetheine binding ACP domain|Polyketide synthase, phosphopantetheine-binding domain 14\n", - "Anthrax toxin receptor, C-terminal|Anthrax toxin receptor, C-terminal 14\n", - "PKD/REJ-like domain|REJ domain 14\n", - "Phosphatidylinositol 3-/4-kinase, catalytic domain|FATC domain|FATC domain 14\n", - "Enhancer of polycomb-like, N-terminal 14\n", - "Clusterin, N-terminal 14\n", - "Serine-threonine protein phosphatase, N-terminal 14\n", - "MAGE homology domain|MAGE homology domain 14\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Class I myosin, motor domain 14\n", - "Centrosomin, N-terminal motif 1 14\n", - "RNA polymerase Rpb1, domain 1|DNA-directed RNA polymerase III subunit RPC1, N-terminal 14\n", - "Death domain|Death domain|Death domain|CRADD, Death domain 14\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase domain|Tyrosine-protein kinase, catalytic domain|Janus kinase 2, pseudokinase domain 14\n", - "Glycoside hydrolase family 38, central domain|Glycoside hydrolase family 38, central domain 14\n", - "Germinal-centre associated nuclear protein, MCM3AP domain 14\n", - "Helicase-associated domain|Helicase-associated domain 14\n", - "Adenosine deaminase/editase 14\n", - "Patatin-like phospholipase domain|Lysophospholipase patatin, conserved site|Patatin-like phospholipase domain 14\n", - "Peptidase S54, rhomboid domain 14\n", - "AP complex, mu/sigma subunit|Clathrin adaptor complex, small chain 14\n", - "Factor I / membrane attack complex 14\n", - "UNC-45/Cro1/She4, central domain 14\n", - "W2 domain 14\n", - "Lipin/Ned1/Smp2 (LNS2) 14\n", - "Methionyl/Valyl/Leucyl/Isoleucyl-tRNA synthetase, anticodon-binding|Valyl tRNA synthetase, anticodon-binding domain 14\n", - "PEA3-type ETS-domain transcription factor, N-terminal 14\n", - "LEM domain|LEM domain 14\n", - "KASH domain 14\n", - "ADAM17, membrane-proximal domain|ADAM17, membrane-proximal domain 14\n", - "GAF domain 14\n", - "BEACH domain 14\n", - "NADP-dependent oxidoreductase domain|Aldo/keto reductase, conserved site|NADP-dependent oxidoreductase domain 14\n", - "Myelin-PO, C-terminal|Myelin P0 protein, conserved site 14\n", - "MoaB/Mog domain|MoaB/Mog domain 14\n", - "Tetrapyrrole biosynthesis, 5-aminolevulinic acid synthase 14\n", - "I-kappa-kinase-beta NEMO binding domain|I-kappa-kinase-beta NEMO binding domain 14\n", - "Trans-Isoprenyl Diphosphate Synthases, head-to-head 14\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Class III myosin, motor domain 14\n", - "Domain of unknown function DM14 14\n", - "Rho GTPase-binding/formin homology 3 (GBD/FH3) domain 14\n", - "Yip1 domain 14\n", - "Ubiquitin-activating enzyme E1, FCCH domain 14\n", - "FAD-binding domain, ferredoxin reductase-type 14\n", - "AGC-kinase, C-terminal|Protein kinase B alpha, catalytic domain 14\n", - "Mediator complex, subunit Med25, synapsin 1|Mediator complex, subunit Med25, NR box 14\n", - "SH2 domain|SH2 domain 14\n", - "Myc-type, basic helix-loop-helix (bHLH) domain 14\n", - "Thiamin pyrophosphokinase, catalytic domain 14\n", - "MALT1, death domain 14\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Class II myosin, Myh3, motor domain 14\n", - "Schlafen, AAA domain 14\n", - "tRNA nucleotidyltransferase/poly(A) polymerase, RNA and SrmB- binding domain 14\n", - "Male sterility, NAD-binding 14\n", - "SAND domain|SAND domain 14\n", - "[2Fe-2S]-binding|Xanthine dehydrogenase, small subunit 14\n", - "Transcription factor, T-box, region of unknown function 14\n", - "Raf-like Ras-binding|Raf-like Ras-binding 14\n", - "Alanine dehydrogenase/pyridine nucleotide transhydrogenase, N-terminal|Alanine dehydrogenase/pyridine nucleotide transhydrogenase, N-terminal 14\n", - "Formamidopyrimidine-DNA glycosylase, catalytic domain|Formamidopyrimidine-DNA glycosylase, catalytic domain|Formamidopyrimidine-DNA glycosylase, catalytic domain 14\n", - "PAS fold-3|PAS domain|PAS domain 14\n", - "C-type lectin-like|C-type lectin, conserved site|C-type lectin-like|C-type lectin-like|Aggrecan/versican, C-type lectin-like domain 14\n", - "Cyclin, C-terminal domain|Cyclin-like|Cyclin, C-terminal domain|Cyclin-like 14\n", - "ATPase, F1/V1/A1 complex, alpha/beta subunit, nucleotide-binding domain|ATP synthase, F1 complex, alpha subunit nucleotide-binding domain 14\n", - "Reeler domain|Reeler domain 14\n", - "Dilute domain|Myosin 5b, cargo-binding domain 14\n", - "PUB domain 14\n", - "Protein of unknown function DUF3504 14\n", - "DNA ligase, ATP-dependent, C-terminal|DNA ligase, ATP-dependent, central 14\n", - "RGS domain|RGS domain|RGS domain|SNX14, RGS domain 14\n", - "ERCC3/RAD25/XPB helicase, C-terminal domain|Helicase, C-terminal 14\n", - "PKD/Chitinase domain 14\n", - "Ferrochelatase, C-terminal 14\n", - "D-isomer specific 2-hydroxyacid dehydrogenase, NAD-binding domain|D-isomer specific 2-hydroxyacid dehydrogenase, NAD-binding domain conserved site 1 14\n", - "RNA polymerase Rpb1, domain 3 14\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain|Citron Rho-interacting kinase, catalytic domain 13\n", - "S-adenosyl-L-homocysteine hydrolase, NAD binding domain|S-adenosyl-L-homocysteine hydrolase, NAD binding domain 13\n", - "Zinc finger, double-stranded RNA binding|Zinc finger C2H2-type|Zinc finger C2H2-type|Matrin/U1-C-like, C2H2-type zinc finger 13\n", - "Aldehyde oxidase/xanthine dehydrogenase, a/b hammerhead|Aldehyde oxidase/xanthine dehydrogenase, a/b hammerhead 13\n", - "P-type ATPase, N-terminal 13\n", - "ATP-sulfurylase PUA-like domain|Sulphate adenylyltransferase|Sulphate adenylyltransferase 13\n", - "Transthyretin, conserved site|Transthyretin/hydroxyisourate hydrolase domain|Transthyretin/hydroxyisourate hydrolase domain 13\n", - "ALG11 mannosyltransferase, N-terminal 13\n", - "Nucleoside diphosphate kinase-like domain 13\n", - "TRASH domain 13\n", - "tRNA-splicing endonuclease, subunit Sen54, N-terminal 13\n", - "Cystatin domain 13\n", - "WHEP-TRS domain|WHEP-TRS domain|WHEP-TRS domain 13\n", - "Formin, FH3 domain|Formin, FH3 domain 13\n", - "DEAD/DEAH box helicase domain 13\n", - "DEAD2|Helicase superfamily 1/2, ATP-binding domain, DinG/Rad3-type 13\n", - "LSM domain, eukaryotic/archaea-type|LSM domain, eukaryotic/archaea-type 13\n", - "Pancreatic trypsin inhibitor Kunitz domain|Pancreatic trypsin inhibitor Kunitz domain|Proteinase inhibitor I2, Kunitz, conserved site|Pancreatic trypsin inhibitor Kunitz domain|Pancreatic trypsin inhibitor Kunitz domain|Pancreatic trypsin inhibitor Kunitz domain 13\n", - "PPM-type phosphatase domain|PPM-type phosphatase domain|PPM-type phosphatase domain|PPM-type phosphatase domain|PPM-type phosphatase domain 13\n", - "Peptidase S8/S53 domain|Kexin/furin catalytic domain 13\n", - "DNA polymerase, Y-family, little finger domain 13\n", - "Death effector domain 13\n", - "Endothelin-like toxin 13\n", - "K Homology domain 13\n", - "Fork head domain|Fork head domain|Fork head domain 13\n", - "FANCI helical domain 1 13\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase domain|Protein kinase domain 13\n", - "MIF4G-like, type 3|MIF4G-like, type 3 13\n", - "Mu homology domain 13\n", - "B30.2/SPRY domain|SPRY domain|Ryanodine receptor, SPRY domain 3 13\n", - "Voltage-dependent L-type calcium channel, IQ-associated domain|EF-hand domain 13\n", - "UbiA prenyltransferase conserved site 13\n", - "Glutamate/phenylalanine/leucine/valine dehydrogenase, C-terminal|Glutamate/phenylalanine/leucine/valine dehydrogenase, C-terminal|NAD(P) binding domain of glutamate dehydrogenase 13\n", - "Codanin-1, C-terminal domain 13\n", - "N-acetylneuraminic acid synthase, N-terminal 13\n", - "Arginyl-tRNA synthetase, catalytic core domain 13\n", - "Serine/threonine-protein kinase OSR1/WNK, CCT domain 13\n", - "Metallo-beta-lactamase|Hydroxyacylglutathione hydrolase, MBL domain 13\n", - "Tyrosine-protein kinase ephrin type A/B receptor-like|Tyrosine-protein kinase ephrin type A/B receptor-like 13\n", - "Zinc finger, LIM-type|LIM3 prickle 13\n", - "Tyrosinase copper-binding domain|Tyrosinase copper-binding domain|Tyrosinase copper-binding domain 13\n", - "Poly(A)-specific ribonuclease, RNA-binding 13\n", - "rRNA-processing arch domain 13\n", - "Cadherin, N-terminal 13\n", - "RNA polymerase, beta subunit, protrusion 13\n", - "Anthrax toxin receptor, extracellular 13\n", - "Formin Homology 1|Formin, FH2 domain 13\n", - "Fibronectin, type I|Fibronectin, type I|Fibronectin, type I|Fibronectin, type I 13\n", - "Regulatory factor X-associated protein, RFXANK-binding domain|Regulatory factor X-associated protein, RFXANK-binding domain 13\n", - "P domain 13\n", - "START domain 13\n", - "Multicopper oxidases, conserved site 13\n", - "Cold-shock protein, DNA-binding|Cold shock domain 13\n", - "Carbamoyl-phosphate synthetase large subunit-like, ATP-binding domain|Carbamoyl-phosphate synthase large subunit, CPSase domain|ATP-grasp fold 13\n", - "Protein ASX-like, PHD domain 13\n", - "CRAL/TRIO, N-terminal domain|CRAL/TRIO, N-terminal domain 13\n", - "GPCR, family 2, extracellular hormone receptor domain|GPCR, family 2, extracellular hormone receptor domain 13\n", - "VPS9 domain|VPS9 domain 13\n", - "Phox homologous domain|Phox homologous domain|Phox homologous domain|SNX27, PX domain 13\n", - "RNA polymerase Rpb1, domain 4|DNA-directed RNA polymerase III subunit RPC1, N-terminal 13\n", - "Anillin, N-terminal domain 13\n", - "MAM domain|MAM domain|MAM domain|MAM domain|MAM domain 13\n", - "Peptidase M1, membrane alanine aminopeptidase, N-terminal 13\n", - "AMP-dependent synthetase/ligase|AMP-binding, conserved site 13\n", - "Mst1 SARAH domain|SARAH domain 12\n", - "Microtubule-associated serine/threonine-protein kinase, domain 12\n", - "DNA replication factor Cdt1, C-terminal 12\n", - "Glycosyltransferase family 23 (GT23) domain 12\n", - "Cullin protein, neddylation domain|Cullin, conserved site|Cullin protein, neddylation domain 12\n", - "RH2 domain 12\n", - "Molybdopterin oxidoreductase, 4Fe-4S domain 12\n", - "Zasp-like motif 12\n", - "Alpha-D-phosphohexomutase, alpha/beta/alpha domain III 12\n", - "CXC domain 12\n", - "Serpin domain|Serpin domain|Alpha2-antiplasmin 12\n", - "RBP-Jkappa, IPT domain 12\n", - "BCS1, N-terminal 12\n", - "PAZ domain 12\n", - "Dual specificity phosphatase, catalytic domain|Dual specificity protein phosphatase domain|Tyrosine specific protein phosphatases domain|Dual specificity protein phosphatase domain|Protein-tyrosine phosphatase, catalytic 12\n", - "SH2 domain|SH2 domain|SH2 domain|SH2 domain|Tyrosine-protein kinase Lck, SH2 domain 12\n", - "Cytoskeleton-associated protein 2, C-terminal 12\n", - "Tyrosine-protein kinase ephrin type A/B receptor-like 12\n", - "Kank N-terminal motif 12\n", - "CH-like domain in sperm protein|Calponin homology domain 12\n", - "Domain of unknown function DUF1087 12\n", - "Electron transfer flavoprotein, alpha subunit, C-terminal|Electron transfer flavoprotein subunit alpha, conserved site 12\n", - "Fibrinogen, alpha/beta/gamma chain, C-terminal globular domain|Fibrinogen, alpha/beta/gamma chain, C-terminal globular domain 12\n", - "Domain of unknown function DUF4457 12\n", - "Di-trans-poly-cis-decaprenylcistransferase-like, conserved site 12\n", - "Zinc finger, UBR-type|Zinc finger, UBR-type|Zinc finger, UBR-type 12\n", - "Peptidase, metallopeptidase 12\n", - "Anaphylatoxin/fibulin|Anaphylatoxin/fibulin|Anaphylatoxin/fibulin|Anaphylatoxin/fibulin 12\n", - "Ras-like guanine nucleotide exchange factor, N-terminal|Ras-like guanine nucleotide exchange factor, N-terminal 12\n", - "R3H domain|R3H domain|PARN, R3H domain 12\n", - "Domain of unknown function DUF4503 12\n", - "SWIB/MDM2 domain|SWIB domain 12\n", - "Anti-Mullerian hormone, N-terminal 12\n", - "Porphobilinogen deaminase, C-terminal|Porphobilinogen deaminase, dipyrromethane cofactor binding site 12\n", - "Ferrochelatase, N-terminal 12\n", - "Fibrinogen, alpha/beta/gamma chain, C-terminal globular domain|Fibrinogen, conserved site|Fibrinogen, alpha/beta/gamma chain, C-terminal globular domain|Fibrinogen, alpha/beta/gamma chain, C-terminal globular domain|Fibrinogen, alpha/beta/gamma chain, C-terminal globular domain 12\n", - "SH2 domain|SH2 domain|SH2 domain|SH2 domain|VAV1, SH2 domain 12\n", - "Interleukin-12 beta, central domain 12\n", - "Ras guanine-nucleotide exchange factors catalytic domain|Ras guanine-nucleotide exchange factor, conserved site|Ras guanine-nucleotide exchange factors catalytic domain|Ras guanine-nucleotide exchange factors catalytic domain|Ras guanine-nucleotide exchange factors catalytic domain 12\n", - "Zinc finger CCHC FOG-type 12\n", - "Saposin B type domain 12\n", - "Helix-hairpin-helix DNA-binding motif, class 1 12\n", - "Sugar isomerase (SIS)|Sugar isomerase (SIS)|GlmS/FrlB, SIS domain 2 12\n", - "Phox homologous domain|Phox homologous domain|Phox homologous domain|SH3PXD2, PX domain 12\n", - "Zinc finger, DNA-directed DNA polymerase, family B, alpha 12\n", - "RNA polymerase, N-terminal 12\n", - "SPRY-associated|Butyrophylin-like, SPRY domain|B30.2/SPRY domain 12\n", - "SRCR-like domain|SRCR domain|SRCR domain|SRCR-like domain 12\n", - "Isoleucyl tRNA synthetase type 2, anticodon-binding domain 12\n", - "Ras guanine-nucleotide exchange factors catalytic domain|Ras guanine-nucleotide exchange factors catalytic domain 12\n", - "PDZ-associated domain of NMDA receptors 12\n", - "Beta-adaptin appendage, C-terminal subdomain 12\n", - "14-3-3 protein, conserved site|14-3-3 domain 12\n", - "Malonyl-CoA decarboxylase, N-terminal 12\n", - "Dynein regulatory complex protein 1, C-terminal 12\n", - "NADH-ubiquinone oxidoreductase 51kDa subunit, iron-sulphur binding domain 12\n", - "IMP dehydrogenase/GMP reductase|CBS domain|IMP dehydrogenase/GMP reductase 12\n", - "FOXP, coiled-coil domain|Zinc finger C2H2-type 12\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain, eukaryote|TIA-1, RNA recognition motif 2 12\n", - "Insulin-like growth factor-binding protein, IGFBP|Insulin-like growth factor binding protein, N-terminal, Cys-rich conserved site|Insulin-like growth factor-binding protein, IGFBP|Insulin-like growth factor-binding protein, IGFBP 12\n", - "Tripartite DENN domain|uDENN domain 12\n", - "Domain of unknown function DUF775 12\n", - "Cytidine and deoxycytidylate deaminase domain|Cytidine and deoxycytidylate deaminase domain 12\n", - "2Fe-2S ferredoxin-type iron-sulfur binding domain|2Fe-2S ferredoxin-type iron-sulfur binding domain 12\n", - "SH3 domain|SH3 domain|SH3 domain|PSTPIP1, SH3 domain 12\n", - "Methyltransferase small domain 12\n", - "Protein kinase domain|Protein kinase, ATP binding site|Protein kinase domain|Protein kinase domain|Myosin Light Chain Kinase 1, Kinase domain 12\n", - "FAS1 domain 12\n", - "Tumour necrosis factor receptor 7, N-terminal 12\n", - "SynGAP, PH domain 12\n", - "Calcium/calmodulin-dependent protein kinase II, association-domain 12\n", - "von Hippel-Lindau disease tumour suppressor, beta/alpha domain 12\n", - "SMCs flexible hinge|SMCs flexible hinge 12\n", - "RAP domain|RAP domain|RAP domain 12\n", - "Glutamyl/glutaminyl-tRNA synthetase, class Ib, catalytic domain|Aminoacyl-tRNA synthetase, class I, conserved site 12\n", - "DHR-2 domain 12\n", - "Death domain|Death domain|Death domain|RIP1, Death domain 12\n", - "Histidine triad, conserved site|HIT-like domain 12\n", - "von Willebrand factor, type D domain|von Willebrand factor, type D domain 12\n", - "Protein-arginine deiminase (PAD) N-terminal 12\n", - "Uroporphyrinogen decarboxylase (URO-D)|Uroporphyrinogen decarboxylase (URO-D) 12\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Class X myosin, motor domain 12\n", - "MCM domain|MCM domain|MCM domain 12\n", - "Tetrahydrofolate dehydrogenase/cyclohydrolase, catalytic domain 12\n", - "Tensin/EPS8 phosphotyrosine-binding domain|PTB/PI domain 12\n", - "Polymerase, nucleotidyl transferase domain|2-5-oligoadenylate synthetase, N-terminal 12\n", - "MutL, C-terminal, dimerisation 12\n", - "SH3 domain|SH3 domain|SH3 domain|Neutrophil cytosol factor 2, SH3 domain 1 12\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tumor necrosis factor receptor 11A, N-terminal 12\n", - "RAVE complex protein Rav1 C-terminal 12\n", - "Major prion protein N-terminal domain 12\n", - "Dilute domain|Dilute domain|Dilute domain|Myosin 5b, cargo-binding domain 12\n", - "SH2 domain|SH2 domain|SH2 domain|SH2 domain|PI3K p85 subunit, C-terminal SH2 domain 12\n", - "Major sperm protein (MSP) domain|Major sperm protein (MSP) domain 12\n", - "Ribosomal protein S10 domain|Ribosomal protein S10 domain 12\n", - "Histidine kinase/HSP90-like ATPase|Histidine kinase/HSP90-like ATPase 12\n", - "Anaphylatoxin/fibulin|Anaphylatoxin/fibulin 12\n", - "CRIB domain|CRIB domain|CRIB domain|p21 activated kinase binding domain 12\n", - "Transketolase, N-terminal 12\n", - "Serine-threonine protein phosphatase, N-terminal|Serine/threonine-specific protein phosphatase/bis(5-nucleosyl)-tetraphosphatase 12\n", - "Helix-hairpin-helix motif, class 2 12\n", - "Glutamine amidotransferase 12\n", - "C2 domain|C2 domain|C2 domain|E3 ubiquitin-protein ligase HECW, C2 domain 12\n", - "UDP-glucose/GDP-mannose dehydrogenase, dimerisation 12\n", - "Molybdenum cofactor synthesis C-terminal|Radical SAM|Elp3/MiaB/NifB 12\n", - "DHR-1 domain 12\n", - "R3H domain|R3H domain|R3H domain|DNA-binding protein SMUBP-2, R3H domain 12\n", - "SH3 domain|SH3 domain|SH3 domain|Jouberin, SH3 domain 12\n", - "Transcription factor, GTP-binding domain 12\n", - "FerIin domain|FerIin domain 12\n", - "UME domain|UME domain 12\n", - "WW domain 12\n", - "Citron homology (CNH) domain|Citron homology (CNH) domain|Citron homology (CNH) domain 12\n", - "PLAC 12\n", - "Ets domain|Ets domain 12\n", - "Cobalamin (vitamin B12)-binding domain|Cobalamin (vitamin B12)-binding domain|Methionine synthase, B12-binding domain 12\n", - "OAR domain|OAR domain 12\n", - "Pyridoxal-phosphate binding site 12\n", - "Cytochrome b561/ferric reductase transmembrane|Cytochrome b561/ferric reductase transmembrane|Cytochrome b561/ferric reductase transmembrane 11\n", - "Zinc finger, nuclear hormone receptor-type|Zinc finger, nuclear hormone receptor-type|Zinc finger, nuclear hormone receptor-type|Zinc finger, nuclear hormone receptor-type|Zinc finger, nuclear hormone receptor-type|Zinc finger, nuclear hormone receptor-type 11\n", - "Krueppel-associated box|Krueppel-associated box 11\n", - "Macrophage scavenger receptor 11\n", - "IMP dehydrogenase/GMP reductase|CBS domain|CBS domain 11\n", - "Zinc finger, RanBP2-type|Zinc finger, RanBP2-type 11\n", - "Intermediate filament protein, conserved site|Intermediate filament, rod domain 11\n", - "Basic-leucine zipper domain|Basic-leucine zipper domain 11\n", - "APOBEC-like, N-terminal|APOBEC/CMP deaminase, zinc-binding|Cytidine and deoxycytidylate deaminase domain 11\n", - "Carbamoyl-phosphate synthetase large subunit-like, ATP-binding domain|Carbamoyl-phosphate synthetase large subunit-like, ATP-binding domain|ATP-grasp fold|Biotin carboxylation domain 11\n", - "Haem NO binding associated|Adenylyl cyclase class-3/4/guanylyl cyclase 11\n", - "Malic enzyme, N-terminal domain|Malic enzyme, N-terminal domain 11\n", - "PI3Kalpha, catalytic domain 11\n", - "CUB domain 11\n", - "Agenet-like domain|Agenet-like domain 11\n", - "U2A'/phosphoprotein 32 family A, C-terminal 11\n", - "CG-1 DNA-binding domain|CG-1 DNA-binding domain|CG-1 DNA-binding domain 11\n", - "VWFC domain|von Willebrand factor, type D domain 11\n", - "Sugar isomerase (SIS)|Sugar isomerase (SIS)|GlmS/AgaS, SIS domain 1 11\n", - "SCAN domain|SCAN domain|SCAN domain|SCAN domain 11\n", - "EGF-like domain, extracellular|EGF-like, conserved site|EGF-like, conserved site|EGF-like domain|EGF-like domain 11\n", - "Interferon/interleukin receptor domain 11\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain|Rhodopsin kinase, catalytic domain 11\n", - "Cytochrome b561/ferric reductase transmembrane 11\n", - "RING-type zinc-finger, LisH dimerisation motif|Zinc finger, RING-type, conserved site|Zinc finger, RING-type|Zinc finger, RING-type 11\n", - "Insulin-like growth factor-binding protein, IGFBP|Insulin-like growth factor-binding protein, IGFBP 11\n", - "DDE superfamily endonuclease domain 11\n", - "MnmE, helical domain 11\n", - "C2 domain|C2 domain|Calpain C2 domain 11\n", - "PA domain|Transferrin receptor protein 1/2, PA domain 11\n", - "Cation channel complex component UNC80, N-terminal 11\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Class V myosin, motor domain 11\n", - "Zinc finger, CHCC-type 11\n", - "Pleckstrin homology domain|FERM central domain|Pleckstrin homology domain|Pleckstrin homology domain|Band 4.1 domain|Kindlin/fermitin, PH domain 11\n", - "SATB, ubiquitin-like oligomerisation domain 11\n", - "Fragile X-related mental retardation protein, C-terminal region 2 11\n", - "Integrin beta subunit, cytoplasmic domain|Integrin beta subunit, cytoplasmic domain 11\n", - "Sorting nexin, C-terminal 11\n", - "Vitamin K epoxide reductase 11\n", - "Phosphatidic acid phosphatase type 2/haloperoxidase 11\n", - "Double-stranded RNA-binding domain|Double-stranded RNA-binding domain 11\n", - "Pancreatic trypsin inhibitor Kunitz domain|Pancreatic trypsin inhibitor Kunitz domain|Pancreatic trypsin inhibitor Kunitz domain|Pancreatic trypsin inhibitor Kunitz domain|Pancreatic trypsin inhibitor Kunitz domain 11\n", - "Membrane attack complex component/perforin (MACPF) domain|Membrane attack complex component/perforin (MACPF) domain 11\n", - "PAS fold-3|PAS domain 11\n", - "Endothelin-like toxin, conserved site|Endothelin-like toxin 11\n", - "Helicase superfamily 1/2, ATP-binding domain|Helicase, C-terminal 11\n", - "Rotatin, N-terminal 11\n", - "Ribosomal protein S19e, conserved site 11\n", - "SAP domain|SAP domain|SAP domain 11\n", - "MAGE homology domain 11\n", - "Alpha carbonic anhydrase domain|Alpha carbonic anhydrase domain 11\n", - "Rad4 beta-hairpin domain 1|Rad4 beta-hairpin domain 1 11\n", - "Tubulin binding cofactor C-like domain|Tubulin binding cofactor C-like domain|C-CAP/cofactor C-like domain 11\n", - "MAP2/Tau projection 11\n", - "Ribosome maturation protein SBDS, N-terminal 11\n", - "Helicase/SANT-associated domain 11\n", - "DOMON domain|DOMON domain 11\n", - "Glutamate/phenylalanine/leucine/valine dehydrogenase, C-terminal|NAD(P) binding domain of glutamate dehydrogenase 11\n", - "Immunoglobulin C1-set|Immunoglobulin/major histocompatibility complex, conserved site|Immunoglobulin-like domain|Immunoglobulin C1-set 11\n", - "SNF2-related, N-terminal domain|Chromo/chromo shadow domain 11\n", - "Inter-alpha-trypsin inhibitor heavy chain, C-terminal 11\n", - "Mucin-like domain 11\n", - "Adenylate kinase, conserved site 10\n", - "GPCR, family 2, secretin-like, conserved site|GPCR, family 2-like 10\n", - "Ephrin receptor ligand binding domain|Ephrin receptor ligand binding domain|Ephrin receptor ligand binding domain|Ephrin type-B receptor 4, ligand binding domain 10\n", - "Abnormal spindle-like microcephaly-associated protein, ASH domain 10\n", - "RNA polymerase, alpha subunit|RNA polymerase, N-terminal 10\n", - "Inositol polyphosphate-related phosphatase|OCRL1/INPP5B, INPP5c domain 10\n", - "Actinin-type actin-binding domain, conserved site 10\n", - "Rel homology domain (RHD), DNA-binding domain 10\n", - "Extracellular sulfatase, C-terminal 10\n", - "Cadherin, N-terminal|Cadherin-like 10\n", - "Reticulon 10\n", - "MATH/TRAF domain|MATH/TRAF domain|MATH/TRAF domain|TRIM37, MATH domain 10\n", - "DRF autoregulatory|Diaphanous autoregulatory (DAD) domain|Formin, FH2 domain 10\n", - "Methylmalonyl-CoA mutase, alpha/beta chain, catalytic|Methylmalonyl-CoA mutase, alpha/beta chain, catalytic|Methylmalonyl-CoA mutase, alpha chain, catalytic 10\n", - "Phosphatidylinositol-4-phosphate 5-kinase, core|Phosphatidylinositol-4-phosphate 5-kinase, core|Phosphatidylinositol-4-phosphate 5-kinase, core 10\n", - "PLD-like domain 10\n", - "LRRC8, pannexin-like TM region 10\n", - "Laminin, N-terminal 10\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain|RPK118-like, kinase domain 10\n", - "PH-BEACH domain|PH-BEACH domain 10\n", - "FAST kinase leucine-rich 10\n", - "Transcription regulator GCM domain|Transcription regulator GCM domain|Transcription regulator GCM domain 10\n", - "MoeA, C-terminal, domain IV 10\n", - "Nuclear receptor-interacting protein 1, repression domain 1 10\n", - "GDSL lipase/esterase|Phospholipase B 10\n", - "Phosphoribosyl pyrophosphate synthetase, conserved site 10\n", - "RNA polymerase Rpb1, domain 5|DNA-directed RNA polymerase III subunit RPC1, N-terminal 10\n", - "Pre-mRNA-processing-splicing factor 8, U6-snRNA-binding 10\n", - "E3 ubiquitin-protein ligase RNF220, middle domain 10\n", - "Zinc finger, LIM-type|Zinc finger, LIM-type|LIM3 prickle 10\n", - "EF-Hand 1, calcium-binding site|EF-hand domain 10\n", - "CO dehydrogenase flavoprotein, C-terminal|CO dehydrogenase flavoprotein, C-terminal|Xanthine dehydrogenase, small subunit 10\n", - "HEPN domain|HEPN domain|HEPN domain 10\n", - "Sortilin, C-terminal|VPS10 10\n", - "Transcription factor, MADS-box|Transcription factor, MADS-box|Transcription factor, MADS-box 10\n", - "Vacuolar protein 14 C-terminal Fig4-binding domain 10\n", - "Stn1, C-terminal 10\n", - "AIMP2, lysyl-tRNA synthetase binding domain 10\n", - "Inositol polyphosphate 5-phosphatase, clathrin binding domain|OCRL1, PH domain 10\n", - "Pre-mRNA-processing-splicing factor 8, U5-snRNA-binding 10\n", - "EGF-like calcium-binding, conserved site 10\n", - "Glutamine synthetase, beta-Grasp domain 10\n", - "ERAP1-like C-terminal domain 10\n", - "Oxidoreductase, N-terminal 10\n", - "EGF-like domain|EGF-like, conserved site|EGF-like, conserved site|EGF-like domain|EGF-like domain 10\n", - "VPS9 domain 10\n", - "XPG-I domain|Helix-hairpin-helix motif, class 2 10\n", - "Vitamin B12-dependent methionine synthase, activation domain 10\n", - "LEM domain 10\n", - "Link domain 10\n", - "TNFR/NGFR cysteine-rich region|Fas receptor, N-terminal 10\n", - "DNA-directed RNA polymerase, insert domain|DNA-directed RNA polymerase, RpoA/D/Rpb3-type 10\n", - "Ribosomal protein S14, conserved site 10\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain|Microtubule-associated serine/threonine-protein kinase, catalytic domain 10\n", - "AGC-kinase, C-terminal 10\n", - "LsmAD domain|LsmAD domain 10\n", - "YjeF N-terminal domain|YjeF N-terminal domain 10\n", - "CARMIL, C-terminal domain 10\n", - "Stealth protein CR4, conserved region 4 10\n", - "Galactokinase galactose-binding domain 10\n", - "RNA recognition motif domain|RNA recognition motif domain|FOX1, RNA recognition motif 10\n", - "SH2 domain|SH2 domain|SH2 domain|PI3K p85 subunit, C-terminal SH2 domain 10\n", - "DDH domain 10\n", - "CD80-like, immunoglobulin C2-set|Immunoglobulin-like domain 10\n", - "Lipoyl synthase, N-terminal|Radical SAM 10\n", - "WHIM2 domain 10\n", - "Coproporphyrinogen III oxidase, conserved site 10\n", - "Rrp40, S1 domain 10\n", - "EGF domain|EGF-like, conserved site|EGF-like domain 10\n", - "Vitellinogen, open beta-sheet|Lipid transport protein, N-terminal|Vitellinogen, open beta-sheet 10\n", - "WHEP-TRS domain|WHEP-TRS domain 10\n", - "HELP 10\n", - "CD3 gamma/delta subunit, Ig-like domain|Immunoglobulin subtype 2 10\n", - "Insulin-like growth factor II E-peptide, C-terminal|Insulin-like growth factor II E-peptide, C-terminal 10\n", - "Methionyl/Leucyl tRNA synthetase|Methioninyl-tRNA synthetase core domain|Methioninyl-tRNA synthetase core domain 10\n", - "Catalase core domain|Catalase core domain 10\n", - "Laminin G domain|EGF-like domain 10\n", - "Immunoglobulin V-set domain|Immunoglobulin/major histocompatibility complex, conserved site|Immunoglobulin-like domain|Immunoglobulin V-set domain|Immunoglobulin subtype 10\n", - "SH2 domain|Ras and Rab interactor 2, SH2 domain 10\n", - "Frizzled domain 10\n", - "Zinc finger, Btk motif|Zinc finger, Btk motif|Zinc finger, Btk motif 10\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase domain|Tyrosine-protein kinase, catalytic domain|Protein kinase domain 10\n", - "Helix-hairpin-helix motif|HhH-GPD domain|HhH-GPD domain|HhH-GPD domain 10\n", - "Nuclear coactivator 10\n", - "Somatomedin B domain|Somatomedin B domain|Somatomedin B domain|Somatomedin B domain 10\n", - "Transcription factor TFIIE beta subunit, DNA-binding domain|Transcription factor TFIIE beta subunit, DNA-binding domain|Transcription factor TFIIE beta subunit, DNA-binding domain 10\n", - "Domain of unknown function DUF3498|Ras GTPase-activating domain 10\n", - "Peptidase family A1 domain 10\n", - "CARD domain|CARD domain|CARD8/ASC/NALP1, CARD domain 10\n", - "SH2 domain|SH2 domain|SH2 domain|Protein kinase domain|SH2 domain 10\n", - "UDP-glucose/GDP-mannose dehydrogenase, C-terminal|UDP-glucose/GDP-mannose dehydrogenase, C-terminal 10\n", - "3'-5' exonuclease domain 10\n", - "Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3/4-kinase, conserved site|Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3-/4-kinase, catalytic domain|PI3Kdelta, catalytic domain 10\n", - "Retinoblastoma-associated protein, B-box 10\n", - "TNFR/NGFR cysteine-rich region|Tumor necrosis factor receptor 11A, N-terminal 10\n", - "Epithelial sodium channel, conserved site 10\n", - "PAN/Apple domain|PAN/Apple domain 10\n", - "DIX domain|DIX domain 10\n", - "Hydantoinaseoxoprolinase, N-terminal 10\n", - "Peptidase S53, activation domain|Sedolisin domain|Peptidase S53, activation domain|Peptidase S53, activation domain 10\n", - "YjeF N-terminal domain 10\n", - "Rho GTPase-activating protein domain|Rho GTPase-activating protein domain|Chimaerin, RhoGAP domain 10\n", - "Calmodulin-binding domain|Calmodulin-binding domain 10\n", - "W2 domain|W2 domain|W2 domain 10\n", - "Importin-alpha, importin-beta-binding domain|Importin-alpha, importin-beta-binding domain 10\n", - "U box domain|U box domain|U box domain 10\n", - "AT hook, DNA-binding motif 10\n", - "Alpha-2-macroglobulin, thiol-ester bond-forming|Alpha-2-macroglobulin, conserved site 10\n", - "Ubiquitin interacting motif|Ubiquitin interacting motif|Ubiquitin interacting motif 10\n", - "Bromo adjacent homology (BAH) domain|Bromo adjacent homology (BAH) domain 10\n", - "Mon2, dimerisation and cyclophilin-binding domain 10\n", - "Sulfite reductase [NADPH] flavoprotein alpha-component-like, FAD-binding|Flavoprotein pyridine nucleotide cytochrome reductase|FAD-binding domain, ferredoxin reductase-type 10\n", - "NADH-ubiquinone oxidoreductase 51kDa subunit, FMN-binding domain|NADH:ubiquinone oxidoreductase, 51kDa subunit, conserved site 10\n", - "Kazal domain|Major facilitator superfamily domain|Kazal domain 10\n", - "Ephrin receptor ligand binding domain|Ephrin receptor ligand binding domain|Ephrin receptor ligand binding domain|Ephrin type-A receptor 2, ligand binding domain 10\n", - "STAT1, SH2 domain 9\n", - "Talin, N-terminal F0 domain 9\n", - "ISWI, HAND domain 9\n", - "Peptidase C1A, papain C-terminal|Peptidase C1A, papain C-terminal|Peptidase C1A, papain C-terminal|Papain-like cysteine endopeptidase 9\n", - "SH2 domain|SH2 domain|SH2 domain|Tyrosine-protein kinase Blk, SH2 domain 9\n", - "Alpha-2-macroglobulin, thiol-ester bond-forming 9\n", - "Motilin/ghrelin-associated peptide 9\n", - "T-box transcription factor-associated 9\n", - "Calcium-dependent secretion activator domain|Calcium-dependent secretion activator domain 9\n", - "Thiamin pyrophosphokinase, thiamin-binding domain 9\n", - "Rad4 beta-hairpin domain 2|Rad4 beta-hairpin domain 2 9\n", - "Rho GTPase-activating protein domain|Rho GTPase-activating protein domain|Rho GTPase-activating protein domain|Chimaerin, RhoGAP domain 9\n", - "Aminopeptidase P, N-terminal|Aminopeptidase P, N-terminal 9\n", - "SH2 domain|SH2 domain|SH2 domain|STAT2, SH2 domain 9\n", - "FERM adjacent (FA) 9\n", - "Alpha carbonic anhydrase domain|Carbonic anhydrase, alpha-class, conserved site|Alpha carbonic anhydrase domain|Alpha carbonic anhydrase domain 9\n", - "CELF1/2, RNA recognition motif 3 9\n", - "CFTR regulator domain|ABC transporter-like 9\n", - "Pre-SET domain 9\n", - "GPS motif 9\n", - "Stealth protein CR3, conserved region 3 9\n", - "RNA polymerase, alpha subunit|RNA polymerase, N-terminal|DNA-directed RNA polymerase III subunit RPC1, N-terminal 9\n", - "Chitin binding domain|Chitin binding domain|Chitin binding domain 9\n", - "mRNA decay factor PAT1 domain 9\n", - "Basic-leucine zipper domain|Basic-leucine zipper domain|Basic-leucine zipper domain|Basic-leucine zipper domain 9\n", - "SANT/Myb domain|SANT/Myb domain 9\n", - "DnaJ domain|DnaJ domain|DnaJ domain|DnaJ domain|DnaJ domain|DnaJ domain 9\n", - "SEA domain 9\n", - "Glutamine amidotransferase type 2 domain|Glutamine amidotransferase type 2 domain 9\n", - "XPG N-terminal|XPG conserved site|XPG N-terminal 9\n", - "Immunoglobulin-like domain|Immunoglobulin subtype 2 9\n", - "Aminoacyl-tRNA synthetase, class II|Threonine-tRNA ligase catalytic core domain 9\n", - "CRAL/TRIO, N-terminal domain 9\n", - "Peptidase S16, Lon proteolytic domain 9\n", - "SH3 domain|SH3 domain|SH3 domain|SH3 domain|Fyn/Yrk, SH3 domain 9\n", - "Alpha crystallin/Hsp20 domain|Alpha crystallin/Hsp20 domain|Heat shock protein beta-3|Heat shock protein beta-3 9\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tumour necrosis factor receptor 7, N-terminal 9\n", - "Link domain|Link domain 9\n", - "SH2 domain|SH2 domain|SH2 domain|SH2 domain|Tyrosine-protein kinase Blk, SH2 domain 9\n", - "Formamidopyrimidine-DNA glycosylase, catalytic domain 9\n", - "EGF-like calcium-binding domain|EGF-like calcium-binding, conserved site|EGF-like calcium-binding domain 9\n", - "Cystine knot, C-terminal 9\n", - "Aromatic amino acid hydroxylase, C-terminal|ACT domain 9\n", - "SH2 domain|SH2 domain|SH2 domain|SH2 domain|Ras GTPase-activating protein 1, N-terminal SH2 domain 9\n", - "Peptidase M10A, cysteine switch, zinc binding site 9\n", - "Calponin homology domain|Actinin-type actin-binding domain, conserved site|Calponin homology domain 9\n", - "Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain 9\n", - "Domain of unknown function DUF3338 9\n", - "Aminoacyl-tRNA synthetase, class II (G/ P/ S/T)|Aminoacyl-tRNA synthetase, class II 9\n", - "THIF-type NAD/FAD binding fold|Ubiquitin-activating enzyme E1, C-terminal 9\n", - "Heat shock factor (HSF)-type, DNA-binding|Heat shock factor (HSF)-type, DNA-binding|Heat shock factor (HSF)-type, DNA-binding|Heat shock factor (HSF)-type, DNA-binding 9\n", - "SAND domain 9\n", - "Janus kinase and microtubule-interacting protein, C-terminal domain 9\n", - "Cullin protein, neddylation domain 9\n", - "Importin-alpha, importin-beta-binding domain 9\n", - "JAB1/MPN/MOV34 metalloenzyme domain 9\n", - "Phox homologous domain|Phox homologous domain 9\n", - "RNA binding activity-knot of a chromodomain|Chromo/chromo shadow domain|Chromo/chromo shadow domain 9\n", - "Myosin head, motor domain|Myosin head, motor domain|Class IX myosin, motor domain 9\n", - "STAT transcription factor, protein interaction 9\n", - "Protein kinase, ATP binding site 9\n", - "Josephin domain|Josephin domain|Josephin domain 9\n", - "Galectin, carbohydrate recognition domain|Galectin, carbohydrate recognition domain|Galectin, carbohydrate recognition domain|Galectin, carbohydrate recognition domain|Galectin, carbohydrate recognition domain 9\n", - "Sialate O-acetylesterase domain 9\n", - "Alpha-D-phosphohexomutase, alpha/beta/alpha domain I|Alpha-D-phosphohexomutase, conserved site 9\n", - "Alpha 1,4-glycosyltransferase domain 9\n", - "Xanthine dehydrogenase, small subunit 9\n", - "High mobility group box domain|High mobility group box domain 9\n", - "Ubiquitin specific protease domain 9\n", - "Protein tyrosine phosphatase, receptor type, N-terminal 9\n", - "Ubiquitin-associated domain 9\n", - "POU-specific domain|POU domain|POU-specific domain|POU-specific domain 9\n", - "Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A|Carboxypeptidase A, carboxypeptidase domain 9\n", - "Frizzled domain|Frizzled domain|Frizzled domain|Collagen alpha-1(XVIII) chain, frizzled domain 9\n", - "WH2 domain 9\n", - "Pseudouridine synthase, RsuA/RluB/C/D/E/F|Pseudouridine synthase, RsuA/RluB/C/D/E/F 9\n", - "Double-stranded RNA-binding domain 9\n", - "Cyclic nucleotide-gated channel, C-terminal leucine zipper domain|Cyclic nucleotide-binding domain 9\n", - "PKD domain|Polycystin cation channel 9\n", - "Zinc finger, ZZ-type|Zinc finger, ZZ-type 9\n", - "Guanine nucleotide exchange factor, N-terminal 9\n", - "Protein kinase domain|Protein kinase, ATP binding site|Protein kinase domain|Protein kinase domain|p21-activated kinase 3, catalytic domain 9\n", - "Nuclear receptor-interacting protein 1, repression domain 2 9\n", - "Sodium:neurotransmitter symporter, serotonin, N-terminal|Sodium:neurotransmitter symporter, serotonin, N-terminal 9\n", - "YjeF N-terminal domain|YjeF N-terminal domain|YjeF N-terminal domain 9\n", - "Clp ATPase, C-terminal 9\n", - "Alanine dehydrogenase/pyridine nucleotide transhydrogenase, NAD(H)-binding domain 9\n", - "Protein kinase domain|Fibroblast growth factor receptor 1, catalytic domain 9\n", - "Target SNARE coiled-coil homology domain|Target SNARE coiled-coil homology domain|Target SNARE coiled-coil homology domain 9\n", - "Interferon-related developmental regulator, N-terminal 9\n", - "Ubiquitin-associated domain|Ubiquitin-associated domain 9\n", - "CIDE-N domain|CIDE-N domain|CIDE-N domain 9\n", - "Fibronectin type III|Fibronectin type III|Fibronectin type III|Immunoglobulin subtype|Fibronectin type III 9\n", - "C2 domain|Ferlin, fifth C2 domain 9\n", - "Sugar isomerase (SIS) 9\n", - "Legume-like lectin|Legume-like lectin 9\n", - "DnaJ domain|DnaJ domain|DnaJ domain|DnaJ domain 9\n", - "SH2 domain|SH2 domain|SH2 domain|SH2 domain|PI3K p85 subunit, N-terminal SH2 domain 9\n", - "Serum albumin, N-terminal 9\n", - "Clp1, N-terminal beta-sandwich domain 9\n", - "Serine rich protein interaction domain 9\n", - "Pterin-binding domain 9\n", - "DNA-directed RNA polymerase, RBP11-like dimerisation domain 9\n", - "Peptidase M14, carboxypeptidase A|Cytosolic aminopeptidase 1 9\n", - "Sterile alpha motif domain|Sterile alpha motif domain|USH1G, SAM domain 9\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|U1 small nuclear ribonucleoprotein A, RNA recognition motif 1 9\n", - "GTP-binding protein TrmE, N-terminal 9\n", - "Protein kinase, C-terminal|AGC-kinase, C-terminal|AGC-kinase, C-terminal|Novel protein kinase C delta, catalytic domain 8\n", - "Transcription factor, GTP-binding domain|Transcription factor, GTP-binding domain|Tr-type G domain, conserved site|Transcription factor, GTP-binding domain 8\n", - "DNA polymerase beta, palm domain|DNA-directed DNA polymerase X|DNA-directed DNA polymerase X 8\n", - "Signal transducer and activation of transcription 2, C-terminal 8\n", - "Trimerisation motif 8\n", - "Coactivator CBP, pKID|Coactivator CBP, pKID 8\n", - "NAD/GMP synthase 8\n", - "Retinoblastoma-associated protein, N-terminal 8\n", - "Dihydroorotate dehydrogenase domain|Dihydroorotate dehydrogenase, conserved site 8\n", - "Zinc finger, PARP-type|Zinc finger, PARP-type|Zinc finger, PARP-type 8\n", - "Peptidase M16C associated|Peptidase M16C associated 8\n", - "Cobalamin (vitamin B12)-binding module, cap domain|Cobalamin (vitamin B12)-binding module, cap domain|Cobalamin (vitamin B12)-binding module, cap domain|Methionine synthase, B12-binding domain 8\n", - "RNA polymerase Rpb2, domain 7 8\n", - "YjeF N-terminal domain|YjeF N-terminal domain|YjeF N-terminal domain|YjeF N-terminal domain 8\n", - "Teneurin intracellular, N-terminal|Teneurin intracellular, N-terminal 8\n", - "Thiamine pyrophosphate enzyme, N-terminal TPP-binding domain 8\n", - "Protein kinase domain|Protein kinase, ATP binding site|Protein kinase domain|Protein kinase domain|Protein kinase B gamma, catalytic domain 8\n", - "FERM, C-terminal PH-like domain|FERM, C-terminal PH-like domain 8\n", - "C2 domain|C2 domain|Freud, C2 domain 8\n", - "Zinc finger, double-stranded RNA binding|Zinc finger C2H2-type|Zinc finger C2H2-type|Zinc finger C2H2-type|Matrin/U1-C-like, C2H2-type zinc finger 8\n", - "Tripeptidyl-peptidase II domain 8\n", - "Acyl-CoA-binding protein, ACBP|Acyl-CoA-binding protein, ACBP|Acyl-CoA-binding protein, ACBP 8\n", - "Serine/threonine-specific protein phosphatase/bis(5-nucleosyl)-tetraphosphatase 8\n", - "NADH:ubiquinone oxidoreductase, 30kDa subunit 8\n", - "Rho guanine nucleotide exchange factor, coiled-coil domain 8\n", - "Myelin gene regulatory factor C-terminal domain 1|Intramolecular chaperone auto-processing domain 8\n", - "Gcp-like domain|Peptidase M22, conserved site|Gcp-like domain 8\n", - "Zinc finger, C5HC2-type 8\n", - "Anthrax toxin receptor, C-terminal 8\n", - "RQC domain 8\n", - "Phosphomannose isomerase, type I, conserved site 8\n", - "Concentrative nucleoside transporter C-terminal domain 8\n", - "Nck-associated protein 5, C-terminal 8\n", - "Asparagine synthase, N-terminal domain 8\n", - "Ras-like guanine nucleotide exchange factor, N-terminal 8\n", - "Arrestin-like, N-terminal|Arrestin, conserved site 8\n", - "EGF-like domain|EGF-like, conserved site|EGF-like calcium-binding, conserved site|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 8\n", - "Serpin domain|Serpin domain|Protein Z-dependent peptidase inhibitor 8\n", - "IRS-type PTB domain|Dok-7, PTB domain 8\n", - "Immunoglobulin C2-set-like, ligand-binding|Immunoglobulin-like domain 8\n", - "Saccharopine dehydrogenase, NADP binding domain 8\n", - "C-type lectin-like|C-type lectin-like|C-type lectin-like|Natural killer cell receptor-like, C-type lectin-like domain 8\n", - "HTTM 8\n", - "PTB/PI domain|PTB/PI domain 8\n", - "Pyridoxamine kinase/Phosphomethylpyrimidine kinase 8\n", - "Oxoglutarate/iron-dependent dioxygenase|Prolyl 4-hydroxylase, alpha subunit 8\n", - "DNA-directed DNA polymerase, family B, multifunctional domain|DNA-directed DNA polymerase, family B, conserved site 8\n", - "Urocanase, Rossmann-like domain 8\n", - "Laminin G domain|Fibronectin type III 8\n", - "Peptide N glycanase, PAW domain 8\n", - "Basic leucine zipper domain, Maf-type 8\n", - "Membrane insertase YidC/Oxa1, C-terminal 8\n", - "Butyrophylin-like, SPRY domain|B30.2/SPRY domain|SPRY-associated 8\n", - "Cobalamin (vitamin B12)-binding domain|Methionine synthase, B12-binding domain 8\n", - "RNA polymerase Rpb1, domain 1 8\n", - "P domain|P domain 8\n", - "COMPASS complex Set1 subunit, N-SET domain|COMPASS complex Set1 subunit, N-SET domain 8\n", - "Cation-transporting P-type ATPase, N-terminal 8\n", - "Fibrinogen alpha C domain 8\n", - "Carbohydrate kinase, FGGY, N-terminal 8\n", - "SH3 domain|SH3 domain|SH3 domain|SH3 domain|Rho guanine nucleotide exchange factor 7, SH3 domain 8\n", - "PAS fold|PAS domain 8\n", - "SH3 domain|SH3 domain|SH3 domain|RasGAP, SH3 domain 8\n", - "Pleckstrin homology domain|Pleckstrin homology domain|Pleckstrin homology domain|FGD1, N-terminal PH domain 8\n", - "Alpha-L-fucosidase, C-terminal 8\n", - "Somatomedin B domain|Somatomedin B domain|Somatomedin B domain 8\n", - "Pseudouridine synthase, TruD, insertion domain 8\n", - "STAT6, C-terminal 8\n", - "Yippee/Mis18/Cereblon|CULT domain|CULT domain 8\n", - "Post-SET domain|Post-SET domain 8\n", - "EGF domain|EGF-like domain 8\n", - "Tyrosine-protein kinase, non-receptor, TYK2, N-terminal|SH2 domain 8\n", - "Insulin-like growth factor-binding protein, IGFBP 8\n", - "RNA polymerase Rpb2, domain 2 8\n", - "SH2 domain|PLC-gamma, C-terminal SH2 domain 8\n", - "INPP5B, PH domain|INPP5B, PH domain 8\n", - "Lipoxygenase, C-terminal|Lipoxygenase, C-terminal|Lipoxygenase, conserved site|Lipoxygenase, C-terminal 8\n", - "C1q domain|C1q domain 8\n", - "Domain of unknown function DUF4209 8\n", - "Domain of unknown function DUF1115 8\n", - "Ribosomal protein L35Ae, conserved site 8\n", - "Pleckstrin homology domain|Protein Kinase B, pleckstrin homology domain 8\n", - "Platelet-derived growth factor, N-terminal 8\n", - "DNA topoisomerase, type IA, central|DNA topoisomerase, type IA, DNA-binding domain|DNA topoisomerase, type IA, central 8\n", - "Ribosomal protein L10e/L16|Ribosomal protein L10e, conserved site|Ribosomal protein L10e/L16 8\n", - "WH2 domain|WH2 domain|Formin, FH2 domain 8\n", - "SH3 domain|SH3 domain|SH3 domain|Lck, SH3 domain 8\n", - "Transforming growth factor-beta, C-terminal|Transforming growth factor-beta, C-terminal 8\n", - "Calpain C2 domain 8\n", - "GRAM domain|Myotubularin-related protein 13, PH-GRAM domain 8\n", - "RHIM domain 8\n", - "Protein Kinase B, pleckstrin homology domain 8\n", - "Lysophospholipase, catalytic domain|Lysophospholipase, catalytic domain|Lysophospholipase, catalytic domain 8\n", - "Actin-depolymerising factor homology domain|Actin-depolymerising factor homology domain 8\n", - "EGF-like domain|EGF-like, conserved site|EGF-like, conserved site|EGF-like calcium-binding, conserved site|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 8\n", - "4Fe-4S ferredoxin-type, iron-sulphur binding domain|4Fe-4S ferredoxin, iron-sulphur binding, conserved site|4Fe-4S ferredoxin-type, iron-sulphur binding domain 8\n", - "ATP-dependent RNA helicase Ski2, C-terminal|ATP-dependent RNA helicase Ski2, C-terminal 8\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Tyrosine-protein kinase, receptor class III, conserved site|Protein kinase domain|Tyrosine-protein kinase, catalytic domain|Protein kinase domain 8\n", - "PI31 proteasome regulator, N-terminal 8\n", - "DNA polymerase alpha/epsilon, subunit B 8\n", - "Periplasmic copper-binding protein NosD, beta helix domain|Carbohydrate-binding/sugar hydrolysis domain 8\n", - "Tensin/EPS8 phosphotyrosine-binding domain 8\n", - "Phosphoinositide 3-kinase, accessory (PIK) domain|Phosphoinositide 3-kinase, accessory (PIK) domain 8\n", - "Domain of unknown function DUF1899|Domain of unknown function DUF1899 8\n", - "Beta-trefoil DNA-binding domain|Beta-trefoil DNA-binding domain 8\n", - "Delta/Serrate/lag-2 (DSL) protein|Delta/Serrate/lag-2 (DSL) protein|Delta/Serrate/lag-2 (DSL) protein 8\n", - "Phosphatidylinositol 3-kinase adaptor-binding (PI3K ABD) domain 8\n", - "Fumarate reductase/succinate dehydrogenase, FAD-binding site 8\n", - "Tim10-like 8\n", - "LNS2/PITP 8\n", - "Poly(ADP-ribose) polymerase, catalytic domain 8\n", - "Polymerase, nucleotidyl transferase domain|2-5-oligoadenylate synthetase, conserved site|2-5-oligoadenylate synthetase, N-terminal 8\n", - "MHC class II, alpha chain, N-terminal|MHC class II, alpha chain, N-terminal 8\n", - "CASP, C-terminal 8\n", - "Nuclear receptor coactivator 6, putative nucleic acid-binding region 8\n", - "Keratin type II cytoskeletal 1, tail 8\n", - "Protein kinase, C-terminal|AGC-kinase, C-terminal|AGC-kinase, C-terminal|Protein kinase B gamma, catalytic domain 8\n", - "EH domain 8\n", - "UPF3 domain|UPF3B, RNA recognition motif-like domain 8\n", - "Ureohydrolase, manganese-binding site 8\n", - "Glutathione S-transferase, N-terminal|Glutathione S-transferase, N-terminal|Glutathione S-transferases, class Zeta , N-terminal 8\n", - "SPRY-associated|B30.2/SPRY domain|SPRY-associated 8\n", - "SH3 domain|SH3 domain|SH3 domain|Tyrosine-protein kinase ITK, SH3 domain 8\n", - "SH2 domain|SH2 domain|PLC-gamma, N-terminal SH2 domain 8\n", - "CVC domain 8\n", - "Factor I / membrane attack complex|EGF-like domain|Follistatin-like, N-terminal 8\n", - "Laminin, N-terminal|Laminin, N-terminal|LamG-like jellyroll fold 8\n", - "Repulsive guidance molecule, N-terminal 8\n", - "C-type lectin-like|Aggrecan/versican, C-type lectin-like domain 8\n", - "SH3 domain|SH3 domain|SH3 domain|Pleckstrin homology domain|SH3 domain|1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase gamma-2, SH3 domain 8\n", - "PD-(D/E)XK endonuclease-like domain, AddAB-type 8\n", - "Zinc finger, PHD-finger|Zinc finger, PHD-finger|Zinc finger, RING-type|Zinc finger, PHD-type 8\n", - "Forkhead-associated (FHA) domain|Forkhead-associated (FHA) domain|Forkhead-associated (FHA) domain 8\n", - "Zinc finger, A20-type|Zinc finger, A20-type|Zinc finger, A20-type 8\n", - "Drought induced 19 protein type, zinc-binding domain 8\n", - "Phosphatidylinositol 3-kinase Ras-binding (PI3K RBD) domain|Phosphatidylinositol 3-kinase, C2 domain|Phosphatidylinositol 3-kinase Ras-binding (PI3K RBD) domain 8\n", - "Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A|Carboxypeptidase A6 8\n", - "Alpha-D-phosphohexomutase, alpha/beta/alpha domain II 8\n", - "Galactokinase galactose-binding domain|Galactokinase, conserved site 8\n", - "Synaptotagmin 7\n", - "Glycoside hydrolase, family 2, immunoglobulin-like beta-sandwich 7\n", - "Scaffold protein Nfu/NifU, N-terminal|Scaffold protein Nfu/NifU, N-terminal 7\n", - "MRG domain|MRG domain 7\n", - "Extracellular Endonuclease, subunit A|DNA/RNA non-specific endonuclease 7\n", - "Zinc finger, FYVE-related|Rab-binding domain 7\n", - "TOPRIM domain|TOPRIM domain|DNA topoisomerase 2, TOPRIM domain 7\n", - "Ephrin receptor-binding domain|Ephrin receptor-binding domain|Ephrin receptor-binding domain|Ephrin-B ectodomain 7\n", - "Roc domain 7\n", - "Tumour necrosis factor domain|Tumour necrosis factor, conserved site|Tumour necrosis factor domain|Tumour necrosis factor domain|Tumour necrosis factor domain 7\n", - "WWE domain 7\n", - "Transcription factor TFIIB, cyclin-like domain|Cyclin-like|Cyclin-like 7\n", - "Carboxylesterase, type B|Carboxylesterase type B, conserved site 7\n", - "GTP binding domain|MnmE, helical domain|TrmE-type guanine nucleotide-binding domain|Small GTP-binding protein domain|TrmE-type guanine nucleotide-binding domain 7\n", - "ALIX V-shaped domain 7\n", - "Type I cytokine receptor, cytokine-binding domain|Fibronectin type III 7\n", - "Tubulin/FtsZ, GTPase domain|Beta tubulin, autoregulation binding site 7\n", - "Tubulin binding cofactor C-like domain|C-CAP/cofactor C-like domain 7\n", - "Cyclic nucleotide-binding, conserved site|Cyclic nucleotide-binding domain|Cyclic nucleotide-binding domain 7\n", - "EBP50, C-terminal 7\n", - "Centromere kinetochore component CENP-T, N-terminal domain 7\n", - "Adenylate cyclase, N-terminal|Adenylyl cyclase class-3/4/guanylyl cyclase 7\n", - "POU-specific domain|POU-specific domain|POU-specific domain 7\n", - "Peptidase M12A|Peptidase M12A|Peptidase, metallopeptidase|Tolloid/BMP1 peptidase domain 7\n", - "Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Diacylglycerol/phorbol-ester binding|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain 7\n", - "C-type lectin-like|C-type lectin-like|C-type lectin-like|CD209-like, C-type lectin-like domain 7\n", - "Superoxide dismutase, copper/zinc binding domain 7\n", - "Complement Clr-like EGF domain|EGF-like calcium-binding, conserved site|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 7\n", - "Kringle|Kringle|Kringle 7\n", - "Zona pellucida domain|Zona pellucida domain|Zona pellucida domain, conserved site|Zona pellucida domain|Zona pellucida domain 7\n", - "Tetrahydrofolate dehydrogenase/cyclohydrolase, NAD(P)-binding domain 7\n", - "MAM domain 7\n", - "Enolase, C-terminal TIM barrel domain 7\n", - "Molybdenum cofactor sulfurase, C-terminal|Molybdenum cofactor sulfurase, C-terminal 7\n", - "FERM, N-terminal|FERM domain 7\n", - "Sec7 domain|Sec7 domain 7\n", - "Frizzled/Smoothened, transmembrane domain|Frizzled/Smoothened, transmembrane domain 7\n", - "Coiled-coil domain-containing protein 50, N-terminal 7\n", - "Ribosomal protein L6, alpha-beta domain 7\n", - "Chromo/chromo shadow domain|Chromo/chromo shadow domain 7\n", - "tRNAHis guanylyltransferase catalytic domain 7\n", - "DNA-repair protein Xrcc1, N-terminal 7\n", - "Ephrin receptor-binding domain|Ephrin receptor-binding domain|Ephrin receptor-binding domain|Ephrin receptor-binding domain|Ephrin-B ectodomain 7\n", - "Aminoacyl-tRNA synthetase, class II (D/K/N)|GAD domain|Aminoacyl-tRNA synthetase, class II 7\n", - "Exoribonuclease, phosphorolytic domain 2 7\n", - "Carbamoyl-phosphate synthetase large subunit-like, ATP-binding domain|Carbamoyl-phosphate synthase large subunit, CPSase domain|Carbamoyl-phosphate synthetase large subunit-like, ATP-binding domain|ATP-grasp fold 7\n", - "Cystatin domain|Cystatin domain 7\n", - "PRELI/MSF1 domain|PRELI/MSF1 domain 7\n", - "MnmG-related, conserved site 7\n", - "Translation elongation factor EFG/EF2, domain IV|Translation elongation factor EFG/EF2, domain IV|Translation elongation factor EFG/EF2, domain IV 7\n", - "Glutaredoxin|Glutaredoxin 7\n", - "Thioredoxin domain|Thioredoxin, conserved site|Thioredoxin domain|Disulphide isomerase 7\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain, eukaryote|PUF60, RNA recognition motif 1 7\n", - "B30.2/SPRY domain|SPRY domain|Ryanodine receptor, SPRY domain 2 7\n", - "PA14/GLEYA domain|IPT domain 7\n", - "Matrilin, coiled-coil trimerisation domain|Matrilin, coiled-coil trimerisation domain 7\n", - "Transmembrane protein TMEM132, N-terminal 7\n", - "Transcription factor COE, DNA-binding domain|Transcription factor COE, conserved site|Transcription factor COE, DNA-binding domain 7\n", - "Aminoacyl-tRNA synthetase, class II (D/K/N)|Lysyl-tRNA synthetase, class II, C-terminal|Aminoacyl-tRNA synthetase, class II|Lysyl-tRNA synthetase, class II, C-terminal 7\n", - "Cadherin, N-terminal|Cadherin-like|Cadherin-like 7\n", - "Dpy-30 motif 7\n", - "Pleckstrin homology domain|DOK4/5/6, PH domain 7\n", - "Mammalian uncoordinated homology 13, domain 2 7\n", - "Peptidase M10, metallopeptidase 7\n", - "Domain of unknown function DUF4502 7\n", - "MD-2-related lipid-recognition domain 7\n", - "Aminoacyl-tRNA synthetase, class II (G/ P/ S/T)|Aminoacyl-tRNA synthetase, class II|Prolyl-tRNA synthetase, catalytic domain 7\n", - "STAT2, SH2 domain 7\n", - "ATP-citrate lyase/succinyl-CoA ligase|ATP-citrate lyase/succinyl-CoA ligase, conserved site 7\n", - "Macrophage scavenger receptor|Macrophage scavenger receptor 7\n", - "Heat shock chaperonin-binding 7\n", - "WxxW domain 7\n", - "F-box domain|F-box domain|F-box domain 7\n", - "CPH domain 7\n", - "Ubiquitin carboxyl-terminal hydrolase, C-terminal 7\n", - "Proteasome component (PCI) domain|Proteasome component (PCI) domain|Proteasome component (PCI) domain 7\n", - "Mab-21 domain|Mab-21 domain 7\n", - "SH2 domain|SH2 domain|SH2 domain|SH2 domain|SH2D1A, SH2 domain 7\n", - "UbiB domain|UbiB domain, ADCK3-like 7\n", - "Elongation factor EFG, domain V-like|Elongation factor EFG, domain V-like 7\n", - "FY-rich, C-terminal 7\n", - "Fascin domain 7\n", - "Ribonuclease H domain|Ribonuclease H domain 7\n", - "Dual specificity phosphatase, catalytic domain|Dual specificity protein phosphatase domain|Tyrosine specific protein phosphatases domain|Dual specificity protein phosphatase domain|Dual specificity protein phosphatase domain 7\n", - "Valyl tRNA synthetase, anticodon-binding domain 7\n", - "Long hematopoietin receptor, soluble alpha chain, conserved site|Fibronectin type III 7\n", - "Threonyl/alanyl tRNA synthetase, SAD|Threonyl/alanyl tRNA synthetase, SAD 7\n", - "NDT80 DNA-binding domain|NDT80 DNA-binding domain 7\n", - "8-oxoguanine DNA glycosylase, N-terminal 7\n", - "Band 7 domain|Band 7/stomatin-like, conserved site|Band 7 domain 7\n", - "Aminoacyl-tRNA synthetase, class II (G/ P/ S/T)|Aminoacyl-tRNA synthetase, class II|Prokaryote proline-tRNA ligase core domain 7\n", - "Tim44-like domain|Tim44-like domain 7\n", - "Initiation factor eIF-4 gamma, MA3|Initiation factor eIF-4 gamma, MA3|Initiation factor eIF-4 gamma, MA3 7\n", - "Cyclic nucleotide-binding domain|Cyclic nucleotide-binding, conserved site|Cyclic nucleotide-binding domain|Cyclic nucleotide-binding domain 7\n", - "Dihydrofolate reductase domain|Dihydrofolate reductase domain|Dihydrofolate reductase domain 7\n", - "Fibronectin type III|Long hematopoietin receptor, Gp130 family 2, conserved site|Fibronectin type III|Fibronectin type III|Fibronectin type III 6\n", - "G-protein gamma-like domain|G-protein gamma-like domain 6\n", - "NADH:ubiquinone oxidoreductase, 75kDa subunit, conserved site 6\n", - "IMP dehydrogenase/GMP reductase|CBS domain 6\n", - "YgfZ/GcvT conserved site 6\n", - "TNRC6, PABC binding domain 6\n", - "AGC-kinase, C-terminal|AGC-kinase, C-terminal|Novel protein kinase C delta, catalytic domain 6\n", - "Sterile alpha motif domain|Sterile alpha motif domain|Stromal interaction molecule 1, SAM domain 6\n", - "Cationic amino acid transporter, C-terminal 6\n", - "Heavy metal-associated domain, HMA|Heavy metal-associated domain, HMA 6\n", - "PAN2 domain|Ubiquitin specific protease domain 6\n", - "ERCC3/RAD25/XPB helicase, C-terminal domain|Helicase, C-terminal|Helicase, C-terminal|Helicase, C-terminal 6\n", - "Maelstrom domain 6\n", - "SH2 domain|STAT1, SH2 domain 6\n", - "ROK, N-terminal 6\n", - "PPM-type phosphatase domain|PPM-type phosphatase domain 6\n", - "Peptide methionine sulphoxide reductase MrsB|Peptide methionine sulphoxide reductase MrsB|Peptide methionine sulphoxide reductase MrsB|Peptide methionine sulphoxide reductase MrsB 6\n", - "FAST kinase-like protein, subdomain 2 6\n", - "Vitamin D binding protein, domain III 6\n", - "Zinc finger, CCCH-type 6\n", - "Storkhead-box protein, winged-helix domain 6\n", - "Laminin EGF domain|EGF-like, conserved site|EGF-like, conserved site|EGF-like domain|Laminin EGF domain|EGF-like domain 6\n", - "SPRY domain|B30.2/SPRY domain|SPRY domain|RING finger and SPRY domain-containing protein 1, SPRY domain 6\n", - "Intercellular adhesion molecule, N-terminal 6\n", - "EGF-like domain|Selectin, C-type lectin-like domain 6\n", - "Nucleoporin, Nup133/Nup155-like, N-terminal 6\n", - "RelB transactivation domain 6\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain|Checkpoint kinase 1, catalytic domain 6\n", - "Lethal giant larvae homologue 2 6\n", - "NOT2/NOT3/NOT5 6\n", - "Par3/HAL, N-terminal 6\n", - "NIF system FeS cluster assembly, NifU, N-terminal 6\n", - "Stromal interaction molecule, Orai1-activating region 6\n", - "Glutamate/phenylalanine/leucine/valine dehydrogenase, dimerisation domain 6\n", - "Glucosamine/galactosamine-6-phosphate isomerase|6-phosphogluconolactonase, DevB-type|6-phosphogluconolactonase, DevB-type 6\n", - "V-ATPase proteolipid subunit C-like domain 6\n", - "Rab effector MyRIP/Melanophilin 6\n", - "PLC-gamma, N-terminal SH2 domain 6\n", - "PRP1 splicing factor, N-terminal 6\n", - "SMP-30/Gluconolactonase/LRE-like region 6\n", - "ATP synthase, F1 complex, delta/epsilon subunit, N-terminal|ATP synthase, F1 complex, delta/epsilon subunit, N-terminal 6\n", - "Actin-depolymerising factor homology domain 6\n", - "Zinc finger, CW-type|Zinc finger, CW-type 6\n", - "Acyl-CoA-binding protein, ACBP|Acyl-CoA-binding protein, ACBP 6\n", - "SH2 domain|STAT3, SH2 domain 6\n", - "Carbohydrate kinase, FGGY, C-terminal 6\n", - "PAS domain|PAS domain|PAS domain|PAS domain|PAS domain 6\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|p54nrb, RNA recognition motif 2 6\n", - "C-terminal associated domain of TOPRIM 6\n", - "Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain 6\n", - "Carbohydrate-binding WSC|Carbohydrate-binding WSC 6\n", - "2-oxoglutarate dehydrogenase E1 component, N-terminal domain 6\n", - "Elongation factor EFG, domain V-like|Elongation factor EFG, domain V-like|116kDa U5 small nuclear ribonucleoprotein component, C-terminal 6\n", - "Homeobox domain|Homeobox domain, metazoa|Homeobox domain, metazoa|Homeobox, conserved site|Homeobox domain|Homeobox domain|Homeobox domain 6\n", - "EGF-like calcium-binding, conserved site|EGF-like calcium-binding domain 6\n", - "GIT, Spa2 homology (SHD) domain|GIT, Spa2 homology (SHD) domain 6\n", - "Signal-induced proliferation-associated 1-like protein, C-terminal 6\n", - "Alpha-2-macroglobulin RAP, C-terminal|Alpha-2-macroglobulin RAP, domain 2 6\n", - "Pumilio homology domain 6\n", - "CTLH, C-terminal LisH motif|CTLH, C-terminal LisH motif 6\n", - "SMARCC, C-terminal 6\n", - "Homeobox domain|Homeobox domain, metazoa|Helix-turn-helix motif|Homeobox, conserved site|Homeobox domain|Homeobox domain|Homeobox domain 6\n", - "p21-activated kinase 3, catalytic domain 6\n", - "Tripartite DENN domain, FNIP1/2-type 6\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tumor necrosis factor receptor 14/UL144, N-terminal 6\n", - "Acetylcholinesterase, tetramerisation domain|Carboxylesterase, type B 6\n", - "Heat shock protein beta-3 6\n", - "Aminoacyl-tRNA synthetase, class II|Prokaryote proline-tRNA ligase core domain 6\n", - "C2 domain|Perforin-1, C2 domain 6\n", - "VWFC domain|VWFC domain|VWFC domain|VWFC domain|VWFC domain 6\n", - "GoLoco motif|GoLoco motif 6\n", - "SH3 domain|SH3 domain|Nebulin, SH3 domain 6\n", - "Frizzled/Smoothened, transmembrane domain|GPCR, family 2-like|Frizzled/Smoothened, transmembrane domain|Smoothened, transmembrane domain 6\n", - "Pleckstrin homology domain|Pleckstrin homology domain|Pleckstrin homology domain|APBB1IP, PH domain 6\n", - "PPM-type phosphatase domain 6\n", - "Sec7, C-terminal 6\n", - "DNA-binding RFX-type winged-helix domain 6\n", - "Laminin EGF domain|EGF-like, conserved site|Laminin EGF domain|Laminin EGF domain|EGF-like domain 6\n", - "THO complex, subunitTHOC2, N-terminal 6\n", - "Mitochondrial ABC-transporter, N-terminal five TM domain 6\n", - "PTP type protein phosphatase 6\n", - "Netrin module, non-TIMP type|Netrin domain|Netrin module, non-TIMP type 6\n", - "R3H domain 6\n", - "B-box-type zinc finger|B-box-type zinc finger 6\n", - "Tubby, N-terminal 6\n", - "B30.2/SPRY domain|Fibronectin type III|Fibronectin type III|Fibronectin type III 6\n", - "Ubiquitin system component Cue|Ubiquitin system component Cue|Ubiquitin system component Cue 6\n", - "EGF-like domain|EGF-like domain|Follistatin-like, N-terminal 6\n", - "Dihydroprymidine dehydrogenase domain II|4Fe-4S ferredoxin-type, iron-sulphur binding domain 6\n", - "TOPRIM domain|TOPRIM domain|TOPRIM domain|DNA topoisomerase 3-like, TOPRIM domain 6\n", - "Laforin, CBM20 domain 6\n", - "GOLD domain|GOLD domain 6\n", - "Sodium/solute symporter, conserved site 6\n", - "Acyl transferase 6\n", - "ABC transporter-like|ABC transporter, conserved site|ABC transporter-like 6\n", - "PAS domain|PAS domain 6\n", - "Transient receptor ion channel domain 6\n", - "Tensin/EPS8 phosphotyrosine-binding domain|PTB/PI domain|PTB/PI domain|Epidermal growth factor receptor kinase substrate, phosphotyrosine-binding domain 6\n", - "JNK/Rab-associated protein-1, N-terminal 6\n", - "Ribosomal protein S7 domain 6\n", - "Ribosome maturation protein SBDS, N-terminal|Ribosome maturation protein SBDS, conserved site 6\n", - "Peptidase S8, pro-domain 6\n", - "Pancreatic trypsin inhibitor Kunitz domain|Pancreatic trypsin inhibitor Kunitz domain|Pancreatic trypsin inhibitor Kunitz domain 6\n", - "CAAX prenyl protease 1, N-terminal 6\n", - "RNA polymerase Rpb2, domain 3 6\n", - "Histidine kinase/HSP90-like ATPase|Signal transduction histidine kinase-related protein, C-terminal|Histidine kinase domain|Histidine kinase/HSP90-like ATPase|Histidine kinase/HSP90-like ATPase 6\n", - "Cyclin, N-terminal 6\n", - "Protein-arginine deiminase (PAD), central domain 6\n", - "PI3K-C2-gamma, catalytic domain 6\n", - "NADH:ubiquinone oxidoreductase, 30kDa subunit|NADH:ubiquinone oxidoreductase, 30kDa subunit 6\n", - "CDC48, domain 2 6\n", - "SH3 domain|SH3 domain|SH3 domain|SH3PXD2B, SH3 domain 4 6\n", - "SH2 domain|SH2 domain|SH2 domain|SOCS1, SH2 domain 6\n", - "Zinc finger, AN1-type 6\n", - "Sec23, C-terminal 6\n", - "Misato Segment II tubulin-like domain 6\n", - "Ribonuclease II/R 6\n", - "Lamin-B receptor of TUDOR domain|Tudor domain 6\n", - "Zinc finger, MYM-type 6\n", - "Cadherin conserved site 6\n", - "DG-type SEA domain 6\n", - "Aminotransferase, class IV, conserved site 6\n", - "Apx/Shrm Domain 2 6\n", - "Aprataxin, C2HE/C2H2/C2HC zinc finger 6\n", - "Enolase, N-terminal 6\n", - "Low-density lipoprotein (LDL) receptor class A, conserved site|SRCR domain 6\n", - "Gcp-like domain 6\n", - "Ubiquitin interacting motif|Ubiquitin interacting motif 6\n", - "Protein kinase domain|Protein kinase, ATP binding site|Protein kinase domain|Protein kinase domain|cGMP-dependent protein kinase, catalytic domain 6\n", - "Zinc finger, MIZ-type|Zinc finger, MIZ-type 6\n", - "Homeobox domain|Helix-turn-helix motif|Homeobox domain|Homeobox domain|Homeobox domain 6\n", - "Diacylglycerol kinase, accessory domain|Diacylglycerol kinase, accessory domain 6\n", - "Carbohydrate binding module family 20|Laforin, CBM20 domain 6\n", - "Succinyl-CoA synthetase, beta subunit, conserved site 6\n", - "C2 domain|C2 domain|Ferlin, fourth C2 domain 6\n", - "Transferrin receptor-like, dimerisation domain 6\n", - "W2 domain|W2 domain 6\n", - "Laminin EGF domain|EGF-like, conserved site|Laminin EGF domain|Laminin EGF domain 6\n", - "Tubby, C-terminal|Tubby, C-terminal, conserved site 6\n", - "Protein kinase domain|Mitogen-activated protein (MAP) kinase, conserved site|Protein kinase domain|Protein kinase domain 6\n", - "Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3-/4-kinase, catalytic domain|PI3Kbeta, catalytic domain 6\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tyrosine-protein kinase ephrin type A/B receptor-like|Tumour necrosis factor receptor 4, N-terminal 6\n", - "CoA-binding 6\n", - "GDNF/GAS1|GDNF/GAS1 6\n", - "Dynamin, GTPase region, conserved site|Dynamin-type guanine nucleotide-binding (G) domain|Dynamin, GTPase domain|Dynamin, GTPase domain 6\n", - "Thioredoxin domain|Thioredoxin domain|Disulphide isomerase 6\n", - "Domain of unknown function DUF4477 6\n", - "SH3 domain|SH3 domain|SH3 domain|SH3 domain|Neutrophil cytosol factor P40, SH3 domain 6\n", - "FeS cluster biogenesis 6\n", - "Multicopper oxidase, type 2|Multicopper oxidases, conserved site 6\n", - "NIF system FeS cluster assembly, NifU, N-terminal|NIF system FeS cluster assembly, NifU, N-terminal|NIF system FeS cluster assembly, NifU, N-terminal 6\n", - "JNK/Rab-associated protein-1, N-terminal|RH1 domain 6\n", - "Domain of unknown function DUF3528, homeobox protein, eukaryotic 6\n", - "SH3 domain|SH3 domain|SH3 domain|SH3PXD2B, SH3 domain 2 6\n", - "Death domain|Death domain|Death domain|Ankyrin-3, death domain 6\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase domain|Tyrosine-protein kinase, catalytic domain|Janus kinase 2, catalytic domain 6\n", - "BRICHOS domain|BRICHOS domain 6\n", - "Freud, C2 domain 6\n", - "FERM domain|FERM central domain 6\n", - "Peptidase C1A, papain C-terminal|Peptidase C1A, papain C-terminal|Peptidase C1A, papain C-terminal|Cathepsin C 6\n", - "Nerve growth factor-related|Nerve growth factor-related|Nerve growth factor-related 6\n", - "JmjN domain|JmjN domain|JmjN domain 6\n", - "Poly(ADP-ribose) polymerase, catalytic domain|Poly(ADP-ribose) polymerase, catalytic domain 6\n", - "Regulator of K+ conductance, N-terminal 6\n", - "Superoxide dismutase, copper/zinc binding domain|Superoxide dismutase, copper/zinc binding domain|Superoxide dismutase, copper/zinc, binding site 6\n", - "Zinc finger, UBP-type|Zinc finger, UBP-type|Zinc finger, UBP-type 6\n", - "Protein kinase domain|Protein kinase, ATP binding site|Protein kinase domain|Protein kinase domain|Checkpoint kinase 1, catalytic domain 6\n", - "G-protein gamma-like domain 6\n", - "SPX domain|SPX domain 6\n", - "Formyl transferase, C-terminal 6\n", - "Saposin A-type domain 6\n", - "FYVE zinc finger|FYVE zinc finger 6\n", - "Methyltransferase small domain|DNA methylase, N-6 adenine-specific, conserved site 6\n", - "DNA topoisomerase I, DNA binding, eukaryotic-type|DNA topoisomerase I, eukaryotic-type 6\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|PUF60, RNA recognition motif 2 6\n", - "C-myb, C-terminal 6\n", - "Delta/Serrate/lag-2 (DSL) protein|Delta/Serrate/lag-2 (DSL) protein 6\n", - "Histone deacetylase, glutamine rich N-terminal domain 6\n", - "Glutaredoxin 6\n", - "SH2 domain|PI3K p85 subunit, N-terminal SH2 domain 6\n", - "Immunoglobulin-like domain|Immunoglobulin C1-set|Immunoglobulin subtype 6\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|hnRNP DL, RNA recognition motif 1 6\n", - "Cytoplasmic activation/proliferation-associated protein-1 C term 6\n", - "PA domain 6\n", - "Phox homologous domain|Phox homologous domain|Phox homologous domain|Neutrophil cytosol factor 1, PX domain 6\n", - "RH1 domain 6\n", - "Cysteine-rich flanking region, C-terminal|Polycystin cation channel 6\n", - "Serpin domain|Serpin, conserved site|Serpin domain|Antithrombin serpin domain 6\n", - "1-acyl-sn-glycerol-3-phosphate acyltransferase 6\n", - "Delta/Serrate/lag-2 (DSL) protein|Delta/Serrate/lag-2 (DSL) protein|Delta/Serrate/lag-2 (DSL) protein|EGF-like domain 6\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain, eukaryote|PUF60, RNA recognition motif 3 6\n", - "Zinc finger, SIAH-type 6\n", - "MoaB/Mog domain|Molybdenum cofactor biosynthesis, conserved site|MoaB/Mog domain|MoaB/Mog domain|MoaB/Mog domain 6\n", - "Anillin homology domain 6\n", - "Fucosyltransferase, N-terminal 6\n", - "La-type HTH domain|La-type HTH domain|La-related protein 7, La domain 6\n", - "C-type lectin-like|C-type lectin, conserved site|C-type lectin-like|C-type lectin-like|Collectin, C-type lectin-like domain 6\n", - "XPG N-terminal 6\n", - "Domain of unknown function DUF4071 6\n", - "Potassium channel, voltage dependent, Kv3, inactivation domain 6\n", - "Molybdopterin cofactor biosynthesis C (MoaC) domain 6\n", - "Melanoma associated antigen, N-terminal|Melanoma associated antigen, N-terminal 6\n", - "SRCR domain|SRCR domain 6\n", - "Zinc finger, PHD-finger|Zinc finger, RING-type|Zinc finger, RING-type|Zinc finger, PHD-type 6\n", - "Clusterin, C-terminal 6\n", - "SH3 domain|SH3 domain|SH3 domain|SH3 domain|Tyrosine-protein kinase BTK, SH3 domain 6\n", - "Hepatitis delta antigen (HDAg) domain 6\n", - "FERM central domain|Band 4.1 domain|FERM central domain 6\n", - "POU-specific domain|POU-specific domain 6\n", - "Vacuolar protein sorting protein 11, C-terminal 6\n", - "Alpha crystallin/Hsp20 domain|Heat shock protein beta-1, ACD domain 6\n", - "P-type trefoil domain 6\n", - "Sequestosome-1, UBA domain|Ubiquitin-associated domain|Ubiquitin-associated domain|Sequestosome-1, UBA domain 6\n", - "DNA-directed RNA polymerase, insert domain|DNA-directed RNA polymerase, 30-40kDa subunit, conserved site|DNA-directed RNA polymerase, RpoA/D/Rpb3-type 6\n", - "ATP synthase, F1 complex, alpha subunit nucleotide-binding domain 6\n", - "RNA polymerase Rpb1, domain 1|RNA polymerase, N-terminal|DNA-directed RNA polymerase III subunit RPC1, N-terminal 6\n", - "Phosphoribosyltransferase domain|Orotate phosphoribosyl transferase domain|Phosphoribosyltransferase domain 6\n", - "Aprataxin, C2HE/C2H2/C2HC zinc finger|Zinc finger C2H2-type 6\n", - "DHHA2 domain|DHHA2 domain 6\n", - "Glycosyltransferase family 1, N-terminal 6\n", - "RNA polymerase Rpb2, domain 4 6\n", - "RNA recognition motif, spliceosomal PrP8 6\n", - "CRAL-TRIO lipid binding domain 6\n", - "TMEM248/TMEM219 domain 6\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain|Protein Kinase B beta, catalytic domain 5\n", - "Peptidase M10, metallopeptidase|Peptidase, metallopeptidase 5\n", - "Major intrinsic protein, conserved site 5\n", - "Factor I / membrane attack complex|Follistatin-like, N-terminal 5\n", - "Nucleoporin, NSP1-like, C-terminal 5\n", - "Carbamoyl-phosphate synthetase large subunit-like, ATP-binding domain 5\n", - "PWWP domain|PWWP domain|PWWP domain|BR140-related, PWWD domain 5\n", - "Hydin adenylate kinase-like domain 5\n", - "Biotin/lipoyl attachment|2-oxo acid dehydrogenase, lipoyl-binding site 5\n", - "CIDE-N domain 5\n", - "FCP1 homology domain 5\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain|Serine/threonine-protein kinase greatwall, catalytic domain 5\n", - "Cadherin, cytoplasmic C-terminal domain 5\n", - "FDF domain|FDF domain 5\n", - "bMERB domain|bMERB domain 5\n", - "Zinc finger, UBP-type 5\n", - "Laminin G domain|Laminin G domain|Fibronectin type III|Laminin G domain 5\n", - "HSR domain 5\n", - "Nucleoporin, Nup133/Nup155-like, C-terminal 5\n", - "Cryptic/Cripto, CFC domain 5\n", - "PEHE domain 5\n", - "S1 domain|S1 domain|RNA-binding domain, S1 5\n", - "Oestrogen-type nuclear receptor final C-terminal domain 5\n", - "Nmd3, N-terminal 5\n", - "FIIND domain 5\n", - "Syntaxin, N-terminal domain|Target SNARE coiled-coil homology domain|Target SNARE coiled-coil homology domain 5\n", - "Hepatocyte nuclear factor 1, beta isoform, C-terminal|Homeobox domain 5\n", - "Carbohydrate-binding/sugar hydrolysis domain 5\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain|Serine/threonine-protein kinase Sgk3, catalytic domain 5\n", - "Immunoglobulin I-set|Immunoglobulin V-set domain|Immunoglobulin subtype 5\n", - "B3/B4 tRNA-binding domain|B3/B4 tRNA-binding domain 5\n", - "Translation elongation factor Ts, conserved site 5\n", - "C-type lectin-like|C-type lectin-like 5\n", - "Gamma-butyrobetaine hydroxylase-like, N-terminal 5\n", - "Poly A polymerase, head domain 5\n", - "Beta/gamma crystallin|Beta/gamma crystallin 5\n", - "DEP domain 5\n", - "TNFR/NGFR cysteine-rich region|Tumour necrosis factor receptor 7, N-terminal 5\n", - "C2 domain|C2 domain|Ferlin, third C2 domain 5\n", - "PUL domain|PUL domain 5\n", - "Tensin phosphatase, C2 domain|Tensin phosphatase, C2 domain 5\n", - "Serum albumin, conserved site|Serum albumin, N-terminal|Serum albumin, N-terminal|Serum albumin, N-terminal 5\n", - "Acetylcholinesterase, tetramerisation domain 5\n", - "Tumour necrosis factor domain 5\n", - "Distal-less-like homeobox protein, N-terminal domain 5\n", - "Apx/Shrm Domain 1|Apx/Shrm Domain 1 5\n", - "Laminin EGF domain|Laminin EGF domain|EGF-like calcium-binding domain|Laminin EGF domain|EGF-like domain 5\n", - "Phospholipase/carboxylesterase/thioesterase 5\n", - "Netrin module, non-TIMP type|Netrin domain|Netrin module, non-TIMP type|Complement C3-like, NTR domain 5\n", - "Notch, NODP domain 5\n", - "TauD/TfdA-like domain 5\n", - "ATF7-interacting protein , protein binding domain 5\n", - "C2 domain|C2 domain|C2 domain|C2 domain|Ferlin, fifth C2 domain 5\n", - "TGS 5\n", - "SWIRM domain|SWIRM domain 5\n", - "Glutaredoxin|Glutaredoxin|Glutaredoxin, PICOT-like 5\n", - "Uncharacterised domain KLRAQ/TTKRSYEDQ, C-terminal 5\n", - "Angiomotin, C-terminal 5\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tyrosine-protein kinase ephrin type A/B receptor-like|Tumour necrosis factor receptor 4, N-terminal 5\n", - "Glycoside hydrolase family 2, catalytic domain|Glycoside hydrolase, family 2, conserved site 5\n", - "Phospholipase D/Transphosphatidylase|Phospholipase D/Transphosphatidylase|Phospholipase D/Transphosphatidylase 5\n", - "Polyprenyl synthetase, conserved site 5\n", - "MIT|MIT 5\n", - "Peptidase M12A|Peptidase, metallopeptidase|Tolloid/BMP1 peptidase domain 5\n", - "Katanin p80 subunit, C-terminal 5\n", - "Threonine-tRNA ligase catalytic core domain 5\n", - "AP-4 complex subunit epsilon-1, C-terminal|AP-4 complex subunit epsilon-1, C-terminal 5\n", - "Uncharacterised domain, cysteine-rich 5\n", - "Syntaxin, N-terminal domain|Syntaxin, N-terminal domain 5\n", - "Peripherin/rom-1, conserved site 5\n", - "START domain|START domain 5\n", - "Rho GTPase-binding/formin homology 3 (GBD/FH3) domain|Formin, GTPase-binding domain 5\n", - "DNA-directed RNA polymerase, RpoA/D/Rpb3-type 5\n", - "Zinc finger protein DZIP1, N-terminal 5\n", - "Peptide methionine sulphoxide reductase MrsB|Peptide methionine sulphoxide reductase MrsB 5\n", - "L-fucokinase 5\n", - "Kinesin-associated|Kinesin motor domain 5\n", - "PurM-like, C-terminal domain 5\n", - "FAD linked oxidase, N-terminal 5\n", - "Aldehyde oxidase/xanthine dehydrogenase, molybdopterin binding|Oxidoreductase, molybdopterin binding site 5\n", - "Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3/4-kinase, conserved site|Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3-/4-kinase, catalytic domain|PI3Kalpha, catalytic domain 5\n", - "Peptidase S8/S53 domain 5\n", - "Glutathione S-transferase, C-terminal|Glutathione S-transferase, C-terminal-like|Glutathione S-transferases, class Zeta , C-terminal 5\n", - "Sema domain|Sema domain|Sema domain|RON, Sema domain 5\n", - "Aminotransferase, class I/classII|Aminotransferase, class-II, pyridoxal-phosphate binding site 5\n", - "FYVE zinc finger 5\n", - "EGF-like, conserved site|EGF-like calcium-binding domain|EGF-like domain 5\n", - "Follistatin-like, N-terminal 5\n", - "SH2 domain|SH2 domain|SH2 domain|SH2D1A, SH2 domain 5\n", - "Period circadian-like, C-terminal 5\n", - "Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A|Carboxypeptidase A, carboxypeptidase domain 5\n", - "tRNA methyltransferase TRM10-type domain 5\n", - "Kringle|Kringle 5\n", - "Prokineticin domain|Prokineticin domain 5\n", - "Carbohydrate-binding WSC|Carbohydrate-binding WSC|Carbohydrate-binding WSC 5\n", - "Hydroxyacylglutathione hydrolase, MBL domain 5\n", - "GRIP domain 5\n", - "CREB-binding protein/p300, atypical RING domain|CREB-binding protein/p300, atypical RING domain 5\n", - "Methionyl/Valyl/Leucyl/Isoleucyl-tRNA synthetase, anticodon-binding|Isoleucyl tRNA synthetase type 1, anticodon-binding domain 5\n", - "Polyribonucleotide nucleotidyltransferase, RNA-binding domain 5\n", - "Rpn11/EIF3F, C-terminal 5\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|p54nrb, RNA recognition motif 1 5\n", - "Conserved oligomeric Golgi complex, subunit 2, N-terminal 5\n", - "POU-specific domain 5\n", - "Mib-herc2 5\n", - "FY-rich, N-terminal|FY-rich, N-terminal 5\n", - "Squalene/phytoene synthase, conserved site|Trans-Isoprenyl Diphosphate Synthases, head-to-head 5\n", - "Pleckstrin homology domain|FGD1-4, C-terminal PH domain 5\n", - "Folliculin, C-terminal 5\n", - "Insulin-like 5\n", - "Aminoacyl-tRNA synthetase, class Ia|Aminoacyl-tRNA synthetase, class I, conserved site 5\n", - "SH3 domain|SH3 domain|SH3 domain|SH3 domain|Stac3, first SH3 domain 5\n", - "TILa domain|VWFC domain|von Willebrand factor, type D domain 5\n", - "Myosin 5a, cargo-binding domain 5\n", - "PB1 domain|PB1 domain|TFG, PB1 domain 5\n", - "Urocanase, C-terminal domain 5\n", - "Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain 5\n", - "EGF-like domain|Follistatin-like, N-terminal 5\n", - "UmuC domain 5\n", - "Alpha crystallin/Hsp20 domain|Alpha crystallin/Hsp20 domain|Heat shock protein beta-7, ACD domain 5\n", - "V(D)J recombination-activating protein 1, Zinc finger|V(D)J recombination-activating protein 1, Zinc finger 5\n", - "MOSC, N-terminal beta barrel 5\n", - "SOCS box domain|SOCS box domain|SOCS box domain 5\n", - "Vps16, N-terminal 5\n", - "CBM21 (carbohydrate binding type-21) domain|CBM21 (carbohydrate binding type-21) domain 5\n", - "Cell morphogenesis protein C-terminal 5\n", - "Pleckstrin homology domain|Pleckstrin homology domain|FGD1-4, C-terminal PH domain 5\n", - "Dilute domain|Dilute domain|Dilute domain|Myosin 5a, cargo-binding domain 5\n", - "NADH:ubiquinone oxidoreductase, subunit G, iron-sulphur binding|NADH:ubiquinone oxidoreductase, subunit G, iron-sulphur binding|NADH:ubiquinone oxidoreductase, subunit G, iron-sulphur binding 5\n", - "Peptidase M12A 5\n", - "NIF system FeS cluster assembly, NifU, N-terminal|NIF system FeS cluster assembly, NifU, N-terminal 5\n", - "Dickkopf, N-terminal cysteine-rich 5\n", - "Serum albumin, conserved site|Serum albumin, N-terminal|Serum albumin, N-terminal 5\n", - "Phosphatidylinositol 3-/4-kinase, catalytic domain|PI3Kdelta, catalytic domain 5\n", - "Phosphoadenosine phosphosulphate reductase|Phosphoadenosine phosphosulphate reductase 5\n", - "2-5-oligoadenylate synthetase, N-terminal 5\n", - "SH3 domain|SH3 domain|SH3 domain|SH3 domain|Nebulette, SH3 domain 5\n", - "Tudor domain|Tudor domain|Tudor domain 5\n", - "Cytochrome b5-like heme/steroid binding domain|Cytochrome b5-like heme/steroid binding domain 5\n", - "14-3-3 domain|14-3-3 protein, conserved site|14-3-3 domain 5\n", - "DNA-directed RNA polymerase III subunit RPC1, C-terminal 5\n", - "Zinc finger, nuclear hormone receptor-type|Zinc finger, nuclear hormone receptor-type|Zinc finger, nuclear hormone receptor-type|Zinc finger, nuclear hormone receptor-type|Nuclear hormone receptor, ligand-binding domain|Zinc finger, nuclear hormone receptor-type 5\n", - "Zinc finger, nuclear hormone receptor-type|Zinc finger, nuclear hormone receptor-type|Zinc finger, nuclear hormone receptor-type|Nuclear hormone receptor, ligand-binding domain|Zinc finger, nuclear hormone receptor-type 5\n", - "RNA recognition motif domain|RNA recognition motif domain|PUF60, RNA recognition motif 1 4\n", - "RecQ helicase-like 5|RecQ helicase-like 5 4\n", - "Zinc finger, CCCH-type|Zinc finger, CCCH-type 4\n", - "Pirin, N-terminal domain 4\n", - "SH2 domain|SH2 domain|SH2 domain|SH2 domain|SH2B3, SH2 domain 4\n", - "Cyclin, C-terminal domain|Cyclin-like|Cyclin, C-terminal domain 4\n", - "Lipocalin family conserved site 4\n", - "VHS domain|VHS domain|VHS domain 4\n", - "Sterile alpha motif domain|Sterile alpha motif domain|Sterile alpha motif domain|SASH1, SAM domain repeat 2 4\n", - "EVA1 domain 4\n", - "Transcriptional repressor p66, coiled-coil MBD2-interaction domain 4\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|Splicing factor ELAV/Hu, RNA recognition motif 1 4\n", - "DEAD2|DNA/RNA helicase, ATP-dependent, DEAH-box type, conserved site|Helicase superfamily 1/2, ATP-binding domain, DinG/Rad3-type|Helicase-like, DEXD box c2 type 4\n", - "Marvel domain 4\n", - "Aminotransferase, class I/classII|Aminotransferase, class-II, pyridoxal-phosphate binding site|Tetrapyrrole biosynthesis, 5-aminolevulinic acid synthase 4\n", - "Fragile X-related 1 protein, C-terminal region 3 4\n", - "Tyrosine-protein kinase JAK2, SH2 domain 4\n", - "SANT/Myb domain|SANT domain|SANT/Myb domain|SANT/Myb domain 4\n", - "E3 ubiquitin-protein ligase HECW, C2 domain 4\n", - "WD40 repeat, conserved site 4\n", - "DNA repair Nbs1, C-terminal 4\n", - "Cobalamin (vitamin B12)-binding module, cap domain|Cobalamin (vitamin B12)-binding module, cap domain|Methionine synthase, B12-binding domain 4\n", - "ELM2 domain|ELM2 domain 4\n", - "Integrin beta subunit, VWA domain|PSI domain 4\n", - "RNA recognition motif domain|RNA recognition motif domain|ESRP1, RNA recognition motif 1 4\n", - "Kazal domain|Kazal domain|EGF-like domain|Kazal domain 4\n", - "Ribosome biogenesis protein BMS1/TSR1, C-terminal|Ribosome biogenesis protein BMS1/TSR1, C-terminal 4\n", - "Ribosomal protein S23/S25, mitochondrial 4\n", - "Erythropoietin/thrombopoeitin, conserved site 4\n", - "Peptidase C1A, papain C-terminal 4\n", - "Cyclin, C-terminal domain|Cyclin, C-terminal domain 4\n", - "Flavodoxin-like fold 4\n", - "APC10/DOC domain|HERC2, APC10 domain 4\n", - "Alpha-2-macroglobulin receptor-associated protein, domain 1|Alpha-2-macroglobulin receptor-associated protein, domain 1 4\n", - "MATH/TRAF domain|TRAF3, MATH domain 4\n", - "Phospholipase D-like domain 4\n", - "PDGF/VEGF domain 4\n", - "IMP dehydrogenase/GMP reductase|IMP dehydrogenase / GMP reductase, conserved site|IMP dehydrogenase/GMP reductase 4\n", - "Heat shock protein Hsp90, N-terminal 4\n", - "von Hippel-Lindau disease tumour suppressor, alpha domain 4\n", - "Pancreatic trypsin inhibitor Kunitz domain 4\n", - "S-adenosyl-L-homocysteine hydrolase, conserved site 4\n", - "Phosphopantetheine binding ACP domain|Polyketide synthase, phosphopantetheine-binding domain 4\n", - "Formamidopyrimidine-DNA glycosylase, catalytic domain|Formamidopyrimidine-DNA glycosylase, catalytic domain 4\n", - "DNA fragmentation factor 45kDa, middle domain|DNA fragmentation factor 45kDa, middle domain 4\n", - "Immunoglobulin V-set domain|Immunoglobulin-like domain|Immunoglobulin V-set domain|Immunoglobulin subtype 2|Immunoglobulin subtype 4\n", - "GINS subunit, domain A 4\n", - "EXS, C-terminal 4\n", - "Kinesin motor domain|Kinesin motor domain, conserved site|Kinesin motor domain|Kinesin motor domain 4\n", - "FerIin domain|Ferlin, second C2 domain 4\n", - "Acetylserotonin O-methyltransferase, dimerisation domain 4\n", - "Ribosomal protein S10 domain|Ribosomal protein S10, conserved site|Ribosomal protein S10 domain 4\n", - "Synaptobrevin|Synaptobrevin|Synaptobrevin|Synaptobrevin 4\n", - "Pointed domain|Pointed domain|Pointed domain 4\n", - "Ephrin receptor-binding domain|Ephrin receptor-binding domain|Ephrin receptor-binding domain|Ephrin, conserved site|Ephrin receptor-binding domain|Ephrin-B ectodomain 4\n", - "Adaptor protein ClpS, core 4\n", - "Acyl-CoA-binding protein, ACBP|Acyl-CoA-binding protein, ACBP|Acyl-CoA-binding protein, ACBP, conserved site|Acyl-CoA-binding protein, ACBP|Acyl-CoA-binding protein, ACBP 4\n", - "Staufen, C-terminal|Double-stranded RNA-binding domain|Double-stranded RNA-binding domain|Double-stranded RNA-binding domain 4\n", - "Zinc finger, RanBP2-type 4\n", - "Protein kinase domain|Protein kinase, ATP binding site|Protein kinase domain|Protein kinase domain|Salt-Inducible kinase, catalytic domain 4\n", - "Laminin EGF domain|EGF-like domain|Laminin EGF domain 4\n", - "Zinc finger, Btk motif|Zinc finger, Btk motif|Zinc finger, Btk motif|Zinc finger, Btk motif 4\n", - "SH3 domain|SH3 domain|SH3 domain|GRAF, SH3 domain 4\n", - "Long hematopoietin receptor, Gp130 family 2, conserved site|Fibronectin type III 4\n", - "SANT/Myb domain|SANT domain|SANT/Myb domain 4\n", - "Set2 Rpb1 interacting domain 4\n", - "Dual specificity phosphatase, catalytic domain|Dual specificity protein phosphatase domain|Protein-tyrosine phosphatase, catalytic 4\n", - "3-hydroxyacyl-CoA dehydrogenase, conserved site 4\n", - "Helicase/UvrB, N-terminal|Helicase superfamily 1/2, ATP-binding domain, DinG/Rad3-type 4\n", - "Germinal-centre associated nuclear protein, nucleoporin homology domain 4\n", - "Cullin protein, neddylation domain|Cullin protein, neddylation domain 4\n", - "Talin-1/2, rod-segment 4\n", - "Linker histone H1/H5, domain H15|Linker histone H1/H5, domain H15|Linker histone H1/H5, domain H15|Linker histone H1/H5, domain H15 4\n", - "ARID DNA-binding domain 4\n", - "CCR4-NOT transcription complex subunit 1, CAF1-binding domain 4\n", - "Peptidase M16, N-terminal|Peptidase M16, zinc-binding site 4\n", - "Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A|AEBP1/CPX, carboxypeptidase domain 4\n", - "Rel homology dimerisation domain 4\n", - "Immunoglobulin V-set domain|Immunoglobulin-like domain|Immunoglobulin V-set domain 4\n", - "DNA topoisomerase, type IA, central|DNA topoisomerase, type IA, central 4\n", - "WAP-type 'four-disulfide core' domain|WAP-type 'four-disulfide core' domain|WAP-type 'four-disulfide core' domain|WAP-type 'four-disulfide core' domain 4\n", - "Major facilitator superfamily domain|Kazal domain 4\n", - "Metastasis-associated protein MTA1, R1 domain 4\n", - "Disintegrin domain 4\n", - "Orn/DAP/Arg decarboxylase 2, N-terminal 4\n", - "Calcineurin-binding protein cabin-1, MEF2-binding domain|Calcineurin-binding protein cabin-1, MEF2-binding domain 4\n", - "VWFC domain|VWFC domain|von Willebrand factor, type D domain 4\n", - "Phosphoribosyltransferase C-terminal 4\n", - "Domain of unknown function DUF4208 4\n", - "Lipocalin/cytosolic fatty-acid binding domain|Cytosolic fatty-acid binding 4\n", - "Hotdog acyl-CoA thioesterase (ACOT)-type domain 4\n", - "Pirin, C-terminal domain 4\n", - "SH3 domain|SH3 domain|Disks large homologue 5, SH3 domain 4\n", - "S phase cyclin A-associated protein in the endoplasmic reticulum, N-terminal 4\n", - "Myotonic dystrophy protein kinase, coiled coil 4\n", - "EGF domain|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 4\n", - "CCR4-Not complex, Not1 subunit, domain of unknown function DUF3819 4\n", - "Aminopeptidase P, N-terminal 4\n", - "PAP/25A-associated 4\n", - "Syndecan/Neurexin domain|Neurexin/syndecan/glycophorin C 4\n", - "Signal transducer and activation of transcription 1, TAZ2 binding domain, C-terminal 4\n", - "Lysyl oxidase, conserved site 4\n", - "Endonuclease/exonuclease/phosphatase|Deoxyribonuclease I, conservied site 4\n", - "Rad4 beta-hairpin domain 3|Rad4 beta-hairpin domain 3 4\n", - "von Hippel-Lindau disease tumour suppressor, beta domain 4\n", - "Complement Clr-like EGF domain|EGF-like domain 4\n", - "Urocanase, N-terminal domain 4\n", - "2Fe-2S ferredoxin-type iron-sulfur binding domain|2Fe-2S ferredoxin-type iron-sulfur binding domain|Xanthine dehydrogenase, small subunit 4\n", - "Phosphopantetheine binding ACP domain|Phosphopantetheine binding ACP domain 4\n", - "DNA-dependent protein kinase catalytic subunit, catalytic domain 4\n", - "SH2 domain|SH2 domain|SH2 domain|Protein kinase domain|SH2 domain|Tyrosine-protein kinase Lck, SH2 domain 4\n", - "Zona pellucida domain|Zona pellucida domain 4\n", - "Peptidase S8/S53 domain|Site-1 peptidase catalytic domain 4\n", - "SCAN domain 4\n", - "EamA domain 4\n", - "Zinc finger, RING-type|E3 ubiquitin-protein ligase CBL-B, RING finger, HC subclass 4\n", - "SRCR-like domain|SRCR domain 4\n", - "Replication factor Mcm10, C-terminal|Replication factor Mcm10, C-terminal 4\n", - "A-kinase anchor protein 10, PKA-binding (AKB) domain 4\n", - "Phostensin/Taperin N-terminal domain 4\n", - "SPARC/Testican, calcium-binding domain 4\n", - "Alcohol dehydrogenase, N-terminal 4\n", - "PI31 proteasome regulator, C-terminal 4\n", - "THUMP domain|THUMP domain|THUMP domain 4\n", - "ERCC3/RAD25/XPB helicase, C-terminal domain|Helicase, C-terminal|Helicase, C-terminal 4\n", - "Mre11, DNA-binding 4\n", - "Zinc finger, RING-type|Zinc finger, RING-type|Zinc finger, PHD-type 4\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|Heterogeneous nuclear ribonucleoprotein R, RNA recognition motif 1 4\n", - "Electron transfer flavoprotein, alpha/beta-subunit, N-terminal|Electron transfer flavoprotein, beta-subunit, conserved site|Electron transfer flavoprotein, alpha/beta-subunit, N-terminal|Electron transfer flavoprotein, beta subunit, N-terminal 4\n", - "Brf1, TBP-binding domain 4\n", - "Dynamin GTPase effector|Dynamin GTPase effector 4\n", - "SH3 domain|Pleckstrin homology domain 4\n", - "Myosin, N-terminal, SH3-like|Myosin head, motor domain 4\n", - "E1A-binding protein p400, N-terminal 4\n", - "EGF-like domain|EMI domain 4\n", - "C2 domain|C2 domain|Ferlin, fifth C2 domain 4\n", - "Cleavage/polyadenylation specificity factor, A subunit, C-terminal 4\n", - "SH3 domain|Obscurin, SH3 domain 4\n", - "Kazal domain|EGF-like domain|Follistatin-like, N-terminal 4\n", - "5'-Nucleotidase, C-terminal 4\n", - "Rho GTPase-activating protein domain|Rho GTPase-activating protein domain|Rho GTPase-activating protein domain|ARAP, RhoGAP domain 4\n", - "PAS fold-3|PAS domain|PAS domain|PAS domain 4\n", - "Interferon regulatory factor-3|Interferon regulatory factor DNA-binding domain 4\n", - "Sequestosome-1, UBA domain 4\n", - "Dilute domain 4\n", - "Ricin B, lectin domain|Ricin B, lectin domain|Ricin B, lectin domain 4\n", - "Anion-transporting ATPase-like domain 4\n", - "RNA recognition motif domain|RNA recognition motif domain|hnRNP A1, RNA recognition motif 2 4\n", - "DNA methyltransferase 1-associated 1|DNA methyltransferase 1-associated 1 4\n", - "Ras-associating (RA) domain|SNX27, RA domain 4\n", - "Predicted HAD-superfamily phosphatase, subfamily IA/Epoxide hydrolase, N-terminal 4\n", - "Mediator complex, subunit Med1 4\n", - "Stealth protein CR1, conserved region 1 4\n", - "EYA domain 4\n", - "ATPase, V1 complex, subunit H, C-terminal 4\n", - "DhaL domain|DhaL domain|DhaL domain 4\n", - "Seven-in-absentia protein, TRAF-like domain 4\n", - "S-adenosylmethionine synthetase, central domain|S-adenosylmethionine synthetase, conserved site 4\n", - "TROVE domain|TROVE domain 4\n", - "HAT, C-terminal dimerisation domain 4\n", - "APC10/DOC domain|APC10/DOC domain|APC10/DOC domain|HERC2, APC10 domain 4\n", - "Acetylcholinesterase, tetramerisation domain|Acetylcholinesterase, tetramerisation domain 4\n", - "Double-stranded RNA-specific adenosine deaminase (DRADA) 4\n", - "Gamma-carboxyglutamic acid-rich (GLA) domain|Gamma-carboxyglutamic acid-rich (GLA) domain|Gamma-carboxyglutamic acid-rich (GLA) domain 4\n", - "OTU domain 4\n", - "Phosphotyrosine protein phosphatase I 4\n", - "Peptidase C14A, caspase catalytic domain|Peptidase C14, caspase non-catalytic subunit p10|Peptidase C14A, caspase catalytic domain|Peptidase C14A, caspase catalytic domain 4\n", - "Low-density lipoprotein (LDL) receptor class A, conserved site|EGF-like calcium-binding domain 4\n", - "Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A|Carboxypeptidase B, carboxypeptidase domain 4\n", - "Neuralized homology repeat (NHR) domain|Neuralized homology repeat (NHR) domain 4\n", - "C2 domain|Ferlin, first C2 domain 4\n", - "Notch domain 4\n", - "Pleckstrin homology domain|Pleckstrin homology domain|Pleckstrin homology domain|Vav, PH domain 4\n", - "C2 domain|C2 domain|C2 domain|C2 domain 4\n", - "FERM central domain|Pleckstrin homology domain|Band 4.1 domain|Kindlin/fermitin, PH domain 4\n", - "SRCR domain|SRCR domain|SRCR domain|SRCR domain|SRCR-like domain 4\n", - "Somatomedin B domain|Somatomedin B, chordata|Somatomedin B domain|Somatomedin B domain 4\n", - "Zinc finger, CCHC-type 4\n", - "2-oxo acid dehydrogenase, lipoyl-binding site|Biotin/lipoyl attachment 4\n", - "Tyrosine-protein kinase, non-receptor, TYK2, N-terminal|FERM domain 4\n", - "OCRL1, PH domain 4\n", - "Myelin gene regulatory factor C-terminal domain 2 4\n", - "GTP binding domain|MnmE, helical domain|TrmE-type guanine nucleotide-binding domain|TrmE-type guanine nucleotide-binding domain 4\n", - "Phospholipase C, phosphatidylinositol-specific, Y domain|Phospholipase C, phosphatidylinositol-specific, Y domain 4\n", - "Notch domain|Notch domain 4\n", - "Ubiquitin carboxyl-terminal hydrolase 37, pleckstrin homology-like domain|Ubiquitin carboxyl-terminal hydrolase 37, pleckstrin homology-like domain 4\n", - "CTF transcription factor/nuclear factor 1, DNA-binding domain|MAD homology 1, Dwarfin-type 4\n", - "Hox9, N-terminal activation domain 4\n", - "RNA polymerase Rpb1, domain 4 4\n", - "L27-2|L27 domain 4\n", - "Dihydroprymidine dehydrogenase domain II 4\n", - "Alcohol dehydrogenase, C-terminal 4\n", - "FERM domain|JAK2, FERM domain C-lobe 4\n", - "D domain of beta-TrCP|D domain of beta-TrCP 4\n", - "Protein-only RNase P, C-terminal 4\n", - "MOFRL domain 4\n", - "Syntaxin, N-terminal domain|Target SNARE coiled-coil homology domain 4\n", - "Carboxypeptidase A6 4\n", - "SH2 domain|Tyrosine-protein kinase Lck, SH2 domain 4\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tumour necrosis factor receptor 5, N-terminal 4\n", - "Condensin complex subunit 1, C-terminal 4\n", - "SH2 domain|SH2 domain|PI3K p85 subunit, N-terminal SH2 domain 4\n", - "SH3BP2, SH2 domain 4\n", - "Domain of unknown function DUF2075 4\n", - "Autophagy-related protein 16 4\n", - "MATH/TRAF domain|MATH/TRAF domain|TRIM37, MATH domain 4\n", - "Transferrin-like domain|Transferrin-like domain 4\n", - "Domain of unknown function DUF1866|RNA recognition motif domain|Domain of unknown function DUF1866 4\n", - "EGF-like domain|SEA domain 4\n", - "Autophagy-related, C-terminal 4\n", - "Carbamoyl-phosphate synthetase large subunit-like, ATP-binding domain|Carbamoyl-phosphate synthetase large subunit-like, ATP-binding domain|ATP-grasp fold 4\n", - "Cadherin prodomain|Pleckstrin homology domain 4\n", - "JAB1/MPN/MOV34 metalloenzyme domain|MPN domain 4\n", - "Mib-herc2|Mib-herc2 4\n", - "LISCH7 4\n", - "Opiodes neuropeptide|Pro-opiomelanocortin/corticotropin, ACTH, central region|Opiodes neuropeptide 4\n", - "U1 small nuclear ribonucleoprotein of 70kDa N-terminal|F-BAR domain 4\n", - "Lipid-binding serum glycoprotein, C-terminal|Lipid-binding serum glycoprotein, N-terminal 4\n", - "XPG-I domain|XPG conserved site|XPG-I domain 4\n", - "Putative zinc-RING and/or ribbon|Putative zinc-RING and/or ribbon 4\n", - "Rapsyn, myristoylation/linker region, N-terminal|43kDa postsynaptic, conserved site 4\n", - "Cobalamin (vitamin B12)-binding domain|Methylmalonyl-CoA mutase, C-terminal 4\n", - "Myotonic dystrophy protein kinase, coiled coil|Myotonic dystrophy protein kinase, coiled coil 4\n", - "Laminin EGF domain|Laminin EGF domain|Laminin IV type B|Laminin EGF domain 4\n", - "Metabotropic glutamate receptor, Homer-binding domain|Metabotropic glutamate receptor, Homer-binding domain 4\n", - "Protein kinase domain|Protein kinase, ATP binding site|Protein kinase domain|Protein kinase domain|Novel protein kinase C delta, catalytic domain 4\n", - "Mediator complex, subunit Med12|Mediator complex, subunit Med12 4\n", - "Fibrous sheath-interacting protein 2, C-terminal 4\n", - "CD80-like, immunoglobulin C2-set|Immunoglobulin subtype 4\n", - "Zinc finger, XPA-type, conserved site|Zinc finger, XPA-type, conserved site 4\n", - "Zinc finger, CCCH-type|Zinc finger, CCCH-type|Zinc finger, CCCH-type 4\n", - "DHR-1 domain|DHR-1 domain|Dedicator of cytokinesis B, C2 domain 4\n", - "Methionine synthase, B12-binding domain 4\n", - "C2 domain|Ferlin, third C2 domain 4\n", - "Interleukin-1 propeptide 4\n", - "OST-HTH/LOTUS domain|OST-HTH/LOTUS domain 4\n", - "Disintegrin domain|Disintegrin domain 4\n", - "Transcription factor TFIIIB component B'', Myb domain 4\n", - "Squalene cyclase, C-terminal|Terpene synthase, conserved site 4\n", - "Heparin cofactor II 4\n", - "Lipocalin/cytosolic fatty-acid binding domain|Lipocalin family conserved site 4\n", - "C2 domain|C2 domain|E3 ubiquitin-protein ligase HECW, C2 domain 4\n", - "Proliferating cell nuclear antigen, PCNA, C-terminal 4\n", - "Tyrosine-protein kinase, receptor Tie-2, Ig-like domain 1, N-terminal 4\n", - "SH2 domain|SH2 domain|SH2 domain|STAT5b, SH2 domain 4\n", - "NOPS 4\n", - "Chaperonin TCP-1, conserved site 4\n", - "C1q domain 4\n", - "Transcription factor, GTP-binding domain|Tr-type G domain, conserved site|Transcription factor, GTP-binding domain 4\n", - "L27 domain, C-terminal|L27 domain 4\n", - "Zinc finger, MIZ-type 4\n", - "FERM central domain|FERM, N-terminal|FERM domain|Band 4.1 domain 4\n", - "Creatinase, N-terminal 4\n", - "TAFII-230 TBP-binding 4\n", - "OST-HTH/LOTUS domain 4\n", - "Pancreatic trypsin inhibitor Kunitz domain|Proteinase inhibitor I2, Kunitz, conserved site|Pancreatic trypsin inhibitor Kunitz domain|Pancreatic trypsin inhibitor Kunitz domain|Pancreatic trypsin inhibitor Kunitz domain 4\n", - "MHCK/EF2 kinase 4\n", - "Disks large homologue 1, N-terminal PEST domain|Disks large homologue 1, N-terminal PEST domain 4\n", - "Domain of unknown function DUF4211 4\n", - "Plk4, C-terminal polo-box domain 4\n", - "Interleukin-4 receptor alpha, N-terminal 4\n", - "NADP-dependent oxidoreductase domain 4\n", - "Ribosomal protein S10 domain|Ribosomal protein S10, conserved site 4\n", - "Unconventional myosin-X, coiled coil domain 4\n", - "HD/PDEase domain 4\n", - "Cyclin, N-terminal|Cyclin-like 4\n", - "Myc-type, basic helix-loop-helix (bHLH) domain|Myc-type, basic helix-loop-helix (bHLH) domain|Myogenic basic muscle-specific protein|Myc-type, basic helix-loop-helix (bHLH) domain 4\n", - "La-type HTH domain|La-type HTH domain 4\n", - "Tumour necrosis factor receptor 9, N-terminal 4\n", - "Fibronectin type II domain|Fibronectin type II domain|Fibronectin type II domain 4\n", - "Polymerase, nucleotidyl transferase domain 4\n", - "C-type lectin-like|C-type lectin-like|Aggrecan/versican, C-type lectin-like domain 4\n", - "Phosphatidylinositol 3-kinase, C2 domain|Phosphatidylinositol 3-kinase Ras-binding (PI3K RBD) domain 4\n", - "BCAS3 domain 4\n", - "CCR4-Not complex component, Not1, C-terminal 4\n", - "Bromodomain|Bromodomain|Bromodomain|Bromodomain, conserved site|Bromodomain|Bromodomain 4\n", - "Telomere length regulation protein, conserved domain 4\n", - "POU-specific domain|POU-specific domain|POU-specific domain|Cro/C1-type helix-turn-helix domain 4\n", - "Cyclophilin-type peptidyl-prolyl cis-trans isomerase domain 4\n", - "TAF6, C-terminal HEAT repeat domain 4\n", - "G2 nidogen/fibulin G2F|G2 nidogen/fibulin G2F 4\n", - "Peptidase M14, carboxypeptidase A|Carboxypeptidase A, carboxypeptidase domain 4\n", - "Ribosomal protein/NADH dehydrogenase domain|Ribosomal protein/NADH dehydrogenase domain 4\n", - "Bicarbonate transporter, C-terminal|Anion exchange, conserved site 4\n", - "Mab-21 domain|Ricin B, lectin domain|Mab-21 domain 4\n", - "SH3 domain|SH3 domain|Pleckstrin homology domain|SH3 domain|1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase gamma-2, SH3 domain 4\n", - "Sphingolipid delta4-desaturase, N-terminal|Sphingolipid delta4-desaturase, N-terminal 4\n", - "Tumor necrosis factor receptor 27, N-terminal 4\n", - "Pleckstrin homology domain|FERM central domain|Pleckstrin homology domain|Pleckstrin homology domain|Kindlin/fermitin, PH domain 4\n", - "SH2 domain|SH2 domain|SH2 domain|VAV1, SH2 domain 4\n", - "Spermidine synthase, tetramerisation domain|Polyamine biosynthesis domain 4\n", - "Aminoacyl-tRNA synthetase, class II|WHEP-TRS domain|WHEP-TRS domain 4\n", - "Usher syndrome type-1C protein-binding protein 1, PDZ domain 4\n", - "C2 domain|Ferlin, fourth C2 domain 4\n", - "Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A 4\n", - "SNAP-25 4\n", - "C2 domain|FerIin domain|Ferlin, second C2 domain 4\n", - "SH3 domain|SH3 domain|SH3 domain|SH3 domain|Nebulin, SH3 domain 4\n", - "DNA topoisomerase, type IA, zn finger 4\n", - "Sox, C-terminal 3\n", - "Homeobox domain|Homeobox domain, metazoa|Helix-turn-helix motif|Homeobox domain|Homeobox domain|Homeobox domain 3\n", - "CRIB domain|CRIB domain|CRIB domain 3\n", - "Cytochrome b5-like heme/steroid binding domain|Cytochrome b5-like heme/steroid binding domain|Cytochrome b5, heme-binding site|Cytochrome b5-like heme/steroid binding domain|Cytochrome b5-like heme/steroid binding domain 3\n", - "CABIT domain 3\n", - "Glyoxalase/fosfomycin resistance/dioxygenase domain 3\n", - "Cytochrome c-like domain 3\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain|MSK2, N-terminal catalytic domain 3\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase domain|Sterile alpha motif domain|Tyrosine-protein kinase, catalytic domain 3\n", - "Folliculin-interacting protein, middle domain|Tripartite DENN domain, FNIP1/2-type 3\n", - "SecY conserved site 3\n", - "V-ATPase proteolipid subunit C-like domain|ATP synthase, F0 complex, subunit C, DCCD-binding site 3\n", - "Alanine dehydrogenase/pyridine nucleotide transhydrogenase, NAD(H)-binding domain|Alanine dehydrogenase/pyridine nucleotide transhydrogenase, NAD(H)-binding domain 3\n", - "F-BAR domain|SLIT-ROBO Rho GTPase-activating protein 1, F-BAR domain 3\n", - "Tox-GHH domain 3\n", - "Vertebrate-like NAGS Gcn5-related N-acetyltransferase (GNAT) domain 3\n", - "FY-rich, N-terminal 3\n", - "Fibrillar collagen, C-terminal|Fibrillar collagen, C-terminal 3\n", - "Calreticulin/calnexin, conserved site 3\n", - "Villin headpiece|Villin headpiece|Villin headpiece 3\n", - "MHC class II, beta chain, N-terminal|MHC class II, beta chain, N-terminal|MHC class II, beta chain, N-terminal 3\n", - "Lipase, N-terminal 3\n", - "Zinc finger, DNA glycosylase/AP lyase-type 3\n", - "Aspartyl/asparaginy/proline hydroxylase 3\n", - "Domain of unknown function DUF2439 3\n", - "DBINO domain 3\n", - "Pop1, N-terminal 3\n", - "Tachykinin/Neurokinin-like, conserved site 3\n", - "Peptidase M24|FACT complex subunit SPT16 3\n", - "Phospholipase C, phosphatidylinositol-specific, Y domain 3\n", - "CEP170, C-terminal 3\n", - "OAR domain 3\n", - "Sterile alpha motif domain|Stromal interaction molecule 1, SAM domain 3\n", - "Myb/SANT-like DNA-binding domain 3\n", - "Cytosolic aminopeptidase 1 3\n", - "SH2 domain|SH2 domain|SH2 domain|Chimaerin, SH2 domain 3\n", - "Septin-type guanine nucleotide-binding (G) domain 3\n", - "G8 domain|G8 domain|G8 domain|Cell surface hyaluronidase, PANDER-like domain 3\n", - "Zinc finger, C2HC5-type 3\n", - "Immunoglobulin-like domain|Immunoglobulin V-set domain 3\n", - "Zinc finger, RING-type|Zinc finger, RING-type, conserved site|Zinc finger, RING-type|Zinc finger, RING-type 3\n", - "Band 4.1, C-terminal 3\n", - "Lysophospholipase, catalytic domain 3\n", - "PI3Kbeta, catalytic domain 3\n", - "Integrin alpha chain, C-terminal cytoplasmic region, conserved site 3\n", - "Beta/gamma crystallin 3\n", - "Peptide methionine sulphoxide reductase MrsB|Peptide methionine sulphoxide reductase MrsB|Peptide methionine sulphoxide reductase MrsB 3\n", - "Protein kinase domain|Protein kinase, ATP binding site|Protein kinase domain|Protein kinase domain|Citron Rho-interacting kinase, catalytic domain 3\n", - "Anaphylatoxin/fibulin 3\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tumor necrosis factor receptor 6B, N-terminal 3\n", - "Butyrophylin-like, SPRY domain|B30.2/SPRY domain|SPRY domain 3\n", - "SKI-interacting protein SKIP, SNW domain 3\n", - "Ribosomal protein L3, conserved site 3\n", - "NADH:ubiquinone oxidoreductase, 75kDa subunit, conserved site|2Fe-2S ferredoxin-type iron-sulfur binding domain|2Fe-2S ferredoxin-type iron-sulfur binding domain 3\n", - "PARP-10, RNA recognition motif 1 and 2 3\n", - "Thioredoxin domain|Thioredoxin domain|Thioredoxin-related transmembrane protein 2, thioredoxin domain 3\n", - "Kazal domain|Factor I / membrane attack complex|Follistatin-like, N-terminal|Kazal domain 3\n", - "Zinc finger, SWIM-type 3\n", - "Exonuclease, RNase T/DNA polymerase III|Exonuclease, RNase T/DNA polymerase III 3\n", - "Apple domain 3\n", - "Transketolase-like, pyrimidine-binding domain|Transketolase binding site|Transketolase-like, pyrimidine-binding domain 3\n", - "Domain of unknown function DUF4819 3\n", - "Laminin EGF domain|Laminin EGF domain|Laminin EGF domain|EGF-like calcium-binding domain|Laminin EGF domain|EGF-like domain 3\n", - "BEN domain|BEN domain|BEN domain 3\n", - "Sterile alpha motif domain|Sterile alpha motif domain|Sterile alpha motif domain|Liprin-alpha, SAM domain repeat 3 3\n", - "Speriolin, C-terminal 3\n", - "Carbohydrate-binding WSC|Carbohydrate-binding WSC|Polycystin cation channel 3\n", - "Cyclophilin-type peptidyl-prolyl cis-trans isomerase domain|Cyclophilin-type peptidyl-prolyl cis-trans isomerase domain|Cyclophilin-type peptidyl-prolyl cis-trans isomerase, conserved site|Cyclophilin-type peptidyl-prolyl cis-trans isomerase domain 3\n", - "Knr4/Smi1-like domain|Knr4/Smi1-like domain 3\n", - "Sodium/sulphate symporter, conserved site 3\n", - "Glutamyl-tRNA synthetase 3\n", - "Frizzled/Smoothened, transmembrane domain|Frizzled/Smoothened, transmembrane domain|GPCR, family 2-like|Frizzled/Smoothened, transmembrane domain|Smoothened, transmembrane domain 3\n", - "Electron transfer flavoprotein, alpha/beta-subunit, N-terminal|Electron transfer flavoprotein, alpha subunit, N-terminal 3\n", - "Ribosomal protein L19/L19e|Ribosomal protein L19/L19e|Ribosomal protein L19/L19e|Ribosomal protein L19, eukaryotic 3\n", - "Rab-binding domain FIP-RBD|Rab-binding domain FIP-RBD 3\n", - "Peptidase M12A|Peptidase M12A|Tolloid/BMP1 peptidase domain 3\n", - "GRAM domain|TBC1D8B, PH-GRAM domain 1 3\n", - "Ribosomal RNA methyltransferase FtsJ domain|Mononegavirus L protein 2-O-ribose methyltransferase 3\n", - "Biotin carboxylation domain|Biotin carboxylase, C-terminal 3\n", - "Domain of unknown function DUF4764 3\n", - "Domain of unknown function DUF3677 3\n", - "CRAL-TRIO lipid binding domain|CRAL-TRIO lipid binding domain 3\n", - "DZF domain|DZF domain|DZF domain 3\n", - "Fas receptor, death domain 3\n", - "Domain of unknown function DUF4171 3\n", - "Translation initiation factor 3, N-terminal 3\n", - "SH3 domain|SH3 domain|SH3 domain|CD2-associated protein, second SH3 domain 3\n", - "Aminoacyl-tRNA synthetase, class I, conserved site 3\n", - "Sterile alpha motif domain|USH1G, SAM domain 3\n", - "EGF-like, conserved site|Laminin EGF domain|EGF-like domain|EGF-like domain 3\n", - "DNA recombination and repair protein Rad51-like, C-terminal|DNA recombination and repair protein RecA, monomer-monomer interface|AAA+ ATPase domain|Rad51/DMC1/RadA 3\n", - "EH domain|EH domain|EF-hand domain|EH domain|EF-hand domain|EH domain 3\n", - "Antithrombin serpin domain 3\n", - "Pre-mRNA cleavage complex subunit Clp1, C-terminal 3\n", - "Timeless C-terminal 3\n", - "SPARC/Testican, calcium-binding domain|Osteonectin-like, conserved site 3\n", - "Serpin H1 inhibitory domain 3\n", - "Complement Clr-like EGF domain|EGF-like calcium-binding domain|EGF-like domain 3\n", - "Fetuin-B-type cystatin domain|Cystatin domain|Cystatin domain 3\n", - "Multicopper oxidase, type 2|Multicopper oxidases, conserved site|Multicopper oxidase, copper-binding site 3\n", - "Condensin complex subunit 1, N-terminal 3\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tumor necrosis factor receptor 11A, N-terminal 3\n", - "FMP27, GFWDK domain|FMP27, GFWDK domain 3\n", - "Retinoblastoma-associated protein, C-terminal 3\n", - "Alpha/beta hydrolase fold-3 3\n", - "Long hematopoietin receptor, single chain, conserved site|Fibronectin type III|Fibronectin type III|Fibronectin type III 3\n", - "Protein kinase domain|Protein kinase, ATP binding site|Protein kinase domain|Protein kinase domain|MST4, kinase domain 3\n", - "FCP1 homology domain|FCP1 homology domain|FCP1 homology domain|FCP1-like phosphatase, phosphatase domain 3\n", - "Poly(ADP-ribose) polymerase, regulatory domain|Poly(ADP-ribose) polymerase, regulatory domain 3\n", - "BEN domain|BEN domain 3\n", - "FERM central domain|Pleckstrin homology domain|Pleckstrin homology domain|Band 4.1 domain|Kindlin/fermitin, PH domain 3\n", - "NAD(P) transhydrogenase, alpha subunit, C-terminal 3\n", - "Vitellinogen, open beta-sheet 3\n", - "I/LWEQ domain 3\n", - "Fibronectin type III|Long hematopoietin receptor, soluble alpha chain, conserved site|Fibronectin type III|Fibronectin type III|Fibronectin type III 3\n", - "Long hematopoietin receptor, soluble alpha chain, conserved site|Fibronectin type III|Fibronectin type III 3\n", - "Glutathione S-transferase, C-terminal-like|Prostaglandin E synthase 2, C-terminal 3\n", - "Glycosyl transferase, family 3|Glycosyl transferase, family 3 3\n", - "Nuclear Testis protein, N-terminal 3\n", - "Bromodomain protein 4, C-terminal 3\n", - "Tyrosine-protein kinase ephrin type A/B receptor-like|Tumour necrosis factor receptor 4, N-terminal 3\n", - "von Willebrand factor, type D domain|VWFC domain|von Willebrand factor, type D domain 3\n", - "Homeobox KN domain|Homeobox domain|Homeobox domain 3\n", - "GRAM domain|GRAM domain|TBC1D8B, PH-GRAM domain 1 3\n", - "DNA glycosylase/AP lyase, H2TH DNA-binding|DNA glycosylase/AP lyase, H2TH DNA-binding 3\n", - "RasGAP protein, C-terminal 3\n", - "Pleckstrin homology domain|Pleckstrin homology domain|DOK4/5/6, PH domain 3\n", - "RecF/RecN/SMC, N-terminal|Smc2, ATP-binding cassette domain 3\n", - "SLIDE domain|SANT/Myb domain 3\n", - "VRR-NUC domain|VRR-NUC domain 3\n", - "Ephrin receptor ligand binding domain 3\n", - "XPA, C-terminal 3\n", - "Peptidase S53, activation domain|Sedolisin domain|Peptidase S53, activation domain 3\n", - "Thrombospondin, C-terminal 3\n", - "C-type lectin-like 3\n", - "EGF-like domain, extracellular|EGF-like, conserved site 3\n", - "XPA, C-terminal|XPA, conserved site 3\n", - "Transmembrane protein 135, N-terminal domain 3\n", - "CD4, extracellular|Immunoglobulin subtype 3\n", - "Olfactomedin-like domain 3\n", - "Citron homology (CNH) domain|Citron homology (CNH) domain 3\n", - "C-type lectin-like|C-type lectin-like|Polycystin cation channel 3\n", - "CDC48, N-terminal subdomain 3\n", - "R3H domain|R3H domain|R3H domain|Sperm-associated antigen 7, R3H domain 3\n", - "SH3 domain|SH3 domain|SH3 domain|Neutrophil cytosol factor 1, first SH3 domain 3\n", - "PDGF/VEGF domain|Platelet-derived growth factor, conserved site|PDGF/VEGF domain|PDGF/VEGF domain|PDGF/VEGF domain 3\n", - "HMG box transcription factor BBX, domain of unknown function DUF2028 3\n", - "Homeobox KN domain|Homeobox domain 3\n", - "Phox-associated domain|Phox-associated domain 3\n", - "Macro domain|Macro domain|Macro domain 3\n", - "Transferrin-like domain|Transferrin family, iron binding site|Transferrin-like domain|Transferrin-like domain 3\n", - "FERM central domain|Band 4.1 domain|Kindlin/fermitin, PH domain 3\n", - "Polyamine biosynthesis domain, conserved site|Polyamine biosynthesis domain 3\n", - "SH2 domain|STAT2, SH2 domain 3\n", - "Wings apart-like protein, C-terminal|WAPL domain 3\n", - "Ribosomal protein L18e/L15P|Ribosomal protein L18e, conserved site 3\n", - "Peptidase C14A, caspase catalytic domain|Peptidase C14A, caspase catalytic domain|Peptidase C14, p20 domain|Peptidase C14A, caspase catalytic domain|Peptidase C14A, caspase catalytic domain 3\n", - "DRF autoregulatory|Diaphanous autoregulatory (DAD) domain 3\n", - "HTH CenpB-type DNA-binding domain|HTH CenpB-type DNA-binding domain|HTH CenpB-type DNA-binding domain 3\n", - "Nerve growth factor-related|Nerve growth factor-related 3\n", - "Ran binding domain 3\n", - "DNA/pantothenate metabolism flavoprotein, C-terminal 3\n", - "Frizzled/Smoothened, transmembrane domain 3\n", - "PSP, proline-rich|PSP, proline-rich 3\n", - "Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3/4-kinase, conserved site|Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3-/4-kinase, catalytic domain|PI3K-C2-gamma, catalytic domain 3\n", - "SH2 domain|SYK/ZAP-70, N-terminal SH2 domain 3\n", - "FMP27, C-terminal 3\n", - "STAS domain 3\n", - "S100/CaBP-9k-type, calcium binding, subdomain|EF-hand domain|S100/CaBP-9k-type, calcium binding, subdomain|S-100 3\n", - "Phospholipase A2 domain|Phospholipase A2 domain 3\n", - "SH3 domain|ZO-2, SH3 domain 3\n", - "ILEI/PANDER domain|Cell surface hyaluronidase, PANDER-like domain 3\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tyrosine-protein kinase ephrin type A/B receptor-like 3\n", - "DNA polymerase alpha catalytic subunit, N-terminal domain 3\n", - "Lethal giant larvae (Lgl)-like, C-terminal domain 3\n", - "UCH-binding domain 3\n", - "CTLH/CRA C-terminal to LisH motif domain 3\n", - "RAP domain 3\n", - "STAT transcription factor, DNA-binding|STAT1, SH2 domain 3\n", - "Pleckstrin homology domain|Zinc finger, Btk motif|Pleckstrin homology domain|Pleckstrin homology domain 3\n", - "Rho binding domain 3\n", - "Josephin domain 3\n", - "Laminin IV|Laminin IV|Laminin EGF domain 3\n", - "FERM central domain|FERM central domain 3\n", - "Alkyl hydroperoxide reductase subunit C/ Thiol specific antioxidant|Thioredoxin domain 3\n", - "JmjN domain|JmjN domain 3\n", - "SH3 domain|SH3 domain|SH3 domain|SH3 domain|SH3 domain-containing kinase-binding protein 1, third SH3 domain 3\n", - "Putative adherens-junction anchoring domain 3\n", - "Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A|Metallocarboxypeptidase Z, carboxypeptidase domain 3\n", - "Resistance to inhibitors of cholinesterase protein 3, N-terminal 3\n", - "CCR4-NOT transcription complex subunit 1, HEAT repeat 3\n", - "Flotillin, C-terminal domain 3\n", - "2'-5'-oligoadenylate synthetase 1, domain 2/C-terminal|2-5-oligoadenylate synthetase, conserved site 3\n", - "CNNM, transmembrane domain 3\n", - "Chromatin assembly factor 1 subunit B, C-terminal domain 3\n", - "SH2 domain|STAT5b, SH2 domain 3\n", - "Ribosome receptor lysine/proline rich 3\n", - "von Willebrand factor, type D domain|von Willebrand factor, type D domain|VWFC domain|VWFC domain|von Willebrand factor, type D domain 3\n", - "Cdk-activating kinase assembly factor MAT1, centre 3\n", - "Tumour necrosis factor receptor 4, N-terminal 3\n", - "3'5'-cyclic nucleotide phosphodiesterase N-terminal 3\n", - "Domain of unknown function DUF3480 3\n", - "Zinc finger, piccolo-type 3\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tumor necrosis factor receptor 11A, N-terminal 3\n", - "RNA recognition motif domain|RNA recognition motif domain|RBM12, RNA recognition motif 4 3\n", - "Zinc finger, GRF-type 3\n", - "FAD dependent oxidoreductase, central domain 3\n", - "Small-subunit processome, Utp21 3\n", - "Glucose/Sorbosone dehydrogenase 3\n", - "Peptidase C19, ubiquitin-specific peptidase, DUSP domain 3\n", - "Amyloidogenic glycoprotein, copper-binding|Amyloidogenic glycoprotein, extracellular 3\n", - "CRIB domain|CRIB domain|CRIB domain|CRIB domain 3\n", - "Iron sulphur domain-containing, mitoNEET, N-terminal 3\n", - "Heat shock protein Hsp90, N-terminal|Histidine kinase/HSP90-like ATPase|Histidine kinase/HSP90-like ATPase 3\n", - "Aspartyl/Glutamyl-tRNA(Gln) amidotransferase, subunit B/E, catalytic 3\n", - "ATP:guanido phosphotransferase, catalytic domain|ATP:guanido phosphotransferase, catalytic domain 3\n", - "Domain of unknown function DUF1736 3\n", - "Elongation factor 1 beta central acidic region, eukaryote|Elongation factor 1 beta central acidic region, eukaryote 3\n", - "Amyloidogenic glycoprotein, extracellular 3\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|Set1B, RNA recognition motif 3\n", - "Signal recognition particle, SRP54 subunit, M-domain 3\n", - "R3H domain|R3H domain|DNA-binding protein SMUBP-2, R3H domain 3\n", - "Coenzyme A transferase binding site|3-oxoacid CoA-transferase, subunit A 3\n", - "F-box associated (FBA) domain|F-box associated (FBA) domain|F-box associated (FBA) domain 3\n", - "Bms1/Tsr1-type G domain|Ribosome biogenesis protein Bms1, N-terminal 3\n", - "Electron transfer flavoprotein, alpha subunit, N-terminal 3\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tumor necrosis factor receptor 6B, N-terminal 3\n", - "Pleckstrin homology domain|Pleckstrin homology domain|Pleckstrin homology domain|Anillin, PH domain 3\n", - "RNA polymerase Rpb2, domain 5 3\n", - "Zinc finger, PARP-type|Zinc finger, PARP-type|Zinc finger, PARP-type|Zinc finger, PARP-type 3\n", - "FERM central domain|FERM, N-terminal|FERM domain|Band 4.1 domain|FERM central domain 3\n", - "Molybdenum cofactor sulfurase, C-terminal 3\n", - "Noelin domain 3\n", - "Laminin G domain|Fibronectin type III|Fibronectin type III 3\n", - "N-acetyltransferase ESCO, acetyl-transferase domain 3\n", - "Basic leucine zipper domain, Maf-type|Basic-leucine zipper domain 3\n", - "Peptidase C12, ubiquitin carboxyl-terminal hydrolase|Peptidase C12, ubiquitin carboxyl-terminal hydrolase|Peptidase C12, ubiquitin carboxyl-terminal hydrolase|Peptidase C12, ubiquitin carboxyl-terminal hydrolase 3\n", - "Peptidase C14, p20 domain|Peptidase C14A, caspase catalytic domain 3\n", - "Aconitase/3-isopropylmalate dehydratase large subunit, alpha/beta/alpha domain|Aconitase/3-isopropylmalate dehydratase large subunit, alpha/beta/alpha domain|Aconitase family, 4Fe-4S cluster binding site 3\n", - "Signal recognition particle, SRP54 subunit, GTPase domain|Signal recognition particle, SRP54 subunit, GTPase domain|AAA+ ATPase domain|Signal recognition particle, SRP54 subunit, GTPase domain 3\n", - "Tumor necrosis factor receptor 19-like, N-terminal 3\n", - "Pigment epithelium derived factor 3\n", - "Guanylate-binding protein/Atlastin, C-terminal|Guanylate-binding protein, C-terminal 3\n", - "SH3 domain|SH3 domain|SH3 domain|SH3 domain|Unconventional myosin-Ie/If, SH3 domain 3\n", - "DHR-1 domain|DHR-1 domain|Dedicator of cytokinesis D, C2 domain 3\n", - "Ferritin-like diiron domain 3\n", - "Translocon Sec61/SecY, plug domain 3\n", - "GS domain 3\n", - "PB1 domain|PB1 domain|PB1 domain|Neutrophil cytosol factor P40, PB1 domain 3\n", - "Telomeric repeat-binding factor 2, Rap1-binding domain|Telomeric repeat-binding factor 2, Rap1-binding domain 3\n", - "SNF2-related, N-terminal domain|DNA/RNA helicase, ATP-dependent, DEAH-box type, conserved site|Helicase superfamily 1/2, ATP-binding domain|Helicase superfamily 1/2, ATP-binding domain 3\n", - "Laminin G domain|Fibronectin type III|Fibronectin type III|Laminin G domain 3\n", - "Neuronal tyrosine-phosphorylated phosphoinositide-3-kinase adapter, N-terminal 3\n", - "APC10/DOC domain|APC10/DOC domain|HERC2, APC10 domain 3\n", - "Protein OS9-like 3\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain|Novel protein kinase C epsilon, catalytic domain 3\n", - "Motilin/ghrelin 3\n", - "GPR domain|Gamma-glutamyl phosphate reductase GPR, conserved site|GPR domain|GPR domain 3\n", - "Spen paralogue and orthologue SPOC, C-terminal 3\n", - "GRAF, BAR domain 3\n", - "Immunoglobulin I-set|GPCR, family 2, extracellular hormone receptor domain|Immunoglobulin-like domain|Immunoglobulin subtype 3\n", - "PI3K p85 subunit, inter-SH2 domain|PI3K p85 subunit, N-terminal SH2 domain 3\n", - "Integrin alpha chain, C-terminal cytoplasmic region, conserved site|Integrin alpha chain, C-terminal cytoplasmic region, conserved site 3\n", - "Zinc-finger domain of monoamine-oxidase A repressor R1 3\n", - "Nerve growth factor-related|Nerve growth factor-related|Nerve growth factor-related|Nerve growth factor conserved site|Nerve growth factor-related 3\n", - "Fibrillar collagen, C-terminal 3\n", - "3'5'-cyclic nucleotide phosphodiesterase PDE8 3\n", - "Zinc finger, C3HC4 RING-type|Zinc finger, RING-type 3\n", - "ERV/ALR sulfhydryl oxidase domain 3\n", - "Paired box protein 7, C-terminal 3\n", - "Zinc finger, UBR-type|Zinc finger, UBR-type 3\n", - "Adaptor protein Cbl, EF hand-like|Adaptor protein Cbl, PTB domain|Adaptor protein Cbl, SH2-like domain 3\n", - "PAS-associated, C-terminal|PAS domain 3\n", - "Dihydrofolate reductase domain|Dihydrofolate reductase conserved site|Dihydrofolate reductase domain|Dihydrofolate reductase domain 3\n", - "uDENN domain|Tripartite DENN domain 3\n", - "Matrin-3, RNA recognition motif 1 3\n", - "Ubiquitin-activating enzyme E1, C-terminal 3\n", - "RAI1-like 3\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain, eukaryote 3\n", - "Kazal domain|Kazal domain|Factor I / membrane attack complex|Follistatin-like, N-terminal|Kazal domain 3\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Class IX myosin, motor domain 3\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|RBM40, RNA recognition motif 1 3\n", - "SH3 domain|SH3 domain|Alpha-(1,6)-fucosyltransferase, SH3 domain 3\n", - "Tumour necrosis factor receptor 13C, TALL-1 binding domain 3\n", - "FCP1 homology domain|FCP1 homology domain 3\n", - "Endonuclease VIII-like 1, DNA binding 3\n", - "GTP-binding protein LepA, C-terminal 3\n", - "Transcription factor, MADS-box 3\n", - "NIDO domain 3\n", - "STAT transcription factor, DNA-binding|STAT3, SH2 domain 3\n", - "Cell surface hyaluronidase, PANDER-like domain 3\n", - "Phosphoglucose isomerase, conserved site|Phosphoglucose isomerase, SIS domain 2 3\n", - "Galanin|Galanin|Galanin 3\n", - "Meiosis-specific protein Spo11 3\n", - "NADH:ubiquinone oxidoreductase, subunit G, iron-sulphur binding|NADH:ubiquinone oxidoreductase, 75kDa subunit, conserved site|NADH:ubiquinone oxidoreductase, subunit G, iron-sulphur binding|NADH:ubiquinone oxidoreductase, subunit G, iron-sulphur binding 3\n", - "MAN1, RNA recognition motif 3\n", - "SH3 domain|SH3 domain|SH3 domain|Protein kinase domain|SH3 domain|Tyrosine-protein kinase BTK, SH3 domain 3\n", - "DNA-directed RNA polymerase, subunit 2, hybrid-binding domain|RNA polymerase, beta subunit, conserved site 2\n", - "Zinc finger, Btk motif|Zinc finger, Btk motif|Zinc finger, Btk motif|RASA2, PH domain 2\n", - "EGF-like domain|Tyrosine-protein kinase ephrin type A/B receptor-like 2\n", - "Interferon/interleukin receptor domain|Fibronectin type III|Fibronectin type III|Fibronectin type III 2\n", - "SH2 domain|SH2 domain|PLC-gamma, C-terminal SH2 domain 2\n", - "KIND domain|KIND domain|KIND domain 2\n", - "PAS fold|PAS domain|PAS domain 2\n", - "2Fe-2S ferredoxin-type iron-sulfur binding domain|2Fe-2S ferredoxin-type iron-sulfur binding domain|2Fe-2S ferredoxin-type iron-sulfur binding domain 2\n", - "Pleckstrin homology domain|Pleckstrin homology domain|ARHGEF2, PH domain 2\n", - "MDM2-binding protein, N-terminal domain 2\n", - "Copper type II, ascorbate-dependent monooxygenase, N-terminal|Copper type II, ascorbate-dependent monooxygenase, histidine-cluster-1 conserved site 2\n", - "DNA binding HTH domain, Psq-type|DNA binding HTH domain, Psq-type 2\n", - "RNA polymerase II elongation factor ELL, N-terminal 2\n", - "von Hippel-Lindau disease tumour suppressor, beta domain|von Hippel-Lindau disease tumour suppressor, alpha domain|von Hippel-Lindau disease tumour suppressor, beta/alpha domain 2\n", - "Exonuclease-1, H3TH domain 2\n", - "DSBA-like thioredoxin domain 2\n", - "Treslin, N-terminal 2\n", - "SWIRM domain 2\n", - "Gamma-carboxyglutamic acid-rich (GLA) domain|EGF-like calcium-binding, conserved site|EGF-like domain|Gamma-carboxyglutamic acid-rich (GLA) domain|EGF-like calcium-binding domain 2\n", - "SH3 domain|SH3 domain|Neutrophil cytosol factor 2, SH3 domain 1 2\n", - "Glutathione S-transferase, N-terminal|Glutathione S-transferase, C-terminal-like|Glutathione S-transferases, class Zeta , C-terminal 2\n", - "SH3 domain|SH3 domain|SH3 domain|Tyrosine-protein kinase BTK, SH3 domain 2\n", - "Myosin head, motor domain|Myosin head, motor domain|DNA recombination and repair protein RecA, monomer-monomer interface|Myosin head, motor domain|Myosin head, motor domain|Class XVIII myosin, motor domain 2\n", - "RNA 3'-terminal phosphate cyclase domain 2\n", - "Allantoicase domain 2\n", - "Alpha-2-macroglobulin, N-terminal 2 2\n", - "WIF domain|WIF domain|WIF domain|WIF domain 2\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Class XIX myosin, motor domain 2\n", - "Ribonucleotide reductase large subunit, C-terminal|Ribonucleotide reductase, class I , alpha subunit 2\n", - "Cdc6, C-terminal|Cdc6, C-terminal 2\n", - "Shugoshin, N-terminal coiled-coil domain 2\n", - "Asteroid domain 2\n", - "Domain of unknown function DUF4592 2\n", - "APCDD1 domain|APCDD1 domain 2\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tumour necrosis factor receptor 5, N-terminal 2\n", - "cGMP-dependent protein kinase, interacting domain 2\n", - "Pleckstrin homology domain|Anillin, PH domain 2\n", - "Alpha carbonic anhydrase domain 2\n", - "Yippee/Mis18/Cereblon|CULT domain 2\n", - "SLC41 divalent cation transporters, integral membrane domain 2\n", - "I/LWEQ domain|I/LWEQ domain|I/LWEQ domain|I/LWEQ domain 2\n", - "DNA mismatch repair protein MutS, clamp|DNA mismatch repair protein MutS, core 2\n", - "ATP-sulfurylase PUA-like domain|Sulphate adenylyltransferase 2\n", - "Myogenic determination factor 5 2\n", - "SH3 domain|SH3 domain|SH3 domain|Shank1, SH3 domain 2\n", - "Zinc finger, nuclear hormone receptor-type|Zinc finger, nuclear hormone receptor-type|Nuclear hormone receptor, ligand-binding domain|Zinc finger, nuclear hormone receptor-type 2\n", - "Zinc finger C2H2-type|Matrin/U1-C-like, C2H2-type zinc finger 2\n", - "Laminin EGF domain|EGF-like domain|EGF-like domain 2\n", - "PIG-P 2\n", - "CHRD|CHRD 2\n", - "Requiem/DPF N-terminal domain 2\n", - "Major vault protein, shoulder domain|Major vault protein, shoulder domain 2\n", - "Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A 2\n", - "GTP binding domain 2\n", - "dDENN domain|Tripartite DENN domain|dDENN domain 2\n", - "TNFR/NGFR cysteine-rich region|Tumour necrosis factor receptor 3, N-terminal 2\n", - "Serpin domain|Serpin, conserved site|Serpin domain|Heparin cofactor II 2\n", - "Cortactin-binding protein-2, N-terminal 2\n", - "Phospholipid/glycerol acyltransferase|1-acyl-sn-glycerol-3-phosphate acyltransferase 2\n", - "OST-HTH/LOTUS domain|OST-HTH/LOTUS domain|TDRD5, second LOTUS domain 2\n", - "NHR2-like 2\n", - "Collagen IV, non-collagenous 2\n", - "Transferrin-like domain|Transferrin-like domain|Transferrin family, iron binding site|Transferrin-like domain|Transferrin-like domain 2\n", - "F-actin binding|F-actin binding 2\n", - "POU-specific domain|POU domain|POU-specific domain|POU-specific domain|Cro/C1-type helix-turn-helix domain 2\n", - "WAP-type 'four-disulfide core' domain|WAP-type 'four-disulfide core' domain|WAP-type 'four-disulfide core' domain 2\n", - "Cdc6, C-terminal 2\n", - "Transcription factor COE, helix-loop-helix domain 2\n", - "ELMO domain 2\n", - "2Fe-2S ferredoxin-type iron-sulfur binding domain 2\n", - "CAP domain|CAP domain 2\n", - "PB1 domain|PB1 domain|Neutrophil cytosol factor 2, PB1 domain 2\n", - "SH2 domain|PI3K p85 subunit, C-terminal SH2 domain 2\n", - "Domain of unknown function DUF1232 2\n", - "SH3 domain|SH3 domain|RasGAP, SH3 domain 2\n", - "TNFR/NGFR cysteine-rich region|Tumor necrosis factor receptor 1B, N-terminal 2\n", - "tRNA pseudouridylate synthase B, C-terminal 2\n", - "Galectin, carbohydrate recognition domain|Galectin, carbohydrate recognition domain|Galectin, carbohydrate recognition domain 2\n", - "Zinc finger C2HC RNF-type 2\n", - "SH3 domain|SH3 domain|SH3 domain|c-Cbl associated protein, SH3 domain 2\n", - "B30.2/SPRY domain|Fibronectin type III 2\n", - "Orn/DAP/Arg decarboxylase 2, C-terminal|Orn/DAP/Arg decarboxylase 2, N-terminal|Orn/DAP/Arg decarboxylase 2, pyridoxal-phosphate binding site 2\n", - "Zinc finger, PHD-finger|Zinc finger, PHD-finger|Zinc finger, PHD-finger|Zinc finger, RING-type|Zinc finger, PHD-type 2\n", - "Possible tRNA binding domain 2\n", - "TOPRIM domain|DNA topoisomerase, type IA, central|TOPRIM domain|TOPRIM domain|DNA topoisomerase 3-like, TOPRIM domain 2\n", - "Target SNARE coiled-coil homology domain|Target SNARE coiled-coil homology domain|BET1, SNARE domain 2\n", - "Clathrin, heavy chain, linker, core motif 2\n", - "Tyrosine-protein kinase, non-receptor, TYK2, N-terminal|FERM domain|Band 4.1 domain|FERM central domain 2\n", - "C-type lectin-like|C-type lectin, conserved site|C-type lectin-like|C-type lectin-like 2\n", - "Argonaute linker 2 domain 2\n", - "DAN|Cystine knot, C-terminal 2\n", - "Sigma-54 interaction domain, ATP-binding site 1 2\n", - "IRS-type PTB domain 2\n", - "Chemokine interleukin-8-like domain|CXC chemokine, conserved site|Chemokine interleukin-8-like domain|CXC Chemokine domain 2\n", - "Pre-mRNA processing factor 4 (PRP4)-like 2\n", - "XPG-I domain 2\n", - "AGE domain 2\n", - "Ribonuclease Zc3h12a-like, NYN domain 2\n", - "Telomerase ribonucleoprotein complex - RNA-binding domain 2\n", - "Mad3/Bub1 homology region 1|Mad3/Bub1 homology region 1 2\n", - "Threonyl/alanyl tRNA synthetase, SAD 2\n", - "Huntingtin-interacting protein 1, clathrin-binding domain 2\n", - "Helicase domain 2\n", - "Fas receptor, N-terminal 2\n", - "Domain of unknown function DUF4062 2\n", - "14-3-3 domain 2\n", - "Catalase immune-responsive domain 2\n", - "Zinc finger, nuclear hormone receptor-type|Nuclear hormone receptor, ligand-binding domain 2\n", - "Alpha-2-macroglobulin RAP, C-terminal|Alpha-2-macroglobulin RAP, domain 3 2\n", - "Interferon regulatory factor 2-binding protein 1 & 2, zinc finger 2\n", - "FAD dependent oxidoreductase|D-amino acid oxidase, conserved site 2\n", - "PI3K p85 subunit, C-terminal SH2 domain 2\n", - "ELO family, conserved site 2\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tyrosine-protein kinase ephrin type A/B receptor-like|Tumour necrosis factor receptor 4, N-terminal 2\n", - "CRIC domain|CRIC domain 2\n", - "Thrombospondin/cartilage oligomeric matrix protein, coiled-coil domain|Thrombospondin-5, coiled coil region 2\n", - "Polyketide synthase, acyl transferase domain 2\n", - "APC10/DOC domain|APC10/DOC domain 2\n", - "Extracellular Endonuclease, subunit A 2\n", - "Chemokine interleukin-8-like domain|Chemokine interleukin-8-like domain|Chemokine CC, DCCL motif-cointaining domain 2\n", - "Laminin G domain|EGF-like calcium-binding domain 2\n", - "Carbamoyl-phosphate synthase small subunit, N-terminal domain 2\n", - "RWD domain|RWD domain|RWD domain 2\n", - "SH3 domain|SH3 domain|SH3 domain|Disks Large homologue 3, SH3 domain 2\n", - "Zinc finger, TAZ-type|Zinc finger, TAZ-type 2\n", - "Sterile alpha motif domain|Tumour protein p63, SAM domain 2\n", - "Apple domain|PAN/Apple domain|Apple domain 2\n", - "Multicopper oxidase, type 1|Multicopper oxidases, conserved site 2\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tumor necrosis factor receptor 27, N-terminal 2\n", - "Peptidase family A1 domain|Renin-like domain 2\n", - "KIND domain|KIND domain 2\n", - "BEN domain 2\n", - "Glutathione S-transferase, C-terminal-like|Glutathione S-transferases, class Zeta , C-terminal 2\n", - "SPATA31/FAM205 2\n", - "SGNH hydrolase-type esterase domain 2\n", - "Mrp, conserved site 2\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tyrosine-protein kinase ephrin type A/B receptor-like 2\n", - "Importin-beta, N-terminal domain 2\n", - "SPRY domain|B30.2/SPRY domain|SPRY domain|Butyrophilin subfamily 1/2, SPRY/PRY domain 2\n", - "EH domain|EH domain 2\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|SRSF1, RNA recognition motif 1 2\n", - "CHRD|CHRD|CHRD 2\n", - "Ubiquitin carboxyl-terminal hydrolase 7, ICP0-binding domain 2\n", - "Sterile alpha motif domain|Sterile alpha motif domain|Sterile alpha motif domain|BICC1, SAM domain 2\n", - "Estrogen receptor beta, N-terminal 2\n", - "Nucleoprotein TPR/MLP1 2\n", - "Pleckstrin homology domain|Pleckstrin homology domain|Pleckstrin homology domain|Ras GTPase-activating domain|RASA2, PH domain 2\n", - "SRCR domain|SRCR-like domain 2\n", - "CRIB domain|p21 activated kinase binding domain 2\n", - "CD80-like, immunoglobulin C2-set|Immunoglobulin-like domain|CD80, IgC-like domain 2\n", - "EGF-like domain|Fibrinogen, alpha/beta/gamma chain, C-terminal globular domain|EGF-like domain 2\n", - "SMARCC, N-terminal|Chromo/chromo shadow domain|Chromo/chromo shadow domain 2\n", - "Phosphotyrosine protein phosphatase I|Phosphotyrosine protein phosphatase I 2\n", - "Signal recognition particle, SRP72 subunit, RNA-binding 2\n", - "THAP-type zinc finger 2\n", - "Protein kinase, C-terminal|AGC-kinase, C-terminal|AGC-kinase, C-terminal|Protein Kinase B beta, catalytic domain 2\n", - "RNA recognition motif domain|RNA recognition motif domain|Heterogeneous nuclear ribonucleoprotein Q, RNA recognition motif 1 2\n", - "KEN domain|KEN domain|PUB domain 2\n", - "Ancestral coatomer element 1, Sec16/Sec31 2\n", - "MHC class II, beta chain, N-terminal|MHC class II, beta chain, N-terminal 2\n", - "ASCH domain|ASCH domain 2\n", - "Thioredoxin domain|Thioredoxin, conserved site|Thioredoxin domain 2\n", - "Elongation factor EFG, domain V-like|Elongation factor 4, domain IV 2\n", - "Peptide methionine sulphoxide reductase MsrA|Peptide methionine sulphoxide reductase MsrA|Peptide methionine sulphoxide reductase MsrA 2\n", - "SRCR domain 2\n", - "Sugar transporter, conserved site|Major facilitator superfamily domain 2\n", - "GB1/RHD3-type guanine nucleotide-binding (G) domain 2\n", - "Alanine dehydrogenase/pyridine nucleotide transhydrogenase, N-terminal|Alanine dehydrogenase/NAD(P) transhydrogenase, conserved site-1|Alanine dehydrogenase/pyridine nucleotide transhydrogenase, N-terminal 2\n", - "Alcohol dehydrogenase, N-terminal|Alcohol dehydrogenase, zinc-type, conserved site 2\n", - "Myosin head, motor domain|Myosin head, motor domain|Class XVIII myosin, motor domain 2\n", - "Nucleoside transporter/FeoB GTPase, Gate domain 2\n", - "Transmembrane protein TMEM132, C-terminal 2\n", - "Myelin gene regulatory factor C-terminal domain 1 2\n", - "Uncharacterised domain CHP00451 2\n", - "EGF-like, conserved site|EGF-like, conserved site|EGF-like calcium-binding, conserved site|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 2\n", - "Elongin A binding-protein 1 2\n", - "Complement Clr-like EGF domain|EGF-like calcium-binding, conserved site|EGF-like domain|EGF-like calcium-binding domain 2\n", - "Growth hormone/erythropoietin receptor, ligand binding|Fibronectin type III|Fibronectin type III|Fibronectin type III 2\n", - "LEM domain|LEM domain|Emerin, LEM domain 2\n", - "Protein kinase domain|Protein kinase domain|Protein kinase B gamma, catalytic domain 2\n", - "BAG domain|BAG domain 2\n", - "SH3 domain|SH3 domain|SH3 domain|Nebulette, SH3 domain 2\n", - "Pleckstrin homology domain|Pleckstrin homology domain|ARHGEF28, PH domain 2\n", - "SH2 domain|SH2 domain|SH2 domain|Protein kinase domain|Tyrosine-protein kinase, catalytic domain|SH2 domain|Tyrosine-protein kinase Lck, SH2 domain 2\n", - "AGC-kinase, C-terminal|AGC-kinase, C-terminal|Protein kinase B gamma, catalytic domain 2\n", - "Tripartite DENN domain|cDENN domain 2\n", - "PRP8 domain IV core 2\n", - "C2 domain|Freud, C2 domain 2\n", - "SPARC/Testican, calcium-binding domain|SMOC-1, extracellular calcium-binding domain 2\n", - "SOCS box domain 2\n", - "ABC-type uncharacterised transport system 2\n", - "Immunoglobulin-like domain|Immunoglobulin/major histocompatibility complex, conserved site|Immunoglobulin-like domain|Immunoglobulin subtype 2|Immunoglobulin subtype 2\n", - "PAN/Apple domain|Apple domain|PAN/Apple domain|Apple domain 2\n", - "Integrin beta subunit, tail 2\n", - "Krueppel-associated box-related|Krueppel-associated box 2\n", - "UPF3 domain 2\n", - "I/LWEQ domain|I/LWEQ domain|I/LWEQ domain 2\n", - "AGC-kinase, C-terminal|AGC-kinase, C-terminal|Protein Kinase B beta, catalytic domain 2\n", - "DNA topoisomerase I, catalytic core, eukaryotic-type|DNA topoisomerase I, eukaryotic-type|DNA topoisomerase I, catalytic core, eukaryotic-type 2\n", - "TILa domain|von Willebrand factor, type D domain 2\n", - "NADH:ubiquinone oxidoreductase, subunit G, iron-sulphur binding|NADH:ubiquinone oxidoreductase, subunit G, iron-sulphur binding 2\n", - "DNA polymerase epsilon subunit B, N-terminal 2\n", - "SH2 domain|SH2B3, SH2 domain 2\n", - "Zinc finger, Mcm10/DnaG-type 2\n", - "Kringle|Kringle, conserved site|Kringle|Kringle 2\n", - "DMRTA motif 2\n", - "Zn-dependent metallo-hydrolase, RNA specificity domain 2\n", - "Diacylglycerol kinase, catalytic domain 2\n", - "PWWP domain|PWWP domain|PWWP domain|Bromodomain|ZMYND8/11, PWWP domain 2\n", - "uDENN domain 2\n", - "Anti-proliferative protein|Anti-proliferative protein|Anti-proliferative protein|Anti-proliferative protein 2\n", - "KH-like RNA-binding domain|KH-like RNA-binding domain 2\n", - "NACHT-associated domain 2\n", - "Chaperonin Cpn60, conserved site 2\n", - "Sequestosome-1, UBA domain|Ubiquitin-associated domain|Sequestosome-1, UBA domain 2\n", - "Transcription factor COE, DNA-binding domain 2\n", - "SH2 domain|SH2 domain|SH2 domain|Tyrosine-protein kinase JAK2, SH2 domain 2\n", - "Cold-shock protein, DNA-binding|Cold shock domain|Cold-shock protein, DNA-binding 2\n", - "Glycine cleavage T-protein, C-terminal barrel domain|YgfZ/GcvT conserved site 2\n", - "Uracil-DNA glycosylase-like 2\n", - "Thg1 C-terminal domain 2\n", - "FATC domain|Phosphatidylinositol 3-/4-kinase, catalytic domain|FATC domain|Phosphatidylinositol 3-/4-kinase, catalytic domain|FATC domain 2\n", - "RecQ helicase-like 5 2\n", - "Synphilin-1, alpha-Synuclein-binding domain 2\n", - "Manganese/iron superoxide dismutase, C-terminal 2\n", - "Helically-extended SH3 domain|SH3 domain 2\n", - "Fetuin-A-type cystatin domain|Cystatin domain 2\n", - "Phosphopantetheine binding ACP domain 2\n", - "Threonyl/alanyl tRNA synthetase, SAD|Threonine-tRNA ligase catalytic core domain 2\n", - "Putative adherens-junction anchoring domain|Villin headpiece 2\n", - "AT hook, DNA-binding motif|AT hook, DNA-binding motif 2\n", - "Peptidase M12B, GON-ADAMTSs|Peptidase M12B, GON-ADAMTSs 2\n", - "A-kinase anchor 110kDa, C-terminal 2\n", - "Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Myosin head, motor domain|Class XVIII myosin, motor domain 2\n", - "Cyclin-A, N-terminal 2\n", - "Chemokine interleukin-8-like domain|Chemokine interleukin-8-like domain 2\n", - "Seven-in-absentia protein, TRAF-like domain|Zinc finger, SIAH-type 2\n", - "TILa domain|VWFC domain|VWFC domain 2\n", - "Condensin-2 complex subunit H2, C-terminal 2\n", - "Vacuole morphology and inheritance protein 14, Fab1-binding region 2\n", - "Ras GTPase-activating protein 1, N-terminal SH2 domain 2\n", - "AP180 N-terminal homology (ANTH) domain 2\n", - "Signaling lymphocytic activation molecule, N-terminal|Signaling lymphocytic activation molecule, N-terminal 2\n", - "Uncharacterised domain NUC173 2\n", - "Tumor necrosis factor receptor 11A, N-terminal 2\n", - "Letm1 ribosome-binding domain 2\n", - "Zinc finger C2H2-type|Zinc finger C2H2-type|Matrin/U1-C-like, C2H2-type zinc finger 2\n", - "Conserved oligomeric Golgi complex, subunit 4 2\n", - "Quinone oxidoreductase/zeta-crystallin, conserved site|Polyketide synthase, enoylreductase domain 2\n", - "Golgin subfamily A member 7/ERF4 2\n", - "Transferrin-like domain 2\n", - "Chemokine interleukin-8-like domain|Chemokine interleukin-8-like domain|CX3C chemokine domain 2\n", - "FAM92, BAR domain 2\n", - "Cullin, N-terminal|APC10/DOC domain|APC10/DOC domain 2\n", - "tRNA-binding domain 2\n", - "Interferon/interleukin receptor domain|Short hematopoietin receptor, family 1, conserved site|Fibronectin type III|Fibronectin type III 2\n", - "NtA (N-terminal agrin) domain 2\n", - "p21 activated kinase binding domain 2\n", - "NADH:ubiquinone oxidoreductase, 30kDa subunit|NADH:ubiquinone oxidoreductase, 30kDa subunit|NADH:ubiquinone oxidoreductase, 30kDa subunit, conserved site 2\n", - "Histidine triad, conserved site 2\n", - "Ubiquitin interacting motif 2\n", - "DHR-1 domain|Dedicator of cytokinesis C, C2 domain 2\n", - "Ferlin, second C2 domain 2\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|hnRNPM, RNA recognition motif 3 2\n", - "Intercellular adhesion molecule, N-terminal|Immunoglobulin subtype 2\n", - "CHAT domain 2\n", - "SH3 domain|SH3 domain|SH3 domain|SH3 domain|1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase gamma-2, SH3 domain 2\n", - "Lipid-binding serum glycoprotein, conserved site 2\n", - "CD59 antigen, conserved site|Ly-6 antigen/uPA receptor-like 2\n", - "Ras and Rab interactor 2, SH2 domain 2\n", - "Sodium/hydrogen exchanger, regulatory region 2\n", - "N2227-like 2\n", - "Ribosomal protein L46, N-terminal 2\n", - "Bromodomain|Brd8, Bromo domain 2\n", - "Zinc finger, RING-type|Zinc finger, RING-type|Zinc finger, RING-type|RNF126, RING finger, H2 subclass 2\n", - "RAWUL domain 2\n", - "Domain of unknown function DUF4537 2\n", - "Zinc finger, CW-type 2\n", - "Signal recognition particle, SRP54 subunit, helical bundle|Signal recognition particle, SRP54 subunit, helical bundle 2\n", - "Pleckstrin homology domain|Pleckstrin homology domain|Pleckstrin homology domain|RASA2, PH domain 2\n", - "Homocysteine-binding domain 2\n", - "E3 ubiquitin-protein ligase makorin 1, C-terminal 2\n", - "RNA polymerase Rpb1, domain 3|RNA polymerase, N-terminal|DNA-directed RNA polymerase III subunit RPC1, N-terminal 2\n", - "PLAA family ubiquitin binding domain|PLAA family ubiquitin binding domain 2\n", - "Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3/4-kinase, conserved site|Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3-/4-kinase, catalytic domain|DNA-dependent protein kinase catalytic subunit, catalytic domain 2\n", - "TAFH/NHR1|TAFH/NHR1|TAFH/NHR1 2\n", - "Class I myosin tail homology domain|Myosin head, motor domain|Class I myosin tail homology domain|Myosin head, motor domain 2\n", - "IPT domain|IPT domain|Transcription factor COE, IPT domain 2\n", - "Glyceraldehyde 3-phosphate dehydrogenase, NAD(P) binding domain 2\n", - "YrdC-like domain|YrdC-like domain|YrdC-like domain 2\n", - "SPRY domain 2\n", - "CHORD domain|CHORD domain 2\n", - "Glycoside hydrolase, family 29, conserved site 2\n", - "Cytidine and deoxycytidylate deaminase domain 2\n", - "CS domain|Dynein axonemal assembly factor 4, CS domain 2\n", - "WGR domain|WGR domain 2\n", - "Pancreatic trypsin inhibitor Kunitz domain|Pancreatic trypsin inhibitor Kunitz domain 2\n", - "Carboxypeptidase A, carboxypeptidase domain 2\n", - "Centromere protein H, C-terminal 2\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|SF3B4, RNA recognition motif 2 2\n", - "Aconitase/3-isopropylmalate dehydratase large subunit, alpha/beta/alpha domain|Aconitase family, 4Fe-4S cluster binding site 2\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|SHARP, RNA recognition motif 2 2\n", - "PAS fold-3|PAS domain|PAS domain|PAS domain|PAS domain 2\n", - "Protein kinase A anchoring, WSK motif 2\n", - "FAM91, C-terminal domain 2\n", - "APPL1, BAR domain 2\n", - "PAN/Apple domain|Apple domain 2\n", - "Radical SAM|MoaA/nifB/pqqE, iron-sulphur binding, conserved site|Radical SAM|Elp3/MiaB/NifB 2\n", - "Cwf19-like protein, C-terminal domain-2 2\n", - "Legume lectin, beta chain, Mn/Ca-binding site 2\n", - "Short hematopoietin receptor, family 1, conserved site|Fibronectin type III|Fibronectin type III|Fibronectin type III 2\n", - "Phox homologous domain|Phox homologous domain|Phox homologous domain|Sorting nexin-14, PX domain 2\n", - "Gcn5-related N-acetyltransferase (GNAT) domain, ATAT-type|Gcn5-related N-acetyltransferase (GNAT) domain, ATAT-type|Gcn5-related N-acetyltransferase (GNAT) domain, ATAT-type 2\n", - "AGC-kinase, C-terminal|AGC-kinase, C-terminal|Citron Rho-interacting kinase, catalytic domain 2\n", - "Syntaxin/epimorphin, conserved site|Target SNARE coiled-coil homology domain|Target SNARE coiled-coil homology domain 2\n", - "SPRY domain|Butyrophylin-like, SPRY domain|B30.2/SPRY domain|SPRY domain|Butyrophilin subfamily 1/2, SPRY/PRY domain 2\n", - "FACT complex subunit POB3-like, N-terminal PH domain 2\n", - "NUDIX hydrolase domain|NUDIX hydrolase domain 2\n", - "Tyrosine aminotransferase ubiquitination region 2\n", - "Immunoglobulin I-set|Immunoglobulin-like domain|Immunoglobulin subtype|Palladin, C-terminal immunoglobulin-like domain 2\n", - "Ran binding domain|Ran binding domain 2\n", - "Serum albumin, N-terminal|Serum albumin, conserved site|Serum albumin, N-terminal 2\n", - "Fibronectin, type I 2\n", - "MAM domain|MAM domain 2\n", - "Xylose isomerase-like, TIM barrel domain 2\n", - "Frizzled domain|Frizzled domain|Frizzled domain|Frizzled-5, CRD domain 2\n", - "Serpin domain|Serpin, conserved site|Serpin domain|Alpha2-antiplasmin 2\n", - "Anaphylatoxin/fibulin|Anaphylatoxin/fibulin|Anaphylatoxin/fibulin|Anaphylatoxin/fibulin|Anaphylatoxin/fibulin 2\n", - "PCAF, N-terminal 2\n", - "GNAT domain|GNAT domain|Radical SAM 2\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|SH2 domain|Protein kinase domain|Tyrosine-protein kinase, catalytic domain 2\n", - "Ulp1 protease family, C-terminal catalytic domain 2\n", - "Histidyl tRNA synthetase-related domain 2\n", - "RNA recognition motif domain|RBM6, RNA recognition motif 2 2\n", - "Protein kinase domain|Protein kinase domain|GS domain 2\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase domain|SH2 domain 2\n", - "Mucin-like domain|Mucin-like domain 2\n", - "Sterile alpha motif domain|Sterile alpha motif domain|Liprin-beta, SAM domain repeat 3 2\n", - "Target SNARE coiled-coil homology domain|Syntaxin/epimorphin, conserved site|Target SNARE coiled-coil homology domain|Target SNARE coiled-coil homology domain 2\n", - "Protein kinase domain|Protein kinase, ATP binding site|Protein kinase domain|Protein kinase domain|Rhodopsin kinase, catalytic domain 2\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase domain|Sterile alpha motif domain|Tyrosine-protein kinase, catalytic domain 2\n", - "DhaK domain|DhaK domain 2\n", - "SH3 domain|SH3 domain|Nephrocystin-1, SH3 domain 2\n", - "Ataxin, AXH domain|Ataxin, AXH domain|Ataxin, AXH domain 2\n", - "GTP binding protein, second domain|OBG-type guanine nucleotide-binding (G) domain|Small GTP-binding protein domain 2\n", - "FAD-binding domain|Ubiquinone biosynthesis hydroxylase, UbiH/UbiF/VisC/COQ6, conserved site 2\n", - "MDN2-binding protein, C-terminal domain 2\n", - "GPR domain|GPR domain 2\n", - "Roadblock/LAMTOR2 domain|Roadblock/LAMTOR2 domain 2\n", - "RIG-I-like receptor, C-terminal regulatory domain 2\n", - "Alpha-2-macroglobulin receptor-associated protein, domain 1 2\n", - "Disintegrin domain|Disintegrin, conserved site|Disintegrin domain|Disintegrin domain 2\n", - "GUCT 2\n", - "Lamin-B receptor of TUDOR domain 2\n", - "Thioredoxin-like fold|Thioredoxin domain 2\n", - "Laminin EGF domain|Laminin EGF domain|TNFR/NGFR cysteine-rich region|Laminin EGF domain|EGF-like domain 2\n", - "Lipid-binding serum glycoprotein, conserved site|Lipid-binding serum glycoprotein, N-terminal 2\n", - "ATP synthase, alpha subunit, C-terminal 2\n", - "Electron transfer flavoprotein, alpha/beta-subunit, N-terminal|Electron transfer flavoprotein, alpha/beta-subunit, N-terminal 2\n", - "PI3K p85 subunit, N-terminal SH2 domain 2\n", - "Circularly permuted (CP)-type guanine nucleotide-binding (G) domain 2\n", - "SSXRD motif 2\n", - "Cdc37, C-terminal|Cdc37, C-terminal 2\n", - "Ubiquitin/SUMO-activating enzyme ubiquitin-like domain 2\n", - "La protein, RNA-binding domain|LARP7, RNA recognition motif 2 2\n", - "RII binding domain 2\n", - "G-patch domain|G-patch domain|G-patch domain 2\n", - "Serine-tRNA ligase catalytic core domain 2\n", - "Basic leucine zipper domain, Maf-type|Basic-leucine zipper domain|Basic-leucine zipper domain|Basic-leucine zipper domain 2\n", - "Glutaredoxin|Glutaredoxin, PICOT-like 2\n", - "MDM2-binding protein, central domain 2\n", - "Dishevelled protein domain|DEP domain 2\n", - "S100/CaBP-9k-type, calcium binding, subdomain|S100/CaBP-9k-type, calcium binding, subdomain|S-100 2\n", - "Deoxyribonuclease I, conservied site 2\n", - "VIT domain|VIT domain|VIT domain 2\n", - "AJAP1/PANP, C-terminal 2\n", - "Notch, NOD domain 2\n", - "Prolyl-tRNA synthetase, catalytic domain 2\n", - "L-asparaginase, N-terminal 2\n", - "Natriuretic peptide, conserved site 2\n", - "MIRO domain 2\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA-binding protein 10, RNA recognition motif 2 2\n", - "Domain of unknown function DUF382 2\n", - "TGF-beta, propeptide|Transforming growth factor-beta, C-terminal 2\n", - "Diacylglycerol/phorbol-ester binding|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain 2\n", - "AMMECR1 domain|AMMECR1 domain 2\n", - "Orn/DAP/Arg decarboxylase 2, C-terminal|Orn/DAP/Arg decarboxylase 2, N-terminal 2\n", - "B30.2/SPRY domain|SPRY domain|Ryanodine receptor, SPRY domain 1 2\n", - "SMARCC, SWIRM-associated domain 2\n", - "Nucleoporin Nup153, N-terminal 2\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|Heterogeneous nuclear ribonucleoprotein Q, RNA recognition motif 1 2\n", - "SH3 domain|SH3 domain|SH3 domain|CD2-associated protein, first SH3 domain 2\n", - "OBG-type guanine nucleotide-binding (G) domain 2\n", - "F-actin capping protein, alpha subunit, conserved site 2\n", - "Modifier of rudimentary, Modr|Modifier of rudimentary, Modr 2\n", - "Domain of unknown function DUF4061 2\n", - "Brix domain|Brix domain|Brix domain 2\n", - "NET domain|NET domain 2\n", - "EF-Hand 1, calcium-binding site|EF-hand domain|EF-hand domain|EF-hand domain|EF-hand domain 2\n", - "Forkhead box protein, C-terminal 2\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|RBM8, RNA recognition motif 2\n", - "Homeobox KN domain|Homeobox, conserved site|Homeobox domain|Homeobox domain|Homeobox domain 2\n", - "THIF-type NAD/FAD binding fold|Ubiquitin-activating enzyme E1, conserved site 2\n", - "ITPR-interacting domain|ITPR-interacting domain 2\n", - "Calcium permeable stress-gated cation channel 1, N-terminal transmembrane domain 2\n", - "Zinc finger, PHD-finger|Zinc finger, PHD-finger|Zinc finger, PHD-finger|Zinc finger, PHD-type 2\n", - "Apx/Shrm Domain 1 2\n", - "Peptidase C2, calpain, domain III 2\n", - "EGF-like domain|Laminin EGF domain 2\n", - "Cyclophilin-type peptidyl-prolyl cis-trans isomerase domain|Cyclophilin-type peptidyl-prolyl cis-trans isomerase, conserved site|Cyclophilin-type peptidyl-prolyl cis-trans isomerase domain 2\n", - "DOK4/5/6, PH domain 2\n", - "Rhs repeat-associated core 2\n", - "Caudal-like activation domain 2\n", - "TRAM domain|TRAM domain|Radical SAM 2\n", - "BRO1 domain 2\n", - "STAT4, SH2 domain 2\n", - "Apoptosis regulator, Bcl-2, BH3 motif, conserved site 2\n", - "Phospholipase B 2\n", - "Serine/threonine-protein kinase, C-terminal 2\n", - "Fibronectin type III|PKD/Chitinase domain 2\n", - "Ubiquitin-like domain, USP-type 2\n", - "Cystatin domain|Proteinase inhibitor I25, cystatin, conserved site|Kininogen-type cystatin domain|Cystatin domain|Cystatin domain 2\n", - "TATA-binding protein interacting (TIP20) 2\n", - "EGF-like domain|EGF-like, conserved site|EGF-like domain|EGF-like domain 2\n", - "Kringle 2\n", - "PAS fold 2\n", - "Major facilitator superfamily associated domain 2\n", - "Fork-head N-terminal 2\n", - "NDT80 DNA-binding domain 2\n", - "SMARCC, N-terminal|Chromo/chromo shadow domain 2\n", - "PiggyBac transposable element-derived protein 2\n", - "Regulator of G protein signalling-like domain|p115RhoGEF, RGS domain 2\n", - "DDHD domain|DDHD domain 2\n", - "Phosphatidylinositol-4-phosphate 5-kinase, core 2\n", - "Protein TOPAZ1 domain 2\n", - "EBP50, C-terminal|EBP50, C-terminal 2\n", - "Heat shock factor (HSF)-type, DNA-binding|Heat shock factor (HSF)-type, DNA-binding|Heat shock factor (HSF)-type, DNA-binding 2\n", - "Glycoside hydrolase 35, catalytic domain|Glycoside hydrolase, family 35, conserved site 2\n", - "Protein argonaute, Mid domain 2\n", - "PDZ domain|Pleckstrin homology domain|PDZ domain|Pleckstrin homology domain 2\n", - "AGC-kinase, C-terminal|Novel protein kinase C delta, catalytic domain 2\n", - "DTHCT 2\n", - "IPT domain|RBP-Jkappa, IPT domain 2\n", - "ARF7 effector protein, C-terminal 2\n", - "SH2D1A, SH2 domain 2\n", - "POPLD domain 2\n", - "Ephrin receptor ligand binding domain|Tyrosine-protein kinase, receptor class V, conserved site|Ephrin receptor ligand binding domain|Ephrin receptor ligand binding domain|Ephrin type-B receptor 4, ligand binding domain 2\n", - "Peptidase M28|M28 Zn-Peptidase Glutaminyl Cyclase 2\n", - "WHEP-TRS domain|WHEP-TRS domain|Aminoacyl-tRNA synthetase, class II|WHEP-TRS domain|WHEP-TRS domain 2\n", - "EGF domain|EGF-like, conserved site|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 2\n", - "Heat shock factor (HSF)-type, DNA-binding 2\n", - "Fibronectin type II domain|Fibronectin type II domain|Fibronectin type II domain|Fibronectin type II domain|Fibronectin type II domain 2\n", - "Fibronectin, type I|Fibronectin, type I|Fibronectin, type I 2\n", - "Anti-proliferative protein|Anti-proliferative protein 2\n", - "Glucagon/GIP/secretin/VIP|Glucagon/GIP/secretin/VIP|Glucagon/GIP/secretin/VIP 2\n", - "PLAA family ubiquitin binding domain 2\n", - "SH2 domain|Serine-threonine/tyrosine-protein kinase, catalytic domain|Serine-threonine/tyrosine-protein kinase, catalytic domain|SH2 domain|Protein kinase domain|SH2 domain 2\n", - "TILa domain 2\n", - "SRCR-like domain|SRCR domain|Serine proteases, trypsin domain 2\n", - "SH3 domain|SH3 domain|Neutrophil cytosol factor P40, SH3 domain 2\n", - "cGMP-dependent protein kinase, N-terminal coiled-coil domain 2\n", - "Pleckstrin homology domain|Pleckstrin homology domain|Pleckstrin homology domain|Net1, PH domain 2\n", - "DNA topoisomerase, type IA, central|DNA topoisomerase, type IA, domain 2|DNA topoisomerase, type IA, central 2\n", - "Calcium-dependent channel, 7TM region, putative phosphate 2\n", - "DNA-directed RNA polymerase III subunit RPC1, N-terminal 2\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tumour necrosis factor receptor 9, N-terminal 2\n", - "XLR/SYCP3/FAM9 domain 2\n", - "Transcription initiation factor TFIID component TAF4 2\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain|Novel protein kinase C theta, catalytic domain 2\n", - "Ku70/Ku80 C-terminal arm 2\n", - "DAN|Cystine knot, C-terminal|Cystine knot, C-terminal 2\n", - "SprT-like|SprT-like 2\n", - "GAGE|GAGE 2\n", - "TOPRIM domain|DNA topoisomerase, type IIA, conserved site|TOPRIM domain|DNA topoisomerase 2, TOPRIM domain 2\n", - "SH3 domain|SH3 domain|Tyrosine-protein kinase ITK, SH3 domain 2\n", - "P-type trefoil domain|P-type trefoil domain|P-type trefoil domain 2\n", - "Dopey, N-terminal 2\n", - "Peptidase M12A|Peptidase, metallopeptidase 2\n", - "Immunoglobulin|Immunoglobulin subtype 2\n", - "Acyl-CoA dehydrogenase, conserved site 2\n", - "EXS, C-terminal|EXS, C-terminal 2\n", - "DM DNA-binding domain|DM DNA-binding domain|DM DNA-binding domain 2\n", - "RPA-interacting protein, C-terminal domain 2\n", - "SOCS box domain|SOCS box domain|SOCS box domain|SOCS box domain 2\n", - "Peptidase C19, ubiquitin-specific peptidase, DUSP domain|Peptidase C19, ubiquitin-specific peptidase, DUSP domain|Peptidase C19, ubiquitin-specific peptidase, DUSP domain 2\n", - "Ephrin receptor ligand binding domain|Tyrosine-protein kinase, receptor class V, conserved site|Ephrin receptor ligand binding domain|Ephrin receptor ligand binding domain|Ephrin type-A receptor 2, ligand binding domain 2\n", - "JMY/WHAMM, middle domain 2\n", - "SH2 domain|SH2 domain|PI3K p85 subunit, C-terminal SH2 domain 2\n", - "Ras-associating (RA) domain|Ras-associating (RA) domain 2\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|DAZ, RNA recognition motif, vertebrates 2\n", - "BRICHOS domain 2\n", - "MTMR14, PH-GRAM domain 2\n", - "Arginyl-tRNA synthetase, catalytic core domain|Aminoacyl-tRNA synthetase, class I, conserved site|Arginyl-tRNA synthetase, catalytic core domain 2\n", - "Glycosyl hydrolases family 1, N-terminal conserved site 2\n", - "Peptidase M12A|Peptidase M12A|Peptidase, metallopeptidase 1\n", - "Teneurin intracellular, N-terminal 1\n", - "Sulfite reductase [NADPH] flavoprotein alpha-component-like, FAD-binding 1\n", - "Bcr-Abl oncoprotein oligomerisation 1\n", - "Pleckstrin homology domain|PLEKHN1, PH domain 1\n", - "Major facilitator superfamily associated domain|Major facilitator superfamily domain 1\n", - "Calmodulin-regulated spectrin-associated protein, CH domain|Calponin homology domain|Calponin homology domain|Calponin homology domain 1\n", - "Proline-tRNA ligase, class II, C-terminal|Proline-tRNA ligase, class II, C-terminal 1\n", - "POTRA domain 1\n", - "Laminin G domain|EGF-like domain|EGF-like calcium-binding domain 1\n", - "Defensin propeptide 1\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tumour necrosis factor receptor 4, N-terminal 1\n", - "PAN/Apple domain|Apple domain|Apple domain 1\n", - "Transcription factor Elf, N-terminal 1\n", - "NUC194 1\n", - "PB1 domain|PB1 domain|PB1 domain|Protein kinase C, PB1 domain 1\n", - "Neuroblastoma breakpoint family (NBPF) domain|Neuroblastoma breakpoint family (NBPF) domain 1\n", - "DNA glycosylase/AP lyase, H2TH DNA-binding 1\n", - "Butyrophylin-like, SPRY domain|B30.2/SPRY domain|TRIM1, PRY/SPRY domain 1\n", - "SH3 domain|SH3 domain|SH3 domain|srGAP1/2/3, SH3 domain 1\n", - "Phospholipase D-like domain|Phospholipase D/Transphosphatidylase|Phospholipase D/Transphosphatidylase 1\n", - "Cytosolic fatty-acid binding 1\n", - "KELK-motif containing domain 1\n", - "Peptidase M1, membrane alanine aminopeptidase, N-terminal|Peptidase M1, membrane alanine aminopeptidase, N-terminal 1\n", - "Bacterial Ig-like, group 2|Bacterial Ig-like, group 2 1\n", - "Concentrative nucleoside transporter C-terminal domain|Nucleoside transporter/FeoB GTPase, Gate domain 1\n", - "Diphthamide synthase domain|Diphthamide synthase domain|Diphthamide synthase domain 1\n", - "Thioredoxin domain|Thioredoxin-related transmembrane protein 2, thioredoxin domain 1\n", - "N-acetylmuramoyl-L-alanine amidase domain|Peptidoglycan recognition protein family domain, metazoa/bacteria|N-acetylmuramoyl-L-alanine amidase domain 1\n", - "Uncharacterised domain KLRAQ/TTKRSYEDQ, N-terminal|Uncharacterised domain KLRAQ/TTKRSYEDQ, N-terminal 1\n", - "EGF domain|EGF-like calcium-binding, conserved site|EGF-like domain|EGF-like calcium-binding domain|EGF-like domain 1\n", - "4-diphosphocytidyl-2C-methyl-D-erythritol synthase, conserved site 1\n", - "PAN/Apple domain|PAN/Apple domain|Apple domain 1\n", - "Homeobox domain|Helix-turn-helix motif|Helix-turn-helix motif|Homeobox, conserved site|Homeobox domain|Homeobox domain|Homeobox domain 1\n", - "BEACH domain|BEACH domain 1\n", - "PWWP domain|BR140-related, PWWD domain 1\n", - "MyTH4 domain 1\n", - "Homeobox domain|Homeobox domain, metazoa|Helix-turn-helix motif|Helix-turn-helix motif|Homeobox, conserved site|Homeobox domain|Homeobox domain|Homeobox domain 1\n", - "Vertebrate heat shock transcription factor, C-terminal domain 1\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase, ATP binding site|Protein kinase domain|Tyrosine-protein kinase, catalytic domain|Janus kinase 2, catalytic domain 1\n", - "Ribosomal protein/NADH dehydrogenase domain 1\n", - "tRNA wybutosine-synthesis 1\n", - "U box domain|U box domain 1\n", - "FERM, C-terminal PH-like domain 1\n", - "Post-SET domain 1\n", - "E2F/DP family, winged-helix DNA-binding domain|E2F/DP family, winged-helix DNA-binding domain 1\n", - "PTB/PI domain|Tensin, phosphotyrosine-binding domain 1\n", - "Peroxidases heam-ligand binding site 1\n", - "DJBP, EF-hand domain|EF-hand domain 1\n", - "Protein NPAT, C-terminal domain 1\n", - "Arrestin C-terminal-like domain 1\n", - "Nuclear RNA export factor Tap, RNA-binding domain 1\n", - "Bromodomain|Bromodomain|Bromodomain|Bromodomain|Brd8, Bromo domain 1\n", - "Interleukin-10, conserved site 1\n", - "SH3 domain|SH3 domain|SH3 domain|SH3 domain|Rho guanine nucleotide exchange factor 6, SH3 domain 1\n", - "Alpha crystallin/Hsp20 domain|Heat shock protein beta-3|Heat shock protein beta-3 1\n", - "Immunoglobulin C2-set|Immunoglobulin subtype 2|Immunoglobulin subtype 1\n", - "Aspartic peptidase, DDI1-type|Aspartic peptidase, DDI1-type 1\n", - "GRASP55/65 PDZ-like domain 1\n", - "CID domain|CID domain|CID domain 1\n", - "SPX domain 1\n", - "GIY-YIG endonuclease 1\n", - "Histone-arginine methyltransferase CARM1, N-terminal 1\n", - "SH2 domain|SH2 domain|STAT5b, SH2 domain 1\n", - "Fibronectin type III|Tissue factor, conserved site 1\n", - "Protein-tyrosine phosphatase, catalytic 1\n", - "Adenosine deaminase/editase|Adenosine deaminase/editase 1\n", - "Cholecystokinin A receptor, N-terminal 1\n", - "Fetuin-A-type cystatin domain|Cystatin domain|Cystatin domain 1\n", - "Sterile alpha motif domain|Sterile alpha motif domain|Caskin1/2, SAM repeat 2 1\n", - "Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3-/4-kinase, catalytic domain|PI3K-C2-alpha, catalytic domain 1\n", - "Transforming acidic coiled-coil-containing protein, C-terminal 1\n", - "Serpin domain|Serpin domain|Serpin domain 1\n", - "P-type trefoil, conserved site|P-type trefoil domain|P-type trefoil domain|P-type trefoil domain 1\n", - "Fork head domain|Fork head domain|Fork head domain conserved site 2|Fork head domain|Fork head domain 1\n", - "Alpha crystallin/Hsp20 domain|Alpha crystallin/Hsp20 domain|Heat shock protein beta-3 1\n", - "USH1G, SAM domain 1\n", - "Cytosolic fatty-acid binding|Cytosolic fatty-acid binding 1\n", - "SH3 domain|SH3 domain|SH3 domain|SH3 domain|Endophilin-A, SH3 domain 1\n", - "LEM domain|Ankyrin repeat and LEM domain-containing protein 2, LEM domain 1\n", - "Thioredoxin domain|Disulphide isomerase 1\n", - "SNF2-related, N-terminal domain|Chromo/chromo shadow domain|Chromo/chromo shadow domain 1\n", - "HIN-200/IF120x 1\n", - "Bromodomain|Zinc finger, PHD-finger|Bromodomain 1\n", - "Molybdopterin dehydrogenase, FAD-binding|FAD-binding domain, PCMH-type 1\n", - "CLASP N-terminal domain|TOG domain 1\n", - "Platelet-derived growth factor, N-terminal|PDGF/VEGF domain 1\n", - "FACT complex subunit Spt16p/Cdc68p|FACT complex subunit Spt16p/Cdc68p 1\n", - "Zinc finger, Btk motif|Zinc finger, Btk motif 1\n", - "Peroxin domain|Peroxin/Ferlin domain 1\n", - "WWC, C2 domain 1\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain, eukaryote|PUF60, RNA recognition motif 1 1\n", - "DNA methylase, C-5 cytosine-specific, conserved site 1\n", - "Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain|Interferon regulatory factor DNA-binding domain|Interferon regulatory factor-3|Interferon regulatory factor DNA-binding domain 1\n", - "B-box-type zinc finger|Zinc finger, B-box, chordata|B-box-type zinc finger|B-box-type zinc finger|B-box-type zinc finger 1\n", - "3'5'-cyclic nucleotide phosphodiesterase, catalytic domain|HD/PDEase domain 1\n", - "Aminomethyltransferase, folate-binding domain|Glycine cleavage T-protein, C-terminal barrel domain 1\n", - "Tudor domain|Tudor domain 1\n", - "E3 ubiquitin-protein ligase RNF31, UBA-like domain 1\n", - "RESP18 domain 1\n", - "Target SNARE coiled-coil homology domain|BET1, SNARE domain 1\n", - "Staphylococcal nuclease (SNase-like), OB-fold|Staphylococcal nuclease (SNase-like), OB-fold|Staphylococcal nuclease (SNase-like), OB-fold 1\n", - "Anti-proliferative protein 1\n", - "Zinc finger, double-stranded RNA binding|Zinc finger C2H2-type|Matrin/U1-C-like, C2H2-type zinc finger 1\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tumor necrosis factor receptor 16, N-terminal 1\n", - "Pleckstrin homology domain|Pleckstrin homology domain|Pleckstrin homology domain|Ras GTPase-activating domain 1\n", - "TSG101 and ALIX binding domain of CEP55 1\n", - "Prenylcysteine lyase 1\n", - "CAP Gly-rich domain 1\n", - "Vacuolar protein sorting-associated protein 54, N-terminal 1\n", - "Galanin message associated peptide (GMAP) 1\n", - "SH3 domain|SH3 domain|Dedicator of cytokinesis 4, SH3 domain 1\n", - "TNFR/NGFR cysteine-rich region|Tumour necrosis factor receptor 5, N-terminal 1\n", - "Domain of unknown function DUF5050 1\n", - "HIN-200/IF120x|HIN-200/IF120x 1\n", - "Iron hydrogenase, large subunit, C-terminal 1\n", - "Dendritic cell-specific transmembrane protein-like 1\n", - "DNA ligase 3, BRCT domain|BRCT domain|BRCT domain 1\n", - "3'-5' exonuclease domain|3'-5' exonuclease domain|Exonuclease Mut-7, DEDDy 3'-5' exonuclease domain 1\n", - "Death effector domain|Peptidase C14A, caspase catalytic domain 1\n", - "BMP-2-inducible protein kinase, C-terminal 1\n", - "EGF-like calcium-binding domain|EGF-like, conserved site|EGF-like calcium-binding domain|EGF-like domain 1\n", - "Linker histone H1/H5, domain H15 1\n", - "Pancreatic hormone-like, conserved site 1\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tumor necrosis factor receptor 10, N-terminal 1\n", - "SH3 domain|SH3 domain|SH3PXD2B, SH3 domain 3 1\n", - "Arginyl tRNA synthetase N-terminal domain|Arginyl tRNA synthetase N-terminal domain 1\n", - "DNA polymerase lambda, fingers domain|DNA-directed DNA polymerase X|DNA-directed DNA polymerase X 1\n", - "Alcohol dehydrogenase, N-terminal|Alcohol dehydrogenase, zinc-type, conserved site|Polyketide synthase, enoylreductase domain 1\n", - "CIS, SH2 domain 1\n", - "COS domain 1\n", - "RmlD-like substrate binding domain 1\n", - "DNA ligase 3, BRCT domain|BRCT domain|BRCT domain|BRCT domain 1\n", - "PWI domain 1\n", - "SH3 domain|SH3 domain|SH3 domain|Dedicator of cytokinesis 4, SH3 domain 1\n", - "cDENN domain|Tripartite DENN domain 1\n", - "Tetrahydrofolate dehydrogenase/cyclohydrolase, NAD(P)-binding domain|Tetrahydrofolate dehydrogenase/cyclohydrolase, conserved site 1\n", - "PPM-type phosphatase domain|PPM-type phosphatase, divalent cation binding|PPM-type phosphatase domain|PPM-type phosphatase domain|PPM-type phosphatase domain 1\n", - "Zona pellucida domain|Zona pellucida domain|Zona pellucida domain, conserved site|Zona pellucida domain 1\n", - "Pleckstrin homology domain|IRS-type PTB domain|Pleckstrin homology domain|Dok-7, PH domain 1\n", - "Raf-like Ras-binding 1\n", - "Domain of unknown function DUF3730 1\n", - "SH2 domain|SH2 domain|Protein kinase domain|SH2 domain 1\n", - "Myosin head, motor domain|Myosin head, motor domain|Class I myosin tail homology domain|Myosin head, motor domain 1\n", - "Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A|Carboxypeptidase E, carboxypeptidase domain 1\n", - "Krueppel-like factor 1, transactivation domain 1 1\n", - "Transketolase-like, pyrimidine-binding domain 1\n", - "Cyclin-like 1\n", - "Pleckstrin homology domain|Pleckstrin homology domain|Pleckstrin homology domain|ASAP, PH domain 1\n", - "AMPK, C-terminal adenylate sensor domain|5'-AMP-activated protein kinase alpha 1 catalytic subunit, C-terminal 1\n", - "Collagen alpha-1(XVIII) chain, frizzled domain 1\n", - "HORMA domain 1\n", - "Ras GTPase-activating domain|Ras GTPase-activating domain|Ras GTPase-activating domain|RASAL, RasGAP domain 1\n", - "Orotate phosphoribosyl transferase domain 1\n", - "Ribosomal protein L7/L12, C-terminal|Ribosomal protein L7/L12, C-terminal 1\n", - "Protein Lines, C-terminal 1\n", - "Lysophospholipase, catalytic domain|Lysophospholipase, catalytic domain 1\n", - "ENTH domain 1\n", - "Phosphoinositide 3-kinase, accessory (PIK) domain 1\n", - "CCR4-NOT transcription complex subunit 1, TTP binding domain 1\n", - "Endoplasmic reticulum vesicle transporter, C-terminal 1\n", - "Abl-interactor, homeo-domain homologous domain 1\n", - "Myotubularin-related 12-like C-terminal domain|Myotubularin-like phosphatase domain 1\n", - "Copine 1\n", - "Integrase, catalytic core 1\n", - "Chemokine interleukin-8-like domain|CC chemokine, conserved site|Chemokine interleukin-8-like domain 1\n", - "EMI domain|EMI domain 1\n", - "AARP2CN|Bms1/Tsr1-type G domain|AARP2CN|Ribosome biogenesis protein Bms1, N-terminal 1\n", - "B30.2/SPRY domain|HERC1, SPRY domain 1\n", - "C2 domain|Lysophospholipase, catalytic domain|C2 domain 1\n", - "SOCS box domain|SOCS box domain|SOCS box domain|Ankyrin repeat and SOCS box protein 9/11, SOCS box domain 1\n", - "Zinc finger, CHHC-type 1\n", - "Helicase, C-terminal|Helicase superfamily 1/2, ATP-binding domain|Helicase, C-terminal 1\n", - "Kexin/furin catalytic domain 1\n", - "Transcription factor, GTP-binding domain|Transcription factor, GTP-binding domain|Tr-type G domain, conserved site|Transcription factor, GTP-binding domain|Small GTP-binding protein domain 1\n", - "Ephrin receptor-binding domain|Ephrin receptor-binding domain|Ephrin, conserved site|Ephrin receptor-binding domain|Ephrin-B ectodomain 1\n", - "Uncharacterised domain KLRAQ/TTKRSYEDQ, N-terminal 1\n", - "Methionyl/Leucyl tRNA synthetase|Methioninyl-tRNA synthetase core domain|Aminoacyl-tRNA synthetase, class I, conserved site|Methioninyl-tRNA synthetase core domain 1\n", - "Galanin|Galanin 1\n", - "Notch, NOD domain|Notch domain|Notch, NOD domain 1\n", - "Chromo/chromo shadow domain|Chromo/chromo shadow domain|Chromo/chromo shadow domain 1\n", - "SH2 domain|Serine-threonine/tyrosine-protein kinase, catalytic domain|SH2 domain|SH2 domain|Protein kinase domain|SH2 domain 1\n", - "MnmE, helical domain|TrmE-type guanine nucleotide-binding domain|TrmE-type guanine nucleotide-binding domain 1\n", - "Domain of unknown function DUF3350 1\n", - "SH2 domain|SH2 adaptor protein C, SH2 domain 1\n", - "Threonyl/alanyl tRNA synthetase, SAD|Threonyl/alanyl tRNA synthetase, SAD|Threonine-tRNA ligase catalytic core domain 1\n", - "EF-hand domain|Prolyl 4-hydroxylase, alpha subunit|EF-hand domain 1\n", - "Fanconi Anaemia group E protein, C-terminal 1\n", - "Ribosomal protein L19/L19e|Ribosomal protein L19, eukaryotic 1\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|HuB, RNA recognition motif 2 1\n", - "Peptidase C19, ubiquitin carboxyl-terminal hydrolase|Ubiquitin specific protease domain|Ubiquitin-specific peptidase 48 1\n", - "Ribosomal protein L2, C-terminal 1\n", - "Iron hydrogenase, small subunit 1\n", - "Zinc finger, LIM-type|Zinc finger, LIM-type|Testin, LIM domain 3 1\n", - "HTH CenpB-type DNA-binding domain 1\n", - "Zinc finger, TTF-type 1\n", - "SH3 domain|SH3 domain|SH3 domain|Stac3, first SH3 domain 1\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|PPARGC1B, RNA recognition motif 1\n", - "Sorting nexin protein, WASP-binding domain 1\n", - "Diacylglycerol/phorbol-ester binding|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain|Protein kinase C-like, phorbol ester/diacylglycerol-binding domain 1\n", - "Glutamine-Leucine-Glutamine, QLQ|Glutamine-Leucine-Glutamine, QLQ 1\n", - "Domain of unknown function DUF1747|Domain of unknown function DUF1747 1\n", - "Ribosome recycling factor domain 1\n", - "Lsm14-like, N-terminal|Lsm16, N-terminal 1\n", - "Krueppel-associated box|Krueppel-associated box-related|Krueppel-associated box 1\n", - "FAD-binding domain, PCMH-type|Xanthine dehydrogenase, small subunit 1\n", - "Phosphatidylinositol-4-phosphate 5-kinase, core|Phosphatidylinositol-4-phosphate 5-kinase, core 1\n", - "Serpin domain|Serpin, conserved site|Serpin domain|Angiotensinogen serpin domain 1\n", - "Zinc finger, XPA-type, conserved site 1\n", - "Myogenic basic muscle-specific protein|Myogenic basic muscle-specific protein|Myc-type, basic helix-loop-helix (bHLH) domain 1\n", - "Activator of Hsp90 ATPase, N-terminal|Activator of Hsp90 ATPase, N-terminal 1\n", - "Peptide methionine sulphoxide reductase MsrA|Peptide methionine sulphoxide reductase MsrA 1\n", - "Flavoprotein pyridine nucleotide cytochrome reductase 1\n", - "Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A|Carboxypeptidase A6 1\n", - "Synapsin, ATP-binding domain|Synapsin, conserved site 1\n", - "Phosphatidylinositol 3-kinase, C2 domain|Phosphatidylinositol 3-kinase, C2 domain|Phosphatidylinositol 3-kinase, C2 domain|C2 domain 1\n", - "Proteinase inhibitor I25C, fetuin, conserved site 1\n", - "GC-rich sequence DNA-binding factor-like domain 1\n", - "Protein kinase domain|Protein kinase, ATP binding site|Protein kinase domain|Protein kinase domain|Novel protein kinase C eta, catalytic domain 1\n", - "RON, Sema domain 1\n", - "TUP1-like enhancer of split 1\n", - "Sodium/calcium exchanger domain, C-terminal extension 1\n", - "SoHo domain|SoHo domain 1\n", - "Peptidase M24|Peptidase M24B, X-Pro dipeptidase/aminopeptidase P, conserved site 1\n", - "Lipid-binding serum glycoprotein, N-terminal|Lipid-binding serum glycoprotein, C-terminal 1\n", - "CAS family, C-terminal 1\n", - "Carboxypeptidase D, carboxypeptidase-like domain 2 1\n", - "CREB-binding protein/p300, atypical RING domain|Bromodomain 1\n", - "Transcription regulator Wos2-domain 1\n", - "Srp40, C-terminal 1\n", - "SH3 domain|SH3 domain|SH3 domain|SH3 domain|CD2-associated protein, first SH3 domain 1\n", - "Glycolipid transfer protein domain 1\n", - "SH2 domain|SH2D1A, SH2 domain 1\n", - "Transcription factor, MADS-box|Transcription factor, MADS-box|Transcription factor, MADS-box|Transcription factor, MADS-box 1\n", - "Zinc finger, Rad18-type putative 1\n", - "Neuroblastoma breakpoint family (NBPF) domain 1\n", - "Sec63 domain 1\n", - "HYR domain 1\n", - "Mitochondria-eating protein, C-terminal domain 1\n", - "PAS domain|PAS-associated, C-terminal|PAS domain 1\n", - "Acyltransferase, C-terminal domain 1\n", - "Zinc-binding domain|Zinc-binding domain 1\n", - "Frizzled/Smoothened, transmembrane domain|Frizzled/Smoothened, transmembrane domain|Frizzled/Smoothened, transmembrane domain|Smoothened, transmembrane domain 1\n", - "Interferon regulatory factor-3 1\n", - "C-type lectin-like|C-type lectin, conserved site|C-type lectin-like|C-type lectin-like|Eosinophil major basic protein, C-type lectin-like domain 1\n", - "ARID DNA-binding domain|ARID DNA-binding domain 1\n", - "Tissue inhibitor of metalloproteinase, conserved site|Netrin domain 1\n", - "Salt-Inducible kinase, catalytic domain 1\n", - "Gamma-glutamyl cyclotransferase-like 1\n", - "Lunapark domain 1\n", - "SH2 domain|Tyrosine-protein kinase ABL, SH2 domain 1\n", - "PPM-type phosphatase domain|PPM-type phosphatase, divalent cation binding|PPM-type phosphatase domain 1\n", - "Thymidylate kinase-like domain 1\n", - "Tyrosine hydroxylase, conserved site|Tyrosine hydroxylase, conserved site 1\n", - "Acetyl-CoA carboxylase|Acetyl-coenzyme A carboxyltransferase, N-terminal|Acetyl-coenzyme A carboxyltransferase, C-terminal 1\n", - "Glycoside hydrolase, family 85 1\n", - "GTPase effector domain 1\n", - "P-type trefoil domain|P-type trefoil domain 1\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tumour necrosis factor receptor 4, N-terminal 1\n", - "Macro domain|Macro domain|Macro domain|GDAP2, macro domain 1\n", - "TROVE domain 1\n", - "FMP27, GFWDK domain 1\n", - "Pleckstrin homology domain|Pleckstrin homology domain|Pleckstrin homology domain|PHLDB1/2/3, PH domain 1\n", - "Importin-beta, N-terminal domain|Importin-beta, N-terminal domain 1\n", - "PPM-type phosphatase, divalent cation binding 1\n", - "Flavoprotein 1\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain|Maternal embryonic leucine zipper kinase, catalytic domain 1\n", - "Hyaluronan-mediated motility receptor, C-terminal 1\n", - "Synaptotagmin|C2 domain 1\n", - "Timeless protein 1\n", - "Frizzled/Smoothened, transmembrane domain|Frizzled/Smoothened, transmembrane domain|Smoothened, transmembrane domain 1\n", - "Zinc finger, PHD-finger|SAND domain|SAND domain 1\n", - "Immunoglobulin V-set domain|Immunoglobulin-like domain|Immunoglobulin V-set domain|Immunoglobulin subtype|VSIG4, immunoglobulin variable (IgV)-like domain 1\n", - "FERM central domain|Band 4.1 domain|Band 4.1 domain|FERM domain|Band 4.1 domain|FERM central domain 1\n", - "SH2 domain|SH2 domain|SH2 domain|SH2 domain|Grb14, SH2 domain 1\n", - "Tensin phosphatase, C2 domain 1\n", - "Sharpin, PH domain 1\n", - "Phosphatidylinositol 3/4-kinase, conserved site|Phosphatidylinositol 3-/4-kinase, catalytic domain|Phosphatidylinositol 3-/4-kinase, catalytic domain 1\n", - "Methylmalonyl-CoA mutase, C-terminal 1\n", - "Phox homologous domain|Phox homologous domain|Neutrophil cytosol factor 4, PX domain 1\n", - "SH3 domain|SH3 domain|SH3 domain|GRAF2, SH3 domain 1\n", - "Death effector domain|Death effector domain 1\n", - "EGF-like calcium-binding domain|CUB domain|EGF-like calcium-binding, conserved site|EGF-like domain|CUB domain|EGF-like calcium-binding domain 1\n", - "TNFR/NGFR cysteine-rich region|Tumor necrosis factor receptor 21, N-terminal 1\n", - "SH2 domain|SH2 domain|SH2 domain|Ras GTPase-activating protein 1, N-terminal SH2 domain 1\n", - "Ras GTPase-activating domain|RASA2, PH domain 1\n", - "Anticodon-binding|Aminoacyl-tRNA synthetase, class II 1\n", - "G-protein gamma-like domain|G-protein gamma-like domain|G-protein gamma-like domain|G-protein gamma-like domain 1\n", - "Ubiquitin-like modifier-activating enzyme Atg7, N-terminal 1\n", - "Post-SET domain|SET domain|Post-SET domain 1\n", - "Zinc finger, TAZ-type 1\n", - "Immunoglobulin C2-set 1\n", - "Pre-mRNA splicing factor component Cdc5p/Cef1 1\n", - "Protein kinase domain|Protein kinase domain|Protein kinase domain|p21-activated kinase 2, catalytic domain 1\n", - "Aminoacyl-tRNA synthetase, class II|Threonyl/alanyl tRNA synthetase, SAD|Threonine-tRNA ligase catalytic core domain 1\n", - "Immunoglobulin/major histocompatibility complex, conserved site|Immunoglobulin-like domain 1\n", - "CPL domain|Pumilio homology domain 1\n", - "Laminin EGF domain|Laminin EGF domain|EGF-like domain|EGF-like domain 1\n", - "Protein kinase domain|Protein kinase, ATP binding site|Protein kinase domain|Protein kinase domain|Protein Kinase B beta, catalytic domain 1\n", - "Peptidase M14, carboxypeptidase A|Carboxypeptidase E, carboxypeptidase domain 1\n", - "Transferrin-like domain|Transferrin-like domain|Transferrin-like domain|Transferrin-like domain|Transferrin-like domain 1\n", - "Proteinase inhibitor I25C, fetuin, conserved site|Fetuin-A-type cystatin domain|Cystatin domain|Cystatin domain 1\n", - "DNA-directed RNA polymerase, N-terminal|DNA-directed RNA polymerase, N-terminal 1\n", - "Ubiquinol-cytochrome C reductase hinge domain 1\n", - "Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A|Peptidase M14, carboxypeptidase A|AEBP1/CPX, carboxypeptidase domain 1\n", - "RAD51 interacting motif 1\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain, eukaryote 1\n", - "Coilin, N-terminal domain 1\n", - "tRNA synthetase, B5-domain|tRNA synthetase, B5-domain 1\n", - "PTP type protein phosphatase|PTP type protein phosphatase|Protein-tyrosine phosphatase, catalytic 1\n", - "FCH domain|F-BAR domain|FCH domain|SLIT-ROBO Rho GTPase-activating protein 1, F-BAR domain 1\n", - "Ubiquitin conjugation factor E4, core 1\n", - "Peptidase M14, carboxypeptidase A|Metallocarboxypeptidase Z, carboxypeptidase domain 1\n", - "SH3 domain|SH3 domain|Protein kinase domain|SH3 domain|Tyrosine-protein kinase BTK, SH3 domain 1\n", - "C2 domain|C2 domain|FerIin domain|Ferlin, second C2 domain 1\n", - "Electron transfer flavoprotein, alpha/beta-subunit, N-terminal|Electron transfer flavoprotein, beta subunit, N-terminal 1\n", - "GTPase effector domain|Dynamin GTPase effector 1\n", - "Macrophage migration inhibitory factor, conserved site 1\n", - "Peroxiredoxin, C-terminal 1\n", - "Glutamate/phenylalanine/leucine/valine dehydrogenase, C-terminal 1\n", - "Kazal domain|Factor I / membrane attack complex|Kazal domain 1\n", - "N-acetyltransferase ESCO, zinc-finger 1\n", - "FERM central domain|Band 4.1 domain|FERM domain|Band 4.1 domain 1\n", - "APC10/DOC domain 1\n", - "Galanin|Galanin|Galanin|Galanin 1\n", - "ATP-dependent RNA helicase Ski2, C-terminal 1\n", - "Ubiquitin-conjugating enzyme E2|Ubiquitin-conjugating enzyme E2 1\n", - "Zinc finger, RING-type, conserved site|Zinc finger, RING-type 1\n", - "TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|TNFR/NGFR cysteine-rich region|Tumour necrosis factor receptor 8, N-terminal 1\n", - "Domain of unknown function DUF3454, notch 1\n", - "Dienelactone hydrolase 1\n", - "Kazal domain|Insulin-like growth factor-binding protein, IGFBP|Kazal domain|Kazal domain 1\n", - "Zinc finger, ZPR1-type|Zinc finger, ZPR1-type|Zinc finger, ZPR1-type 1\n", - "Zinc finger, C3HC4 RING-type|Zinc finger, RING-type, conserved site|Zinc finger, RING-type 1\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|SHARP, RNA recognition motif 4 1\n", - "Domain of unknown function DUF676, lipase-like 1\n", - "Bromodomain|Bromodomain|Bromodomain|Brd8, Bromo domain 1\n", - "EGF-like, conserved site|Laminin EGF domain|EGF-like domain 1\n", - "Copine|von Willebrand factor, type A|von Willebrand factor, type A|Copine 1\n", - "Immunoglobulin C2-set|Immunoglobulin subtype 1\n", - "Nuclear protein 96 1\n", - "SH3 domain|SH3 domain|SH3 domain|SH3PXD2B, SH3 domain 3 1\n", - "Developmental pluripotency-associated protein 2/4, C-terminal domain 1\n", - "Immunoglobulin-like domain|Immunoglobulin C1-set 1\n", - "Ricin B, lectin domain 1\n", - "Cullin homology domain 1\n", - "E3 ubiquitin-protein ligase RNF31, UBA-like domain|Ubiquitin-associated domain 1\n", - "DDT domain|DDT domain|DDT domain 1\n", - "Transcription regulator GCM domain|Transcription regulator GCM domain 1\n", - "Thiamine pyrophosphate enzyme, central domain 1\n", - "SEP domain|SEP domain|SEP domain 1\n", - "Kazal domain|Kazal domain|Factor I / membrane attack complex|EGF-like domain|Kazal domain 1\n", - "Glutathione S-transferase, C-terminal|Glutathione S-transferase, N-terminal|Glutathione S-transferase, C-terminal-like|Glutathione S-transferases, class Zeta , C-terminal 1\n", - "GPR domain 1\n", - "SH2 domain|Serine-threonine/tyrosine-protein kinase, catalytic domain|Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase domain|SH2 domain 1\n", - "Ribosomal protein L10e/L16 1\n", - "SH2 domain|SH2 domain|SH2 domain|Tyrosine-protein kinase ABL, SH2 domain 1\n", - "Domain of unknown function DUF4939 1\n", - "Histidine kinase domain|Histidine kinase/HSP90-like ATPase 1\n", - "Phosphoinositide-specific phospholipase C, EF-hand-like domain|EF-hand domain 1\n", - "2-5-oligoadenylate synthetase, conserved site|2-5-oligoadenylate synthetase, N-terminal 1\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Serine-threonine/tyrosine-protein kinase, catalytic domain|Tyrosine-protein kinase, catalytic domain 1\n", - "Alpha-defensin|Alpha-defensin|Beta/alpha defensin 1\n", - "EndoU ribonuclease, C-terminal 1\n", - "Intramolecular chaperone auto-processing domain|Intramolecular chaperone auto-processing domain 1\n", - "Thrombomodulin-like, EGF-like|EGF-like domain 1\n", - "LIS1 homology motif 1\n", - "ABC-transporter extension domain 1\n", - "TBC1 domain family member 30, C-terminal 1\n", - "Protein argonaute, N-terminal 1\n", - "EF-hand domain|SMOC-2, extracellular calcium-binding domain 1\n", - "Immunoglobulin C1-set|Immunoglobulin C1-set 1\n", - "Ribosomal protein L19/L19e|Ribosomal protein L19/L19e|Ribosomal protein L19, eukaryotic 1\n", - "Integrin beta subunit, VWA domain|Integrin beta subunit, VWA domain|von Willebrand factor, type A 1\n", - "MHCK/EF2 kinase|MHCK/EF2 kinase 1\n", - "Double-stranded RNA-specific adenosine deaminase (DRADA)|Double-stranded RNA-specific adenosine deaminase (DRADA) 1\n", - "UHMK1, RNA recognition motif 1\n", - "Ras guanine-nucleotide exchange factors catalytic domain 1\n", - "CXC chemokine receptor 4 N-terminal domain 1\n", - "Zinc finger, RING-CH-type 1\n", - "Peptidase M1, leukotriene A4 hydrolase/aminopeptidase C-terminal 1\n", - "SH2 domain|SH2 domain|STAT2, SH2 domain 1\n", - "Alpha crystallin/Hsp20 domain|Heat shock protein beta-3 1\n", - "Fibronectin type II domain|Peptidase M10, metallopeptidase|Fibronectin type II domain|Fibronectin type II domain|Fibronectin type II domain|Peptidase, metallopeptidase|Fibronectin type II domain|Peptidase M10A, catalytic domain 1\n", - "Interleukin 17 receptor D, N-terminal 1\n", - "Sulphate anion transporter, conserved site 1\n", - "SPRY-associated|Butyrophylin-like, SPRY domain|B30.2/SPRY domain|SPRY-associated|Butyrophilin subfamily 3, PRY/SPRY domain 1\n", - "Lipid-binding serum glycoprotein, C-terminal|Lipid-binding serum glycoprotein, N-terminal|Lipid-binding serum glycoprotein, C-terminal 1\n", - "Laminin G domain|Fibronectin type III|Laminin G domain 1\n", - "Aminoacyl-tRNA synthetase, class II|Prolyl-tRNA synthetase, catalytic domain 1\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Serine-threonine/tyrosine-protein kinase, catalytic domain|Protein kinase domain|Tyrosine-protein kinase, catalytic domain|Janus kinase 2, catalytic domain 1\n", - "Cystatin domain|Fetuin-B-type cystatin domain|Cystatin domain|Cystatin domain 1\n", - "F-BAR domain|GAS7, F-BAR domain 1\n", - "Neutrophil cytosol factor 1, C-terminal 1\n", - "PKD domain|PKD domain|Polycystin cation channel 1\n", - "uDENN domain|Tripartite DENN domain|cDENN domain 1\n", - "ABC transporter-like|ABC transporter-like|AAA+ ATPase domain|RLI, domain 1 1\n", - "LSM domain, eukaryotic/archaea-type|Sm-like protein Lsm11, middle domain 1\n", - "MHC class I-like antigen recognition-like|MHC class I alpha chain, alpha1 alpha2 domains|Immunoglobulin-like domain 1\n", - "Lipocalin/cytosolic fatty-acid binding domain|Cytosolic fatty-acid binding|Cytosolic fatty-acid binding 1\n", - "Ets domain|Ets domain|Ets domain 1\n", - "SMOC-1, extracellular calcium-binding domain 1\n", - "Carbamoyl-phosphate synthetase large subunit-like, ATP-binding domain|Biotin carboxylation domain 1\n", - "Metaxin, glutathione S-transferase domain 1\n", - "CO dehydrogenase flavoprotein, C-terminal|CO dehydrogenase flavoprotein, C-terminal 1\n", - "Ribosomal protein L7/L12, oligomerisation 1\n", - "Methylmalonyl-CoA mutase, alpha chain, catalytic 1\n", - "PA14/GLEYA domain|PA14 domain 1\n", - "SH3 domain|SH3 domain|SH3 domain|SAM and SH3 domain-containing protein 1, SH3 domain 1\n", - "Cytochrome b5-like heme/steroid binding domain 1\n", - "POU domain|Homeobox domain|Homeobox domain 1\n", - "GTP binding domain|Era-type guanine nucleotide-binding (G) domain|Small GTP-binding protein domain|Era-type guanine nucleotide-binding (G) domain 1\n", - "Bromodomain|Extended PHD (ePHD) domain 1\n", - "Netrin module, non-TIMP type|Netrin domain|Netrin module, non-TIMP type|Secreted frizzled-related protein 3, NTR domain 1\n", - "Pleckstrin homology domain|IRS-type PTB domain|Pleckstrin homology domain|Dok-7, PTB domain 1\n", - "N-acetylmuramoyl-L-alanine amidase domain|N-acetylmuramoyl-L-alanine amidase domain|Peptidoglycan recognition protein family domain, metazoa/bacteria|N-acetylmuramoyl-L-alanine amidase domain 1\n", - "Carbohydrate kinase PfkB|Carbohydrate/puine kinase, PfkB, conserved site 1\n", - "B30.2/SPRY domain|Fibronectin type III|Fibronectin type III 1\n", - "Importin-beta, N-terminal domain|Importin-beta, N-terminal domain|Importin-beta, N-terminal domain 1\n", - "Pancreatic trypsin inhibitor Kunitz domain|Pancreatic trypsin inhibitor Kunitz domain|Pancreatic trypsin inhibitor Kunitz domain|Proteinase inhibitor I2, Kunitz, conserved site|Pancreatic trypsin inhibitor Kunitz domain|Pancreatic trypsin inhibitor Kunitz domain|Pancreatic trypsin inhibitor Kunitz domain 1\n", - "Ephrin receptor ligand binding domain|Ephrin receptor ligand binding domain|Ephrin receptor ligand binding domain 1\n", - "Oxoglutarate/iron-dependent dioxygenase 1\n", - "Dilute domain|Dilute domain|Dilute domain 1\n", - "Rrp7A, RNA recognition motif 1\n", - "Zinc finger, HIT-type|Zinc finger, HIT-type 1\n", - "Nucleolar 27S pre-rRNA processing, Urb2/Npa2, C-terminal 1\n", - "Inositol monophosphatase, conserved site 1\n", - "TNFR/NGFR cysteine-rich region|Tyrosine-protein kinase ephrin type A/B receptor-like 1\n", - "Glycoside hydrolase family 18, catalytic domain 1\n", - "Phosphatidylinositol-specific phospholipase C, X domain|Phosphatidylinositol-specific phospholipase C, X domain 1\n", - "Sequestosome-1, UBA domain|Ubiquitin-associated domain 1\n", - "WAP-type 'four-disulfide core' domain|WAP-type 'four-disulfide core' domain|WAP-type 'four-disulfide core' domain|WAP-type 'four-disulfide core' domain|WAP-type 'four-disulfide core' domain 1\n", - "Tetratricopeptide repeat protein 5, OB fold domain 1\n", - "MCM domain|MCM domain|MCM domain|AAA+ ATPase domain 1\n", - "DNA replication factor RFC1, C-terminal 1\n", - "SH3 domain|SH3 domain|SH3 domain|SH3 domain|CD2-associated protein, third SH3 domain 1\n", - "FERM central domain|Pleckstrin homology domain|Kindlin/fermitin, PH domain 1\n", - "Sugar isomerase (SIS)|Sugar isomerase (SIS) 1\n", - "HTH CenpB-type DNA-binding domain|HTH CenpB-type DNA-binding domain 1\n", - "BTB/Kelch-associated 1\n", - "A-kinase anchor protein 2, C-terminal domain 1\n", - "Cwf19-like, C-terminal domain-1 1\n", - "Major sperm protein (MSP) domain 1\n", - "Chemokine interleukin-8-like domain 1\n", - "GMP synthase, C-terminal 1\n", - "Cathelicidin, antimicrobial peptide, C-terminal 1\n", - "Fibronectin, type I|Fibronectin, type I 1\n", - "PPM-type phosphatase domain|PPM-type phosphatase, divalent cation binding|PPM-type phosphatase domain|PPM-type phosphatase domain|PPM-type phosphatase domain|PPM-type phosphatase domain 1\n", - "Neurohypophysial hormone, conserved site|Neurohypophysial hormone, conserved site 1\n", - "Syndetin, C-terminal 1\n", - "E2F transcription factor, CC-MB domain|E2F transcription factor, CC-MB domain 1\n", - "PH-BEACH domain 1\n", - "tRNA wybutosine-synthesis|Radical SAM 1\n", - "Beta-2-glycoprotein-1 fifth domain 1\n", - "Dedicator of cytokinesis C, C2 domain 1\n", - "Fibronectin type II domain|Fibronectin type II domain|Fibronectin type II domain|Fibronectin type II domain 1\n", - "SEFIR domain 1\n", - "SH3 domain|SH3 domain|SH3 domain|CD2-associated protein, third SH3 domain 1\n", - "Iron hydrogenase, small subunit|Iron hydrogenase, small subunit 1\n", - "Beta-trefoil DNA-binding domain 1\n", - "ADAM, cysteine-rich|Disintegrin domain|ADAM, cysteine-rich 1\n", - "Jun-like transcription factor 1\n", - "Peptidase M14, carboxypeptidase A|Carboxypeptidase A6 1\n", - "SNAP-25|Target SNARE coiled-coil homology domain 1\n", - "Mediator complex, subunit Med12 1\n", - "FERM central domain|Pleckstrin homology domain|Pleckstrin homology domain|Kindlin/fermitin, PH domain 1\n", - "Nucleolar pre-ribosomal-associated protein 1, N-terminal 1\n", - "SPRY domain|B30.2/SPRY domain|SPRY domain|TRIM1, PRY/SPRY domain 1\n", - "MnmE, helical domain|TrmE-type guanine nucleotide-binding domain|Small GTP-binding protein domain|TrmE-type guanine nucleotide-binding domain 1\n", - "Immunoglobulin V-set domain|Immunoglobulin-like domain 1\n", - "Ephrin receptor-binding domain|Ephrin receptor-binding domain|Ephrin-B ectodomain 1\n", - "G8 domain|G8 domain 1\n", - "T cell CD4 receptor C-terminal region 1\n", - "RNA recognition motif domain|RNA recognition motif domain|RNA recognition motif domain|SHARP, RNA recognition motif 1 1\n", - "G2 nidogen/fibulin G2F|G2 nidogen/fibulin G2F|G2 nidogen/fibulin G2F 1\n", - "Minichromosome loss protein Mcl1, middle region 1\n", - "Serine-threonine/tyrosine-protein kinase, catalytic domain|Tyrosine-protein kinase, catalytic domain 1\n", - "ATP:guanido phosphotransferase, catalytic domain 1\n", - "Chemokine interleukin-8-like domain|Chemokine interleukin-8-like domain|CXC Chemokine domain 1\n", - "Ubiquinol-cytochrome c reductase 8kDa, N-terminal 1\n", - "Fatty acyl-CoA reductase, C-terminal|Fatty acyl-CoA reductase, C-terminal 1\n", - "Glyceraldehyde 3-phosphate dehydrogenase, catalytic domain 1\n", - "EF-Hand 1, calcium-binding site 1\n", - "Protein kinase domain|Protein kinase, ATP binding site|Protein kinase domain|Protein kinase domain|Atypical Protein Kinase C zeta, catalytic domain 1\n", - "Copper amine oxidase, N2-terminal 1\n", - "Immunoglobulin V-set domain|Immunoglobulin-like domain|Immunoglobulin subtype|VSIG4, immunoglobulin variable (IgV)-like domain 1\n", - "Tubulin/FtsZ, GTPase domain|Tubulin, conserved site 1\n", - "Spindle assembly abnormal protein 6, N-terminal 1\n", - "EGF-like, conserved site|Laminin G domain|EGF-like domain|EGF-like domain 1\n", - "FCH domain|F-BAR domain 1\n", - "Emerin, LEM domain 1\n", - "Tetrahydrofolate dehydrogenase/cyclohydrolase, catalytic domain|Tetrahydrofolate dehydrogenase/cyclohydrolase, conserved site 1\n", - "SH2 domain|VAV3, SH2 domain 1\n", - "2Fe-2S ferredoxin-type iron-sulfur binding domain|2Fe-2S ferredoxin, iron-sulphur binding site|2Fe-2S ferredoxin-type iron-sulfur binding domain|Xanthine dehydrogenase, small subunit 1\n", - "Pyruvate carboxyltransferase 1\n", - "SPARC/Testican, calcium-binding domain|EF-Hand 1, calcium-binding site|SMOC-1, extracellular calcium-binding domain 1\n", - "TNFR/NGFR cysteine-rich region|Tumor necrosis factor receptor 6B, N-terminal 1\n", - "Cytochrome b5-like heme/steroid binding domain|Cytochrome b5-like heme/steroid binding domain|Cytochrome b5-like heme/steroid binding domain|Cytochrome b5, heme-binding site|Cytochrome b5-like heme/steroid binding domain|Cytochrome b5-like heme/steroid binding domain 1\n", - "Sigma-54 interaction domain, ATP-binding site 1|Small GTP-binding protein domain 1\n", - "HIF-1 alpha, transactivation domain, C-terminal 1\n", - "TNFR/NGFR cysteine-rich region|Tyrosine-protein kinase ephrin type A/B receptor-like|Tumour necrosis factor receptor 4, N-terminal 1\n", - "Zinc finger, ZPR1-type 1\n", - "Aminoacyl-tRNA synthetase, class II|Lysyl-tRNA synthetase, class II, C-terminal 1\n", - "Laminin EGF domain|Laminin EGF domain|GPCR, family 2, extracellular hormone receptor domain|Laminin EGF domain 1\n", - "Janus kinase 2, catalytic domain 1\n", - "Fibrinogen, alpha/beta/gamma chain, C-terminal globular domain|Fibrinogen, alpha/beta/gamma chain, C-terminal globular domain|Fibrinogen, alpha/beta/gamma chain, C-terminal globular domain 1\n", - "Zinc finger, FPG/IleRS-type 1\n", - "Immunoglobulin-like domain|Immunoglobulin-like domain 1\n", - "Phox homologous domain|Phox homologous domain|SNX27, PX domain 1\n", - "Peptide methionine sulphoxide reductase MsrA 1\n", - "von Willebrand factor, VWA N-terminal domain|von Willebrand factor, type A 1\n", - "SH2 domain|Chimaerin, SH2 domain 1\n", - "tRNA synthetase, B5-domain|tRNA synthetase, B5-domain|tRNA synthetase, B5-domain 1\n", - "Heat shock protein Hsp90, N-terminal|Histidine kinase/HSP90-like ATPase 1\n", - "Protein kinase, C-terminal|AGC-kinase, C-terminal|AGC-kinase, C-terminal|Classical Protein Kinase C alpha, catalytic domain 1\n", - "Aminoacyl-tRNA synthetase, class II (D/K/N)|Lysyl-tRNA synthetase, class II, C-terminal 1\n", - "DZF domain 1\n", - "SH3 domain|SH3 domain|SH3 domain|SH3 domain|VAV3 protein, second SH3 domain 1\n", - "Protein kinase domain|Protein kinase, ATP binding site|Protein kinase domain|Protein kinase domain|Maternal embryonic leucine zipper kinase, catalytic domain 1\n", - "PUL domain 1\n", - "IRG-type guanine nucleotide-binding (G) domain 1\n", - "B-cell lymphoma 9, beta-catenin binding domain 1\n", - "Immunoglobulin I-set|Immunoglobulin subtype|Palladin, C-terminal immunoglobulin-like domain 1\n", - "PKD domain|REJ domain|Polycystin cation channel 1\n", - "Radical SAM|Lipoyl synthase, N-terminal|Elp3/MiaB/NifB 1\n", - "Prp31 C-terminal|Nop domain 1\n", - "AARP2CN|AARP2CN 1\n", - "Phosphoinositide 3-kinase, accessory (PIK) domain|Phosphoinositide 3-kinase, accessory (PIK) domain|PI3Kalpha, catalytic domain 1\n", - "Protein of unknown function DUF3719 1\n", - "SH3 domain|SH3 domain|SH3 domain|DBS, SH3 domain 1\n", - "RIO kinase|Serine/threonine-protein kinase Rio2 1\n", - "Bromodomain|Bromodomain|Bromodomain, conserved site|Bromodomain|Bromodomain|BAZ2A/BAZ2B, bromodomain 1\n", - "SH2 domain|SH2 domain|SH2 domain|SH2 domain|Tyrosine-protein kinase Frk, SH2 domain 1\n", - "S-adenosyl-L-homocysteine hydrolase, NAD binding domain|S-adenosyl-L-homocysteine hydrolase, conserved site|S-adenosyl-L-homocysteine hydrolase, NAD binding domain 1\n", - "RNA recognition motif domain|RNA recognition motif domain|Set1A, RNA recognition motif 1\n", - "DNA topoisomerase, type IIA, subunit B, domain 2 1\n", - "Spt5 C-terminal domain 1\n", - "EF-hand domain|S-100 1\n", - "Pleckstrin homology domain|PDZ domain|Pleckstrin homology domain 1\n", - "Class II aldolase/adducin N-terminal|Class II aldolase/adducin N-terminal 1\n", - "PWI domain|PWI domain|PWI domain 1\n", - "Prokineticin domain 1\n", - "Netrin domain|Netrin module, non-TIMP type|Secreted frizzled-related protein 3, NTR domain 1\n", - "Retro-transposon transporting motif 1\n", - "Glycosyl hydrolase, C-terminal (DUF3459) 1\n", - "Orotate phosphoribosyl transferase domain|Phosphoribosyltransferase domain 1\n", - "Name: Interpro_domain, dtype: int64\n" - ] - } - ], - "source": [ - "#Drop variants with leass than 30% of data along with duplicates. Also delete columns with all null values.\n", - "print('Dropping empty columns and rows along with duplicate rows...')\n", - "#df.dropna(axis=1, thresh=(df.shape[0]*0.15), inplace=True) #thresh=(df.shape[0]/4)\n", - "df.dropna(axis=0, thresh=(df.shape[1]*0.3), inplace=True) #thresh=(df.shape[1]*0.3), how='all',\n", - "df.drop_duplicates()\n", - "df.dropna(axis=1, how='all', inplace=True) #thresh=(df.shape[0]/4)\n", - "print('\\nData shape (nsSNV) =', df.shape)\n", - "print('\\nclinvar_CLNSIG:\\n', df['clinvar_clnsig'].value_counts())\n", - "print('\\nclinvar_review:\\n', df['clinvar_review'].value_counts())\n", - "print('\\nInterpro_domain:\\n', df['Interpro_domain'].value_counts())" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Data shape (nsSNV) = (1521194, 268)\n", - "\n", - "clinvar_CLNSIG:\n", - " Uncertain_significance 1184498\n", - "Benign 73635\n", - "Likely_pathogenic 72897\n", - "Likely_benign 72434\n", - "Pathogenic 68392\n", - "Benign/Likely_benign 26904\n", - "Pathogenic/Likely_pathogenic 22434\n", - "Name: clinvar_clnsig, dtype: int64\n", - "\n", - "clinvar_review:\n", - " criteria_provided,_single_submitter 1171873\n", - "criteria_provided,_multiple_submitters,_no_conflicts 336920\n", - "reviewed_by_expert_panel 12328\n", - "practice_guideline 73\n", - "Name: clinvar_review, dtype: int64\n" - ] - } - ], - "source": [ - "#Filter variants for clinvar_review\n", - "df= df.loc[df['clinvar_review'].isin(config_dict['CLNREVSTAT'])]\n", - "df= df.loc[df['clinvar_clnsig'].isin(config_dict['ClinicalSignificance'])]\n", - "print('\\nData shape (nsSNV) =', df.shape)\n", - "print('\\nclinvar_CLNSIG:\\n', df['clinvar_clnsig'].value_counts())\n", - "print('\\nclinvar_review:\\n', df['clinvar_review'].value_counts())" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['MutationAssessor_pred',\n", - " 'Polyphen2_HDIV_pred',\n", - " 'ClinPred_pred',\n", - " 'MetaRNN_pred',\n", - " 'MetaLR_pred',\n", - " 'BayesDel_noAF_pred',\n", - " 'DEOGEN2_pred',\n", - " 'PROVEAN_pred',\n", - " 'fathmm-MKL_coding_pred',\n", - " 'genename',\n", - " 'Interpro_domain',\n", - " 'Ensembl_transcriptid',\n", - " 'clinvar_clnsig',\n", - " 'clinvar_review',\n", - " 'aaref',\n", - " 'SIFT_pred',\n", - " 'cds_strand',\n", - " 'alt',\n", - " 'SIFT4G_pred',\n", - " 'Ensembl_geneid',\n", - " 'MetaSVM_pred',\n", - " 'ref',\n", - " '#chr',\n", - " 'PrimateAI_pred',\n", - " 'fathmm-XF_coding_pred',\n", - " 'M-CAP_pred',\n", - " 'Ensembl_proteinid',\n", - " 'BayesDel_addAF_pred',\n", - " 'LRT_pred',\n", - " 'FATHMM_pred',\n", - " 'Uniprot_acc',\n", - " 'Polyphen2_HVAR_pred',\n", - " 'LIST-S2_pred',\n", - " 'aaalt']" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Check if there are any categorical columns\n", - "num_cols = df._get_numeric_data().columns\n", - "\n", - "list(set(df.columns) - set(num_cols))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "34" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(list(set(df.columns) - set(num_cols)))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "

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5 rows × 34 columns

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" - ], - "text/plain": [ - " MutationAssessor_pred Polyphen2_HDIV_pred ClinPred_pred MetaRNN_pred \\\n", - "0 NaN NaN D D \n", - "1 NaN NaN D D \n", - "2 M D D D \n", - "3 NaN NaN D D \n", - "4 NaN NaN D D \n", - "\n", - " MetaLR_pred BayesDel_noAF_pred DEOGEN2_pred PROVEAN_pred \\\n", - "0 T D D NaN \n", - "1 T D NaN D \n", - "2 T D T D \n", - "3 T D T NaN \n", - "4 T D T NaN \n", - "\n", - " fathmm-MKL_coding_pred genename ... fathmm-XF_coding_pred M-CAP_pred \\\n", - "0 D SAMD11 ... D T \n", - "1 D SAMD11 ... D T \n", - "2 D SAMD11 ... D T \n", - "3 D SAMD11 ... D T \n", - "4 D SAMD11 ... D T \n", - "\n", - " Ensembl_proteinid BayesDel_addAF_pred LRT_pred FATHMM_pred Uniprot_acc \\\n", - "0 ENSP00000411579 D N NaN A6PWC8 \n", - "1 ENSP00000393181 D N NaN Q5SV95 \n", - "2 ENSP00000342313 D N NaN Q96NU1 \n", - "3 ENSP00000480870 D N NaN A0A087WXB3 \n", - "4 ENSP00000482138 D N NaN A0A087WYW1 \n", - "\n", - " Polyphen2_HVAR_pred LIST-S2_pred aaalt \n", - "0 NaN D E \n", - "1 NaN D E \n", - "2 P D E \n", - "3 NaN D E \n", - "4 NaN D E \n", - "\n", - "[5 rows x 34 columns]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[list(set(df.columns) - set(num_cols))].head()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "df1 = df" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Benign', 'Pathogenic', 'Likely_pathogenic', 'Likely_benign']" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Classes for training\n", - "config_dict['Clinsig_train']" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Pathogenic/Likely_pathogenic', 'Benign/Likely_benign']" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "config_dict['Clinsig_test']" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "train_df = df.loc[df['clinvar_clnsig'].isin(config_dict['Clinsig_train'])]\n", - "test_df= df.loc[df['clinvar_clnsig'].isin(config_dict['Clinsig_test'])]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Test data shape (nsSNV) = (49338, 268)\n", - "\n", - "Train data shape (nsSNV) = (287358, 268)\n" - ] - } - ], - "source": [ - "print('\\nTest data shape (nsSNV) =', test_df.shape)\n", - "print('\\nTrain data shape (nsSNV) =', train_df.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "clinvar_clnsig:\n", - " Benign 73635\n", - "Likely_pathogenic 72897\n", - "Likely_benign 72434\n", - "Pathogenic 68392\n", - "Name: clinvar_clnsig, dtype: int64\n" - ] - } - ], - "source": [ - "df = train_df\n", - "print('\\nclinvar_clnsig:\\n', df['clinvar_clnsig'].value_counts())" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "#chr 0\n", - "pos(1-based) 0\n", - "ref 0\n", - "alt 0\n", - "aaref 10879\n", - "aaalt 10879\n", - "genename 0\n", - "Ensembl_geneid 0\n", - "Ensembl_transcriptid 0\n", - "Ensembl_proteinid 0\n", - "Uniprot_acc 4942\n", - "cds_strand 148801\n", - "SIFT_score 121119\n", - "SIFT_converted_rankscore 49567\n", - "SIFT_pred 121119\n", - "SIFT4G_score 96653\n", - "SIFT4G_converted_rankscore 51163\n", - "SIFT4G_pred 96653\n", - "Polyphen2_HDIV_score 163106\n", - "Polyphen2_HDIV_rankscore 59543\n", - "Polyphen2_HDIV_pred 163106\n", - "Polyphen2_HVAR_score 163106\n", - "Polyphen2_HVAR_rankscore 59543\n", - "Polyphen2_HVAR_pred 163106\n", - "LRT_score 35854\n", - "LRT_converted_rankscore 35854\n", - "LRT_pred 35854\n", - "LRT_Omega 35854\n", - "MutationTaster_converted_rankscore 2113\n", - "MutationAssessor_score 180402\n", - "MutationAssessor_rankscore 71607\n", - "MutationAssessor_pred 180402\n", - "FATHMM_score 113784\n", - "FATHMM_converted_rankscore 50370\n", - "FATHMM_pred 113784\n", - "PROVEAN_score 119262\n", - "PROVEAN_converted_rankscore 47819\n", - "PROVEAN_pred 119262\n", - "VEST4_score 78158\n", - "VEST4_rankscore 13431\n", - "MetaSVM_score 44210\n", - "MetaSVM_rankscore 44210\n", - "MetaSVM_pred 44210\n", - "MetaLR_score 44210\n", - "MetaLR_rankscore 44210\n", - "MetaLR_pred 44210\n", - "Reliability_index 44210\n", - "MetaRNN_score 41806\n", - "MetaRNN_rankscore 41806\n", - "MetaRNN_pred 41806\n", - "M-CAP_score 101124\n", - "M-CAP_rankscore 101124\n", - "M-CAP_pred 101124\n", - "REVEL_score 118656\n", - "REVEL_rankscore 46625\n", - "MutPred_score 159156\n", - "MutPred_rankscore 159156\n", - "MVP_score 99284\n", - "MVP_rankscore 62412\n", - "MPC_score 227128\n", - "MPC_rankscore 68671\n", - "PrimateAI_score 52368\n", - "PrimateAI_rankscore 52368\n", - "PrimateAI_pred 52368\n", - "DEOGEN2_score 170142\n", - "DEOGEN2_rankscore 56186\n", - "DEOGEN2_pred 170142\n", - "BayesDel_addAF_score 901\n", - "BayesDel_addAF_rankscore 901\n", - "BayesDel_addAF_pred 901\n", - "BayesDel_noAF_score 901\n", - "BayesDel_noAF_rankscore 901\n", - "BayesDel_noAF_pred 901\n", - "ClinPred_score 38605\n", - "ClinPred_rankscore 38605\n", - "ClinPred_pred 38605\n", - "LIST-S2_score 90506\n", - "LIST-S2_rankscore 48353\n", - "LIST-S2_pred 90506\n", - "CADD_raw 0\n", - "CADD_raw_rankscore 0\n", - "CADD_phred 0\n", - "CADD_raw_hg19 302\n", - "CADD_raw_rankscore_hg19 302\n", - "CADD_phred_hg19 302\n", - "DANN_score 302\n", - "DANN_rankscore 302\n", - "fathmm-MKL_coding_score 302\n", - "fathmm-MKL_coding_rankscore 302\n", - "fathmm-MKL_coding_pred 302\n", - "fathmm-XF_coding_score 32454\n", - "fathmm-XF_coding_rankscore 32454\n", - "fathmm-XF_coding_pred 32454\n", - "Eigen-raw_coding 21121\n", - "Eigen-raw_coding_rankscore 21121\n", - "Eigen-phred_coding 21121\n", - "Eigen-PC-raw_coding 21121\n", - "Eigen-PC-raw_coding_rankscore 21121\n", - "Eigen-PC-phred_coding 21121\n", - "GenoCanyon_score 302\n", - "GenoCanyon_rankscore 302\n", - "integrated_fitCons_score 19336\n", - "integrated_fitCons_rankscore 19336\n", - "integrated_confidence_value 19336\n", - "GM12878_fitCons_score 19336\n", - "GM12878_fitCons_rankscore 19336\n", - "GM12878_confidence_value 19336\n", - "H1-hESC_fitCons_score 19336\n", - "H1-hESC_fitCons_rankscore 19336\n", - "H1-hESC_confidence_value 19336\n", - "HUVEC_fitCons_score 19336\n", - "HUVEC_fitCons_rankscore 19336\n", - "HUVEC_confidence_value 19336\n", - "LINSIGHT 276051\n", - "LINSIGHT_rankscore 276051\n", - "GERP++_NR 395\n", - "GERP++_RS 395\n", - "GERP++_RS_rankscore 395\n", - "phyloP100way_vertebrate 0\n", - "phyloP100way_vertebrate_rankscore 0\n", - "phyloP30way_mammalian 0\n", - "phyloP30way_mammalian_rankscore 0\n", - "phyloP17way_primate 0\n", - "phyloP17way_primate_rankscore 0\n", - "phastCons100way_vertebrate 0\n", - "phastCons100way_vertebrate_rankscore 0\n", - "phastCons30way_mammalian 0\n", - "phastCons30way_mammalian_rankscore 0\n", - "phastCons17way_primate 0\n", - "phastCons17way_primate_rankscore 0\n", - "SiPhy_29way_logOdds 697\n", - "SiPhy_29way_logOdds_rankscore 697\n", - "bStatistic 4834\n", - "bStatistic_converted_rankscore 4834\n", - "1000Gp3_AF 187396\n", - "1000Gp3_AFR_AF 187396\n", - "1000Gp3_EUR_AF 187396\n", - "1000Gp3_AMR_AF 187396\n", - "1000Gp3_EAS_AF 187396\n", - "1000Gp3_SAS_AF 187396\n", - "TWINSUK_AF 235842\n", - "ALSPAC_AF 235842\n", - "UK10K_AF 235842\n", - "ESP6500_AA_AF 191491\n", - "ESP6500_EA_AF 191491\n", - "ExAC_AF 129971\n", - "ExAC_Adj_AF 129971\n", - "ExAC_AFR_AF 129971\n", - "ExAC_AMR_AF 129971\n", - "ExAC_EAS_AF 129971\n", - "ExAC_FIN_AF 129971\n", - "ExAC_NFE_AF 129971\n", - "ExAC_SAS_AF 129971\n", - "ExAC_nonTCGA_AF 134546\n", - "ExAC_nonTCGA_Adj_AF 134546\n", - "ExAC_nonTCGA_AFR_AF 134546\n", - "ExAC_nonTCGA_AMR_AF 134546\n", - "ExAC_nonTCGA_EAS_AF 134546\n", - "ExAC_nonTCGA_FIN_AF 134546\n", - "ExAC_nonTCGA_NFE_AF 134546\n", - "ExAC_nonTCGA_SAS_AF 134546\n", - "ExAC_nonpsych_AF 134102\n", - "ExAC_nonpsych_Adj_AF 134102\n", - "ExAC_nonpsych_AFR_AF 134102\n", - "ExAC_nonpsych_AMR_AF 134102\n", - "ExAC_nonpsych_EAS_AF 134102\n", - "ExAC_nonpsych_FIN_AF 134102\n", - "ExAC_nonpsych_NFE_AF 134102\n", - "ExAC_nonpsych_SAS_AF 134102\n", - "gnomAD_exomes_AF 114245\n", - "gnomAD_exomes_AFR_AF 114269\n", - "gnomAD_exomes_AMR_AF 114270\n", - "gnomAD_exomes_ASJ_AF 114274\n", - "gnomAD_exomes_EAS_AF 114288\n", - "gnomAD_exomes_FIN_AF 114259\n", - "gnomAD_exomes_NFE_AF 114245\n", - "gnomAD_exomes_SAS_AF 114260\n", - "gnomAD_exomes_POPMAX_AF 116232\n", - "gnomAD_exomes_controls_AF 114260\n", - "gnomAD_exomes_non_neuro_AF 114256\n", - "gnomAD_exomes_non_cancer_AF 114245\n", - "gnomAD_exomes_non_topmed_AF 114245\n", - "gnomAD_exomes_controls_AFR_AF 114299\n", - "gnomAD_exomes_controls_AMR_AF 114273\n", - "gnomAD_exomes_controls_ASJ_AF 114311\n", - "gnomAD_exomes_controls_EAS_AF 114317\n", - "gnomAD_exomes_controls_FIN_AF 114287\n", - "gnomAD_exomes_controls_NFE_AF 114262\n", - "gnomAD_exomes_controls_SAS_AF 114273\n", - "gnomAD_exomes_controls_POPMAX_AF 135658\n", - "gnomAD_exomes_non_neuro_AFR_AF 114269\n", - "gnomAD_exomes_non_neuro_AMR_AF 114272\n", - "gnomAD_exomes_non_neuro_ASJ_AF 114292\n", - "gnomAD_exomes_non_neuro_EAS_AF 114303\n", - "gnomAD_exomes_non_neuro_FIN_AF 114272\n", - "gnomAD_exomes_non_neuro_NFE_AF 114257\n", - "gnomAD_exomes_non_neuro_SAS_AF 114262\n", - "gnomAD_exomes_non_neuro_POPMAX_AF 119590\n", - "gnomAD_exomes_non_cancer_AFR_AF 114287\n", - "gnomAD_exomes_non_cancer_AMR_AF 114270\n", - "gnomAD_exomes_non_cancer_ASJ_AF 114274\n", - "gnomAD_exomes_non_cancer_EAS_AF 114291\n", - "gnomAD_exomes_non_cancer_FIN_AF 114259\n", - "gnomAD_exomes_non_cancer_NFE_AF 114245\n", - "gnomAD_exomes_non_cancer_SAS_AF 114260\n", - "gnomAD_exomes_non_cancer_POPMAX_AF 118491\n", - "gnomAD_exomes_non_topmed_AFR_AF 114269\n", - "gnomAD_exomes_non_topmed_AMR_AF 114270\n", - "gnomAD_exomes_non_topmed_ASJ_AF 114274\n", - "gnomAD_exomes_non_topmed_EAS_AF 114288\n", - "gnomAD_exomes_non_topmed_FIN_AF 114259\n", - "gnomAD_exomes_non_topmed_NFE_AF 114245\n", - "gnomAD_exomes_non_topmed_SAS_AF 114260\n", - "gnomAD_exomes_non_topmed_POPMAX_AF 117047\n", - "gnomAD_genomes_AF 124588\n", - "gnomAD_genomes_POPMAX_AF 126660\n", - "gnomAD_genomes_AFR_AF 124588\n", - "gnomAD_genomes_AMI_AF 124595\n", - "gnomAD_genomes_AMR_AF 124588\n", - "gnomAD_genomes_ASJ_AF 124589\n", - "gnomAD_genomes_EAS_AF 124588\n", - "gnomAD_genomes_FIN_AF 124588\n", - "gnomAD_genomes_MID_AF 124589\n", - "gnomAD_genomes_NFE_AF 124588\n", - "gnomAD_genomes_SAS_AF 124588\n", - "gnomAD_genomes_controls_and_biobanks_AF 124588\n", - "gnomAD_genomes_non_neuro_AF 124588\n", - "gnomAD_genomes_non_cancer_AF 124588\n", - "gnomAD_genomes_non_topmed_AF 124588\n", - "gnomAD_genomes_controls_and_biobanks_AFR_AF 124588\n", - "gnomAD_genomes_controls_and_biobanks_AMI_AF 124626\n", - "gnomAD_genomes_controls_and_biobanks_AMR_AF 124588\n", - "gnomAD_genomes_controls_and_biobanks_ASJ_AF 124603\n", - "gnomAD_genomes_controls_and_biobanks_EAS_AF 124589\n", - "gnomAD_genomes_controls_and_biobanks_FIN_AF 124588\n", - "gnomAD_genomes_controls_and_biobanks_MID_AF 124591\n", - "gnomAD_genomes_controls_and_biobanks_NFE_AF 124588\n", - "gnomAD_genomes_controls_and_biobanks_SAS_AF 124588\n", - "gnomAD_genomes_non_neuro_AFR_AF 124588\n", - "gnomAD_genomes_non_neuro_AMI_AF 124595\n", - "gnomAD_genomes_non_neuro_AMR_AF 124588\n", - "gnomAD_genomes_non_neuro_ASJ_AF 124589\n", - "gnomAD_genomes_non_neuro_EAS_AF 124588\n", - "gnomAD_genomes_non_neuro_FIN_AF 124588\n", - "gnomAD_genomes_non_neuro_MID_AF 124589\n", - "gnomAD_genomes_non_neuro_NFE_AF 124588\n", - "gnomAD_genomes_non_neuro_SAS_AF 124588\n", - "gnomAD_genomes_non_cancer_AFR_AF 124588\n", - "gnomAD_genomes_non_cancer_AMI_AF 124595\n", - "gnomAD_genomes_non_cancer_AMR_AF 124588\n", - "gnomAD_genomes_non_cancer_ASJ_AF 124589\n", - "gnomAD_genomes_non_cancer_EAS_AF 124588\n", - "gnomAD_genomes_non_cancer_FIN_AF 124588\n", - "gnomAD_genomes_non_cancer_MID_AF 124589\n", - "gnomAD_genomes_non_cancer_NFE_AF 124588\n", - "gnomAD_genomes_non_cancer_SAS_AF 124588\n", - "gnomAD_genomes_non_topmed_AFR_AF 124588\n", - "gnomAD_genomes_non_topmed_AMI_AF 124621\n", - "gnomAD_genomes_non_topmed_AMR_AF 124588\n", - "gnomAD_genomes_non_topmed_ASJ_AF 124591\n", - "gnomAD_genomes_non_topmed_EAS_AF 124589\n", - "gnomAD_genomes_non_topmed_FIN_AF 124588\n", - "gnomAD_genomes_non_topmed_MID_AF 124590\n", - "gnomAD_genomes_non_topmed_NFE_AF 124588\n", - "gnomAD_genomes_non_topmed_SAS_AF 124588\n", - "clinvar_clnsig 0\n", - "clinvar_review 0\n", - "Interpro_domain 195998\n", - "dtype: int64" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Check how many columns are null\n", - "df.isnull().sum(axis = 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Impact (Class):\n", - " low_impact 146069\n", - "high_impact 141289\n", - "Name: clinvar_clnsig, dtype: int64\n" - ] - } - ], - "source": [ - "#Convert classes from HGMD and ClinVar to either \"high_impact\" or \"Low_impact\"\n", - "y = df['clinvar_clnsig'].str.replace(r'Likely_pathogenic','high_impact').str.replace(r'Pathogenic','high_impact')\n", - "y = y.str.replace(r'Likely_benign','low_impact').str.replace(r'Benign','low_impact')\n", - "print('\\nImpact (Class):\\n', y.value_counts())" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "#Drop the class column\n", - "df = df.drop('clinvar_clnsig', axis=1)\n", - "#df['hgmd_class'] = y" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['#chr',\n", - " 'pos(1-based)',\n", - " 'ref',\n", - " 'alt',\n", - " 'aaref',\n", - " 'aaalt',\n", - " 'genename',\n", - " 'Ensembl_geneid',\n", - " 'Ensembl_transcriptid',\n", - " 'Ensembl_proteinid',\n", - " 'Uniprot_acc',\n", - " 'clinvar_review',\n", - " 'Interpro_domain']" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "config_dict['var']" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Drop variant info columns so we can perform one-hot encoding\n", - "var = df[config_dict['var']]\n", - "df = df.drop(config_dict['var'], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(287358, 254)" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['MutationAssessor_pred',\n", - " 'Polyphen2_HDIV_pred',\n", - " 'ClinPred_pred',\n", - " 'MetaRNN_pred',\n", - " 'MetaLR_pred',\n", - " 'BayesDel_noAF_pred',\n", - " 'DEOGEN2_pred',\n", - " 'PROVEAN_pred',\n", - " 'fathmm-MKL_coding_pred',\n", - " 'SIFT_pred',\n", - " 'cds_strand',\n", - " 'SIFT4G_pred',\n", - " 'MetaSVM_pred',\n", - " 'PrimateAI_pred',\n", - " 'fathmm-XF_coding_pred',\n", - " 'M-CAP_pred',\n", - " 'BayesDel_addAF_pred',\n", - " 'LRT_pred',\n", - " 'FATHMM_pred',\n", - " 'Polyphen2_HVAR_pred',\n", - " 'LIST-S2_pred']" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Check if there are any categorical columns\n", - "num_cols = df._get_numeric_data().columns\n", - "\n", - "list(set(df.columns) - set(num_cols))" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " MutationAssessor_pred Polyphen2_HDIV_pred ClinPred_pred MetaRNN_pred \\\n", - "11 NaN NaN T T \n", - "12 NaN NaN T T \n", - "13 M D T T \n", - "14 NaN NaN T T \n", - "15 NaN NaN T T \n", - "\n", - " MetaLR_pred BayesDel_noAF_pred DEOGEN2_pred PROVEAN_pred \\\n", - "11 T T T NaN \n", - "12 T T NaN D \n", - "13 T T T D \n", - "14 T T T NaN \n", - "15 T T T NaN \n", - "\n", - " fathmm-MKL_coding_pred SIFT_pred ... SIFT4G_pred MetaSVM_pred \\\n", - "11 D NaN ... NaN T \n", - "12 D D ... T T \n", - "13 D D ... T T \n", - "14 D NaN ... T T \n", - "15 D NaN ... 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'Polyphen2_HDIV_rankscore': 0.5759900212287903,\n", - " 'Polyphen2_HVAR_score': 0.5410000085830688,\n", - " 'Polyphen2_HVAR_rankscore': 0.57014000415802,\n", - " 'LRT_score': 2.4000000848900527e-05,\n", - " 'LRT_converted_rankscore': 0.5587499737739563,\n", - " 'LRT_Omega': 0.08483199775218964,\n", - " 'MutationTaster_converted_rankscore': 0.8100100159645081,\n", - " 'MutationAssessor_score': 1.9850000143051147,\n", - " 'MutationAssessor_rankscore': 0.5646899938583374,\n", - " 'FATHMM_score': -0.9700000286102295,\n", - " 'FATHMM_converted_rankscore': 0.7842699885368347,\n", - " 'PROVEAN_score': -2.3399999141693115,\n", - " 'PROVEAN_converted_rankscore': 0.5808500051498413,\n", - " 'VEST4_score': 0.5770000219345093,\n", - " 'VEST4_rankscore': 0.6790800094604492,\n", - " 'MetaSVM_score': -0.3720000088214874,\n", - " 'MetaSVM_rankscore': 0.7289999723434448,\n", - " 'MetaLR_score': 0.37599998712539673,\n", - " 'MetaLR_rankscore': 0.7340099811553955,\n", - " 'Reliability_index': 10.0,\n", - " 'MetaRNN_score': 0.19587162137031555,\n", - " 'MetaRNN_rankscore': 0.35601499676704407,\n", - " 'M-CAP_score': 0.19680799543857574,\n", - " 'M-CAP_rankscore': 0.8650100231170654,\n", - " 'REVEL_score': 0.335999995470047,\n", - " 'REVEL_rankscore': 0.6969599723815918,\n", - " 'MutPred_score': 0.7049999833106995,\n", - " 'MutPred_rankscore': 0.8413800001144409,\n", - " 'MVP_score': 0.788725733757019,\n", - " 'MVP_rankscore': 0.8119099736213684,\n", - " 'MPC_score': 0.48845353722572327,\n", - " 'MPC_rankscore': 0.5411499738693237,\n", - " 'PrimateAI_score': 0.6072239875793457,\n", - " 'PrimateAI_rankscore': 0.5392400026321411,\n", - " 'DEOGEN2_score': 0.2666434943675995,\n", - " 'DEOGEN2_rankscore': 0.7485550045967102,\n", - " 'BayesDel_addAF_score': 0.09952180087566376,\n", - " 'BayesDel_addAF_rankscore': 0.6424099802970886,\n", - " 'BayesDel_noAF_score': 0.04204289987683296,\n", - " 'BayesDel_noAF_rankscore': 0.7306100130081177,\n", - " 'ClinPred_score': 0.3046443462371826,\n", - " 'ClinPred_rankscore': 0.2519400119781494,\n", - " 'LIST-S2_score': 0.8972100019454956,\n", - " 'LIST-S2_rankscore': 0.657829999923706,\n", - " 'CADD_raw': 3.4916319847106934,\n", - " 'CADD_raw_rankscore': 0.6387900114059448,\n", - " 'CADD_phred': 24.700000762939453,\n", - " 'CADD_raw_hg19': 3.3808820247650146,\n", - " 'CADD_raw_rankscore_hg19': 0.6408200263977051,\n", - " 'CADD_phred_hg19': 24.600000381469727,\n", - " 'DANN_score': 0.9943440556526184,\n", - " 'DANN_rankscore': 0.6442300081253052,\n", - " 'fathmm-MKL_coding_score': 0.9360299706459045,\n", - " 'fathmm-MKL_coding_rankscore': 0.5867000222206116,\n", - " 'fathmm-XF_coding_score': 0.548860490322113,\n", - " 'fathmm-XF_coding_rankscore': 0.5619750022888184,\n", - " 'Eigen-raw_coding': 0.42638349533081055,\n", - " 'Eigen-raw_coding_rankscore': 0.6287699937820435,\n", - " 'Eigen-phred_coding': 4.511109828948975,\n", - " 'Eigen-PC-raw_coding': 0.40067073702812195,\n", - " 'Eigen-PC-raw_coding_rankscore': 0.6160799860954285,\n", - " 'Eigen-PC-phred_coding': 4.363158226013184,\n", - " 'GenoCanyon_score': 0.9999986290931702,\n", - " 'GenoCanyon_rankscore': 0.7476599812507629,\n", - " 'integrated_fitCons_score': 0.6717699766159058,\n", - " 'integrated_fitCons_rankscore': 0.525950014591217,\n", - " 'integrated_confidence_value': 0.0,\n", - " 'GM12878_fitCons_score': 0.615513026714325,\n", - " 'GM12878_fitCons_rankscore': 0.5265799760818481,\n", - " 'GM12878_confidence_value': 0.0,\n", - " 'H1-hESC_fitCons_score': 0.6589829921722412,\n", - " 'H1-hESC_fitCons_rankscore': 0.5588099956512451,\n", - " 'H1-hESC_confidence_value': 0.0,\n", - " 'HUVEC_fitCons_score': 0.6355509757995605,\n", - " 'HUVEC_fitCons_rankscore': 0.5308799743652344,\n", - " 'HUVEC_confidence_value': 0.0,\n", - " 'LINSIGHT': 0.9746059775352478,\n", - " 'LINSIGHT_rankscore': 0.7730500102043152,\n", - " 'GERP++_NR': 5.320000171661377,\n", - " 'GERP++_RS': 4.639999866485596,\n", - " 'GERP++_RS_rankscore': 0.573989987373352,\n", - " 'phyloP100way_vertebrate': 4.349999904632568,\n", - " 'phyloP100way_vertebrate_rankscore': 0.5916900038719177,\n", - " 'phyloP30way_mammalian': 1.0260000228881836,\n", - " 'phyloP30way_mammalian_rankscore': 0.45945999026298523,\n", - " 'phyloP17way_primate': 0.5989999771118164,\n", - " 'phyloP17way_primate_rankscore': 0.4025000035762787,\n", - " 'phastCons100way_vertebrate': 1.0,\n", - " 'phastCons100way_vertebrate_rankscore': 0.7163800001144409,\n", - " 'phastCons30way_mammalian': 0.9890000224113464,\n", - " 'phastCons30way_mammalian_rankscore': 0.517520010471344,\n", - " 'phastCons17way_primate': 0.9649999737739563,\n", - " 'phastCons17way_primate_rankscore': 0.5289700031280518,\n", - " 'SiPhy_29way_logOdds': 13.576299667358398,\n", - " 'SiPhy_29way_logOdds_rankscore': 0.6134099960327148,\n", - " 'bStatistic': 717.0,\n", - " 'bStatistic_converted_rankscore': 0.5583500266075134,\n", - " '1000Gp3_AF': 0.0029952076729387045,\n", - " '1000Gp3_AFR_AF': 0.0022692889906466007,\n", - " '1000Gp3_EUR_AF': 0.0,\n", - " '1000Gp3_AMR_AF': 0.0014409221475943923,\n", - " '1000Gp3_EAS_AF': 0.0,\n", - " '1000Gp3_SAS_AF': 0.0,\n", - " 'TWINSUK_AF': 0.0021574972197413445,\n", - " 'ALSPAC_AF': 0.0023352361749857664,\n", - " 'UK10K_AF': 0.0022480825427919626,\n", - " 'ESP6500_AA_AF': 0.0031364348251372576,\n", - " 'ESP6500_EA_AF': 0.00023255814448930323,\n", - " 'ExAC_AF': 0.0003896999987773597,\n", - " 'ExAC_Adj_AF': 0.00043590000132098794,\n", - " 'ExAC_AFR_AF': 0.000192299994523637,\n", - " 'ExAC_AMR_AF': 0.00011010000162059441,\n", - " 'ExAC_EAS_AF': 0.0,\n", - " 'ExAC_FIN_AF': 0.0,\n", - " 'ExAC_NFE_AF': 4.501000148593448e-05,\n", - " 'ExAC_SAS_AF': 0.0,\n", - " 'ExAC_nonTCGA_AF': 0.00045190000673756003,\n", - " 'ExAC_nonTCGA_Adj_AF': 0.000499650021083653,\n", - " 'ExAC_nonTCGA_AFR_AF': 0.0002209999947808683,\n", - " 'ExAC_nonTCGA_AMR_AF': 0.00017830000433605164,\n", - " 'ExAC_nonTCGA_EAS_AF': 0.0,\n", - " 'ExAC_nonTCGA_FIN_AF': 0.0,\n", - " 'ExAC_nonTCGA_NFE_AF': 3.968999953940511e-05,\n", - " 'ExAC_nonTCGA_SAS_AF': 0.0,\n", - " 'ExAC_nonpsych_AF': 0.000506900018081069,\n", - " 'ExAC_nonpsych_Adj_AF': 0.0005537500255741179,\n", - " 'ExAC_nonpsych_AFR_AF': 0.00020450000010896474,\n", - " 'ExAC_nonpsych_AMR_AF': 0.00017299999308306724,\n", - " 'ExAC_nonpsych_EAS_AF': 0.0,\n", - " 'ExAC_nonpsych_FIN_AF': 0.0,\n", - " 'ExAC_nonpsych_NFE_AF': 5.237000004854053e-05,\n", - " 'ExAC_nonpsych_SAS_AF': 0.0,\n", - " 'gnomAD_exomes_AF': 0.00026651800726540387,\n", - " 'gnomAD_exomes_AFR_AF': 6.454940012190491e-05,\n", - " 'gnomAD_exomes_AMR_AF': 8.673530101077631e-05,\n", - " 'gnomAD_exomes_ASJ_AF': 0.0,\n", - " 'gnomAD_exomes_EAS_AF': 0.0,\n", - " 'gnomAD_exomes_FIN_AF': 0.0,\n", - " 'gnomAD_exomes_NFE_AF': 2.8809599825763144e-05,\n", - " 'gnomAD_exomes_SAS_AF': 3.266270141466521e-05,\n", - " 'gnomAD_exomes_POPMAX_AF': 0.0019165199482813478,\n", - " 'gnomAD_exomes_controls_AF': 0.00026527600130066276,\n", - " 'gnomAD_exomes_non_neuro_AF': 0.0002777430054266006,\n", - " 'gnomAD_exomes_non_cancer_AF': 0.0002677690063137561,\n", - " 'gnomAD_exomes_non_topmed_AF': 0.00024912800290621817,\n", - " 'gnomAD_exomes_controls_AFR_AF': 0.0,\n", - " 'gnomAD_exomes_controls_AMR_AF': 6.501110328827053e-05,\n", - " 'gnomAD_exomes_controls_ASJ_AF': 0.0,\n", - " 'gnomAD_exomes_controls_EAS_AF': 0.0,\n", - " 'gnomAD_exomes_controls_FIN_AF': 0.0,\n", - " 'gnomAD_exomes_controls_NFE_AF': 2.353489981032908e-05,\n", - " 'gnomAD_exomes_controls_SAS_AF': 0.0,\n", - " 'gnomAD_exomes_controls_POPMAX_AF': 0.0030525000765919685,\n", - " 'gnomAD_exomes_non_neuro_AFR_AF': 6.468310311902314e-05,\n", - " 'gnomAD_exomes_non_neuro_AMR_AF': 6.759950338164344e-05,\n", - " 'gnomAD_exomes_non_neuro_ASJ_AF': 0.0,\n", - " 'gnomAD_exomes_non_neuro_EAS_AF': 0.0,\n", - " 'gnomAD_exomes_non_neuro_FIN_AF': 0.0,\n", - " 'gnomAD_exomes_non_neuro_NFE_AF': 3.3503798476886004e-05,\n", - " 'gnomAD_exomes_non_neuro_SAS_AF': 3.2671199733158574e-05,\n", - " 'gnomAD_exomes_non_neuro_POPMAX_AF': 0.002087059896439314,\n", - " 'gnomAD_exomes_non_cancer_AFR_AF': 6.78334035910666e-05,\n", - " 'gnomAD_exomes_non_cancer_AMR_AF': 8.7570799223613e-05,\n", - " 'gnomAD_exomes_non_cancer_ASJ_AF': 0.0,\n", - " 'gnomAD_exomes_non_cancer_EAS_AF': 0.0,\n", - " 'gnomAD_exomes_non_cancer_FIN_AF': 0.0,\n", - " 'gnomAD_exomes_non_cancer_NFE_AF': 2.9329499739105813e-05,\n", - " 'gnomAD_exomes_non_cancer_SAS_AF': 3.2758998713688925e-05,\n", - " 'gnomAD_exomes_non_cancer_POPMAX_AF': 0.0020483199041336775,\n", - " 'gnomAD_exomes_non_topmed_AFR_AF': 8.325010276166722e-05,\n", - " 'gnomAD_exomes_non_topmed_AMR_AF': 8.707259985385463e-05,\n", - " 'gnomAD_exomes_non_topmed_ASJ_AF': 0.0,\n", - " 'gnomAD_exomes_non_topmed_EAS_AF': 0.0,\n", - " 'gnomAD_exomes_non_topmed_FIN_AF': 0.0,\n", - " 'gnomAD_exomes_non_topmed_NFE_AF': 2.7773099645855837e-05,\n", - " 'gnomAD_exomes_non_topmed_SAS_AF': 3.266270141466521e-05,\n", - " 'gnomAD_exomes_non_topmed_POPMAX_AF': 0.0019623199477791786,\n", - " 'gnomAD_genomes_AF': 0.0003352130006533116,\n", - " 'gnomAD_genomes_POPMAX_AF': 0.0022930449340492487,\n", - " 'gnomAD_genomes_AFR_AF': 0.0001205869994009845,\n", - " 'gnomAD_genomes_AMI_AF': 0.0,\n", - " 'gnomAD_genomes_AMR_AF': 0.00013102700177114457,\n", - " 'gnomAD_genomes_ASJ_AF': 0.0,\n", - " 'gnomAD_genomes_EAS_AF': 0.0,\n", - " 'gnomAD_genomes_FIN_AF': 0.0,\n", - " 'gnomAD_genomes_MID_AF': 0.0,\n", - " 'gnomAD_genomes_NFE_AF': 4.4094300392316654e-05,\n", - " 'gnomAD_genomes_SAS_AF': 0.0,\n", - " 'gnomAD_genomes_controls_and_biobanks_AF': 0.0004559550143312663,\n", - " 'gnomAD_genomes_non_neuro_AF': 0.0003415349929127842,\n", - " 'gnomAD_genomes_non_cancer_AF': 0.000332591007463634,\n", - " 'gnomAD_genomes_non_topmed_AF': 0.0003965799987781793,\n", - " 'gnomAD_genomes_controls_and_biobanks_AFR_AF': 0.00011005900159943849,\n", - " 'gnomAD_genomes_controls_and_biobanks_AMI_AF': 0.0,\n", - " 'gnomAD_genomes_controls_and_biobanks_AMR_AF': 0.0002133109956048429,\n", - " 'gnomAD_genomes_controls_and_biobanks_ASJ_AF': 0.0,\n", - " 'gnomAD_genomes_controls_and_biobanks_EAS_AF': 0.0,\n", - " 'gnomAD_genomes_controls_and_biobanks_FIN_AF': 0.0,\n", - " 'gnomAD_genomes_controls_and_biobanks_MID_AF': 0.0,\n", - " 'gnomAD_genomes_controls_and_biobanks_NFE_AF': 0.0,\n", - " 'gnomAD_genomes_controls_and_biobanks_SAS_AF': 0.0,\n", - " 'gnomAD_genomes_non_neuro_AFR_AF': 0.0001231299975188449,\n", - " 'gnomAD_genomes_non_neuro_AMI_AF': 0.0,\n", - " 'gnomAD_genomes_non_neuro_AMR_AF': 0.00013493500591721386,\n", - " 'gnomAD_genomes_non_neuro_ASJ_AF': 0.0,\n", - " 'gnomAD_genomes_non_neuro_EAS_AF': 0.0,\n", - " 'gnomAD_genomes_non_neuro_FIN_AF': 0.0,\n", - " 'gnomAD_genomes_non_neuro_MID_AF': 0.0,\n", - " 'gnomAD_genomes_non_neuro_NFE_AF': 4.693369919550605e-05,\n", - " 'gnomAD_genomes_non_neuro_SAS_AF': 0.0,\n", - " 'gnomAD_genomes_non_cancer_AFR_AF': 0.00012151800183346495,\n", - " 'gnomAD_genomes_non_cancer_AMI_AF': 0.0,\n", - " 'gnomAD_genomes_non_cancer_AMR_AF': 0.00013262599532026798,\n", - " 'gnomAD_genomes_non_cancer_ASJ_AF': 0.0,\n", - " 'gnomAD_genomes_non_cancer_EAS_AF': 0.0,\n", - " 'gnomAD_genomes_non_cancer_FIN_AF': 0.0,\n", - " 'gnomAD_genomes_non_cancer_MID_AF': 0.0,\n", - " 'gnomAD_genomes_non_cancer_NFE_AF': 4.628629903891124e-05,\n", - " 'gnomAD_genomes_non_cancer_SAS_AF': 0.0,\n", - " 'gnomAD_genomes_non_topmed_AFR_AF': 0.00012085100024705753,\n", - " 'gnomAD_genomes_non_topmed_AMI_AF': 0.0,\n", - " 'gnomAD_genomes_non_topmed_AMR_AF': 0.00015491900558117777,\n", - " 'gnomAD_genomes_non_topmed_ASJ_AF': 0.0,\n", - " 'gnomAD_genomes_non_topmed_EAS_AF': 0.0,\n", - " 'gnomAD_genomes_non_topmed_FIN_AF': 0.0,\n", - " 'gnomAD_genomes_non_topmed_MID_AF': 0.0,\n", - " 'gnomAD_genomes_non_topmed_NFE_AF': 4.752400127472356e-05,\n", - " 'gnomAD_genomes_non_topmed_SAS_AF': 0.0}" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "median_scores = df.median().to_dict()\n", - "median_scores" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['1000Gp3_AF',\n", - " '1000Gp3_AFR_AF',\n", - " '1000Gp3_EUR_AF',\n", - " '1000Gp3_AMR_AF',\n", - " '1000Gp3_EAS_AF',\n", - " '1000Gp3_SAS_AF',\n", - " 'TWINSUK_AF',\n", - " 'ALSPAC_AF',\n", - " 'UK10K_AF',\n", - " 'ESP6500_AA_AF',\n", - " 'ESP6500_EA_AF',\n", - " 'ExAC_AF',\n", - " 'ExAC_Adj_AF',\n", - " 'ExAC_AFR_AF',\n", - " 'ExAC_AMR_AF',\n", - " 'ExAC_EAS_AF',\n", - " 'ExAC_FIN_AF',\n", - " 'ExAC_NFE_AF',\n", - " 'ExAC_SAS_AF',\n", - " 'ExAC_nonTCGA_AF',\n", - " 'ExAC_nonTCGA_Adj_AF',\n", - " 'ExAC_nonTCGA_AFR_AF',\n", - " 'ExAC_nonTCGA_AMR_AF',\n", - " 'ExAC_nonTCGA_EAS_AF',\n", - " 'ExAC_nonTCGA_FIN_AF',\n", - " 'ExAC_nonTCGA_NFE_AF',\n", - " 'ExAC_nonTCGA_SAS_AF',\n", - " 'ExAC_nonpsych_AF',\n", - " 'ExAC_nonpsych_Adj_AF',\n", - " 'ExAC_nonpsych_AFR_AF',\n", - " 'ExAC_nonpsych_AMR_AF',\n", - " 'ExAC_nonpsych_EAS_AF',\n", - " 'ExAC_nonpsych_FIN_AF',\n", - " 'ExAC_nonpsych_NFE_AF',\n", - " 'ExAC_nonpsych_SAS_AF',\n", - " 'gnomAD_exomes_AF',\n", - " 'gnomAD_exomes_AFR_AF',\n", - " 'gnomAD_exomes_AMR_AF',\n", - " 'gnomAD_exomes_ASJ_AF',\n", - " 'gnomAD_exomes_EAS_AF',\n", - " 'gnomAD_exomes_FIN_AF',\n", - " 'gnomAD_exomes_NFE_AF',\n", - " 'gnomAD_exomes_SAS_AF',\n", - " 'gnomAD_exomes_POPMAX_AF',\n", - " 'gnomAD_exomes_controls_AF',\n", - " 'gnomAD_exomes_non_neuro_AF',\n", - " 'gnomAD_exomes_non_cancer_AF',\n", - " 'gnomAD_exomes_non_topmed_AF',\n", - " 'gnomAD_exomes_controls_AFR_AF',\n", - " 'gnomAD_exomes_controls_AMR_AF',\n", - " 'gnomAD_exomes_controls_ASJ_AF',\n", - " 'gnomAD_exomes_controls_EAS_AF',\n", - " 'gnomAD_exomes_controls_FIN_AF',\n", - " 'gnomAD_exomes_controls_NFE_AF',\n", - " 'gnomAD_exomes_controls_SAS_AF',\n", - " 'gnomAD_exomes_controls_POPMAX_AF',\n", - " 'gnomAD_exomes_non_neuro_AFR_AF',\n", - " 'gnomAD_exomes_non_neuro_AMR_AF',\n", - " 'gnomAD_exomes_non_neuro_ASJ_AF',\n", - " 'gnomAD_exomes_non_neuro_EAS_AF',\n", - " 'gnomAD_exomes_non_neuro_FIN_AF',\n", - " 'gnomAD_exomes_non_neuro_NFE_AF',\n", - " 'gnomAD_exomes_non_neuro_SAS_AF',\n", - " 'gnomAD_exomes_non_neuro_POPMAX_AF',\n", - " 'gnomAD_exomes_non_cancer_AFR_AF',\n", - " 'gnomAD_exomes_non_cancer_AMR_AF',\n", - " 'gnomAD_exomes_non_cancer_ASJ_AF',\n", - " 'gnomAD_exomes_non_cancer_EAS_AF',\n", - " 'gnomAD_exomes_non_cancer_FIN_AF',\n", - " 'gnomAD_exomes_non_cancer_NFE_AF',\n", - " 'gnomAD_exomes_non_cancer_SAS_AF',\n", - " 'gnomAD_exomes_non_cancer_POPMAX_AF',\n", - " 'gnomAD_exomes_non_topmed_AFR_AF',\n", - " 'gnomAD_exomes_non_topmed_AMR_AF',\n", - " 'gnomAD_exomes_non_topmed_ASJ_AF',\n", - " 'gnomAD_exomes_non_topmed_EAS_AF',\n", - " 'gnomAD_exomes_non_topmed_FIN_AF',\n", - " 'gnomAD_exomes_non_topmed_NFE_AF',\n", - " 'gnomAD_exomes_non_topmed_SAS_AF',\n", - " 'gnomAD_exomes_non_topmed_POPMAX_AF',\n", - " 'gnomAD_genomes_AF',\n", - " 'gnomAD_genomes_POPMAX_AF',\n", - " 'gnomAD_genomes_AFR_AF',\n", - " 'gnomAD_genomes_AMI_AF',\n", - " 'gnomAD_genomes_AMR_AF',\n", - " 'gnomAD_genomes_ASJ_AF',\n", - " 'gnomAD_genomes_EAS_AF',\n", - " 'gnomAD_genomes_FIN_AF',\n", - " 'gnomAD_genomes_MID_AF',\n", - " 'gnomAD_genomes_NFE_AF',\n", - " 'gnomAD_genomes_SAS_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_AF',\n", - " 'gnomAD_genomes_non_neuro_AF',\n", - " 'gnomAD_genomes_non_cancer_AF',\n", - " 'gnomAD_genomes_non_topmed_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_AFR_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_AMI_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_AMR_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_ASJ_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_EAS_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_FIN_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_MID_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_NFE_AF',\n", - " 'gnomAD_genomes_controls_and_biobanks_SAS_AF',\n", - " 'gnomAD_genomes_non_neuro_AFR_AF',\n", - " 'gnomAD_genomes_non_neuro_AMI_AF',\n", - " 'gnomAD_genomes_non_neuro_AMR_AF',\n", - " 'gnomAD_genomes_non_neuro_ASJ_AF',\n", - " 'gnomAD_genomes_non_neuro_EAS_AF',\n", - " 'gnomAD_genomes_non_neuro_FIN_AF',\n", - " 'gnomAD_genomes_non_neuro_MID_AF',\n", - " 'gnomAD_genomes_non_neuro_NFE_AF',\n", - " 'gnomAD_genomes_non_neuro_SAS_AF',\n", - " 'gnomAD_genomes_non_cancer_AFR_AF',\n", - " 'gnomAD_genomes_non_cancer_AMI_AF',\n", - " 'gnomAD_genomes_non_cancer_AMR_AF',\n", - " 'gnomAD_genomes_non_cancer_ASJ_AF',\n", - " 'gnomAD_genomes_non_cancer_EAS_AF',\n", - " 'gnomAD_genomes_non_cancer_FIN_AF',\n", - " 'gnomAD_genomes_non_cancer_MID_AF',\n", - " 'gnomAD_genomes_non_cancer_NFE_AF',\n", - " 'gnomAD_genomes_non_cancer_SAS_AF',\n", - " 'gnomAD_genomes_non_topmed_AFR_AF',\n", - " 'gnomAD_genomes_non_topmed_AMI_AF',\n", - " 'gnomAD_genomes_non_topmed_AMR_AF',\n", - " 'gnomAD_genomes_non_topmed_ASJ_AF',\n", - " 'gnomAD_genomes_non_topmed_EAS_AF',\n", - " 'gnomAD_genomes_non_topmed_FIN_AF',\n", - " 'gnomAD_genomes_non_topmed_MID_AF',\n", - " 'gnomAD_genomes_non_topmed_NFE_AF',\n", - " 'gnomAD_genomes_non_topmed_SAS_AF']" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "config_dict['allele_freq_columns']" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "#Fill NAs in dataframe\n", - "df[config_dict['allele_freq_columns']] = df[config_dict['allele_freq_columns']].fillna(0)\n", - "df = df.fillna(df.median())" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(287358, 279)" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Perform one-hot encoding\n", - "df = pd.get_dummies(df, prefix_sep='_')\n", - "df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "SIFT_score 0\n", - "SIFT_converted_rankscore 0\n", - "SIFT4G_score 0\n", - "SIFT4G_converted_rankscore 0\n", - "Polyphen2_HDIV_score 0\n", - "Polyphen2_HDIV_rankscore 0\n", - "Polyphen2_HVAR_score 0\n", - "Polyphen2_HVAR_rankscore 0\n", - "LRT_score 0\n", - "LRT_converted_rankscore 0\n", - "LRT_Omega 0\n", - "MutationTaster_converted_rankscore 0\n", - "MutationAssessor_score 0\n", - "MutationAssessor_rankscore 0\n", - "FATHMM_score 0\n", - "FATHMM_converted_rankscore 0\n", - "PROVEAN_score 0\n", - "PROVEAN_converted_rankscore 0\n", - "VEST4_score 0\n", - "VEST4_rankscore 0\n", - "MetaSVM_score 0\n", - "MetaSVM_rankscore 0\n", - "MetaLR_score 0\n", - "MetaLR_rankscore 0\n", - "Reliability_index 0\n", - "MetaRNN_score 0\n", - "MetaRNN_rankscore 0\n", - "M-CAP_score 0\n", - "M-CAP_rankscore 0\n", - "REVEL_score 0\n", - "REVEL_rankscore 0\n", - "MutPred_score 0\n", - "MutPred_rankscore 0\n", - "MVP_score 0\n", - "MVP_rankscore 0\n", - "MPC_score 0\n", - "MPC_rankscore 0\n", - "PrimateAI_score 0\n", - "PrimateAI_rankscore 0\n", - "DEOGEN2_score 0\n", - "DEOGEN2_rankscore 0\n", - "BayesDel_addAF_score 0\n", - "BayesDel_addAF_rankscore 0\n", - "BayesDel_noAF_score 0\n", - "BayesDel_noAF_rankscore 0\n", - "ClinPred_score 0\n", - "ClinPred_rankscore 0\n", - "LIST-S2_score 0\n", - "LIST-S2_rankscore 0\n", - "CADD_raw 0\n", - "CADD_raw_rankscore 0\n", - "CADD_phred 0\n", - "CADD_raw_hg19 0\n", - "CADD_raw_rankscore_hg19 0\n", - "CADD_phred_hg19 0\n", - "DANN_score 0\n", - "DANN_rankscore 0\n", - "fathmm-MKL_coding_score 0\n", - "fathmm-MKL_coding_rankscore 0\n", - "fathmm-XF_coding_score 0\n", - "fathmm-XF_coding_rankscore 0\n", - "Eigen-raw_coding 0\n", - "Eigen-raw_coding_rankscore 0\n", - "Eigen-phred_coding 0\n", - "Eigen-PC-raw_coding 0\n", - "Eigen-PC-raw_coding_rankscore 0\n", - "Eigen-PC-phred_coding 0\n", - "GenoCanyon_score 0\n", - "GenoCanyon_rankscore 0\n", - "integrated_fitCons_score 0\n", - "integrated_fitCons_rankscore 0\n", - "integrated_confidence_value 0\n", - "GM12878_fitCons_score 0\n", - "GM12878_fitCons_rankscore 0\n", - "GM12878_confidence_value 0\n", - "H1-hESC_fitCons_score 0\n", - "H1-hESC_fitCons_rankscore 0\n", - "H1-hESC_confidence_value 0\n", - "HUVEC_fitCons_score 0\n", - "HUVEC_fitCons_rankscore 0\n", - "HUVEC_confidence_value 0\n", - "LINSIGHT 0\n", - "LINSIGHT_rankscore 0\n", - "GERP++_NR 0\n", - "GERP++_RS 0\n", - "GERP++_RS_rankscore 0\n", - "phyloP100way_vertebrate 0\n", - "phyloP100way_vertebrate_rankscore 0\n", - "phyloP30way_mammalian 0\n", - "phyloP30way_mammalian_rankscore 0\n", - "phyloP17way_primate 0\n", - "phyloP17way_primate_rankscore 0\n", - "phastCons100way_vertebrate 0\n", - "phastCons100way_vertebrate_rankscore 0\n", - "phastCons30way_mammalian 0\n", - "phastCons30way_mammalian_rankscore 0\n", - "phastCons17way_primate 0\n", - "phastCons17way_primate_rankscore 0\n", - "SiPhy_29way_logOdds 0\n", - "SiPhy_29way_logOdds_rankscore 0\n", - "bStatistic 0\n", - "bStatistic_converted_rankscore 0\n", - "1000Gp3_AF 0\n", - "1000Gp3_AFR_AF 0\n", - "1000Gp3_EUR_AF 0\n", - "1000Gp3_AMR_AF 0\n", - "1000Gp3_EAS_AF 0\n", - "1000Gp3_SAS_AF 0\n", - "TWINSUK_AF 0\n", - "ALSPAC_AF 0\n", - "UK10K_AF 0\n", - "ESP6500_AA_AF 0\n", - "ESP6500_EA_AF 0\n", - "ExAC_AF 0\n", - "ExAC_Adj_AF 0\n", - "ExAC_AFR_AF 0\n", - "ExAC_AMR_AF 0\n", - "ExAC_EAS_AF 0\n", - "ExAC_FIN_AF 0\n", - "ExAC_NFE_AF 0\n", - "ExAC_SAS_AF 0\n", - "ExAC_nonTCGA_AF 0\n", - "ExAC_nonTCGA_Adj_AF 0\n", - "ExAC_nonTCGA_AFR_AF 0\n", - "ExAC_nonTCGA_AMR_AF 0\n", - "ExAC_nonTCGA_EAS_AF 0\n", - "ExAC_nonTCGA_FIN_AF 0\n", - "ExAC_nonTCGA_NFE_AF 0\n", - "ExAC_nonTCGA_SAS_AF 0\n", - "ExAC_nonpsych_AF 0\n", - "ExAC_nonpsych_Adj_AF 0\n", - "ExAC_nonpsych_AFR_AF 0\n", - "ExAC_nonpsych_AMR_AF 0\n", - "ExAC_nonpsych_EAS_AF 0\n", - "ExAC_nonpsych_FIN_AF 0\n", - "ExAC_nonpsych_NFE_AF 0\n", - "ExAC_nonpsych_SAS_AF 0\n", - "gnomAD_exomes_AF 0\n", - "gnomAD_exomes_AFR_AF 0\n", - "gnomAD_exomes_AMR_AF 0\n", - "gnomAD_exomes_ASJ_AF 0\n", - "gnomAD_exomes_EAS_AF 0\n", - "gnomAD_exomes_FIN_AF 0\n", - "gnomAD_exomes_NFE_AF 0\n", - "gnomAD_exomes_SAS_AF 0\n", - "gnomAD_exomes_POPMAX_AF 0\n", - "gnomAD_exomes_controls_AF 0\n", - "gnomAD_exomes_non_neuro_AF 0\n", - "gnomAD_exomes_non_cancer_AF 0\n", - "gnomAD_exomes_non_topmed_AF 0\n", - "gnomAD_exomes_controls_AFR_AF 0\n", - "gnomAD_exomes_controls_AMR_AF 0\n", - "gnomAD_exomes_controls_ASJ_AF 0\n", - "gnomAD_exomes_controls_EAS_AF 0\n", - "gnomAD_exomes_controls_FIN_AF 0\n", - "gnomAD_exomes_controls_NFE_AF 0\n", - "gnomAD_exomes_controls_SAS_AF 0\n", - "gnomAD_exomes_controls_POPMAX_AF 0\n", - "gnomAD_exomes_non_neuro_AFR_AF 0\n", - "gnomAD_exomes_non_neuro_AMR_AF 0\n", - "gnomAD_exomes_non_neuro_ASJ_AF 0\n", - "gnomAD_exomes_non_neuro_EAS_AF 0\n", - "gnomAD_exomes_non_neuro_FIN_AF 0\n", - "gnomAD_exomes_non_neuro_NFE_AF 0\n", - "gnomAD_exomes_non_neuro_SAS_AF 0\n", - "gnomAD_exomes_non_neuro_POPMAX_AF 0\n", - "gnomAD_exomes_non_cancer_AFR_AF 0\n", - "gnomAD_exomes_non_cancer_AMR_AF 0\n", - "gnomAD_exomes_non_cancer_ASJ_AF 0\n", - "gnomAD_exomes_non_cancer_EAS_AF 0\n", - "gnomAD_exomes_non_cancer_FIN_AF 0\n", - "gnomAD_exomes_non_cancer_NFE_AF 0\n", - "gnomAD_exomes_non_cancer_SAS_AF 0\n", - "gnomAD_exomes_non_cancer_POPMAX_AF 0\n", - "gnomAD_exomes_non_topmed_AFR_AF 0\n", - "gnomAD_exomes_non_topmed_AMR_AF 0\n", - "gnomAD_exomes_non_topmed_ASJ_AF 0\n", - "gnomAD_exomes_non_topmed_EAS_AF 0\n", - "gnomAD_exomes_non_topmed_FIN_AF 0\n", - "gnomAD_exomes_non_topmed_NFE_AF 0\n", - "gnomAD_exomes_non_topmed_SAS_AF 0\n", - "gnomAD_exomes_non_topmed_POPMAX_AF 0\n", - "gnomAD_genomes_AF 0\n", - "gnomAD_genomes_POPMAX_AF 0\n", - "gnomAD_genomes_AFR_AF 0\n", - "gnomAD_genomes_AMI_AF 0\n", - "gnomAD_genomes_AMR_AF 0\n", - "gnomAD_genomes_ASJ_AF 0\n", - "gnomAD_genomes_EAS_AF 0\n", - "gnomAD_genomes_FIN_AF 0\n", - "gnomAD_genomes_MID_AF 0\n", - "gnomAD_genomes_NFE_AF 0\n", - "gnomAD_genomes_SAS_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_AF 0\n", - "gnomAD_genomes_non_neuro_AF 0\n", - "gnomAD_genomes_non_cancer_AF 0\n", - "gnomAD_genomes_non_topmed_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_AFR_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_AMI_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_AMR_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_ASJ_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_EAS_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_FIN_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_MID_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_NFE_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_SAS_AF 0\n", - "gnomAD_genomes_non_neuro_AFR_AF 0\n", - "gnomAD_genomes_non_neuro_AMI_AF 0\n", - "gnomAD_genomes_non_neuro_AMR_AF 0\n", - "gnomAD_genomes_non_neuro_ASJ_AF 0\n", - "gnomAD_genomes_non_neuro_EAS_AF 0\n", - "gnomAD_genomes_non_neuro_FIN_AF 0\n", - "gnomAD_genomes_non_neuro_MID_AF 0\n", - "gnomAD_genomes_non_neuro_NFE_AF 0\n", - "gnomAD_genomes_non_neuro_SAS_AF 0\n", - "gnomAD_genomes_non_cancer_AFR_AF 0\n", - "gnomAD_genomes_non_cancer_AMI_AF 0\n", - "gnomAD_genomes_non_cancer_AMR_AF 0\n", - "gnomAD_genomes_non_cancer_ASJ_AF 0\n", - "gnomAD_genomes_non_cancer_EAS_AF 0\n", - "gnomAD_genomes_non_cancer_FIN_AF 0\n", - "gnomAD_genomes_non_cancer_MID_AF 0\n", - "gnomAD_genomes_non_cancer_NFE_AF 0\n", - "gnomAD_genomes_non_cancer_SAS_AF 0\n", - "gnomAD_genomes_non_topmed_AFR_AF 0\n", - "gnomAD_genomes_non_topmed_AMI_AF 0\n", - "gnomAD_genomes_non_topmed_AMR_AF 0\n", - "gnomAD_genomes_non_topmed_ASJ_AF 0\n", - "gnomAD_genomes_non_topmed_EAS_AF 0\n", - "gnomAD_genomes_non_topmed_FIN_AF 0\n", - "gnomAD_genomes_non_topmed_MID_AF 0\n", - "gnomAD_genomes_non_topmed_NFE_AF 0\n", - "gnomAD_genomes_non_topmed_SAS_AF 0\n", - "cds_strand_+ 0\n", - "SIFT_pred_D 0\n", - "SIFT_pred_T 0\n", - "SIFT4G_pred_D 0\n", - "SIFT4G_pred_T 0\n", - "Polyphen2_HDIV_pred_B 0\n", - "Polyphen2_HDIV_pred_D 0\n", - "Polyphen2_HDIV_pred_P 0\n", - "Polyphen2_HVAR_pred_B 0\n", - "Polyphen2_HVAR_pred_D 0\n", - "Polyphen2_HVAR_pred_P 0\n", - "LRT_pred_D 0\n", - "LRT_pred_N 0\n", - "LRT_pred_U 0\n", - "MutationAssessor_pred_H 0\n", - "MutationAssessor_pred_L 0\n", - "MutationAssessor_pred_M 0\n", - "MutationAssessor_pred_N 0\n", - "FATHMM_pred_D 0\n", - "FATHMM_pred_T 0\n", - "PROVEAN_pred_D 0\n", - "PROVEAN_pred_N 0\n", - "MetaSVM_pred_D 0\n", - "MetaSVM_pred_T 0\n", - "MetaLR_pred_D 0\n", - "MetaLR_pred_T 0\n", - "MetaRNN_pred_D 0\n", - "MetaRNN_pred_T 0\n", - "M-CAP_pred_D 0\n", - "M-CAP_pred_T 0\n", - "PrimateAI_pred_D 0\n", - "PrimateAI_pred_T 0\n", - "DEOGEN2_pred_D 0\n", - "DEOGEN2_pred_T 0\n", - "BayesDel_addAF_pred_D 0\n", - "BayesDel_addAF_pred_T 0\n", - "BayesDel_noAF_pred_D 0\n", - "BayesDel_noAF_pred_T 0\n", - "ClinPred_pred_D 0\n", - "ClinPred_pred_T 0\n", - "LIST-S2_pred_D 0\n", - "LIST-S2_pred_T 0\n", - "fathmm-MKL_coding_pred_D 0\n", - "fathmm-MKL_coding_pred_N 0\n", - "fathmm-XF_coding_pred_D 0\n", - "fathmm-XF_coding_pred_N 0\n", - "dtype: int64" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Check how many columns are null\n", - "df.isnull().sum(axis = 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(35, 25))\n", - "corr_matrix = df.corr()\n", - "sns.heatmap(corr_matrix, fmt=\".2g\", cmap=\"coolwarm\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create correlation matrix\n", - "corr_matrix_abs = corr_matrix.abs()\n", - "\n", - "# Select upper triangle of correlation matrix\n", - "upper = corr_matrix_abs.where(\n", - " np.triu(np.ones(corr_matrix_abs.shape), k=1).astype(np.bool)\n", - " )\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Find features with correlation greater than 0.9\n", - "to_drop = [column for column in upper.columns if any(upper[column] > 0.95)]\n", - "print(len(to_drop))\n", - "print(\n", - " f\"Correlated columns being dropped: {to_drop}\"\n", - " )\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "123" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "279-156" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [], - "source": [ - "# Drop features\n", - "df.drop(to_drop, axis=1, inplace=True)\n", - "df = df.reset_index(drop=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(287358, 279)" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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6RJ3MzMzMzMzMzMzMzFDTajdy4RqrxBMbzPoXEUuoNSgdD8wGzpd0XD/R/SNi6ooaywpfotYgtWRFIUmfkzRd0lMriN0CvLvoJfaKiFgMvBK4PiIeLuo/r8juBZxTTLsGGCup96FEl0ZEW/HzgcAPJE0HLgVGShrRT/2Ol3SrpFt/ffdDAyyymZmZmZmZmZmZmZkNFvcws5dERPQA04Bpku4C3vUiitsdeIukbwKjgaqkduBPwA6SKhFRjYhTgFMkLbdhLSKul7QP8HrgHEmnAQuA/h6k2F9Tfm9uad20CrBHXQPa8j77dOB0gCc+8rY18sGNZmZmZmZmZmZmZmZrAvcwsxdN0paSNq+bNBV49IWWFxF7R8SUiJgC/B/wtYj4QUT8G7gV+GoxvCKSWui/oau3bpOBWRHxM+AXwE7AP4B9JW1cZHqHZLweOLaYth+1Z5st6qfYq4AT6j5j6gtdVjMzMzMzMzMzMzMzG3zuYWYvheHA9yWNBrqBf1MbnvHClfBZ/wWcBvxb0jygjdrzzpZnP+CTkrqAJcA7I2K2pOOBiyRVgFnUnpd2MvBLSTOAZSy/l9xHgR8WuUZqDW0feLELZmZmZmZmZmZmZmZrl0qjn2G2qlCER4ozW9k++4uO9IH29stely73lK3OTmeP+dar09kDzn5PKjfv5unpMkdvt/nAocJl25yczr7xkdPS2X/vmh8pdONrf5DOVlqHprML77w3lbvq0F+ly3xb55np7EkPHpnOfujWo9LZMdttms62PzMnnR0yZtTAoUKlpSWdjZ6eVG7B69+XLnPYr/9fOtu60frpbGXsuuls95NPpLNtTz2Tzg4/4MB0Vk/n61DtaE/lOnbcL11m6703prM9i/rrRNy/hskbp7Mdd9+VzpbRvWyFIwE/x7CttkhnO5/IbbPmKSXWwYP/Tmeb15uQznbNnTdwqNA0blw6G91d6SzKf5HpeGJmOtu6WW79tm+5W7rM5kWz0tnH1n9VOltGLH8ggJdNRL4OFVVTOfU7uveLV2Z9lalDTzSksw3KXSOhXH1bYlk6u96Df01WIL8OYvbT6azGT8yX21zi/qOhKZ2tDmlNZxvn5s81tOevJdUJG6RyUeK8WG3Kr68ymp7MX3f+ue0x6ezTS/L3oj0lzjWbjsrfh208/bep3MnzPpgu83/n/Hc6+4mOL6azP973L+ns/RsclM5O+2f+mv6BYeeks49P2Ted3fDuP6RyP618OF3mQdus6HHsfT5/9m3p7I1DX5vO7tB4Zzr7x1n5e5DDR09LZ6/t3DudPeSJ3Pf0/9d5wsChwifav5rOfqvpc+nspxq/nc5+t+HEdHbWrPx5/JT1fp7OXrLeR1O5w5eelS7zgiHHpbNvbroknf1DHJbOvv62T6Wz2d8TAJwy9JR09ksbn5/O/qwz93ur93V8L10mDfn7wJ8ofw77+BtL3ADYgK6Zsv0a2Ujz6kdmrHb7iYdkNDMzMzMzMzMzMzMzs7Wah2S0NYKkVwB9/5SsIyJ2H4z6mJmZmZmZmZmZmZnZ6sMNZrZGiIi7gKmDXQ8zMzMzMzMzMzMzM1v9uMHMzMzMzMzMzMzMzMxsEKhptXvU1xrLzzAzMzMzMzMzMzMzMzOztZobzMzMzMzMzMzMzMzMzGyt5gYzMzMzMzMzMzMzMzMzW6v5GWZmZmZmZmZmZmZmZmaDoNLoZ5itKtxgtoqQ1APcRW2b/BN4V0QsW072OGCXiDjhBXzOycCSiPjWC6/tf8r6b+C/gG5gNvCeiHh0OdkpwGURsV1/dZF0JrAvsAhoBW4EPhMRTxbZR4BdgAuBr0fElXXlfBzYIiI+9GKXaWUZO7Y5nd34rQemszu2TExnDzj7Pens1e88I5V75Rf2TZf5wAXT0tmWr0Y6y7gJ6ajIl9swanQ6W23r91Dt1+hdtk/l1h3ZlS6zs5rfDzaN1nR2wshXprPVpUvT2eGbb5zOVtbfKJ2lqzOf7enJZ5OG7blXOts1enw6W+3pTmcbm/LnmuEjR6azS8Zvls6OKLEdKlFN5Xoa8stV5pzQMHJUOts1Jn+cDdksf06IZfljZ8iodfLltg5LZ5uVvDFPbi+AxtH5davx+XXb3NiUzjIiv48ruw6A6vDR6WxTW1s6y7ARqVhX89B0kRo+Jp3tivy6bVT+vBSRX7c9NKyUOkj5639nNXe+aa6UuOaUUGY7NCl/rxDkt0N35L8ilrm3qkT+2lttzR0PqubL1IT1858/LH/+6GlqSWcpca7pbs6fxyvD8teSypB8fdPLVskfu9VKfv+KEtmmEte9BvLXs9am/Lmmp8T5rlKiDsu22DWVG3lv/vzRsNNh6ew2j6ybzpYxpMR59MKfXZ/OHv/p/L4wtGdROltdJ3f/Pqq6cn7huXRU/hw2f2F+X1g6bnQ6++TT+XPu3I3y3+O6nsmvs44p26ZylX+ni6S69U7p7Dpz8ue7zvV2SGdHzckPANbUlL8XbJsyNV+Hau58t2xE/rth46L8fcKS0Rums3MeTkdp2jBfLl35e6vxJa7/7etOSWdHL0oeD80lvsuW+P3DBtUSv48rcX9ptjrxkIyrjraImFo0KHUCHxjsCiXcQa3hbntqDVnffJHlfTIidgC2LMq+VlLf31qcBxzVZ9pRxfSVTpIbmc3MzMzMzMzMzMzM1jBuMFs1/RXYTNIYSZdImiHpRknP6ZoiaYSkhyU1Fa9HSnpEUpOkaZL+T9LfJd0tabe6Wbcp3n9I0kfrynu7pJslTZf0U0kNxfQlkk6RdGdRjwkAEXFtXS+4G4ENXoqFj5rvAE8Dh/R5+0LgUElDirpNASYBN/RXlqSJkq4vluluSXsX0w+WdHuxTFcX0/pd35JOlnS6pKuAsyWtK+l3km4p/u35Uiy3mZmZmZmZmZmZmZkNDjeYrWKKHkyHUBue8UvAHUUPrs8CZ9dnI2IxMA14fTHpKOB3EdHbh3hYRLwK+BBQP8beVsBrgd2ALxYNbFsDRwJ7RsRUoAc4trcc4Mai99f1wPv6qfp7gSsGWLxNi4ar6ZKmM3AvutuLuv5HRMwFbgYOLiYdBZwfEcvrM3wMcGWxTDsA0yWtC/wMOKJYprcW2RWt752BwyLiGOC7wHciYlfgCODnAyyHmZmZmZmZmZmZmdnzqEFr5L/VkYeXW3W0Fo1IUOth9gvgJmoNMkTENZLGSuo7SO3PgU8BlwDv5rmNWecV815f9D4bXUz/Y0R0AB2SZgETgAOoNQrdUjzHoxWYVeQ7gcuKn28DXlNfAUlvp/Z8sYEeaPVg0XDVO9/JA+SXd1T1Dsv4++L/FT2c6xbgjKIX3iURMV3SfsD1EfEwQETMK7J7sfz1fWlE9D6I5EBqvfR6P2OkpBFFA6aZmZmZmZmZmZmZma1m3GC26mirb0wCUP9PoH9OT6qI+JukKZL2BRoi4u7lZeted9RN66G2Hwg4KyI+089ndtX14OrN99bxQOBzwL5FI9xLaUfg6n6mXwJ8W9JOQGtE3L68AorGwn2o9cI7R9JpwAKev26g/wa63lz9k7QrwB51DWj9knQ8cDzAWz74E1550PEripuZmZmZmZmZmZmZ2SDxkIyrtusphkUsekXNiYhF/eTOptbr6pd9ph9ZzLsXsDAiFq7gs64G3iJpfDHPGEmTV1Q5STsCPwXeGBGzVpQtQzUfBSYCf+r7fkQsoTYU5RkUvehWUNZkYFZE/Ixar72dgH8A+0rauMiMKeLZ9X0VcELdZ0zt77Mj4vSI2CUidnFjmZmZmZmZmZmZmZnZqss9zFZtJwO/lDQDWAa8azm5XwNf5fmNR/Ml/R0YyYqHLSQi7pX0eeAqSRWgC/gw8OgKZjsNGA5cUHSGeywi3rjCJVqx0yR9ARgK3AjsHxGdy8meB1xEbUjGFdkP+KSkLmAJ8M6ImF30/rqoWNZZ1IaZPJnc+v4o8MMi10itoW2g57GZmZmZmZmZmZmZmT1HZTV93teayA1mq4iIGN7PtHnAYf1MPxM4s27SXsCFEbGgT/R3fYdYjIiT+7zeru7n84HzV1S3iLgQuLD4+cDlLM7zRMQjwHZ9pp1c9/NxA8w/pc/ri1n+M87qc2cBZ/Uz/Qrgij7Tlre+T+7zeg5F7z0zMzMzMzMzMzMzM1v9ucFsNSfp+8AhwOsGuy62fB0d1XR23k3T09lFO3Xny52eL/eVX9g3lbvxK9ely9z547umszOWNaSz1cceSmfZOB/tmjkznW0YPSqdnXfjHancnIlN6TKbeuaks48+nn/U4LwZ09PZ4Ruul84umzk7nW15Jp9tGNKczka1v8cYPl/jVvuky2y7+aZ0tnWTKeksI0amo10P54+HpY/l9/ERm2yVzsYzT+azncvrRPxcjePzB2/PQw+ks92Ll6SzQ5QfxbrjgXwdKFFuz0OPpLOtkzdMZ5c9+ngqN3TTTdJltj2e37+GNueP3aUPPJwvd8NJ6Wx0d6WzZbbZogdW1FH/uUYn10PzpP5Gi15OduEz+eyoLdPZMnrIX9Mblb+vKSMi/9eaQyq562QD+br2lPjK1aT8vqh+H8nbvxj478z+Y2VtB0W+vpV5T+eCXfn11TNvXjrbsP4G6WyluSWdpSF/PGhYfjtU5uaP9Whfls42NuTuR6PfR2/3r8z6ihLn2+rMJ9LZnk3y5bZ15Y/f7mp+PcTQfHboU/encj3VV6bLbH7o7oFDhaVtr05nactf97o2yH/f2euNu6ezevQ76Wznejuks5Unc/cgzzTlv/uTv11j6JL8cT5mWO4eG2Dksny548dtka/D4tz9JUBTwwqfBvIcQx6/L5WrVvdPl1m559Z0dnbDa9PZ5iV3prPzSP89Ogvm57dva+TrsHB87rvv0CX57zo9rXums8MX5PeZ0SN2Sme773oqna22t6ezs4bnsy0t/05nZy7NbYfoyH/fapqTP84fKLGPkz81m61W3GC2mouIjyxn+n4vc1UAkPQK4Jw+kzsiYqWeRgfrc83MzMzMzMzMzMzMbPXnBjN7SUXEXcDUteVzzczMzMzMzMzMzMxs9ecGMzMzMzMzMzMzMzMzs0GgSn64Zlu58oN3m5mZmZmZmZmZmZmZma2B3GBmZmZmZmZmZmZmZmZmazU3mJmZmZmZmZmZmZmZmdlazc8wMzMzMzMzMzMzMzMzGwRqcL+mVYW3hJmZmZmZmZmZmZmZma3V3MNskEnqAe6iti3+CbwrIpYtJ3scsEtEnPACPudkYElEfOuF1/Y/Zf038F9ANzAbeE9EPLqc7BTgsojYrm9dgDnAayPi6Lr3xlFbDxtERIek3wPjI2KPPvO/r/jsZuArEXHei12ulWnUyIZ0dp3tt0xnx45pSmdHb7d5OvvABdNSuZ0/vmu6zNv+75Z0dsTbetLZyoSJ6WwZTRtulM52PflEOpvdDkOaqukyu1tGp7PDh+dP+2X2GaqRjo4cNSKdbRiez9KQP86yFlbyx1jL5A3T2ZgwKZ8dMjSdbXhmZjo7YrPJ6WzX0DHpbMs6Y9NZJXNN7YvSZTasv0E+u2xpOsuSfB2GbJTfF6jk99vo6EhnNTG/Hlqzx874/Pl2WHdXOqtJ+fPt8Ib8OUwjR6WzVPPXnTJGNeXrW5mU22+6moflyxw2Op2N9BG5+pHy16jseugp8TVqZa3bMuWK/DpYWarKn++q64xP5VTi2G0Y0pL//FHj8tmmIelslDjn9zS1prMN6+Trq872dLa7dXgqF8r/HW40NuezJfbxxvXWz2eV32+GNXems9VYSX+P3Ji7Hx0xrMS9cGP+HnudIfnz3byr70xn23fJHzt33/xQOjt/+Ix0dsT2+6WzjM2dl9ZtXDn7QUPbknS2szG/LzR1L05nu7vT0VLnhZYS331pzO2Pw4bmP78yfL10dmKJcxiVddPR8SW+yw5tzV/PYkT+u9mo1twG7q7kyxzdXOI6PWd+OlsdUuL6UOL7ISW+w0xsym+H7tG58wfApGG5ZVNbfr8tY1yJexWzNZV7mA2+toiYWjQodQIfGOwKJdxBreFue+BC4JsvsJyLgNdIqv9t8FuAS4vGstHATsBoSRv3mfc7ETEVOAz4qaT8b7VfJKnEt30zMzMzMzMzMzMzM1vlucFs1fJXYDNJYyRdImmGpBslbV8fkjRC0sO9jUSSRkp6RFKTpGmS/k/S3yXdLWm3ulm3Kd5/SNJH68p7u6SbJU2X9NPeBiFJSySdIunOoh4TACLi2rpecDcCJf5c41kRsQi4HnhD3eSjgN7eYkcAfwB+U0zvr4wHgGXAOsv7HEkflXRvsT5/U0wbLumXku4qph9RTD+6mHa3pFPrylgi6cuSbgL2WN46MzMzMzMzMzMzMzPLqjRojfy3OnKD2SpCUiNwCLXhGb8E3FH04PoscHZ9NiIWA9OA1xeTjgJ+FxG9fYeHRcSrgA8BZ9TNuhXwWmA34ItFA9vWwJHAnkWPrR7g2N5ygBsjYgdqDVvv66fq7wWuGGDxNi0alqZLms5ze9GdV9QfSZOALYBri/eOLt4/r/j5eSTtBDwQEbNW8PmfBnYs1mfvZ38BWBgRryimX1N8/qnAq4GpwK6SDi/yw4C7I2J3YC7LX2dmZmZmZmZmZmZmZraacYPZ4GstGpFuBR4DfgHsBZwDEBHXAGMl9X0Qx8+Bdxc/vxv4Zd175xXzXg+MLIY2BPhjRHRExBxgFjABOADYGbilqMcBwCZFvhO4rPj5NmBKfQUkvR3YBThtgGV8sBh2cmrRwPSTuvcuA/aSNBJ4G3BhRPQUvdk2A26IiH8B3ZK2q5vvE5LuB24CTh7g82cAvy7q2zso84HAD3sDETEf2BWYFhGzI6Ib+DWwTxHpAX5X/LyidfYfko6XdKukW2+44vQBqmhmZmZmZmZmZmZmZoMl//RWW1naikak/5DUX3/F5zypOyL+JmmKpH2Bhoi4e3nZutcdddN6qG1/AWdFxGf6+cyuiIg++d46Hgh8Dtg3Ijr6mTclItok/Ql4E7WeZp8o3jqS2jCLDxerY2Tx/ueL978TEd+S9GbgbEmbRsTynmD9emoNX28EviBpW2rL3Xc9raifaHtE9NTllrfO6pftdOB0gB9esQo8ad3MzMzMzMzMzMzMzPrlHmarpusphviTtB8wp3jeV19nU+tN9ss+048s5t2L2rCDC1fwWVcDb5E0vphnjKTJK6qcpB2BnwJvHGAoxKzzgP+m1uPtxmLa0cDBETElIqZQ69H1vOeYRcRF1HrnvWs5da0AG0bEtcCngNHAcOAq4IS63DrUeqvtK2lc8Uyyo4Hr+im29DozMzMzMzMzMzMzM+tLFa2R/1ZHbjBbNZ0M7CJpBvANltMYRG3IwHUohmCsM1/S36kNffjeFX1QRNxLrdfWVcXn/RmYOED9TqPW6HRB8VyySwfID+QqYBJwfkSEpCnARjzbeEZEPAwskrR7P/N/GfjvonGsrwbgV5LuAu6g1jNtAfBVYB1Jd0u6E9g/ImYCn6H2DLU7gdsj4vd9C3yB68zMzMzMzMzMzMzMzFZRHpJxkEXE8H6mzQMO62f6mcCZdZP2ovbMrwV9or/rO1xgRJzc5/V2dT+fD5y/orpFxIXAhcXPBy5ncZ4nIh4BtuszrW9duoF1+8yzfj9l7VT8eFOf6bcBWy7n87uorae+05fQT0NkRJwLnNvP9OF9Xve7zszMzMzMzMzMzMzMbPWjZx9RZasTSd8HDgFeFxH/qps+DTgxIm4drLrZ8y2445r0gXZV+37pcg9puiqdvaLroHS2pSlX3YXLGtJljmjtGThUaHrVNunsNd+9PZ39r9cuSGevvn9SOrvF+l3p7MK23N8p7DcyfwjP6Nk+nd2l64Z09vK2A9LZru58N+u2Ek89XHd0NR8uoTu5O04d/0S6zBlzNkhn1xnamc42VfLHztylLensgqX54/cVk+ans08sGpnOZm9BJo5cki7z8QUj0tl5i/Id7TebuLzHZD7frEVD0tmeErt4Q4lxARoq+fu7JW25goe25Mtc1p4/Jwxvza+EZR35lfDk0/ljZ9jQfLmjRuSXbeHi/Dpbb1wyNzK/Ly7pbEpntxj1VDrbHfm/uWtUdzpbIb/NumguUW5+H8tmuyK/bivKf35DiXXQGfl10KT8vUoZscJHAD/X3M7R6ew9T+bO5T351VXKqOH5Y7fM+TZ7jw3Q1JDfb6Y/kD+HVUoMjTN6ZK7c5vzhQFNjmXWbL3fhkvxyvWbzh9LZ5p78ObcS+R1yccM66ezcjtGp3Cva/pYu846WvdPZnZZNS2fvHLZvOjtuSP7+cuLC+/J1GLJHOjtmyOJ09oYH10vl3jX7a+kyZ+5xZDo7t3tsOjuhMjOdnRPj09nJnfens4829/s3zf3aoPvhdPbe6rap3M7t/T1do383NOS/9+7V/ed09qYh+XJ3674+na10tqWzNw59bTq7dcu/Bg4B/+rYLF3mDtyWzt7XuEM6u3n13nT2r0t3TWfLfDd7XfWSfB2GHprO7tX+p1TuR0/my9wk/6sKXtf5u3R26N5vXT3H21tF3bTH7mtkI83u/7hptdtP3MNsNRURH1nO9P1e5qoAIOkVwDl9JndERH9DKK6sOvwQ2LPP5O9GRN9nvJmZmZmZmZmZmZmZmf2HG8zsJRERdwFTB7kOHx7MzzczMzMzMzMzMzMzK6PSsNp1xFpjlRjkwMzMzMzMzMzMzMzMzGzN4wYzMzMzMzMzMzMzMzMzW6u5wczMzMzMzMzMzMzMzMzWan6GmZmZmZmZmZmZmZmZ2SCQn2G2ynAPMzMzMzMzMzMzMzMzM1urucHMzMzMzMzMzMzMzMzM1moeknElkLQkIob3mXYy8D5gNtAMfAVoAT5WRLYB7gd6gD9FxKdftgqXJGk/oDMi/l5yvkeAXSJizkqoVu86XhIR31oZ5b8Yly/dP5198+PfTGfPnfC/6ewxz5yazjJuQipWfeyhdJGVCRPT2c999/Z09tUf2ymd7bj3hnT2Hfd8NJ1tGrZnOlt56tFU7vJNP5ku87U9l6azZy88PJ1919NfSmer7e3pbNf8hels65TJ6SwNDflsT08q9sT4t6WLPGTGSelsZfNt09melmHpbOOj96WznTOfTme7x74pnd3umbvTWbq7UrFlLTumi5z6cP7S1PbQI+nskL32TWd5+P58NrkvAlS7cusLoHH8euls98wnc2Wuv0G6zJ5nnklnGyZvnM5Wn3o8nW1/Kr+PR3d+OwxZd0w6271kWTrbstGGqVzXptuly2yanV9fM0cdmM62xpJ0dgFj09mhlaX5bDVfhyWMTGdbKm2p3Ihqvq6LGZ3ONqsjnR0Z+TosJL/fNih/PJSxTfst6eyOT9+YylWX5tdB55x56WzrK/LHWZn7jxg2Kp9tak5n95qZv/Z1Lc4fO62Tc+elqFbTZVaGj0hnKVFu55O5axnAg5t8Np1d1jU+ne2J/FBK6zYsSGe37bg5lbtw4WvTZb6tK//94VcL35jOHqs/pbOPNOfv736/OH+NenPXH9LZJ1umprOH/O7wVO6Y5tPSZf7vLuPS2c2770ln/1nZIZ3dpOHBdPbvHbuls7s13JnO3tQ2NZ09oP33qdxZi49Il/nunp+ksz/X+9PZ/xpyTjp7TrwznZ09N3+d/sS656azfx17ZCq3zzO/Tpd5Yetx6ewbh16Vzl5TzZ8TDvl3id+Flfhu9q3G/K9tT5xwXjp7+tK3p3IffOpT6TKbenK/4wP4Dp9IZz+TTpqtXtxg9vL6TkR8S9LmwG3A2Ij4JfynMWn/ldWY9FKR1AjsBywBSjWYlfgMAYqI/Dekl5GkhohYOb9JMDMzMzMzMzMzM7O1hioeCHBV4S0xCCLiAWAZsE6Z+SQNl/RLSXdJmiHpiGL60cW0uyWdWpdfIukUSXdKulHSBEmjJD0iqVJkhkp6XFKTpE0l/UnSbZL+KmmrInOmpG9LuhY4H/gA8AlJ0yXtLWldSb+TdEvxb89ivrGSrpJ0h6SfAsv9kztJUyT9U9KPgNuBDSX9WNKtku6R9KW67COSviTp9mK5t+qnvPdJukJSq6SPSrq3WGe/eRHr8suSbgL2kPR2STcX6+Cnkkp0bTEzMzMzMzMzMzMzs1WJG8wGgaSdgAciYlbJWb8ALIyIV0TE9sA1kiYBpwKvBqYCu0o6vMgPA26MiB2A64H3RcRC4E6gd2ypNwBXRkQXcDrwkYjYGTgR+FHdZ28BHBgRRwA/odZbbmpE/BX4bvF6V+AI4OfFPF8EboiIHYFLgY0GWL4tgbMjYseIeBT4XETsAmwP7Ctp+7rsnIjYCfhxUdf/kHRCsVyHR0Qb8Glgx2KdfeBFrMu7I2J3YC5wJLBnREylNozmsQMsm5mZmZmZmZmZmZmZraLcYPby+oSk+4GbgJNfwPwHAj/sfRER84FdgWkRMTsiuoFfA/sUkU7gsuLn24Apxc/nU2vwATgKOF/ScOBVwAWSpgM/BeofOnXBCoYhPBD4QTHfpcBISSOKevyqqOsfgfkDLN+jEVH/wIK3SboduAPYltpz3npd1M9yAbwDOAQ4IiJ6HwIxA/i1pLcD3XV1LrMue4DfFT8fAOwM3FIs8wHAJn0XRtLxRQ+5W6+59PQBFt3MzMzMzMzMzMzMzAaLn2H28up9htmbgbMlbRoR7SXmFxD9TFuerojozffw7Pa+FPi6pDHUGn6uodaDakHRY6o/K3qidgXYo+jN9WzFJPqp74r85zMkbUyt59iuETFf0plAS122tzGsfrkA7qbWO2wD4OFi2uupNXy9EfiCpG0pvy7b6xoMBZwVESt8vmVEnE6t1x7n3hBl1oOZmZmZmZmZmZmZrQVUWdGvpe3l5B5mgyAiLgJuBd5VctargBN6X0hah1pvtX0ljSueo3U0cN0An78EuJnaUIqXRURPRCwCHpb01qJsSdphOUUsBkasoF5Tix+vpxiqUNIhlHtm20hqDWgLJU2g1mss4w7g/cClkiYVz2rbMCKuBT4FjAaG91PnMuvyauAtksYX846RNLnEspmZmZmZmZmZmZmZ2SrEDWYrx1BJT9T9++9+Ml8G/rto0Mn6KrCOpLsl3QnsHxEzgc8A11J7NtntEfH7RFnnA28v/u91LPDeoux7gMOWM+8fgDdJmi5pb+CjwC6SZki6l2efE/YlYJ9iWMWDgMeyCxoRd1Jr/LoHOAP4W4l5b6DWO+2PwFjgV5LuKsr7TkQs4EWsy4i4F/g8cJWkGcCfee7wlWZmZmZmZmZmZmZmthpReKQ4s5XuXw8+lj7QuqIpXW6TutLZzmhOZ1VqJM2XXkXVdLajml+uJ7fZK53d6J/Xp7MNLO/xfs8XKxz5s65MvfRlQrm6ltlnylhZ9ZXy+21Erg5l9sWeaEhny5RbRna5oNx2GOxzQpltW42V87dAZdZBdSX9PVKZOpTZvoNtZS3XytrHV9a+UCF3Xihz/ihzTihznJWxsrZvmWN9ZZ1zs1baPl5i+w72OoBy97iDfd1ZFawK152sVeH6lD2HQrnvUKvCNT17j9mo7oFDhe7IP52jTLk95O+Hy6yDMtu3zLKVufZlt8MQdQwcKpTZv8pkV9YxuSqcm7PX/5W135bZF1fW+a7Md+TulfAknjL3H2V+r7Gyvk+XOieU2MfLXEvK3AM1qzOV64gh6TLL7Ldljp0tNt1o9fnSuRq4/YC9Bv8kuxLsdPUNq91+4h5mZmZmZmZmZmZmZmZmtlZ76f/UwF40Se8GPtZn8t8i4sODUZ+XkqSx1J4B1tcBETH35a6PmZmZmZmZmZmZmdlgqTSsdh2x1lhuMFsFRcQvgV8Odj1WhqJRbOpg18PMzMzMzMzMzMzMzKyXh2Q0MzMzMzMzMzMzMzOztZobzMzMzMzMzMzMzMzMzGyt5iEZzczMzMzMzMzMzMzMBoEqfobZqsI9zMzMzMzMzMzMzMzMzGyt5gYzMzMzMzMzMzMzMzMzW6u97EMySgrgVxHxjuJ1IzATuCkiDl3BfKOBYyLiRwOU/5ycpEnA9yLiLS+grjcBQ4AxQCvwZPHW4RHxSGL+KcCrIuLcsp+9qspuh37mOxlYEhHfWkn1mgJcFhHbrYzyX6zL79kwnf2vJz6Tzv5owtfS2Q/POymdbRg1OpXrmjkzXWbThhulsz9v+Wg6+4578tmGf16fzj629T7p7AGXfSqdrT7xaCp32TYnp8t84+Kz0tmfdx+Xzr7rgRPT2fbZ89LZtjkL09l199k1ne1ZtCidrbZ3pHKdB741XebQ2/6czmry5vlsV66uAMx5Jh1dfM996Wzzm49NZ5vu+ns6G52dqVz7Hq9Llzn0jmvS2WUPPpLOtr7mkHS2+5a/pbPz730onR27wxbprBqb0tnOObnjt31e/thd8vSCdHb9V++SznbNz9dh6ZOz0tmG5vwt8chXbJ3OPn3NjensmG02TuUaX7VvusyG2U+lsw9sW+J8V1maznZUW9LZ7shvhzJ16IwhJbK5Y2dkJX/N6SC/DjqqzelsmTq0xdB0tlHd6ayIdHbywunpbMOtuXu2avI6AtC9JL/PtGy4QTqrocPSWRrz+3iMGJ3Ott+Yv+70tOXvK1o3nJjKqTm/31Za8scDlYZ0tO3fD6azD775lHR25pIR6WxPNT+U0rZjnkhnR3flrmc/v3NquswPbvuPdPa7d+yezp6w8+3p7GNN+fvhafeuk86+e5Mb0tmnhubrsMn1P07lTm38fLrMt7wq/x1qvc7c90iA2zqnprObjXhy4FDhH0/m7lUA9p70r3T2yofy97hHrpu71//OXXuny/zfKRens5+5I/+d4JRXTktnv3Lngens7JmL09nvvGFGOvvnrlencof0XJIu82v3vT6d/cRu+fPHxU/vkc4edvU709lqdzWd/fSw/5fOfu8N09PZr92eu9f/5Mz878KG7Z4/j/+i+t50dotN01Gz1cpg9DBbCmwnqbV4/RqebYhakdHAh8rmIuKpF9JYVsy7e0RMBU4Czo+IqcW/R5JFTAGOKfOZkvLfCl5mRd1Gk9sOL+ZzVtln663KdTMzMzMzMzMzMzOz1YsqlTXy3+posGp9BdD7ZwZHA+f1viHpZEkn1r2+u+g99A1gU0nTJZ0mabikqyXdLukuSYcVs/TNTZF0d1FWi6RfFvk7JO1fTD9O0kWS/iTpAUnfXF7FJb1B0k3F/H+RNKGYvm/xmdOL90YUddm7mPYJSQ1FnW6RNEPS+4t595N0raRzgbtW8NnvLOa7U9I5xbTJxXqYUfy/UTH9TEnfk/R3SQ9Jeksx/XxJr6sr80xJR5So23PWb5H5ZN18X6or+3OS7pf0F2DL5S1XkZ0m6WuSrgM+toL1fLKkM4r8Q5Ke9ycVkjYp5ttV0raSbi7qO0PS5i9wXX5b0rXAqZI2LfaV2yT9VdJWK1o2MzMzMzMzMzMzMzNbtQ1Wb5nfACdJugzYHjgDGKi/9KeB7YoeX709fd4UEYskjQNulHRpP7kpdWV8GCAiXlE0clwlqbfv91RgR6ADuF/S9yPi8X7qcQPwyogISf8FfAr4H+BE4MMR8TdJw4H2oi4n9g41Kel4YGFE7CppCPA3SVcV5e5W1Pvh/hZe0rbA54A9I2KOpDHFWz8Azo6IsyS9B/gecHjx3kRgL2Ar4FLgQmrr/kjgcknNwAHAB4H3ZupWrM/69XsQsHmREXCppH2o9SQ8qlinjcDtwG39LVud0RGxb1HuOstZzxTLsz8wgtq2+s/YCJK2LJbx3RExXdL3ge9GxK+L5W14getyC+DAiOiRdDXwgYh4QNLuwI+AXN91MzMzMzMzMzMzMzNb5QxKg1lEzCgaXo4GLn+BxQj4WtE4UwXWByYMMM9ewPeLOtwn6VFqDSEAV0fEQgBJ9wKTgf4azDYAzpc0EWgGehu4/gZ8W9KvgYsi4gnpeWOYHwRs39vbCxhFrbGpE7h5eY1lhVcDF0bEnKL+vYNd7wG8ufj5HKC+d9wlEVEF7u3toUWtd9/3ikaxg4HrI6KtaPh6IXU7qPh3R/F6eDHfCODiiFgGUDRmDuT8up+Xt54B/hgRHUCHpFk8u93XBX4PHBER9xTT/gF8TtIG1LbLA5JeyLq8oGgsGw68CrigbvvmH4xhZmZmZmZmZmZmZmarnMEcSPJS4FvUDcdY6Oa59VreU4GPpdZAsnPR2+mZFWR7regpvPVPP+5h+Y2J3wd+EBGvAN7f+5kR8Q3gv4BWar3d+humT8BH6p6FtnFE9PbiGugp1ILUU7XrM/XLpKKe7cA04LXUepr95kXWTcDX6+bbLCJ+0U9dMuo/p9/13M9y1W+rhdQaOffsfTMizgXeCLQBVxaNZS9kXfbWrQIsqFveqRGxdX8zSzpe0q2Sbv37lacnPs7MzMzMzMzMzMzM1iaqaI38tzoazAazM4AvR0TfZ3Y9AuwEIGknYONi+mJqvZZ6jQJmRUSXas8im7ycXL3rqTW0UQzFuBFwf8l6jwKeLH5+V+9ESZtGxF0RcSpwK7VhA/vW5Urgg5KaeusgaVjyc68G3iZpbDFv7zCCf6c29CHFst2QKOs3wLupDYN5Zcm69bdM7yl6XiFpfUnjqa3rN0lqVe15bm/ILeZ/9LueB9BJbQjFd0o6pqjPJsBDEfE9ao202/Mi1mVELAIelvTWYl5J2qG/ykTE6RGxS0Ts8qrXHp9cBDMzMzMzMzMzMzMze7kN1jPMiIgngO/289bvqDV4TAduAf5V5OdK+puku6kNK3gq8AdJtwLTgfuWk/thXdk/An4i6S5qPdmOi4iOfoZOXJGTqQ3H9yRwI8826H28aLjrAe4tPrsKdEu6EzizWN4pwO2qfehsnn1G1gpFxD2STgGuk9RDbQjE44CPAmdI+mRR3rsTxV0FnA1cGhGdxbSfZ+rWd/1GxCclbQ38o1iPS4C3R8Ttks6ntm0eBf6aWc46J9P/el6hiFgq6VDgz5KWAtsAb5fUBTxNrZF23otcl8cCP5b0eaCJWgPknSWXz8zMzMzMzMzMzMzMVhEve4NZRAzvZ9o0asMEEhFt1J6J1d+8x/SZtEcyt10xvZ1aw0jf/JnUGrR6Xx+6vPcj4vfUnpPVt4yP9FcX4IA+rz9b/Ks3rfi3QhFxFnBWn2mPUHu+Wd/scX1eD6/7uQsY2+f9arZufddvRHyXfho/I+IU4JR+F+b52f36vF7eej65z+vt6l72bucFwK7FtN8DX++nnBezLh+m9vw3MzMzMzMzMzMzMzNbAwxaDzOztUlDQ4nsiOe1KS/XkOZ878hK69B0ttq2LJVrGD0qXWbXk0+ks1vs05XONg3bc+BQoYGedPaAyz6Vzl596DfT2Z0/vuvAIaBnq/y27Ro9Pp1d+FA1nW0Ynh0xFkaMz9dhRJkxjCv5g6dxg43y5Xbn9rH5LWMGDhWGD82vL7o6Bs4UukeOHThUaJw3K50d8Yp+H7/Yr87IHzuVDaaks1Rz+2Nz+6J0kTF5i3R22IT18+U++q90tnnDDdPZCZtsms7S1TlwphBj10tnWyfMTuWa1puSLnOdeU+nswwZ6BG0ddH1ctcngCHbT83XIfLnxjImHrxPvgrjJqRyi0fnz3XNraPzWeX3r65oTmfLaK7k69BN00qpQ2ul/SX/fJV4rG/28wG6WDnbIVb42OcXnp0/ako6O2bH7lROJY7d1iXz09nu4euks9GwcvbF7qbWdLbp1Yeks0M68/tY54h1U7koMWJLlLi3qyqfHbpe/tw4pCF/rmltyt8DVSO/HnrIL9uIWQ+kcuPG7Jwuc+ish9LZSevtlc6OeOSOdLZly/w2KzMo0NBHZqSzrdvl7wXZcvtUbNsS+3iTSnzv/cPZ6WwctGM6O+np29PZq6/J/07h0DfNTGcnj5+SzrY883AqN3HC8/4Werkqy/LfNbbbdnlPgHm+xhLXna02z5/zN55c4vrQtjCdXWdk7tzY05n/3rvvzvmDd/gT96SzQ1r77T/Rr5H75u/Hs99PAfZozN27A6D8E5F22yF3f9e6RX4fr5Y4L40qcd9KiftAs9WJG8xWMcVzta7u560DImLuy12fl5qkHwJ9Wzi+GxG/HIz6mJmZmZmZmZmZmZkNlkqDGyBXFW4wW8UUjWJTB7seK0tEfHiw62BmZmZmZmZmZmZmZlYv3yfUzMzMzMzMzMzMzMzMbA3kBjMzMzMzMzMzMzMzMzNbq3lIRjMzMzMzMzMzMzMzs0Ggip9htqpwDzMzMzMzMzMzMzMzMzNbq7nBzMzMzMzMzMzMzMzMzNZqbjAzMzMzMzMzMzMzMzOztZqfYbYckgL4VUS8o3jdCMwEboqIQ1cw32jgmIj40QDlPycnaRLwvYh4y4uo847A7cDBEXHlCy1ndSVpP+DEFW2fwbKsLdLZhTPuS2fnvaI7X+5d96azo3fZPvf5N96RL3O7zdPZhW35U1PlqUfT2Vg/Px5w9Yl8uTt/fNd09rb/uyWVa39dvq5Ny2ans4sWdaaz8+7K74ujtpicznbOnZ/OUsn/XUfT8KH5crMfv21POrv0nn+msy3rr5fONo5fks52z5mTzrbNzO83I8aMT2dj9sx8tq0tleuasGm6zKaH7k5nq0sWp7OVMWPT2fb7/5Uvd0hzOtu9ZGk6O3Sb/LHT9eQTqVxTU4m6PvJQOtu43sR8uc88k842jByZzkZ3VzpLtZqOtj/1dDrb2tGeyjWOm5Ius6ltQTobI1evMfNF/t7KVg2K/LHTtDB5jYr8fhDPPJnONq6Xr2u1uSWdpdKQz5ZYX40LZuXLbc9dewGaGptSuSixXNXG/LUE5a9llXn560P3evn6ltjF6Knmz6M9ka9DpT13/Z+3MH/fSsPcdPSZ9vx3zpg4LJ2tlvj77YYyf+rdkq9DRH6bVebk7nFnNZW4nuZvsWnZYWo6211iX6w25I5zgE02z98PV3ry91bLukv8anJp7rvRvK788dA9/8F09tGW/Pfpqh5JZ59oLvG7nQX5OlQm5b+bza3kzs/Ny/Ln23mt+f0rShy7jQ35k3P3g/nvZtGd328eHpq7dwdoaspfp5/pyJ3wKh357/705JdrVolbFXtpqcTvv2zl8pZYvqXAdpJai9evATLfskYDHyqbi4inXkxjWeFo4Ibi/1WWpAFPv5nMYCkaT83MzMzMzMzMzMzMbA3hBrMVuwJ4ffHz0cB5vW9IOlnSiXWv75Y0BfgGsKmk6ZJOkzRc0tWSbpd0l6TDiln65qZIursoq0XSL4v8HZL2L6YfJ+kiSX+S9ICkb9Z9voC3AMcBB0lqKaYPk/RHSXcWdTyymP4NSfdKmiHpW8W0dSX9TtItxb89i+n7FvWcXtRnhKSJkq4vpt0tae8ie3RR77slnVpXvyWSvizpJmCP/la2pEcknSTpBuCtkt5X1OPOol5Di9yZkr4n6e+SHpL0vIZGSbsWdd2kv/oXmU8Vdb1T0jeKaVMl3Visl4slrVNMnybpa5KuAz4maWdJ10m6TdKVkvJ/Hm9mZmZmZmZmZmZmZqsU95RZsd8AJ0m6DNgeOAPYe4B5Pg1sFxFT4T+9kd4UEYskjQNulHRpP7kpdWV8GCAiXiFpK+AqSVsU700FdgQ6gPslfT8iHgf2BB6OiAclTQNeB1wEHAw8FRGvLz5nlKQxwJuArSIiiuEhAb4LfCcibpC0EXAlsDVwIvDhiPibpOFAO3A8cGVEnFL0BhtaDCt5KrAzML+o9+ERcQkwDLg7Ik4aYP21R8ReRV3HRsTPip+/CrwX+H6RmwjsBWwFXApc2FuApFcVucMi4jFJ3+1bf0mHAIcDu0fEsmKdAJwNfCQirpP0ZeCLwMeL90ZHxL6SmoDrivJnF42QpwDvGWDZzMzMzMzMzMzMzMxsFeQeZisQETOAKdR6l13+AosR8DVJM4C/AOsDEwaYZy/gnKIO9wGPAr0NZldHxMKIaAfuBXofHHQ0tQY+iv97h2W8CzhQ0qmS9o6IhcAiao1eP5f0ZmBZkT0Q+IGk6dQaoUYWvbH+Bnxb0kepNRp1A7cA75Z0MvCKiFgM7ApMi4jZRebXwD5F2T3A7xLr6/y6n7eT9FdJdwHHAtvWvXdJRFQj4l6euz63Bk4H3hARjxXT+qv/gcAvI2IZQETMkzSqeP+6Yr6z6upfX7ctge2APxfr6vPABn0XRNLxkm6VdOtNfz49sehmZmZmZmZmZmZmtjZRRWvkv9WRe5gN7FLgW8B+QP0TTrt5boPj8p72fCywLrBzRHRJemQF2V4r2ps66n7uARqLHl5HAG+U9Lli/rGSRkTEvyTtTK3H2dclXRURX5a0G3AAcBRwAvDqYnn2iIi+T4L+hqQ/FmXcKOnAiLhe0j7Uhqw8R9Jp1Brilqc9IjJPmax/ovGZwOERcaek46htg/7WQ/36mklt/e4IPAUQEc+rfzFP2afF99ZNwD0R0e/Qkr0i4nRqjXecemHVT6Y3MzMzMzMzMzMzM1tFuYfZwM4AvhwRd/WZ/giwE4CknYCNi+mLgRF1uVHArKKxbH+e7RHWN1fvemoNbRRDMW4E3L+COh4I3BkRG0bElIiYTK031+HFMInLIuJX1Br+diqGJRwVEZdTG25walHOVdQazyg+e2rx/6YRcVdEnArcCmwlaXKxXD8DflGsi5uAfSWNKxrxjqY2dOELNQKYWQyBeGxyngXUGvG+Jmm/5dW/WNb31D0XbUzR+25+7/PYgHcsp/73A+tK2qOYt0nStv3kzMzMzMzMzMzMzMxsNeAeZgOIiCeoPdurr98B7yyG5LsF+FeRnyvpb5LuBq6g9kyvP0i6FZgO3Lec3A/ryv4R8JNiKMJu4LiI6JCW2/HsaODifur3QWAWcJqkKtBVTBsB/F5SC7XeUp8o5vko8MNi+MhGag13HwA+XjT29VAbBvIKaj3TPimpC1gCvDMiZkr6DHBtUe7lEfH75VU64QvUGuEepTa05PIaGJ8jIp6R9AbgCknvAd7et/7F+pwK3Cqpk9qQm58F3kVt3Q8FHgLe3U/5nZLeAnyvGMaxEfg/4J4XsaxmZmZmZmZmZmZmZjZI3GC2HBExvJ9p04Bpxc9twEHLmfeYPpP6Hbqvn9x2xfR24Lh+8mdSG6aw9/WhxY/T+sleSm04SYAr+/n43fqZZw5wZD/TP9LP/GcV//pmzwXO7Wf689ZnP5kpfV7/GPhxP7nj+iu7z/Z5jGefeXbTcj7vG8A3+kybDryyn+x+/eT26ZszMzMzMzMzMzMzM7PVjyL8aCWzlW3xrX9KH2jnLzw4Xe6RIy9PZy9e9rp0dt2RXancnMVN6TKHNFXT2f1H3ZrO/nXJLunsNmOfSmdnzJqUzvZU8w+xbO/MZccftGW6zF988I/p7Ll7XZLOnjPmxHS2vSN/LamUeOjn7bfOTmdbWvP7Y0dHdyr3wbfmRy7+4y0jS3x+5pGONRPXa05nb7nxmXR25Jih6exBe+ez027qGDhUaB2a22b77JgustR5qaMrvy8uWpLfxxctyW/fMoY05/fHLTbK13fB0oZUboMx+W07b2l+O4xszR2PAG2duboC9ER++3b3vPTncYDGhvx2GN6Su06OH74sXeayrvz5Y9LQuemslF8ulX5kbE6F/H1FtcQo9D2R38eyVto6UH4dlFlfPbz06wCgGvntMHPZmJf888ucl9YZmrsXBqhU8tu3ocSx01TJX0sWtg9JZzt78tthREtuPVRK7ONl1leZY2dRR34dbD/igXS2qSd/7aukHtlds6B5fDp7+9MbpHJvaLwsXealXYcOHOottzn/nfOvjf3+TXG/thz9eDo7dtFj6ez13XsPHCpssc7MdPafcyemcgc0XpMu88lR27zknw+w2+h709k5mpDOTuzOb4eP/HxsOvvjEV9NZy/a6+ep3LGtv0uXedKMQ9LZLzd/Y+BQ4bPLPpXOfrUhvw4aN9gwnf1h9UPp7AfG9x24qn9XNx+WLvNVLTens5fN6bevQb+OGJr/HcjPZuZ/F1btyV93TmjJ7YsAf57wnnR2X12byl0X+6fL7OjOX/tf153bDwCG7nlE/ouRDej+I1+7RjbSbHn+lavdfuIeZvayk3Qxzz7zrdf/RkR/PeHMzMzMzMzMzMzMzMxWKjeY2csuIt402HUwMzMzMzMzMzMzMzPrle+TaWZmZmZmZmZmZmZmZrYGcg8zMzMzMzMzMzMzMzOzQaDKaveorzWWe5iZmZmZmZmZmZmZmZnZWs0NZmZmZmZmZmZmZmZmZrZWc4OZmZmZmZmZmZmZmZmZrdX8DDMzMzMzMzMzMzMzM7NBoIr7Na0qFBGDXYeXnaQe4K66SYdHxCOSPgF8HZhArTHx6uL99YAeYHbxejdgXkQMryvzOGCXiDhB0snAF4HNI+LfxfufAL4N7BoRt0p6BHg8IvauK2M60BgR2720S/zSkjQVmBQRl5ecbxpwYkTcupLqdRzFNlgZ5b8Yl9zSkz7QDp79i3S5l439r3T20EVnp7OdYyamck2L5qTL7B42Op29ufWAdPaVS/6Uzj42ftd0duMHr0pnu0aPT2ebFs4eOAQc84e90mW+98evT2cXTbsvnX3Tgp+ms3R357Pty9LROTfkTxddS9vzVVjYlsqN+Pp30mWOvfnSdDa6u9JZlH/wa7Utt1wAi+5/JJ0d9o735utw5UXpbE9bRyrXfUz+tD7q6fw+ro78vkhjUzraff+96WzX/IXpbOvmm6azPRtsks42LMnVoTqkNV1mZemidLZr3Q3S2YbO/D5OtVoi25PPltgXKgvnprPROiyVWzBx23SZQ7qWpLNPtGyRzlaUX7cNyq/b9uqQdFbkv8O0VHLnGoDuyP09YaPy170ydW2rtqSzjSXWbYX8Ngvy150yy9as/HYYt/jRdDb9+Ytz92AAncPHprPVxuYXUp0BdTXmz7lDl+aXTd2d6Wx3y8hUrlppSJdZ6r5G+XKHLJuXzt4/6lXp7LLu/PatRv4XXeu2LEhnx3U9lcpdt2DHdJkHjLgxnb1q4R7p7P5jp6ezMxs2TGcfX7hOOrvH0NvS2WeaN0pnp8y9JZX7S+V16TK3HPN0Ojth2cPp7H2NO6SzY5sXpLNPLRuXzu5+yzfS2f9d9D/p7JdefWcqd/nC/Pfpw6Z/Kp09celn0tlvNn4pnf3Yks+ms5tumb9GfbL1R+nsL4Z8NJV7+4S/pMu8oj3/u53XN12Rzv6x65B09g13fT6dLfM9/fvrfj2dfd82N6ez1yzePZV7XeWP6TJ7mvL3FNf27J/OHr5rQ/6ibgN64NjXrZGNNJv/+vLVbj9ZW5su2yJiat2/R4rpRwO3AG+KiLm97wM/Ab5Tl898y7gLOKru9VuAvr9BGyFpQwBJW7+YBXq5SGoEpgL5u8AX9jklvnW9vFblupmZmZmZmZmZmZmZWXlra4PZ80jaFBgOfJ5aw9mLdQlwWFH2JsBCnu2h1uu3wJHFz0cD5w1QxwZJ35J0l6QZkj5STD9A0h3F9DMkDSmmPyLpS5JuL97bSlKlmD66rtx/S5ogaV1Jv5N0S/Fvz+L9kyWdLukq4Gzgy8CRkqZLOlLSsOJzbynq0bvcrZJ+U9T1fGCFf9IgaYmkL0u6CdhD0klFmXcXn68iN03SqZJulvQvSXv3U9brJf1D0jhJby3KuFPS9S9iXZ4k6QbgrZIOKsq/XdIFkob3rYOZmZmZmZmZmZmZma0e1tYGs9aisWe6pIuLab0NVn8FtpQ00Bhr9WVMp9aIVG8R8Lik7Yqyz++njAuBNxc/vwH4wwCfeTywMbBjRGwP/FpSC3AmcGREvILaUJIfrJtnTkTsBPyY2nCIVeD3wJsAJO0OPBIRzwDfpdaTblfgCODndeXsDBwWEccAJwHnF73tzgc+B1xTzLc/cJqkYUU9lhV1PaUoY0WGAXdHxO4RcQPwg4jYtRiishU4tC7bGBG7AR+nNvzlf0h6E/Bp4HURMaeo72sjYgfgjS9iXbZHxF7AX6g1rB5YrNtbgf8eYNnMzMzMzMzMzMzMzJ6j0qA18t/qaG1tMKsfkvFNxbSjgN8UDUoXAW8tUcZUao0yff2mKPdw4OJ+3p8HzJd0FPBPYKCHqRwI/CQiugEiYh6wJfBwRPyryJwF7FM3T+/DZG4DphQ/n8+zPduO4tnGvAOBHxQNgJcCIyWNKN67NCKW9+CQg4BPF/NNA1qAjYp6/Kqo6wxgxgDL1wP8ru71/pJuknQX8Gqg/qEd/S0X1Brs/hd4fUTML6b9DThT0vuA3uEUX8i67F1PrwS2Af5WLPO7gMl9F0bS8ZJulXTrVRf/bIBFNzMzMzMzMzMzMzOzwZJ7qvUaTtL2wObAn4tR/5qBh4Afvsii/wCcBtwaEYvU/wOOzy8+57hMVeF5T9YeqKm29+naPTy7vf8BbCZpXWqNeV8tpleAPfo2jBX1XjpAvY6IiPv7ma/MAwvbI6KnmLcF+BGwS0Q8Lulkag1xK1ouqG23TYAtqPX8IiI+UPSkez0wXdJUXti67F0HAv4cESscujMiTgdOB7jklp418sGNZmZmZmZmZmZmZmZrgrW1h1lfRwMnR8SU4t8kYH1Jz+s1VEbR8PS/1IYjXJ6LgW8CVyaKvAr4gKRGAEljgPuAKZI2KzLvAK4boF5RfO63gX9GxNy68k/ozRUNS/1ZDIyoe30l8JG6Z4ztWEy/Hji2mLYdsP3Ai/gfvY1jc4rng70lOd+j1Ia5PFvStsVnbxoRN0XEScAcYENe3Lq8EdizNydpqKQtSiybmZmZmZmZmZmZmZmtQtxgVnMUzx8y8eJi+osSEb+JiNtX8P7iiDg1IjoTxf0ceAyYIelO4JiIaAfeDVxQDF1YBX6SKOt84O0899lqHwV2kTRD0r3AB5Yz77XANsXz244EvgI0FfW6u3gNteemDZc0A/gUcHOiXgBExALgZ8BdwCXALSXmvZ9aQ90Fkjal9ky1u4q6XQ/cyYtYlxExm1qPwPOKZbsR2CpbPzMzMzMzMzMzMzMzW7Wo1tnIzFamtnO/nj7QvrDwY+lyvzr6++nsSQs/ms5uuklrKvfo4x0DhwrDh+dHgP3w5tems+fOOjCdPWDzx9LZq+7fMJ1duKiazi5alGkbhy+Nzo8Ie/H6/5POjtwv37Z75gl/Smc3325SOtvYmH/oZ7XEJWpEiX2smix431cM9GjJZ11yXf7zJ6zXMnCoMGnddJQ/Xj4znd1gyph0dudXNKezjz2d375DksWOG50ukrkL85/f3rFy7oEWLuxKZ0eNakpn587NnT8AJm84JF/u/J5UbuuN00Xy2DP5v8kaOTy/zZa15+tQRldXfl9Yuix/zi9zvpswLrfOJo9d3iNln29BW/7YnbLO/IFDhQby66Clkt9oy6q5+w+ASolRv4dU8vcrbdX8+TmrZSV9vkqsg2GV/PWsK/LnJSlfh2U9+e374NzRqVx3T/4Ym53fxZkwNp9tqOTXQWOJ7JDG/HG2sC1/D9LemV9n40Z2p7NZTQ355aqUeE78U/Pz57t9NnwwnR29LH9vpWruegrwzPBN09lfXp3bIct8f/haW/674WdHZf4et+asoflyD9j04XR2/UdvSGfP6HhHOrvv5k+nszc+ul4qd2zl1+kyH91o3/znP75BOnvIhNvS2Rmd26Wzr1p2RTr7q8WHp7NHjp+Wzh5/zpRU7ucbfi9d5g8nnZbOfmL4L/Lldr4vnf3wsLPSWRblL2g/b/14Onvs+tencn9YkN9vXz/27+nsxc+8Kp09avgf0tlfzDo0ne3JX6L4UE9+Hztvnfzv+Y5s+G0qd37P29JlLlmWv/94X3f+WtJy2AklrtQ2kIeOO3SNbKTZ5MzLVrv9xD3MzMzMzMzMzMzMzMzMbK2W/1M0e9lIei1wap/JD0fEmwajPi81STcBff/0/R0Rcddg1MfMzMzMzMzMzMzMzNZubjBbBUXElcCVg12PlSUidh/sOpiZmZmZmZmZmZmZmfVyg5mZmZmZmZmZmZmZmdkgUMVPzlpVeEuYmZmZmZmZmZmZmZnZWs0NZmZmZmZmZmZmZmZmZvayknSwpPsl/VvSp/t5/5OSphf/7pbUI2lM8d4jku4q3rv1paiPh2Q0MzMzMzMzMzMzMzOzl42kBuCHwGuAJ4BbJF0aEff2ZiLiNOC0Iv8G4BMRMa+umP0jYs5LVSc3mJmZmZmZmZmZmZmZmQ0CVTTYVRgsuwH/joiHACT9BjgMuHc5+aOB81ZmhdbYBjNJPcBd1Jbxn8C7ImJZn+kPA++IiAXFPNsC3wc2AAScDXwV2Bf4ekTsUVd+I/AkMBX4epFZWLy9LCJeVZf9PTC+z/wnA58CpkTErGLakogY/pKuiJeYpNHAMRHxo5LznQwsiYhvraR6TQEui4jtVkb5L9bXu/4nnf3QTW9OZz+/+2/T2RNuOyqdnTDylancvBnT02WO3m7zdPbStq+ks+96+kvp7EObvT9f7gMnprMNw4els/Puui+VO+ew/LZ9x4Kf5LMn/CmdPe4HB6eze3xxv3R22TPz09nWdUensw1DmtPZ6O5J5arbvCNd5omP5rfD8OaN09lKdUw6e8jIB9LZxXfMTGfHbZY/L8WCf6ez1WVtudwrD0iX2XjfDels94KFA4cKzRtumM7On3FbvtxR+ct+99Lc+gIY0bBZOtv22JOp3DDyZS69P78fDN14o3S2a+68gUOFhtbWdLba2ZnOUq2mo+2z8/UdvuWmqVzHqFcNHCo0t81KZ58Ymy+3Qu4cCtAe+e3QUukoUYf8duiKpnR2aCV3nDWoO11mR7UlnR1WWZbOikhn2yNfh8YSy1bGeuTONQDbzj0/F+zJ74vdTz6RzjZqcjpLU/7+g8b8vlhtyd9fVmbn120sW5rOoin5bFK1echLXiZAw8z8PdC9k/4rn+3K7wvdPflfdG3J0+nsl9Y/K5X72pyPpsv8bMv/pbNfnvuxdPYLo36dzt5fPSid/fHit6ezHxp6Zjr7uPZPZ98+55up3A+HfDJd5iGRvx9/65BL0tkbu/Pf43au3JLOnrv08HT26HF/Tmf/1Ja/1//FRrn1+8n2L6TL/H/zT0pnv7w4n/1C8ynp7KlL/jednfV0/jz+/yZ/O529aPF/p3JvW/jDdJm/5cPp7LGV/PnjN8uOTWePu+8j6Wy1M38P9Mlhp6azpw39eTr7g2XvSeVO6Mh/fkT+vvn/VT6Vzn4unTRbofWBx+tePwHs3l9Q0lDgYOCEuskBXCUpgJ9GxOkvtkJr8jPM2iJiatGA0gl8oJ/p86B29pbUClwKfCMitgB2AF4FfAi4HtigaJTpdSBwd8R/7nA+WZQ7tU9j2WhgJ2C0pL6/IZ0D5FtSBlnRRXI0tXWyMj9nlW3IXZXrZmZmZmZmZmZmZma2KpB0vKRb6/4d3zfSz2zL+8vANwB/6zMc454RsRNwCPBhSfu82DqvyQ1m9f4K/f5Z9D+otWICHENthV8FEBHLqLVWfjpqTfEXAEfWzXsUue5/RwB/AH5TzFPvDODI3ofUDUTSOyXNkHSnpHOKaZMlXV1Mv1rSRsX0MyV9T9LfJT0k6S3F9PMlva6uzDMlHSGpQdJpkm4pynp/8f5+kq6VdC61nnnfADYtHqTXO3boJ+vm+1Jd2Z8rHtj3F2DLAZZtmqSvSboO+JikN0i6SdIdkv4iaUKRO1nSGUX+IUnP+zM6SZsU8+0qaVtJNxf1nSFp8xe4Lr8t6VrgVEmbSvqTpNsk/VXSVpntZ2ZmZmZmZmZmZma2NoiI0yNil7p/fXuAPQHUD+mzAfDUcop7XntMRDxV/D8LuJjaEI8vyhrfW6boEXQI8Kc+0xuAA4BfFJO2BZ4zhlJEPChpuKSR1DbG6dQaTIYArwM+URc/TdLni5/viYje/sFHA18CngEupDZ8Y68l1BrNPgZ8cYDl2JZab9c9I2JOXSPbD4CzI+IsSe8BvgccXrw3EdgL2Ipa77kLqTXcHQlcLqm5WAcfBN4LLIyIXYvl+5ukq4pydgO2i4iHi15220XE1KJeBwGbFxkBlxYtuUup7cQ7UtvPbu+7fvsxOiL2LcpdB3hlRISk/6I2fGVvb7ytgP2BEcD9kn5ct562LJbx3RExXdL3ge9GxK+L5W14getyC+DAiOiRdDXwgYh4QNLuwI+AVw+wbGZmZmZmZmZmZmZmz7EWP8PsFmDzYmS+J6m1JxzTNyRpFLVHYr29btowoBIRi4ufDwK+/GIrtCY3mLVKml78/FeebRjrnT6FWgNO78DKYvnd/SIibikaz7YEtgZujIj6B/F8MiIurJ+p6BW1GXBD0fDTLWm7iLi7LvY9YLqk/zfA8rwauDAi5hQV6u16uAfQ+3CZc4D6gbUvKXrH3dvbQwu4Avhe0Sh2MHB9RLQVDV/b9/ZEA0ZRawjrBG6OiIeXU6+Din93FK+HF/ONAC4ueuoh6dIBlg+g/mEFGwDnS5oINFN73lyvP0ZEB9AhaRbQu2zrAr8HjoiIe4pp/wA+J2kD4KKikeuFrMsLisay4dSG6rxA+s+JbOUMxG9mZmZmZmZmZmZmtgaKiG5JJwBXAg3AGRFxj6QPFO//pIi+CbgqIuof5DgBuLj4HX0jcG5EPKfT1AuxJjeYtfX2gupvetEqeRm1Z5h9D7gHeM4Yl5I2AZZExOJiUu+wiluTG47xSGAd4OFiw40s5u/tiUZELCiGOxzouWAratCrV5+pf3K6is9rlzQNeG1Rv/Pq3v9IRFz5nA+V9qPWW2xF9fp6RPy0z3wfT9a3Xv3nfB/4dkRcWtTh5Lr36perh2f344XUHhK4J7XtSUScK+km4PXAlUVvtReyLnvrVgEWLGffeg7VxmQ9HuD1x/2QnfbLP1zazMzMzMzMzMzMzGxNFhGXA5f3mfaTPq/PBM7sM+0hYIeXuj5ryzPMniciFgIfBU6U1AT8GthL0oEAklqpNaTV9zI6j1q3v1dTG+JwIEcDB0fElIiYAuzM859jBvBt4P2suAHzauBtksYW9esdRvDvdWUeC9yQqNdvgHcDe1NrvaX4/4PFukDSFkVXxr4WU+s9Rt187yl6XiFpfUnjgeuBN0lqlTSC2kP5yhhFrRsmwLuS83RSG0LxnZKOKeqzCfBQRHyP2jbbnhexLiNiEbUG0LcW80pSvwdm/RitbiwzMzMzMzMzMzMzM1t1rck9zAYUEXdIuhM4KiLOkXQY8H1JP6TWBfAcas+16s3fK2kZcFuf7n/w3GeYAbwN2Ai4sW7+hyUtKp57VV+POZIu5rnPROtb13sknQJcJ6mH2hCIx1Fr9DtD0ieB2dQawgZyFXA2cGlEdBbTfk5tmMrbVesON5tnn99VX4+5kv4m6W7gioj4pKStgX8UveiWAG+PiNslnQ9MBx6lNixmGSdTG/bwSWrrcOPMTBGxVNKhwJ8lLQW2Ad4uqQt4GvhyRMx7kevyWODHxfZuotYAeWfJ5TMzMzMzMzMzMzOztZwqa22/plXOGttgFhHDM9Mj4g11P98F7DdAuc/rTRQRxy0nvn4/2Z2KH2/qM/2/gf8e4LPPAs7qM+0Raj3eVlin+uWOiC5gbJ/3q8Bni3/1phX/6rPH9Hn9XeC7/dThFOCUfhfm+dn9+rz+PbXnkfXNndzn9XZ1L7crpi0Adi2m/R74ej/lvJh1+TC157+ZmZmZmZmZmZmZmdkaYI1tMDNblQwZ0pDOjt021ZkOgMbG/F8fjNlu03S2unRFj6171vAN10uXSTX/SLuubuWLbW/P16GE9tnz0tkR48ens6O2mJz7/I4SjwDs7k5HN99uUjq7xxf3S2f/8aVp6ex6e41LZ7d8y5R0tqetY+BQIbp7UrmGrvz+1Th2nXS2MnbddJam5nQ0qtV0trF1SDpbbelvhN7+9SxYmM5WWpJ1iPxyqakpnY2e3H4AwIhR6ejQ9cYOHCrMv/+xdHbUps/7O5zlK7EvVJpyt4PLHngwXWbb7AXp7LAtN0tn1ZC/nvYkr2UAUeIa1Twpf+2rPjUrne1ZvCSVq0R+v1VPVzrboPy1pKknf77tUEs620i+vo2Rz3aX+MpTIbd+m3vy14fOSv58m/18gOZqvg4dytehoUQdyhjSmdvHAejsHDgD0J3fD0qd88tkG0vcs5U4N5cRixels2XOjY3D5uaCyt+7NwzJnxNQ/rtOdVmJcz4lvmtU89kosSs0kj/ndq27YSrX+VR+/4oNJ6azS59OHo9A97DR+Ww1f25evDi/vqrjRgwcKnRF/r6xuiR3DutuyO8IpfbFpvyxs6wzv26XjhwzcKhQ4lCnuzFf36eeyBfctM0rUrnW+/PbtmFi/jvy0ofzx4O2yh9nix/KX88WL2xLZ6vj898fGpP7bnVU/rvOzNn581L3RqPT2c4F6Shts+ans9Xk7wkAlnbn78N6RuV/B7J4Vq4O1a78da+y/kbp7PjG/PctszWVG8xWMcVzta7u560DIiL5jWXVVQx3uWefyd+NiF8ORn3MzMzMzMzMzMzMzMzcYLaKKRrFpg52PVaWiPjwYNfBzMzMzMzMzMzMzMysnhvMzMzMzMzMzMzMzMzMBoEqJca9tZUqPyi4mZmZmZmZmZmZmZmZ2RrIDWZmZmZmZmZmZmZmZma2VnODmZmZmZmZmZmZmZmZma3V/AwzMzMzMzMzMzMzMzOzQaCK+zWtKrwlzMzMzMzMzMzMzMzMbK3mHmZrIUnTgK9HxJV10z4OHATsD9xfF/92RJwt6T3AJ4Cg1tD6uSK/J9AMbFw331cj4sKi3BOB04B1I2LOSl6mEyPi1pX1GS+Xtlnz09mGsUpn25/Jr/7hm2+cyi2bOTtd5shRI9LZto50lK75C9PZIL++2ubkyx1RyZfbOTe3fSub5cukfVk62tiYL3fZM/l9cb29xqWzT9+Q3xen7L8gne3p6Exnu5fldrJhUU2X2T57XjrbvN6idFZDhqSzlcaGdHbxU/n6jupsT2el/D6WPX5VaUqX2bMov26XPT03nR2ydVc6W+Y8vvjpfH2bRwxNZxtaW9LZbH3L7F9LZ+XPoeuU2GbZcyiUOydENdLZhtb8MblkZonzwujcdbKxO79cam9LZ3si/7WgUsmfG8mvWnpKfDWpaHDr0KOV8zWqSv446ylxbiyzDsrcL6lEwZVqdzpbXZw7h0Rn/ngoc/5oXGeddFbd+esDjfltVinxV8ZdC/Ln3O5FS9LZxlGj09m0KLEzltA1L799I/L7eEd3PttTzWfLHGck760qJb6TREN+X2xsyu+LKnHvXOY8Xmkosb5KKFOH7sW5Y2eR8ueEMufQKHGP3aCVc5yV2MWISv561pCPEsnzaENDfr+NJfl70cUL8vdWLM2fb5csyv8SZNHcxelsZWl+2TqUW2eV9qXpMpct60lny5w/urrz+/iy2QvS2Z6ufB0WVfPbt9KR32+WLMnd23Qty1/7h4zMZxc2lLjHdj8cW0O5wWztdB5wFHBl3bSjgE8CG0XE1PqwpA2oNZDtFBELJQ2n1gD2++L9KcBl/cy3IfAa4LFsxSQ1RkT+2/TLSFJDROSv9mZmZmZmZmZmZmZmtlpwU/Da6ULgUElD4D8NXpOAJ5aTHw8sBpYARMSSiHg48TnfAT7FAH/TKuk4SRdI+gNwlaThkq6WdLukuyQd1ltPSf+U9DNJ90i6SlJrn7Iqks6S9FVJDZLOlHR3Uc4nisxmkv4i6c7iMzZVzWl12SOL7H6SrpV0LnBXUeZpkm6RNEPS+xPrwczMzMzMzMzMzMzs+aQ1899qyD3M1kIRMVfSzcDBwO+p9S47n1rD1qaSptfFPwL8HXgGeFjS1cBFEfGHFX2GpDcCT0bEncnhufYAto+IeZIagTdFxCJJ44AbJV1a5DYHjo6I90n6LXAE8KvivUbg18DdEXGKpJ2B9SNiu6JOo4vcr4FvRMTFklqoNRy/GZgK7ACMA26RdH2R3w3YLiIelnQ8sDAidi0aHP8m6apkA6KZmZmZmZmZmZmZma2C3MNs7dU7LCPF/+cVPz8YEVPr/v21GIbwYOAtwL+A70g6eXkFSxpKbQjHk0rU588R0fugDwFfkzQD+AuwPjCheO/hiJhe/HwbMKWujJ9SNJYVrx8CNpH0fUkHA4skjaDWiHYxQES0R8QyYC/gvIjoiYhngOuAXYtybq5rEDsIeGfRqHgTMJZaI15/6+F4SbdKuvWWq39WYlWYmZmZmZmZmZmZmdnLyQ1ma69LgAMk7QS0RsTtKwpHzc0R8XVqDWxHrCC+KbAxcKekR4ANgNslrbeCeeqfGnossC6wc/FctGeAluK9+ieh9vDcXpJ/B/Yveo0REfOp9RibBnwY+Dks9+nKK+oGV183AR+pa1DcOCKu6m+miDg9InaJiF12PeB9KyjezMzMzMzMzMzMzMwGkxvM1lIRsYRaQ9IZPNu7rF+SJhUNa72mAo+uoOy7ImJ8REyJiCnUno22U0Q8nazeKGBWRHRJ2h+YnJzvF8DlwAWSGovhHCsR8TvgC0UdFgFPSDq8WLYhRY+464Eji2eUrQvsA9zcz2dcCXxQUlMx/xaShiXrZ2ZmZmZmZmZmZmb2H6pojfy3OvIzzNZu5wEX8ezQjPD8Z5idQe05Z9+SNAloB2YDH1iJ9fo18AdJtwLTgfuyM0bEtyWNAs4BvgH8UlJvw/Bniv/fAfxU0peBLuCtwMXUnqN2J7VnuX0qIp6WtFWfj/g5tWEgb1ft4WyzgcPLLqCZmZmZmZmZmZmZma063GC2Fiue46W6148ArcuJv3oF5TwCbLeC96cMUI8zgTPrXs+h1njVn+3qct+q+3m/up+/WJev7xnX+/4D9L88nyz+1WenUeuJ1/u6Cny2+GdmZmZmZmZmZmZmZmsAN5iZvQwmTWhIZ5tH5Ud43GDSkHR2yNxR6Wxl/Y1SuZZnZqfLbBg+Ip1dd3Q1nW2dkh2xExroyddhn13TWSr57UslNxLu7bfm1+1hs25NZ6s7p6O0rjs6nd3yLVPS2Sn7L0hnb/zKdensuF1Gp7NDRjSncpNa8mXO+3d21Flon784nR07tW9H1+Vb8MDj6WzH4o6BQwVV88dO5/yF6WzX0vZUbmi1K12mmnPbFmDoxHXT2eqs/PatdufX18SdNklnlzw1J51tnjIlnR2ezDXsvk+6zFEP/zOdZb0N0tGhrUPTWTXkz83VtmXpbNfceenshFdtn842Tlw/lWtvWt7fNj1fw7CR+ay609nOyN9/iEhnK+Sv/21RYl8oUYdmcufGnkr+a1Ql8stVZjt0RMvAoRegzPqKFT4G+LkWDJuUzk7ccONUrtLVmS6zNXkPBsCkDdPRKLEvVIfkj99qY/561rT5lvns0vw9SHV8/vycLrN55ey3Q0ocZ9XI7wujW/P3IGWOh2XV/L7QNG9mKrfh+vlzc2XWk+nsxlPy59vGJfPz2TH5+6UN1itxzl2cr0PDevk6NE9c0SPZn7XBqBLbQfn9tmnhrHR26Pj8tWRIV/4eaNzI/Poact0l6eyisbuns1qWuyefMjl/jN1x0kXp7OK9jxo4VLjztHPT2XmvOiyd3WCzCensor/ekM62H3B8LrhgbrrMqVvlz7eNTz2czo4akz/fjpqSO3YBerryx86GE8ens1qQ//3OFpvlfic4pH2LdJnVOfnzx+LG/HHuJz3ZmsoNZvaykfRa4NQ+kx+OiDcNRn3MzMzMzMzMzMzMzMzADWb2MoqIK4ErB7seZmZmZmZmZmZmZmarApUZDcFWKm8JMzMzMzMzMzMzMzMzW6u5wczMzMzMzMzMzMzMzMzWam4wMzMzMzMzMzMzMzMzs7Wan2FmZmZmZmZmZmZmZmY2CFTRYFfBCu5hZmZmZmZmZmZmZmZmZms1N5iZmZmZmZmZmZmZmZnZWs1DMq7GJAXwq4h4R/G6EZgJ3BQRh65gvqnApIi4fIDyhwI/A7YHBCwADgYuA74eEVfWZT8ObAF8E3gY+GpEfKF4b1xRr59GxAkvZFkzJC2JiOErq/wXo7Mrn21oaSlRbqSzlRLl0tWZijUMac6X2dCQz5ZRotzaIZPTs2hROtu4wUbpbNPwoalcS7UpXWbX0vZ0dsTw/Gm/zPbtaevIZzty+xfAuF1Gp7Nzbl2Qzo7aZlgq19iTX7frbDIhnR26/vh0tozWdUens/MfmZvO9rSOyGdLbN/u9ly22jAkXWZjc36/bWjNl1sZMTKdLTOUwrJZ89PZoRPWSWeplrg+NOXOCw1PPJgus3PW7HS2edx66WxEtUS2xJAWlfzfkDWvlz/WO5/Jr4eGpYtTuaiUuJ6WyLZXW/PFKr8dymiP/L2KKLGPl6hvW+Su0410p8sM8vtiWzX3+QAVVs526I78vUKZe6uGyK8zRb7cdJklrg9lhEqca8qcw8oc69WVsy9Q7cnlVOLvcMts2zLrtqvEF64SekpcS6rVfLZJ+eOhe3TuvnHZzPy6jXUnpbNL5+TL7R6bv1fpjhLXqPxXDaoj8nWolvkb8iG5a1RPiXuwMteH7hFj89lqfrk6m/PX3u4S+3jjuvnvO8uWlLimTsmVu3RefjtM3nOTdLanJ3leBDbYbUo629WRP4e1Lc1/32qZMC6dXbQkuc7G5LftrAX54zxGr5vOllhdpX5fkv1+CtA2Ml+J6tiJ+XLnJ7dDietpZUL+89fpcVOBmY+C1dtSYDtJrRHRBrwGeDIx31RgF2CFDWbAx4BnIuIVAJK2BLqA84CjgCvrskcBnyx+fgg4FPhC8fqtwD2JelF8TmNEiW/UL6NVuW5mZmZmZmZmZmZmtnpRiT/itJXLW2L1dwXw+uLno6k1ZgEgaZikMyTdIukOSYdJaga+DBwpabqkIyXtJunvRebvRcMYwETqGuAi4v6I6AAuBA6VNKT4nCnAJOCGItoG/FPSLsXrI4HfrmghJJ0p6duSrgVOXV6dJB0n6SJJf5L0gKRv9lPWOEn/kPR6SRMlXV8s692S9i4yB0u6XdKdkq4upo2RdImkGZJulLR9Mf1kSadLugo4W9K6kn5XrNdbJO058GYyMzMzMzMzMzMzM7NVlXuYrf5+A5wk6TJqQyeeAexdvPc54JqIeI+k0cDNwF+Ak4BdeodHlDQS2CciuiUdCHwNOKIo6ypJbwGuBs6KiAciYq6km6kNz/h7ar3Lzo+I0LPDZvwGOErS00AP8BS1RrUV2QI4MCJ6VlAnqPWQ2xHoAO6X9P2IeLxYlgnApcDnI+LPkv4HuDIiTpHUAAyVtC61oSb3iYiHJY0pyv0ScEdEHC7p1cDZxWcB7AzsFRFtks4FvhMRN0jaiFpPu60HWDYzMzMzMzMzMzMzM1tFucFsNRcRM4oeXkfz/CEWDwLeKOnE4nUL0N/DlkYBZ0naHAigqSh7uqRNinIOBG6RtEdE/JNnh2XsbTB7T58y/wR8BXgGOD+5OBdERO+A0P3WqXB1RCwEkHQvMBl4vMhcDXw4Iq4rsrcAZ0hqAi4plmk/4PqIeLhYznlFdi+KRrmIuEbSWEmjivcuLYa9pFgX29Q1Do6UNCIinvPwEUnHA8cDHPuxn7L3645PrgYzMzMzMzMzMzMzM3s5ucFszXAp8C1gP6D+SbACjoiI++vDknbvM/9XgGsj4k1F49u03jciYglwEXCRpCrwOuCfwCXAtyXtBLRGxO31BUZEp6TbgP8BtgXekFiOpZk6UetZ1quHZ/fjbuA24LXAdUU9rpe0D7VhK8+RdBqwAPp9Unx/T7DtzdXXrQLsUdeA1q+IOB04HeCnV5V4Mr2ZmZmZmZmZmZmZrRVU6e/X0jYY/AyzNcMZwJcj4q4+068EPqKiK5SkHYvpi4ERdblRPPussuN6J0raU9I6xc/NwDbAo/CfhrRpxWefR//+H/C/ETH3BSxTv3UaQFDr6baVpE8X9Z4MzIqInwG/AHYC/gHsK2njItM7JOP1wLHFtP2AORGxqJ/PuQo4ofeFpKnJ+pmZmZmZmZmZmZmZ2SrIDWZrgIh4IiK+289bX6E2TOEMSXcXrwGupTak4HRJRwLfBL4u6W9AQ938mwLXSboLuAO4Ffhd3fvnATtQe15Zf/W6JyLOeoGLtbw6rVAxpONRwP6SPkSt1910SXdQG27xuxExm9pQiRdJupNnh4w8GdhF0gzgG8C7lvMxH+3NFUNCfqDswpmZmZmZmZmZmZmZ2arDQzKuxiJieD/TplEMX1gMGfj+fjLzgF37TN6i7ucvFLmzgbNX8PkX02cYw4h4BNiun+yZwJkrKOu4Pq//sZw6PaeciDi07ufhxf+d1IZl7PW8RruIuAK4os+0ecBh/WRP7vN6DnDk8palP23t+REZo6dn4FChvWPllEsyG9WVM9Jkd4mqZusKEJHv3lxt7xg41Ku7K59N6ujoTmfbF65wdNDnqJbYZlFiQ5TJdi/Lr9shI5rT2VHbDEtnF967dOAQ0NSxJF1mV4nu85Xm/HI1jByZzvLk0+lotSe/L/Q0D01nK01NA4cKDc35/Twt8stV7VoJnw/0dObL7Vjcns62rPO8y/5yVRcvTGd7luXOIQ1dnfnP78hnWbwgX257fn2VUeYaWWlpSWe7l+TONQDNyTqoWuIiWeJ4KEMlRpqOfke8fnnrsCZ+PoBUog6DX10aqyXOCx3JY73E8RBd+fu1Snv+3kpDytyP5+sQlRJf1Uucn+nMZyvduWyoxD1QiSyVEn/f252/9lbzpTJrYf6ercx3mMklbu9CufXQU+LeLirpv0kt9/0hWVcodw7rKVOHEstW6lyevE6X+dpd6npaYrkaK2X28ryOrpVzTe/qLFHf5LmxzH7b2JI/zslfHqg0ljjOyqyDEspc+4YPTW7fEufmaolflZS5lqykW9xSenpKbLMS9ysdncmF61mW//xhIwbOFFbSr/nMVivuYWZmZmZmZmZmZmZmZmZrNfcws5eVpM8Bb+0z+YKIOGUw6mNmZmZmZmZmZmZmNlhUYtQiW7ncYGYvq6JhzI1jZmZmZmZmZmZmZma2yvCQjGZmZmZmZmZmZmZmZrZWc4OZmZmZmZmZmZmZmZmZrdU8JKOZmZmZmZmZmZmZmdlgqLhf06rCW8LMzMzMzMzMzMzMzMzWam4wMzMzMzMzMzMzMzMzs7WaImKw62C2xvvHPxelD7QNGp9Il/tkz/rp7KSGp9LZrMZqVzrbXWlKZ9srw9LZCj35OpCvw7i2x9PZpS1j0tlK5Oo7K9ZLlzmB/LZ9rGdyOrtlz13pbENXezqrqKazHS2j09nGnnwdmjqWpHLX7/GxdJnb33txOttVGZLOhpTOtnbllgugtX1+Ojtr+Cbp7IjufLnZZWtrGJ4uc3jXgvznk1+3XQ35bTa8fW46m90XAZYOn5DOjpz1QDpbbW5J5aLSkC6zzHHe1TIyna305K87ofzfhZVZtvYho9LZYctmp7PdjbntsKR1XLpMkb/P72zIfT5Ac4nz7Xzy9W2ttKWzQ6rL0tnFGp3ONim3jw3tWZQuc2Fl7Ev++QAt1aX5Oih/r1KmDmX2sTHtJe5Fk9eHKPH3n5US961l9DQ0l6hDdzrbXeK609jTkc6WOT9nl63M+XZlaezOn5dmtuTva+6cOT6dbSixGnZZ77F0tiNy+8KS7tZ0mSMb8+ePRd3572Zjmxaks12R/262rCe/bKMbS9SB/PG7/sJ7U7k5o/L7V89KekpKd4lym+hMZxdXR6SzQ0tc0+d0rJPOThiSu7d6fFn++/QmrfnjccbCTdPZ7Uc9mM7euWCzdLapIX8e33JkftmWRe5YHxn573uzI//9pcyxO7szf2+1Vfsd6SwlrpE3xD7p7DajHk1n71u4USr3ysYb02UuGprfDrN78te9Xbccnf9CbQOa9bnj1shGmvGnnLna7SeDf2drqwRJPZKmS7pb0h+kFf9mQdLJkk4sfv6ypAMHyE+TtEs/098o6dMrKlPSxyUNfYHL9feS+f0kXfZCPsvMzMzMzMzMzMzMrAxJa+S/1ZEbzKxXW0RMjYjtgHnAh7MzRsRJEfGXF/KhEXFpRHxjgDI/DrygBrOIeNULmc/MzMzMzMzMzMzMzNYebjCz/vwDWB9A0qaS/iTpNkl/lbRV37CkMyW9pfj5JEm3FD3VTtdzm5LfLunvxXu7FfnjJP1geWVK+igwCbhW0rWS3ivpO3W590n69vIWRNKS4v/9il5uF0q6T9Kve+sm6eBi2g3Am+vmHSbpjGJ57pB0WDH9e5JOKn5+raTrpVVgHBIzMzMzMzMzMzMzM3tB/Et+ew5JDcABwKXFpNOBj0TEzsCJwI8GKOIHEbFr0VOtFTi07r1hRY+vDwFnZOoTEd8DngL2j4j9gd8Ab5TUO+D5u4FfZsoCdqTWW20bYBNgT0ktwM+ANwB7A/UDXX8OuCYidgX2B06TNAz4NHCkpP2B7wHvjigx0LGZmZmZmZmZmZmZma1S3GBmvVolTQfmAmOAP0saDrwKuKB476fAxAHK2V/STZLuAl4NbFv33nkAEXE9MFIDPCetPxGxFLgGOLTo7dYUEXclZ785Ip4oGremA1OArYCHI+KBiAjgV3X5g4BPF8s+DWgBNoqIZcD7gD9TayDs90muko6XdKukWy/5bbZNz8zMzMzMzMzMzMzWFqpU1sh/q6PGwa6ArTLaImKqpFHAZdSeYXYmsCAipmYKKHpr/QjYJSIel3QytUamXtFnlr6vs34OfBa4j3zvMoCOup97eHb/X149BBwREff3894rqDUuTlreh0XE6dR66PGPfy56octqZmZmZmZmZmZmZmYr2erZzGcrTUQsBD5KbfjFNuBhSW8FUM0OK5i9t3FsTtE77S193j+yKGcvYGHxWRmLgRF1dbwJ2BA4hqLX2otwH7CxpE2L10fXvXcl8JG6Z53tWPw/GfgfakM8HiJp9xdZBzMzMzMzMzMzMzMzG0RuMLPniYg7gDuBo4BjgfdKuhO4BzhsBfMtoPY8sLuAS4Bb+kTmS/o78BPgvSWqdDpwhaRr66b9FvhbRMwvUc7zREQ7cDzwR0k3AI/Wvf0VoAmYIelu4CtF49kvgBMj4qliOX5e9K4zMzMzMzMzMzMzM7PVkIdkNAAiYnif12+oe3lwP/mT634+ru7nzwOf7ye/33I+90xqQz+uqMzvA9/vM+tewHf6K7NP+cOL/6dRew5Z7/QT6n7+E7VnmfWdtw14fz/FHliXuY3a8IwrdP8zIweK/Mdmfzktnb1jr/yIlJvf+D/p7LA990rl2m6+KV1my+QN09k/bv7pdPaQGSels4/s/+F0duhtf05nhw8dls4uveefqdwfN/9JusxPRj77vSWfSGdPfDRfbuPYddLZ9tnz0tl5/346nV1nkwnpbFdFqdz2916cLnPGNm9KZ3f4wIo66z7XuH3ynVjnXJ8/JucvXpbOTn7H29LZrrvvTGernZ2p3Og990+X2f23awcOFToWLE5nR206OZ1d8sDD6Wy05v/e4l+/7ft3KMu325ePS2c77u9v5OHna3htfh9vuv/2dLZhvQ3SWZ58dOBMr6imoz2Ll6Sz3U89k842bpy/9jVvkNvHGsflj93mOU+ks7M2eVU629mQ32+boqtEtmPgUKGtMnzgUEGRHxm7tZrbF7pKrIMGetLZlurSdLazkq9DmXXQpNy5GaAaDenssiGj09kJD/8jF+xsT5fZ9e8H0tmmbQa8vX9BqkNHDBzqzTY0pbONsx5PZ2PJonSWSVNyuWp3ushqS/6+WT35Y6cy+6l0tm2bbdLZiaPz56VqNXd/CdBWbU1nt7j3t6ncL4ecMHCo8K5nvpvO/mLcF9LZ9zVckM4+sNkbBg4V7n06/13jbQvPTWcf3/bQdLbxrtx56dJ1Dhw4VDh42yfT2cmP5u9xb55wRDq7beTv3e/rzN/XHPj4b9LZW8bkv6fv9Mj/pXJXjPlcuszJF+R/V3LTfhels1Nv/mw6e8er8uvrvhkz09nT2j6Tzt7+9lwdXntj/ndWN78ynz30yXPS2dsnfSSdnfK7n6ezzaPy16jfdmydzv50i/zgWFeNf96vVPv16rY70mU2PZr/DvX9pi+ns7tumY6arVbcYGarFUmjgZuBOyPi6kGujpmZmZmZmZmZmZnZC6bkH3bbyucGM1utFMM+blE/TdJYoL/GswMiYu7LUS8zMzMzMzMzMzMzM1t9ucHMVntFo9jUwa6HmZmZmZmZmZmZmZmtniqDXQEzMzMzMzMzMzMzMzOzweQeZmZmZmZmZmZmZmZmZoOh4n5NqwpvCTMzMzMzMzMzMzMzM1urucHMzMzMzMzMzMzMzMzM1mpuMDMzMzMzMzMzMzMzM7O1mp9hZi8pSQH8KiLeUbxuBGYCN0XEof3km4CvAEcAHcAy4IsRcUXx/o7A7cDBEXFl3Xw9wF3U9uF/Au+KiGUrc9lejLEjutPZoRtOSmfHja6ms60brZ/Odo0enytzkynpMmNCfrnWGdqZzlY23zafVX59afLm6SxdHeloy/rrpXIdHT3pMqPSlc5OWK8lnR3evHE6Wxm7bjrbvN6idLZ9/uJ0duj6uf0WoNLcnMotrQxJl7nDB3ZIZ+/8yZ3p7O7rjEhnR26cP86qXfnzEh3t6WjD6FHprNraUrmelvw6GDJ5cjrbND5/2Sizjw9vaEhnVWKc8m3flo7Svf4m6WxrY+52cHFLfts2Tdwone0YNSGdba7k1y2NTflo+9J0dsT6G+TrMHxkOtozNJftaF0nXabGRjrbXhmWznZWc+dQgCHKXyOXMTydbevJX8+GNuTONQAdak3lusivg0by59sy26Ej8teoRuXrUI38cVYt8feXc6r58+iodaekciGly2xVvq4dY/PHeZQot6cpv9/2VPL7WGtD/mt9Q2f+eGgfmTs/q5q/b+1qyu/jIn/v3tKSL7eMMS1L0tmI/P5YRnX0uFRubHN+fVW688fj2Pzln+7G/P14mfW1zvAS57BKvg7dJX4lpo02TeUmteSvvU3Kf++lUuI4V74Oy5pHp7PDyX/vpDufrVbz+4Im5H6vMWFEfh0Mm5C/t2pqzNd1SInvcSpxPdtq+4np7Jju/O9L5i7MXc+axuePscZKfjvEmHy5rSXOdyO3zh27AGrKf3/Yeki+vhqb/2603sjcNbWLzdJlNo7On/M371g511MbmCor5z7CynMPM3upLQW2k/7z24bXAE+uIP8VYCKwXURsB7wBqL+rOBq4ofi/XltETC3m6QQ+8FJUfiBFA6CZmZmZmZmZmZmZma1B3GBmK8P/Z+++4ywt6/v/v97nTN/ZwhaWzgJLcYFl6aCAKIiKJnbREA3GiBpbYkziN5agxlh/Go0aRaPYRRQNsYFBAUGlSO9IX9r23dnpc87n98e5R84OUz63sv39fDzmMefc532u+7r7fc8193X/FHhe8fqVwLfHC0nqAl4HvCUiBgEi4rGI+G7xuYCXAmcAp0ia6F8yfwXj/2uFpGmSfizpBkk3SzqtGH6kpF8Xw6+SNF1Sh6SvSLpJ0nWSnlFkz5B0nqT/BS4qyvyypKuL3AvKzyIzMzMzMzMzMzMzM9tSuMHMNobvAK8oGrgWA1dOkFsIPBARE/XP9jTg3oi4G7gEOHVsoLjj67k0umccz3OAhyPikOJutJ9JagPOBd4WEYcAJwP9wJsAIuJgGg19X21qpDuWRrePzwTeBfwiIo4EngF8TJLvWTYzMzMzMzMzMzMz20q5wcyedBFxI7CARqPTT/6Eol5Jo/GN4ndzt4ydkq4HrgEeAP57gjJuAk6W9BFJx0fEWmB/4JGIuLqo77qIGAGOA75eDLsduB/Yryjn5xGxqnh9CvDOYvyXAB3AEzoklnSmpGskXfOz879YdtrNzMzMzMzMzMzMbBsnVbbJn62Rn8dkG8sFwMeBE4E5owMlXQjMp9HQ9VZgD0nTI6Kn+cuSqsBLgD+X9C5AwJymbH9ELJmqEhFxp6TDadyd9iFJFwE/BMZ78uhkT1fsHZN7SUTcMcW4zwbOBvjf343kn3RqZmZmZmZmZmZmZmab1NbZzGdbgy8D74+IDbpKjIhnR8SSiPibiOijcWfYp4tuEpG0s6S/pNFN4g0RsXtELIiIPYHvAy8sUwlJuwB9EfENGg14hwG3A7tIOrLITC+6drwMOL0Yth+Nu8bGaxS7EHhL8Yw1JB1apk5mZmZmZmZmZmZmZrZlcYOZbRQRsTQiPpWIvhtYDtwq6WYad38tp9H94g/GZL8P/EXJqhwMXFV0n/gu4N8iYgg4DfhPSTcAP6fRreLngKqkm2g84+yMiBgcp8wPAK3AjUWdP1CyTmZmZmZmZmZmZmZmtgVxl4z2pIqI7nGGXULjWV/j5YeAfyp+ml04TvYCGl09jjueCcq/cIKyrgaOGecrZ4yTPQc4p+l9P/D6zPjNzMzMzMzMzMzMzGzL5wYzs00gYrLHo22oOm/HEuXm61CZMy+drddGcsHpM9JlRntXOttaqaWztY5p6WwZGh7v5sLxjcyYM3Wo0LLj+lRu59a2dJksz69fu+RXAyr12flwifqqvT2dnbPkgHwdSqjOyK27ofy8nXvC0ens0TtMT2ev/NDl6exJ5/5tOjvyyEPpLC2t6WgM5LedWk9uexjozK+LrfN2Tmcrw0PpbG36DulsdWQ4nY31uXkAMO2QxensQFt+31iZldsxdD866aM7N6B1q/PjnzE3X26JA99IW2c6W2Zbr82an862LX8wna3090wdAobm5Ker2p76/yIAeuv543SVejo76RNix+ir5aetonwdNO6ja8fXU8/tn1uVPFcCqsqf1/TU8suszDxoVX6/NBj543SlxLpQZj7UK9VULpI5gPrM/PnaSFt+e6hX8pfUdeXrO9SS3x5aS+zzy8yz4WQdosQD3Yer+fUrSuxA2iv5405ny0A6263cvrmsIfLzoX/OHqlcbW1+fg3tsjCdra3Pl9s/PX+MrJdYvsO1/Do2MCt/LljGSHfuXLBD+WNOPfLbY9/cPUuUm44yUslfx9VK/F2jtste+Tr0p6MMzMvNh5HefF1nHnVYOju0Nn/cm3FkvtzBVfljZFtbic7C9n5KOvrdz12Vyv3FOw5Jl9nTl1/HB0tsuyPDJU4wS4jh/PlSpTNfh7W7HJivQ/KwU2vtSJc5ODd/LbtniXnQ6HzLnjSVjbNeW3luMLNtgqQ5wMXjfHRSRKzc1PUxMzMzMzMzMzMzM7OthxvMbJtQNIot2dz1MDMzMzMzMzMzMzOzrU+J+3jNzMzMzMzMzMzMzMzMtj2+w8zMzMzMzMzMzMzMzGwzUMX3NW0pvCTMzMzMzMzMzMzMzMxsu+YGMzMzMzMzMzMzMzMzM9uuucHMzMzMzMzMzMzMzMzMtmt+hpmZmZmZmZmZmZmZmdlmoIo2dxWs4AYze1JJqgE30Vi37gVeFRFrJC0AbgPuaIp/Angm8JuI+EJTGS8EzoyIU5vKG/WdiPiwpEuAd0TENRtzep4sj6yqprPDDz6Qzq7syt8kOvLQ0nS2pbUtlRu+9550mdXHHklnVx78wnS25f7b09mYe0Q6y4rH8nVYtSydHVmxIpW7+rH8+M/YpT+d/fFP8svhuTPuSmejXk9nKy357WHNXQ+ms53zZqWzPPRorswj16eLXHHZlensjL12SWdPOvdv09mLT/tcOrvoVfuns/MXH5nODi7PreMALd3TUrkZj9yaLpNH8/u6kTVr01m15k+ZBlatSWerHe3pbP9td6azM6r57ayWPD7UDzk6XWZLmX1oz8p0VssfTmdbS2RjcDCdra9enc/OmJ7OVnbaNZWb1pefX21rc/s6gFm77ZTO1pU//xisd+Tr0LImXwfy6/hI5LffWZVcHYL8Be0wufMqgB2q+fWrVuJSbjDy+5o2DaWzZebDLPLH1M6VufNhjQyny6w/nD/H7tqzxDxQfh7UO3LHPYCuams627ryoXSW3vxyaJm7cy5YZh605tfFMuW2LM8f/4em589rHhjOn7PV6/n6zuvMn4O0/fIHqdyyBcemy6w8eHE6u26vZ6Wz01dfnc62HLhnOru2N3/c6bo/f07ednh++bY+cMfUIeDh7lPTZe67Q34/Pu2R/Hlg7LIknZ3Vmz9f6hvOL7OBn/8knR086oXp7ND3vpbKPXz4ieky1151bTpbWxjpbM8116Wz7J2PDg7mr73rN+X/ZPa6t74slWt9OH/NOWe3/HG6467fpbP9uzw9na335/9eUh/MH/97qyPp7My7r0pnV3cdnsp1DPw+XWasyV8/XFc9Kp197qHpqNlWxQ1m9mTrj4glAJK+CrwJ+GDx2d2jn42S9BjwTuALTYNfAXx7bHlbAkktEZE/KpqZmZmZmZmZmZmZ2RbPzzCzjek3wFT/Lv1/wAGSdgaQ1AWcDPzwyaiApAMlXSXpekk3Stq3GP7q4v0Nkr5eDNtT0sXF8Isl7VEMP0fSJyT9EviIpH0k/UzS7yT9StIBT0ZdzczMzMzMzMzMzMxs8/AdZrZRSKoCJwH/3TR4H0nXN71/S0T8StL5wMuBTwF/DvwyInqKTOeY73woIs4tUZU3AJ+KiG9KagOqkg4E3gU8LSJWSJpdZD8DfC0ivirpr4FPAy8sPtsPODkiapIuBt4QEXdJOhr4HI2uJc3MzMzMzMzMzMzM8kp0e28bl5eEPdlGG7hWArOBnzd9dndELGn6+VUx/Ns0umGEDbtjhKJLxqafMo1l0LjL7V8k/TOwZ0T002jc+l5ErACIiFVF9ljgW8XrrwPHNZVzXtFY1g08FTivmM4vAON27i/pTEnXSLrmVz85u2S1zczMzMzMzMzMzMxsU3GDmT3ZRp85tifQRuMZZlO5AthZ0iE0GqPyT4edQkR8i8Zda/3AhZKeCQjIPKm1OdNb/K4Aa8Y04j1lgnGfHRFHRMQRx5965p8wFWZmZmZmZmZmZmZmtjG5wcw2iohYC7wVeIek1imyAXwX+Crwk4gYeLLqIWlv4J6I+DRwAbAYuBh4uaQ5RWa0S8Zf8/idbqcDl49T13XAvZJeVnxXRUOfmZmZmZmZmZmZmZltpdxgZhtNRFwH3MDjjVD7SLq+6eetTfFvA4cA3xlTTOeY73y46bMfS1pa/Jw3QTVOA24uuk88gMYzym4BPghcKukG4BNF9q3AayTdCLwKeNsEZZ4OvLb47i3ACyafE2ZmZmZmZmZmZmZmtiVr2dwVsG1LRHSPef9nTW87J/nedTS6Shw7vDpB/sRkfT4EfGic4V+lcUdb87D7aDzfbGz2jDHv7wWekxm/mZmZmZmZmZmZmdlEVHnCn8VtM3GDmdkmsL4v88i0ht6lj6WzK3YcSWf7H86X2z1jRirX+8Aj6TKnL9wznV3TO2476biGHnk0nY0ntslOqOeW29PZ6QeP+xi7cfU/sjyVmzG3K13mujvuS2d3WzR76lCh57r88m3pbM+X+/CqdHawZzCdXX3fynS2Xsttk3u9fHV+/D19+fEP57fdkUceSmcXvWr/dPbWr9+Rzu706hL7muVr0tnh+3Lb75zjTkmXWb/2mnR2YHl+XZxxUH7errrtvnR2uH84nW2blt/Ouh/Lb7+9D+TWse7269Jlrr3t9+nszMPTUYaWLk1ny2xntcGhdLZSzR+jeu7J13fmolwdqrN3TpdZ6V+fzg5M/H9NTyw36ulsGYPRka+DNlIdyNWhhfy2W+b8YyDyy6GqWjpbxkjkLxGl/Dlu+0j+OKnluX1YDOW33cGl+f1iZ/f0dFatbelsZbA/na2359eF+iP5fU2tJ79faG1JrguVfMc1lRLTVabckQfuT2drC/P78b6hSZ8usGEd6vltfcfO/LbTcvBhqdyapfnjXsse+WuzRx7Lb2exYE46W4/88l2+qsQ+f/a8dDQiv8xqK1akcssG8ssh9smPf2R6/jpucCS/Hx9pzW+TPevy2860gw5MZ8usYzOOzG0Pq1aWOE7X89vj/Xcty5dbzR+n770z93cCgKjnt4f6Tvl5+3C2CiP5Mu9+JL8uPnNmfh1f15vfdlbdlL8uGe7L//3hnt1z+wQAnpqv74MP59bdWv8D6TJV4vqll/w+DPLlmm1N3GBm2wRJzwY+MmbwvRHxos1RHzMzMzMzMzMzMzMz23q4wcy2CRFxIXDh5q6HmZmZmZmZmZmZmZltfdxgZmZmZmZmZmZmZmZmtjmU6IraNi4vCTMzMzMzMzMzMzMzM9uuucHMzMzMzMzMzMzMzMzMtmtuMDMzMzMzMzMzMzMzM7Ptmp9hZmZmZmZmZmZmZmZmthlI2txVsIIbzMw2gWP3X5/Ozpjzgny5M2vpbPdOJ6ez63dcmMpN3/uAdJnDXbPT2YM7V6ezI3NelM6KSGfbXnx6OjsU+eUwffaOqdwps7vSZU474bXp7OFDbens3IUvTmfrHdPS2ZlDA+ms6vl5W+ucns+25ebvo917p8vc81UvT2cZzM8DWlrT0fmLj0xnd3r1SDr7i2edlc6ecMUn0tlqf08qt2LmnukyO059VTrbUh9KZ4eG+tLZuYvyy6Eykq9DvSW//a6bltvXAHQcvjaVG6jmTxu7DszPg1UzdsuXu9+qdHawPb9PaKkNprPDlfxy6B7IH8/qyXVh2Zz8sbdj5u7pbC2q+Sz5bIvy+5rhyO/v6pHvKKNKiWNJcj5EiQvaCvX8+EvM2zLLrFXD6WyZeVvG2ta56WxtyXNSuTLndp2L16SzyzrzdS2jVsmv42W07XxgOlut57fJ/tbcfrSu/DoTkd926iW2h5m75OfB9Nb8tdkOrWvS2Y1l/Y77pHInzMkv28HhfdPZp+6SX2brO3J1BaiTL/eo/fPH6V5y17IAA9GZzz7teancicrXtV6i06d6W76uM9r709k1rflzxr3nrktn1886Kp09qcS5wsDw4lTuxL3y69f0RS9NZ08d3iOd7ez6i3T2hcO7pLO9A/n1ZtXuf5vOLlyTO19ZO+2EdJmLRvLbQx/5/dJTKr3p7NzTX5nOUuLvDy+fNj+dXTnjaensicO5c7Y1raely1Tkz5eeX88vM2gvkTXberhLRkuTFJK+3vS+RdJyST+a4nuzJP1t0/sFkvolXS/pVkmfl0pcZT2x/EskHfHHft/MzMzMzMzMzMzMzLZvbjCzMnqBgySN/mvTs4CHEt+bBYz9t5a7I2IJsBhYBLyw+UNJW+Tdj5Ly/+ZoZmZmZmZmZmZmZmZbBTeYWVk/BUb7Ingl8O3RDySdJekdTe9vlrQA+DCwT3FH2ceaC4uIEeDXwEJJZ0g6T9L/AhdJmibpy5KulnSdpBcU5XZK+o6kGyWdC0zYN4GkqqRzirrcJOnvi+ELJf2fpBskXStpHzV8rCl7WpE9UdIvJX0LuKko82NFvW6U9Po/ea6amZmZmZmZmZmZ2fanUtk2f7ZCW+RdPLZF+w7w3qIbxsXAl4Hjp/jOO4GDijvKKBrRKF53AScB7wXmA8cCiyNilaR/B34REX8taRZwlaT/A14P9EXEYkmLgWsnGfcSYNeIOKgY36xi+DeBD0fEDyR10Gg8fnGRPwSYC1wt6bIif1QxDfdKOhNYGxFHSmoHrpB0UUTcO8V8MDMzMzMzMzMzMzOzLdDW2cxnm01E3AgsoHF32U/+hKL2kXQ9cAXw44j4aTH85xGxqnh9CvDOIncJ0AHsAZwAfKOpPjdOMp57gL0l/aek5wDrJE2n0Yj2g6KMgYjoA44Dvh0RtYh4DLgUOLIo56qmBrFTgFcX9boSmANPfDqppDMlXSPpmv/57ldKzBozMzMzMzMzMzMzM9uUfIeZ/TEuAD4OnEijsWjUCBs2wnZMUsboM8zG6m16LeAlEXFHc0ASQGQqGhGrJR0CPBt4E/By4O8miGuSosbW6y0RceEU4z4bOBvg17f1pOprZmZmZmZmZmZmZmabnu8wsz/Gl4H3R8RNY4bfBxwGIOkwYK9ieA8w/Y8Yz4XAW1S0kEk6tBh+GXB6MewgGl1DjkvSXKASEd8H3gMcFhHrgKWSXlhk2ouuIS8DTiueUTaPxp1sV01QrzdKai2+v5+kaX/E9JmZmZmZmZmZmZmZ2RbAd5hZaRGxFPjUOB99n8e7KrwauLPIr5R0haSbgZ8Cn02O6gPAfwA3Fo1m9wHPB/4L+IqkG4HrGb9Ra9SuRXa0cfj/Fb9fBXxB0vuBYeBlwA9oPEPtBhp3sP1TRDwq6YAxZX6JRreU1xb1Wg68MDlNZmZmZmZmZmZmZmYAqDJZx2e2KbnBzNIionucYZfQeL4YEdFP4/le4333L8YMOmiczDnAOU3v+4HXj5PrB16RrPMNFHe9jRl+F/DMcb7yj8VPc/YSimks3teBfyl+Um5/JH+D3aEP3TF1aLTcOW3p7HFrl6az04eHUrl47KF0mR07zJk6VFg6//h09qDHbk5nOWCfdLT1pl+ns5XdFqSzsfyRVO6SuwbTZR639vx09oGnnJTOxprfp7O1NWvT2eKm0ZSh1flya4O59Rag0tqayk0//W3pModvviGdrc6amc7GQH5dGFy+Ip3tX74mnT3hik+ks5c97e3p7PT9u1K5xd/4ZLrMjruvS2ejr3fqUEGz56azg7fdls4Ol1hvW2Y84TA8oVn7L0pnY8WjqZym5Y9ltWW5MgFmL1iYzrI6v453lNjXECV6Tm6frMfpDdUeze3zAaq77pbKzeraIV1m+/r8/OqZly+3Qj2dHYn85UaVWj6rfLYW1XS2oty0tZLfdkfIHXNg482DUsuhRLlldNV70tm5912dC5bZztetTkc7d8jv86OlvUQ2vxxq7fkOLNqWP5jOMpQ/r5gxZ6dULir5bazekr9+QfkOcVpX5K91Htr7Rens9Or6dHakxJ9XyuxHO/pWTR0CblmWPz4d3Z0rE+D21fnpOm73/Ll7pTV/7L13eWc6e3j3mnS2tXM4nZ12/2SPTn/cPdOemi5z3u5r0tm68tvZUC2/zKZV8svs9vW7prNLHr0+nb2s58ipQ4Ujd8mdY960On/sPW7ltenstf0np7Mv6cz/TeE3/cels9Vqft/4ynUXpbM/1JtSuZPm3p8u8+GRw9PZY1qWpbNL67nrSIBDrrw8na0P5/cJV8x5QTr7jCUPpLO3rjo4lTu6dkW6zJiW//vDT2oTduL1BMc+JR0126q4S0YzMzMzMzMzMzMzMzPbrvkOM9tmSLoSGPsvnq8a51lrZmZmZmZmZmZmZmZmf+AGM9tmRMTRm7sOZmZmZmZmZmZmZmZpJbqito3LS8LMzMzMzMzMzMzMzMy2a24wMzMzMzMzMzMzMzMzs+2aG8zMzMzMzMzMzMzMzMxsu+ZnmJmZmZmZmZmZmZmZmW0OFW3uGljBd5iZmZmZmZmZmZmZmZnZds13mNmkJAXwjYh4VfG+BXgEuBJ4M3A5sEdE1Ju+cz1wJnAq8DpgOY117V8i4oKNWNf1EdG9scr/U7SW2NJiaKhEufn/PqgPDqSzlccX56TK1LXM/0lElAiPDJcIl6hDiWmjnptfANHfn8p1drWmy6w9OpjOtrelo9T7cnUFqHS0p7PDq9fms7359XZkIL/Mqm0jqVyoxDZWZntIrgcAtZ716WxL97R0dvi+R9PZan9POjt9/650tueOvlSuXi2xE63V0tGNtQ+rDZRYb/vz22+1M7+dkdyPA8Rwbj+qEseRMvuPan0jLbMSB98osd6oJb9/LrNfqCbnr0os2zLZWlTT2VIbRAk1StShxLlC43Q2px65/yesq0RdS6iX+H9GlZkJW4BQif/VzO4XShynKbP/KHEyGiW2szLK1KHMuWh63pYot9QuodSJfonp2kjXBCpRhzLbZJlsrZo7gW+p5pdEtOYvCspcy24spVabjSV5rlBmc9xYosRWWSZbL7McSqxj7W3540N23W3Ln65RL3Hu3rc+fyypV/LZ/t78Pkxl7kIpcYzq7U/u84fz0zVQZtdcYl9TZl0cWd+bztaGc38nABjsLnE8LfN3hey0lbh+KbMelCnWbFvlO8xsKr3AQZI6i/fPAh4CiIj7gAeB40fDkg4ApkfEVcWgT0bEEuBlwJelqa+Ui0a5LdKWXDczMzMzMzMzMzMzM/vjuMHMMn4KPK94/Urg202ffRt4RdP7V4z5HICIuA0YAeaONwJJ50j6hKRfAh+RdJSkX0u6rvi9f5E7Q9L5kn4m6S5JHx2nrLmSfiPpeZJ2lnSZpOsl3Szp+CLzHEnXSrpB0sXFsNmSfijpRkm/lbS4GH6WpLMlXQR8TdI8Sd+XdHXx87RSc9PMzMzMzMzMzMzMDJAq2+TP1sh3y1jGd4D3SvoRsBj4Mo/fVfZd4DpJb4mIEeA0GneTbUDS0TT61Fg+yXj2A06OiJqkGcAJETEi6WTg34GXFLklwKHAIHCHpP+MiAeL8cwHLgDeHRE/l/QPwIUR8UFJVaBL0jzgi0X590qaXZT7PuC6iHihpGcCXyvGBXA4cFxE9Ev6Fo075y6XtAdwIfCU3Kw0MzMzMzMzMzMzM7MtjRvMbEoRcaOkBTTuLvvJmM8elXQLcJKkx4DhiLi5KfL3kv4S6AFOi5i09/HzImK0t9yZwFcl7UvjKRXNPVBfHBFrASTdCuxJo2vIVuBi4E0RcWmRvZpGV5CtwA8j4npJJwKXRcS9xTSsKrLHUTTKRcQvJM2RNLP47IKIGH0gy8nAIj3eB/EMSdMjYoOH/Eg6k8az3HjNP3yBZ/z5mZNMupmZmZmZmZmZmZmZbS5uMLOsC4CPAycCc8Z8Ntot42M8sTvGT0bEx5PjaH4S5weAX0bEi4rGukuaPhtsel3j8fV4BPgd8GzgUoCIuEzSCTS6lPy6pI8Baxj/UfHjPYVzNNdctwpwbFMD2rgi4mzgbICvX7aVPZXdzMzMzMzMzMzMzGw7snV2JGmbw5eB90fETeN89n3gVBrdMX7nSRrfTOCh4vUZye8E8NfAAZLeCSBpT2BZRHwR+G/gMOA3wNMl7VVkRrtkvAw4vRh2IrAiItaNM56LgDePvpG0JFk/MzMzMzMzMzMzMzPbAvkOM0uJiKXApyb4bI2k3wLzR7s5fBJ8lEaXjG8HfpH9UvH8s1cA/ytpHY07w/5R0jCwHnh1RCwvuks8X42nDy4DngWcBXxF0o1AH/BXE4zmrcBni1wLjYa2N/wxE2lmZmZmZmZmZmZm27HKeB2f2ebgBjObVER0jzPsEjbsIpGIeME4ubNKjOeMMe9/A+zXNOg9xfBzgHOacs8fW9eIGKLRLeOor44zvp8CPx0zbBUw5XRExAoad9OZmZmZmZmZmZmZmdk2wA1mZpvAXnN7pw4VhqafmM4+pS1f7uD8fLm1alsq17LjXukyWwfG691yfDvPWJ/O9nUcms5K+UfJDRx7ajrbVmLahufvk8qdUOKpdyPHvHnqUGHuqny59WNOyoejno6q0prOdtWH09l6tT2dzVpRfcL/DExo1tOekc7WOqanswOds6cOFWY8cms6O+e4U9LZFTP3TGcXf+OT6Wy9mjsN+c1hr02XedL33pLOxm57p7P05/dL1VNfli+3Jb/eVntXprM9M3ZOZ1uT+/J13fkyuxatSWf7ksccgNZdJn186AZC+Z7HVa+lsy0jA+nsyMLD0tnettz+plbJn75nj+cAXcqfU8S4j30d36A60tkO8vO2XqJn+TL17ajk17GseuSXQ2elL52NyE/XsPJ1UIlH75aZtwPqSmfX7pk7v1Pk69q6U4nz8eT2COX2NXVVS2Tz5bZ1zU1nKyXOrbL7kErk96G1EueBKnF+WZmWnwctyte3p54/ZyuzTXZUBqcOFarDuf3SQbvn1/F6X37ffPjeJc6BVveksx0z8/v8/XfK16GyJl9uR+T3uX17HZLK7a38caTM/rbM9jC3fW0621niWna3Gflsf9vCdPaUrvw5rpbl5tmhe+WXbX3nZ6Wzp7fnz91HhvLXW3/Znj9O9w3n96NDtaeks8+ellu+tbX5/eKiefltV8uH0tmF8/Prol48UedRT9Si/H781Hp+mQ1VZ6Wzh+yZ25f3cGS6zGotP2/3LJGF/Dww25q4wcw2KUnvAsb+JfG8iPjg5qiPmZmZmZmZmZmZmZmZG8xskyoaxtw4ZmZmZmZmZmZmZmbbPVXyvQvYxuUlYWZmZmZmZmZmZmZmZts1N5iZmZmZmZmZmZmZmZnZds0NZmZmZmZmZmZmZmZmZrZd8zPMzMzMzMzMzMzMzMzMNgdpc9fACr7DzMzMzMzMzMzMzMzMzDYpSc+RdIek30t65zifnyhpraTri5/3Zr/7x/AdZmZmZmZmZmZmZmZmZrbJSKoCnwWeBSwFrpZ0QUTcOib6q4h4/h/53VLcYGaTkhTANyLiVcX7FuAR4MqIeL6kM4CPAQ8BbcAnI+KLRfa5wAeAaYCAH0XEOzZiXddHRPfGKv9Pcc/yaensYXdfms7+fsEz09mjHvttOsvc+alY7Z670kVWd90tnX1Q09PZJff+Op2tH7kwne267hfpbOy5Xzrbes/NqdyKXY5Llzlz9e3p7Mr+Q9PZltsvT2fV2prO1taty5fb1pbOtpTIEpGKdR/z5+kiR674ZTrbvuee6WzrvJ3TWR5dmo7Wr70mne049VX57N3XpbPUaqnYSd97S7rIi1/6n+nsEW8/Kp2d8dxT09mWq/PrQksl3+3C0LKV6ez0QxansyNLH0jl5u23KF1mPHhvOlvZefd8uSseTWfVkt8vxchwOptdbwFahobS2a7d90rlhmfn9wmta5els717HpPOVsjtQwGC/DpeV77zizLllslmtdTz68yISqyLka9rhXo6W4/8vG2c/j/5qoykszMeyV3jaiRfZix7JJ3t2CW/X6KSv6Sut7Wns7X2/PVD2/IH01n6evPZmTvkciX2i/WO/HSplt/O1Js/v7x74f7pbP9w/vyyVmI726krP21Rza1jtz1c4pqzM78e3Lo2f2128PT8Oj4U+Xl7z4r8Zf4hbfltcqSS3z933X5lKndLd/46bv5++X1+rSU/b9cN59eFWW355buiL1/u3tVqOvv7VXPS2eyV980P5ut6xNob0tlrO05OZ4+q5679AW5oz/9t57Fl+fPLE6bn/2b86IJnpHJPueE36TKv3SV/DXXwTsljDvDIuvw+YfHN/5fOUmK9vbR2ZDp7/F75a6Obh56ayh0x/Lt0mWW6+rtl+Gnp7HOW5KtgNomjgN9HxD0Akr4DvADI7MD+lO9OyA1mNpVe4CBJnRHRT6PF9qExmXMj4s2SdgRukXQBMA/4DPC8iLi9aGg7MzNCSS0Rkb/63YS25LqZmZmZmZmZmZmZ2Vamsm0+OUvSmWzYJnB2RJzd9H5XoPm/v5YCR49T1LGSbgAeBt4REbeU+G4p2+aSsCfbT4HnFa9fCXx7vFBELAPuBvYE/gn4YETcXnw2EhGfm2gEks6R9AlJvwQ+IukoSb+WdF3xe/8id4ak8yX9TNJdkj46TllzJf1G0vMk7SzpsqJ/05slHV9kniPpWkk3SLq4GDZb0g8l3Sjpt5IWF8PPknS2pIuAr0maJ+n7kq4ufvL/fmFmZmZmZmZmZmZmto2LiLMj4oimn7PHRMa7BXJslxfXAntGxCHAfwI/LPHd0nyHmWV8B3ivpB8Bi4EvA8ePDUnaG9gb+D1wEPD/lRzPfsDJEVGTNAM4ISJGJJ0M/DvwkiK3BDgUGATukPSfEfFgUYf5wAXAuyPi55L+AbgwIj5Y9GvaJWke8MWi/HslzS7KfR9wXUS8UNIzga8V4wI4HDguIvolfYtG15OXS9oDuBB4SslpNTMzMzMzMzMzMzPbXi0FmvtD343GXWR/EBHrml7/RNLnJM3NfPeP4QYzm1JE3ChpAY27y34yTuQ0ScfRaMB6fUSsUon+cZucFxGjHeDPBL4qaV8aLcPNnYtfHBFrASTdSuOOtgeLzMXAmyJi9EFgVwNfltQK/DAirpd0InBZRNxbTN+qInscRaNcRPxC0hxJM4vPLii6pAQ4GVjUNI0zJE2PiJ7miWm+5fSMf/gCz/izVI+UZmZmZmZmZmZmZmbbuquBfSXtReMxUK8A/qI5IGkn4LGICElH0eg1cSWwZqrv/jHcYGZZFwAfB04Exj6R9dyIePOYYbfQuCsr//TUxvPSRn0A+GVEvKhorLuk6bPBptc1Hl+PR4DfAc8GLgWIiMsknUCjS8mvS/oYjY1pvNszJ7uNs7luFeDYpga0cRW3mJ4N8LVL//TbQc3MzMzMzMzMzMzMtgVF73JvptGDWxX4ckTcIukNxeefB14KvFHSCNAPvCIiAhj3u39qnfwMM8v6MvD+iLgpmf8Y8C+S9gOQVJH09hLjm0mjZRjgjOR3Avhr4ABJ7yzGuyewLCK+CPw3cBjwG+DpReszTV0yXgacXgw7EVjRfMtnk4uAPzQQSlqSrJ+ZmZmZmZmZmZmZ2eOkbfMnISJ+EhH7RcQ+EfHBYtjni8YyIuIzEXFgRBwSEcdExK8n++6fyneYWUpELAU+VSJ/o6S/A74tqYtGY9aPS4zyozS6ZHw78IsS461JegXwv5LW0bgz7B8lDQPrgVdHxPKiu8TzJVWAZcCzgLOAr0i6EegD/mqC0bwV+GyRa6HR0PaGEtNmZmZmZmZmZmZmZmZbEDeY2aQionucYZdQdJEYEecA50zw3R8BP0qO54wx738D7Nc06D3jjS8inj+2rhExRKNbxlFfHWd8PwV+OmbYKuAF42TPGvN+BXDaZNNjZmZmZmZmZmZmZmZbDzeYmW0CQyP57Mi6nnR2cChfbm3deL1Ljq86Y2YqN9KzPl9mX+/UocKqdfneYvvvuS+d5ch8tO/ufLnT5u+aztbX55bv4HDutmUADfWlswOD+cfpjaxZm85GrZbO9j26Mp3t2nleOlvtbE9n68O5jTLGfbTh+AbX5Lfd1h3zy6wynN/QyyyzgeWr0tmWer4OUWJbj6FcubHb3ukyj3j7UensNZ+4Kp096RlPT2fL7BvrJXbkZdaxjjWr8+U+ujyVmzZvRYkyl6WzHd3T09kyx7JameUwkt+HlZHd1wBMmz4jlat0547RAKwtsR6oM52tUE9nW8jPg+FoTWfL7J9blK/DSLIOdVXTZZaZXyPk50EZKvE43TLZUsshhtNZHl2aitWH82UOPPjQ1KFCV2uJy+T2jnS02tGVzqorv97W7r83nR0psR9t22l+Kpc9ngNUktcZAIyU2HbXrkln2/fPrze9w/nzyyjx1Oqq8scd1XPZMuf5tOXH39uXL7fSlZ+3ZfYfAyWueysMpLO1yG/rIytz50EP9wxOHSq07Jdfx1tGSkxXS/4YVcZwrcRTXSr59WZ4pMS1b/LaqG8gP/56ieuX3nr+mM7kj73fsNxavtyenhLXZq356856Pbcc1t56d7rM+0fy86AyJ7/tDEd+nSlzjVzG+mp+Oag3f+xd3ZfbP9dW586VANTelh8/Jc7XyJdrtjVxg5ltUpLeBbxszODznqw+Rs3MzMzMzMzMzMzMthaqlPinBNuo3GBmm1TRMObGMTMzMzMzMzMzMzMz22K46dLMzMzMzMzMzMzMzMy2a24wMzMzMzMzMzMzMzMzs+2au2Q0MzMzMzMzMzMzMzPbHOT7mrYUXhJmZmZmZmZmZmZmZma2XXODmZmZmZmZmZmZmZmZmW3X3CWj2Saw0w4j6Wzbfgeks/Nn19PZKnuls8Ozd07l2svcLrx+XTq6cOeBdLb9uKensyLS2c5nPTedjfvvTGcrs+ekcuvW5+tKV2s+W0Lb7rvnw9NnpqPtTxlOZ+vLHk1nK9NnpLNZa6rt6ezMffZMZytz5qWztek7pLNqzR/WZxy0fzo7NNSXr8PsuflsNti/Pl3mjOeems6e9Iz8/uPiP/tYOnvCx5+fzrYfdHA627FqeTpb2/vAdHba3B1TufW75cvs2mmPdHaoM7/ttsxdmc5WKtV0VrX8cVrr16SzMS0/bUMzctvOQGfuOALQ1jkrna2Snwe1EpcQtcgvh1bljw8jka9DPfLnK+2V3DlIvcR01Uv8j2ILJeYB+eN/uTrk14Uy6srPs6EDjkzlSp3b7bEsnR2cOT+djUqJdbFaYtup5Jdvx2Hd6WzLcP48e7BrdioXSh/RiRL75ihxrdHemz8+VMlfQ3W1DKWzkT+zKbVfaunJTdue8/PTVVmdP7fbo0y5v78tne3Y8dB0dve5+eXADbeno507H5TOVg48LJU7or3Edk5+e2h7ID9vpx2Yn7c7PJqfX12z9s1nH8zXd+ZOx6Szeiy3L99t5/zxQb1t6ez8efllxor8PmHuDvl9wszp+X2+hmals11tueP/rJe+NF3mi3bI7z9uO/Pf09nH/t+fpbNtB+avt4j8enNI2/R0dmjm3unsU5KzLPbMbzeV3jXp7D7Kbw9m2yrfYWZ/IKkm6XpJt0i6QdLbpcZViqQTJa0tPh/9Obn4bDdJ/yPpLkl3S/qU9PgeVtJRki4pPr9W0o8lHVx8dpakh8aUO6sYX0j6s6ZyfiTpxOL1NyXdIelmSV+WtHFaDMzMzMzMzMzMzMzMNpaKts2frZAbzKxZf0QsiYgDgWcBpwL/2vT5r4rPR3/+T5KA84EfRsS+wH5AN/BBAEnzge8C/xIR+0bEYcCHgH2ayv3kmHLXFMOXAu+aoK7fBA4ADgY6gb/50yc/R5LvzDQzMzMzMzMzMzMz24a4wczGFRHLgDOBNxeNYhN5JjAQEV8pvlcD/h74a0ldwJuBr0bEr5vKvjwifpioxg3AWknPGqd+P4kCcBWw20SFSHp6091r10maXgz/J0k3FXfTfbgYtkTSbyXdKOkHknYohl8i6d8lXQq8TdLhki6V9DtJF0rK9WFoZmZmZmZmZmZmZmZbHDeY2YQi4h4a68jow02OH9N14j7AgcDvxnxvHfAAsLD4/NopRvX3TWX+csxn/wa8e6IvFl0xvgr42STlvwN4U0QsAY4H+iU9F3ghcHREHAJ8tMh+DfjniFgM3MSGd9jNioinA58G/hN4aUQcDnyZ4o46MzMzMzMzMzMzMzPb+rjBzKbSfHfZ2C4Z7y4+H++pmOMOl3SlpNskfappcHOXjM9ozkfEr4rvHT9B/T4HXDaam8AVwCckvZVGo9cIcDLwlYjoK8azStLM4vNLi+99FTihqZxzi9/7AwcBP5d0PY0GvSfc4SbpTEnXSLrmZ+d/cZLqmZmZmZmZmZmZmZnZ5uRnMdmEJO0N1IBlwFMmiN0CvGTM92YAuwN3F58fBvwPQEQcLemlwPNLVOWDNJ5lNjJmPP8KzANeP9mXI+LDkn5M45lsv5V0MhM39E2md3TUwC0RcewU4z0bOBvgR9eOlB2XmZmZmZmZmZmZmW3jJN/XtKXwkrBxSZoHfB74TPGcsIlcDHRJenXxvSrw/wHnFHdvfRY4Q9JTm77TVaYuEXERsANwSFP9/gZ4NvDKiKhPMS37RMRNEfER4BrgAOAiHn/OGpJmR8RaYHXT3WyvAi4dp8g7gHmSji2+2yrpwDLTZGZmZmZmZmZmZmZmWw7fYWbNOosuBltp3M31deATTZ8fX3w+6t8i4nuSXgR8TtJ7aDTC/gT4F4CIeFTSacBHJO1K4261FcD7m8r5e0l/2fT+hePU7YMUd6kVPg/cD/xGEsD5EfH+cb4H8HeSnkHjbrlbgZ9GxKCkJcA1koaa6vxXwOeLhrR7gNeMLSwihoq75D5ddOPYAvwHjbvpzMzMzMzMzMzMzMxsK+MGM/uDiKhO8tklwMwJPnsQ+LNJvvtb4OkTfHYWcNY4H90HXNKUu4Cm56lFRHrdjYi3TDD8w8CHxwy7HjhmnOyJ4+ROGJubyN0PTThrn+C4W29MZ+/YK9/T40kP3JTOti/sS+UG77orX+Yeu6ezy9a1p7Pce0c6Wt/1xHR25Oor0tm23fPTNnDHnancuj1q6TJHHrw1nV07bTidXX3j79LZrp3mpLP9y1ans/WR/HxQRVOHCrWhkalDQPcBR6fLXH/XvelsdzW/T6iO5JfZwKo16eyq2+5LZ+cuOjKdHbzttnS2NjCQylVPfVm6zJarf5nOjvSsT2dP+Hi+J+HL3vGjdPawtz6Szlbb8qdt02v5bad/6cOpXPfaVekyBx94MJ1t32dhOltbuTydpZ4/RtZLzK8yhteuS2c7F+yZC+5/WLrM1nUr0tmRGfulsxUmvbl/A/USHVqM5E/vkPLLNyJ/fBiOtlSuhfy+Ocjv80doTWer5Nfb4RLllllmZXQP5o//7Y/ekwvW8/Og9shD+fHv3p/O0ppbZwCiml8OtY5p6WzLQ3fn69Cfn7aOefNzwRJdCEVbRz6r/LZbefj+dLZvZvoyjtX9nelsrcS+ZsbM3qlDhXrn9FTu9vvz43/WDvn90q3JzRHgWfsclM721vMdz9z9SH47O2FhvvOX/kp3Olt5OHeuf39nfnvYb3Z+OQzvlj9OrxrIT9e6efnzsMdW5K/Toyu33gLcuTR//H9mSy574x256z2A567Nny9d/+jadPYV7flyr3skf844NJCftlfvujKdXTYrt51V1ueP57eVWBf/8v+dkc5eOpg/D+y9+jfpbJS4frh6+kvT2VcekV8X7liZ25c/e+jmdJkqca6yTPnzfHdcZ9sqN5iZmZmZmZmZmZmZmZltDiX+Cdw2LjeY2TZD0muAt40ZfEVEvGlz1MfMzMzMzMzMzMzMzLYObjCzbUZEfAX4yuauh5mZmZmZmZmZmZmZbV3c2aiZmZmZmZmZmZmZmZlt13yHmZmZmZmZmZmZmZmZ2eYg39e0pfCSMDMzMzMzMzMzMzMzs+2aG8zMzMzMzMzMzMzMzMxsu+YGMzMzMzMzMzMzMzMzM9uu+RlmWwBJNeAmQEANeHNE/HoTjPcSYGdgEGgD/g94d0SsmeJ79wFHRMSKP2HcZxRlvHmcz9ZHRHfT+78HPgTMj4i1xbATgf8B7i1iKyLi5D+2PhvbwGCks/Xh4XR2aKj+x1RnStHXmwuW6V+3Uk1Ha2Umq1YrEc5bfes96ez8vfdJZyvtbX9MdSY1vHptOjtzl9Z0tm1m99Shwuo7Hkhnex5dl87ufNje6WzfstXp7GDPQCo3c3B9uszo7EhnVclvO7E+X4dqR3s6O9yf39dURoby5Q7msyP9g7lgS366WipKZ+sl6tp+0MHp7GFvfSSdvfbTv0tnD/+7I9PZGMpPW3Z9fPinl6XL7F2RPI4AC3feOZ2t9/Xns8Mj6WyUOJZ0HLIknV15/o/S2eH1fbnx75cfv2r57by/nt+HTavk6grQovxy6Kt15utQzdehXuJ8pbfWlcrNqPakywzy+6Uy86BMHSrkT65E/ry1TLmVen59pD+5Dymxr6sPJI85QHUgv36VoRL7GrXlj33pc3eg1pvPtnTnzwWzVC8xD5TfdkbWrklnI/Ll9g7lr2FGavlyO2blj2frdliQyq26Ob899O+bKxNg+S35uvYvnp/PjuTX8bXr8uvN4C5z09neem6fDxDJ84rBan4fWkatJX+c7hnIX/Ot6ZqXzpZZxwdn5c/vlt6WX8cGjj0wletfmj/mtO62Wzp7708fTGdb/myPdPa+nzyUzvasXJPOxlP3S2f7BnPLt941I13mNb/JX6O/7FkL09n6vVNnRt3xoxvS2bZp+T+T3z7/jnR2/an5aVt1V+58Jar57WZk30PS2d2T64FtBCXOe2zj8h1mW4b+iFgSEYcA/49G49CmcnpELAYW02g4+59NOO6sVwJXAy8aM/xXxXxbsikbyyTlr5rMzMzMzMzMzMzMzGyL5wazLc8MYDWApG5JF0u6VtJNkl5QDP+ApLeNfkHSByW9tXj9j5KulnSjpPcVw6ZJ+rGkGyTdLOm0sSONiCHgn4A9JB1SfO8vJV0l6XpJX8g2FEn6oaTfSbpF0plNw18j6U5JlwJPaxq+l6TfFPX+wJiy9gG6gXfTaDgrTdLLium+QdJlxbCqpI8X8/VGSW8php8k6bpi+JcltRfD75P0XkmXAy+TdEpR52slnSfpyf/3SzMzMzMzMzMzMzMz2yTcJeOWoVPS9UAHjS4Sn1kMHwBeFBHrJM0FfivpAuC/gfOBT0mqAK8AjpJ0CrAvcBSN7h0vkHQCMA94OCKeByBp5niViIiapBuAAyQNAacBT4uIYUmfA04HvpaYnr+OiFWSOoGrJX2fRpeP7wMOB9YCvwSuK/KfAv4rIr4m6U1jynol8G3gV8D+knaMiGXFZ8cX8w3gvIj44AT1eS/w7Ih4SNKsYtiZwF7AoRExImm2pA7gHOCkiLhT0teANwL/UXxnICKOK5bF+cDJEdEr6Z+BtwPvT8wbMzMzMzMzMzMzMzPbwvgOsy3DaJeMBwDPAb6mRoftAv5d0o00ni+2K43neN0HrJR0KHAKcF1ErCxen0KjIepa4AAaDWg3ASdL+oik40efAzaB0Q5TT6LRuHV10Sh1EpB9mNBbi4a33wK7F3U4GrgkIpYXd7Od25R/Go1GMYCvjynrFcB3IqJOo5HqZU2fNXfJOFFjGcAVwDmSXgeM3iV3MvD5iBgBiIhVwP7AvRFxZ5H5KnBCUzmjdT4GWARcUcybvwL2HDtSSWdKukbSNVf+/OxJqmdmZmZmZmZmZmZmZpuT7zDbwkTEb4o7mOYBpxa/Dy/u8rqPxl1oAF8CzgB2Ar5cDBPwoYj4wthyJR1elPchSRdFxBPuhiq6XDwYuA3YEfhqRPy/MvWXdCKNxqhjI6JP0iVNdZ7s6bdP+EzSYhqNbT8vHvjcBtwDfLZMnSLiDZKOBp4HXC9pCY15NXacUz1dcfQJ2QJ+HhGTdhEZEWcDZwN85Hv1jfPkXzMzMzMzMzMzMzPbelV8X9OWwktiCyPpABp3Qa0EZgLLisayZ7DhXUw/oHE32pHAhcWwC4G/Hn2elqRdJe0oaRegLyK+AXwcOGyc8bYCHwIejIgbgYuBl0rasfh8tqQn3EU1jpnA6qKx7AAad2MBXAmcKGlOMa7mO8WuoHEnGTS6fRz1SuCsiFhQ/OwC7JqsR/O07RMRV0bEe4EVNO56uwh4g6SW0ekDbgcWSFpYfPVVwKXjFPlb4GmjOUldkvYrUyczMzMzMzMzMzMzM9ty+A6zLcPoM8ygcffSXxXPE/sm8L+SrgGup9GgA0BEDEn6JbAmImrFsIskPQX4TXFH1nrgL4GFwMck1YFhGs/lGvVNSYNAO41uH19QlHWrpHcDFxXPSRsG3gTcP8W0/IxGQ9SNwB00GpeIiEcknQX8BniERpeRo90jvg34lqS3Ad9vKusVwHPHlP+DYviVU9Sj2cck7Utj3l4M3ADcDOwH3ChpGPhiRHxG0muA84qGtKuBz48tLCKWSzoD+Lak9mLwu4E7x2bNzMzMzMzMzMzMzGzL5wazLUBEVCcYvgI4drzPikasY9jwTi0i4lPAp8bE7+bxu9CasydOUa9z2fBZY6PDF0zynUGe2Mg1+tlXgK+MM/xeNpzODxfD9xon+/amt5dMVI8x33nxOINHgLcXP83Zi4FDxyljwZj3v6Bxd5+ZmZmZmZmZmZmZmW3l3GC2FZK0CPgR8IOIuGtz18emNmtGvvfTkf7BfLkz85vwyD396Wz7zB1Sudo996XLjMH8dFVLdBZbHx5OZzXpY/Q2NOeQEr1sDg+loyPre6cOAe2752dC5777pLMrV5aoa29+nZm5z67pbNv0rnR2/cMr0tmu+bn1FqBjh+5Urrd7frrMO797dTp74MvTUaYdsjid7b8tf6Nr27T2qUOFektbOtsyIzdvAaqduTpUe1emyxxals8OrulJZztWLU9nq235ffPhf5f/34vf/Ud+HTvpG/n1pvbQo6nczq8+LV/m7beks5q7Yzpb6e/LZ9NJqPfl93e9V12Vzu6waO90tm2vXHa4XkuXWeb4NIf8Ol4f/3+9xtVHfp8wu5LffmvKb2eD9Y6pQ4V5eiyVi8ivYTVNe9LHD1CjNZ0dJL/Pr1BPZ2PKRwA/bqBtejrb3ZmcZ9X8uhglzhlpy68zZdTbO9NZRf68VV35daxSK7EPyS6HEmVGmXlQYn/XMnNWvlzl5+3MzpF0tlbPbw/9kT8f3u3+X6RyO+54SLrMrtvzHbbstuvT0tnuR+9IZ6ftviCdnTdnRjrb+ejv09nuhfk6xOBAKlfNL1oqyu9v23ryx+mZs0oc/wcfTmend+6czravfiidXbwoP9M67rwmldtxxxPSZa696tp09pBj/zad7b3uP9LZQ596Zjp7x02P5Otw0dnp7Kw/e2kqV7/uunSZxxydv/BtX3ZfOrv7Tvn97R7HPuF/8SfUsUOJc5XH8vul6bf8OJ3deaejUrlKf/4cu371Zensja3PTGf/4riNc7603ZKfnLWlcIPZVigibgXyf4HZCCTNodG94VgnRUT+rx1PIknvYswdd8B5EfHBzVEfMzMzMzMzMzMzMzPbOrjBzP4oRaPYks1dj2ZFw5gbx8zMzMzMzMzMzMzMrBTf62dmZmZmZmZmZmZmZmbbNd9hZmZmZmZmZmZmZmZmtjlU8s/ms43Ld5iZmZmZmZmZmZmZmZnZds0NZmZmZmZmZmZmZmZmZrZdc4OZmZmZmZmZmZmZmZmZbdf8DDMzMzMzMzMzMzMzM7PNQb6vaUvhBjP7k0haHxHdY4adBayPiI9LOgb4FNBe/JwL3A+8rYgvAu4AasDPIuKdTeU84bsRcZak04F/LmLrgTdGxA0baRKfFCO1fHbG4kXpbKXEvnTaAfuls9E5LZXr3HP3dJnaebd0tlqJdLZlx53S2SD/AE21tObLnZOvQ9ei3ELbb/f8PKgN753O7rm+PZ2dXl2YzlKvp6PVzo50tm3BghJ1yM+zes/aXHDZXekyj3r/GensyK75ZTbQltseAWZUq+ls92OPpLPrpu2Yzs7aP78PI3LrTc+MndNFTj9kcTrbsWZ1Olvb+8B8HWr5nX4MDaWzJ30jP20X/+V/p7N7PT+3f95r4V7pMmN4OJ2tP/RAvtwS87a2vi+drXZ3pbPT9s/vGwcffCidpSV3Wl6vtqWLrM2Ync9W8se9lnp+va0pv18aUb4OrSXqMEj+uDNUyWU7ar3pMmuV/DzIjh+grT6Qzoby50AVldiHRb7c6esfTWdZvy6XKzFdrfPm5se/Nn98YMbMdLSyIn/sjdn5Y2/05dfHMuds9CfLLXHerL6e/PjLnI+XOD60V/PHqAdW5JfvyEg6yl4z8nXo2zl3HTd9WX57GNr9kHS2e3n+orN/7oJ0trWSnwfTOvLn+X0z8te9Ir89VHaYk8rN2SE/v0R+unrm5q8fuiuD6exApXvqUKHM9fTqXQ5OZ2esy2+/w3sflCtzRX45zHjuqensrv356+nO/Z6dzu7Slz+/mzYt/3eYafPz0zavO3deEYcdly5zJvll2zs3fx3Z0Zffducef1Q6GyP5/dLTl+yfzjJ4QTp62f/dncq97sDk3zSAloX5uu5V4rzZbFvlBjPb2L4KvDwibpBUBfaPiFuBrwBIug94RkSsyHy3GH4v8PSIWC3pucDZwNEbe0KK+rZERIlLITMzMzMzMzMzMzMz29L5Xj/b2HYEHgGIiFrRWPYnfTcifh0Ro//2+Vtgwn+NlzRN0o8l3SDpZkmnFcOPlPTrYvhVkqZL6pD0FUk3SbpO0jOK7BmSzpP0v8BFRZlflnR1kXtB2ZliZmZmZmZmZmZmZmZbDt9hZhvbJ4E7JF0C/Az4akRk+4/JfPe1wE8nKeM5wMMR8TwASTMltdHoGvK0iLha0gygn6KbyIg4WNIBNBrHRvtzOBZYHBGrJP078IuI+GtJs4CrJP1fRJToC8XMzMzMzMzMzMzMzLYUvsPMNqqIeD9wBHAR8Bc0Gr6elO8Wd4C9lsefZzaem4CTJX1E0vERsZZG146PRMTVxXjWFd0sHgd8vRh2O41nrY02mP08IlYVr08B3inpeuASoAPYY+yIJZ0p6RpJ11z+07Ozk21mZmZmZmZmZmZm2wtp2/zZCvkOM9voIuJu4L8kfRFYLmlORKwcm5P0FeBQGneEnTrZdyUtBr4EPHe8sprGfaekw4FTgQ9Jugj4IYz7dN3JtuLmu8cEvCQi7pgkT0ScTeP5anz2pyWe5mtmZmZmZmZmZmZmZpuU7zCzjUrS86Q/NCfvC9SANeNlI+I1EbFktLFsou9K2gM4H3hVRNw5xfh3Afoi4hvAx4HDgNuBXSQdWWSmS2oBLgNOL4btR+OusfEaxS4E3jJaN0mHTj0nzMzMzMzMzMzMzMxsS+U7zOxP1SVpadP7T4z5/FXAJyX1ASPA6RFRS5Y97nclvReYA3yuaLMaiYgjJijjYOBjkurAMPDGiBiSdBrwn5I6aTy/7GTgc8DnJd1UjO+MiBjUE28f/QDwH8CNRaPZfcDzk9NkZmZmZmZmZmZmZmZbGDeY2Z8kIia9SzEiXjHF5wvKfjci/gb4m2T9LqRxR9jY4VcDx4zzlTPGyZ4DnNP0vh94fWb8ZmZmZmZmZmZmZmYTqrgjwC2FG8zMNoHh4fwjzAYfeDCd7d87X+7Q0qVThwptyYcy9t2fr2tntZrOrt8pf5AYeeShdJbF+ejQilXpbOf85ens8EO55bBmdn5+VYfWprMrV2dv8IT+B/LzttKaP5z0L1udznank+XqUOvrT+Wq+y9Jlzl4x6SPNdxAZ0u+rpVZ89LZWnL9AugtsXw7Ds+vY7Hi0Xx2eDiVa91xr3SZI0sfSGcHH81vu9Pm7pjO9i99OJ1ViZPi2kP5ebvX83dLZ+/9UW692eslJY45q/PrTEdXZ77cEvvm+tBIOjuS3CcAjKztSWeH1qxLZ9vX57LV4Xxdqz35/W11Xm57BKikOwuAmPQRsWPKVT2dLaNMHVoiNx+q9fz6VaYT/Oz4YeMth40llJ8R9b7eqUMAtfw8GFy2Ip1t32WndLYykN8mqefXcdXz0zbSsz6dzZ4DAbR1dKRyKrFsaWvLZzWYjo6snPCx1k8Qkd8eujvzx76R/CIrpb03N209vfm6tlby5xTre/PrbbauALWufdPZVfnDKe2V/PldTNsjnR1ZlptnyyM/v2KP/LrY0Z8/pve157ezakv+eDYwVKK+g/lzwYHh/D6ktTe3HHpKrLcsvzsdXdOa39ArPfens+tb8vVduSK/b6xE/tqod1prKtf6yL35Mmfnl2175Pcf6wfzfy8ZWb4sna0P57eH5V355TC4Nn+d3tGVO/bWepPnSoAezq8HD1fy56KQW2fMtjZuMLNtgqQ5wMXjfHRSRImjrpmZmZmZmZmZmZmZbXfcYGbbhKJRbMnmroeZmZmZmZmZmZmZmW193GBmZmZmZmZmZmZmZma2OSQfj2Mbn58mZ2ZmZmZmZmZmZmZmZts1N5iZmZmZmZmZmZmZmZnZds0NZmZmZmZmZmZmZmZmZrZd8zPMzMzMzMzMzMzMzMzMNgf5vqYthRvMzLYw7QsWpLMR+XLbFuyVD0c9FevaZ+98mTvunI52deQnrGXX3fJ1KGFg1dp0tnWnBflsa1sqt9vswXSZ9dWd6exTSqwG01iYzvbddXc6W2mpprPVo0/IZ5fm61AdHkrlRiol6vrsF6WzPR0z09nuR+9IZ+uHHJ0vt/26dHagmj9d0LTp+ezgQCq3rju//5i336J0dtq8Fens+t0OTGe7165KZx/+6WXp7M6vPi2d3WthfmPf6yW5fe4vXvPVdJmzDupOZw99y/PT2Zbuaeks9dyxDKDS0ZHODh2fr2/fpz6WznL9zalYy6Kn5sssYZ12SGe7Kr3pbEuMpLP90ZWvg/J1qJBfF9bVZ6Vy3a3544NKnLD1UOL4UO1JZ8ssh3rkp62iWjobJY6pWerMnwO177JTOltpa89XosRxj4G+dLTelp+2Snvu/LIRzj9QXi2tuWA2B1AtsR4kz5sBqtPzx52K8vuE2dOG09mRWn7edtXz2+89845N5Xrvy2+P9x/0jHR21b35efDIIYeks33D+XV8YCC/H102f3G+3Mgf/1t22yOVm9mW/4NnRH6dWTErf+092JM/d1/ZPj+dVb663N1+cDq7/MF8uQ8c8MxUrveK/PbwyNNemc4uPT+/7d7/4vy5+4M/yp/XrF2dP5bc8szXpLPrVuTWm0cPPCVd5pU/ys+vo/8sv+32/j4dpW/po+ls1PP7mhXd69PZB1/5rnR278tnp3LVwfx10cqnvSydfX49vy7CrBJZs62Hmy5ts5K0k6TvSLpb0q2SfiJpv+Kzv5c0IGlmU/5ESWslXSfpDkmXSXp+0+dnSXpI0vWS7pJ0vqT8X0/NzMzMzMzMzMzMzGy74wYz22wkCfgBcElE7BMRi4B/AUb/xemVwNXA2Ns2fhURh0bE/sBbgc9IOqnp809GxJKI2Bc4F/iFpHnJOj35//pqZmZmZmZmZmZmZmZbNDeY2eb0DGA4Ij4/OiAiro+IX0naB+gG3k2j4WxcEXE98H7gzRN8fi5wEfAXE5Uh6T5J75V0OfAySa+TdLWkGyR9X1KXpKqke9QwS1Jd0gnF938lKd93nZmZmZmZmZmZmZmZbVH8DDPbnA4CfjfBZ68Evg38Cthf0o4RsWyC7LXAP04ynmuBA6aoy0BEHAcgaU5EfLF4/W/AayPiPyXdCSwC9irqfbykK4HdIqJED8pmZmZmZmZmZmZmZkDF9zVtKbwkbEv1CuA7EVEHzgcme0LlVI+ezTya9tym1wcVd43dBJwOHFgM/xVwQvHzIeA44Ega3UY+caTSmZKukXTNry88O1EFMzMzMzMzMzMzMzPbHNxgZpvTLcDhYwdKWgzsC/xc0n00Gs8m7JYROBS47U/4HKC36fU5wJsj4mDgfUBHMfxXwPHAUcBPgFnAicBl4xUYEWdHxBERccRTn33mFKM3MzMzMzMzMzMzM7PNxQ1mtjn9AmiX9LrRAZKOBD4FnBURC4qfXYBdJe05toCice09wGfHG4GklwCn0OjeMWs68IikVhp3mI26EngqUI+IAeB64PU0GtLMzMzMzMzMzMzMzGwr5WeY2WYTESHpRcB/SHonMADcR+OurTeOif+Axp1mV9J4dth1QBewDHhrRFzclP17SX8JTANuBp4ZEctLVO09xXjuB26i0YBGRAxKehD4bZH7FY07324qUbaZmZmZmZmZmZmZWYMyTxSyTcENZrZZRcTDwMsTubc3vZ05Se4s4KySdVgw5v1/Af81Qfb4ptffAr6VGce6nlq6Pv33/T6dXb3jUDo7uCZfbsusCWfxBvoffCRd5rSR4XS2b9f8QaL22GPpLAflo+sfXZPO7rDq0XR25L57UrlVs1vTZVZ616WzD6zL31jce0d+nelfviZf7rK16ezMe6fqTfVxQ8vy7eL1wdy207JPfqVpvePafHbnPdJZrVudzrasyG8Pa2/LL9+uA49MZ2vL8ttDva8/N/5Fa9JlxoP3prODjy5LZ7t2yi+zwQceTGd7V/ROHSrUbr8lnY3h/D53aHVum5x1UHe6zDU3r09nR9b1pLO9D+bXL5V4aHKlJZ+dcdfv0tkHl+a339ZpHVOHgM7+/D60ZU1+HR+enb8sqGnjXEIM1NvT2dZqfh0vY6ieO/7WS6xfUqSzgyNt6WxHS/5cIVKP9C0vIl+uIj8famvz5zZZQ2vz+5r2Heeks9Wo/zHVmVKlI7+tD67K72tGenPHXoBKW259VGt+XVSJ9Za2Esey5SvT2TLbw1Atv62P1Epcw1Ty82HHoaWpXM/aGfkye/PnS+vWTk9nZ/fm6gqwqm12OtuzPn89vUNPvg6D0xems/WVuWuNVZ35upZZF6cPrEpnR2q7p7PTlD9nGxian87Oasnvw+r13N8fAOatz62769bl168dl+fPsft7j05nd157ezo70P+Ep5VMaN2q/PFsj5H8enPT0FGp3JzV+evIanWXdHbWQP48f3gkf23Wt7zEujiS337XDuSXw04D+ev09T25a66hdfllO+eRm9PZi+uL0tkj909HzbYq7pLRzMzMzMzMzMzMzMzMtmu+w8y2G5J+AOw1ZvA/R8SFm6M+ZmZmZmZmZmZmZma2ZXCDmW03IuJFm7sOZmZmZmZmZmZmZmZ/IHcEuKXwkjAzMzMzMzMzMzMzM7PtmhvMzMzMzMzMzMzMzMzMbLvmBjMzMzMzMzMzMzMzMzPbrvkZZmZmZmZmZmZmZmZmZpuDtLlrYAXfYWZmZmZmZmZmZmZmZmbbNd9hZhuNpBpwE9AKjABfBf4jIupNmf8BdoyIY5uGnQX8E7AgIpYVw9ZHRHfxOoBPRMQ/FO/fAXRHxFmbYrr+GL29w+ls+847prOVyP/3QdtO89NZ7bhzKtfV1pYvc5c90tnuzvrUoUJ1z73ydSDS2V2feUQ6S3tHOtqyU27ezugcSZc53LJbOjtjfX6d6dorv8ym7b8wnd1h3bp0lp3y09Y2d6d8uT1rUrH+jhnpIqsl6jo4M789VmbMTWdbelamszMPT0dZNSM/bbMX5NeFar2WyvVV8/uays67p7Md3dPT2aHO/LrQvk9+HizcObdPANDc/PGh/tAD6WxHV2cqd+hbnp8uc2RdTzr7q3/8cTp75DuOTmc7d5qXzlZn5pcvHbn5BbD7cU9JZ7uOODKVG2xpT5dZmzEnP/5Kfzor8sfpxmlbsg7VfB3KKHP8727pzZVZYroU+ey0lr50toyK8susNQbT2VD+/y9rLfl9edusmalcpWtauszW3fOXvvWetemsZsxKZ2Pdmny2Lb+tt+9W4nwpSmy/HV3JCuTrSi137G+Umz/H7li4T77cEtYPVNPZ4ZH8efYw+e2ho391KrfXgvzxqWVkIJ09+KDc9giU+u/4ua0r0tn99947nR0pcZzsqufPVyo77ZrK7dyeX2dalL/mG2rJbw97zMhfE3QO5+dBS37SaIv8Orbvrvn5kD2mHnJQd7rMqOaPD8cdn7+OK+OYo2ens30D+Wz7wM/T2d1m547/w8qfNx93VPI4AoTyK9g+O+fXmZ1e+Jx0lpH83+5ObMv/Lap94M50dt+FuXW3/eH8tQ7V/Lxtq+TPW8F3RNm2yXeY2cbUHxFLIuJA4FnAqcC/jn4oaRZwGDBL0tgjzQrgHyYodxB4saT8X5GfRFKJo7iZmZmZmZmZmZmZmW3x3GBmm0Rxp9iZwJulP/zb2UuA/wW+A7xizFe+DJwmabx/nRkBzgb+PjNuSS+TdLOkGyRdVgyrSvq4pJsk3SjpLcXwkyRdVwz/sqT2Yvh9kt4r6XLgZZJOkfQbSddKOk9S/t+XzMzMzMzMzMzMzMxsi+IGM9tkIuIeGuvcaJ9SrwS+Xfy8ckx8PY1Gs7dNUNxngdMlZfqHeC/w7Ig4BPjzYtiZwF7AoRGxGPimpA7gHOC0iDiYRpelb2wqZyAijgP+D3g3cHJEHAZcA7w9UQ8zMzMzMzMzMzMzs8dVKtvmz1Zo66y1bc0EIGk+sBC4PCLuBEYkHTQm+2ngr6QndpAcEeuArwFvTYzzCuAcSa8DRrtTPBn4fESMFOWtAvYH7i3qA41nrp3QVM65xe9jgEXAFZKuB/4K2DNRDzMzMzMzMzMzMzMz2wK5wcw2GUl7AzVgGXAasANwr6T7gAWM6ZYxItYA3wL+doIi/wN4LTDp074j4g007gjbHbhe0hwaDXdjn2Q51dMqR58CL+DnxfPZlkTEooh47diwpDMlXSPpmusv++8pijYzMzMzMzMzMzMzs83FDWa2SUiaB3we+ExEBI0uGJ8TEQsiYgFwOE98jhnAJ4DX0+gecQPFXWHfpdFoNtm494mIKyPivcAKGg1nFwFvkNRSZGYDtwMLJC0svvoq4NJxivwt8LTRnKQuSfuNU7+zI+KIiDhiyQmTVtHMzMzMzMzMzMzMzDajJzRCmD2JOosuC1uBEeDrwCckLQD2oNHwBEBE3CtpnaSjmwuIiBWSfgD8/QTj+P+AN09Rj49J2pfGnWEXAzcANwP7ATdKGga+GBGfkfQa4LyiIe1qGo18G4iI5ZLOAL4tqb0Y/G7gzrFZMzMzMzMzMzMzM7OJhKbq+Mw2FTeY2UYTEdUJProP2HWc/GHFyyvHDH878Pam991Nrx8Duqaox4vHGTxSlPn2MdmLgUPHKWPBmPe/AI6cbLxmZmZmZmZmZmZmZrZ1cIOZ2SbQPb01nR1euiqdbdkx/98Hwyvz5ba15Orbe9e96TK7q/ndTd9u+d5i6w8/mM7GwhLza/XadLZ9p750duSxx1K5/t0nam9+oupwfzrbN5COllpnVM3Xd2jl6nS2q3PS9vANRNTT2fpAbkZUasPpMnno/nS0rZKfX4qxj1ucJLv84XR2aOnSdLZrv/y6wOoV6WgMDaVyrbvk1/FY8Wg6W1u3Lp1tmbsyX+7K5elsvS8/bZX+/L4marV0dmhFbvm2dE/6yNAN9D6YXw5HvuPoqUOFqz9+5dShwrH/emI6Wx8eSWdbS/znX99j+f1dR/J4Vpm9c7rMak9+/J1z16ezZfQrvx+fTv7YW0Yf3VOHCtPoSeWqJY4PQ9X2qUOFrsgvh5jy8buPGyRfh5Z6bt9clsocp3tz+7t6f34fSon9YmVafn9HPT9dSp5jA0Qlf+5cW59bbyF/7AVomZs/X8lXID+/qOSvCYZLnNe0HpLf53d35Nebej2/TbaQ34e09uf2jT29+XnbunZZOtuzPl9ux5pH0tmhrgXp7MoSh4fOSr4O/fNmprP1pblz/bU75s/dRyK/nbcP904dKvRU9khnd67lzy+l/LS1j+TLHRjJ72vaBnLr7pp1+bq29uX3Hyv78ttDG/l1cfX6fH3Xrs3vw1q68tcwPZ259bFjOPc3DYChyO8X2wfWpLNl1pn6svx1SX1gMJ1d3l1iOXTml8OatbnjTq3EdWTrivy6uCp/qmK2zXKDmW0zJL0LeNmYwedFxAc3R33MzMzMzMzMzMzMzGzr4AYz22YUDWNuHDMzMzMzMzMzMzOzrYPyd9bbxuUlYWZmZmZmZmZmZmZmZts1N5iZmZmZmZmZmZmZmZnZds0NZmZmZmZmZmZmZmZmZrZd8zPMzMzMzMzMzMzMzMzMNgc/w2yL4SVhZmZmZmZmZmZmZmZm2zU3mJmZmZmZmZmZmZmZmdl2zV0y/pEkvRV4I3BtRJw+zudLgF0i4ifF+7OA9RHx8U1YxwXAvcC/RcR7imFzgUeAL0TEm5vrJakD+F/g8oh4n6T1EdG9kevYPP73A5dFxP9tzHFuDrNmVNPZ1nlz8+XOas2XW82Xy/QZqVjX7ruki9SMmensQ4/W0tmBhx9NZwOls70PLUtn2xcvSWerM3Lzthb5ulKv57MlVDs709lab28+OziUzqqa33aizDzLllnmlvgosRxa8tvuSFt+ObQufzidrQ+PpLOD7dPT2Q7ll4Nac6chZZaDSszbWs/6dLZSya+L1CMfLbEcyvyXU219X74OQ8k6lNjXqJKvbedO89LZY//1xHT2N++7JJ2de8SsdHafZx2UzvatWJfOzurvT+Va+vJl0teTjvZX8qd87ZGrK5TbN/dXpqWzHWycOvQpNx+mKb8c6pHfHvqVnwedkT/2ljkHGq52pLNldKxams6uuvXuXLDEMWekfzCdnb5bfr/Uump1Oltpb0tnW2r58+Hbv/XLfLnt+ePZ7sc9JZWrduTXmWpX/rwme54AsP6BR9LZ/lq+vsMjJdaxej47SIk6dOauo6Z15fc1I907pLPtfSXmwbRZ6WwZnR35OtTa8/vR4Up7vty+3HFn2fIS1zpPyZ8zltnfSflyh1q60tmWEqfDZY47ZdTbcttOicvIUuGWlvx0RYnrh7b8JQwtrSXWhcivC9M7ctcEUWK/WGYtiEp+n1/icgtKXJeUOe6UKBYN5/cLtVpu4q759EXpMo9+5wvS2aX1/PkSJY5lZlsT32H2x/tb4NTxGssKS4BTN111JnQP8Pym9y8DbhkbktQGfB/4XUS8bxPVbQMR8d4tubFMkhuYzczMzMzMzMzMzMy2QW4w+yNI+jywN3CBpH+W9GtJ1xW/9y8an94PnCbpekmnFV9dJOkSSfcUd6ghaYGk2yV9SdLNkr4p6WRJV0i6S9JRRe4sSV+VdJGk+yS9WNJHJd0k6WeSJvp/lH7gNklHFO9PA747JtMCfAe4KyLe+UfMj/mSfiDphuLnqcXwtxfTdLOkv2vKv0vSHZL+D9i/afg5kl5avL5P0vskXVtM4wHF8HmSfl4M/4Kk+4u75sar1zRJPy7qdPPocpB0ZLGsbpB0laTpkjokfaUY13WSnlFkz5B0nqT/BS4qyvyypKuLXP7fNMzMzMzMzMzMzMzMmoS0Tf5sjdxg9keIiDcADwPPAP4LOCEiDgXeC/x7RAwVr8+NiCURcW7x1QOAZwNHAf/a1Mi1EPgUsLjI/AVwHPAO4F+aRr0P8DzgBcA3gF9GxME0GsWeN0mVvwO8QtJuQK2oe7N/AkYi4u/KzIcmnwYujYhDgMOAWyQdDrwGOBo4BnidpEOL4a8ADgVeDBw5SbkrIuIwGvP4HcWwfwV+UQz/AbDHJN9/DvBwRBwSEQcBPysaM88F3lbU92Qa8+9NAMX8fCXw1aKLSoBjgb+KiGcC7yrGfySN5f8xqUTfOWZmZmZmZmZmZmZmtsVxg9mfbiZwnqSbgU8CB06S/XFEDEbECmAZML8Yfm9E3BQRdRrdJV4cEQHcBCxo+v5PI2K4GF4FflYMH5sb62fAs2g0BJ07zueXA8dK2m+SMibzTBqNWkRELSLW0mjw+0FE9EbEeuB84Pji5wcR0RcR64ALJin3/OL373h8+o6j0QBIRPwMmOyhATcBJ0v6iKTji3rtDzwSEVcXZayLiJGi3K8Xw24H7gdG58fPI2JV8foU4J2SrgcuodFh77iNdpLOlHSNpGuu+NnZk1TTzMzMzMzMzMzMzMw2JzeY/ek+QONOr4OAP2PyJx42PzmxRqMrxLHD603v602ZP+SKhrXholHtDzlJRxddQF4v6c9Hv1Tc8fY74B9oPKdsrMuAvwN+KmmXSepfxmT3XGYfzzk6H5rnVfpezoi4EzicRsPZhyS9t/j+eOOfrNzmJ6oLeElx5+CSiNgjIm6bYPxnR8QREXHE055zZrbaZmZmZmZmZmZmZma2ibnB7E83E3ioeH1G0/AeYPqmrEhEXNnUkDP2zq3/D/jniFg5wXe/D3yMRreFs0qO+mLgjQCSqpJm0GiEe6GkrqLLwhcBvyqGv0hSp6TpNBoZy7gceHkxrlOAHSYKFo1/fRHxDeDjNLqLvB3YRdKRRWa6pJaiXqcXw/ajcdfYHeMUeyHwFqnRCaukQ0vW38zMzMzMzMzMzMysQZVt82crtHXWesvyURp3L11Bo5vEUb8EFhV3e522ear2uIi4JSK+OkXm8zS6QbygeH5Xl6SlTT9vn+CrbwOeIekmGneyHRgR1wLnAFcBVwJfiojriuHnAtfTuNvtVyUn5X3AKZKuBZ4LPEKjcXI8BwNXFd0nvgv4t+Juu9OA/5R0A/BzGncFfg6oFtNwLnBGRAyOU+YHgFbgxqIbzg+UrL+ZmZmZmZmZmZmZmW1hWqaO2HgiYkHxcgWPP+sK4D3F56uAIyf5/kFNbw9qGn5G0+v7Rj+LiLPGfL+76fUGn433/THDz6HRmDVeuWcBo8NSDaoR8RjwgnGGfwL4xDjDPwh8cJzhZzS9XtD0+hrgxOLtWuDZETEi6VjgGRM0bBERF9K4I2zs8KuBY8b5yhljBzTPq+J9P/D68cZnZmZmZmZmZmZmZmZbJzeY2dZmD+C7kirAEPC6zVyflJZq+tFrxPBwOlstcY9ojOTLLXqcfFLLpF5LR6d15ScsRvLlKv34PKi2ldg9Rj0fTc6zkVp+nSkzb4eH8/OgPjSUzkY9X26ZbL2/L52lUmK9qeXmWVSqU4cKtZ716WzLQO/UodE6JLdHgBgc9/8HxlUbzC/fllq+XKLEupBcDiqxjpfZL9XL7D9qI/lyk9MF+XkAUO/rT2er3V3p7Eiy3ErHZI9pHZNtyW+P1Zkz0tn6cH45zD1iVjq74po16ezux+T3S9XW/D5k4LFxe81+gu6F+fHX16xOZ2uRr2uU6Vojv0ugHiXKLXGYLKNUHZKiRGU3xvgBIkrMsBLRSuT3YWW6ZGmb3pnKDa7NH09X3ZvbxgBm7bt7OlvmvKYybVq+3BLnl/P2n5/O9q/Oz7O22RP2fr+hSn6lqXZvnKcWDPcObJRyW1vyy1clNocqJc5Bymxn2TLLnNfkV8VS52wba39XatrKdLqUnBElToWpkT/2Vmv5c/d6meN0iX1+tVLierpeYjmUmGeV4dx1SYlTbBjJn2OPjJTYJwyWKTcdZaC/xP5jKL9vHK7l1ptqf/66d6Q1v4JV6vnruDLbWZS47i3zN5ChaomdYwnV5N8P93xa/lylVuI6sr/Ecmh02GW27XGDmaVJehfwsjGDzyvuGNskIuIuYIPnhkmaQ+M5amOdNNEz28zMzMzMzMzMzMzMNrsS/yxtG5cbzCxtoq4UN7eiUWzJ5q6HmZmZmZmZmZmZmZltnTbOPfBmZmZmZmZmZmZmZmZmWwk3mJmZmZmZmZmZmZmZmdl2zV0ympmZmZmZmZmZmZmZbQ4V39e0pfCSMDMzMzMzMzMzMzMzs+2aG8zMzMzMzMzMzMzMzMxsu+YuGc02gZndkc6qWs2XO135SqzKZ+vds3JBbZw29zLT1T5vdjor8sthxsFPSWdLqddTsYGhEsu2tTUd7e3LjR9I1xWgbZed0tlqZ3s6O7xyVb4OO81PZysdHancQPvMdJkjDz+Wzk7fdbd0tjYrP1311avT2UqJfc1wpS2dpT03bwHUklt3W0YG8uOv1fLZErR+zUYpt+OQJels71VXpbPT9l+Yzo6s7Unlho5/frrMGXf9Lp2lozMdbVV+37jPsw5KZ3c/pi+dve4z16azx3/seels2wGLUrl6x7R0mZXO/Lxt02A6O0yJfUIJLRpJZ/siPx/KHP/bK7n9zVDk93VV8vulVobS2UHyy7fMPBgkP22UOF3pm717Ott9QG4f1tWb33bLnGO3775rOku9xHn+7Ln5ckuYvWT/dHZ49dp0tiV7vlJi30xHVz5botxZByxIZ9dU8ttZR0t++61X8/XtrefnQ9tj96dy83bIn7u3PHR3OrvLrvl1vHX1o/nsDvnrrdnT89PWumZZOtu2w97pbHXmjFRuelf+z2xljnutA+vS2Y6Z+XW8s29NOtvdnt8eulfck87uOCe/LlRuvjeVm7tLukgGf3tHOrvHU/Pb+fC1+XJ3Oyq/nXVPy19PD91wQzrbecorUrl47KF0mYuOyF/Pty7N7z9mz8uv49W98tdF1aH8decebfnlULs/v8+dv3fufGXeoflj/63fuiydvXH3W9NZ3nZ0Pmu2FfEdZhuRpLdKuk3SNyf4fImkU5venyXpHZuuhiDpAkmvanr/RUn/WLy+RNIdkq4vfl66keqwvvi9i6TvbYxxmJmZmZmZmZmZmZmZTcR3mG1cfws8NyIm+heYJcARwE82WY2e6K3ALyX9L7AIOJpGvUedHhHXbIqKRMTDwEZplHuySKpGxMa5fcHMzMzMzMzMzMzMtitR5o5926h8h9lGIunzwN7ABZL+WdKvJV1X/N5fUhvwfuC04u6t04qvLiru7LpH0luLshZIul3SlyTdLOmbkk6WdIWkuyQdVeTOkvRVSRdJuk/SiyV9VNJNkn4m6Ql9X0XEfcDZwEeBzwFvjojhP2J6Xy3pRkk3SPp6MWxPSRcXwy+WtEcxfC9Jv5F0taQPNJWxQNLNxeszJJ1f1PsuSR9tyr1W0p3FfPqipM9MUq+XFfPsBkmXFcOqkj5ezJcbJb2lGH5SsYxukvRlSe3F8PskvVfS5cDLJJ1S1P9aSedJ6i47v8zMzMzMzMzMzMzMbMvhBrONJCLeADwMPAP4L+CEiDgUeC/w7xExVLw+NyKWRMS5xVcPAJ4NHAX8a1Mj10LgU8DiIvMXwHHAO4B/aRr1PsDzgBcA3wB+GREHA/3F8PF8HHgOcEtEjO3Y9ptNXTLOGe/Lkg4E3gU8MyIOAd5WfPQZ4GsRsRj4JvDpYvingP+KiCOByTopXgKcBhxMo2Fxd0m7AO8BjgGeVcyLybwXeHZRrz8vhp0J7AUcOlo3SR3AOcBpxfxqAd7YVM5ARBwH/B/wbuDkiDgMuAZ4+xR1MDMzMzMzMzMzMzOzLZgbzDaNmcB5xd1TnwQOnCT744gYjIgVwDJgfjH83oi4KSLqwC3AxRERwE3Agqbv/7S4Q+wmoAr8rBg+NtdsMY1Hdh8gaew6cXrRoLckIlZO8P1nAt8r6kxEjD7V81jgW8Xrr9No4AN4GvDtpuETuTgi1kbEAHArsCeNhsRLI2JVMZ3nTfJ9gCuAcyS9jsb8ADgZ+HxEjDTVd38a8/jOIvNV4ISmckYbNI+h0XXlFZKuB/6qqNcTSDpT0jWSrvnFBWdPUU0zMzMzMzMzMzMzM9tc/AyzTeMDNO70epGkBcAlk2QHm17XeHwZNQ+vN72vs+FyHASIiLqk4aJR7Q85SUcDXyiGvRf4EY2uGF8FvIHGXVWfTU9Zg4CYMrVhJpMfb16U6tA1It5QTPPzgOslLWH8+k5Vbm9T7ucR8crEuM+m0d0l37o8MtNrZmZmZmZmZmZmZtuTJ9zDYpuLl8SmMRN4qHh9RtPwHmD6pqxIRFzZdMfYBcDrgbsi4hIaXQv+k6R5JYu9GHj5aJeNkmYXw38NvKJ4fTpwefH6ijHDy7gKeLqkHSS1AC+ZLCxpn2Ka3wusAHYHLgLeUHx/tL63AwskLSy++irg0nGK/C3wtNGcpC5J+5WcBjMzMzMzMzMzMzMz24K4wWzT+CjwIUlX8Hi3gAC/BBYVzwc7bVNXStKOwD/TeA4aEfEwjeeLfbRMORFxC/BB4FJJNwCfKD56K/AaSTfSaIAafbbZ24A3SbqaRmNimXE9BPw7cCWN54ndCqyd5Csfk3RT0R3mZcANwJeAB4Abi/r+RdHt42todJ15E4078j4/zviX02j0/HYxXb9l6ueomZmZmZmZmZmZmZnZFsxdMm5EEbGgeLkCaL4L6T3F56uAIyf5/kFNbw9qGn5G0+v7Rj+LiLPGfL+76fUGnxXDljHmuWYR8Ymm1ydOVLdxyvoqjed+NQ+7j8bzzcZm76XxfLNRH27Kj07LOcA5Td95flP+WxFxdnGH2A9o3DE2Ub1ePM7gERp30719TPZi4NBxylgw5v0vmGS5jef2u4fT2f4HHpo6NFpubSCdHVz2SDrb2t+fyq276/50mTNb87ubtZV8D5Yj6/vS2XqJ/xF49Be/TWd3fs4JU4cKAw8/msq17JufB5W1Ez1ecJxyW/K9mg4sXzV1qFB/eFk6u/6RfLnzn7o4nR16bHk6O7K+d+oQMG1xvsyWvXZPZ+mekY62LX8wna3PyN+03HPP0nS2e2B1Olt7NL+vqQ8NpXIjCw9Ll9mSLBOgPjySzsa0/DIbXrsunV15/o/S2R0W7Z3ODj6YP5YMrcnVt+9TH0uX+eDS/Dqz+3FPSWf7HsuX27civxyqrdWpQ4XjP/a8dPZX//jjdHbhi29J5XZ/02vTZdLSmo6uGZmVzna35PahABXV09nVw/ntbGbr+nS2jOVDc1K5HVrz61dE/ti7YmT21KHCrNaedFap3tAbapHfHlqVP8ftWJ8/pq688oZUTpX8uV19pJbO9tx0WzrbuVO+c47aQw+nsx175M8rekqck4/0D04dKlTb21K5SjIHoBL7JZUot/fB3Dk2QO9IRzq7biBf3xKrGHt25663AAZ2Xjh1CLjhuvz2eOqBuTIBrr4uv84857D89jAQ+eVw8135Y8kpC+ems0OVfB1iODd/e3ry55f1yO/Dhtvz5/mrBrqnDhXmd+WOewAP3JffHlYtWJTO3nbftHT2sD1z/6986y35a4KXLl6Szl7268n+X3tDLzwyfw1z+VX5v2v0rMnvP17xrCf8SW5Cj67LbQ/1Bfuny/zZtfn/kd/ngPxx757H8tvu4ssuS2drg/n96JXdk3Z4tYHTn31UOnvXjbnlu/b3+b8THPLOV6WzJ6zNr7dm2yo3mNnW6CxJJwMdNBrLfrh5q2NmZmZmZmZmZmZmVl74GWZbDDeYWVrxjLKLx/nopIjI3+LyJ4qId4wdJuldwMvGDD4vIj64aWplZmZmZmZmZmZmZmZbKzeYWVrRKLZkc9djPEXDmBvHzMzMzMzMzMzMzMysNN/rZ2ZmZmZmZmZmZmZmZts132FmZmZmZmZmZmZmZma2OUibuwZW8B1mZmZmZmZmZmZmZmZmtl1zg5mZmZmZmZmZmZmZmZlt19xgZmZmZmZmZmZmZmZmZpuUpOdIukPS7yW9c5zPT5d0Y/Hza0mHNH12n6SbJF0v6Zonoz5+htk2QFINuKlp0Hci4sOSvgR8IiJu3Qx1OhF4R0Q8f0ssb0zZZwBHRMSbJb0B6IuIrz2Z41iwe1s629W6Tzq75+yOdLZzxl7pLNOmp2Kz2vLTVdll93R2pxnpKB175MutUE9nZy/Kz6+YOz+d7RwcSOW6O/J1jZFp6ez8rvz/SXTvn18Xaz3r09m2Wbn1C6Bl513T2WpvT74OtVoq19+S38badtszna115VfySn9+uio75efXzEVD6Wx9JJ+t7rpbPpvcHnrbutNldu2e33anTc8vh6EZc9PZzgX5dWF4fV8627bX3uksLflTvPb163LB629Ol9k6Lb/tdB1xZDrb8fCD6eys/v50duCxlels2wGL0tmFL74lnf39+felcrv9Xf7Yi/L7/BX9+WNJdVpuHwrQrvz+Y81AZzrbUc2X217JZ1f05eZD+/Th/PhLzIPs+AE6pufLbdVIOttSIts9siadjWp+vzTnqMWp3NBjy9NlDvf0prPTD82NH4CW1nS0tTO/fKMtvx+ddXj+vLG2Zm0621LimJ7WWmIfVsnvwzoeejSfbclvOzt0DqaztcjXt0X5fcja6bnzu3lz8/O2Z2b+GmrWDvly+7vz10Xrh/P7/Dmz8/uPoa4d0tmeev66ZLc9cueYc/rz82s48tNVqeePvSPkn4OzppI/x91xVr4OA9X8/q61xF8m+2bsksrNmpXfNw/PyV9DdU3LlzuwQ66uAB0d+ZlQm96ezq6eszCdbVsdqdxw2+x0mbVarkyAgY6Z6WyZdaZ1eol1Mb9LYEZXVzq7as6+6ez8+bnj/5yuJekyh3bLj3/fEvtbsyeDpCrwWeBZwFLgakkXjGnPuBd4ekSslvRc4Gzg6KbPnxERK56sOnkr2Db0R8SSsQMj4m82Q11KkVSNiPxZ10YWEZ/f3HUwMzMzMzMzMzMzs+1DlPhnx23MUcDvI+IeAEnfAV4A/KHBLCJ+3ZT/LbAR/qvrcdvtktgeSLpE0hHF69dKurMY9kVJnymGz5P0fUlXFz9PK4afJenLRf4eSW+dYBxnSfq6pF9IukvS65o+7pb0PUm3S/qmJBXfuU/SeyVdDrxM0imSfiPpWknnSeoucs8pvns58OIpprVb0leKWzBvlPSSYvgri2E3S/pIU/41xfy4FHjamOl5R9P8+4ikq4rs8cXwLknfLcZzrqQrR+ezmZmZmZmZmZmZmZlNaVeguSuZpcWwibwW+GnT+wAukvQ7SWc+GRXyHWbbhk5J1ze9/1BEnDv6RtIuwHuAw4Ae4BfADcXHnwI+GRGXS9oDuBB4SvHZAcAzgOnAHZL+KyLG6ztiMXAMMA24TtKPi+GHAgcCDwNX0GiYurz4bCAijpM0FzgfODkieiX9M/B2SR8Fvgg8E/g98IfpmcB7gLURcXAxzTsU0/0R4HBgNY2N54XAlcD7iuFrgV8C101QbktEHCXpVOBfgZOBvwVWR8RiSQcB109RNzMzMzMzMzMzMzOz7UbRiNXckHV2RJzdHBnna+P25yrpGTQazI5rGvy0iHhY0o7AzyXdHhGX/Sl1doPZtmHcLhmbHAVcGhGrACSdB+xXfHYysKi4+QtghqTRXnt/HBGDwKCkZcB8Gq28Y/1PRPQD/ZJ+WYxvDXBVRCwtxnk9sIDHG8xGG8COARYBVxR1aAN+Q6Ox7t6IuKv4/jfYcOMa62TgFaNvij5NTwAuiYjlRRnfBE4oIs3Dz22aH2OdX/z+XVF/aGyUnyrGc7OkGyepl5mZmZmZmZmZmZnZdqVoHDt7kshSoPnhqrvRuPlmA5IWA18CnhsRf3gIeUQ8XPxeJukHNNol/qQGM3fJuH2Y7ImvFeDYiFhS/OwaET3FZ81POK4BLZLeJOn64mf0CaZjW31H3z/h+03vR5+6LeDnTeNfFBGvnaDcyWic/GTTnS17dBqa6596gq6kMyVdI+maS3802X7BzMzMzMzMzMzMzLZL0rb5M7WrgX0l7SWpjcYNMRdsOGu0B42bWl4VEXc2DZ82euOPpGnAKcDNf+qicIPZ9uEq4OlFN4UtwEuaPrsIePPoG0lLJisoIj7b1Lg12tr7AkkdkuYAJ9JY0bN+CzxN0sJi/F2S9gNuB/aStE+Re+UU5Yydjh1odL34dElzJVWLMi4thp8oaY6kVuBlJeoLjbvkXl6MZxFw8HihiDg7Io6IiCOe/vwnpQtVMzMzMzMzMzMzM7OtXkSM0Pib/oXAbcB3I+IWSW+Q9IYi9l5gDvC54iaea4rh84HLJd1Ao/3jxxHxsz+1Tu6Scdsw9hlmP4uId46+iYiHJP07jYaih4FbaTy7C+CtwGeLbgVbaNyy+AbKuQr4MbAH8IGi39CJujjcQEQsl3QG8G1J7cXgd0fEnUUfpz+WtIJGI9VBkxT1b8V03EzjbrD3RcT5kv4fjWeUCfhJRPwPgKSzaHT9+AhwLVAtMb2fA75azLPrgBt5fH6amZmZmZmZmZmZmdkUIuInwE/GDPt80+u/Af5mnO/dAxzyZNfHDWbbgIgYt7EnIk5sevutiDi7uMPsBzTuyCIiVgCnjfPds8a8n6yx6s6I2OAWqoi4BLik6f2bm14vGJP9BXDkOHX4GY1nmU0pItYDfzXO8G8B3xpn+FeAr4wz/Kym1yc2vV7B488wGwD+MiIGijvgLgbuz9TTzMzMzMzMzMzMzMy2PG4w236cJelkoINGY9kPN291tmpdwC+L7hwFvDEihib7wsL5vZN9vIHhWUeks4va+9LZgZ2OytehrSuVa9tlXYkyp6WzOzGQL3efydpyN1RRPZ1teerT09meWXvky527IJXbsTW/bNdMPzCd3XO4P50dnPnUdLYStXS2ZWTSzWUDA62d6WxU8jeKqp6r7/rOuekyW+bml9lg5w7p7NCc/DyY1rdy6lChOnvndHbZnNT/LgAwqys/bYrcNlmr5E9XhktMV6V7Zjo70DknnWX/w9LRjv2WpLPDyfUWoF5tS2eryf1Cy6L8PqGzP3/j9WBL+9ShQqXE8m3pyx+juhfmt996R/54tvubXjt1qLDb3+WW2SUn/HO6zINfmz8+LPzHP09n2+r54/SgcucUALt1r0pnWzWczlbIH//3nrEslWuv5OdBGQtnPprOdkR+vR1SRzpbIb+v6WuZns4yY7d0dPqi3OOGO3Z6JF1mR20knR3eMX9uV8Zwe35+lTmvaeuakc5WB/LXJX2zcvvc7PEcYKQlvy6WKbc7nQSVeFT29Lb8th65R12X1jaSO04ftk9+n1BmHhx3UH4ejJQ4/5jV0jN1qLBkz/zTRAYr+bVhWqXEfnRubh/2FOX3NR2VwalDhXXT5qezu7Amne1Ufh7sNjO/zFon/xPJBg7edU06G/XcdnbMAfnr3uF6fp15/vH5dXGkkt/fnfrU/LF3YCRf7rSB/LnVnrPWpHK1ofx2ftKh+f1HmWu+A3dek87Gc/JPYcn+nQDg2eSvYaYNrk5nF+2ZO8etDY77dJhxqcQ5UEuZ/rfsySU/OWtL4Qaz7UREvGMjlXvWxih3IpJeA7xtzOArIuJNm6oOEdED5Fu1zMzMzMzMzMzMzMxsi+YGM9uqTNSVopmZmZmZmZmZmZmZ2R/L9/qZmZmZmZmZmZmZmZnZds13mJmZmZmZmZmZmZmZmW0GoY3zLFQrz3eYmZmZmZmZmZmZmZmZ2XbNDWZmZmZmZmZmZmZmZma2XXODmZmZmZmZmZmZmZmZmW3X3GBmZmZmZmZmZmZmZmZm27WWzV2B7ZWk+cAngWOA1cAQ8NGI+MGTOI5W4APAS4BBoA/414j46ZM1ji2FpHOAH0XE9zZ3XcbTO9SWzrauXZbO9szMl9u2Pl+uumfnylz7WLrMyrRZ6ez6ltZ0tnX5g+lszD40na0ufzidbeuclc629q9J5fq688u2XevT2TX9JdaZ/hLrTG04nx3oT2er02aks1Sq+WxEKqbundJFtq1Yms5qTm78ANX27nwd1j6azlb68+tNx8zd09n29SvSWUU9latVN84+lLWr09FS2/m6EvOgxLbD8FA6WpuR248DVHvy8yGrZU1+OdRmzElnS9W1rycdra/Jl1vp7MzXocTxDOX+j+3g1x6YLvKm/74lnd3nH/N1rVZq6WwL+XV8fUxLZ8voUm86O0BHKjcS+fnVHvnj3gD59WtEJZaZRtLZMqr1fLnVen5daF27PJWrP5w/D6z15I97LdNmprMbS73Msa9nZb7gMse+9uT6mDyvAqi25Y9lJM8TAEbu+X06W9knX9+RyP+PcYQ2SrZ9OLfurh8ucf1QXZfOrq+VKDfy21lU56azPSWup9ur+Tr0tObPl7LnmCuq+X3zPjPz62JbbSCdHa7k/9RXjfx+fLBW4jqdwY1SbvtQ7vxufb3EOjOYP3dfU2Lf3KH8vnldtT2f7ctf97a15vf5A8lp6+jJn+evbc9PV3tbftsdrJRYZ0r83Ur1/DnuihJ1aOvK73N7RnLbb0uZY3+Z6cpH7cmWvB60jc9LYjOQJOCHwGURsXdEHA68AtjtSR7VB4CdgYMi4iDgz4DpT/I4nlSSSvzFe9NSg7cZMzMzMzMzMzMzM7NtjP/4v3k8ExiKiM+PDoiI+yPiPyVVJX1M0tWSbpT0egBJJ0q6RNL3JN0u6ZtFwxuSTpJ0naSbJH1ZUrukLuB1wFsiYrAYx2MR8d3iO/8l6RpJt0h632g9JN0n6X2Sri3KO0BSRdJdkuYVmYqk30uaK2lPSRcXdb1Y0h5F5hxJn5b0a0n3SHrpRDOjmLZfSvoWcFMx7IeSflfU78ym7HpJH5R0g6TfFnfqjS3vA8X4K5I+LOnWon4fLz6fL+kHRRk3SHpqMfztkm4ufv6uGLZA0m2SPgdcC+wu6R+bls/7xo7fzMzMzMzMzMzMzMy2Lm4w2zwOpNH4Mp7XAmsj4kjgSOB1kvYqPjsU+DtgEbA38DRJHcA5wGkRcTCNbjbfCCwEHoiIie77fVdEHAEsBp4uaXHTZysi4jDgv4B3REQd+AZwevH5ycANEbEC+AzwtYhYDHwT+HRTOTsDxwHPBz48+SzhqKJOi4r3f13ceXcE8FZJo/01TQN+GxGHAJfRaBT8A0kfBXYEXgPMAl4EHFjU79+K2KeBS4syDgNukXR48Z2jaXST+TpJo/337V9M46HF632L+i4BDpd0whTTZmZmZmZmZmZmZmZmWzA3mG0BJH22uNPpauAU4NWSrgeuBObQaKABuCoilhYNWNcDC2g04NwbEXcWma8CmQacl0u6FriORgPeoqbPzi9+/64YB8CXgVcXr/8a+Erx+ljgW8Xrr9NoIBv1w4ioR8StwBPuBBvjqoi4t+n9WyXdAPwW2J3H58EQ8KNx6gfwHmBWRLw+IgJYBwwAX5L0YhrPcIPGHX7/BRARtYhYW9T7BxHRGxHri3lwfJG/PyJ+W7w+pfi5jkaj5wFNdduApDOLu/iu+en3vzTF5JuZmZmZmZmZmZnZ9ibQNvmzNco/CdSeTLcALxl9ExFvkjQXuAZ4gEY3ihc2f0HSibDBU1NrNJbfRGve74E9JE2PiA2eilrcsfYO4MiIWC3pHNjgCeej4xkdBxHxoKTHJD2Txl1YpzO+5qfWNtd3qi3kD09jL6b1ZODYiOiTdElT/YaLxrAN6le4msYdX7MjYlVEjEg6CjiJxjPi3kyjsWw8k9Wv+UnxAj4UEV+YYnqIiLOBswF+et1w/mm+ZmZmZmZmZmZmZma2SfkOs83jF0CHpDc2Desqfl8IvFFSK4Ck/SRNm6Ss24EFkhYW719Fo7vBPuC/gU9LaivK2lnSXwIzaDQCrS2eAfbcZL2/RKNrxu9GRK0Y9msajVHQaES7PFnWZGYCq4vGsgNodJGY8TMaXT/+WNJ0Sd3AzIj4CY2uLJcUuYtpdFtJ8cy4GTS6d3yhpK5ifr8I+NU447gQ+OuibCTtKmnHP2YizczMzMzMzMzMzMxsy+AGs82guEPqhTSeHXavpKtodKX4zzQapW4FrpV0M/AFJrkTMCIGaDx76zxJNwF14PPFx+8GlgO3FmX9EFgeETfQ6FLwFhpdLV6RrPoFQDePd8cI8FbgNZJupNFY97ZkWZP5GdBSlPkBGt0ypkTEecAXi7pOB35UlHMp8PdF7G3AM4r59Tsazzi7lsaz4K6i0RXmlyLiunHKv4hGF5S/Kb7/vWI8ZmZmZmZmZmZmZma2lXKXjJtJRDzC43dmjfUvxU+zS4qf0e+/uen1xcCh44xjCPin4mfsZ2dMUK8FTa+vAU5sv9ujsAABAABJREFU+vgQ4IaIuL0pcx/jdHM4tvyI6B5vfMVnl7DhtA0ywV1vzeVExPdoNFhtML6I+DKNhkCAo8Yp4zHgBeMM/wTwiTHD7gMOGjPsU8CnJpoeMzMzMzMzMzMzM7OMkO9r2lLo8cdBmU1M0jtpdGN4ekQ8Gd0ubld+f/e96Q1thNZ0ua0aSmeHo61ENleHthLjL/Ogxwr1dLaVfB0G6ExnRyL//wQbYz7U/3/27jtetqq+///rPafexr106RdBQEC4CIgo3RK7YFQwREWTGI01+YmaaBA1KpavJmosaBS7KIohGkWDdOlw4dJUmkoRqZdbT5n5/P6YdWQ4nPJZyHBueT8fj/M4M3s+e+211+6zZq0V+QtlTV6bFQ2L5zRWpmN7NJrPQ0V+a9Jd3cpv36zBxqp0bF9zaPqgYnVjqp52H2pFa/b0QcWCxv35PFQcD83oScf2Vu0LuXRna8X0QYUif/4YUr4MesivV815fFVrcPqgYmPuSsc2G/k89LRGUnEPaMN0miMVx/nsiuNsVmt5OnZVY9Lf6jxMzT7er/yxfv/ognTs3aty54Ud592WTrNmX7xxl2ekY7c6NN8b9S0fPCsd+4R/e1Y6dvM9t0/H3vrKE9KxvPElqbBNdnpcOsnb3/SldOystx+ejt18z+3Ssb9/9SemDyp2uv+CdOyquZunY1f35a99y7QgFVdzfylm/rm3Jg9SRWxFujVlNsDqVFxfK39erNHbzN9j39ObPyZryqCH5vRBj8As5e+z+5q57TDayD/v9bbyZdtS/hpZk4eaa1TNM4EqvuOqycMW912birt//rbpNJc35qdja/bFmnNCQ/l0R5PfEwAMRn4fb0Q+D9nYkcZAOs2ac1jN+aNbao7JgdH8dljZn9sfa7bXrOFl6djlA/lnjZp9fGkrf5zV2FD3pmNnDy9Nx94zsGUqrqYM5o/cnY5tNvLPcVvt9KSZPyDWIfdf8YuZv1ntggV7HbbW7SduYWYpEXEC7fHBHjFJTwK+Pm7yUETs9+eka2ZmZmZmZmZmZmZm9udwhZk9ZiJiCbBopvNhZmZmZmZmZmZmZmbWyRVmZmZmZmZmZmZmZmZmM8FjmK0xvCXMzMzMzMzMzMzMzMxsveYKMzMzMzMzMzMzMzMzM1uvucLMzMzMzMzMzMzMzMzM1muuMDMzMzMzMzMzMzMzM7P1Wu9MZ8BsfTBKXzq2QSufbuTTrdGr0a6kmzUa+VPTrFieT7gxKx06u7EiHTsS/fk8JEmRjq3ZZ8gnS4NmOravOZRPt5HP73AM5NNVPl0lC6K/uTqd5nDPYD62ld9neiq2b6tikNhG5NNt0pNPt2Z/VC4ssoFAo2Inr8lrs+KWqSbdOY2V6dhW5LdDb2s4HduI3LFec15sKl9eqtlnKgzEqnRsVBw7I+SP37m9+TLrmZPbDv2t/Hmpp5E/j2916Gbp2NvO/GM6dqPZ+fw+bu8d07Ejy/LHTs15Ycun7pSKW/GHe/LLr7g+bbnPDunYVfc8kI7tUX5fWDnvcenY1X1z07E156W+xkgqLns9r1Vz3emWmnXroTv37tltlr2O1Kopg37l96+hivvLVpd+Y6yK+7Ch3tmpuJpnw1ZP/p6ipgxqnntrtCqet2r2m6p168s9Sw5VPHPWPPfWnJeqniWjogwq8jukfDk0Kq5R2fvsmvu1Zm9+vWrux2vucaNiX6x5Nhvtz5dDKLeP1Sy/ObBhOrZb54/ZjfwzQY2V5O+Benpz9zUAq1u5a9T8xtJ0mjXfVbSU37726Moeg9Z9bmFWSPplIuZtknJ3q39eXhZK+qtHMN9Jkl46xecHSrpG0mJJW0k6pUxfJOl542KfK+lSSddJul7Sx+vX5LEl6RhJn5npfJiZmZmZmZmZmZmZ2drFFWZFRDwtEfY24FGpMJOm/On1QqC6wizhaODjEbEoIm6LiLHKtUXAnyrMJO0OfAb464h4IrA7cFMX8jMpac39ScOanDczMzMzMzMzMzMzM6vnCrNC0vLy/xBJZ0k6pbSs+qba3gJsCZwp6cwS+2xJF0i6XNL3JM0t059X5j1P0qck/ahMP17SiZJ+BnyttCQ7t8x/uaSxSrsTgANLS7B/lNQj6WOSLpF0laS/L+lJ0mckXSvpx8CkfelI+lvg5cBxZZ0WSrpaUj/wfuDIsrwjgXcAH4yI6wEiYjQiPlvS2U7SGSUfZ0jatkw/qazrLyXdNNbSTdIWks4paV8t6cCptoGk90u6CNhf0nFlna8u5aYSd5akj0i6WNKvJ0pT0vPLttlE0stKGldKOqd83iPp45KWlHV5c5n+DElXlOlfljRQpt9S8nMe8LLJtr2ZmZmZmZmZmZmZma19XGE2sb1otybbFXg88PSI+BRwO3BoRBwqaRPgPcAzI+LJwKXAP0kaBL4APDciDgA2HZf23sCLI+KvgD8CzyrzHwl8qsS8Czi3tAT7JPA3wNKI2BfYF/g7SdsDRwA7A08C/g6YtJVcRHwJOA04NiKO7pg+DBwHnFyWdzLtFmWXTZLUZ4CvRcQewDc78gywBXAA8ALalX7Qbil3ekQsAvYEFk+WR2AOcHVE7BcR5wGfiYh9I2J3YFZJd0xvRDyF9nZ6b2ciko6gXYbPi4i7y/r9RUTsCbyohL0O2B7Ya2xdyrY7CTgyIp5Ee4y/N3Qkvbps0/9jgm0/xXqZmZmZmZmZmZmZmT1MqLFO/q2N1s5cd9/FEXFrRLRoV/AsnCDmqbQr1M6XtBh4NbAdsAtwU0TcXOK+PW6+0yL+NAp9H/BFSUuA75X0JvJs4FVlORcBGwNPAA4Cvh0RzYi4HfhF5Xo+EvsD3yqvv067gmzMDyOiFRHXApuXaZcAr5F0PPCkiFg2RdpN4Psd7w+VdFEpn8OA3To++0H5fxkP3T6HAu8Enh8R95Vp5wMnSfo7+NPopM8EPh8RowARcS/tysebI+LXJeartMt4zMnl/2Tb/iEkvU7tceAu/e53vjnFapuZmZmZmZmZmZmZ2Uyaahyt9dlQx+smE5eTgJ9HxCseMlHaa5q0V3S8/kfgTtotrxrA6knmEfDmiDh93LKeB8Q0y3skrqHdEu7KRGzn8jvLTQARcY6kg4DnA1+X9LGI+Nokaa2OiCZAae31WWCfiPh9qXAbnGBZ47fPTbRbBe5Eu+UXEfF6SfuVPCyWtKjkb3zZaZp1Hdt2E2778SLiROBEgOtvvLUb28nMzMzMzMzMzMzMzB4FbmFWZxkwr7y+EHi6pB0BJM2WtBNwPfB4SQtL3JFTpDcfuKO0ZHslD7Z+6lwOwOnAGyT1lWXtJGkOcA5wVBmPawvarav+3PUC+BjwL2V9kNSQNNbl4C+Bo8rro4HzpkpY0nbAHyPii8B/AU9O5mmscuzuMj7YS5Pz/RZ4Ce0x4nYredghIi6KiOOAu4FtgJ8Br5fUW2I2or3tFo5tU9rb5OwJljHZtjczMzMzMzMzMzMzs7WQW5jVORH4iaQ7yjhmxwDfljRQPn9PRPxa0j8AP5V0N3DxFOl9Fvi+pJcBZ/JgC6argFFJV9IeU+s/aHc7eLkkAXcBhwOn0u6qcAnwayau3Mk4E3hX6V7wwxFxsqS3lXWbTbsl1o9L7FuAL0s6tuTjNdOkfQhwrKQRYDnwqkyGIuJ+SV+kvW630O7aMSUifiXpaOB7kl4IfEzSE2i3DDuDdsu5q2m3Qruq5O2LEfEZSa8p8/WWZX5+gvTvmmjb094GZmZmZmZmZmZmZmY5mq7jM3usKMI9xT3aJM2NiOWlcus/gd9ExCdnOl82c6678bb0gaau9LIJMW2Pkx2xMbMn6V6NpmOHYmD6oKJPI+nYVsxsA9yGWunYmm3bQzMdOxz96dhu7bfdSjdrNPK/K+lr5PevXvL7eI2R6OtKujX7YzeOHSm/H9Scv2rONTX7QquiAX9NHmrKtvmnRuvTy5ZZTV5r1Gzfmu0w09cyqDt2GuRia65lveRjr7x3+3TsRrMn60X84ZYtynYuAHOuWJyO7e/J749PHL48Hbukd59UXF8jfz3defSqdOzVjel6d3/QYEUZ7LTqsnTsrfMmG1b54XqUL4eVzVnp2MHG0PRB1J1va9TcL9Xch9Xc13Rr3WZat+6xa64lNdezNeHZLJuHmjRrVO23Fdu35hpZoyYPNfvNSCt3n93fGE6nWXOdHq547u3WvXu3nlFrNCN3j9uv/Haoub+sue5l8wrdOx5qZPeFmrzWPJ/WXHtrtkO3rqc158aafSG7766OwemDiprnotmNFdMHFTvusP3MP3CtQ+5dct46WUmz0ZMOWOv2k3XzLnzm/V1prXUN7W4XvzCz2TEzMzMzMzMzMzMzM7PJuEvGLiityWasRZmkU4HxPxN+Z0ScPhP5GU/SRcD4n0e9MiKWzER+zMzMzMzMzMzMzMxs/eYKs3VQRBwx03mYSkTsN9N5MDMzMzMzMzMzMzObaSF3BLim8JYwMzMzMzMzMzMzMzOz9ZorzMzMzMzMzMzMzMzMzGy95gozMzMzMzMzMzMzMzMzW6+5wszMzMzMzMzMzMzMzMzWa70znQEzMzMzMzMzMzMzM7P1UaCZzoIVrjCbAZI2Bz4JPBW4DxgGPlpenwn8bUT8V4ndC7gcODYiPi7pZcDxwBOBp0TEpSXuWcAJQH9J79iI+EX57BXAvwAB3A78dUTcLemTwKElW7OBzSJiQZnno8DzabdC/Dnw1oiISdbnQODzwEiZ5z8i4qWSFgFbRsT/dsQ+F/gAMAcQ8KOIePsjK8m1R7+G0rFDrcF07EBjdTp2OAbSsU16UnG9Gk2nWaNBMx07u7EiHVtTBqORPz32N4bTsd2wupVfLzHhYTyhwUZ+v+1lJB3brLj0NGilY1dH/tjJmtVYlY7ti3x5rWRuPrY5Kx27oPf+dOxQRXmNRF86tqfi+M2eawbJn+tayjeer1mvPuX38ZrzR8323ahxTz4Pyq9bQ7njbFXMTqdZc16a3ZM/zuaxNB27qjEnHduK/H5Tc+27b2SDdOz9q3P7wtZz702nuTzyZfCEf3tWOvZxe++Yjl1yxeJ07Iq9FqVjN33eVunY89/7s3Tszh99USpu8313Saf5q+cel47d+B0vTMdufcBu6dgbX3x8Onbnu85Nx67cYIt07HBf/hxyH5um4mruE5qRu+YANJWPrclDzRchPcpfTyPy6bYqOpkZSD7D9Ef+Ol3zXVBfM39vdW/PZunYoVZ/OnagS/f5syL/DNMTuevOaCO/Xj2t/H1NVNxbNZW/B6p6Jqg4Hmr2sWbFPdtWD1ydintgXv76tLSxcTq25l605pmvr2IfH624dx6ouH/vbeXzoMidc2uOh8HI71+9zYpngka+vGrSbTby+21PK3/fOtKTu3+vec7YePVt6dgHZuWu/QCN5H4AcH9rQTq2xib8MR3b28zv48v6NsqlWfFMstHIH9KxjYrjAbaviDVbe7hLxseYJAE/BM6JiMdHxN7AUcDWJWQJcGTHLEcBV3a8vxp4CXDOuKTvBl4YEU8CXg18vSyvF/gP4NCI2AO4CngTQET8Y0QsiohFwKeBH5R5ngY8HdgD2B3YFzh4itU6Gvh4Seu2iHhpmb4IeF7Huu8OfIZ2hd0TS9o3TZHuGkeqeHo2MzMzMzMzMzMzM7O1givMHnuHAcMR8fmxCRHx24j4dHn7O2BQ0ualcu05wE86Yq+LiF+NTzQiroiI28vba0oaA7R/XyVgTklvA9qtzMZ7BfDtseSAQdqt1QaAPuDOiVZG0t8CLweOk/RNSQslXS2pH3g/cKSkxZKOBN4BfDAiri95Ho2Iz5Z0tpN0hqSryv9ty/STJH1K0i8l3STppWX6FpLOKWlfXVq5TZS/npLG1ZKWSPrHMn1HSf8n6UpJl0vaQW0f64g9ssQeIulMSd8ClpQ0PybpkpLfv59o2WZmZmZmZmZmZmZmtnZwl4yPvd1od7E4lVOAlwFXlNh8XxRtfwlcEdHuH0zSG2i3XFsB/AZ4Y2ewpO1ot6P9BUBEXCDpTOAO2pVtn4mI6yZaUER8SdIBtLtWPEXSwjJ9WNJxwD4R8aaynHcC/2+SPH8G+FpEfFXSa4FPAYeXz7YADgB2AU6jXT5/BZweER8srb4m6+NlEbBVROxe8rCgTP8mcEJEnCppkHbl8UtK/J7AJsAlksZa8j0F2D0ibpb0OmBpROxbKiXPl/SziLh5kjyYmZmZmZmZmZmZmT1MTRfI1l3eEjNM0n+WVk6XdEz+Lu0Ks85WX9n0dgM+Avx9ed8HvAHYC9iSdpeM/zxutqOAUyLaHdVK2pH2GGlbA1sBh0k6qHLVau0PfKu8/jrtCrIxP4yIVkRcC2xepl0CvEbS8cCTImLZJOneBDxe0qclPQd4QNI82pVopwJExOqIWFmW+e2IaEbEncDZtLujBLi4o0Ls2cCrJC0GLgI2Bp7w56y8mZmZmZmZmZmZmZnNHFeYPfauAZ489iYi3gg8Ax4c2Toi/gCMAM8CzsgmLGlr4FTgVRFxY5m8qKR5Y0QE7cq4p42b9SgeWjF3BHBhRCyPiOW0u4R8ajYfU7gG2DsZ2zlKbWcLOwFExDnAQcBtwNclvWrCRCLuo91i7CzaLeu+xOTDAE81PHDnqMwC3jw2/ltEbB8RDxtNXtLrJF0q6dLvfLuq3tPMzMzMzMzMzMzMzB5DrjB77P2C9vhib+iYNlF3gscB7xxr9TWd0tXgj4F/jojzOz66DdhV0liF3LOA6zrm2xnYELigY57fAQdL6i0t1A7unKfCMmBex/uPAf8iaaey7Iakfyqf/ZJ2xR3A0cB5UyVcupH8Y0R8EfgvOiohx8VtAjQi4vvAvwJPjogHgFslHV5iBiTNBs6hPeZaTymvg4CLJ0j2dOANpWyQtJOkOeODIuLEiNgnIvY56hWvmGp1zMzMzMzMzMzMzMxsBnkMs8dYRESpqPmkpHcAd9FuvfTOcXG/nGh+SUcAn6bdIu3HkhZHxF8AbwJ2BP5V0r+W8GdHxO2S3gecI2kE+C1wTEeSrwC+U1qfjTkFOIz2uGcB/DQi/ucRrO6ZwLtK14UfjoiTJb0N+HapoAralXwAbwG+LOlY2mXymmnSPgQ4tqzTcmDCFma0u5T8ivSnjmDHuqN8JfAFSe+n3ZrvZbRb5+0PXFny9o6I+IOkXcal+SVgIXC5JJX8Hj5Nfs3MzMzMzMzMzMzMHkpTdXxmjyVXmM2AiLiDB1tTjXfWBPHHd7w+lXbFzviYfwP+bZLlfR74/CSfHT/BtCZlDLSMiDim4/UtwO7l9b08OAbY2Oc/An40QRq30K6kmzTt8n5u+f9V4KuJvF3JBK3PIuI3Ey0POLb8dcaeRcd2iYgW8C/lz8zMzMzMzMzMzMzM1nKuMDN7DIxEfzpWiumDitHoeyTZmVavRruSbtYI+fKa3Vqejl3NrHy6jRXTBxWj5LeDyG3fbFxtbKMqtpWO7Y2RfLrKp7sqJuqxdmLdKLOB1sp0mqsac/OxzcF0bE15tejpTrqR78G5R6mehNuSm6xV0YN0TDkc5SOPHY38LVPNeXxOT34fayqfh77WcDo2a7by58W+nvw5oVsGWZUPrvgx38p4WC/Mk5rfl79GDfbktlmfulO2m++5fTp2ZFl+v+3vyd9TbPq8rdKxt/zvbenYDT+8Oh27xdN2T8U1Vw1NH1TUnG+3PmC3dGxzKH+c1+Rh2YbbpWNX9+aPhzlD96Vj+wZz+3kP+f2r5hxao+b+o0aDiutpl36QnL2/62l157ykyO+3/cofD8Ot/H5bcw9Uo5EbeQGA1Y1cfmvul9TIP2/V6Nb9EhXpRuQPiKpn7/7cdhjqzT+/DDe78zxf93yYf35oRj62pfz+2NfIp5vdZjXfwdScQ3sb+fNdzfcafY38Oazq+4dGfl/oI5eHoVb+WXb54Mbp2Jp0a545ZzcqngkqrGCDdOxAI38vurKV+95qg8YD6TSXD2yYjh0Yzd/nm62rXGFmaZJOBcZ/m/LOiDh9JvIznqSLgIFxk18ZEUtmIj9mZmZmZmZmZmZmZrZ2cIWZpUXEETOdh6lExH4znQczMzMzMzMzMzMzs6yoaClu3eUtYWZmZmZmZmZmZmZmZus1V5iZmZmZmZmZmZmZmZnZes0VZmZmZmZmZmZmZmZmZrZe8xhmZmZmZmZmZmZmZmZmMyCkmc6CFW5hZmZmZmZmZmZmZmZmZus1V5iZmZmZmZmZmZmZmZnZek0RMdN5WKNJ2hz4JPBU4D5gGPhoeX0m8LcR8V8ldi/gcuDYiPi4pJcBxwNPBJ4SEZeWuGcBJwD9Jb1jI+IX5bNXAP8CBHA78NcRcbekTwKHlmzNBjaLiAVlno8Cz6ddAfpz4K3R5Q1b1u39wB+AY4FXRcRbJoi7BdgnIu7uZn4eLd3K73U33taV7SHyybYiXz8u5dKNyDcXzqYJ0KCVjh2NfM+yDeXTrVm3bqjJa42afaambGvS7ZaafSyr6ripKIOavHbrOF8TZMuhpgyCivPSGlC2NftCt85L2TJbE85LNduhZl+o0a1jPauX0XRsT0XsA60N0rGNijLYZuV16djzm09Px244uDode/+ee6djt7r2vFRcTRnMazyQjl3amp+O7ak4JjcZvSMde1/vZunYmvNC1T1bxb1gN3Tr3LwmnPNrzPR1ukZNeXXrelajGT3p2Gx+u3U9rXqG6tJ9WI1u5SF7DutRM51mzTV9JPrSsWvCNpvpe/Ka7dCN4xG6d5zVqNkO2fPomnAt69Y+3q1rSU0esvdA3fq+pubY2XmHbWb+ZmUdcud1l838F2tdsPkT917r9pO165u1x5gkAT8EzomIx0fE3sBRwNYlZAlwZMcsRwFXdry/GngJcM64pO8GXhgRTwJeDXy9LK8X+A/g0IjYA7gKeBNARPxjRCyKiEXAp4EflHmeBjwd2APYHdgXOPjPXfeEvwH+ISIOjYhLJ6osMzMzMzMzMzMzMzMzWxu4wmxqhwHDEfH5sQkR8duI+HR5+ztgUNLmpXLtOcBPOmKvi4hfjU80Iq6IiNvL22tKGgOAyt+ckt4GtFuZjfcK4NtjyQGDtFurDQB9wJ2TrZCkfSX9UtKVki6WNE/SoKSvSFoi6QpJh5bYYyT9QNJPJf2mtGRD0nHAAcDnJX1M0iGSflQ+21jSz0o6XyjrM7bsvy7LXCzpC5J6yvTlkj5Y8nRhadVHKddTy/QrS+XgpOlMsK5vGMtzx/p8urz+oaTLJF0j6XUTzLtQ0tUd798u6fjyeodSJpdJOlfSLpOVt5mZmZmZmZmZmZnZZEKNdfJvbbR25vqxsxvtLhancgrwMuBpJXaochl/CVwREUMRMQK8gXbLtduBXYH/6gyWtB2wPfALgIi4gHbXkHeUv9MjYsK+byT1AyfT7rJxT+CZwCrgjSWtJ9GujPuqpMEy2yLareieBBwpaZuIeD9wKXB0RBw7bjHvBc6LiL2A04Bty7KfWNJ5emkl1wSOLvPMAS4seToH+Lsy/VPA2WX6k4FrpklnvFNot/Abc2RZf4DXlhaD+wBvkbTxJGlM5ETgzWX+twOfrZjXzMzMzMzMzMzMzMzWMK4wqyDpP0tLp0s6Jn+XdoVZZ6uvbHq7AR8B/r6876NdYbYXsCXtLhn/edxsRwGnRESzzLMj7THStga2Ag6TdNAki9wZuCMiLgGIiAciYpR2a7Gvl2nXA78FdirznBERSyNiNXAtsN00q3UQ8I2S1o9pj/UG8Axgb+ASSYvL+8eXz4aBH5XXlwELy+vDgM+VtJoRsXSadB4iIu4CbpL01FIhtjNwfvn4LZKuBC4EtgGeMM16ASBpLu3K0e+V5X8B2GKS2NdJulTSpd/9zjcyyZuZmZmZmZmZmZmZ2QzIjxC4frqGdgswACLijZI2od26amzaHySNAM8C3kq7MmVakrYGTgVeFRE3lsmLSpo3lpjvAu8aN+tRlBZhxRG0W2ctL/P8BHgqDx83DdrdI040gOBUg+91tphrkttnJlvGVyNifAUgwEhEjM0z3TKmSmciJwMvB64HTo2IkHQI7dZ1+0fESkln0e7WstMoD61QHvu8AdxfWrdNKSJOpN0ajetuvG2dHLjRzMzMzMzMzMzMzGxd4BZmU/sF7fHF3tAxbfYEcccB7xxr9TUdSQuAHwP/HBHnd3x0G7CrpE3L+2cB13XMtzOwIXBBxzy/Aw6W1FtaqB3cOc841wNbStq3pDdPUi/tyrWjy7SdaHej+LCx15I603puyS/AGcBLJW1WPtuodC85lTNot7hDUo+kDR5BOj8ADqfdAnCsO8b5wH2lsmwX2hWM490JbFbGZBsAXgDtVnnAzZJeVpYvSXtOsx5mZmZmZmZmZmZmZg8TaJ38Wxu5wmwKpdXT4bQrpG6WdDHwVeCd4+J+GRE/HD+/pCMk3QrsD/xY0unlozcBOwL/Kmlx+dssIm4H3gecI+kq2i3OPtSR5CuA73S0xoL2OF030h737Ergyoj4n0nWZ5j2OF6fLt0R/px2y6nPAj2SltCuVDomImrHYhvzPuAgSZcDz6ZdoUdEXAu8B/hZWbefM0lXhh3eChxa8nUZsFttOhFxH6UryYi4uEz+KdBb5v8A7W4Zx883ArwfuIh2d5HXd3x8NPA3pQyvAV48zXqYmZmZmZmZmZmZmdkaTA+tezGzbuhWl4yasPfLibUiXz8u5dKNyP9SIJsmQINWOnY08j3LNpRPt2bduqEmrzVq9pmasq1Jt1tq9rGsquOmogxq8tqt43xNkC2HmjKo+QXTmlC2NftCt85L2TJbE85LNduhW79m69axntXLaDq2pyL2gdYG6dhGRRlss3Kyjg8e7vzm09OxGw6uTsfev+fe6ditrj0vFVdTBvMaD6Rjl7bmp2N7Ko7JTUbvSMfe17tZOrbmvFB1z1ZxL9gN3To3rwnn/BozfZ2uUVNe3bqe1WhGTzo2m99uXU+rnqG6dB9Wo1t5yJ7DepTqeAiou6aPRF86dk3YZjN9T16zHbpxPEL3jrMaNdshex5dE65l3drHu3UtqclD9h6oW9/X1Bw7O++wzczfrKxD7rh+8cx/sdYFW+yyaK3bTzyGmdljoF/5Bnsj0Z+O7dNwPl3y6Q63crEDjfx6deMGAWCwsSodOxwDFbH5B5JZjfyXdlk1Nz81ZTta8TAwu6JsG1Q8kFRcevrJ72OrYqIec/88fRpJx85qLU/HDmlWOnZZa146dkHj/nweHjZ04+Rm+iGy5jivMVpxnA9UHOc15/EVzfx+u6nuTMcON/Lbtzdy+/kDrQX55bfyZTu3d0U6dg7L0rErNTcdW/OFRs2+cNfwxunYu1fOScU9foM/ptNcXXGc88aXpEO3fOpO6dglR34qHbvzR1+Ujt3iabunY29IVoIB3LbrAam4x79o23SaZ7/55+nYp/7ns9OxWx+yKB17w7OPTcfuePcF0wcVKzfYMh27qj9/PVtGruKwaxXjXfpRaU263apMaFV0MjOQvA/re8Sdk0ytt5V/1rmvsen0QUXNfU2v8hUaNWrubQZHc9fJkUb+Waevld9mLeXLq6n8ff6I8vmt+QFIVSUY+fuVLZdek4pbusE26TRXNPI/Vqn5TqFG1Y9VK8qr5tmoRiM3Egqjjfz9eP9o/nhsNSqei1r5Z+SadGuOycGR/HZYPrBRKq6nVfGMPJz/0dDywfx9c437mhtOH/QIbEb+x0gDI/nnnfsGp+uMq63muXuTodvSsc1G/jiH/PnObG3iCrN1lKRTge3HTX5nRJw+UfzaTtJFwPg77ldGxJKZyI+ZmZmZmZmZmZmZ2XRCa1ePQesyV5itoyLiiJnOw2MpIvab6TyYmZmZmZmZmZmZmdnayVWXZmZmZmZmZmZmZmZmtl5zhZmZmZmZmZmZmZmZmZmt19wlo5mZmZmZmZmZmZmZ2QwIaaazYIVbmJmZmZmZmZmZmZmZmdl6zRVmZmZmZmZmZmZmZmZmtl5zhZmZmZmZmZmZmZmZmZmt1zyGWQVJyyNibsf7Y4B9IuJNkg4C/h3YAzgqIk6ZJI2TgB+N/1zSQuA64Fcdkz8REV+T9FrgH4GgXcn57oj47zLf24G/BUaBJvD/IuJrkyz7QODzwAjwfOA/IuKlkhYBW0bE/3bEPhf4ADAHUMnz26cro5nUuT1mOi/jDcdAOraX0XTsaPSlY0WkY/sbw6m4noq8NitONyMV6zWvtSIdO9zIb4cNGg+kY0fJ57cbelWzz/SkY3sq0u1vrk7HNpXfF5qNfGzNsZM9HmY1l6XTHOkZzMfSn47tq9gOQb7P615G8ulW9KXdR+78AdBSfn/M6m3l16tm+a2KY6embDfoye9jEfnfOQ028+fGnlZuH5vbV1FejXxepfz1qaeZL9s5yp/HawxH/ljfsC+fh4F5uXUbaOTPtzX3CXN2elw6dsUf7knH9jWa6djN990lHdtcNZSObVTcAz3+Rdum4m467XfpNDd/T36/3XL/XdOxNWVQY/mCbdKxQ72z07H9o6vSsQN9ueNMtNJp1pxras753VKzbj3kj7Oae4WeyF0fFBXboeJ4rImdpZXp2FWR32+bXdoXsmULMNSTy2/N/WWrJ79eNc9xNftXk3weRivyUHM81OSh2Zs7L61uzEmnORoVX8l1aWibmuOsJr+Nxqx0bKtiO2SfNYbI369Fb75wh5Rfr/6e/HV6mPx3Fa2Ktg/N/vw2y36v0erJL390cJN0bM25pubYndezPB1bYxkbpmMbFdfJFa3cOX8j5e/HV/fNnT6oGKn43sxsXeUKs0fP74BjgD+nUunGiFjUOUHS1sC7gSdHxFJJc4FNy2evB54FPCUiHpA0Hzh8ivSPBj4eEV8p719a/i8C9gH+t6S7O/AZ4PkRcb2kXuB1f8Z6VZPUExH5O93H0JqcNzMzMzMzMzMzMzNbe9T86MS6y10yPkoi4paIuApSPwU8SNIvJd0k6aXTxG4GLAOWl+Usj4iby2f/AvxDRDxQPlsaEV+dKBFJfwu8HDhO0jclLZR0taR+4P3AkZIWSzoSeAfwwYi4vqQ7GhGfLelsJ+kMSVeV/9uW6SdJ+tT49ZK0haRzStpXl1ZuE5K0XNL7JV0E7C/pOEmXlPlOlNpNHCSdJekjki6W9OuJ0pT0fEkXSNpE0stKGldKOqd83iPp45KWlHV5c5n+DElXlOlfljRQpt9S8nMe8DJJzy7pXy7pe6Ui08zMzMzMzMzMzMzM1kKuMKszq1T8LJa0mHZF0yOxBXAA8ALghI7pO3SmXyqCrgTuBG6W9BVJLwSQNA+YFxE3ZhYYEV8CTgOOjYijO6YPA8cBJ0fEoog4GdgduGySpD4DfC0i9gC+CXxqmvX6K+D00nJuT2DxFNmcA1wdEftFxHnAZyJi34jYHZhV0h3TGxFPAd4GvLczEUlHAO8CnhcRd5f1+4uI2BN4UQl7HbA9sNfYukgaBE4CjoyIJ9FugfmGjqRXR8QBwP8B7wGeGRFPBi4F/mmK9TIzMzMzMzMzMzMzszWYK8zqrCqVSotKBdBxjzCdH0ZEKyKuBTbvmH5jZ/oRcW7p+u85tLtP/DXwSUnH0+7BOt/Z9KNnf+Bb5fXXaVeQjZlovS4BXlPy/KSImGqwlibw/Y73h0q6SNIS4DBgt47PflD+XwYs7JwHeCft7iTvK9POB06S9Hfwp46Onwl8PqLdeXxE3AvsDNwcEb8uMV8FDupI++Ty/6nArsD5peL01cB241dG0uskXSrp0u9+5xtTrLaZmZmZmZmZmZmZmc0kj2HWZZI+CDwfoGN8ss6RP6ftoDQiArgYuFjSz4GvRMTxklZIenxE3PQoZ/saYG/ardumzV7H64etV0ScI+kg2mXwdUkfi4ivTZLW6rGxwUprr88C+0TE70uFW+eorWPLavLQ/fgm4PHATrRbfhERr5e0X8nDYkmLmLjCcbptsaIj7ucR8YqpgiPiROBEgOtuvG0mKjfNzMzMzMzMzMzMbA0WcrumNYW3RJdFxLs7WqRVk7SlpCd3TFoE/La8/jDwn5I2KLEbSHrdI1jMMmBex/uPAf8iaaeSbkPSWJeDvwSOKq+PBs6bJv/bAX+MiC8C/wU8ear4DmOVY3eX8cGmG+ttzG+BlwBfk7RbycMOEXFRRBwH3A1sA/wMeL2k3hKzEXA9sFDSjiWtVwJnT7CMC4Gnj8VJmj1WVmZmZmZmZmZmZmZmtvZxC7NHiaR9gVOBDYEXSnpfROw2zWzj7VC6+BvzZeC/gY9L2hJYDdwFvL58/jlgLnCJpBFgBPh/jyD7ZwLvKsv+cEScLOltwLclzabdEuvHJfYtwJclHVvy8ppp0j4EOLbkbznwqkyGIuJ+SV8ElgC30O7aMSUifiXpaOB7Zcy3j0l6Au2WYWfQbjl3Ne1WaFeVvH0xIj4j6TVlvt6yzM9PkP5dko6hXT4DZfJ7aHeZaWZmZmZmZmZmZmZmaxm1e/szs26q6ZKxQSudbquikWhNujF9T6EAqGIYvWyatVpRUQZ69MsA6sohq2a9etRMx9asV00epO5cS7qx39ZoRs/0QUXNduihO9usW7FrwrGeVZPXbp1v14TtULPvdmP5NeeEbu1fNeewmnSrzrnx6B8PA43V6die1mg69o+jm08fVNRcTx8/fE069sb+3buSh4167k3Hnv37HacPAjafP5JOs/WU/Ho97poL0rE1Nm7cnY5dzgZdycNwqy8dO9gYmj6I7t0L15zDao7zmnSr7sNmZGjrB3Xrulejpgx6lT83diu/Nelm992ZfjaENeOesVt5yN5b1Vyf1oRngm6p2Q41+2423W6dF7t1nNWkW6MmD9l9vOZeeDTy7TRq0q2xJpzDau4VsuVQ87zXre/CnrjDVjN/slmH/P43166TlTTbPGHXtW4/cQszMzMzMzMzMzMzMzOzGbAm/NjB2lxhtg6SdCqw/bjJ74yI02ciP+NJuggYGDf5lRGxZCbyY2ZmZmZmZmZmZmZm6zdXmK2DIuKImc7DVCJiv5nOg5mZmZmZmZmZmZmZ2ZjudJJrZmZmZmZmZmZmZmZmtpZwCzMzMzMzMzMzMzMzM7MZEHK7pjWFt4SZmZmZmZmZmZmZmZmt11xhZmZmZmZmZmZmZmZmZus1V5iZmZmZmZmZmZmZmZnZes1jmK0FJC2PiLkd748B9omIN0k6CfhRRJwyPl7SzcBzIuJXHZ/9O3A7cDHw38DNHYt6e0T8n6THAf8O7AsMAbcAb4uIX0+Sv48BzwP+F7gRWBkRXyv5/FlE3F7i+oAPAH9Z0l0JvDcifvIIi2at0a+hdOxI9KdjB1idjh1moCIPfam4Po2k06zRQzMdW1O2QwzmY1v57TCrkd8O3bCqlV+vQOnYOY2V6dhGxTZr0ZOO7dFoOnZVa3Y6Nqtfw+nYwdaKdOzqxpx07LLm3OmDig177svnIWalY5s126xqX8j9bmdWxb4Ykd/HR8md6wB6yZ/vatJd2cxvh011Zzp2uJE/L/RGbt2WMT+d5tBo/hw6pze/fWfH8nTsKuWPs1bkf0PWR/68cPfoRvnYlbn87jj/D+k0V5Pfv2a9/fB07Jb77JCOvfpl/5GO3fgdL0zHbn3AbunYmw8/Lh371P98dipuy/13Taf5m2suSMf+Ybf907GPf9G26dgb3vujdOz+K/K35ivnb5mOHerNX6fvZ5N0bFbNtYzIh6oiWJGPbaiVjq259mWvvQCDWpWK62/l74Vrxufoa+bv8+/t2SwdO9TKPxf1NbrzvFPzHDc4mrvHHOnJr1dPK79eLeWPnWYjfw9Uc7/UQ/6ZoOaYrMnDVvddlYq7f8HCdJrLG/l7q5p77Ab580dN2daUV82zUSMq1i1y61ZzPPS28vd2NefxUP7c3Gjly6DZyH+V2z+aP9es6p+Xiqu55my8+p507AOz8ufxmuP8vuaG6dgam3FHOnZgNP+8c9/A41JxNfcJG6++LR3bqti/YKuKWLO1hyvM1m3fAY4C3gcgqQG8FHg6sD1wbkS8oHMGSQJOBb4aEUeVaYuAzYEJK8yAvwc2jYjxTzTHAFfTrqCDdmXZFsDuETEkaXPg4D9j/R5zknojIn9HaWZmZmZmZmZmZmY2iZofuFt3uUvGddu3aVeYjTkIuCUifjvFPIcCIxHx+bEJEbE4Is6dKFjSacAc4CJJR0o6XtLbJb0U2Af4pqTFkuYAfwe8eaxiLSLujIjvlnReIWmJpKslfaQj/eWSPijpSkkXlko2JL2sxF4p6ZzJVkbSbpIuLnm4StITyvRXlfdXSvp6mbadpDPK9DMkbVumnyTpE5LOBD4iaQdJP5V0maRzJe0yRXmamZmZmZmZmZmZmdkazhVma4dZpcJnsaTFwPszM0XEVUBL0p5l0lG0K9HGHNiZrqQdgN2By7IZi4gXAasiYlFEnNwx/RTgUuDoiFgE7AD8LiIeGJ+GpC2BjwCHAYuAfSUdXj6eA1wYEXsC59CudAM4DviLMv1FU2Tx9cB/lDzsA9wqaTfg3cBhZf63ltjPAF+LiD2AbwKf6khnJ+CZEfH/ASfSrvjbG3g78NkpC8nMzMzMzMzMzMzMzNZo7pJx7bCqVPgAD45hVt5O1HFv57RvA0dJugZ4Me2KpjETdcn4aOS31r7AWRFxV8nDN2m3hvshMAyMDbpwGfCs8vp84CRJ3wV+MEXaFwDvlrQ18IOI+I2kw4BTIuJugIi4t8TuD7ykvP468NGOdL4XEU1Jc4GnAd/rKKt859hmZmZmZmZmZmZmZrbGcQuztd89wJ9GsJS0EXB3x+ffBl4OPBO4KiL+OE161wB7P9qZBG4AtpU00SiiU9XSjUT8aVTVJqWSNyJeD7wH2AZYLGnjiWaOiG/RboG2Cji9VJaJ3FDenTFjI9Y2gPtLi7qxvydONLOk10m6VNKl3/n2tycKMTMzMzMzMzMzM7P1WKixTv6tjdbOXFuns4AjJfWX98cAZ459GBE30q5UO4GHdsc4mV8AA5LGuj5E0r6SDn4EeVsGzCv5WAn8F/CpsbxK2kLSXwMXAQdL2kRSD/AK4OypEpa0Q0RcFBHH0a4g3GaSuMcDN0XEp4DTgD2AM4CXj1WylUpGgF/y4JhvRwPnjU+vdCl5s6SXlXnV0eXl+NgTI2KfiNjnqFe8YqrVMTMzMzMzMzMzMzOzGeQKs7VcRPwIOBe4rIxv9nTgnePCvg3sApw6bvr4McxeWlpzHQE8S9KNpSvH44HbH0H2TgI+X9KeRbtF2F3AtZKupt3l4l0RcQfwz7Qr+q4ELo+I/54m7Y9JWlLSOafMN5EjgatL2exCe4yya4APAmdLuhL4RIl9C/AaSVcBr+TBsc3GOxr4mzLvWFeXZmZmZmZmZmZmZma2lvIYZmuBiJg77v1JtCujxt6/D3jfFPN/EvjkuGlnAfMnib+ddjeO1fmLiOM7Xn8f+P648HeUv/FpfAv41jRpnwKcUl6/ZHzsJHn7MPDhCaZ/FfjquGm3AIdNEHvMuPc3A8/JLN/MzMzMzMzMzMzMzNZ8rjAzewyMRP/0QUWDVj5d8ukqNWxbW59GHvU0Y8qh6h5quKK8NogV0wcVqzUrn27jgXRszXbIaii/H/TSTMeORk86tmb79rdWp2Objb507FAMpmNrjh0pt26Drfz+NdzI53UoBtKxNftCs+Ky3qP8ftOs2G9q0s3uYxH580fNflBjlPx+21NxTG7Qsywd26zIQ80x2YhcfudW5HWwN5/XGjXXklkV14caQ+SvJQv6Ksps3nAuLlam0xxVfjtssOd26dhV9+SvkYM9o+nYrQ/YLR3bHMqVF0BPxXl060MW5Za/aiidZo3Hv2jbdOxNp/0uHbvRv+Xzu2zD/L6wundOOnb2cH6/GRjI5beH/P5Vc42sOdd06364UXEtqblXqNHbyh1nPZHfDlXX9OT1CWBQq9Kxy2KiYbUny0P+HqhGzfZd1Td3+iBgNCruVSrOzVHRMdFol75iqkm3W8fkaH/ufLeqkdteACOt/DZrVYwDU1MGNemORsV5tKIcas7l2Xv9mmf0Rk/+Oa5G1Tm/p+KZvuKZoLcn990O5Mt2mPyzbMzK71816daY29OdZ4JlbJiOrRnHaWUr96yxQPfl0xxYkI6t2W/t0eWyX3O4wsxSJD0J+Pq4yUMRsd9M5Gc8SX8BfGTc5Jsj4oiZyI+ZmZmZmZmZmZmZma09XGFmKRGxBFg00/mYTEScDpw+0/kwMzMzMzMzMzMzM7O1T75NqJmZmZmZmZmZmZmZmdk6yC3MzMzMzMzMzMzMzMzMZkDIY5itKdzCzMzMzMzMzMzMzMzMzNZrrjAzMzMzMzMzMzMzMzOz9ZorzMzMzMzMzMzMzMzMzGy95gozMzMzMzMzMzMzMzMzW68pImY6D+scScsjYm7H+2OAfSLiTZJOAn4UEaeMj5d0M/CciPhVx2f/DtwOXAz8N3Bzx6LeHhH/J+lxwL8D+wJDwC3A2yLi191Zwz/l7dvAbsBXgA2BcyLi/8bFHFLy+YJu5uXR0q38Xn/jrekDLcgP8tiglY5tRk86NpuHmryK/LmmTyPp2OHoT8f2ajQdW1Ne3VBTBqPR25V0V8dgOrZm+65NurWP96jZlXRr9oUaM33sNJQ/17Ui/1ugmrJtVfzGqFvn8Zp0q2IjF1uzH1SVQcX27ca1DPJlACDl95uafSwbW3Me76nYZvePLqhIN38OW7h8STr2pjl7pmNr9pvNm7elY+/s2Sodm7VA96Vjb1i5XTp2Tt9QOvbePfZNxy687qx0bI2a4ze7j3XrvNhDfh+vUXP+qDkvdascZvr+rlvr1a/hrqTbLdk8dOvZsOacX5NuzXm8Zt1Goq8recjeZ9fsX926z6/ZZjX3uFX3zhX35DX5zW7fAeWvkUMxkI6t2b4131XUpFujJg/pa2/F9alGzfm25n645pxQo1t5yO4LNd/X1JillenYHXfYfuYvkuuQG268eZ38Ym1t3E+6882aPVLfAY4C3gcgqQG8FHg6sD1w7viKHEkCTgW+GhFHlWmLgM2BrlWYlUq6p0VE/qnezMzMzMzMzMzMzMxsDeQuGdcs36ZdYTbmIOCWiPjtFPMcCoxExOfHJkTE4og4d7IZJL1D0hJJV0o6oUxbJOlCSVdJOlXShmX6WZI+IuliSb+WdGBJ5mfAZpIWSzpQ0kmSXlrmeY6k6yWdB7ykY7lzJH1Z0iWSrpD04jL9GEk/kPRTSb+R9NGOeZ4j6fKS1zOmSmeSdb1I0m4d78+StLekp0j6ZZn/l5J2nmDe4yW9veP91ZIWltd/XcpksaQvSJrZ5khmZmZmZmZmZmZmZvaIucKsO2aVipTFkhYD78/MFBFXAS1JY/3RHEW7Em3MgZ3pStoB2B24LJsxSc8FDgf2i4g9gbHKqa8B74yIPYAlwHs7ZuuNiKcAb+uY/iLgxohY1Fk5J2kQ+CLwQuBA4HEd6bwb+EVE7Eu7ou9jkuaUzxYBRwJPAo6UtI2kTUtaf1ny+rJEOuN9B3h5ydsWwJYRcRlwPXBQROwFHAd8aMqC6yDpiSWvT4+IRUATODo7v5mZmZmZmZmZmZmZrVncJWN3rCoVKcCDY5iVtxP1R9o57dvAUZKuAV5MuzJnzERdMtbm7ZnAVyJiJUBE3CtpPrAgIs4uMV8Fvtcxzw/K/8uAhdOkvwtwc0T8puTvG8DrymfPBl7U0WprENi2vD4jIpaWea4FtuPBcdFuHsvrNOlcN0F+vgv8nHZF38s71ms+8FVJT6Bd/jWdGj8D2Bu4pJT/LOCP44MkvW5s3d/3byfw8qNcp2ZmZmZmZmZmZmZmDwq3a1pjuMLssXcP7YogACRtBNzd8fm3aXd3eDZwVUQ8rCJmnGtoj3OWJSautJvK2GipTXL7zGTpi3ZrsV89ZKK0X8cyOpczWV4nTGfCjETcJukeSXvQbhX29+WjDwBnRsQRpZvFsyaYfZSHtsIcG1FTtMeM++dpln0icCLA9Tfeuk4O3GhmZmZmZmZmZmZmti5w1eVj7yzaXQ72l/fHAGeOfRgRN9KuVDuBh3bHOJlfAAOS/m5sgqR9JR08SfzPgNdKml1iNyotu+7rGJ/slbQr7B6J64HtS3eRAK/o+Ox04M0qzbIk7TVNWhcAB0vafiyvjzCd7wDvAOZHxJIybT5wW3l9zCTz3QI8uSzjycD2ZfoZwEslbTaWL0nbTZMHMzMzMzMzMzMzMzNbQ7nC7DEWET8CzgUuK+ObPR1457iwb9Pu2vDUcdPHj2H20ogI4AjgWZJuLF05Hg/cPsnyfwqcBlxalj/WreGraY8FdhXt8cRS465NkP5q2t0Q/ljSecBvOz7+AO2uD6+SdHV5P1Vad5W0fiDpSuDkR5IOcArt8eC+2zHto8CHJZ0P9Ewy3/eBjUo5vQH4dcnXtcB7gJ+V8vo5sMU0eTAzMzMzMzMzMzMzszWU2vUtZtZNNV0yBvlx6Rq00rHNmKxe8JHnoSavqugJtE8j6djh6J8+qOjVaDq2pry6oaYMRiPfu25NuqtjcPqgomb7rk26tY/3qNmVdGv2hRozfew0lD/XtSL/W6Casm1V/MaoW+fxmnSrYiMXW7MfVJVBxfbtxrUM8mUAIOX3m5p9LBtbcx7vqdhm948uqEg3fw5buHzJ9EHFTXP2TMfW7DebN2+bPqi4s2erdGzWAt2Xjr1hZb7Tgjl9Q9MHFffusW86duF1Z6Vja9Qcv9l9rFvnxR7y+3iNmvNHzXmpW+Uw0/d33Vqvfg13Jd1uyeahW8+GNef8mnRrzuM16zYS+eHJa/KQvc+u2b+6dZ9fs81q7nGr7p0r7slr8pvdvgPKXyOHYiAdW7N9a76rqEm3Rk0e0tfeiutTjZrzbc39cM05oUa38pDdF2q+r6kxSyvTsTvusP3MXyTXIb++8Xfr5BdrO+2w7Vq3n3gMM7PHQM1D92iXDsuam9DszXjNl6fdUrNeNbr1xXBWc9KGj3/e8mtu1GrKoGYf79YXNd2oLKp5iO6reMhpVXyZ0K0HuJry6tYDb1ZVpUOXKjN6yR8P3frioebLokYXtkPNflujL/JfaPS28vv4SE/FQ2TFaXyIfLo1Xxxmz7mNLn2Rv9P9F6RjV857XDr21nm7pmN3vuvcdOyyDfMVS/f2bp6O3fHuXDksX7BNOs27e/MdEey/4ifp2Joy2KCiEuyWJx6Sjt3+BVunYxd89DPp2FHlvuBrRMWXwsofj3U/kuhOpy2h/Impl/yXdjWy1yhF/vrULTXX0xUxLx3brQqzmnvMAVan4qr2mYovemuu/zU/1GhVPO/UbN9uVYjOZnkqblT5563RLj2b1ai5x62q0GhUVBxW/JC/t/Hon+9msyIdW/X8UHGc1aQ7Qr4SbB5L07HDyXtcVZy/eqOiUkn5595uVa7VPR/m0635rmA1s1Jxc5Q7J0Hd/dI6+ltosyquMFtHSXoS8PVxk4ciYr+ZyE+3SfoL4CPjJt8cEUfMRH7MzMzMzMzMzMzMzGzt4QqzdVRELKE9Ftl6ISJOB06f6XyYmZmZmZmZmZmZmdnaxxVmZmZmZmZmZmZmZmZmM2BNGDfV2rrT4bqZmZmZmZmZmZmZmZnZWsIVZmZmZmZmZmZmZmZmZrZec4WZmZmZmZmZmZmZmZmZrddcYWZmZmZmZmZmZmZmZmbrNVeYmZmZmZmZmZmZmZmZzYBA6+RfhqTnSPqVpBskvWuCzyXpU+XzqyQ9OTvvI9H7aCRiM0fS8oiYO27a8cDyiPi4pJOAZwGPj4ghSZsAl0bEQkkN4N+Bw4AAVgMvj4ibJd0C7BMRd0vaHPgk8FTgPmAY+GhEnCrpEODtEfGCjuWfBPwIOBrYHpgLbArcXEL+ISJ+2YWyeFhe1hStLtVNZ088tUR0Jd2sbq1XjZoymOn81uRVmtltC90r226sW01eW9GTj604JzRopWNnury6pVvH2Ewfu9C97Rvx6K9bQ82uLD8087/fakR+3Wp2mz6NpGPnjt6filvZOy+dZk9rNB27au7m6djVfXOnDxrLQ8V+s3KDLfJ56J2Tjm0of5yt3GDLVNxQ7+x0mjVWzs8tH+rKoMb2L9g6HXvzj25Nx+75kYrjLKkn8vt4jabyj8ndupbU3CuEKq7/kb/+V50b08uvuO5VXB96W8P5dBsV5bUGPBM0k1/bdOPaD5X3lzX3Kl2K7dY2G230peJqnglqrG33w83In0ervn9Yex5huqZJfh/L7rcArcidc2uO85GK83jVda8iD73k78drjJIv2xrZc3nNtaxbeTV7NEjqAf6Tdv3FrcAlkk6LiGs7wp4LPKH87Qd8DtgvOW81V5itH5rAa2nvTJ2OBLYE9oiIlqStgRWdAZIE/BD4akT8VZm2HfCi6RYaEUeU+EOorMiS1BPRhSe0R4Gk3oguPZ2bmZmZmZmZmZmZma37ngLcEBE3AUj6DvBioLPS68XA1yIigAslLZC0BbAwMW+1mf9Jrz0W/h34R+lhP5XcArgjov0zv4i4NSLuGxdzGDAcEZ8fmxARv42ITz/amZR0i6TjJJ0HvEzS30m6RNKVkr4vaXaJO6k0w/ylpJskvXSCtPaVdIWkx0s6WNLi8neFpHkl5h2SlpT0TyjTFkm6sDTvPFXShmX6WZI+JOls4K2S9pZ0tqTLJJ1eDlIzMzMzMzMzMzMzM5veVsDvO97fWqZlYjLzVnMLs/XD74DzgFcC/9Mx/bvAeZIOBM4AvhERV4ybdzfg8mnSP1DS4o7329LukvGRWB0RBwBI2jgivlhe/xvwN8BYRd0WwAHALsBpwCljCUh6Wol7cUT8TtJ/AG+MiPMlzQVWS3oucDiwX0SslLRRmf1rwJsj4mxJ7wfeC7ytfLYgIg6W1AecXdK/S9KRwAdpt+IzMzMzMzMzMzMzM0tZE4aM6AZJrwNe1zHpxIg4sTNkgtnGd8A7WUxm3mpuYbb++BBwLB3bPCJuBXYG/hloAWdIesZUiUj6z9Ii65KOyedGxKKxP9oVWI/UyR2vd5d0rqQltMdD263jsx9GRKv0Sdo56MYTgROBF0bE78q084FPSHoL7UqvUeCZwFciYiVARNwraX75/Owy31eBgybI287A7sDPS0Xhe4CHDfYg6XWSLpV06cnf+WZlMZiZmZmZmZmZmZmZrZ0i4sSI2Kfj78RxIbcC23S83xq4PRmTmbeaW5itJyLihlK58/Jx04eAnwA/kXQn7VZXZ3SEXAP8ZUf8GyVtAlzapax2jqF2EnB4RFwp6RjgkI7Phjped9Ym3wEMAntRDpCIOEHSj4Hn0e7n9Jllntoa57G8CbgmIvafKricAE4E+NWNv/fQtGZmZmZmZmZmZmZmbZcAT5C0PXAbcBTwV+NiTgPeVMYo2w9YGhF3SLorMW81tzBbv3wQePvYG0lPlrRled0A9gB+O26eXwCDkt7QMW12tzNazAPuKF0gHp2c537g+cCHJB0CIGmHiFgSER+hXdG3C/Az4LUd46JtFBFLgftKF5XQ7sLybB7uV8CmkvYv8/ZJ2m2CODMzMzMzMzMzMzMzG6f0BPcm4HTgOuC7EXGNpNdLen0J+1/gJuAG4IvAP0w175+bJ7cwW/vNlnRrx/tPTBZYdrbLgSeXSZsBX5Q0UN5fDHxm3Dwh6XDgk5LeAdxFu6XVOx+l/E/lX4GLaFfiLaFdgTatiLhT0gtpt5p7LfDXkg4FmsC1wE8iYkjSIuBSScO0D7x/AV4NfL5UpN0EvGaC9IclvRT4VOnGsRf4d9qt8czMzMzMzMzMzMzMUiLWzTHMMiLif2l/N9857fMdrwN4Y3beP5fayzOzbrr81/ekD7QNe+5Lp3tvc6N07MaNu9OxjWim4lTRq2VLPenY20a2TMfuuvqS6YOK38/LNwTcbunidOx98xemYxWtVNwyLUinOaDV6dilzfnp2MdxWz4Pw8vTsY3WaDr2/jn5faEn8un2toZTcf0jK9NprhxYkI69u7VpOrZHueMRYIHy54+B0fy6Le3bJB07u7UsHRvKNXRfrXzD5h4q9oMYScfWnMPmDuW3Q6OVz8Pq/tTvRgCYt/wP6djsdohGvgxUcX/Z7O2vSDd3DgUYvPfW6YP+lHC+04WVG20zfdBYHpbflY6Nntzv2JZt8LBhUyfVU7F/DfVWHGcV59s/sFU6tua6U3N+vnNgu3TsPJbmlj+6Kp3mHY38PrMZd6Rj+0fz1/8/9G2bjl3APenY7D0jwJW7/eX0QcWhZ3wgFaff3ZBOc+SJe6djW42+dGxNGdSk21OxfRsj+dgaGk6mW3N9qMlrxblZrfx2uGLLw9Oxg725e0ao+6Jrdk/+HBITjmf/cKuag+k0Bxr59Voxmr8+zO97IB07EvnjoWbdFvTmzuMAw5G/B9lm6VWpuF/PfUo6zftW58t2u7l3pmNr1qtX+Wv68tE56diBnvw+1oz8sT6nkbv+3zeyIJ3mhn33p2OXjm6Qjp3Xm39GXt7Ml+1wM9/2YeP++9OxDXLn0VHyx27Ncd6v/D5TU15btcZ3pDW5mmeNG9kpHbt1b/655Kahham4fZednk5z2Yb5e+GbYsd07NOeOG/9reHpgmtvuH2drKTZdcct17r9xF0ympmZmZmZmZmZmZmZ2XrNXTLaY07SqcD24ya/MyLyP48wMzMzMzMzMzMzMzN7lLjCzB5zEXHETOfBzMzMzMzMzMzMzGymZbtgtu5zl4xmZmZmZmZmZmZmZma2XnOFmZmZmZmZmZmZmZmZma3XXGFmZmZmZmZmZmZmZmZm6zVXmJmZmZmZmZmZmZmZmdl6rXemM2BmZmZmZmZmZmZmZrY+CjTTWbDCFWbWNZI2Bz4JPBW4DxgGPlpe/zdwc0f42yPi/yQ1gSW0982bgVdGxP2SFgLXAb8C+oFzgH+IiFaX8n4M8DHgNmAQ+EJEfLJ8tjPwBWABMACcGxGvmyq96/+4UXrZL3ng++nY8+ZOudiHeNmqU9OxrVnzUnGNe/+QT3PDzdKxPxvZOR271x8uTMfqqbumY3suPScdu9Feo+nYvqV3peJ+vfkr0mnu07ogHXv5qoPSsbvdc3I6luHhdGhr2dJ07BbbbJ+OVUQ6lqHVqbBlCxelk9z85vx2mL/pwnRsq9GTjp11z+/SsbrrjnRsc9Fz0rGb3HJJOpZWMxW2dLu90klucMe1+eX/4dZ06PAu+6ZjB/5wUz4Pq1akQ+fOmpNPd/kD6dDWynwesppL88vvXzA/HdtasTIde++1N+bzMG9WOnbuLjumY++56Mp07MZP2SMVN2/X/Lkue80BuHH7v8in2xhJxw7GUDr2PjbN52Ewn4dGxe3iMnL740DfYDrNmjK4n03SsQMD+XR7Ine+BRhVfzq2xqFnfCAde+Yz/jUVt9Wh+fvLx+2Zvz4MLV2ejp2/w9bp2OZQ/n5pYMf8PdCyJdelY1uj+eNhg92ekIpTT/5eRbMrrmXKd4iz+je/Scf2Hf7CdOzKkfyx3oz8F12zenL3ogCbr8jdV5y+7IB0ms+Zm3/W+eny/PPDX2ywJB17z+xt0rG/Xpo/1neYdXU6dunsx6Vj+3//q1TcJYP56+mBO+afp+c0889Qy9kyHbsh+XuFG1bky2uPeflj8sbVC9OxC5vXpOIuW3lwOs0X6sx07CXNF+fTHcg/H/5kNP+8tXxV/tz4snnnpWOvmJ0rsz2GL0qneXEjf15a1HdVOvb3o09Kx+72h8XpWEby91bXDu6djt1zVv46/b/LnpiK2+3c/06nuWD//dKxF44uSsc+LZdVs7WOu2S0rpAk4IfAORHx+IjYGzgKGHuiPDciFnX8/V+Zvqq83x24F3hjR7I3RsQiYA9gV+Dwccs8RtLx0+TrlorVOLks7+nAuyWN3dF/CvhkyecTgU9XpGlmZmZmZmZmZmZmZmsYV5hZtxwGDEfE58cmRMRvI6KmcukCYKvxEyNiFPglkP+J958hIu4BbgC2KJO2AG7t+Dz/MzozMzMzMzMzMzMzM1vjuMLMumU34PIpPj9Q0uKOvx06P5TUAzwDOG38jJJml88ek4oqSdvS7pZxrH34J4FfSPqJpH+UtOCxyIeZmZmZmZmZmZmZrVsCrZN/ayOPYWaPCUn/CRxAexyzY2l3yfiCCUJnSVoMLAQuA37e8dkO5bMA/jsifiJpY+CM8vlGQL+kw8v7V0bEkrLsp5dpW5Y0AL4XER+cIttHSjoU2Bn4u4hYDRARX5F0OvAc4MXA30vaM6JigAozMzMzMzMzMzMzM1tjuIWZdcs1wJPH3kTEG2m3CptuNPdVZdyw7YB+JhjDLCL2iojjS7r3jI2DBhwHfL5jXLQlY8vuiLm94/OpKsugPYbZbsCBwP+T9KcRbiPi9oj4ckS8GBgFdh8/s6TXSbpU0qW/OO3EaRZlZmZmZmZmZmZmZmYzxRVm1i2/AAYlvaFj2uzszBGxFHgL8HZJfY925mpExAXA14G3Akh6zlieSiXaxsBtE8x3YkTsExH7HPai1z2WWTYzMzMzMzMzMzMzswruktG6IiKidI34SUnvAO4CVgDvLCEHdnSNCPBvEXHKuDSukHQlcBRwbvdzPaWPAJdL+hDwbOA/JK0unx0bEX+YuayZmZmZmZmZmZmZ2dooYu0c72td5Aoz65qIuIN2ZddE5k8yz9xx71/Y8fZh3R6Oiz0pkaeF08V0pHVSx/vbgbEuGf+p/JmZmZmZmZmZmZmZ2TrAFWZmj4FGTeen0UqHSlGRbj5WrWYucGTk0U8TaOZDaa1YkQ+u0BoeTseqYpvVbIduGG1W/GKlZkOM5veFqCjbxkg+tkpyf4yanouHV08fM5au8tshGj3pWHVpO4iK/bZi3bKxqjl/jY6mY1s157CaMqg431GxHejJ7wtV2yF5rGvWrHyaFRqz56RjW6tW5ROuKIOhpflryewVK/NZqLgBGL7zrlTc4OPuSKfZuv336djYPl9eNcdDq+I82iB/Pe0hf6w3yR87QfK8VJHXmjKoUVMGwUA6thH5c1hP5POg392Qjt3q0M1Scbed+cd0mhs9frqhlB80e7MN07HRyu8Lg5vn81CjZ9ZgOra1LH8Oy4qa62nN/WUzf42897pb0rFxeMV9WPKcUBvbDd16zGhWPOpUPfNVnJtHK3abmjy0lD8/x7IHUnHLKgpsqNWfjm3UlK0qrnsV90uNiut/VJRtq+LYUSt33ak4NSNVlG2r4jivOChbFS1LhvOn3KrjoZHMQk/FuXm0Yr3UV3FvVdMSZ2SoIja/biM1j2Z9+ful0WZuv2n0V4xeszL/rLOq5qRfcR43W5u4wszWa5JeQxmbrMP5EfHGmciPmZmZmZmZmZmZmZk99lxhZuu1iPgK8JWZzoeZmZmZmZmZmZmZrX9qWttad3WnfxAzMzMzMzMzMzMzMzOztYQrzMzMzMzMzMzMzMzMzGy95gozMzMzMzMzMzMzMzMzW6+5wszMzMzMzMzMzMzMzMzWa70znQEzMzMzMzMzMzMzM7P1UaCZzoIVbmFmZmZmZmZmZmZmZmZm6zW3MOsySbcA+0TE3cn444HlEfHxKWJOAg4GlgIt4I0RcYGklwHHA08EnhIRl3bM88/A3wBN4C0RcXqZvjdwEjAL+F/grRERVSs5QyQtAraMiP+dJu4Y2tvgTX/Gst4GnBgRKx/J/KuH878SaN15Rzp2RV8+3bjrD+lYbb5VKq55773pNHsGBtOxDORDh+/O56HG6PIV6dhZy+9Lx8adt6Xi7p3Xl06zf/SudOxd96dDGb3t1nRsNJvp2OF78uU1q5H/XYf6+9OxMTKSimsszMUBjNzwm3TsLOXXqzV/43zs7b9Lxw7dmj/XzNrj/nQsD+S3L8PDqbC+x+WPx/hjfr1W/z53PALM2vaP6djmHfl0W6uH0rHZ/Ragb9NN0rFDf0zdojCw5ePSaQ4vXZaO7dum4na04lwzuipftvfefE86Vj096djWaD6/I8ty+/lgczSdZnPZ8nSsmPnbv2bky7apmX2MkfLl1YhWOrZJRRlUPMrV/GK1pXweaow8ce907OP2vDYVt9HjN02nueS/rknHPu34w9KxraHctQxAAxX3KqtXp2Obq/Kxy27P3zvP3zN5P1qxj9Obv8el4nzbNzv/ANHbyJ9HV4zk021G/jjrJZ+HvuHc9WHF6vzyB5S79gMsW1GxXgP5605zTn77rh7O3zv3k78Xbc1dmI9duSoVtyLy1/7BRv5eZdbK+9OxIwP5su1Rfl8cbeW3w+yh+9OxK4Z2TMf2rcjtu/etzO+3jeW3pGPvGchf//sa+Wez+3rz+f3DH/PPBL2j+eeSZcnn6b7bb0ynee+Gf5GOHWzlr08rWhX343+4PR0bFffuf5yT3w6NTR5Ix65cldzHWvlr78gf8t8H/qGZf/aGDSpizdYebmG29jo2IhYB7wK+UKZdDbwEOKczUNKuwFHAbsBzgM9Kf3oS/hzwOuAJ5e85Xc/5o0BSL7AIeN6jlJ6kKb/Bfhsw+9FYlpmZmZmZmZmZmZmZrVlcYfYokbRQ0vWSvirpKkmnSBqrYHmzpMslLZG0i6SGpN9I2rTM25B0g6RNxqW5SNKFJb1TJW04waLPAXYEiIjrIuJXE8S8GPhORAxFxM3ADcBTJG0BbBARF5RWZV8DDpfUI+mmUom0QFJL0kElT+dK2lHSUyT9UtIV5f/OHZ8v6liH8yXtMUF5NSTdImlBx7QbJG0uaVNJ35d0Sfl7evn8eEknSvpZyev7gSMlLZZ0pKQ5kr5c5rlC0os7FrmNpJ9K+pWk93Zss+skfRa4vMR8TtKlkq6R9L4S9xZgS+BMSWeWac+WdEHZrt+TNHeCcjczMzMzMzMzMzMzm1SE1sm/tZErzB5dO9Putm8P4AHgH8r0uyPiybRbc709IlrAN4Cjy+fPBK6coNvGrwHvLOktAd47wTJfWD6bylbA7zve31qmbVVeP2R6RDSBXwO7AgcAlwEHShoAto6IG4DrgYMiYi/gOOBDJY0vAccASNoJGIiIq8ZnqJTBfwNHlNj9gFsi4k7gP4BPRsS+wF+WNMfsDbw4Iv6qLPfkiFgUEScD7wZ+UeY7FPiYpDllvqfQLu9FwMsk7VOm7wx8LSL2iojfAu+OiH2APYCDJe0REZ8CbgcOjYhDS8Xme4Bnlu16KfBPkxW+mZmZmZmZmZmZmZmt2Vxh9uj6fUScX15/g3ZlE8APyv/LgIXl9ZeBV5XXrwW+0pmQpPnAgog4u0z6KnBQR8jHJC2m3Z3i30yTr4mqc2OK6QDnluUdBHy4rMu+wCXl8/nA9yRdDXySdnePAN8DXiCpr6zXSVPk62TgyPL6qPIe2hWInynrdxqwgaR55bPTImKyzsOfDbyrzHcWMAhsWz77eUTcU+b9AQ9um99GxIUdabxc0uXAFWWddp1gOU8t088vy3o1sN34IEmvK63VLj3rf06cJMtmZmZmZmZmZmZmZjbTZna07HXP+JEZx96PjeTapJR5RPxe0p2SDgP248HWZlnHRsQpydhbgW063m9Nu8XUreX1+OnQrjB7Pe2uCI8DjgUO4cHx0T4AnBkRR0haSLuCiohYKenntLuBfDkw1pJrIhcAO5auKQ8H/q1MbwD7j68YkwQw1eiTAv5yfLeUpfXaZNtmRUfc9sDbgX0j4j5JJ9GudJtoOT+PiFdMkRci4kTgRICTznrY8s3MzMzMzMzMzMzMbA3hFmaPrm0l7V9evwI4b5r4L9Fuifbd0g3in0TEUuA+SQeWSa8EzuaROQ04StJAqRR6AnBxRNwBLJP0VLVro15Fu5tEgIuApwGtiFgNLAb+nnZFGrRbmN1WXh8zwXp9CrgkIu6dLFNl3LRTgU8A10XEPeWjnwFvGovrHBNtnGXAvI73p9MeL05lvr06PnuWpI0kzaJdOXc+D7cB7Qq0pZI2B547ybIuBJ4uaceynNml+0kzMzMzMzMzMzMzs7RA6+Tf2sgVZo+u64BXS7oK2Ij2mGVTOQ2Yy7juGDu8mnbXi1fRHnvr/VMlJukISbcC+wM/lnQ6QERcA3wXuBb4KfDGjgq6N9Cu4LoBuBH4SZlniPa4Z2PdFZ5Lu8JobLy0jwIflnQ+0NOZj4i4jPYYbpOtV6eTgb/mwe4YAd4C7CPpKknX0m7pNpEzgV0lLZZ0JO1Wb33AVaWryA90xJ4HfJ12xd/3I+LS8YlFxJW0u2K8hnaXmZ2VaicCP5F0ZkTcRbuS8Ntl21wI7JJYVzMzMzMzMzMzMzMzWwO5S8ZHVysixlfuLBx7USppDun4bE/gyoi4viPm+I7Xi2mPl/UQEXHMRAuPiFNpt9ia6LMPAh+cYPqlwO6TzHNgx+tvAd/qeH8B0Nmq6l/HXkjaknZl7M8mSneC5WvctLt5cGyzzunHj3t/L+1x1Tr9/QTzncQEY6lFxC2MW/cpyvbTwKc73v9igmWbmZmZmZmZmZmZmdlayBVmM0TSu2i37qodu2yNJulVtCvm/ikiWjOdnzVFT0VbzsbmW6RjZ080wtoktFk+3dacDVJxPVttPX3QWJrzN0nHzm/kh3yb9aQJ63v/bIPb5NdtdO6G6djex+UOiw1nj6TTHG5tnI7dvGf6mDG92i4f3GxOHzOW7ob58mLLbaaPeQQaq1dNH1Spb9cnpWOHNq7Yv/pnp2Nnbzecjp01d970QcUfZ+WP31kb5mMVuWN9uH9uOs3Bin1mdl/+Nmho/ubp2IFt8vtXz+qV6Vj6K076S+9Lhw5s+bhUXKN/IJ/mZvnzUmvZ0nRsY86cdOy8rTdNxy54Qn6/Gdhmq3TssiXXpWPn7bVHKm5ks23TafbOmZ+OrVHTtUYP+etDUxUXqQpS/r4ie15qRT6vNcuvGfW2W9uhVdEJSVP582ir0ZeOHVq6PBU3e7P8PcXTjj8sHfvL43+Rjt3h8Pz90tCFv07Hbv3UfI/vg5tulI7tX5C7zwdorcpdo9TI7zMayN/jovw+vvF+e6Zj/xj5/A4388d6s5XPb5N8uiP9uWvfvNn5E8jw7Px1esOKc9jw7Pwx2a/8fesGs/PnsNWzNkvH9pLfH3u22z4Vt11//lzXo/x6rajYZnOVvxetuZ41Kp7Th/oq7tla+X1huDd337rZQP7rqOaCHdKxj6u4To/05a8PmzTz+Z01mN/HRmfl71s3GBhKxa3e5onpNDdr5I+x1X35a9m80dF0bGPhjulYRvL74rYD+e3QHMiv24a9uWtU3wb5Z+S+nfKdYu00mv+ewGxd5QqzR8lErZWmiT8BOKFrGZohEfE14Gud0yS9BnjruNDzI+KNj1nGzMzMzMzMzMzMzMzWMBFr53hf6yJXmFnXRcRXyI1nZmZmZmZmZmZmZmZm9pir6CjOzMzMzMzMzMzMzMzMbN3jCjMzMzMzMzMzMzMzMzNbr7nCzMzMzMzMzMzMzMzMzNZrHsPMzMzMzMzMzMzMzMxsBgSa6SxY4RZmZmZmZmZmZmZmZmZmtl5zhZmZmZmZmZmZmZmZmZmt19aILhkl3QLsExF3J+OPB5ZHxMeniDkJOBhYCrSAN0bEBZI+ALy4TPsjcExE3F7m+Wfgb4Am8JaIOP2RrtO6qJTpjyLiFElfAj4REdfOcLb+pHY/eiwN9rfywf2DXUk3mvl0m3252EZFXlt9A+nYnoh0LD09+dgKmj0nHRs9fenYVrLMGo18GbQa/enYnop06cunS28+XY2OpGOjkb9MhfLN1zWQy2+zp6IMKoTyv1dpdasMarZvhejNH+sRuXNYTXlRUV4M5M9hNfti1bHTLRvMT4c2Vq/KBc6Zl06zJ7ltAbTBgnQsrXy6fffel46NVsW5sSJ21uM2zafbm7+WrE1quhZpULHfkN8OETPbvUnN8mvWqya2RlT8prJq+0YzHTt/h61zy684J7SGhtOxOxy+XTr2xh/+Nh371H89OB17+8W/Scfu8JKD0rHNFSvTsT1zc+f9qLi3Y3BWPrbimaBbvwTuadScl/LHQw/544Hk/d1Ab8WzYcU9Y1W6XepOqr+n4nm6W5JlVlG0jEb+/lIVx0OrIhM122ygp2K/rVBznGUPnd6a596K1eqveO6tMVCR7kiX8tDTyBWEKu7z+5Jp1uqt2GdavflnM1UcO30126HieXYwmd2eeXPzy685f6wBp1uzmbZGVJh10bGlcufZwBeAPYCPRcS/Akh6C3Ac8HpJuwJHAbsBWwL/J2mniIonu/VIRPxtt9KW1BsRo91K/8+xJufNzMzMzMzMzMzMzNYuM/0jP3vQY9olo6SFkq6X9FVJV0k6RdLs8vGbJV0uaYmkXSQ1JP1G0qZl3oakGyRtMi7NRZIuLOmdKmnDCRZ9DrAjQEQ80DF9Dvzpp5kvBr4TEUMRcTNwA/AUSS+X9ImyrLdKuqm83kHSeeX1cZIukXS1pBPVtoOkyzvy+QRJl01RNrdI+pCkCyRdKunJkk6XdKOk15eYuZLO6CinF48r1y+VPHxT0jMlnV/K8Ckl7vhS9j8ry3uJpI+WtH4qqW+y9Zkgv2dJ2qe8/lzJ8zWS3jdund7XuV2nWP/jy7J+BnytrNO5Zd7LJT2txB1Sln1KWedvjs+fpFllff5O0hxJP5Z0ZVmfI0vMvpJ+WaZfLGmepEFJXyl5vULSoSX2GEnfk/Q/wM9Kml8uZXTF2HYwMzMzMzMzMzMzM7O100yMYbYzcGJE7AE8APxDmX53RDwZ+Bzw9mj30/QN4Ojy+TOBKyfobu9rwDtLekuA906wzBeWzwCQ9EFJvy9pH1cmbwX8vmOeW8u0c4ADy7QDgXskbQUcAJxbpn8mIvaNiN2BWcALIuJGYKmkRSXmNcBJUxUM8PuI2L+kexLwUuCpwPvL56uBI0o5HQr8v47Koh2B/6Ddim4X4K9KHt8O/EvHMnYAnk+7gvAbwJkR8SRgVZk+4fpMk+93R8Q+ZdkHS9qj47OHbNdp0tkbeHFE/BXt7jKfVeY9EvhUR9xewNuAXYHHA0/v+Gwu8D/AtyLii8BzgNsjYs+yPj+V1A+cDLw1IvakvW+tAt4IUMrjFcBXJY3117U/8OqIOAx4N/CLiNiX9nb4mKR8/31mZmZmZmZmZmZmZrZGmYkKs99HxPnl9TdoV+oA/KD8vwxYWF5/GXhVef1a4CudCUmaDyyIiLPLpK8CnR24f0zSYuB1tMcmAyAi3h0R2wDfBN40ltwEeY2I+AMwV9I8YBvgW2UZB/Jghdmhki6StAQ4jHa3jgBfAl4jqYd2pc+3JiqQDqeV/0uAiyJiWUTcBayWtKDk8UOSrgL+j3aF3uZlnpsjYkmpaLwGOCMioqS1sGMZP4mIkTK9B/hpxzLH4iZbn8m8vLSmu6LE7trx2UTbddL1j4ixgVT6gC+WPHxvXJoXR8StZV0Xj0v3v4GvRMTXOtbrmZI+IunAiFhKu9L2joi4BNqtDks3iwcAXy/Trgd+C+xU0vl5RNxbXj8beFfZt84CBoFtx6+MpNeVlneX/t8PT5xm1c3MzMzMzMzMzMzMbKbMxBhm40caHHs/VP43KfmKiN9LulPSYcB+PNjaLOvYiDhlis+/BfyYdqu0W2lXiI3ZGri9vL6AdguxX9GuJHst7RZH/19pgfRZYJ+S3+NpV6AAfL+k/Qvgsoi4Z5r8jpVBq+P12Pte2uu/KbB3RIxIuqVjWePjh8bN+5BlRERL0kipVPtT3DTr8zCStqfdcmzfiLhP0knj4h+2XaewouP1PwJ3AnvSrthdPUGaE6V7PvBcSd+Ktl9L2ht4HvDh0uXjD3n4fggTV5pOlDcBfxkRv5pqZSLiROBEgO9d2OrOqKxmZmZmZmZmZmZmttZqzXQG7E9mooXZtpL2L69fAZw3TfyXaLdE+25ENDs/KK2F7pM01mXiK4GzmYKkJ3S8fRFwfXl9GnCUpIFSCfQE4OLy2Tm0K4XOod2K6lBgqCx/rHLobklzaXejOJa/1cDptLsjfEjruEdoPvDHUll2KLDdo5DmeJOuzyQ2oF2ZtFTS5sBzH6V8zKfdCqxFe7v2JOc7DriHdqUfkrYEVkbEN4CPA0+mvc23lLRviZknqZf29j26TNuJdquxiSrFTqc95p5K7F6PaA3NzMzMzMzMzMzMzGyNMBMtzK4DXi3pC8BvaFcmvXmK+NNoVzZNVuH0auDzkmYDN9FuCTaVEyTtTLvi9rfA6wEi4hpJ3wWuBUaBN3ZU0J1Lu/XZORHRLOOfXV/mu1/SF2l3/XcLcMm45X0TeAnws2nylfFN4H8kXUq7K8Lrpw6vl1if8fFXSrqCdjeQN9Fu4fVo+CzwfUkvA87koS28pvM24MuSPgqcQbtrzhYwArwhIoYlHQl8WtIs2uOXPbMs8/OlG8hR4JiIGHpwmLg/+QDw78BVpdLsFqYf583MzMzMzMzMzMzMzNZQM1Fh1oqI14+btnDsRURcChzS8dmewJVlTKmxmOM7Xi8Gnjp+IRFxzEQLj4i/nCxjEfFB4IMTTL+Rju76IuLZ4z5/D/CeSZI9APjy+NZxEyxjYcfrk4CTJvqMdleQE9m9I/6Yjte3jH3WWW7l/dyO18d3vJ5wfcale8hE08fFL+x4PX67jo8dn7ffAHt0TPrnMv0s2uOGjcW9qeP1wo74zorT0ydY3iVMsN8Ax0wQexIP3R6rgL+fYN5JNVtT9fY4bnm9fd1JtyefLg+vJJxYT7bhH0QjHzvYyPdgGXPmp2Or9Hbp9Jgshx5V9OJZEdpbUbZU7Iu0KhqPV6TbGpiVTzfyeYjmSCqu0RpNp9maPS8d2+ybtKfbh6er/LHTGpyTjm0MrZo+qGg28tssunDsVJVB/0A6tmdwdj7dnvx61Zxv1Zzy9uCheag4Hhp335GOTR+/q1fm06wQD9yfjlXF+aMx0J+PnZM/drTRJunY5m23Tx9U9M3K5WFkIH+uqaGai0mX0o0pe8f+M/JQcU1VPPrl0K3l15RXTR66pVVxLWkODafiBjffNJ2mKs4JQxf+Oh371H89OB174Qem7AzlIfZ+277pWPXly7Zn3tzpg/4UnLv+qj9ftozktm074YoOcWZXnMcrzktDI/l7kGbkj8lmugOVuvuwdJo9FdusQs1xXiP7eArQbOTXreY82pqVu/7O6cs/k/Qq/6wxWlG2vcrfX0ZFx1OjrXxs1fNDxXYY7c3d6/fki5Zmb8X5YzR//mj25p/5yG8yensq8tBX8fyQjBvpz5dXze3laMWxW3W/VvH8UHO31Js/jdOqeP4n91VF1bW/VfG92YYV3zOaratmosIsTdK7gDdQP3bZGkHSqcAOwGEznRczMzMzMzMzMzMzM1uzRMUPb6y7HtMKs87WTsn4E4ATupahLouII8ZPK5Vo24+b/M6IeFgrqHWRpNcAbx03+fyIeONM5MfMzMzMzMzMzMzMzGyNbmG2LpqoEm19EhFTjUdnZmZmZmZmZmZmZmb2mKvoFNzMzMzMzMzMzMzMzMxs3eMKMzMzMzMzMzMzMzMzM1uvuUtGMzMzMzMzMzMzMzOzGRBoprNghVuYmZmZmZmZmZmZmZmZ2XrNFWZmZmZmZmZmZmZmZma2Xlsnu2SUdAuwT0TcnYw/HlgeER+fIuYk4GBgKdAC3hgRF0h6GXA88ETgKRFxaYk/Gji2I4k9gCdHxOLK1ekqSS8Cdo2IEx6FtN4GnBgRK//sjNUv+xDg7RHxgsd62RkbDI6mY1uak093oCLdkVnp2NH+XB40J7/8Zl9++X200rHR15+OFZFPd96CdOxoxboRuXXrazTTSY4ov/yB3nzZtgbz+2KNRiP/W41Wb377RqOnIjZ3+RvtGUin2erpS8c2G/n1Gu7Nb9/ZFXloDVTstxWaA/n9RpE7JlvK7zNVy59dcQ5rVGzfimNH/fl9LFteALHRZvl0W7nzTas/v880BpemY6OiDLLHLkBvM38ejeS5udbgttvk89A/mIurONe1eiqukcrvXzXX01bF7/N6lN9mDfKxo5Hfb7Lrpop7lVbky6ChfLrdKoNQvjuYqu07ujodO7Dj9unYrFidX/7WT90pHXv7xb9Jx+79tn3TsZf9+yXp2Gd8f/90rIby5cC8+bm4Zv56Sl/+nM/oSDq0NXdBOrbmfDerr+JeoeJY76k4fkO58/6cgYo0K64lNem2KtKtMdhb8dxb80xQcb6L3tx5tL83v38Nt/LX6dnpyDo1XYDVHA81Bnryx3o0c8fZYF/FftvqzvEQ5NOd3ZMvWyXPCVB379yX3A7ZbQAwWLNtK575+hsV5+aK50Mq9pvZjYp7wYrtMG9WLg+NBRvml5+ONDNYRyvMuujYiDhF0rOBL9CuBLsaeEl5/ycR8U3gmwCSngT89xpYWdYbEacBpz1KSb4N+AbwZ1WYSeqJiPxV6jFUyqw7d4lmZmZmZmZmZmZmtl6J8Bhma4q1uktGSQslXS/pq5KuknSKpLEf3rxZ0uWSlkjaRVJD0m8kbVrmbUi6QdIm49JcJOnCkt6pkiaqsj8H2BEgIq6LiF9Nk9VXAN8u6b9c0ifK67dKuqm83kHSeeX1cZIukXS1pBPVtoOkyzvy+QRJl01RNrdI+oiki8vfjmX6SZI+IelM4COSjpH0mY7PPifpTEk3STpY0pclXVda2I2l/TlJl0q6RtL7yrS3AFsCZ5a0kfRsSReU7fA9SXOnye9xpQxeJunvShlcKen7Y9u15PFTkn5Z8vjSCdLaV9IVkh5f1mFx+btC0rwS846yb1wp6YQybcJtL+ksSR+SdDbwVkl7Szpb0mWSTpe0xZRb38zMzMzMzMzMzMzM1mhrdYVZsTPtbgD3AB4A/qFMvzsingx8jnZXfS3arZ+OLp8/E7hygm4bvwa8s6S3BHjvBMt8Yfks60hKhRntyrYDy+sDgXskbQUcAJxbpn8mIvaNiN2BWcALIuJGYKmkRSXmNcBJ0yz3gYh4CvAZ4N87pu8EPDMi/r8J5tkQOAz4R+B/gE8CuwFP6lj2uyNiH9ot7A6WtEdEfAq4HTg0Ig4tFZHvKct5MnAp8E/T5Hd1RBwQEd8BflDKYE/gOuBvOuK2oF1eLwAe0pWkpKcBnwdeHBE3AW+n3X3mItrlvUrSc4HDgf1K+h8ts0+17RdExMHAp4BPAy+NiL2BLwMfnGa9zMzMzMzMzMzMzMxsDbYuVJj9PiLOL6+/QbsiBeAH5f9lwMLy+svAq8rr1wJf6UxI0nzaFSNnl0lfBQ7qCPmYpMXA63hoBc6kJO0HrIyIqwEi4g/A3NLSaRvgW2UZB/Jghdmhki6StIR25dVuZfqXgNeo3WHxkWXeqXy7439nx/bfm6LLw/+JiKBdYXRnRCwplY3X8GA5vry0drui5G3XCdJ5apl+fimzVwPbTZPfkzte7y7p3FIGR/NgGQD8MCJaEXEtsHnH9CcCJwIvjIjflWnnA58oLeAWlO4Unwl8ZWystYi4N7Htx/K2M7A78POyXu8Btp5mvczMzMzMzMzMzMzMbA22LlSYjR9Rdez9UPnfpIzVFhG/B+6UdBiwH/CTymUdGxGLIuJZYxVgCUfxYMXVmAtotxD7Fe1KsgNpV2idL2kQ+CztFkxPAr4IjI0C/33gubRbVl0WEfdMs+yY5PWKKeYZK7dWx+ux972StqfdausZpSXWjzvy10nAz0t5LYqIXSNiukrGznydBLyplMH7xi2jM1+dHbzeAawG9hqbEBEnAH9Lu6XehZJ2KfPkR+J9aN4EXNOxXk+KiGdPNIOk15WuKy/9yQ++VLk4MzMzMzMzMzMzM1vXBVon/9ZG60KF2baSxlpPvQI4b5r4L9Fuifbd8a2sImIpcJ+ksS4TXwmczSMkqQG8DPjOuI/OoV3pdA7tVlqHAkNl+WMVQ3eXMb/+NEZXRKwGTqfdzeRDWsdN4siO/xc8wtUYbwPalUdLJW1OuwJvzDJgXnl9IfD0jrHTZkvaqWI584A7JPXxYDea07kfeD7wIUmHlOXuUFrJfYR2t5C7AD8DXtsxLtpGFdv+V8CmY/ucpD5Ju00QR0ScGBH7RMQ+z33J3yZXwczMzMzMzMzMzMzMHmu9M52BR8F1wKslfQH4De3KpDdPEX8a7cqmySqcXg18vlSm3ES7JdikJB1Be0yrTYEfS1ocEX9RPj4IuLWMpdXpXNrdMZ4TEU1JvweuB4iI+yV9kXaXiLcAl4yb95vAS2hX+kxnQNJFtCtGX5GIn1ZEXCnpCtpdNN5Eu8vDMScCP5F0RxnH7Bjg25IGyufvAX6dXNS/AhcBv6VdFvOmDv9T/u6U9MKSj9cCfy3pUNotDa8FfhIRQ2U8tkslDQP/C/wLiW0fEcOSXgp8qnTj2Et7fLhrkutlZmZmZmZmZmZmZmZrmHWhwqwVEa8fN23h2IuIuBQ4pOOzPYErI+L6jpjjO14vpj3+1kNExDETLTwiTgVOneSzsyZJ60Y6uhIc36VfRLyHduXSRA4AvjzFGGSd/jMi3jcu7WPGvT+JdveHD/ksIm6hPVbXw+aboiw+TbvycOz9L4B9E/kkIhaOe/852pWf4+PG539u+X8WcFZ5/TseHPPsokmWdwJwwrhpi5l4ex0yQdxB4+PMzMzMzMzMzMzMzGzttC5UmKVJehfwBvJd/K1RJJ0K7AAcNtN5sToPrM4faj333pGOvWeDvnRs7/J8uo05Uw1z1xF3z53pNHs23CQdu/iufG+xB9zxy3Rs6/kHpGNXX3j+9EFF32HPnT6o6L3/j6m4pT0D0wcVs1t3pWOXDuX3xcZdt6VjY9kD6diR+5emY/uesHM6llYrHzsynArrnbd5OsneP/4+HTurJ78d+vrn5GPvyW+z1h23pmP7t5iw59mJY+/Kl0N2m/XPzp8/apbf/O3N6djBJ89Nx/bedmM6NlbmzrcAmp3fF2rSHV22PBXXGOhPpzl0733p2IGtt07HNpcvS8de/60z07Gb7pw/1jdalD8vLfvNb9OxC/bOHg8bpNPsWzbdcLcP0vxd0rE9jKZjIX89i6jo436Gu8PvIfPbtTZVDJ1bUwYN5a97NeMH9DKST1cV+R1ZnY5dtuS6VFzPrImGUp5Yc1V++YObbpSO3eEl+d/RqS9/7/6M7+8/fVBxxl9+Kh272X4bpmN3fO5e0wcBPXNmpdNsDObPCY3B/PYd/fX10wcV8bxnpGMrdvGqY30k8vtCTyt33/rHB/Jp9s1bmY79w9L89X+fDXP3FACjfZulY+9clt8XFs3JP5cs71uQjm3clNvHfj3r8HSaO22Yv572jQ5NH1QsJ39MLmjcnY5dOVzxvUZ//lpy/+p8fvtbuXvBPyzL77f9jfz90q0j+XSfVrEv3jGUPzcuXZ4/1xy6cf7eeVlfbjsMrsrf598Z+eeXJ8zKP7/cN5zfZ/puzz+bMZo/Jn87kP/eqm9u/jj77QO5dH994g/Sae70t0ekY++o+V5lnRjpac3Ryh/a1mVrdYXZ+FZQifiHtSpam0TEw85wpRJt+3GT3zm+xdaaYor8nj4T+TEzMzMzMzMzMzMzM1urK8xs4kq0Ndnall8zMzMzMzMzMzMzM1v3ue2kmZmZmZmZmZmZmZmZrddcYWZmZmZmZmZmZmZmZmbrNXfJaGZmZmZmZmZmZmZmNgMCzXQWrHALMzMzMzMzMzMzMzMzM1uvucLMzMzMzMzMzMzMzMzM1muuMDMzMzMzMzMzMzMzM7P12iMew0zSLcA+EXH3n5HGAuCvIuKzHdN2Av4d2AkYAZYAb46IOx/pcsYt82XA8cATgadExKUdn/0z8DdAE3hLRJxepu8NnATMAv4XeGtExKORn26TtAjYMiL+d5q4Y2hvzzf9Gct6G3BiRKx8pGn8Gcs+BHh7RLzgsV52xshoRT+0q/LFNzqnIhOrV6VDGwODqbhYnc+rhlfnl9/Il9fIsuXp2BrNVUPp2IGKdctuh+Fm/vcMag7nFz+cL9tYuSId21yRjx19IL/N+lYsS8dWGc6VmaKVTjKWP5CO7RnOH4/R6EnHsiJfts2KY6enNZrPw3D+2KHVTIU1WiP5NCv229EH8tusdyR/nMeq/PatOXYazVx5AdDK77vNlcn8VpybR1fky6C/5jhLHrsAvQP5Y2fVffntMHLf0nTsaMW1pHl/Lt2e1fm8svS+dOia0Gd+aw34LV82D90qr26VQbfyqy49ErVGc+eF1rL8veiy2+9Nx/Yv2CAd21yRz0PPvLnpWA3lrzub7bdhOvaPF+XPC49/dvK6s6riPr+/Px3bWp1Pd/je/Lm55nio2cUjunOcNZq5+6DhkfzyG8389Wmo4jaskby3g7ryqnmezpYXQFScc5vLc88ly5sV980Velv5e6CWunMtaVZss0bF/d1IxbNvI1kOVftt5O+tVlVs3kZv/n64Jr8rVuZPTI25Fc8ls3PboWdV/hl9dU9+2/b05/fx0VbFPl7xjByj+Q2xPHmvAtAYzK/b0HBu+w7Oz31vB9Balr9GLq0oAxioiLXpdOs+wurN9FPpAuAfxt5IGgR+DHwuInaMiCcCnwM2fRSXeTXwEuCczomSdgWOAnYDngN8VtLYNy2fA14HPKH8PedRzE/XSOoFFgHPe5TSkzTlndfbgNmPwnIqvh1+bJUyNTMzMzMzMzMzMzOzdci0FWaSFkq6XtJXJV0l6RRJY5Uib5Z0uaQlknYp8U+R9EtJV5T/O5fpu0m6WNLiks4TgBOAHcq0jwF/BVwQEf8ztvyIODMirpY0KOkrZVlXSDq0pHuMpB9I+qmk30j6aJneI+kkSVeXef6xpHddRPxqglV9MfCdiBiKiJuBG4CnSNoC2CAiLiityr4GHF7Sv6lUIi2Q1JJ0UFn2uZJ2nKIszi0tv8bK+HxJe0xQ9g1Jt5SWeGPTbpC0uaRNJX1f0iXl7+nl8+MlnSjpZyWv7weOLGV8pKQ5kr5c5rlC0os7FrlNKcdfSXpvx/a/TtJngctLzOckXSrpGknvK3FvAbYEzpR0Zpn2bEkXlH3ke5Im/UllWc/jJJ0HvEzS35U8XlnWc3aJO0nSp0p53iTppROktW9Zt8dLOris++IybV6JeUfZL66UdEKZtkjShWX/PFXShmX6WZI+JOls4K2S9pZ0tqTLJJ1e9hEzMzMzMzMzMzMzM1tLZVvL7Az8TUScL+nLPNgq7O6IeLKkfwDeDvwtcD1wUESMSnom8CHgL4HXA/8REd+U1A/0AO8Cdo+IRQCSPgFcNkke3ggQEU9Su3LuZ2p33wjtVlR7AUPAryR9GtgM2Coidi9pL5hmHbcCLux4f2uZNlJeP2R6RDQl/RrYFdi+5PtASRcBW0fEDZI2mKQsvgQcA7ytrMNARFw1PkMR0ZL038ARwFck7QfcEhF3SvoW8MmIOE/StsDptLuZBNgbOCAiVmlcV4uSPgT8IiJeW8rkYkn/V+Z7CrA7sBK4RNKPgbtpb//XRMQ/lDTeHRH3qt0S7AxJe0TEpyT9E3BoRNwtaRPgPcAzI2KFpHcC/0S7Am8yqyPigLKMjSPii+X1v9HuKvPTJW4L4ABgF+A04JSxBCQ9rcS9OCJ+J+k/gDeWfXcusFrSc4HDgf0iYqWkjcrsX6Pd/efZkt4PvJd2qzmABRFxsKQ+4OyS/l2SjgQ+CLx2ivUyMzMzMzMzMzMzM7M1WLZLxt9HxPnl9TdoV1YA/KD8vwxYWF7PB74n6Wrgk7S7OAS4APiXUnGyXUTkO9JtOwD4OkBEXA/8lvY4ZwBnRMTSiFgNXAtsB9wEPF7SpyU9B5huoJKJOgqNKaYDnAscVP4+XPK4L3BJ+Xyysvge8IJS+fJa2uOjTeZk4Mjy+qjyHuCZwGckLaZdabTBWOsp4LQpyvfZwLvKfGcBg8C25bOfR8Q9Zd4f8OB2/m1EdFYmvlzS5cAVZZ12nWA5Ty3Tzy/LejXt7TKVkzte7652S7wlwNE8WHYAP4yIVkRcC2zeMf2JwInACyPid2Xa+cAnSgu4BRExSrvsvjI21lqp/JtfPj+7zPdV2tt1fN52pl2p+POyXu8Btp5oZSS9rrTEu/TM006cZtXNzMzMzMzMzMzMbH0TsW7+rY2yLczGr97Y+7GRYpsdaX0AODMijpC0kHalDBHxrdL66vnA6ZL+lnalVqdrgIMnycNUI991jljbBHoj4j5JewJ/Qbt12suZuhXQrcA2He+3Bm4v07eeYDq0K8xeT7srwuOAY4FDeHB8tMnKYqWkn9PuBvLlwD5T5OsCYEdJm9JuFfVvZXoD2H98xZgkgKlGLBXwl+O7pSyt1ybbzis64ran3Zpw31LGJ9GudJtoOT+PiFdMkZfxOvN9EnB4RFxZWskd0vFZ5/bu3C/uKHnZi7KNIuKE0lLuecCFpaWfePi6ZvMm4JqI2H+6GSLiRNoVeHz9nOrlmZmZmZmZmZmZmZnZYyTbwmxbSWMVBK8Azpsidj5wW3l9zNhESY8HboqIT9FuEbUHsAyY1zHvt4CnSXp+x3zPkfQk2pVQR5dpO9FuFTXRWGRj820CNCLi+8C/Ak+eZh1PA46SNFAqhZ4AXBwRdwDLJD1V7dqoVwH/Xea5CHga0Cqt2xYDf0+7Im3Ssii+BHwKuCQi7p0sU2XctFOBTwDXRcQ95aOfAW/qWN9FkyQxvoxPpz32nMp8e3V89ixJG0maRbty7nwebgPalUdLJW0OPHeSZV0IPF3SjmU5szu60MyYB9xRWuEdnZznftoVsh+SdEhZ7g4RsSQiPgJcSrsbx58Br9WD46JtFBFLgfskHVjSeiXtrhfH+xWw6djxIKlP0m4TxJmZmZmZmZmZmZmZ2VoiW2F2HfBqSVcBGwGfmyL2o8CHJZ1Pe5yyMUcCV5du7HYBvlYqf86XdLWkj5XWUi+gXaHzG0nX0q5o+iPwWaCndNF3MnBMRHS2NBpvK+CssryTgH8GkHSEpFuB/YEfSzodICKuAb5Lu0vHn9Ie96pZ0noD7QquG4AbgZ+UeYaA3/Pg2Gfn0q7oWTJNWRARl9HuJvIrU6zDmJOBv+ahXRa+BdhH0lWlnF4/ybxnArtKWlzG2/oA0AdcVbqK/EBH7Hm0u71cDHw/Ii4dn1hEXEm7K8ZrgC/z0Eq1E4GfSDozIu6ive2+XfabC2lv96x/pV0h+XPa4+KlRMSdwAuB/yyt5t5W9q8rgVXATyLip7QrSC8t+8fby+yvBj5W8ruICcZbi4hh4KXAR0qai2lXmpqZmZmZmZmZmZmZ2Voq2yVjKyLGV8gsHHtRKlYOKa8v4MGxxaBd8UFEfJj2OF8PERF/Ne799cBzJsnHMRPMfxIdY4BFxAs6Pn5Yq7KIOJV2i62HiYgPAh+cYPqltMetmmieAztef4t2K7mx9xOWBYCkLWlXWP5sonQnWL7GTbubB8c265x+/Lj399IeV63T308w30lMMJZaRNzCuHWPiGMmyeengU93vP/FBMueUEQsHPf+c0xQMTt+2RExt/w/iwe7vPwdD455dtEkyzsBOGHctMW0x14bH3vIBHEHjY8zMzMzMzMzMzMzM6vRmnI0KnssZSvM7FEk6VW0K+b+KSJaM50f674Fc0bTsa3Nt5k+qNhwVnP6oLF0+7eePqho9k00LN3D9fb0pdMcnTU3HbtA2cavMGu7fHnVmLXNFunY4XmbpmP7enNlNm9wJJ3maHODdOwmffl9ES1Mh/bOuWf6oLHY+QvSsa3N8vstrfzx0BgdTsU1e/rzy99yYTp09Qabp2NHemelY3s3ye+3fb35W4BVffOmDyo22Phx6VhauUtg1XaYv2E6tP9x+e0wNHujdOzgpvl0e+fmz43MmpOPXTXVcKYP1T+YO+cref4CaPTnt5kGZ6djezfpmT6o2OaAJ6Zj+zfK7ze9W+XPSz0D+XLIprtyQf447x/Inz8GWJ2O7W3lzqEAKL/fDmiqziPG5SHy18kR5bfDALk89ETF9bTCoFZNH1TUbIfSE3kutmLo3Ubkr70azu9jG+z2hHRs1vw98+ew1qqV6dieuflrJD35cxjz5qdDd3zuXtMHFY9/dn6bXfiBiXqof7ht/2LLdJrRyu9f87dekI7d/Bn5Tj9Wj+bPCT2N/OO6KkatVkXwqsHcNWrDufltu3LOZunYBSP5Mlg9mN9va8yfnV+3ocEF6dio+HKy93G56++Ws/L7V5P8OWF1X/6esa/iuWi4J3+vMLc/f+0d7sndXwIM9ubzu7pvk1TcxhVftQ335O+tNh7KH7vDsyuOs6F8fnsa+e9LhuflygtgTl/yGXmw4t6uYjsMDeS/1xgkv8+wSf7ZTBXHzqa9+eP3/2fvvuPsLOv8/7/eZ2Yykx5ICIQaCL2GKl0QLNgQG6Cu4qqsu9i/uq6r66KuFX+WtaMrCEpRimIFRRBFSgIEEkIPoQVIgfSp53x+f5x75DBM+dyQSX0/H495zJn7vM91X3c99znXXNfdPXZiOrt5cjC4rQ4uMYjXjvnsLtXWfLlmG6khvy3rr4eRvTARcR5wXuM0Se8EPtgnen1EnLHWKrYWSLoc2LHP5I9HxJXroj5mZmZmZmZmZmZmZmbuYbaeiIhzyN3PbIMWESet6zqYmZmZmZmZmZmZmZk1yvfjNTMzMzMzMzMzMzMzM9sIuYeZmZmZmZmZmZmZmZnZOhCRv6+mDS/3MDMzMzMzMzMzMzMzM7NNmhvMzMzMzMzMzMzMzMzMbJPmBjMzMzMzMzMzMzMzMzPbpPkeZmZmZmZmZmZmZmZmZutAxLqugfV63g1mkuYDB0XE4hdQxgTgLRHx3YZpuwLfAHYFuoHZwPsj4snnO58+8/wccCJQAxYCp0XEguK5TwDvAqrAByLiyjUxz42FpHOB30TEJZJ+BHwtIuau42r9w5rYJ4eLhum+jZUS5UaZSlSa1niZoXyH1hEt6ShRq6WzIv/uoxEj8nUosx6S67ZSoq61ZJlA/ew2HIZrJy9Vh/w+lt1mZfZbaj3pqGr5DVGqDqWO83y5tTLrtsT+mK1tJUrsuNUS67arK58ts27LbLMySiwbzfkTqbL1LVNmS4kTeWtrPltCU1tbPlzmDbXEvlBpzb+XpGcf+fe9Mp+6Wmqd6WypY7LEqh0RHelsU607X4WmUelsS+TWQ5ntUOb8MaJWYh1E/n2nzHYotY+VUeb9oSl5Ldqd3w8osVwq8R4ZPSX2xRLXl1Tz27dp9Mh8ue35fWz7l2+dyj185YJ0mbu+eVo6u/i+RenslseX2GZlDogNSJlLlTLroKnEZc1wrVtpeL5FLFXf7PmmxOE4XMp87i3zHlVqHytxPbyuP0qqxPVSU6XEvljifaepxD5e6iNfiWu2inL1bepYlS6zu3mYzgkl9vFyBa/7gdiyH0sqZT7rlLCuj0ez9cG6PhNMAP6t9w9JbcBvge9FxM4RsQfwPWCLNTjPsyJi34iYDvwG+HQx7z2BU4C9gFcA35VU4lvoTUtEvHu4Gsskrbc9H9fnupmZmZmZmZmZmZmZ2fMzZIOZpKmS7pb0E0l3SLpEUu+/ab5f0q2SZkvavcgfIunvkm4rfu9WTN9L0s2SZhXl7AJ8CZhWTDsLeAtwQ0T8unf+EXFNRMyR1CbpnGJet0k6tij3NEmXSfqDpPskfaWY3iTpXElzitd8uChvecPijYZ//FvCicBFEdEZEQ8C9wOHSHqzpK8VZX5Q0rzi8TRJfysef1rSjGJeZ6tumqRbG9bjLpJuGWQ9z5f0BUk3SJop6QBJV0p6QNJ7i8wYSVc3rPMT+2yjHxV1+Jmk4yVdX6yTQ4rcmcV2vKqY3+slfaUo6w+SWgZann7qe62kg4rH3yvqfKekz/RZps/03UcGWP4zi3ldBZxXLNNfi9feKunwIndMMe9LimX+Wd/6SRpZLM97JI2W9FtJtxfLc3KRObjYP28v9suxQ+xjv5D0a+CqoswfF+vott7tYGZmZmZmZmZmZmZmG6ZsD7PdgLMjYl9gOc/0ClscEQdQ7wX20WLa3cDREbE/9d5bXyimvxf4ZtGz6yDgUeA/gAciYnpEfAzYGxioUekMgIjYBzgV+InqPdIApgMnA/sAJ0varpi2TUTsXbzmnN6CJH1e0iPAW4s6AmwDPNIwv0eLadcBRxXTjgKWSNoGOBL4azH92xFxcETsTb0D/qsj4gFgmaTpReadwLkDLFuvRyLisKLcc4E3AocCny2e7wBOKtb5scD/19BYtDPwTWBfYHfqjY9HUt8u/9kwj2nAq6g3EP4UuKZYP+3F9H6XZ4h6fzIiDirm/WJJ+zY8198+MpADgRMj4i3Uh8t8afHak4H/bcjtD3wI2BPYCTii4bkxwK+BCyLih9R7Cy6IiP2K5fmDpBHAxcAHI2I/4Phi+Qfbxw4D3hERLwE+Cfw5Ig6mvh3OkjR6iGUzMzMzMzMzMzMzM3uWQBvlz4Yo22D2SERcXzz+KfWGGIDLit+3AFOLx+OBX0iaA3yd+hCHADcA/ynp48AOEdFesq5HAucDRMTdwEPU73MGcHVELIuIDmAusAMwD9hJ0rckvYJ6Qx/F6z8ZEdsBPwPeV0zubwtGRDwBjJE0FtgOuAA4mnrjWW+D2bGSbpI0G3hJwzL/CHin6kM7nly8djBXFL9nAzdFxIqIWAR0qH6/NwFfkHQH8CfqDXpbFq95MCJmR0QNuLNYJ1GUNbVhHr+PiN57wzUBf2iYZ29uoOUZyJuL3nS3Fdk9G57rbx8ZcPkb9osW4IdFHX7Rp8ybI+LRYlln9Sn3V8A5EXFew3IdL+nLko6KiGXUG4Afj4gZUO91GBE9DL6P/TEinioevwz4D0mzgGuBNmD7vgsj6fSi593MP1z2wyEW3czMzMzMzMzMzMzM1pVsg1nfuyn2/t17V+wq0Htvp89R77W0N/Aa6o0JRMQFwGup9+S5UtJL+pnPndR7GfVnsCbJxrtzV4HmiHga2I96g8YZ1Buv+roAeEPx+FHqDWK9tgV67158A/UeYvdQbyQ7inqPo+uLHkjfBd5Y9Ez6IcUyA5cCJ1DvoXVLRCwZZBkal6PWZ5lq1NfvW6nfz+3Aoqfekw3z6pvv7PPaZ82jaGzqLhrV/pEbYnmeQ9KO1HuOHVf0QPxtn3x/+8hAGu8c+uFi+faj3iOx8Q7Zz9neDX9fD5zQ2/MuIu6lvk/NBr4o6dPU96X+7hA62D7WWDcBbyh6Rk6PiO0j4q6+L4iIsyPioIg46BWvf88gRZuZmZmZmZmZmZmZ2bqUbTDbXtJhxeNTgb8Nkh0PPFY8Pq13oqSdgHkR8b/Ue1LtC6wAxja89gLgcEmvanjdKyTtQ31oxLcW03al3qPnnoEqIWkSUImIS4H/Ag4opu/SEHst9SEkKep0iqTWohFoF+Dm4rnrqDcKXUe9F9WxQGfRW6m3cWixpDHUh1EEoOjxdiX14Qj/MSTkCzAeWBgR3cX9tXZYA2X2NeDyDGAc9cakZZK2pN5AuCaMp94LrAb8E/XecBmfBpZQb/RD0tbA6oj4KfBV6vvB3cDWkg4uMmMlNZPfx66kfv8+Fdn9n9cSmpmZmZmZmZmZmZnZemGoHj+97gLeIekHwH3UG4DeP0D2K9Tv/fQR4M8N008G3iapG3gC+GxEPCXp+mL4xt9HxMckvRr4hqRvAN3AHcAHqTeAfL8Yoq8HOC0iOp+5hddzbAOcI6m3UfATxe8vSdqNeo+qh6jfW42IuFPSz6kP6dgDnBER1eI1f6Xe++y6iKgW9z+7u3jdUkk/pN6DaT4wo089fga8HrhqoIqW8DPg15JmUh+K8O7B4+Ullqdv/nZJt1HvHTiPeg+vNeG7wKWS3gRcw7N7eA3lQ8CPJX0FuJr6PcZq1Penf42ILkknA9+SNJJ6r8fjye9jnwO+AdxRNJrNZ+j7vJmZmZmZmZmZmZmZPUutv7HQbJ3INpjVIuK9faZN7X0QETOBY4rHN/DMfZ+g3ruLiPgi8MW+BUfEW/r8fTfwigHqcVo/rz8XOLfh78aGiwP6yb+h77SG5z4PfL6f6Q/QMFxfRLysz/OfAj41QLFHAj9uaHwbaN5TGx6fy7OXaWpD9DD6t3dD/rSGx/N7n4uIM/vMc0zD4zMbHve7PH3KPaa/6X3yUxse/2MfGSDbt273Ue+F2OsTxfRrqQ+z2Zt7X8PjqQ35dzY8vrKf+c0ADu2nKqf1kz2XZ2+PduBf+nntgJortXS2NmLAETBfWLkt+XJrldypoVKirtE8YuhQoaU5/y5RGTN26FBvHUrcbLLSVmLZKtkOkFBLrodKpcQ7ZS2/XC1NZfbF1nS2qTW/voj8spU5HsqUWxn4ny2et1rb6HS2u6VEtim/HWot+WyldWQ6G5FfX9l9vCg4FatWWvLzL7EdKuPGp7NljvMosd+qNujlwbPLLbHNtHpFOsuI5DZryq8DNee3GdX8OiDy57CmUfn11VTivYS2UeloqfXQktsOPc35/atpRFd+/sOkFtkBLRh8cOwXYDhuNq1+R/d+4fMP5ddXmXPzcFGJY1LdHfnsqNy5XGXOHyWOR7V258tty59r6C5xTJZ5T28rkc2e84FIfnOz65unpcu89+cPpLNTjt4inaXMtWgJlRKHWZQ5L5S5tkqeF5qbSlxjl7iuaS7xuaTMOayMMnUos2ylJM9Lo0rsitUS75E15Zerovy5uYwy5ZZ572sqUW72c0GZz73VSv682FziG+0yn4vKlNvaks+Wum6s5N5TqyPz180jS2zbUvt4mXNCmc9mJb5TaCtxzo2m7Nfv0JxcDWXe+8usg9ZqmVabdX8tajYc8keslSbpcmAa0N/92szMzMzMzMzMzMzMzGw9MGSDWWMPJSsnIk7qO61oRNuxz+SPR8RzekFtjCS9k/oQm42uj4gz1kV9zMzMzMzMzMzMzMzM3MNsLeuvEW1TEhHnAOes63qYmZmZmZmZmZmZmZn1coOZmZmZmZmZmZmZmZnZOrA+3J/Y6obnjqxmZmZmZmZmZmZmZmZmGwg3mJmZmZmZmZmZmZmZmdkmzQ1mZmZmZmZmZmZmZmZmtknzPczMzMzMzMzMzMzMzMzWgYh1XQPr5QYzsw2Yhul+kFHJnRpC+U6qQb6yTWX6vtZqJcIlVJryVVA+S3Kdifw7ZZn5V4brHqIl9oVhU+aAqKz5+qpazWfJ77dljp3hWgc11vw+3ltyqsjIry9Vu/Oz7+lJR0ud70psB5XJ1vL7GM0t+aw6c7mWEfkyR5TYDq1t+WyJ/VYtw3SZW2abtZZYZ8llK3M8UCLbXO1KZ8u8R5U5fbRUk/siJddDiTo013LrodQ6KHFqLrMOKpE/J8QwXQCUOTeWen/IZkvstzSVeS8rsb7KfLtRZh305M+jlbb8ebTW0ZHOjt92Qiq3+L5F6TKnHL1FOvv4dfly9/zg8OzjtfXgy6vscVYpcV4qc30plSh3mD6glqlDGaWus0tc629IIvLroMxbSbnr4RLlJrfZcH1XUuq7ihLvD00l9vEyy1bmeqkp+dms0rU6XWZPc76yZfaZMue7Mu+9JQ6HYZNdDWop83lveI5Hs43VevANZ52k+ZImvcAyJkj6tz7TdpX0O0n3S7pL0s8lbfnCavus8t8k6U5JNUkHNUx/q6RZDT81SdPX1HzXFEmvlfQfa6isD0katSbKeh7zPkbSb9bFvM3MzMzMzMzMzMzMbMO23jSYrSETgH80mElqA34LfC8ido6IPYDvAfl/axvaHOD1wHWNEyPiZxExPSKmA/8EzI+IWWtwvi+YpOaIuCIivrSGivwQ8IIbzKQy3XXWLknulWlmZmZmZmZmZmZmtpFZ6w1mkqZKulvSTyTdIemShl5J75d0q6TZknYv8odI+ruk24rfuxXT95J0c9F76w5JuwBfAqYV084C3gLcEBG/7p1/RFwTEXMktUk6p5jXbZKOLco9TdJlkv4g6T5JXymmN0k6V9Kc4jUfLsq7KyLuGWKxTwUuLMp5s6SvFY8/KGle8XiapL8Vjz8taUYxr7NVN03SrQ3rcRdJtwyynudL+nKxjm6WtHMx/VxJX5N0DfDlYnm/3fDc9yRdI2mepBdL+nHRM+/chrK/J2lm0bPuM8W0DwBbA9cUZSPpZZJuKLbpLySNGaK+ny7WwZskvadYB7dLurR3Hynq+L/FvjBP0hv7KevgYpvuVCxDby+/2ySNLTL/XmzH2yV9qZg2XdKNxf50uaTNiunXSvqCpL8AH5R0oKS/SLpF0pWSpgy69c3MzMzMzMzMzMzM+lFDG+XPhmhd9TDbDTg7IvYFlvNMr7DFEXEA9V5gHy2m3Q0cHRH7A58GvlBMfy/wzaIH10HAo8B/AA8UPbs+BuwNDNSodAZAROxDvUHrJ6r3SAOYDpwM7AOcLGm7Yto2EbF38ZpzSizvyRQNZtR7oh1VPD4KWCJpG+BI4K/F9G9HxMERsTcwEnh1RDwALNMzwzq+Ezh3iPkuj4hDgG8D32iYvitwfET8v35esxnwEuDDwK+BrwN7Afs0zPuTEXEQsC/wYkn7RsT/AguAYyPiWNWH1/xUMZ8DgJnAR4aob0dEHBkRFwGXFetgP+Au4F0NuSnU19erqTeS/oOkw4HvAydGxDzq+9EZxX5yFNAu6QTgdcCLivK/Urz8PODjxX45G/jvhqInRMSLgf8FvgW8MSIOBH4MfH6I5TIzMzMzMzMzMzMzs/XYumoweyQiri8e/5R64wfAZcXvW4CpxePxwC8kzeGZxhuAG4D/lPRxYIeIaC9ZhyOB8wEi4m7gIeoNSQBXR8SyiOgA5gI7APOAnSR9S9IrqDf0DUnSi4DVETGnmNcTwJiip9N2wAXA0dQbc3obzI6VdJOk2dQbr3qX+UfAO1UfsvDk4rWDubDh92EN038RMeBdwn8dEUG9wejJiJgdETXgTp7ZJm8uervdVtRtz37KObSYfr2kWcA7qK/HwVzc8HhvSX8t1sFbeWYdAPwyImoRMRdovB/dHsDZwGsi4uFi2vXA14oecBMiogc4HjgnIlYDRMRTksYXz/+leN1PqG+XvnXbjXpD7B+L5foUsG1/CyPp9KIn3szfXfqjIRbdzMzMzMzMzMzMzMzWlXXVYBYD/N1Z/K4CvfeK+hxwTdHb6jVAG0BEXAC8FmgHrpT0kn7mcydw4AB1GKxPYGfD4yrQHBFPA/sB11LvnZZtATmFZxquet1AvYfYPdQbyY6i3qB1fdHL7bvUezDtA/yQYpmBS4ETqPesuiUilgwx7xjg8apBXtO77DWevR5qQLOkHan32jqu6In124b6NRLwx977uEXEnhHxrn5yjRrrdS7wvmIdfKbPPBrr1bgdHwc6gP17JxT3Z3s39Z56N6o+1Kd47j44lN66CbizYbn2iYiX9feCiDg7Ig6KiINe+YZ3l5ydmZmZmZmZmZmZmZmtLeuqwWx7Sb09nk4F/jZIdjzwWPH4tN6JknYC5hVDAV5BfXjAFcDYhtdeABwu6VUNr3uFpH2oD4341mLarsD21Buw+lUMMViJiEuB/wIOGGohJVWANwEX9XnqOuqNTtdR76V1LNAZEct4pmFocXHPr3/co6vo8XYl9SErM0NCntzw+4ZEPmMc9cajZZK2pN6A16tx/d8IHNFw77RRxXrOGgs8LqmFYjslLAVeBXxB0jHFfKcVveS+TH1YyN2Bq4B/brgv2ubFun9aUu9wmf8E/IXnugfYonf/ldQiaa9+cmZmZmZmZmZmZmZmg4rYOH82ROuqwewu4B2S7gA2p94ANJCvAF+UdD3Q1DD9ZGBOMSze7sB5RY+r6yXNkXRWMUzjq4H3S7pP0lzqjW4LqffiaiqG/LsYOC0iGnsu9bUNcG0xv3OBTwBIOknSo9R7iP1W0pUNrzkaeLS4l1ajv1IfjvG6YmjERygaDSNiKfVeZbOBXwIz+rz2Z9R7R101SF17tUq6Cfgg9XuSvWARcTv1Rr47qd+/6/qGp88Gfi/pmohYRH1dX1hs5xupb6es/wJuAv5I/T522fo9Sb0n4neK4TA/VOwPt1Pvjfj7iPgD9UbWmcX27L1f3juAs4r6Tgc+20/5XdQbMb9clDkLOLzEcpmZmZmZmZmZmZmZ2XqmeejIsKhFxHv7TJva+yAiZgLHFI9v4Jl7i0G9IYWI+CLwxb4FR8Rb+vx9N/CKAepxWj+vP5d6g1jv369uePo5vcoi4nLg8v4Kj4hrqd/Lq+/0B2gYSrDvkH4R8Snq98bqz5HAjwe5B1mj70TEZ/qUfVqfv8+lWN7G5yJiPvV7dT3ndX3LaJj+LeBbDX//GTg4UU8iYmqfv79HPw2p/dR/TPH7WurDZVLcv6y319dNA8zvS8CX+kybRf/b65h+ckf3zQ1m0fIR6WzzogfS2cc3a0lnW5bfn8+OHJ3K1R5/NF1m81bbpLPLNNiIqc/W9dhjQ4cKlf1r6Wz7/fntMGqr7fN1eOrJVG75dsemy2zteSqdXbA6vy82PX5fOltbPdhIr8/W/dTT6Wxr5LcZ3d35bE9PKtY8fut0kZVFC9LZtrbcMQbQWsmvr+ZF+WOy5+GH0tnxW+c70rYszteBntw2q4yelC5Sq1K3GK3PftnSdLZ11VCjID+jsiC/bsvUoXn8hHQ2qpnLhKIOS3LL1jR2TLrMrkX59dW287R0tvvR/P618uHH8+Wu6khnJ+w+NZ1d9cgT6WzbY7lsfitAz7z8e/+Sl30onR2hrnRWJf6t8KmmycNSh4j8dcXTlS1SuZFanS5zuNZBm/K3cC6zDlRi5PLmWol9oZY/L3Xcl7sGeequ+ekyW0a1prMTX7RfOlvqP1BH5d//a2MmpLM996b/v5Cup5als1sel/u/wC2PL7EWWvsbzb9/e34wv99e/Yb/TWe3m/umdHZJx9ihQ4Vq5NdDs3LXogCj23PX+k8szX82PLyyOJ1dsCT/tdFRlUXp7NKx+eu7hcvyn2FG1fLLtmL85ulsbVHuc9xDq/LbdvrW+fPtyK4V6WyH8p9htijxXvLU6vzxu0NL/tpqRWd+3x3dmdsOjy2Zni6zdUSuTICHVuWP8+M2yx8PD6/Ir4NlK/KfkVu2zZ/zn2R8KjdN+XXQ3pXPtpXYx5d15M8JlcceTGdrXYP1o3i2R0bmt0PzlPxn1McX5crteiK/37Y2z01nH9Ar01kObxo6Y7YBWlcNZvY8SLocmAb0d782MzMzMzMzMzMzMzMzex7WeoNZ355LlhcRJ/WdVjSi7dhn8sf79thaXwxS3yv7y5uZmZmZmZmZmZmZmQ039zDbwPXXiLY+29Dqa2ZmZmZmZmZmZmY2XMoMoW7Dq9SQ62ZmZmZmZmZmZmZmZmYbGzeYmZmZmZmZmZmZmZmZ2SbNDWZmZmZmZmZmZmZmZma2SfM9zMzMzMzMzMzMzMzMzNaBWqzrGlgv9zAzMzMzMzMzMzMzMzOzTZoi3Hxp6xdJU4HfRMTefaa/Gvgc9YbeFuCbEfEDSa8D7o2IuUOU+6ycpM8C10XEn9ZEfjDzHnggfaAtrU5IlzuhaWk6u6w2Pp1topbKVUu0uTerms6OqqxKZ5dXx6Wzoyur8+X2jE1nW5u60tmeaErlJrIoXebC2lbp7ObNT6WzZfbFQPls5LO1GJ7/68jt4TC6uSNdZntP2/OrzBBGlqhDV60lna3WcvsiwNiWlelse3XNr4cy54/V1dZ0trWpO53NnhfL1qHM8SDlr9nKLFu2DhXl10GZc0IZLepJZ4djXwRoreTP+atKnBfamnPlivx+UCmRLbN/ldFcYpt11kaks2XeH0Y1taez3ZE/j2aNUH6f6azlzx9lrsNGVfLroJq8VoFyx/ry7tHpbEsld94vM//mSn5fHK7rjzLHb5ljssx7SZl11tGTOyaH65xfxriW/OeHR/Y8Kp3d/Z7fP5/qrFHttVGp3Gjlr9dWRv6zzhitSGc7GJnOljkeWkqcR7sifx4tU4fV1dyyjW9eli6zqZY/L61mTDpb5r23VuK9pIn8NXlH5K+BxpJfZyvJff4fpfw54enqZunsFnoynV0UWw5LuWX22ydrU9LZsc25c0hP5AcrK3MN1BX568Ay23dxz6R0tsy63bLyeDr7NPk6jKnkzrll9ttxTfnz+Kjq8nR2m133WfcXABuRX86obpSNNK87uOl57yeSNgcuBqYC84E3R8TTfTLbAecBW1H/uu/siPhm8dyZwHvgH1+y/mdE/G6o+bqHmW0QJLUAZwOviYj9gP2Ba4unXwfsmSjmWbmI+PQQjV9l82ZmZmZmZmZmZmZm9sL8B3B1ROwCXF383VcP8P8iYg/gUOAMSY3tBF+PiOnFz5CNZeAGM1t/NUv6iaQ7JF0CbEH9nntLACKiMyLukXQ48FrgLEmzJE2T9B5JMyTdLulSSaMGyJ0r6Y0Akr4kaW4xv68m8gdL+nsxj5sl5f9Fz8zMzMzMzMzMzMwMiNg4f16gE4GfFI9/Qr1zS5/1Fo9HxK3F4xXAXcA2L2Sm+X60ZmvXbsC7IuJ6ST8G3gJcATwk6WrgN8CFEfF3SVdQH8LxEgBJSyPih8Xj/ynK+VY/OYrfmwMnAbtHREiaEBFLB8mPoN4d9OSImCFpHJAf58bMzMzMzMzMzMzMzAayZUQ8DvWGMUmTBwsXt3naH7ipYfL7JL0dmEm9J9rT/b22kXuY2frqkYi4vnj8U+DIiHg3cBxwM/BR4McDvHZvSX+VNBt4K7DXEPNaDnQAP5L0emCoG13tBjweETMAImJ5RDxnkHBJp0uaKWnmhRddNESRZmZmZmZmZmZmZmYbh8bvx4uf0/s8/ydJc/r5ObHkfMYAlwIfiojem/F9D5gGTAceB/6/TFnuYWbrq76dNgMgImYDsyWdDzwInNbPa88FXhcRt0s6DThm0BlF9Eg6hHpj3CnA+4CXDPIS9VO//so9m/p915j3wAMb5Y0bzczMzMzMzMzMzMz6avx+fIDnjx/oOUlPSppS9C6bAiwcINdCvbHsZxFxWUPZTzZkfkh9xLohuYeZra+2l3RY8fhUYJakYxqenw48VDxeATTeQ2ws8HhxsLy1YXrfHPCPFujxxY3/PlSUPWAeuBvYWtLBxevHSnLjs5mZmZmZmZmZmZmVEmij/HmBrgDeUTx+B/CrvgHV76H0f8BdEfG1Ps9NafjzJGBOZqZuMLP11V3AOyTdAWwOfAP4d0n3SJoFfIZnepddBHxM0m2SpgH/RX2s0j9Sb9xigFyvscBvinn9BfjwYPmI6AJOBr4l6fZiPm1rbMnNzMzMzMzMzMzMzDZdXwJeKuk+4KXF30jaWtLviswRwD8BL5E0q/h5ZfHcVyTNLr7zP5ZnvvMflHvF2HonIuYDe/bz1Cv7mUZxr7PG/PeKn6FypzU8PqRMvrh/2aH91cfMzMzMzMzMzMzMzJ6fiFhC/RZKfacvoGgniIi/Qf9d2SLin57PfN1gZrYWbLlwdjo7t/XV6ewutRnp7D1Nzzm/DGhkS08q196dP4WMHtGVzk4YsSSdXd09OZ0d2dqRzj6+sr/ROAcot6WazkbybnZbtT6aLnN1dUQ6u1PX4+ns3O4d0tlaLd/NurMnn50wsjudrUa+3IXLcuusUhmXLnPKhM50dvO2lensGK1IZx/u3jqdXd3Vks5u1rI0nR3blF82UUvlVtTyx2N7d/54WNXdms6Oas6fw55uH5mvQ1dTOjt+ZO7cDPDw4vHp7JiRuRPT5qPzx2NXNT+IwcqO/DoY05Y/33aXONe0NOdvNdrWnK/D8o4Sx9nI3Dlk7Ij8e1lP5LfDuOZV6WytxCAVGvq2r//QWskfZ7USy1ZGs3LHWTXy+22ZoUhaKvnjrDJMdSiTLbN920qcR1d35wZvKFPXMuf8rmp+3TZVcu9lAJ3d+XKz1+MAKjHaTfZaFMotW1alRF1rJeq6pCN/rbD7Pb9PZ+/e7YR0duxuo9LZLX6VuoUGAB3V3L67ded96TKXtE5MZ7fu7vd2If16esRu6WxbJX/tPKbr6XT2saap6WyZ953lXbntu133/ekyF7Zun86Wed+pKH/sdkf+WmVi14J09rGWHdPZkT35zzuLK1ukclO656XLfLyyZTo7pmdxOvtI83bp7NgSx1kZ91Z2Smez+02Za7CJ8UQ6u6CSPx42i0Xp7NMd+XNzmfednfu/lVK/HmnN7wvbd6RGjGNOdWq6zM7R+eN8l2du+WS2yfKQjGZmZmZmZmZmZmZmZrZJcw8zMzMzMzMzMzMzMzOzdaBMD0cbXu5hZmZmZmZmZmZmZmZmZps0N5iZmZmZmZmZmZmZmZnZJs0NZmZmZmZmZmZmZmZmZrZJ8z3MzMzMzMzMzMzMzMzM1oHwPczWG+5hZmZmZmZmZmZmZmZmZps0N5iZmZmZmZmZmZmZmZnZJs1DMm5iJP0YeDWwMCL2LqZtDlwMTAXmA2+OiKeL5z4BvAuoAh+IiCuL6QcC5wIjgd8BH4yodx6V9Dbg34EmoAeYAXw0IpYOUq/3AmcU81kJnB4Rc4dYlg8DXwS2jIhlxbRjgF8BDxaxxRFxvKQzgfcAi4ARwOci4sIhym8GngB+GBGfaJh+LTAFaC8m/U9EXDJoWT3dgz39LLUWpbOq5sutVvLlViOX7anly6xFvn2+EtV0NlvXsqqllq3Euk2WW2YdlFm3quXL7anml6tM1/Ey6zYosR1KlNuTXA0jS7xLlpl/DNN+W2odlMiW0VPi0kLkdpwy66ta4ngos9+W2RfLnJfKHGdljp2ennQ0fTyUqWuZbHfPMB3nJbLKnxqpNa35cw3k990y++JwnWvMeg3X/pg9jw7XubnM+VbDVYcy13fJ91Motx00DEMDRYm6llFmfZUxdrdR6eyKe1ans1uUqEN2P6+UuM4vc+yU+fxQptwyynw2Gq46ZI/fCiXe/NcDpa4VSlw8l9kOw7F9FbU1XiaUO87KnPPLHGdl1Mq8Tyezw7W+ynTpKLN9y3wuKbO+KuS/jyv33ptbtmp+FZT6zsjjApq5h9mm6FzgFX2m/QdwdUTsAlxd/I2kPYFTgL2K13xXUlPxmu8BpwO7FD+vKF7zCuDDwAkRsRdwAPB3YMsh6nVBROwTEdOBrwBfSyzLqdQb407qM/2vETG9+Dm+YfrXi/JPBH4gqWWI8l8G3AO8WVLfd7e3Nsxj0MYyMzMzMzMzMzMzM7P+RGycPxsiN5htYiLiOuCpPpNPBH5SPP4J8LqG6RdFRGdEPAjcDxwiaQowLiJuKHqVndfwmk9S7032WDG/akT8OCLuAZA0X9KXJd1c/Oxc5JY31Gc0DP6vh5KmAWOAT1FvOCuzDu4DVgObDRE9Ffgm8DBwaJl5mJmZmZmZmZmZmZnZhsMNZgb1IQ0fByh+Ty6mbwM80pB7tJi2TfG473So90a7dYj5LY+IQ4BvA9/onSjpDEkPUO9h9oEhyjgVuBD4K7CbpMkNzx0laVbx88m+L5R0AHBfRCwcqHBJI4HjgN8U8+nbKPezhnlMHKKuZmZmZmZmZmZmZma2HnODmQ2mv0F2Y5Dpz36xtE/RoPSApJMbnrqw4fdh/ygg4jsRMQ34OPWeY4M5hXrvtxpwGfCmhucah2T8fMP0D0u6B7gJOHOI8l8NXBMRq4FLgZMahqOEZw/JuKS/AiSdLmmmpJk/vuJPQ8zOzMzMzMzMzMzMzMzWleZ1XQFbLzwpaUpEPF4Mt9jb8+pRYLuG3LbAgmL6tv1MB7iT+n3LromI2cB0Sd8GRjbkY4DHvS6ifo+0fknal/p90/5Y3FpsBDAP+M5gC0n9HmZflfR64DxJ0yKiY4DsqcARkuYXf08EjgXSLV8RcTZwNsDqv/5iAx211czMzMzMzMzMzMyGSy36659i64J7mBnAFcA7isfvAH7VMP0USa2SdqTeSHVzMWzjCkmHqt5i9faG13wR+Kqkxga1xsYygJMbft8AIGmXhudfBdw3SH1PBc6MiKnFz9bANpJ2yCxsRFwGzGxY5meRNA44Eti+dx7AGZS8V5qZmZmZmZmZmZmZmW0Y3MNsEyPpQuAYYJKkR4H/Br4E/FzSu4CHKYY3jIg7Jf0cmAv0AGdERLUo6l+Bc6k3hv2++CEifidpC+D3xRCGS4E5wJUN1WiVdBP1BtveRqj3SToe6AaeZoDGrMIpwAl9pl1eTL8puSo+C1wg6YfFsI6NXg/8OSI6G6b9CviKpNZk+WZmZmZmZmZmZmZmtoFQhEeKs7WnGOLwoIhYvK7rsjbNvX9B+kCT8sek+h3Rsn/R763n+lehbxviCy9zuAzHcpXNVmkaOtSbjVy2WT3pMnsi/78PTaoOHSqUWbfN5OtbptzVtb4dVAfWUmKdZY1QVzrbXqKuZbRWOocOFbqjJZ0tc/4osx5qJTqvZ+tQZh8vM/8yx0Mt8uWW2cfb1J7OtseodLZF3els1qjainS2Wsnvi92MSGebyS9XJ23pbBP5fWFVLb8dRlXy27d5GLZZlBjWo8w60HP+12hg7Rqdzo6MVelsJfL1XaEJ6Wxbcps11fLvOWXWQSsDjRb+XGXWQYfy++1wXd8N13VrVplrlTLXdmWOneEqt8z7fxllPpdklTkvlVHm2rmpRLYrhuf/Jeft/pJ0dupd16ZyZY6xMtu2zOeiMtdhZZTZb4ZjvwVoIXc9XC3xf+ll1leZ7VDmHDpc5/zs596yyuznWV0lzqEjSlyvddby17itlfznrTLbrKuWX7ZsHcrst9USn6GG4/M8lPssqxLHWZnPZqXOz8lsRfm6dpfYD8br6XR2h513W/dfCm5ELvr7xtlIc8rh2uD2Ew/JaGZmZmZmZmZmZmZmZps0D8loa1VxP7AUSfsA5/eZ3BkRL1pT9ZH0HeCIPpO/GRHnrKl5mJmZmZmZmZmZmZn1Z+PsX7ZhcoOZrbciYjYwfZjnccZwlm9mZmZmZmZmZmZmZus/D8loZmZmZmZmZmZmZmZmmzQ3mJmZmZmZmZmZmZmZmdkmzUMympmZmZmZmZmZmZmZrQO+h9n6wz3MzMzMzMzMzMzMzMzMbJPmBjMzMzMzMzMzMzMzMzPbpHlIRrO14I6FU9LZ19713+nsb/Y8M1/ugm+ms6t3PTiVG7XgnnSZNLeko7dMeX06u1fnzensgrG7p7MTuhems2MX3pfOVjpWpXKXj/nndJkv2fzWdPZrf90znf3MNj9JZ7u32C6dRUpHW556PJ3tmTA5nQ3l/l/koc0PTJe569yfp7O1CZPS2faJ26ezI665PJ1t3ueAdHbl5GnpbNvqp9LZatOIVK6puz1dZjTlL21Uq6azzSuWpLO1kWPT2eWbTU1nt33oz+ns6im7prOtq3LLNm+Lw9JlTu56NJ1ta386nW1pX5bOdo8cn84q8vvCiCcfSmc7puyczi4bu01u/j3546G1e2U6u7p1Qjrb2TwqnVWJsUWaoied7aiMTmcr1NLZtp7c+3RnU34dDMf8AdpbxqSzEfn33lY60tlqiY+TPSWyW66al85mtXTl1233iPz+Vea6plrJXw+HmtLZplpXOlupdqez7W2bpbNZteQ1GOSv1wBGt+evPxaMzJ+bO6qt6WyQ3xem3nVtOjt/j2NSuQW/z382O+m6d6Wzs9743XT2yPu/n84u2uel6exj1dx7JMD0+y9IZ1fslL/WH9GxPJWb2XJkusxtRy1KZ7deckc6++jE6ens9gtnpLO3Tzgund1/Zv77h5UH5PeFju98JZVb+pH8frvHXRens3fs+rZ0dt95+XLnTHtTOttZzb+XHHh//jP9rOSy7bk6/x1M9voWYNIjt6Wz922b32fGdS9OZ5uq+ffTJyv7pLMHPnZJOnvbtrnvw3brvj1d5qjH701nL2rNvz+cln87NduguMHMho2kTwJvAapADfgX4MvARyNipqT5wIrieYDLgZOKxzsDjwHtwB0R8fa1UN8zgZUR8dUBnj8XeDGwHBgJ3Ah8IiIeG+66mZmZmZmZmZmZmdnGp+Z7mK033GBmw0LSYcCrgQMiolPSJKC/rgTHRkTjv3t8pnj9tRQNay+wHgIUEfl/7R3cxyLikqLcDwHXSNo7IvL/hmJmZmZmZmZmZmZmZusV38PMhssUYHFEdAJExOKIWLAmZyDpNEm/kvQHSfdI+u9i+lRJd0n6LnArsJ2kj0maIekOSZ9pKOOTxWv/BOyWnXfUfR14AjhhTS6XmZmZmZmZmZmZmZmtXW4ws+FyFfWGqnslfVfSiwfIXSNplqSbnud8DgHeCkwH3iTpoGL6bsB5EbF/8XiXIjsdOFDS0ZIOBE4B9gdeD+Ru3PVstwL5G2OZmZmZmZmZmZmZmdl6xw1mNiwiYiVwIHA6sAi4WNJp/USPjYjpEfGi5zmrP0bEkohoBy4Deu+w+1BE3Fg8flnxcxvPNHDtAhwFXB4RqyNiOXDF85j/gHd2lnS6pJmSZl79q7OfR9FmZmZmZmZmZmZmtjGL0Eb5syHyPcxs2EREFbgWuFbSbOAdwzGbAf5e1TBNwBcj4geNQUkf6uf1Ze0PXN1vxSLOBs4GuOjv4Vs3mpmZmZmZmZmZmZmtp9zDzIaFpN0k7dIwaTrw0DDM6qWSNpc0EngdcH0/mSuBf5Y0pqjbNpImA9cBJ0kaKWks8JrsTFX3Aer3avvDC10IMzMzMzMzMzMzMzNbd9zDzIbLGOBbkiYAPcD91IdnvGQNz+dvwPnAzsAFETFT0tTGQERcJWkP4AZJACuBt0XErZIuBmZRb8z7a2J+Z0n6L2AUcCP1ISW71tTCmJmZmZmZmZmZmZnZ2qfwSHG2gSruiXZQRLxvXddlKI++703pA+1z2/5g6FDhrBGfTWe/MPZL6ey4cS2pXLWWP3+MHd2Uzv7zNlels5cse3k6e+QOD6ezv5ixTTo7afP8sj21tJrKvX+H36XL/F3tVensax/5Wjr7hfYPpLNdXbV0tlLJj2G83Tat6ezqjvz+WK3mskfvtSJd5m3zx6ezE8fn11e1ll9fC59KR1m6rCedPXq/fPbOR9rS2eam3LLtvd2qoUOFuxaMTmc7OvP7zA5b5rfZ3Q/lt9lTT+X/52Ly5PzxMHZ0vg4rVuXWw6rVufMXwIpl3ensjlNH5stdld8Oo0cNz0AKW0zI1+H2u/LrYYtJI1K5A6atTpe5sjNXJsCe4x5MZzuV32a1EgNatNGeznaRPx5i4FvOPrcOkVu/w7UORsXKdLZDo9LZMpqV32+rkf//y5XV/Pn5nkWbp3JlPsqu6sjvB2NH5Qtubc6fE8oY3Zo/5y5cnrt2B+jqzq+Hzcbk6qASt6dobsqv20qJ0fOfWJpfB8fucH86O37V4+lspZbfZgvG7JrOXj9/61Ru6xN2S5fZ/re70tljfvuedPbOk7+Vzk5tfSSd3XLGL9PZG/bOf4aZMmpJOnvbE7nPh6/r/Gm6zId2ODadXdy5WTq7b8ff09kHxu6fzu7+yO/T2Ud3ODqd3W7mxens3/b4YCq3+9lvS5d589vz8z/h/i+ns1fv/rF09vgHvpnO1pYvS2f/dtCn0tlDe65J5W5szu+3Rz/643R25o7/lM4esOjX6exXH31jOlvmO/KPtn8mnZ15+H+msy+af14qd8HI96bLnLJZ/truyJ4/prNjD37lhnmDqvXU+de94NsGrZf+6egSH8bWE+5hZmZmZmZmZmZmZmZmtg64T9P6ww1mtt6T9HKg77/xPBgRJwHnDsP8vgMc0WfyNyPinDU9LzMzMzMzMzMzMzMzW/fcYGbrvYi4ErhyLc7vjLU1LzMzMzMzMzMzMzMzW/eG5+YOZmZmZmZmZmZmZmZmZhsI9zAzMzMzMzMzMzMzMzNbB2q+h9l6wz3MzMzMzMzMzMzMzMzMbJPmBjMzMzMzMzMzMzMzMzPbpLnBzMzMzMzMzMzMzMzMzDZpivAAmTY4SZ8E3gJUgRrwL8CXgY9GxExJ84EVxfMAlwMnFY93Bh4D2oE7IuLtRZnbA3OBMyPiq8W0McBZwMuA5cW8vh8RPxzuZSzmfxpwUES8b4DnzwTeAywCRgOzgU9FxNyhyr7s5vxItK+Y85lslPO2yWdPvvXf0tmml52Yyo2YNyddJqPHpqM37fC2dPbAFX9KZx+edFA6u1X7vHR21MJ8lqVLUrHLtvxQusiXT7ghnf3mLfl18J9t30hnY4sp+WxTSzpbWfhYiTpsnc9WmlK5+VsfmS5z6vU/SmcrE7dIZ7u23jlf7o1Xp7PN2++QznZuvUs627LyqXQ2WkakcrWWtnSZTZ2r0lmq1aEzhUrn6nQ2mnL7F0D7pKnp7Ki7b0pnu3beL51tWfpEKvfQDsemy5y86sF0trmnI51tWbYwne0Zs1k6q2pPOtv82AP5OmybP35XjN8ulRP5a/fWzuXp7KqRE9PZrqaR6Wwn+eN3dC1f347K6HS2J/K3bR5fy71PdzaNSpfZTe5cBzC6uiyd7WjKr4OuaE1nWyv5Y7JaYt12R/79f+tV96azWa0rF6ezXaPzx0NI6Wy1Kb8vZK9VAFq68u9RlWpnOrt69ORULsivg1qJ5SpT7pjV+e37yJg90tnOaoltVqa+zfnrlS1+8ulU7tpX5T82jzwyvw7uvOiudPYDTd9OZx/d5zXp7EOr8581jnjw/9LZp3Y7Kp0d9/T8VO66ESeky5w2PncNBrDtwlvS2fsmHZGvw9IZ6exto16czh7ywLnp7GN759fZxF98LZW75ZVfTZd5xJxvprM37vOBdPbQe76fzs7c4/R0dkVn/rx07P3/m6/DXu9N5fZfemW6zCe2mp7Obj3/+nT27u3y+8yOq/LfW1Vq3ensjU354+HoR3+czt409e2p3PTOv6fLbF1wXzr7kxH/ms7+y8tKvPHZkM65psQHvQ3IO4/d8PaT/Ccc2yRJOgx4NXBARHRKmgT9fuo/NiIaP6V8pnj9tRQNa33yXwd+32faj4B5wC4RUZO0BfDPa2AZmiIi/63o4L7e0MB3MvBnSftExKI1VL6ZmZmZmZmZmZmZma1lHpLRhjIFWBwRnQARsTgiFryQAiW9jnrD2J0N06YBh1DvsVUr5rUoIr48SDnHSLpO0uWS5kr6vqRK8dxKSZ+VdBNwmKS3SbpZ0ixJP5DUVOTeKeleSX8B8v+GVa/fxcBV1HvfmZmZmZmZmZmZmZnZBsoNZjaUq4Dtikal70oaqM/xNUVj1KDjRUkaDXycogdag72A23sby0o4BPh/wD7ANOD1xfTRwJyIeBGwBDgZOCIiplMfOvKtkqYU9TgCeCmwZ8l5A9wK7P48XmdmZmZmZmZmZmZmZusJN5jZoCJiJXAgcDr1e3ddXNzrq69jI2J60UA1mM9QH9Zw5WAhSZ8sGuCG6s12c0TMK4ZcvBDoveFQFbi0eHxcsQwzJM0q/t4JeBFwbdGTrQu4eIh59VvVQZbhdEkzJc286vKzn0fRZmZmZmZmZmZmZrYxi9g4fzZEvoeZDalojLoWuFbSbOAdL6C4FwFvlPQVYAJQk9QB/AHYT1IlImoR8Xng85IGbViD59wQsffvjob7lgn4SUR8ojFYDA35Qg/d/YG+92erVyTibOBsgMturm2gpwgzMzMzMzMzMzMzs42fe5jZoCTtJmmXhknTgYeeb3kRcVRETI2IqcA3gC9ExLcj4n7qDU//03B/sTYG6cFVOETSjsW9y04G/tZP5mrqjXSTi3I3l7QDcBNwjKSJklqAN5VZFklvAF5GvWebmZmZmZmZmZmZmZltoNzDzIYyBviWpAlAD3A/9eEZLxmGeb0bOAu4X9JTQDv1+50N5gbgS9TvYXYdcHnfQETMlfQp4KqiYa0bOCMibpR0ZlHG49TvR9Y0xPw+LOltFPdIA14SEYuSy2dmZmZmZmZmZmZmZushN5jZoCLiFuDwfp46piEzdZDXHzPIc2f2+Xs58C8lq7g6Ik7up+wxff6+mH7uURYR5wDnZGZU1PfMkvUzMzMzMzMzMzMzM7P1nGJDvfuabfIkHQN8NCJevY6rMqTV//fp9IH2X51Ddap7xofvemc6+6mJ30pn99xni1Ru1arq0KHCZhPy7fPvmvL7dPZnT70ynX3Jzg/ny71+Sjq79VYt6eyTi3pSuQ/vcmW6zN9059fBSYvy+8Fnl+Tbr1et7Epnm1vyowHvOHVUvg6r8+9nteRtBY/dd6jbKD5jxgPj0tmJ49NRqrWhRqZ9xvJV+XIffzK/zQ7fN1+Hux/JH+styeiBO+W3w9wFY9PZMvvM9lvW8nWYl46yaFF7OrvtNvnjYczo/HG2clVu2Z56ujtd5vJlHensPnvnD4gVK/PbobU1v9/W8sWy9Rb5/WbGrM50dsJmI1K5I/fOr9uVnbkyAfYee38621VpS2d7yL9Hjoz8SaxTI9PZWolR6EfXlqdyHZXR6TKrQw5g8IxRtRXpbGclf04osw4q5A+IMuUu686/Tz6wJJetljh2V6zKnxM2G5c/zlubS1SihNGt+evsJ5bmj/XO/KmcCWNyy9ZU4kYPzZX8upXy2QVL8tcfL5uWP9+Na1+YzqqW32aLRk9NZx9ZNTmVm37Jv6XL/PF+P0xn9zplj3R21K2z0tldW/PbYeJt+c+Hf9vjg+nsDqOfSGdvWrB9KvfG9nPTZT407fh09vH2SensAZ3XpbPzx+2fzu684M/p7CPbHpHO7nDnFensdTu8J5U7+HcfSpd57XH5z8gvn/PZfLnTP5XOHjv3rHS2e/HidHbGcV9IZw/pyG3f61vy++0x876Xzt6827vT2UOeuDSdPXPem9PZMj7Z8V/p7O3HnZnOHnRf6n/6+emY96XL3H5S/rP/oT3XpLPjDnx5/uLKhvSjq9koG2nefdyQt1ta77iHma33JO0DnN9ncmdEvAi4dhjm90meez+zX0TE59f0vMzMzMzMzMzMzMzMbN1zg5mt9yJiNjB9Lc7v84Abx8zMzMzMzMzMzMzMNhElBk8wMzMzMzMzMzMzMzMz2/i4h5mZmZmZmZmZmZmZmdk6EBvlHcw2TO5hZmZmZmZmZmZmZmZmZps0N5iZmZmZmZmZmZmZmZnZJs0NZmZmZmZmZmZmZmZmZrZJ8z3MzMzMzMzMzMzMzMzM1oFabV3XwHopfEe5tUJSFZhNvZHyLuAdEbF6gOxpwEER8b7nMZ8zgZUR8dXnX9t/lPUR4N1AD7AI+OeIeGiA7FTgNxGxd391kXQu8GJgOTASuBH4REQ8VmTnAwcBlwBfjIgrG8r5ELBrRPzbC12moQy17otleg/19TGa+jb9VETMHazca2a3pw+0Qxdflo3yl83elM6++OlfpLNpj/W7O/TrqRm3p7MP/suP09n9Vv81nX10s33S2Ukdj6azY+ffls7GyNGp3DUTTkmXecCoOensrx7eP519e8vP0tme0RPSWUX+KqB55dP5OozZLJ0N5TpYPzFx76FDhe3u+2M62zNhcjrbPnbLdHbs/TPS2Rg/MZ1dOXlaOtvasSydzWrqWJHO1ppb09lKtTufffCudLY6Lb/flNm+Y564J1/upKnpbOuqJanc45P3S5e5+ar8ORQpHW1b+ng6W+q8VKumsy1PP5HOdo/fIp1tH5PbF3qaRqTLbO1emc4uG5nfF7vIH2e1yA9o0ab2dLYjRqazUv7zzshYlcp1qS1dZpDfx0fQmc52kF8HESXqoHwdajTl61BiPWyxOneNWebYbe7MHw9do0pcU5RYrlqlpUQ2v25HdOWXrVJinXW0jU/lyqyD7DVYPZsvd/SqRens4+N2S2fbq8NzrI9qyp/vdrz9klTupt3eky7zRff9KJ29YVq+3NUHTE9nN7tjZjr7VHv+fLfVh49KZ3f76n+lsyT33RtaXpIucocxT+az91+Vzj66c74O2971h3T2jp3fks7u9fQ16eyCyfnPqNvd/stU7pbd35Uu88BH89+VzNz2zflyn8h/t3PblNels509+b4Phy7KnT8Abt/6tancXitvSJf5xGZ7pLPb3nRBOvvAIe9MZ7fszH9v1VTLfz68Qwemswd0/S2dva31iFRu3578Z/+2p/KfzS5tzh/nbzuqxBu1DekHV7FRNtL8y8tKXCCtJzwk49rTHhHTiwalLuC967pCCbdRbzzal3pD1ldeYHkfi4j9gN2Ksq+R1PdbnwuBvi0FpxTTnzdJ+U+cQ/t6sS13AS4G/iwp/42YmZmZmZmZmZmZmZmtV9xgtm78FdhZ0uaSfinpDkk3Stq3MSRprKQHJbUUf4+TNF9Si6RrJX1D0t8lzZF0SMNL9yyenyfpAw3lvU3SzZJmSfpBbyOSpJWSPi/p9qIeWwJExDUNveBuBLZdEwsfdV8HngBO6PP0JcCrJbUWdZsKbA30++8Yko6RdJ2kyyXNlfR9qf6vX8VyfVbSTcBhgyz/OyXdK+kvQO5fOZ5ZlouBq4D8v2CYmZmZmZmZmZmZmdl6xQ1ma5mkZuqNRLOBzwC3FT24/hM4rzEbESuAa4FXFZNOAS6NiN4+wqMj4nDg34DGMex2B14OHAL8d9HAtgdwMnBEREwHqsBbe8sBbix6f11HfcjBvt4F/H6IxZtWNEbNkjSLoXvR3VrU9R8iYglwM/CKYtIpwMUx+NihhwD/D9gHmAa8vpg+GpgTES8CltDP8kuaQn07HAG8FNhziDqnlsPMzMzMzMzMzMzMbCgRG+fPhsgNZmvPyKIRaSbwMPB/wJHA+QAR8WdgoqS+A8X/COgdnPedwDkNz11YvPY6YJykCcX030ZEZ0QsBhYCWwLHAQcCM4p6HAfsVOS7gN8Uj28BpjZWQNLbqN9f7KwhlvGBYqjC6UWj1PeHyA80hmnjsIyZ4Rhvjoh5EVEtskcW06vApcXjgZb/RcC1EbEoIrqoD7FYVr/LIel0STMlzfzNJf/3PIo1MzMzMzMzMzMzM7O1IX+nSHuh2otGpH+Q+r054rPaXiPieklTJb0YaIqIOQNlG/5uvFN3lfp2FvCTiPhEP/PsbujB1ZvvrePxwCeBF0dE/g7gOfsDV/cz/ZfA1yQdAIyMiFuHKGeg9dBRNKLBAMsv6XX9vL6s/ak3hD67EhFnA2cDXDO7fQNtUzczMzMzMzMzMzMz2/i5h9m6dR3FsIiSjgEWR8TyfnLnUe85dU6f6ScXrz0SWBYRywaZ19XAGyVNLl6zuaQdBqucpP2BHwCvjYiFQy5Nkuo+AEwB/tD3+YhYSX0oyh8zdO8ygEMk7Vjcu+xk+r/f2UDLfxNwjKSJxb3i3lRyWd4AvCxZTzMzMzMzMzMzMzMzWw+5h9m6dSZwjqQ7gNXAOwbI/Qz4H57bKPO0pL8D44B/HmxGETFX0qeAq4qGpW7gDOChQV52FjAG+EXRGe7hiHjtoEs0uLMk/RcwCrgROLYYBrE/FwKX8czQjIO5AfgS9XuYXQdc3jcw0PJHxI2SzizKeJz6/ciahpjfh4thKkcDc4CXRMSiRD3NzMzMzMzMzMzMzGw95AaztSQixvQz7SngxH6mnwuc2zDpSOCSiFjaJ3pp3yEGI+LMPn/v3fD4Yvq5R1dj3SLiEuCS4vHxAyzOc0TEfGDvPtPObHh82hCvn9rn78sZ+B5nfa2OiJP7KXNMn78HWv5zeG7vvYHqeSb1hk4zMzMzMzMzMzMzsxckfDOf9YbCW2O9JulbwAnAKyPi3obp1wIfjYjn3DtrU1IMZfnRiHj1Oq7KoO564LH0gdZda0mXO6IyUAe95+qotaazrclyy9S1o5qf/7iWFelsd+Tr0KLudHZVdVQ621bJ396vlhwJd1RlVbrM5dVx6ezYSn7drqw9p51/QD21/P9fVFRLZ5tVHTrUW4cYqnPoM6TcIdmq/DFWHbJz6jMisv8PALX0/w6UW1+1yI/KXKYOlRd8W8jnaqt0pLNdMSKdjRLL1aZ8HVbV8ueP9p78uXF0c74OLZX8+a6aPHZW94xMl1nGpJbF6WwX+fVVRpnjocx7SUe0pbMru3Prd8KI/Hm8zLmmWT3pbPa9rKwmSpzzS/zfn0qcl7J1WNfzH846VMi/T5c5j5a5ZmtV7tqqzHtv9lwHMKLE+//6oMy+UOa8sCEpsy82DdP1UhnZa1GAMbXB7rzwjJry+3i38tdLI6sr09mHqlPT2af3PSidPerG/01n7xnzonR2nwcvSWfn7fzKVK7MdUKZ/bYn8sf55I7BBhLqU4dKfr9Z2rplOqsS7yWjevLXNqubx6ZyY7qXpst8umVyOju+Z0k6u6x5Yjo7rvpUOqsS3+O2t+Q/09eS75Nl3s8nVEtc5zflr5vbevLflzwY09LZMufm7ZU/zlY1j8/XIXnsVEu89zeRv87vLPH5Za+dp2ycFxXryPf+MAxfpqwH/vUVJd7s1hPuYbaei4j3DzD9mLVcFQAk7QOc32dyZ0Tkr0rX/HyvHYb5fZLn3s/sFxHx+TU9LzMzMzMzMzMzMzMzW7fcYGalRMRsYPrGPt+iYcyNY2ZmZmZmZmZmZmZmmwA3mJmZmZmZmZmZmZmZma0DtY1yQMYN0/AMyG1mZmZmZmZmZmZmZma2gXCDmZmZmZmZmZmZmZmZmW3S3GBmZmZmZmZmZmZmZmZmmzTfw8zMzMzMzMzMzMzMzGwdiNhYb2KmdV2B0txgZrYW7Hj3r9PZ71f/JZ19L9/Nl8u/pbOX/PC6VO7I174oXeacm+elsz/71wXp7K9WHJ/OHrrtw+nstXM3S2dV4tzflOzX+47t/pIu886eo9LZly7/fTr7vRVvS2dXrOhJZytN+RW27Vb5t6mOznSUavJuqodOW5ouc+4T+X1mszH59dVdzXcGX7Yqn130VC2dPWS3/Mp9cNHIdDZ7PbjbVivTZc5bPCad7ehKR9luUj78wOMj0tlly6vp7BYTx6Wzo9vyF9tPLc/lOjryZa5YmV+u3XbaKZ1dsiwdZWTb8FyYbz42f+zMuS+fnbh57nw3fYf8cb6iK78v7jLu0XS2Fvlyy6gov98Qw/Mxponc+blnmD5GZedftg4V8vuiyB/rUeIDcHu1LZ29d9nkVK6nxC7T0ZU/dsaNyhc8oqnEui1xWmprzu8LT67Ir9vunnwlxifXg5TfZ5or+WyZchcuy5+Xjtgu/7lkTNfT6Wwl8vvNohHbprOPVbdJ5abf9v10mdfv/cF09oi5/5vO3rbLR9LZo2/Ml/vXQz+QzlZunpPO3rL9KensY4tGpXKv7vp5usxHtz08nV3eMzqd3XFRfh9/ZPv8Z8ntb8kv24MHnprOTnhibjp7z+YnpnKHzvpZuswZe/5HOvvSRy5NZ++Y+t509phHr0hnWb0qHb1pr/z3QPt135zKzWrOfw+046Jr0tm525yQzu41N/+9xg1N09PZMu0V07uuSmfv2eOMdPZFT+e+P7xMb0qXOWlsdzp7eC3/XRRMKZE123B4SMZhJqkqaZakOZJ+IWnAqyxJp0n69vOcz5mSPvr8a/qssj4iaa6kOyRdLWmHQbJTJc3pM+1MSR8tlufCPs9NkrRIUmvx968k3dDP6x8r1ttcSfkrrTVA0nxJkwZ5vneb3i7pVkn5q1wzMzMzMzMzMzMzM1vvuMFs+LVHxPSI2BvoAvL/YrLu3AYcFBH7ApcAX3me5VwGvLRPI+EbgSsiolPSBOAAYIKkHfu89usRMR04EfiBpJbnWQcAJK3JfwPu3ab7AZ8AvrgGyzYzMzMzMzMzMzMzs7XMDWZr11+BnSVtLumXRQ+uGyXt2xiSNFbSg72NRJLGFb2eWiRdK+kbkv5e9Fo7pOGlexbPz5P0gYby3ibp5qJX1A8kNRXTV0r6fNFT6kZJWwJExDURsbp4+Y1AfryIBhGxHLgOeE3D5FOA3l5nbwB+DVxUTO+vjPuA1cCAY50NtE6KnmpnS7oKOE/SFpIulTSj+DmiyE2UdJWk2yT9gHKDq44D8uN0mJmZmZmZmZmZmZkVIjbOnw2RG8zWkqKH0wnAbOAzwG1FD67/BM5rzEbECuBa4FXFpFOASyOid9DZ0RFxOPBvwI8bXro78HLgEOC/iwa2PYCTgSOKHltV4K295QA3Fj2lrgPe00/V3wUMNTjwtKIxbpakWTy7F92FRf2RtDWwK9A7iPGpxfMXFo+fQ9IBwH0RsXCIOgy0Tg4EToyItwDfpN5z7WDqjXU/KjL/DfwtIvYHrgC2H2JeI4tlvbso43ND5M3MzMzMzMzMzMzMbD02PHertkYji0YkqPcw+z/gJuoNNkTEn4seTuP7vO5HwL8DvwTeybMbsy4sXntd0ftsQjH9txHRCXRKWghsCRxHvdFohup3mR4J9DY+dQG/KR7fAry0sQKS3gYcBLx4iGV8oGiM633dmQ3P/Qb4rqRxwJuBSyKiWvRm25l6Q1VI6pG0d0T03g/tw5LeA+wEvGKI+cPA6+SKiGgvHh9PvRde72vGSRoLHA28vnj9byUN1WOsvXd5JR1Gvffa3hHPbjeXdDpwOsC33/cW3vWK/M10zczMzMzMzMzMzMxs7XGD2fBrb2xMAlBDi02DZzW2RMT1kqZKejHQ1NCQ9Jxsw9+dDdOq1LevgJ9ExCf6mWd3QyNPb763jscDnwReXDTCPS8R0S7pD8BJ1Huafbh46mTqwyw+WKyOccXznyqe/3pEfFXS66k3SE2LiI7BZjXA36saplWAwxoa0AAo5v+8OolGxA2SJgFb8ExDZO9zZwNnA3T89vsbaCdUMzMzMzMzMzMzM7ONn4dkXDeuoxgWUdIxwOLifl99nUe959Q5faafXLz2SGBZRCwbZF5XA2+UNLl4zeaSdhiscpL2B34AvDYxFGLGhcBHqPd4u7GYdirwioiYGhFTqfeCe859zCLiMmAm8I4h5pFZJ1cB7+v9Q9L04mHj9jiBQe6X1pek3YEmYEn2NWZmZmZmZmZmZmZmtn5xD7N140zgHEl3AKsZuDHoZ8D/UAw32OBpSX+n3ivrnwebUUTMlfQp4CpJFaAbOAN4aJCXnQWMAX5R9L56OCJeO+gSDe4q4CfA/xXDL06lfp+w3sYzIuJBScslvaif138WuEDSDyOiNsA8MuvkA8B3ivXeTL2h7L3U7yl3oaRbgb8ADw+xPI3DbAp4R0RUh3iNmZmZmZmZmZmZmdmz1Ab6xtvWOvW57ZKtRyS9ETgxIv6pYdq1wEcjYuY6q9h6ZkNYJ9fOaU8faIc++Yt0uddNek6nvAEdvfiidLbWNjqV00P3pst8esYd6ez97/5ROnvAyj+nsw9PPDCd3Wr1A+nsqPn5ZWNkbt1etdk/DR0qHDbqlnT25w8dks6+q/ncdLY2amw6W0ZlxVC3FGyow9h051Ci0pTKPT55v3SZ2975u3S2ttnkdLZjwpR0dtTdN6WzbL5FOrpqy53T2RHtS/N1SKp0DzYi77NFJf+/QJWufLk8eHc+u/Ne6WjnmEnp7Mgn7k9nV0/ZNZ1tXbkolVu45b7pMjdb8Wg629Pcms6OXP54OlttzZ1vAVTtSWdbluY73/eMz2/frlG5c1hny5h0ma3dK9PZpaPy55puRqSz1cidbwFaK/ljsrPWls5K+c87bbQPHaLcOgj6G429fyPIj4TeSX4dlNkOI9SVzpZZNpUYAX3i6kdyZdby/7M2YnX+mqJjbP59uoxqJb/f1JLXKgCtnf0NVtK/SrU7ne1sm5DOZpVZrjJGrV6czj42fs90tr2aP87KHA+jmnLnGoAd77oilbthh9PSZR724I/T2b9NfXc6O+H9x6Szcfbv09nFq/Pv6bVD9k5nJ86+OZ3da/nfUrm/tbwsXeaO455MZ6fe9et0dt7uJ6azOz14VTp7x3YnpbN7PXV1OvvQ5EPT2Z3uyH1fcuOup6fLPPTRn6Wz12/ztnT2iCfy38HcOOXkdLa9O/9555in83WYNSW33+yz7Np0mQ9NOjid3enGvoNrDWzuIe9NZ6etzn9fU6nl3yP/XnlxOnt47S/p7K0jjkzlpvfkP/u3Pv1YOntp81vS2bcd1e8th+x5+uavN85Gmg++ZsPbT9zDbD0l6VvACcAr13VdzMzMzMzMzMzMzMzMNmZuMFtPRcT7B5h+zFquCgCS9gHO7zO5MyL6G0JxuOrwHeCIPpO/ORzrRNJE6vd/6+u4iPD9yszMzMzMzMzMzMzMNiJuMLOUiJgNTF/HdThjLc5rCet4ec3MzMzMzMzMzMxs47ZxDsi4Yaqs6wqYmZmZmZmZmZmZmZmZrUtuMDMzMzMzMzMzMzMzM7NNmhvMzMzMzMzMzMzMzMzMbJPme5iZmZmZmZmZmZmZmZmtAzXfw2y94R5mZmZmZmZmZmZmZmZmtklThJsvh5ukKjCbeo++u4B3RMTqAbKnAQdFxPuex3zOBFZGxFeff23/UdZHgHcDPcAi4J8j4qEBslOB30TE3n3rAiwGXh4RpzY8N4n6etg2Ijol/QqYHBGH9Xn9e4p5jwA+FxEXvtDlypI0n/p2WDzA8+ltCjDrvkXpA21MZWW6nitrY9LZ0ZUBq/cco6rLU7muppHpMse2L0pnn2jbMZ1tUXc6WyvxPwLVaEpnR5ZYtxFK5XpoSZfZE8PTWbhVnelsd+TrW1EtnW2ims6W2b4id0hWye8HzfSksz3D1MF7RIltlt0XAToif6yXOSaz2gY+vT5HTyW/L1ZLHDsja/lzc3slf25eVRuVzo6prEpnRf44i+Sx0xFt6TLL7AejaivS2eyxC9BdaU1ny5w/RtQ60tmuSn6draiNTeXKvJ8PlzLnxjLKnPPL1KHMfpOtQ5nz+HDMv2wdyihThyD/XlLmemWUcue7mvLHbq3EtV0za/69DMqtr1CJbIn39Ow5v57NlVtmucooU+5wnT/KfCYoo8z18KTOx1K5Ja1bp8sc1/NUOruqeXw6u9VTd6azLUsWpLO3bH9KOttUye8LS/Y5JJ3d7e4/pHKdJa6XmpX//FDG2J6n09lR7fl9YcGYXdPZkcpfr7RU89dW2ffUMt9VlDFc57sy1wplNNe60tmVldyxXuZ8W+YzVFOtxOfpEp/5FsfkdLZZ+WUbUxmezzDtkft82Kb2dJkttfz3BAtjq3T2wF03H54DYhP1tV9tnI00HzmxxEXtesI9zNaO9oiYXjQodQHvXdcVSriNeoPRvsAlwFeeZzmXAS+V1HjGfyNwRdFYNgE4AJggqW8rydcjYjpwIvADSfl3xH5IWpPfKmyI29TMzMzMzMzMzMzMzPrhBrO176/AzpI2l/RLSXdIulHSvo0hSWMlPdjbSCRpnKT5klokXSvpG5L+LmmOpMZ/i9qzeH6epA80lPc2STdLmiXpB5KaiukrJX1e0u1FPbYEiIhrGnpM3Qhs+3wWNiKWA9cBr2mYfArQ21vsDcCvgYuK6f2VcR+wGthsoPkMtE4knSnpbElXAedJ2kLSpZJmFD9HFLmJkq6SdJukH0Cpfxv6K7BzibyZmZmZmZmZmZmZGREb58+GyA1ma1HRw+kE6kP5fQa4rejB9Z/AeY3ZiFgBXAu8qph0CnBpRPSODzI6Ig4H/g34ccNLdwdeDhwC/HfRwLYHcDJwRNFjqwq8tbcc4MaI2I96w9Z7+qn6u4DfD7F404rGuFmSZvHsHlcXFvVH0tbArsA1xXOnFs9fWDx+DkkHAPdFxMIh6jDQOjkQODEi3gJ8k3rPtYOpN9b9qMj8N/C3iNgfuALYfoh59datcZuamZmZmZmZmZmZmdkGyA1ma8fIohFpJvAw8H/AkcD5ABHxZ2CipL4DBv8IeGfx+J3AOQ3PXVi89jpgXDG0IcBvI6KzuPfWQmBL4DjqjUYzinocB+xU5LuA3xSPbwGmNlZA0tuAg4CzhljGB4ohCqcXjXLfb3juN8CRksYBbwYuiYhq0ZttZ+oNVfcCPZL2bnjdhyXdA9wEnDnE/GHgdXJFRPQO7ns88O1iPVxR5MYCRwM/LV7/W2CoQb/726bPIul0STMlzbz0ovP6Pm1mZmZmZmZmZmZmZuuJ4blTtPXVXjQi/YPU7w3vntVRMSKulzRV0ouBpoiYM1C24e/GOzlWqW9jAT+JiE/0M8/uiH90kOzN99bxeOCTwIsjIn+HyL4Vi2iX9AfgJOo9zT5cPHUy9WEWHyxWx7ji+U8Vz389Ir4q6fXUh1OcFhGD3Q12oHXSeNfwCnBYQwMaAMX8y3QUfc42fU5lIs4GzgaYdd+iDbQTqpmZmZmZmZmZmZnZxs89zNad6yiGRZR0DLC4uN9XX+dR7zl1Tp/pJxevPRJYFhHLBpnX1cAbJU0uXrO5pB0Gq5yk/YEfAK9NDIWYcSHwEeo93m4spp0KvCIipkbEVOq94J5zH7OIuIx6T653DDGPzDq5Cnhf7x+SphcPG7fHCQxyvzQzMzMzMzMzMzMzszUharFR/myI3GC27pwJHCTpDuBLDNwY9DPqjTcX9pn+tKS/Ux/68F2DzSgi5lLvtXVVMb8/AlOGqN9ZwBjgF8V9ya4YIj+Uq4CtgYsjIiRNpX6fsN7GMyLiQWC5pBf18/rPAh+RNNg+m1knH6BY75Lm8sy91j4DHC3pVuBl1IdZNDMzMzMzMzMzMzOzTYCHZFwLImJMP9OeAk7sZ/q5wLkNk46kfs+vpX2il/YdYjEizuzz994Njy8GLh6sbhFxCXBJ8fj4ARbnOSJiPrB3n2l969IDbNHnNdv0U9YBxcOb+ky/BdhtiKpk1sliip5ofaYvod5Q1uvDfTN98s/ZpmZmZmZmZmZmZmZmtmFyg9l6TNK3gBOAV67rutgLc9+SiensaxZckM5eNvkD6ezrl3wnna1tNjmVqzz2YLpMJubKBPhbHJbOnnDp69LZFf/+/XR2p+u+l86y277paGXx46nc76f8a7rMl8bv0tmLVr4mnX3b4q+ks7WVK9PZnhX57IgpW6WztLbls9VqKta1495DhwrNs29IZ7X9tHS2Z0x+hNiWh+9JZ6uLF6ezHUe8Kp0d/dAd6Wx2O6zecb90kaPuvmnoUKFnSX4dVPY6YOhQb3ZB/twY3T35bOdgt/HsU4fN8u87PQufSOWat90+XWZtyaJ0trLVc/5/ZuByH30ona2ubh869I+Ca+lo0/hx6Wx0d6ez226/YyrXNWnbdJkty/KjaveM22LoUG+2ZWQ6+8ioPdLZbZbPGTrUW4cRo9PZBSN3Tme3XnZnKldtzr/nLBi9azq7zdP5c2iZdfDo6N3T2VGUeE+vtKSzU5bOTWdHPJJ7P4sV/Y1o379aiXNC0w654xGAfm9NPUAdRo5NZ6M5/1G9Mu/udLa6ckU627zVUIOSFEqc6xiV32+z1wkAtUVPprN3H/XBdHZ516h0thr5fWGHUbnPBAAjOnL7+W1PH5wu8zVNt6azf+7Of344ceRd6ey8nfNfczy2KL8dTmj6fTr75N1/SGfv2f0VqdwFH/1zusxT35Q8xoBDR8xIZ+c175nObt+WP84eXjkpnT2o+ZZ0dkb3Qens0e2/TuUu7X59usw3r8h/9v/pyPzn9LdV+95ZZWA/b31nOrt4aX6Is38bc346e+/kN6dyhy/6RbrMqyecms4ezTXp7I1NL86Xe++30llq+ePhO60fS2fPGJvfDv/X9fZU7p1PfiZdZqXEe+9vx380nT0wf4lrtkFxg9l6LCLeP8D0Y9ZyVQCQtA/Q9yzfGRH9DaE4XHX4DnBEn8nfHI51Imki9fu/9XVc0SPNzMzMzMzMzMzMzMw2Am4ws7SImA1MX8d1OGMtzmsJ63h5zczMzMzMzMzMzGzjVct3HrVhVlnXFTAzMzMzMzMzMzMzMzNbl9xgZmZmZmZmZmZmZmZmZps0N5iZmZmZmZmZmZmZmZnZJs33MDMzMzMzMzMzMzMzM1sHwvcwW2+4h5mZmZmZmZmZmZmZmZlt0txgZmZmZmZmZmZmZmZmZps0D8loa4WklRExps+0M4H3AIuAEcDngDbgg0VkT+AeoAr8ISL+Yy3U81zgNxFxyQDPXwuMiYiDir8PAr4aEccMVu6rOn+ersP3mz+Qzr638/x09geVM9LZ8TWlck+21NJlbtGcb59/x4IvpLNvGXFWOvsVdaazX27+VDq7V6UpnV3Yklu3b2/+U7rMP1Vfmc6+tfKzdPY7rR9LZ3ua8n3Hl6s7nd12fGs6W63l61Ct5nKvGT8vXeYVmx2fzm7dlq9rm/LZBWPy+8LCjp509pgSx8680Yens7XkKWQntafLvHPMkensghX55TqoNX/J9NDI/Pmus8Sx0zQqHWXiZvk6LIrchhg/Il/mUyOTBxkwpTV/Dl02Ob++Fi7qSmfLDH8xdlR+X1ixIn+cTWwfkcrtoXyZi5ta0tljx89NZzsrI9PZEeS3w/Kx2+Tr0Jw/IJoivz8uG7ddKtdRGZ0us0L+emnphKnpbHtlzNChQpnt0KP8flOL/PF775hD0tkZbS9P5VZU8+t2VYn9YIcR+XWg3KUdAKNLXDuPaM6fmO4d+bp0dmU1fw7ZemTuvET+lMCotny2jIdW5ZfrLc0Pp7Pbdd+fzlbI72OLyZ1rAGa25K5tXrf0p+kyr5lwSjr7xuXnprPXjXtrOrujHk9nX92V/zz9l3FvSGe3j8Xp7AUf/XMq95avviRd5pi33ZbO3q1909ld4u509pHKTunsYbXr0tl7mg/Ol9v913T2mrbXpnJvXnVuuszLJrw3nX3b8rPT2d9MfHc6++aV56WzVJ/O12HM+9PZl/D3VO63o/PH+auWX5zO/n3iSensoV3XpLM/aPtIOlvmM8EZnV9LZ38z5sPp7LuW/l8q97WR+e+sJk7IX6+dtuLr6Szkl8tsQ+IGM1vXvh4RX5W0C3ALMDEizgGQNB84NqLEVWw/JDVFlPiEPLTJkk6IiN+vwTLNzMzMzMzMzMzMbBNTK/FP4Da8PCSjrRci4j5gNbBZmddJOlPS+ZL+LOk+Se8pph8j6RpJFwCzJTVJOkvSDEl3SPqXIidJ35Y0V9JvgcmJ2Z4F5P+Vw8zMzMzMzMzMzMzM1mvuYWbrBUkHAPdFxMLn8fJ9gUOB0cBtRcMXwCHA3hHxoKTTgWURcbCkVuB6SVcB+wO7AfsAWwJzgR8PMb8bgJMkHQuseB71NTMzMzMzMzMzMzOz9Yh7mNm69mFJ9wA3AWc+zzJ+FRHtxdCN11BvKAO4OSIeLB6/DHi7pFnFvCYCuwBHAxdGRDUiFgC5gcnhfxiil5mk0yXNlDTzx7++utwSmZmZmZmZmZmZmZnZWuMeZrau9d7D7PXAeZKmRURHyTL6DvLa+/eqhmkC3h8RVzYGJb2yn9cPPcOIP0v6HPWebQNlzgbOBlj9l4s8EK2ZmZmZmZmZmZmZPUv4m+P1hnuY2XohIi4DZgLveB4vP1FSm6SJwDHAjH4yVwL/KqkFQNKukkYD1wGnFPc4mwIcW2K+nwf+/XnU18zMzMzMzMzMzMzM1iPuYWZryyhJjzb8/bV+Mp8FLpD0w4iolSj7ZuC3wPbA5yJigaRd+2R+BEwFbpUkYBHwOuBy4CXAbOBe4C/ZmUbE7yQtKlFPMzMzMzMzMzMzMzNbD7nBzNaKiBiyN2NE3ALs1vD31GTx90bE6X3Kuha4tuHvGvCfxU9f70vOh4g4ps/fB2Zfa2ZmZmZmZmZmZmZm6yeFB8i0DZikM4GVEfHVdV2Xwcy9f0H6QJN8TLaSv43dwu5J6exmLcvT2a4Ykc62qDudzQq0xssEUIlb9tWGbuf+hzL1LVOHivKdTYejDk1U02VWaUpnW9SVztYiX25Pif+Dicivr1qJEZyblF9nWWX2mTL7QTM96WyZ7Vstsc3KKHM8DMc6K7PPlNoOym+Hnsjv42XWQZntW6a+Zc6j3clla6t0psssdc4vcZyX2Q4jtTqd7Yy2dLYrWtLZMZVVQ4cK2X2hzDoYUeKcX2Zf7K7l10FbJX9tNVzXIA+vnJzOThn9dCrXWctfr5U5dsq8l5XZF8qcP7pKLFuZcssosz+myyxxXiyjUuJ8N05L09lVjH0etRlamff0CrnscF2PD5fhqkOZ97Mydbhv2ZRUbmxr/py/cvr+6ey0u69OZ4dr3Q7XPjYc7zvZ46bs/De0dVsp8Xk2ex02XNc1w6XMZ7My26HMeigjW4cyy1Xme8Yy+9fu07YdnovGTdQXLq6u+zfnYfCfJzdtcPuJe5jZBkHSO4EP9pl8fUScMUzzuxzYsc/kj0fElcMxPzMzMzMzMzMzMzPb9LhP0/rDDWa2QYiIc4Bz1uL8Tlpb8zIzMzMzMzMzMzMzs3VreMZDMDMzMzMzMzMzMzMzM9tAuMHMzMzMzMzMzMzMzMzMNmkektHMzMzMzMzMzMzMzGwdqPkmZusN9zAzMzMzMzMzMzMzMzOzTZobzMzMzMzMzMzMzMzMzGyT5iEZzdaCZd1j0tl9ll2bzs4ad1w6O3351ensqvHbpHKjVj6ZLrOpfWU6e+eWL0tnd+m5M51d2LJDOrtV10PpbMuvz0tn2/abnsr9bvN/Tpd5zMi/p7NXLDkynX1T6y/T2VpLWzobUjrbsmxhOtszdmK+DpWmVO7JCbuny9zhoWvSWSr5t9/Vk/L77ejH701ne8Zuns7WRozMZ5Vbt2Uoaulstbk1nW3u6UhnRzx8Vzrbve2u6Wy1OX/sjFixKJ1dMWmndLat/elUbvGEfJljO55KZ7tKrIPW7lXpLCXONU3VrnS2pWN5OtvdOjadrdSqqdzy0VumyxxRze/jy1tKnEPJr9uuyB+TLepOZ8vojpZ0doQ6c8H8Kii1vprI7QcANeX/97En8u87zepJZ8ss2w5j8teNo6vLUrnscQMwcvXSdHbVqPzxoBLD5/RU8vviqHQSWnqS+y3QXMuf7zpa8p9hsobjOgFgZNeKdHbxiK3T2WoMT33LHGdbL7kjlbt9zDHpMvdf9sd0dsbYl6ezhz52QTo7b9oJ6ezyntHp7PT783V4aI9Xp7OHjpiRyt2tfdNlTrs7/xn9gd3zn/23nHNjOluL/HtJUyV/zt11Wb4Od487PJ3da2Fu352x+avSZR6y6Ffp7F8mvCGdPXpFvtzrx702na1G/r33iJ4/p7N3jz00ldtmdf5z0YNj8sfDHvdeks7ettNb8uV23prOlvmuYia59QUwveX2dPY+7ZHK7aT702VmP+8B3MBR6Wz+2xKzDYt7mK1hkkLS+Q1/N0taJOk3Q7xugqR/S5T/rJykrSXl31X6L3P/ot75K+GNmKQBW3YkTZU0p8+0MyV9dPhrZmZmZmZmZmZmZmYbk6htnD8bIjeYrXmrgL0l9f47/kuBxxKvmwAM2WDWNxcRCyLijSXr2NepwN+K3+st6fn/O6Ik96Y0MzMzMzMzMzMzM1vPSdpc0h8l3Vf83myA3HxJsyXNkjSz7Ov7coPZ8Pg90Nv/+1Tgwt4n+vZGkjRH0lTgS8C0YsOeJWmMpKsl3Vps8BOLl/TN/aPHk6Q2SecU+dskHVtMP03SZZL+UOwgX2mYv4A3AqcBL5PUVkwfLem3km4v6nhyMf1LkuZKukPSV4tpW0i6VNKM4ueIYvqLi3rOKuozVtIUSdcV0+ZIOqrInlrUe46kLzfUb6Wkz0q6CTisv5VdHBRflnRz8bNzMf1cSV+TdA3wZUnTinVwi6S/Stq9yO0o6Yai7p8ru7HNzMzMzMzMzMzMzGyN+Q/g6ojYBbi6+Hsgx0bE9Ig46Hm+/h/c62Z4XAR8uhiGcV/gxzDkILD/AewdEdPhHz2iToqI5ZImATdKuqKf3NSGMs4AiIh9isagqyT13khlOrA/0AncI+lbEfEIcATwYEQ8IOla4JXAZcArgAUR8apiPuMlbQ6cBOweESFpQlH2N4GvR8TfJG0PXAnsAXwUOCMirpc0BugATgeujIjPFz3GRknaGvgycCDwdFHv10XEL4HRwJyI+PQQ6295RBwi6e3AN4Dewch3BY6PiKqkq4H3RsR9kl4EfBd4SVH/70XEeZLOGGI+UDRYNvy9FfDVxOvMzMzMzMzMzMzMzGxwJwLHFI9/AlwLfHy4X+8eZsMgIu4AplLvXfa751mMgC9IugP4E7ANMNRd3o8Ezi/qcDfwEPUGI6i3pi6LiA5gLrBDMf1U6g18FL97h2WcDRxf9Nw6KiKWAcupN3r9SNLrgdVF9njg20Uj0hXAOEljgeuBr0n6ADAhInqAGcA7JZ0J7BMRK4CDgWsjYlGR+RlwdFF2Fbg0sb4ubPjd2BPtF0Vj2RjgcOAXRT1/AEwpMkc0vP58hvZA0WI9vWi4/H5/IUmnS5opaeYvf35OolgzMzMzMzMzMzMz25RExEb50/j9ePFzeonVsmVEPF6sn8eByQOtPuodcG7pU3729c/iHmbD5wrqvY6OASY2TO/h2Q2VbQO8/q3AFsCBEdEtaf4g2V4a5LnOhsdVoLno4fUG4LWSPlm8fqKksRFxr6QDqfc4+6KkqyLis5IOAY4DTgHeR72HVgU4LCLa+8zzS5J+W5Rxo6TjI+I6SUdTH7LyfElnUW+IG0hHRFSHWG6oHxj9PV5V/K4AS3t75g3x+jUiIs4Gzga44a7la7x8MzMzMzMzMzMzM7P1UeP34/2R9Cfqo7f19ckSszkiIhZImgz8UdLdEXFdyar+g3uYDZ8fA5+NiNl9ps8HDgCQdACwYzF9BTC2ITceWFg0lh3LMz3C+uYaXUe9oY1iKMbtgXsGqePxwO0RsV1ETI2IHaj35npdMUzi6oj4KfWGvwOKXlrjI+J3wIeoD/MIcBX1xjOKeU8vfk+LiNkR8WVgJrC7pB2K5foh8H/FurgJeLGkSUUj3qnAXwapd39Obvh9Q98nI2I58KCkNxV1k6T9iqevp94ACMX6MzMzMzMzMzMzMzOz4RERx0fE3v38/Ap4UtIUgOL3wgHKWFD8XghcDhxSPJV6fV9uMBsmEfFoRHyzn6cuBTYvhgX8V+DeIr8EuF7SnKLX1c+AgyTNpN6Ic/cAuUbfBZokzQYuBk6LiE4Gdir1nahv/d4C7APcXNTzk8D/UG+o+00xTORfgA8Xr/lAUdc7JM0F3ltM/1BRz9uBduD31HvczZJ0G/Xebd8sukR+ArgGuB24tTgoymiVdBPwwYZ69fVW4F1Ffe6kPo4pxWvOkDSDekOlmZmZmZmZmZmZmZmtG1cA7ygevwN4TnuBpNHFraGQNBp4GTAn+/r+KMIjxdmGrRiu8qCIWLyu6zKQR++dkz7Q7u7YJV3uXiPuSmfv7NojnX16dUsqt/nornSZXT1N6exeY+5PZx/u3j6dndT6dDo7f0VqWFsAIgYbDfXZemq57P5j706X+XB1h6FDhWn1NvqUOT17pbOru/Ij/DYp/74zakRPOttTy/8PSHOllsptOfKpdJkLOzZLZ8usg1qJt+kYdGTeZ+vsyW+zca19R9wdWFc1X262vpNal6XLXN49Op2tRv68NLolvw6e6hiTzq7oyJ1vAca35c+5Y0YM9v8yz7a6Z0QqV2af6anm98Xtxy1JZ1d0j0pnVeo4y58/2pry26HMvpB9f9h69NJ0md1R4jhvWpHO9pQY1V0lRr1uIjMKd1135I+dinLnfIBmulO5HvLzL2O41kGT8uWW2WZl3nfK1LerlsuWOY931/LZMc35c36txDpoLrEdyljZMzKdLXO+a6nk6ltmnylzPJbRUc29lwFMGpH/TFBbD/7HOHucbbNsbrrMeWOnp7M7Lb81nV04YdehQ4UJ7U+ks6MXzUtnH9zu2HwdqvmvEB6N3GeuHSL/WXbpiPxnzhU9+WuKJ/c+NJ3d7I6Z6WxzieN3Qkv++n1i+6Pp7COtue9Lpj19c7rMu8cfkc7utmpGOnvfmAPT2Wmr70hnm3o60tmHJkxPZzerLUrlFqm/kdP6t317/nuNxaPz32tsvfj2dPaa5hPSWeXf0jk6rk5n54/bP52dXH0slXtEOw4dKrQ15T8bjlH+M8GO03YuscZsKJ/5afdG2Ujz329red77iaSJwM+pj6L3MPCmiHiqGBnvRxHxSkk78UyHoGbggoj4/GCvH2q+voeZmZmZmZmZmZmZmZnZOlAbnv8p2qAVI+0d18/0BcAri8fzgP36ZgZ7/VDcYGYbDEmX88w933p9PCKmDsO89gHO7zO5MyJetKbnZWZmZmZmZmZmZmZm65YbzGyDEREnrcV5zQamr635mZmZmZmZmZmZmZnZurPuB+Q2MzMzMzMzMzMzMzMzW4fcw8zMzMzMzMzMzMzMzGwdiIh1XQUruIeZmZmZmZmZmZmZmZmZbdLcYGZmZmZmZmZmZmZmZmabNDeYmZmZmZmZmZmZmZmZ2SZNHh9zzZAUwE8j4p+Kv5uBx4GbIuLVg7xuAvCWiPjuEOU/Kydpa+B/I+KNL6DO+wO3Aq+IiCufbzkbC0krI2LMAM9NBR4E/ici/quYNon6Nv5BRLxvsLJvvHtZ+kCbVrsnXef7tEc6u0vclc6uGjEhlRu3+sl0mS2dK9LZ+8a/KJ3dKh5LZ5c1T0xnW+hKZ7d+4tZ0ttbUksrdP/GwdJkjKx3p7MMrJ6ezB1ZmpLOr2jZPZ8to7V6dznY1t63x+Xc0jU5nJ7Q/kc6ubp2QzvZURuTrsGpBvtzmkens0tb8fjO6uiydDZTKjexani6zY8TYdLaMzZ68O51dvsXO6ezSli3S2Ymd+e3b0dLv21m/mmo9qdySpi3TZY7WynR2ZHf+/WFET5lzwqh0toyRHUvT2VWj8u87SyuTcvNXfh00RW7bAvRUcu9PAD2Rz1ajKZ1tLfF+1h35c2P2XAPQSq4O3QzP/EfQmc6WqUOtxP9JNpPfb8qUW2Y9jK0+vcbLzJ7rAGqV/H5bpg6h4Vlflaims2V0NeWuFUL5upYRUeLYrbWns6sq49LZ7hLnuzL1bal0p7PbL8xdk9876ch0mTsvvTmdfXDCQensrvdcms6u2OnAdPbp1q3S2e3n/Tmd7dhiaj7bNj6Ve1T5Msc256+XlnXn99ueyJ9rnt43v303vyP/+XAn7ktnu5ta09nNltyfys3bIv95euuOB9LZhSN3SGe3aH84nV08crt0tiea09mtV+e3w6Mjd0vlJtXyn3urlXxdJz16Wzr7xPb574zKXC81KX+t0FHLf57erLoonV1cyZ3vymyHUe1PpbMzK/lj5/h9W4fnAmAT9alzuzbKRpr/OW3EBrefuIfZmrMK2FtS7xnzpUDmm/wJwL+VzUXEghfSWFY4Ffhb8Xu9JSn/ifW5r82/Ow9tHtDY+Pkm4M41WL6ZmZmZmZmZmZmZma0DbjBbs34PvKp4fCpwYe8Tks6U9NGGv+cUvZa+BEyTNEvSWZLGSLpa0q2SZks6sXhJ39xUSXOKstoknVPkb5N0bDH9NEmXSfqDpPskfaVh/gLeCJwGvExSWzF9tKTfSrq9qOPJxfQvSZor6Q5JXy2mbSHpUkkzip8jiukvLuo5q6jPWElTJF1XTJsj6agie2pR7zmSvtxQv5WSPivpJqDff2+QNF/SlyXdXPzsXEw/V9LXJF0DfFnStGId3CLpr5J2L3I7SrqhqPvnEtu3HbhLUu+/YJ0M/DzxOjMzMzMzMzMzMzMzW4+tyd43BhcBn5b0G2Bf4MfAUUO85j+AvSNiOvyjR9RJEbG8GPLvRklX9JOb2lDGGQARsU/RGHSVpF2L56YD+wOdwD2SvhURjwBHAA9GxAOSrgVeCVwGvAJYEBGvKuYzXtLmwEnA7hERxfCQAN8Evh4Rf5O0PXAlsAfwUeCMiLhe0higAzgduDIiPl/0GBtVDCv5ZeBA4Omi3q+LiF8Co4E5EfHpIdbf8og4RNLbgW/wTA+wXYHjI6Iq6WrgvRFxn6QXAd8FXlLU/3sRcZ6kM4aYT6+LgFMkPQFUgQXA1snXmpmZmZmZmZmZmZnZesg9zNagiLgDmEq9d9nvnmcxAr4g6Q7gT8A2wFA3DTkSOL+ow93AQ9QbjACujohlEdEBzAV6B1w+lXrjD8Xv3mEZZwPHFz23joqIZcBy6o1eP5L0eqD35hnHA9+WNAu4AhgnaSxwPfA1SR8AJkREDzADeKekM4F9ImIFcDBwbUQsKjI/A44uyq4CmQHQL2z43dgT7RdFY9kY4HDgF0U9fwBMKTJHNLz+/MS8AP5AfbjNU4GLBwtKOl3STEkzf/nzc5PFm5mZmZmZmZmZmdmmImqxUf5siNzDbM27AvgqcAzQeKf3Hp7dQNk2wOvfCmwBHBgR3ZLmD5LtNdjN8xrvHF4FmoseXm8AXivpk8XrJ0oaGxH3SjqQeo+zL0q6KiI+K+kQ4DjgFOB91HtoVYDDIqLvHZa/JOm3RRk3Sjo+Iq6TdDT1ISvPl3QW9Ya4gXREpO5eHQM8XlX8rgBLe3vmDfH6oWcW0SXpFuD/AXsBrxkkezZwNsCNdy/bMM8QZmZmZmZmZmZmZmabAPcwW/N+DHw2Imb3mT4fOABA0gHAjsX0FcDYhtx4YGHRWHYsz/QI65trdB31hjaKoRi3B+4ZpI7HA7dHxHYRMTUidqDem+t1xTCJqyPip9Qb/g4oemmNj4jfAR+iPswjwFXUG88o5j29+D0tImZHxJeBmcDuknYoluuHwP8V6+Im4MWSJhWNeKcCfxmk3v05ueH3DX2fjIjlwIOS3lTUTZL2K56+nnoDIBTrL+n/Az4eEUtK1tXMzMzMzMzMzMzMzNZD7mG2hkXEo9TvjdXXpcDbi2EBZwD3Fvklkq6XNAf4PfV7ev1a0kxgFnD3ALnvNJT9XeD7kmZT78l2WkR0SgN2PDsVuLyf+v0rsBA4S1IN6C6mjQV+JamNem+0Dxev+QDwnWL4yGbqDXfvBT5UNPZVqQ8D+XvqDVMfk9QNrATeHhGPS/oEcE1R7u8i4lcDVXoArZJuot74e+oAmbcC35P0KaCF+hCUtwMfBC6Q9EFywz8CEBF3AneWrKeZmZmZmZmZmZmZma2n3GC2hkTEmH6mXQtcWzxuB142wGvf0mfSYcnc3sX0DuC0fvLnAuc2/P3q4uG1/WSvoD6cJMCV/cz+kH5es5hneng1Tn9/P6//SfHTN3sBcEE/05+zPgfwnYj4TJ/Xntbn7weBV/Qzjwd59rr+0kAziYj5FOu7z/RzaVjHZmZmZmZmZmZmZma24XGDmdlaMP2BC9PZC0e9N509tfNH6exFbe9OZx97InP7OJg8add0mT096ShvHvn3dPbvnc9pyx3QLhOeSGdveGzHoUOFq/88Kp3daZeJQ4eAfxl/e7rMuT17prOHr/59OnvBqtelswN3aH2uSonspHG5fRGgp5YvuLM7l919cn7k1bu7tktnx9CdzlYjv1yru3cYOlRYsbwpnd1p0mC3nHy2u1duk85m7/+67bj8/BevHp3OdlfzI1OPmrBLOvvk4tZ0tqea375jR05JZ2PQ25s+W0dXLlvmOO/o2jKdbc7vikj5W4KWKbepki93TGv+vPTw/JZ0dvKEXLnbjl+RLrOzmp//Fq1PpbM9kf8I0aQS5/HI17ca+Q1cqg7k6jBs6yA5/+GsQ5nzRxkre/Ln5/tXbZXKVUrcjrinlj/nV0qcE1qbylyr5OswsiV/8by6K78vlLmuGDMid71S7jqwViKbL/ep1UPd9vsZe2z2aDo7sWtBvhKR328Wt22bzt4+4bhUbv9Hfpkuc8aWr09nD1rw63T2jp37/o/vwDZrWZrObn/Lz/N12Pc96eyY5lVDhwoPr5yUyh1Wuy5d5uPjd09nmyr5cw35w4zN75iRzj6178Hp7Pi7rklnp6y6L529ZdxLU7mDSxwPN07KHw+HPJn/PD1z/HP+b3tABy76UzpbqeY/S87avN//2+/XtJ57U7mHK9PSZe6y8tZ0du42J6Szez7S3//59+/Xrc/5P/8BVUt8p3Bi7ZJ09taJr0xn912V+z7sTz259waAzUd1prMHRP6cAEeWyNpQSlxG2DBzg5mt9yRdzjP3fOv18YiYOgzz2gc4v8/kzoh40Zqel5mZmZmZmZmZmZmZrR/cYGbrvYg4aS3OazYwfW3Nz8zMzMzMzMzMzMzM1r38mBBmZmZmZmZmZmZmZmZmGyH3MDMzMzMzMzMzMzMzM1sHatmbvNuwcw8zMzMzMzMzMzMzMzMz26S5wczMzMzMzMzMzMzMzMw2aW4wMzMzMzMzMzMzMzMzs02a72FmZmZmZmZmZmZmZma2DkT4HmbrC3ljGICkKjC7YdLrImK+pA8DXwS2pN7AenXx/FZAFVhU/H0I8FREjGko8zTgoIh4n6Qzgf8GdomI+4vnPwx8DTg4ImZKmg88EhFHNZQxC2iOiL3X7BL3r6jnyoj46gDPnwu8GFgOjARuBD4REY8NVu6c+59IH2jbL589dKjwwNj909kdV81JZ5eM2z6V23zFI+kyQ/kOrfPb9kxnt6nOT2efHrFlOtsWq9PZ8aseT2cr1e5U7j0/2zld5rl7nZPOnr/tZ9LZUyf9MZ3taW5LZ6PSlM62/uWX6WzzFpPT2ayHDjw5nd1hzq/yBffk9gOA6tY7prMdf/xdOjt6773S2ZW7HpLOjpk/K52lZUQq1r5V/ngos39R4hpo1KN35YsdNTad7ZwwJZ1tfXrQt5pneXrrfdLZts5lqdwDrfkyJzTnygQYER3pbGtP/twcKJ1tqvWks2MWz0tnn9oq/37W0TQ6lWuJrnSZLdXOdLa7qTWd7dTIdLZK/phsoz2d7SBfhzLG1HL7bmelzDrI/4/iyNrKdLa9MmboUG8dosR7byV/TFYjv2ydkd/HxsdTqVyZ68tRnUvT2c6W3PFYVrXSMizlNtXy1xWVqKWzXU2567sy26HMuTmUz46o5vfbpZqYzlZjeJatRfn3nWk3/CiVe/Twf0qXud2dv0lnH9rrtenstgtvSWcfn7xfOttN7poRYPsnb0pnF265bzo7tn3R0CFgXnP+vX98y/J0duun8t8TPL55/uuT8V255QJY0pK/bn1wj2PT2flX3JPOvvPJ3OfZx4/OHw9b/uHsdPZ3+38hnX359R9OZ6845Jvp7PYT89fDL7rzu+ns33Z/fyp3UO2GdJnLR+e/g5k8P3/szt7+denszh23p7PZ72sA5rQdms7ut+KadPa+zQ9L5SZH/nuoCYvvS2evbH5dOvv6Qyr5Nz4b0sfPbt8oG2m+fPrIDW4/8ZCM1qs9IqY3/Mwvpp8KzABOioglvc8D3we+3pDPfHszGzil4e83AnP7ZMZK2g5A0h4vZIF6qW5N7usfi4j9gN2A24BrJOWv4M3MzMzMzMzMzMzMbL3iBjMbkKRpwBjgU9Qbzl6oXwInFmXvBCzjmR5qvX4O9HbpOBW4cIg6nibpV5L+IOkeSf9dTJ8q6S5J3wVuBbaT9DFJMyTdIekzDWV8snjtn6g3gqVE3deBJ4ATsq8zMzMzMzMzMzMzM7P1ixvMrNdISbOKn8uLab0NVn8FdpM01HhnjWXMAj7b5/nlwCOS9i7KvrifMi4BXl88fg3w60TdDwHeCkwH3iTpoGL6bsB5EbF/8XiXIjsdOFDS0ZIOpN7rbf9ivgcn5tfXrcDuz+N1ZmZmZmZmZmZmZrYJi9rG+bMhcoOZ9WockvGkYtopwEURUQMuA95UoozpwKf7yVxUlPs64PJ+nn8KeFrSKcBdQGZw5j8Ww0W2F/U8spj+UETcWDx+WfFzG880cO0CHAVcHhGrI2I5cEVifn31OxarpNMlzZQ08xcXnf88ijUzMyXHqV0AAQAASURBVDMzMzMzMzMzs7Uhf5dm26RI2pd6g9IfVb/Z8ghgHvCdF1j0r4GzgJkRsVz938j54mI+pyXL7HtTxN6/VzVME/DFiPhBY1DSh/p5fVn7A1c/p1IRZwNnA8y5/4mN8saNZmZmZmZmZmZmZmYbA/cws4GcCpwZEVOLn62BbSTt8EIKLXqBfRz4/CCxy4GvAFcmi32ppM0ljaTec+36fjJXAv8saQyApG2KISavA06SNFLSWOrDQKao7gPAFOAP2deZmZmZmZmZmZmZmdn6xT3MbCCnACf0mXZ5Mf3LL6TgiLhoiOdX9M5jgB5off0NOB/YGbggImZKmtqnzKsk7QHcUJS5EnhbRNwq6WJgFvAQ9fu1DeUsSf8FjAJuBI6NiK5MRc3MzMzMzMzMzMzMbP3jBjMDICLG9Pl7x34yH2l4fGaijHOBcwfKF9OPaXg8tZ/n5wN7D1J1gIUR8b6hXhcR3wS+2c88Ps/gPd4as6dlcmZmZmZmZmZmZmZmQ6mF7+azvnCDmdlasPNDV6Wzvxz5T+nsa1f+Ip29su0N6Wz3k6mefbQ05UfobGuppbMH9tyezt7UPj2d3WXEE+nslfN2TWd3mDw1nV3dkzvtfm/s6ekyP778a+ns5yZfm87+of24dHbBo7l9BqCpKR1l+cQXpbOrV/aks91duf3xxM5quswZm5+RztZq+fXV056O0nnI69LZx5/Md4w9Tvl1e92Kg9PZ1hG5kaFfNmpJusz7n5qYznb35LfD+K0OTWfvfTR/efXoXfkNvO+eo9LZccvz+25Hd247LHokXSS12vh0dpdt8vtXR0+JE0gJtRKfTSZP3COdvWv+6HS2Jbnb7LPN0nSZndWWdHaHykPpbEX5/atKfps11/LnpZZKvtwe8ushq1ZiuWolRsGvRH7dNpE/dsif7lCJD+sqcSvgauTXwwMdU1O5WokFW9W5czo7tsS+2FTJX+NGifq2NnWns0s7Rqaz3dX8dmhrzu2PuUFB6pqUX19lyl3RmT/O9xk/L50d2bMinS1z/D45Yvt0duUBL03ltpt5cbrMxw5+Yzq7w5wr0tlH9np1Oju6Z1k6O+GJuens/G2OTmfHVZ9KZ2d0H5TKHdadGbSm7omW/GfOu8cdns7u1J5fX+0jxqWzU1bdl85ec8U96ezU1+6Wzi66K7c/Trn2J+kyL937S+nsa6//YL7cA57z/9oDOumv/5rOtm47JZ294YCPpbOHrsrdFWXWuPz3BAfd97N09rYdT0ln93vid+nsWY/kvwsr4985K52dd+g709ndnvxLKvfnkSemy9x60tbp7GG1O9JZmF4ia7bhcIOZbRAkvZznDgX5YEScRNGLbQ3P7zvAEX0mfzMizlnT8zIzMzMzMzMzMzMzs3XLDWa2QYiIK4Hcv7usmfnlu4qYmZmZmZmZmZmZmdkGzQ1mZmZmZmZmZmZmZmZm60D4HmbrjfwA5mZmZmZmZmZmZmZmZmYbITeYmZmZmZmZmZn9/+zdd5hcZfn/8fe9Jdn0npAASSD0mtB776JIExAR+ClFQbA3UBGsgPpFRRRUQCkiAoL03kINqfSWACEhvW7fnfv3xzkLk8ns7n2STHaTfF7XNdfunPmc5zynz8wz5zkiIiIisk5Tg5mIiIiIiIiIiIiIiIis03QPMxERERERERERERERkQ6Qy+keZp2F6YZyIqV37/jG8I52wNQ/hsu9e4Pzw9kjP/xDOFs/cutQrusHr4fLpCLePv/ciFPD2V0W3RvOThuyezg7bEl83qpmTw1nqV4aiv2zd3zdHjv4qXD27Os3DGf/Njy+zVRutW046xWV4azN/She7oDB4Sxlse3xww13Cxe5/jM3hrM2ZP1wtm7QiHC24T//CGd777xDvA4jtwtnuyzKsM4qu4RyVqL3KtbYEM/Onx0vOMPxrm547HgLUPXmuHC2ceNtwtnKBbF19v7GB4TLHLQ0flzMsn67LI6vh1yXqnC2rLE+np0Rn7emEVuEszW9h4VybhYus2v9knC2uvuAcLa+vHs4W+M9wtm+ubnxOlTE61Cfi28LfXLzQrmG8m7x6ROffu+m+eFsXUV82WZZD93KasLZnJeHs1kMWhLbzyzXFC6zckl8+2ros144m0VTRddw1i3eGUyXDPt6WXP83FfXY2A4G9VcFn8f6MSPdz2qZ4Wz7/fZPpyta86wzjLUt3t5bTjb+7fnhnJvnH1TuMyd7vtWOPvCYb8NZ/d556pwdvr2nwlnP6iJ75O7v3V1ODtvm/h7m75z3wrlHquKz9cmfeLvm4fPeDacfXO9/cLZTeeODWdf6n1wOLvTi5eHs3P2OjGcfXnL2PL94O43wmWetvDScPbpLeOf0/d6+8/h7HObnRnOLm2IH0cPmnZlODtuiy+HcjssuD9c5oz14p85N3gtXu64jb8Yzm5bF993spwjnyqP7w8HzL4+nH12/ZNDuR0bngyX2eW9+Pdbf+sW38bPOTzDiU/a9Y0/Ll0rG2l+d27PNW47UZeM6xgzazaziWb2spndambdiwz/n5n1zRtnazN71MzeNLO3zOxHltjPzJ4tKL/CzGaZ2VAzu87MpqblTjSzZwqydxYZ/yIzqzGzwXnDYi0Mq0A6T3e38fppZjbHzCaky+IBM9tjddVPRERERERERERERERWPTWYrXtq3X20u28DNABnFxk+HzgHwMy6AXcBv3L3zYDtgT2ArwJPAhuY2ci88g8CXnb3menz76Tljnb3jxuW0ga5HYC+ZrZRQR3nAvGfvAWY2ar8+est7j7G3TcFfgXcbmZbrsLyRURERERERERERERkNVKD2brtKWCTIsOfBVr6C/s8MNbdHwRw9xrgXOD77p4DbgVOyBv3RODmwLSPBf4H/CsdJ9/fgRPMrH97hZjZSDN73cyuN7PJZvafvKvmppnZj83saeB4MzvEzJ41s/Hp1XU909xhaRlPA8cE6v4xd38MuBqIXz8vIiIiIiIiIiIiIgK4r52PNZEazNZRZlYBHA5MKRheDhxIclUZwNbAS/kZd38H6GlmvUkax05Mx+0KHAHclhe/LK9Lxvwb/JyUjntz+n++pSSNZtGOczcHrnb37YDFJFe/tahz972Ah4ELgYPcfQdgHPBNM6sCrgE+DewNrMgNC8YD8ZuUiIiIiIiIiIiIiIhIp6IGs3VPNzObSNJg9D7wt4Lh84D+wEPpcANaaw92d3+RpPFsc5IGuOfcfUFeJr9LxpMBzGwIyZVtT7v7m0CTmW1TUPbvgVPTRrn2fODuLXeqvQHYK++1W9K/uwFbAWPT+TwVGEHS0DXV3d9yd0/Hz6rozQvN7EwzG2dm4+67/a8rUKyIiIiIiIiIiIiIiKwOFR1dAVntat19dGvDzawPcDfJPcx+D7wC7JMfNLONgaXuviQd1NKt4pbEumM8AegHTDUzgN7p+Be2BNx9oZndxLJXi7WmsEEv/3l1S7WBh9x9mavZzGx0kfGzGgO8tlyl3K8m6a6Re8c3rqEXoYqIiIiIiIiIiIiIrP10hZksw90XAecB3zazSuBGYC8zOwjAzLqRNKRdmjfazcAXgAP4pCvHtpwEHObuI919JLAjy9/HDOC3wFm037A73Mx2zyv76SKZ54A9zWyTdD66m9lmwOvARmY2Km/8MDPbl+T+ZddkGU9ERERERERERERERDoPNZjJctx9AjAJONHda4GjgAvN7A2Se569CPwxL/8qUAM86u7VBcXl38NsYtpINZykAatl/KnAYjPbtaAec4E7gK7tVPk1ku4bJ5N0J3lVkXmaA5wG3JzmngO2cPc6kgave8zsaeC9dqYFcEI6L28CPwSOdfflrjATEREREREREREREWmL53ytfKyJLLltk8iaycxGAne7e+E90DqVxb/9enhHu2Lo5eFyv1VzcTj7ux4/CWfLgk3puVy4SHp0j7fPf2nYA+HsDXMPC2cP3PT9cPbGsUPD2aFDKsPZ+QubQ7mvb/DfcJm3544JZz/z0rfC2e/W/yic7dYjvgzKy+PbwsgR3cLZ6pr4+SwXPGnvtmVDuMyX368KZ4f0j9e1qbnobRKLmjE7Xu78BY3h7H47xeswZWp8W+gSjI7ZqCZc5ssf9Ahna+riy2uDwfHs5Deawtnamvh6GDw4vj/07hnfz5ZUxw7m1dWx4xfA4sX14ez22/QMZxcujq+H8vJwlOb4rDGwXzz76pvxY0jfvrEdYrctasNlLq3vEs5u1XtqOFtv8W3RMvR83cXrwtk6616SOlR57HhTa/FjTSmmD9mWQRnxN21lFt8hch7f0RY09g1n350fuY1xtveiC5bEz2WD+8ULrijLcFzK8HPVqsr4evhoYXxfr4+fdhjQO8MCDqosj5dp8VXGh3Pjd5s4eNQ74Wyf2lnhrHl83mZ2G9V+KJUL/s653/+dGy7zjbP+Gc7ucPc3w9lXj7q0/VBqVNlb4WyfF+8JZ58fE/+8M6Lbh+HsU9M3CeU+13BduMz3Nz4gnJ1ZNzCc3aH6sXB2Wv+dwtlNPng4nJ0xYvf2Q6mhj18fzv5j/Z+GchseuXm4zPqx8d8/HzblonD20TE/DmcPfDW+7zTNmxfOvnDAz8PZXeofD+WeKDsoXOZBH/w5nB278ZfD2T0/vCGcvWTGKeFslq/If7D0h+HshIPi393t8va1odxNveLH/EF94p9P92+8N5ztudtnMpyppT3nX7FkrWykueL8XmvcdqIrzERERERERERERERERGSdFv8ZlkgHMrMBwCNFXjqwFFeXmdnpwPkFg8e6+zmreloiIiIiIiIiIiIiItKx1GAmawR3nweMXo3TuxaIXQctIiIiIiIiIiIiIrICcrptVqehLhlFRERERERERERERERknaYGMxEREREREREREREREVmnqcFMRERERERERERERERE1mm6h5mIiIiIiIiIiIiIiEgH8JzuYdZZ6AozERERERERERERERERWaeZu1ov1zVm5sAN7n5K+rwCmAk87+5HtjHeaGCYu9/bTvndgWuA7QADFgKHAXcDv3T3B/KyXwc2Ay4FpgI/c/cfpa8NTOv1F3c/d0XmNSszexz4truPa+X1acCS9Gk5cDtwibvXt1XuXeOawzvagS/9OBrl3u0vCWc/9Xo8m9tyh1Cu7JWii6l4dvB64exj6385nN1/xt/C2albfDqcHfHh2HC2rGZxONs07Z1Q7qJkNwj5SdNPwtk/DvlVOHvegvi2WD50WDjrS+PLa8Jvbw9nR+y5cThbUdUllKs748Jwmc0/+1Y422NIv3C2zy6x/RFg0Qvjw9ksv17qdcxx4ay9Gq9Drq4ultv94HCZXd6eFJ9+TXU4a11i2wxA05y54WzlBhuEs1nWb+/Djwhn+SB2XJq5x0nhIgfPeSWc9fJ4hweVc6aHs5SXx7N1teFo/ZtvhLNdtxsdzjYOWD+W69ozPv2l8W1x4YBR4WxdRY9wtj5XFc729EXhbE1Zr3A25/HfCPZrmh3K1Vd0D5dZb93C2Z7NC8PZmvLe8Tp413C2u8WPjc0l6rBk2OwJoZzlmsNlls2YFs42bxDfH7Jo7hrfd3IWP4Z1WTovnC2ri6/fhv5DQznL8J1Cc3n8fJpF10WzwtlXhx0aztY1xevrWDjbozJ+3tnytVtCuXvX+0q4zCPeuSycvW/Ut8PZwxf+I5ydOuqQcPbthUPC2YPnXBfOztx0v3B26GsPhXK39z07XOaOQ+Pva4Z/8HQ4+/LQw8PZjRtfDWcn+5hwduenfhrO3rFN/DPq8TMvDeXu3Si+3Xbdc8tw9q5Lng1nT77hwHD29rOeCmdHjoifS0586pRw9pnjY/vvEVMvD5f56pgvhbNbvXpjOPvYiPjxbv83fxfOekNDOPv3gfHvCr608NfhbPSYe2jtreEy/fUp4eyl3S4KZ3/6xcr4iU/ade5vF62VjTR//GafNW470RVm66ZqYBuzjz+9Hwx8GBhvNBD5Bu58YJa7b+vu2wBfAhqBm4ETC7InpsMB3gXyG+yOB+LfuLUibRBclfZ3922BXYCNgatXcfkiIiIiIiIiIiIiIrIaqcFs3XUf8Kn0/5P4pNEKM+thZn83sxfNbIKZHWVmXYCLgRPMbKKZnWBmu5jZM2nmGTPbPC1iKHkNcO7+RnoF1n+AI82sazqdkcAwoOWnUrXAa2a2U/r8BODfbc2EmV1nZn82s6fM7E0zOzIdfpqZ3Wpm/wMeLDZPaa6bmf3LzCab2S1A+CfA7r4UOBv4rJn1j44nIiIiIiIiIiIiIgJJL0Br42NNpAazdde/gBPNrIqk68Tn8167AHjU3XcG9gcuAyqBHwO3uPtod78FeB3Yx93HpK/9Ih3/78D3zOxZM/uZmW0K4O7zgBdIumeE5OqyW3zZfkFb6rUB0AzMCMzLSGBfkgbAP6fzBLA7cKq7H1BsnsysB/AVoMbdtwN+DuwYmN7H3H0xSVeSm2YZT0REREREREREREREOo/SdDovnZ67T06v8DoJKLwn2SHAZ8yspePcKmB4kWL6ANenDWJO0qiGu080s43Tcg4CXjSz3d39NT7plvHO9O//KyjzfuASYBYQ66wd/u3uOeAtM3sX2CId/pC7z29nnvYBfp/We7KZTQ5OM98a1xeriIiIiIiIiIiIiIh8QleYrdvuAi4nrzvGlAHHpleSjXb34WljV6FLgMfS+5R9mqQRCki6K3T32939q8ANfHLvs/8CB5rZDkA3dx+fX6C7NwAvAd8CbgvOR+H1nS3P8+9o3dY8rfD1oWbWi+QKtzeLvHammY0zs3EP3H7Nik5CRERERERERERERERKTA1m67a/Axe7+5SC4Q8AXzMzAzCzMenwJUCvvFwfPrlX2WktA81sTzPrl/7fBdgKeA8+vu/X4+m0CxvqWvwG+F7ahWPE8WZWZmajgI2BN4pkWpunJ4GT02HbkHRPGWJmPYE/Af919wWFr7v71e6+k7vvdOgxZ0SLFRERERERERERERGR1UwNZuswd5/u7lcUeekSku4VJ5vZy+lzgMeArcxsopmdAFwK/NLMxgLleeOPAp4wsynABGAcy14tdjOwPcn9yorV6xV3vz7DrLwBPAHcB5zt7nUZ5ukqoGfaFeN3Se6x1p7H0jJeAN4HzspQVxERERERERERERERAHK+dj7WRLqH2TrI3XsWGfY4yZVfuHstRRqB0vuB7VwweLO8/3+U5v4B/KON6d9BwX2/3H0asE2R7HXAda2VlRrr7t9oa7w25qmW5F5qIe4+MpoVEREREREREREREZE1g7mvoU19IoCZXQfc7e7/6ei6tKXu3qvDO9olH50aLvdH/f8Szl5a/dVwtl/f8vZDwJy5jeEyhw7pEs5+YcC94ez1cw4PZw/efHo4+6e7+4az22zdq/1Q6r3pDaHcD5t/Fi7zG3O+Hs5esV18V7l4ZnxbrF4amy+AJQtrM2Sr2w+lmpubw9mo008dGc4+PyUXzlZWWPuhVENDvNzm5vg5/b23ZoezRxw5PJwdP3lpOFsT3G5O/nTX+PTf6R7OVtfEl+2QQbHjIsDESYvC2amvfhDObr/7puHs+sPiy2zhoti+M/2DJeEya6vrw9m99h4Szs5bEF9nFRn2s6am+L4zfGi83CefiW8L3XtUhnJH7h3vIGJhbfzcu3PfYrerLa62PH7ey2Xo0KJX03I9XLeqpiJehyZiyxagV3OsDkvL+4bLzLIM+jRFeySH6oo+4awT324riZ/TsyzbRU29w9k35/QN5Zpz8fmatzC+n683MBylS0W8XMtw6+QeXePva6bPi+/rtfHDMwP6xOpbXhafr4oM2fIM/eG8NysePmqz18PZnrVzw9myXHydfdhz83C2uin23mbb8X8Kl/n8dl8PZ3d54Vfh7PjdvhvOblz+Tjjb/7XHw9mxo84MZzfq/mH7odSj00aFcl+ovzpc5vubHxbOvrtkvXB2j9wT4eyHfbYKZ4fPej6cvcc+G84eOvYb7YdSLx5yaSi3x4s/D5eZ5fP0Z360ezj76BXj4+X+Yc9wdsNdNwhnJ59zZzh72KzYtvvwsPgtR/Z77sJw9rFd49+BHPzeleHs5x//VDiba4p/1vh97vvh7IyvXxvOjp7w51Duuv7fC5e58Xrx93a7Nzwczvba+Yj4GzFp11cuy/BmdQ1y1Xf6rnHbia4wkzWCmV0AHF8w+FZ3P61E03seKPy28ZQi93sTEREREREREREREZE1nBrMZI3g7j8H4j8RWvnp7bq6piUiIiIiIiIiIiIi6yZfU2/4tRbK0MmBiIiIiIiIiIiIiIiIyNpHDWYiIiIiIiIiIiIiIiKyTlODmYiIiIiIiIiIiIiIiKzTdA8zERERERERERERERGRDuCue5h1FrrCTERERERERERERERERNZpajATERERERERERERERGRdZrpcj8pxswcuMHdT0mfVwAzgefd/cg2xhsNDHP3e9spfz/g24VlmdnjwFCgDmgAznD3iSs6H1mY2UjgbnffppXX9wPuBN4FugOzgEvd/e72yr53fGN4R9v/lV9HozywxQ/C2cPe/U0427DJ9qFcl7cnhcuk36Bw9On1Ph/O7jXnlnB22sgDwtkN5k4IZyuWLghnc9OnhXI/XPzNcJk/rb8gnP3rJv8Xzn51yc/DWRs8NJylemk4Oumym8LZDXYZGc6WVZSHcg1f+Um8zN/+MJzt2q9XONt75x3C2SXj4tutNzeHs92Oje+T5ZOeCWdz9Q2hXNPuh4TLrHrv5XCW2tp4tszC0aa5c8PZig2Gh7PVEyaGs90OPDScLZv5Xij33o4nhMscuuj1cDaLLgtmhrNeFtvPAaw+vi00vvVGOFuxbXz/res3LJRrqqgKl1lVMy+cXdQnvi3WlfcIZxu8azjb0xeFs9VlvcNZ9/j+269pdihXV9kzXGYDGZZB88Jwtrq8T7wO3iWc7W7V4Wxzhh7+cxl+qzls/pRYMMNn2crZ74ezjUNGhLNZNGfYf7McwyrrFoezZRmOdw29B8eCnguXmauIb4tZ1m/XpXPC2dcH7hfO1jXF69uc4VjTs7IunN3q7dtCuYfX+3/hMg+a+odw9tGNzg1n91/4r3B22oj4Z7P3lsQ/S+43J/75YcbGe4ezw955MpS7u8+p4TK3Gzg9nN1gxvPh7GtDDgxnRzTE39e8YrHvCQBG3xf/ruK2Ha4IZz+/MJZ9eOQ54TJ7f3HncPbOrz4dzh5wfvx94F2XPBvOjh7dP5w96oGTwtnnTroxlDvig/j6enO7k8PZzV67NZx9asSXw9m93/lzOEtTYzh6w4Bvh7NfmB//Pu7hUeeHcvvXt/tV5Mfsjfh3d7+rujCcveDE8viJT9p11q/mr5WNNH/5fv81bjvRPcykNdXANmbWzd1rgYOBDwPjjQZ2AtpsMGvHye4+zsxOBy5Lp73CzKzC3ZtWpow8T7U08qWNg/81s1p3f2QVlS8iIiIiIiIiIiIi64hcbq1sL1sjqUtGact9wKfS/08Cbm55wcx6mNnfzexFM5tgZkeZWRfgYuAEM5toZieY2S5m9kyaecbMNs8w/WeB9dsKmNlSM/uNmY03s0fMbFA6/HEz+4WZPQGcb2Y7mtkTZvaSmT1gZkPT3I5mNsnMngXiP0EC0ivfLgbiP7kTEREREREREREREZFORw1m0pZ/ASeaWRWwHZDfB8AFwKPuvjOwP8mVYJXAj4Fb3H20u98CvA7s4+5j0td+kWH6hwH/bSfTAxjv7jsATwD5/af1dfd9gd8DfwCOc/cdgb8DLX3NXQuc5+67Z6hXvvHAFis4roiIiIiIiIiIiIiIdALqklFa5e6T0/t6ncTyXSweAnzGzFo67a0Cit38og9wvZltCjhJo1p7bjSzHkA50F6nzzmg5SZWNwC3573WMnxzYBvgITMjLXemmfUhaVR7Is39Ezg8UL98rfbDamZnAmcCnHvBnzj8mHgfyyIiIiIiIiIiIiIisvqowUzacxdwObAfMCBvuAHHuvsyd4g1s10Lxr8EeMzdj04b3x4PTPNkYBLwK+BK4JgM9c3v8LXljuUGvFJ4FZmZ9S3Ir4gxwGtFK+J+NXA1wL3jG9URrYiIiIiIiIiIiIhIJ6UuGaU9fwcudvcpBcMfAL5m6SVbZjYmHb4E6JWX6wN8mP5/WnSi7t4IXAjsZmZbthEtA45L//888HSRzBvAIDPbPa1rpZlt7e4LgUVmtleaOzlav7Sc7YAfkTTqiYiIiIiIiIiIiIhk4u5r5WNNpAYzaZO7T3f3K4q8dAlJ94qTzezl9DnAY8BWZjbRzE4ALgV+aWZjSbpCzHegmU3PeyxzBZi71wK/Ab5N66qBrc3sJeAA4OIi89BA0qj2azObBEwE9khfPh240syeBWrbmE6Lvc1sgpm9QdJQdp67PxIYT0REREREREREREREOil1yShFuXvPIsMeJ+1SMW3MOqtIZj6wc8HgzfL+/1FeWd2KTHq/gvJ+E6jrj1rKzRtWWM5EYJ8i474EbJ836KI2pvM4yRVzIiIiIiIiIiIiIiKyFrE19dI4EQAzW1qsca+zqXvgb+Ed7dK5p4bL/W6fa8LZK+qWa99sVZ9esYtP5y9sDpc5eGDhBYat+1y/h8LZW+YfHM7uvdH0cPbaRwa0H0ptsWmxtt/ipn/UFMp9Y8GF4TLPnf2NcPbPe94Xzv56VryX0iVLGsPZpYvrw9n5sxeFs4318TrkmnOh3Je+tEm4zAmvx8oESHuzDamvj+9nWUx9c044+9mjhoWzz75UE87WVsfW2Rc+3SVc5qT3eoSz1TXxdTawX/yi/AmTF4ez0177sP1Qasweo8LZYevFl9nS6thy+OCD6vZDqbra+P642679w9kFi+PvW7tUhqM0xQ7NAGwwJF6Hp1+I7w9VVbHfsR2xR/yYsLi+azg7uufr4Wxtea/2Q6nm5ToYaF3vpvnhbE1FvA5NxDeGXs0LQrml5X3DZeYydOrRtzF+bK6ujP+OK5dhPVTSEM5mWbaLm+Pr7J15fUO5nMfPpwsWx7MD+8bPD10rSvN5unuX+IFp5sL4vp7h7RJ9e8aWQ7nFl0FFeTybpdz358S3xSM2LnoL6qJ61cwOZy0XPz5/2Kutuw4sq7a5KpTbZvLfw2WO2/rscHbH8cU6nCluwo5fC2dHlE8LZwe8WezOC8U9N/K0eB26xd+HPTU99rngc03/CJf53sj9w9n3lw4OZ3f2Z8PZj3rFP+9sMGd8OHtHw2fC2SOf+ko4O+mo34Zyu078XbjMHyw4L5w9+k97tR9K3XTqo+HsZ360e/uh1HZnbBPOPn/qreHskbOuCuUe2/DMcJn7jPtpOPvYjheFswe9/6dw9otPxbdFz8XPO1fww3D2w7NjyxZg25evDeVu6hf/HmjEoLpwdufGp8LZ3jscHH9zJe368s/nrpWNNH+9YOAat53oCjNZI5jZ80DhJ8FTStFYZmaHAr8uGDzV3Y9e1dMSERERERERERERkXVXlgZbKS01mMkawd13XY3TegB4YHVNT0REREREREREREREOla8fxARERERERERERERERGRtZAazERERERERERERERERGSdpi4ZRUREREREREREREREOoDuYdZ56AozERERERERERERERERWaepwUxERERERERERERERETWaeqSUWR1qKsNR+fNjWfdFoazsxfGy62s7B7KLVzQEC6ze7eqcLasR7yuc+Y1h7PlG8Wzc2YuCWc3GtEtnF20MLbMKjbYMFzmqH4DwlkWLwhHZ39UHc4uWRRfZ4vnxZftBpsMCWdrq+PbY1RleS6cfX3yzHB2i+2GhrNdusR/21JfH6+v5+LZ6rp4HcrL41krs1CuprEyXOas2fHtYMmSeLZPr57hbENdU7wO8xaGs29MiW9jPXrEjyHz5taHcosW1ITLXDw/vp/X1PUPZxctii/bisrY9gVQVxs/P/Ts0TWcXZLh3NvcK1ZuXVP8fLq4pjyczfWKZ5uJZ7NoLot/NGkiflzIImexecuV6HeHpVoGZcSP+Y10CWezbAsNzfF5W1obW74NjeEi+Wh2PNytKr5sGyvi3edUlMezFtwWARYtjZdbXRPPlpfF1oPFD7d0rcyyDOLlLloS38aNju/yyInPXH1zbHvMLV4ULnNJfXw/b5w7N5ytb4rv51aWYT3UxD+X1DZm+Jor/jGOuQuD9W2Of97Ksi02e3ybKW+qC2ebPL68yprjx9HhA+LvG7tuEP9stLQhtj80zZsXLnPkRj3C2Q133SCcHT06/h53uzO2CWcnX/NyONv0hQwH0i6x96KzFsXPkfWz5oSzuVyGujbH37vPfPuDcLZ7317h7LRX3g5nG7+c4b3V/Ni2uzDD+4/+veLrrKI2fgwTWVvpCrN1mJm5mf0z73mFmc0xs7vbGW+0mR0RKH8/M1tkZhPM7HUzuzzvtdPMLGdm2+UNe9nMRqb/TzOz2/JeO87Mrss2hyvOzK4zs+Naee0OM5toZm+n8zcxfeyxuuonIiIiIiIiIiIiImu+nPta+VgTqcFs3VYNbGNmLb+rOhj4MDDeaKDdBrPUU+4+BhgDHGlme+a9Nh24oI1xdzKzrYPTaZdl+ZlmG9z9aHcfDXyZZP5Gp49nVkX5IiIiIiIiIiIiIiKyeqnBTO4DPpX+fxJwc8sLZtbDzP5uZi+mV4kdZWZdgIuBE9Krqk4ws13M7Jk084yZbV44EXevBSYC6+cNvhvYulg+dTnww8hMmNlFZvZPM3vUzN4yszPS4fuZ2WNmdhMwxczKzeyydJ4mm9lZac7M7I9m9qqZ3QMMjkxXRERERERERERERETWfGowk38BJ5pZFbAd8HzeaxcAj7r7zsD+wGVAJfBj4Jb0qqpbgNeBfdIryX4M/KJwImbWD9gUeDJvcA64lNYbxf4N7GBmmwTnZTuSxr/dgR+b2bB0+C7ABe6+FfAlYFE6TzsDZ5jZRsDRwObAtsAZgLpXFBERERERERERERFZR6jBbB3n7pOBkSRXl91b8PIhwPfNbCLwOFAFDC9STB/gVjN7GfgdkN+N4t5mNhn4CLjb3T8qGPcmYLe00apQM0kj3Q+Cs3Onu9e6+1zgMZKGMoAX3H1q3jx9MZ2n54EBJA15+wA3u3uzu88AHg1Os1VmdqaZjTOzcX97YOzKFiciIiIiIiIiIiIiIiVS0dEVkE7hLpLuD/cjaUBqYcCx7v5GftjMdi0Y/xLgMXc/2sxGkjSutXjK3Y80s82Ap83sDnef2PKiuzeZ2W+A77VSt3+SNJi9EpiPwjsJtjyvzq8+8DV3fyA/aGZHFBl/pbj71cDVAHV3/nHNvMuhiIiIiIiIiIiIiJSM5/TVcWehK8wE4O/Axe4+pWD4A8DXzMwAzGxMOnwJ0Csv1wf4MP3/tGITcPc3gV9SvGHsOuAgYFCR8RpJrlr7evuzwVFmVmVmA0ga/14sknkA+IqZVQKY2WZm1oOkq8gT03ucDSXpglJERERERERERERERNYBajAT3H26u19R5KVLSO5ZNjntbvGSdPhjwFZmNtHMTiC5D9kvzWwsUN7GpP4M7FPY/aK7NwC/Bwa3Mt7fiF0N+QJwD/AccEnatWKhvwKvAuPTefpLWvYdwFvAFOAq4InA9EREREREREREREREZC1g7rrcT9Z8ZnYRsNTdL+/ouhRz34TG8I62/1vF2i6Le2jU18PZg9+7MpytHTU6lOs2dVK4TO8zoP1QauyA48LZPef+O5ydNuKAcHbYotfC2craReFs2dyZodyVfk64zHP4Qzj71y5fC2e/XP3bcDY3eP1wtqx6cTi7+Kmnw9mqIQPDWW9sDOXmHfP1cJld/i96u0Xov+PW7YdabLxlOJqbMi6erW8IZ+d/5qvh7HqvPBjO4rlQrGF4fBl0mfZqfPJ1NeGs9e4bL3fBvHh2xGbhbPWD94WzPQ49Ipwt++j9UO6V7U8Plzm86e1wtmtd/BhasSS+bC3De1xrqAtnG16On/vK9oifdxYM2CSU61E3P1xml5oF4Wx172HxbJe+4WwDXcPZ3k3xeVtS0S+czWX4jeDA+g/bD5FtGdRbt3C2X/2scHZx1/h7qzqP16EX8X2yqawynG32+N0Ahs5/OZSzXHO4zIq5sXUL0DQw/r4mi+bK+HrwsvjyqqhfEs6W1deGsw29Yu+tyjy+HpoqqsJZC75PgGzvx1/vt3c4u6ghvs5yOQtnB3RbGs5u9fq/QrknR5wRLnOfN+OfH8ZuEf9cssecW8PZGSP3DGen164Xzu4y67Zwdu6GO4azg96Pvc++u+cXwmWOHvheODtkwRvth1Lv9R0dzg6reSucfb0yXu4OU/4Szj67ZXwb2/2tq0O5sZueFS5z6398OZx97nPXh7O73BTfFp458cZwtqk5fqzpte8W4eyM+2Lb2CnzLg2XOX2n48PZDd6N/259wgbHhLPbz7k/nM3yvuK/lSeGs0e9fUn7odTTo78fyu1Zc2+4TCYX64CruF92/Uk4+7PTusQ3RmnXqT/+aK1spLn+4vXWuO1E9zATERERERERERERERHpALqoqfNQg5msUczsdOD8gsFj3TNcjpNtencAGxUM/p67P1CK6YmIiIiIiIiIiIiIyOqnBjNZo7j7tcC1q3F6R6+uaYmIiIiIiIiIiIiISMeId+gvIiIiIiIiIiIiIiIishbSFWYiIiIiIiIiIiIiIiIdIJfTPcw6C11hJiIiIiIiIiIiIiIiIus0NZiJiIiIiIiIiIiIiIjIOk0NZiIiIiIiIiIiIiIiIrJOM3f1jynZmJkDN7j7KenzCmAm8Ly7H1kkXwlcAhwL1AM1wE/c/b709THAeOAwd38gb7xmYArJvfZeA05195pSzls63f2Ab7cyL6cD56dPtwLeAJqB+939+62VOX/yU+Ed7f7Fe4XremT3h8PZe+sOCmf7dGsK5RbVxm+DGC0TYLser4ezr9RuHs5u2HNOvNy5Q8PZft0bwtl5S7uEcp/y/4bLvG7RMeHs54c9Ec7et2SfcLaiPH4uqW+M/1ajLr5oWbw0Xoee3S2U23Pjj8Jlvj5vcDg7b1F8Gfz77y+Es2ect3M4OyO+O7DJ+rlw9t0Z8XmrromVe+iYxeEyP6ruFc7mcrHtAKB7l/gxbPbi2H4OUFMfr0PfnvH1MKhnXThbXV8Zyi2uix/z6xri87VB//pwdkmGOvSqiq+zxub4dtutMl7uR4urwtkuFbFj2Ii+C8Nl1jXFt8VhXePHu5zFl1d9Lr4MerAknG22DNujdwtnu1nsrWYTsf0GoNHj2VItg3ri66GC+Daey/D7y/IM5c6oi51Ty+KHGpbUx/eH3l3jx6XysuZwNsuvVSvLG8PZJQ3xbbw5F69Fj8rYG7Eyi5+fsiyvcuLlzqrpE84O7zk7nK1u7h7OOvENsqosw7mvKVaHraqfC5c5uVv8M+f21fHPD6/02jOcHVC5MJwdtGRqOPta1x3C2b5d4sfcGTUDQrkxjAuXObPbqHC2pjm+nw+zD8LZeTYknB3SFC93SuM24exuNQ+0H0pN7H1gKLd9zVPhMh/KHRLOHjH7L+HsvYPPipf70VXhLF26hqP/rPhyODvs8Nh3K30mjQ+Xuf3Sx8PZyT33DWe3bJoQzv575t7hbJavyE+v/Ec4++qGnwpnN66ZEspNqtwlXGbXDO8pNvK3w9khW+6Y4Z2YtOfkH3y4VjbS3PjL9de47URXmMmKqAa2MbOWd2wHAx+2kb8EGAps4+7bAJ8G8r/RPAl4Ov2br9bdR6fjNABnr0ylzax8ZcYHcPdr0zqNBmYA+6fPW20sExERERERERERERGRzk0NZrKi7gNafiJxEnBzsZCZdQfOAL7m7vUA7j7L3f+dvm7AccBpwCFm1trPX58CNmllGiPN7HUzu97MJpvZf9LpYmbTzOzHZvY0cLyZHWJmz5rZeDO71cx6prnD0jKeBuKX64iIiIiIiIiIiIiIyBpPDWayov4FnJg2cG0HPN9KbhPgfXdvrT+tPYGp7v4O8DhwRGEg7fLxcJLuGVuzOXC1u28HLAa+mvdanbvvBTwMXAgc5O47AOOAb6bzcA3JlW97A+u1MR0REREREREREREREVnLqMFMVoi7TwZGklxddu9KFHUSSeMb6d/8bhm7mdlEkoat94G/tVHOB+4+Nv3/BiC/U/Zb0r+7kdx3bGxa7qnACGALkka7tzy5qd8NKzw3IiIiIiIiIiIiIiKyxonfKVpkeXcBlwP7AR/fAdfMHgCGkDR0nQcMN7Ne7r7MHXXTe4odC3zGzC4ADBiQl61N7xUWUXhjxPzn1S2TBB5y92XulWZmo4uMv9LM7EzgTIDf/ujbnHrcZ1b1JERERERERERERERkDZZcwyGdgRrMZGX8HVjk7lPMbL+Wge5+aH7IzP4G/N7MznL3BjMbChwIzAEm5efN7Hrgs8A/M9ZluJnt7u7Pklyl9nSRzHPAlWa2ibu/nd7nbAPgdWAjMxuVdg15UpFxM3P3q4GrAeZPfkpHPRERERERERERERGRTkpdMsoKc/fp7n5FIHohSePYq2b2MvDf9PlJwB0F2duAz69AdV4DTjWzyUB/4Koi9Z0DnAbcnOaeA7Zw9zqSK8HuMbOngfdWYPoiIiIiIiIiIiIiIrKG0hVmkpm79ywy7HHg8VbyDcB300e+B4pk7yLp6rHodNqQc/ezi5Q3suD5o8DORXL3k9zLLKywbBERERERERERERERWTOpwUxkNZhkO4Wzxyy+Jpx9stdp4exnq68PZ2t6bRLKdV/6VrjMprIB7YdSL1YcHM7uM+vGcPbdHoe2H0od3vzfcLa5oUc426VmVij3YJ+Tw2V+YcjD4eydCw8KZz+36MpwNtcnvn7L6qrbD7VYOC+e7T84ni2LXWA925drX2/Voc9dFs5WDo7X9fPf3j5e7ow/hbM0NYSji3rsE84eODB+ka41xurQvKhXuMwtJz0bzi569Z1wtu9xx4WzZUsXhLO57r3j2QkTwlnfYa9wtnLm1FDuo60PCZc5YMHb4WyjxZdBVWPsGArgTRbOltcujZc768NwNjdy83C2sUv/UK65oUu4zKols8PZuYO2DGebKQ9nyywXzjZZZThbn6sKZ83iPWOX5xpDuVx5vKOO8gyderjHt9sGupakXMuwzox4uU3E1+92Dc+HcuXN8XNZ5Yz4Mb9uw/j+YB5fXo1d4u8ZvTm+3VTVxs875bVL2g+lmqti9S3P8N6uuVv8nF7WUBPOjrL48prWY9dwNucZ9t8M+0Muw3Fhq5oXQrnnKvYPl7nnwrvD2ae6Hxkvd+mj4ewHfbcLZydWxNfZToseCmdnDBodzu4x59ZQ7p4e8c9x23f/IJxdv+a1cHZaz23D2cG5meHs+2WjwtmdcvH35BN7HxjO7rDg/lDu0W5Hhcs84p3Lw9mHN/1mvNypkQ6ZEo9tfG44O2tR/Hx6yqxLw9lxk8aHcou23yFc5iV/mBjO/mjHx8LZST32DWdPrYzf8cUr4u+t/pX7Yjh7wvR4HR7re2Iot1dd/Hjb5cP4Z7O/VZ4Tzp4Tf7skAZ6Lv6eU0lKDmawxzGwA8EiRlw50921KML3TgfMLBo919/jZQ0REREREREREREREOj01mMkaw93nAaNX4/SuBa5dXdMTEREREREREREREZGOEe8HQERERERERERERERERGQtpCvMREREREREREREREREOkAuF7/vspSWrjATERERERERERERERGRdZoazERERERERERERERERGSdpgYzERERERERERERERERWafpHmYiIiIiIiIiIiIiIiIdwF33MOssTCtj3WNmzcAUkgbT14BT3b2mSO4Zd99jFUxvP6DB3Z8J5u8EBrv77nnDLgKWuvvlK1ufYB2mATu5+9yC4QOAR9Kn6wHNwJz0+S7u3lCsvJqxt4V3tJvqjg3X8/MV/wpnb7MTw9mK8lh1m3PhIunbozmc3aPyuXD23oV7hrM7D5sezv7jicHh7L47Wjg7v7oylDuod3wZPLx4t3D24N7PhrP3zIvv/jPnxDeGmpr4tjB6i/iF0LMXloezuWB1d9l4frjMqQv6hrMVZfFz75La+HwN6NkYzr4zM/6bma2G14ezMxZUhbN1RY+YRaY/bGm4zPFTe4Sz771fG84evW98G39tZs9wdty4BeHsbrv2C2f7dI/vZ9X1sf3s+ZeWhMssL4/vu3vt0j2cbWiMH28tHqWpOR7ear34ceH+8X3C2ebm2HHhwDF14TIX1XUNZ3eqmhTOLu0a3xarPb4/rFc/LV6HqgHhbK3Ht7FB9R+EckuqBpZk+kPqpoWzS7oNKkkduhM/5jZafBur8/j5YWZ1bBvLsu/Or46f9wb3jp9PK8vix9ssqiridZi1NH7uq2uMH5+7VsTOfY0Z1kO3LvHzaZb1W9sQn6+dh74fzvZqjB/zy3LxbWFG5YhwdsDHHzfbNujNJ8NlfrjlIeHs+hP/F85+sP1nw9keTYvC2X4zpoSz7254QDjb2+PvwyYt2SyUO3DxreEy39tw73C2Nhc/ho6ofS2cre4WP5/2WvpROLu459BwdvBbT4Wz0zc/OJTb4OV7wmW+uuVJ4exmD18Wzr578DfD2VGP/z6crZ8VOyYAzD/p++HskHmvhnKXjI/vY/t+bXQ4O/K1x8PZjT94NJz9/uTPhLNV3eLvFb4/71vh7OLjzw9nh7wd2x/u6396uMytBswIZysJfkgHNh41KsMnLmnP5741ba1spPn3b0aucduJumRcN9W6+2h33wZoAM7Of9HMygFWRWNZaj8gVJaZ9QV2APqa2UaraPotZa/0FZXuPi9ddqOBPwO/a3neWmOZiIiIiIiIiIiIiIh0bmowk6eATcxsPzN7zMxuIrn6DDNbmv7dz8yeMLN/m9mbZvYrMzvZzF4wsylmNirNfdrMnjezCWb2sJkNMbORJA1y3zCziWa2t5kNMrPbzOzF9JF/idCxwP+AfwHhS6LM7HEz+z8ze8bMXjazXdLhF5nZ1Wb2IPCP1qZtZgPM7MG07n8B1rjWbxERERERERERERGRNZ2Z9Tezh8zsrfTvct1QmNnmaZtDy2OxmX09fe0iM/sw77UjItNVg9k6LL3i6nDSBjJgF+ACd9+qSHx74HxgW+AUYDN33wX4K/C1NPM0sJu7jyFp8Pquu09j2SuxngKuSJ/vTNJA9te86ZwE3Jw+4tfFJ3qkV8V9Ffh73vAdgaPc/fNtTPsnwNNp3e8ChmectoiIiIiIiIiIiIiIrLzvA4+4+6Ykt0harp9Zd38jrze4HYEa4I68SH7vcPdGJrrSXdTJGqmbmU1M/38K+BtJl4kvuPvUVsZ50d1nApjZO8CD6fApwP7p/xsAt5jZUKAL0FpZBwFb2Sc3F+ltZr2A7sAmJA1XbmZNZraNu78cnK+bAdz9STPrnXbvCHCXu7fcqKa1ae8DHJOOf4+ZxTszb4WZnQmcCfCH75zF/zsq1te2iIiIiIiIiIiIiKwbPLdW3sJsZR1FcqsngOuBx4HvtZE/EHjH3d9bmYmqwWzdVJu2un4sbUCqbmOc+rz/c3nPc3yyHf0B+K2732Vm+wEXtVJWGbB7XiNWSx1OB/oBU9P69CbplvHCtmYmT+GRpeV5/ny1Nu1i468Ud78auBqgZuxtOuqJiIiIiIiIiIiIiLRvSMsFPO4+08wGt5M/kfSCmjznmtkXgXHAt9y93Ytk1CWjrEp9gA/T/0/NG74E6JX3/EHg3JYnZjY6/fck4DB3H+nuI0kuowzfxww4IS1vL2CRuy8qkmlt2k8CJ6fDDidpuBMRERERERERERERkYzM7EwzG5f3OLPg9YfN7OUij6MyTqcL8Bng1rzBVwGjgNHATOA3kbJ0hZmsShcBt5rZh8BzwEbp8P8B/0k39K8B5wFXmtlkkm3wSTP7Fcl9w55rKczdp6Y36ts1OP0FZvYMyZVp/6+VzHLTBs4GfgrcbGbjgSeA94PTFBERERERERERERGRPPk9sLXy+kGtvWZms8xsaHp12VBgdhuTOhwY7+6z8sr++H8zuwa4O1JnNZitg9y9Z5Fhj5P0A7pcrvA1d9+v2HjufidwZ5Gy3wS2Kxh8QpGqrV9k3B3Sf58vki90m7v/oGD8iwqezy02bXefBxySN+gb7U2ssGwRERERERERERERkSx0D7Oi7iLpxe5X6d/l2h3ynERBd4wtjW3p06OBlyMTNXetDFnzmdnjwLfdfVxH16WYJ1+pDu9oOy5+KFzucz0PC2d3W3p/OLu074ahXM+FH4TLLF/SbhexHxu/4XHh7DY1z4Sz0/tsG84OrovfH7Ln9FfCWa/qEcrdwknhMo/tcW84e8viI8LZk8tuDGebevQNZ81z4WzFzKnhrPcdFM8m9y1s18wNdwmXuf7E/8Wn37+9bpc/Ud93aDhb9dZL4Sx9+oejNUM3DWe7LmnrBz8rxhobwtmmnvEedcsa69sPpV779i/C2W1/cFo4Wz90k3C26+xp4Wz1BlvFy62eF8rNHFj425fW9a37KJx1Kw9nu9YtjJdbFv9dWFmuMZytXBift/r+sfMpQF1Vn1CuOcN8dW1YGs4u7hY/LjVRGc96hvqW1YWz9bmqcDaLblYTymVZBs3Et/EuxI9LDXSN18Ez1MHix9xciXr471c/q/0QYMTfU1TVzA9n67rHz5FZNJV1CWfd4su2S2Nbt6JeVnlzfP3Wd+0dzkblMhzzo+/XAKoaloSzH1Vt1H4oVZ+Lr7MsupbF18PGHzwayr005LPhMnf86PZw9sUhx4SzO0/6fTg7a5djw9l5uYHh7FYv/DmcXbDzkeFsz+rYcenFyr3DZa7ffW44O/L10I/iAXh/i/hnvg3ffyqcfXX9w8PZrd+7K5ydsEF8GxvzTuwz6tgRp4fL3POdVi98WM5DG341nD34w7+Esw9vcFY4m8vFj42HLPhnOPvS+rHvYbarfjJc5vv9xoSz07bcL5z15+Pfwezjj4SzlmsOZ58oP6T9UGqvsqfD2cmVO4dyW/ukcJndFnzYfih1V3n8+7gT98hwopZ2HXf+u2tlI81/rth4hbcTMxsA/JukV7r3gePdfb6ZDQP+6u5HpLnuwAfAxvm3aDKzf5J0x+jANOCsvAa0VukKM1mjmNmVwJ4Fg6/Iv+ptFU5rAFDszHpgekWaiIiIiIiIiIiIiIisQun37wcWGT4DOCLveQ0woEjulBWZrhrMZI3i7uesxmnNI2mFFhERERERERERERGRtZgazERERERERERERERERDpALsPtS6S0StPpvIiIiIiIiIiIiIiIiMgaQg1mIiIiIiIiIiIiIiIisk5Tg5mIiIiIiIiIiIiIiIis03QPMxERERERERERERERkQ7gOe/oKkjK3LUyREptyQv3hHe0uxuPCJf7mbI7w9n/+VHh7NyFsVzfXuEiyeUsnD184PPh7NiancLZLfrPDGefmrpBONu1Mhylojy2KXy6y73hMu9pim8zR5bfHc7eWvPpcLahMRylsSl+3unTM77d1GeoQ/TUt+0Gi8JlfrAwvkN06xK/mWtTc3wZ1DbELxxfXB0vd8v1q8PZ6Qu6h7PR94ObDFocLnPm4p7hbGOGZTtrfoZtsT6+jWd5T7zhevE6VFXGt7GldeWhXHVtuMhM+/mooU3hbF1TrK6QbdlmeTvcv0dDOPvurKpwtjL4M7athy4Ml1nfHD9Brd81fo5spEs468S32y7Uh7MNdC1JHbpSF8rVE1+3WUSnn7UOZZTmJuK5DB2WVDfHzw8L6nrEpu/xdVtdHz9+9KqKH5cqyuLL1ix+sOlSFq/Dgtpu4WxTLr7OqiqbQzkjPl9lZRmyGcpdVBc/Lm3ff2o427WpJpw1j28L8yuGhLPNHtt2N5nxaLjMN4YdFM5u+X78c8nbIw4JZwc2xc87fV99Mpx9ZZtTwtlBNiucfb1mVCi3W9Nj4TJn9N06nF3Q0Duc3XbxE+HsnIFbhrPrffBiOPvy0MPD2a0/uj+cHT8o9hl1p/dvDpf52JBTw9n9p/4pnH1q1Nnh7N5T/xLO0hw7NgNM2OpL4ezmjZNCuVcrdgiXueOs/4azj/Q5IZy1XeP7ztu3vx7OlmXoh+2MpivD2Ze3ODmc3XJ+bP99tMuR4TIHdo+fy0b5m+Hs4K12ir8Rk3Ydfe5ba2UjzR1/3HSN207UJeM6xsyazWyimb1iZpPM7JtmVpa+tp+ZLUpfb3kclL62gZndaWZvmdk7ZnaFmXXJK3cXM3s8fX28md1jZtumr11kZh8WlNs3nZ6b2afzyrnbzPZL/7/RzN4ws5fN7O9mlqFZYqWX03Vmdlwrr92RzsPbBctrj9VVPxERERERERERERERWXXUYLbuqXX30e6+NXAwcATwk7zXn0pfb3k8bGYG3A781903BTYDegI/BzCzIcC/gR+6+6buvgPwSyD/J1i/Kyh3YTp8OnBBK3W9EdgC2BboBnx5ZWbczOI/K22Dux/t7qPT+uQvr2dWRfkiIiIiIiIiIiIiIrJ6qcFsHebus4EzgXPTRrHWHADUufu16XjNwDeA/2dm3YFzgevzG4zc/Wl3/2+gGpOARWZ2cJH63esp4AWg1T7y0qvY/mlmj6ZXuZ2RDt/PzB4zs5uAKWZWbmaXmdmLZjbZzM5Kc2ZmfzSzV83sHmBwoO4iIiIiIiIiIiIiIrIWCN4tQdZW7v5u2iVjSwPR3mY2MS9yLLA18FLBeIvN7H1gk/T169uZ1DfM7Avp/wvcff+8136WPh4qNmLaFeMpwPntTGM7YDegBzAhbfgC2AXYxt2nmtmZwCJ339nMugJjzexBYAywOcnVbEOAV4G/tzM9EREREREREREREZEV5lluwi0lpSvMBFjmTuiFXTK+k75ebK8tOtzMnjez18zsirzB+V0y5jeW4e5PpePt3Ur9/gQ82ZJrw53uXuvuc4HHSBrKAF5w95Y7Ox8CfDFtFHweGABsCuwD3Ozuze4+A4jfMbkVZnammY0zs3HX3hG/ia2IiIiIiIiIiIiIiKxeusJsHWdmGwPNwGxgy1Zir5BcaZY/Xm9gQ+Cd9PUdgDsB3H1XMzsOODJDVX5Oci+zpoLp/AQYBJwVKKOw8a7leXV+kcDX3P2BgukcUWT8leLuVwNXAyx54R79TEBEREREREREREREpJPSFWbrMDMbBPwZ+GN6n7DWPAJ0N7MvpuOVA78BrnP3GuBK4DQz2yNvnO5Z6uLuDwL9gO3z6vdl4FDgJHfPBYo5ysyqzGwAsB/wYpHMA8BX0m4eMbPNzKwH8CRwYnqPs6HA/kXGFRERERERERERERGRtZCuMFv3dEu7I6wkuZrrn8Bv814vvIfZz9z9P2Z2NPAnM/sRSUPrvcAPAdz9IzM7Afi1ma1PcrXaXODivHLy72EG8Nkidfs56VVqqT8D7wHPmhnA7e5+cZHxWrwA3AMMBy5x9xlmtllB5q/ASGC8JYXOSetyB3AAMAV4E3iijemIiIiIiIiIiIiIiKy0tq9lkdXJtDJkbWBmFwFL3f3yjq5LMVfeF+/u8QuvnB8u95qNfxfOnvXh98LZyg03DOWaZswIl1mx/gbh7L1DvxrOHv72r8PZd3Y5PZwd/LfvhrO9990nnG16581Q7uoN4vN11kcXhrN/HfazcPa0188LZ2tnLwhna+YsDGf7jFwvnG2srgtno6pO/nI4W3fjX8PZ3luOWpHqtCtXWxvOzp/ydjg78OSTwtnG558OZ5uWVrcfAuyYU8NlVo17OD79hYvC2S5bbxvOVj/7bDj7xt2Twtnhu28Uzg7ce5f2Q6mmObNDuZrpH4XLrJkTX7brffawcDY3O14HyuIdKXh9QzhbvtEm4eySJ58MZyt79Qjl/LDjw2V2XTQrnH1zaPzi+u5l8WNNM+XhbF2ua0nqkMvQqUZtc1Uo16t8abjMpgy/UYxOH6BneewYCtDoleFspTWGs2U0h7M9GxeGs33fnxgLNtaHy8x9FH/fWjYyvp/nKrqEs1TE10Nz19gxAaByxjvxOlTHt10GDolng3Jd4ts4Ft93yz6c2n4oNWWHSG//iQV18Y5Tmpqt/VBq0z7x7bF3w9xQ7i/jt28/lDpv6/j7tV+9uFs4+6094u9r3i5r7Y4Qy3v2zV7h7P8bEf/t67vd4+/vtpp0XSj3l6pvhss8aMv4+5r1l74Rzj7VvFc4u3nf+LY4cXb8M/3B3du7Bf0nfj9lj/ZDqa9vFVu/l07cO1zmj3pcEc5+fuwx4exN+90Tzn5x7FHh7My3PwhnH/jWzHD2H7WfC+VOrfxnuMzvT/lsOHvJXs+Es9fOOCSc3eSYLcLZwbv2C2e/v8Hvw9l//Sj+3ftVE3cM5b5b9YdwmY0bFl5L0Lq7G48IZ0/a0+InPmnXUV95Y61spLnzqs3XuO1EXTKKiIiIiIiIiIiIiIjIOk1dMsoaxcxOBwovwRrr7ueUaHp3AIU/6f+euz9QiumJiIiIiIiIiIiIiMjqpwYzWaO4+7XAtatxekevrmmJiIiIiIiIiIiIyLoll8t1dBUkpS4ZRUREREREREREREREZJ2mBjMRERERERERERERERFZp6nBTERERERERERERERERNZpuoeZiIiIiIiIiIiIiIhIB/Ccd3QVJKUrzERERERERERERERERGSdpivMOpCZNQNTAAOagXPd/ZnVMN3HgaFAPdAFeBi40N0XtjPeNGAnd5+7CutSAXwEXOPuPyhSx9p00M/c/T+rarpt1Gc/4NvufmQrr58GXAZMB3oC7wI/bW+95TL8SiDX2BjONufCUby5OR4O1iFXVxcvs6k080WG+TIyrIemDJXIxbPeFKtvrjleVy/Rss01NMWzwfkCaG6MV6K5MV6HprqGcDbM43Xt0qdHOGuVlfEqZDgm5Orjy6Cxpj6cJRdfv5mOYcH1W2EWLpPy8ng2C8+wT2Y45nfpEX8rVtWvV7wOGY4LueB6yDJfWY4JWc4Pubr4dmuV8WWba4jvO+UN8XNfc3183iqDq9cy7I9Zsmsz9wzHkBLI8v6jMyhVfS3DOZXG4L7eGN93o+/BspZrGc5RmZZsZZbjaPz9UpbzQ/gYYvHf4VqW82mGXTfXkOH8kOUzQYaVliNeYSO+P5Q3x7ZHz7Bsy3Lx7SCL8gzlWnmGbSHDesgybxWWYT8L7g9Z6uoZthnPcKzJ9NbZ4seP5ly84LLm0mxjZeH9IV6mZ3gfmOl7ggzH2yzvs7v3jX8msNz0eB2CVfCKruEyq7rF349ned9aluHyj8G79gtnZz+/IJzttV3fcLa8eVY42xz9LijL55fG+OeX+saOfd8s0hnoCrOOVevuo919e+AHwC9X47RPdvftgO1IGs7uXI3TzncI8AbwOVv+0+bJ6fIZvbKNZWa2Kr9BvcXdx7j7psCvgNvNbMtVWL6IiIiIiIiIiIiIiKxGajDrPHoDCwDMrKeZPWJm481sipkdlQ6/xMzObxnBzH5uZuel/3/HzF40s8lm9tN0WA8zu8fMJpnZy2Z2QuFE3b0B+C4w3My2T8f7gpm9YGYTzewvkcYmMxtpZq+Z2TVm9oqZPWhm3dLXRpvZc2nd7jCz/J93nARcAbwP7JZ1oaXTfd3Mrk/L/4+ZdU9fm2ZmPzazp4HjzewQM3s2Xa63mlnPNHdYWsbTwDFZpu/ujwFXA2dmrbuIiIiIiIiIiIiIiHQOajDrWN3SRqnXgb8Cl6TD64Cj3X0HYH/gN+nVV38DTgUwszLgROBGMzsE2BTYBRgN7Ghm+wCHATPcfXt33wa4v1gl3L0ZmARskV4pdQKwp7uPJukq8uTg/GwKXOnuWwMLgWPT4f8Avpde0TYF+Ek6D92AA4G7gZtJGs/y3Zgun4lmNqCN6W4OXJ2Wvxj4at5rde6+F2m3k8BB6XIdB3zTzKqAa4BPA3sD6wXnNd94YIsVGE9ERERERERERERE1mHuubXysSZSg1nHaumScQuSxq1/pA1jBvzCzCaTNPSsDwxx92nAPDMbQ9KV4QR3n5f+fwgwgU8abzYlaZw6yMx+bWZ7u/uiNurS0h3igcCOwItmNjF9vnFwfqa6+8T0/5eAkWbWB+jr7k+kw68H9kn/PxJ4zN1rgNuAowuuZsvvknFeG9P9wN3Hpv/fAOyV99ot6d/dgK2Asel8nQqMIFlWU939LU86fb8hOK/51MGviIiIiIiIiIiIiMgaLH73RSkpd3/WzAYCg4Aj0r87unujmU0DqtLoX4HTSK6E+ns6zIBfuvtfCss1sx3T8n5pZg+6+8VFMuXAtsBrwGDgenf/wQrMRv6dlpuBbu3kTwL2TOcPYADJFXUPZ5xu4R0x859Xp38NeMjdl7mKzcxGFxk/qzEky24ZZnYmaVeNJ5z7Z/Y8TL02ioiIiIiIiIiIiIh0RrrCrJMwsy2AcmAe0AeYnTaW7U9yJVSLO0iuRtsZeCAd9gDw//LuybW+mQ02s2FAjbvfAFwO7FBkupXAL0mu0poMPAIcZ2aD09f7m9mIwvGi0qvaFpjZ3umgU4AnzKw3yZVgw919pLuPBM5h+W4ZI4ab2e7p/ycBTxfJPEfSOLcJgJl1N7PNgNeBjcxsVN74YWa2L0mj2DWFr7n71e6+k7vvpMYyEREREREREREREZHOS1eYdaxuafeAkFwBdaq7N5vZjcD/zGwcMJGkUQcAd28ws8eAhem9x3D3B9N7jz2b9OjIUuALwCbAZWaWAxqBr+RN+0Yzqwe6klzRdVRa1qtmdiHwYHqftEaShqz3VmI+TwX+bGbdgXeB04FjgEfdPf+qtDuBS82sa8byXwNONbO/AG8BVxUG3H2OmZ0G3JxX/oXu/mZ6Jdg9ZjaXpLFtm3amd4KZ7QV0B6YCx7r7cleYiYiIiIiIiIiIiIi0xXMr2wGarCpqMOtA7l7eyvC5wO7FXksbsXYDji8Y5wrgioL4O3xyFVp+dr926nULn9z7K3/4yDbGmUZeQ5O7X573/8S0zvmuSx/5Zcwn6YoSoM06Fsi5+9nt1dfdHyW5Mq8wdz/Jvcza5e7XUVBvERERERERERERERFZs6nBbA1iZlsBdwN3uPtbHV0fiTuj8Q/h7Pd7XhrO/qrxsnD2J91/Hs4OrqxqPwTM7lkXLnNosEyAM3J3hLOXV3w/nD3BZoSz3+/xm3B294oh4ezU7rFldlHVX8Nl/m7QL8PZbzT/Ppz9To9fh7PVTfFtYXFuaTi74dDB4Wxt78Zwtrk5F8od5X3CZf67fstwdsuu8fkq62bhbHV5Uzj77oZzw9nP9Yhv42MHHBXO1vdsDuWOyHUJl/lE83K/i2jV0vKGcHb7Lr3C2Rd7HRfOvj7kjXC256ze4ey+ozcPZ+d0r28/BMztGd93F9UtCWf367JRODunZ3wbL8vQ8XhDeeyYADC8S/xC+Od7HhvO9u7ePZQ7lPj055ZVhrP7WHxbrKFnOGsZbhU7kNnhbDXx/aHM4ut3MDNDuSX0C5eZRXT6WetQafFzZAXxbBPxbewdNgtnX63aMZRrLPrzw+Jm94jP1/Cu8fmqrIhv4xUZ6tu9LL7dvtc1fsBb2hQvd1CWCgdVlZfm19MfdIvP1xfK4se7TTIcl8oy7Dvv+Xbh7KyybUO5b9f+NFzm2PIfh7MX1F0Qzr5ky90yvVWbWfwrjdEND4azj5edF69D2Qfh7JVdvxPKnVP/23CZ79vx7YdS45b7DXLr9vFHwtkPcvFt8ajcf8LZCVWHh7PfJf69xmPlsfXwg6Xx7wn+vln8s//vc8eEszcMuD2cvSLD3UGmvfJ2OPvfyhfC2dOb/xHK/Sv3xXCZ35/31XD2ifL4dxVnNF0Zzn5qg3i5vbbrG6/DNZ8OZyd9ZWI4+73GS0K5K3vHj/kbVcbPvSc03hzOrthddUQ6PzWYrUHc/VVg446sg5kNILnPWaED3X1eB023vS4UV2R6pwPnFwwe6+7nrOppiYiIiIiIiIiIiIhIx1KDmWSSNoqNXtun6+7XAteurumJiIiIiIiIiIiIyLpH9zDrPDJ0ViMiIiIiIiIiIiIiIiKy9lGDmYiIiIiIiIiIiIiIiKzT1GAmIiIiIiIiIiIiIiIi6zTdw0xERERERERERERERKQD5DzX0VWQlK4wExERERERERERERERkXWaGsxERERERERERERERERknWbu3tF1kE7MzJa6e8+CYRcBS939cjPbDbgC6Jo+bgHeA85P41sBbwDNwP3u/v28cpYb190vMrOTge+lsaXAV9x9UolmcRlmth/wbXc/spXXTwMuA6YDPYF3gZ+6+zNtlfvQpPrwjrb3rJuiUR4ffHI4u9+ceLl1g0aGclUfvR0us6nv4HD26arDwtl9598Szr47/KBwdsNFk8NZLP7bg8pFs0O5h3ufEC5z964vhrP/m7N7OHtS/V/D2eY+A8PZsvracNYWzglncwOGhrPkmkOxjzbYOVzk0GduDmdt2PBwdtGwrcPZPu+8EM5iFo7O23TPcLb3wvdXeR0auvUNF9l9ztT45KsXh7MNQzcOZysXzw1nlw7eJJzt9cpT4SzNsW0coH769FDug6MvCJe5Xl18PXStWxTOViyZF85aY0M4m0Xze++Es77tLuHs/AGbhnI96heEy+xSF9/G67r3D2frK7qHs4vKBoSz/ZtmhbN1lT3bD7Vk6RbODqyL7Q+NFfEyF5bHz5GDGmLTh2zLYLH1C2e7W3U420RlONs1VxPODpjzWihnTU3hMstq4vtDc+/4/pDlfWCusiqeLYvfPaFySfy8U9YUPzY29orvv1FeXpq7QlRkWL9vr7dvOLu0Kb6vu8ffW/XpsjSc3WTq/aHc88M+Fy5z12n/CGfHbXRKOLtdXZsfi5cxq89m4ez0mvhnyR0bnw5n5/XZKJwdOi02b3f3/EK4zO0Gxo/5/eo/CmdnVo4MZwfm4uW+2zwqnB295NFw9r0BO4Wzo96+J5R7YcOTwmXuMvmP4ezEMWeHs6OnxD9PT9nuS+FsTVOXcHbncZeHs6/vfk4ot+X0e8Nlzh4efy/cZ+nMcPbtbtuHsyOa3gxny5vj58hJFt9ua3YYHc6+fHPsPdB5A+PfhfFB/PPL/3X9QTj7g8+Vx0980q5DT524VjbSPHD96DVuO9E9zGRlXQ98zt0nmVk5sLm7vwpcC2Bm04D93b3Yp7jlxk2HTwX2dfcFZnY4cDWw68pU0szK3T3+7WHbbnH3c9Ny9wduN7P93T12VhMRERERERERERERkU5FDWaysgYDMwHSBqlXV3bcgqu1ngM2aK0AMxsJ3A88D4wB3gS+6O41aWPd34FDgD+a2XzgpyRXs70DnO7uS83sMOD/gLnA+Az1x90fM7OrgTOBb2QZV0RERERERERERETWbZ5bKy8wWyPpHmaysn4HvGFmd5jZWWYW72ckNu6XgPvaKWdz4Gp33w5YDHw177U6d98LeBi4EDjI3XcAxgHfTKd5DfBpYG9gvQz1bzEe2GIFxhMRERERERERERERkU5ADWayUtz9YmAn4EHg8yRXe62ScdPuDr/EJ/cza80H7j42/f8GYK+811o69d2N5H5qY81sInAqMIKkoWuqu7/lyQ39bojWP7+qRQeanWlm48xs3D3/ifddLSIiIiIiIiIiIiIiq5e6ZJSV5u7vAFeZ2TXAHDMb4O7zCnNmdi1Jt4kz3P2ItsY1s+2AvwKHFyursAptPG+5a7kBD7n7Mnd+NbPRRcbPagyw3P3L3P1qkvuv8dCkel1XKyIiIiIiIiIiIiLSSanBTFaKmX0KuDe9OmtToBlYWCzr7qdHxjWz4cDtwCnu/magGsPNbHd3fxY4CXi6SOY54Eoz28Td3zaz7iT3Rnsd2MjMRqWNdycVGbdVZrYvyf3L9s8ynoiIiIiIiIiIiIiI53IdXQVJqcFM2tPdzKbnPf9tweunAL8zsxqgCTjZ3ZuDZRcd18x+DAwA/mRmAE3uvlMb5bwGnGpmfwHeAq4qDLj7HDM7DbjZzLqmgy909zfN7EzgHjObS9LYtk079T7BzPYCugNTgWPdfbkrzEREREREREREREREZM2gBjNpk7u3eZ87dz+xnddHZh3X3b8MfDlSv1TO3c9ub9ru/iiwc5Hc/ST3MmuXu18HXJehbiIiIiIiIiIiIiIi0slZ0hueyJrJzEYCd7t7e1eFdai6h64L72h/qjk1XO5XK68JZ6/xM8LZvr0slJs5J3658LDBsTIBPlv5v3D2b3M+Hc4esdX78XIfHRLO7rJ9l3B21vw226A/dvLgh8Jl3l9/YDh7ZON/wtk/zT0unF2yNHphKSxd2hDObrZJj3C2ti5+PqtviGW33Tg+X+/PqQxn1+sfLzfLaXrB0vJw9oMZjeHsfmPi2Vc/6B7O5oLztv2I6vZDqZc/iG8zCxbF18OWG4WjvPFe/Hg3f359ODt0vapw9smH3wlnq7rHyt14i8HhMpcuie/nm27SM5xdmGGdNTfHd57y8vg6GzIovp+99U5tvNwhsfWw1Yj4/rikLv7buG0HzQhn63Jd2w+lepTVhLNOfD3U5LqFs93K6sJZC97atjoXP9Z1L4tvB9HpQ7ZlkGU9NGX4TaV7fJ3NrusXzr7+YWzemjLs5zW18Wy/PrH3awBV8beBmfTqFj/evfdRvL7R90AA/fvGyi2LbwZUxA+hWIZys3wuOW701HC2/+L3wlnzeB2m9946nF3cGHtvM+bdm8JlPj/yi+Hsrm//PV7uJv8vnF2/alY4O+zDF8PZF/rHPx8OrloQzj721rBQ7ktlfwuX+d4mh4Sz8xr6hrMb29vh7Ozy9cPZkUsmhbPv9BoTzm4++4lw9vneR4Ryu78bXw/3Df1KOHv4O5eHsw9v/q1w9qCpfwhnm+fPC2fH7vLjcHYXfyaUeya3Z7jMA2ZfH86+sGH8Dik7Lo5/X/Kbd48MZ7N8fvhe4yXh7B/6XBTObnPSlqHca7fEO7oaETt8AfCphvh3Rt33+VyGM7W05+CTX1orG2keunHHNW470RVmskYwswHAI0VeOrAUjWVmdjpwfsHgse5+zqqeloiIiIiIiIiIiIismzz6i2IpOTWYyRrB3ecBo1fj9K4Frl1d0xMRERERERERERERkY4T77tBREREREREREREREREZC2kBjMRERERERERERERERFZp6lLRhERERERERERERERkQ7gnuvoKkhKV5iJiIiIiIiIiIiIiIjIOk0NZiIiIiIiIiIiIiIiIrJOU4OZiIiIiIiIiIiIiIiIrNN0D7NWmNl5wFeA8e5+cpHXRwPD3P3e9PlFwFJ3v3w11nEkMBX4mbv/KB02EJgJ/MXdz82vl5lVAf8Dnnb3n5rZUnfvWeI65k//YuBJd3+4lNNcUWa2H/Btdz+ylddPAy4DpgM9gXeBn7r7M+2V/U87LVyPrzb9Lpy9tus3wtkzaq4IZ+nSJxTz+pnhIq12vXD297NPDWe/MuO74ex7W34rnP3OzPPC2W6bHRDOltXPDeUe8rPDZR5Rdk84e0vz58LZc+t/Hc7mGqvD2caaReFs17rNwlnc49nmmlCspmL3cJEH1E4IZxvZJJxtrqwKZ6vq3o6XW/t+OLuw8oRwdtfmseEszc2h2BJ2Dhe5U+NL8ckvmB7O+ojdwtlDG16Ol1teG86W1cZP2WdsHd/Pmqtj+295fY9wmQ2L54ezXWcMCmeba+LLa9zvHwxnR+y5YTg7aMzm4eyitz8IZwd0Hx3KNddvGy6zYsm8cPa9QQeGs33K4tvXUu8VzlZYUzjbu2xxOFvr3cPZMovdO6C/xZdtdYZlEJ0+QF9bEM4u8dh7O4AetjSc9TILZ3de8kA4u/VTd4ZyZV0qw2WSiy/byt7x4215r3jWKuP1LevbL5x98+rbw9mqPvH3FevtvEUoV1YVL7Osqms4m2V5NXw0K5x9e9ufhrMvN48MZ5sz3HpkC/sonN28cVIod1O3+OeHY+vjH8lv6HluvNymJ8LZ2Wwczt5ux4ezR2aow3yLn/9PnxXbbn7b7cJwmZ/1+Ha7scXf539gG4Wz6+finwkeboq/V9jVXwlnH+12VDi7T8MjodxNveLb7Ym1/wpnr+v/vXD2lPr/hrM39Yt/t7OwIv6596ya2PkUYFzvQ0K5veoeDZd5X//Tw9l9/Plw9tEuRb+2K+q7VX8IZ2loCEev7B0/l5zXN76NXXnLa6HclidsGS7zwJvODGd/m/t6OPvDcFJkzaIGs9Z9FTjc3ae28vpoYCfg3tVWo+LeBY4EfpQ+Px5Y7p2JmXUBbgNecvf4UX0Vcvcfd8R0zazc3WPfyrbvFnc/Ny13f+B2M9vf3WNnNBERERERERERERGRVC6X4UfgUlLqkrEIM/szsDFwl5l9z8yeMbMJ6d/N08ani4ETzGyimbX89H4rM3vczN5Nr1DDzEaa2etm9lcze9nMbjSzg8xsrJm9ZWa7pLmLzOx6M3vQzKaZ2TFmdqmZTTGz+82stZ/X1QKvmdlO6fMTgH8XZCqAfwFvufv3V2B5DDGzO8xsUvrYIx3+zXSeXjazr+flLzCzN8zsYWDzvOHXmdlx6f/TzOynZjY+ncct0uGDzOyhdPhfzOy99Kq5YvVqWbbXm9lkM/uPmXXPK//HZvY0cLyZHWJmz6bl3mpmPdPcYWkZTwPHZFku7v4YcDUQ/6mGiIiIiIiIiIiIiIh0OmowK8LdzwZmAPsDVwH7uPsY4MfAL9y9If3/Fncf7e63pKNuARwK7AL8JK+RaxPgCmC7NPN5YC/g2yx7Beso4FPAUcANwGPuvi1Jo9in2qjyv4ATzWwDoDmte77vAk3u/vUsyyHP74En3H17YAfgFTPbETgd2BXYDTjDzMakw08ExpA0QLXVj9Zcd9+BZBl/Ox32E+DRdPgdwPB26rY5cLW7bwcsJrkysEWdu+8FPAxcCByUljsO+GbaReU1wKeBvYF4n4GfGE+yTkVEREREREREREREZA2lBrP29QFuNbOXgd8BW7eRvcfd6919LjAbGJIOn+ruU9w9R9Jd4iPu7sAUYGTe+Pe5e2M6vBy4Px1emCt0P3AwcBJwS5HXnwZ2N7MMNwNaxgEkjVq4e7O7LyJp8LvD3avdfSlwO0mj097p8Bp3Xwzc1Ua5LZ3tv8Qn87cXSQMg7n4/0N7NGT5w95Yb5tyQjt+iZVnsBmwFjDWzicCpwAiShq6p7v5Wuj5uaGdaxbR64wYzO9PMxpnZuCfvuXoFihYRERERERERERERkdVB9zBr3yUkV3odbWYjgcfbyNbn/d/MJ8s3f3gu73mOZddBPYC758ysMW3E+ThnZrsCf0mH/RiYnOYbzOwl4FskDXqfLqjXk8D1wH1mtre7F16BtiLausN3tNPVluWQv6zidw4vPq3859V5ZT7k7iflB81sdJHxsxoDFL1/mbtfTdJlI9c8vNLTEREREREREREREZG1jOdyHV0FSekKs/b1AT5M/z8tb/gSoNfqrIi7P592ATna3Quv3PoN8D13n9fKuLcBlwH3m1nfjJN+BPgKgJmVm1lvkka4z5pZdzPrARwNPJUOP9rMuplZL5ZvvGvP08Dn0mkdAvRrJz/czHZP/z8pHb/Qc8CeZrZJWm739Gq714GNzGxU3vhhZrYvyf3LrskynoiIiIiIiIiIiIiIdC5qMGvfpcAvzWwsSTeJLR4DtjKziWZ2QsdU7RPu/oq7X99O5s8k3SDeld6/q7uZTc97fLOVUc8H9jezKSTdJ27t7uOB64AXgOeBv7r7hHT4LcBE4DaSRrQsfgocYmbjgcOBmSSNk615DTjVzCYD/Um7jiyY7zkkjZ03p7nngC3cvY6kweseM3saeC9QvxPSdf4myf3njnX3oleYiYiIiIiIiIiIiIjImkFdMrbC3Uem/84F8u/99aP09fnAzm2Mv03e023yhp+W9/+0ltfc/aKC8Xvm/b/Ma8XGLxh+HUljVrFyLwJahoUaTN19FnBUkeG/BX5bZPjPgZ8XGX5a3v8j8/4fB+yXPl0EHOruTemVY/u7e36XloVy7n52kWmNLHj+KEXWV3qftC3aKD8/ex3pcs1qaP+meHhpefuZ1Hr9m+Pl1sfLbew7OJSrnDsrPv0MNt4gnq1sHtJ+KFVG/PLmHrvuGs7myuLLlubYOqtviv+eobmyWzi7tCbeO2hy28WYsvWHh7Ndey8KZ3NzZ8frMGRoOEuP2AXCi7vHt6/K9yJt7omKvoPC2fqB7V1o+wlfWPQi46KsPL7dmmfYbnr0CWcJbmPlzQ3xMi3es6917RLOllUvjJdbGS+3adPtw9nci0+GsxWbbB7O2oz3Q7l5ex4fLnPAzJfDWTJsi5VzZ4azu35/ubcurWquqQ1nX70pvh62//4p4WzDBpuGctac4T1FLv4+oU/j3HC2obwqnPUMvW33b/wonF3aNX5szHIMG1j/YfshoK6yZ/uhVFlZ/Hw6oC42fYCarn3D2SzKPL7dNFEZzi7pNyKc7bt78H1YTXX7mVTjR/Htq3Kz0MeDRIbtK5fhHJmlU57Nvnx0vNwl8fdhbJRhOUSn3yV+/MhyTu9a8Wo427u8rd9jLqu+R3wbz3n8/XtjLl5u95lvhnJDB+8WLrPrh2+Fs8M33CecrZo/PZwt7xn//DCwV2M423VB/Dha2SP2uRegrHuPUG5A3wzvsS1+/Kiqbe/27nnZXsPC2e5L54ez/bu39dXMsvrOjW9jwwbG69vl5ddDuUEjDgyX6eOmhLMb73ZMOGsTJoWzI3Y8LJzt3yt+/GD8i+Fo1333D+W6fPh2uMyttt4qnO02I77vDhxYE842brhZ+6FUeWNdOLtRZYY7r7zxTjg6IvaRgANvOjNc5iOfvzqcHXvqoeEsJyz3lbTIWkENZtLZDAf+bWZlQANwRgfXR0RERERERERERESkJDyXoRFWSkoNZvIxM7sAKPz5+K3pFWOrhbu/BYwpqNcAkvuoFTqw4Eq+VcLMTifphjLfWHc/Z1VPS0REREREREREREREOp4azORjrXWl2NHcfR4wejVO71rg2tU1PRERERERERERERER6VjxTrZFRERERERERERERERE1kK6wkxERERERERERERERKQDuOc6ugqS0hVmIiIiIiIiIiIiIiIisk5Tg5mIiIiIiIiIiIiIiIis09RgJiIiIiIiIiIiIiIiIus03cNsJZjZecBXgPHufnKR10cDw9z93vT5RcBSd798NdbxLuBWd/9n+vwa4E13v8zMHgeGArVp/Gfu/p8S1GGpu/c0s2HA7939uFU9jVUlXSbfdvdxrbw+DXjJ3Y9Nnx8HHOnup7VV7r5ND4TrcCVfC2fPbr4znP2znRPObpDzUO6t8kPDZQ4sKw9nT264NZz9Hd8IZ4+1D8PZv+W+FM72Iba8AGYHF8PZTbeHy3yw7LPh7BlNV4azvyn7bjg7uCK+fheVx/tlXlLRHM72a46f0oKbOHs3LwqX+YfKi8PZTet7hLMjGhvD2Qnlu4Sz1TSFs0fm6sPZe5u3C2ebg6t3RHNDuMxXGvcMZxcQX7ajrEs4O9vi2/iG9RbOTq48IJzdiKpwdkZZbDkcmasOl/lIbqtwtktZ/Bg6vzIcZXqG7bY2F98WJm/4aji7z6IdwtlN+8eOYRkOt8yNH0I5uuzlcDZn8Up0t/h2U+bxCndtqglnm8rjG05zWSzbWNY1XKYT389zZfFzWZZyu1l8eWV4W5PJu75JOPtc0+hQrrY5frz9qDm+LW7W1CuczWW43US/XHydZTEzQyUWNcWPd5s2x7ZzyzBbXZvjG1iWct+xI8LZ05rjx/FNfVa8Eh6ftxrrG87+q2vsc8mxTfeGy7y+y1fC2ZMyfJa9reLz4ewY/yic3SP3REnqsKUvCGfv6fPtUO60Jb8LlzmN48PZZ9k7nN3C3gtnx5XtHs7u4C+Gsw9UfDac3T03OZz9W7fzQ7lTG+8Kl3lpt4vC2W83PBTO/q7qwnD2a42PhrMVtfHt9hddfxKvg08J5f5WGf9u6XDeCWfvKo9/VXiAvxTO3t0YPz/UN8ZPPCc03hzO/l/XH4Sz5zfEvpb9be7r4TLHnhr/7u6r1x8bzvLXN+JZkTWIGsxWzleBw919aiuvjwZ2AuLvXFe984DHzOx/wFbAriT1bnFya41Dq5q7zwBWe2OZmVW4e/yb4fbtZGZbu/srq7BMEREREREREREREVnHePSX3VJy6pJxBZnZn4GNgbvM7Htm9oyZTUj/bm5mXYCLgRPMbKKZnZCOupWZPW5m76ZXqGFmI83sdTP7q5m9bGY3mtlBZjbWzN4ys13S3EVmdr2ZPWhm08zsGDO71MymmNn9ZrbcT2LdfRpwNXAp8CfgXHeP/6zwk/n9oplNNrNJZtZytdoIM3skHf6ImQ1Ph29kZs+a2YtmdkleGSPN7OX0/9PM7Pa03m+Z2aV5uS+Z2ZvpcrrGzP7YRr2uM7M/m9lT6ThH5pV/a9pQ+KCZ9TCzv6d1mmBmR6W5bmb2r3QebgG6BRbH5cAPsy5DERERERERERERERHpnNRgtoLc/WxgBrA/cBWwj7uPAX4M/MLdG9L/b3H30e5+SzrqFsChwC7AT/IauTYBrgC2SzOfB/YCvs2yjTOjgE8BRwE3AI+5+7Yk3Sp+qpXqXg4cBrzi7k8WvHZj2qA30cwGFBvZzLYGLgAOcPftgZZr4P8I/MPdtwNuBH6fDr8CuMrddwba6mdhNHACsC1Jw+KGabeNPwJ2Aw5Ol0V7RgL7ksz/n82spR+q3YFT3f2AtP6PpnXaH7jMzHqQdKlZk87Dz4EdA9P7N7CDmcX7lRERERERERERERERkU5LXTKuGn2A681sU5Je/9u6+cE97l4P1JvZbGBIOnyqe9JhsJm9Ajzi7m5mU0gahFrc5+6N6fBy4P50eGEu33aAAVuYWZm753dyH+mS8QDgP+4+F8Dd56fDdweOSf//J8lVbAB7AsfmDf91K+U+4u6LAMzsVWAEMBB4omUaZnYrsFk79ft3Ok9vmdm7fNLI9lBeXQ8BPmNmLR2PVwHDgX1IG/rcfbKZRTrPbgYuA34A3BfIi4iIiIiIiIiIiIhIJ6YrzFaNS0iu9NoG+DRJY0xr8u9A38wnjZb5w3N5z3Ms27BZD5A2EDW6f3xn4RxQYWa75l0x9hkzKyPpivEU4C2SK6qyMmK3//ZW/m9NsWWxInfCLpxWy/P8O3sbcGx6td9odx/u7q9lqGuhf5I0tg1vLWBmZ5rZODMbd+0d97cWExEREREREREREZF1lOdya+VjTaQGs1WjD/Bh+v9pecOXAL1WZ0Xc/fm8RqG7gLOAt9z9ceCbwHfNbFDGYh8BPtfSZaOZ9U+HPwOcmP5/MvB0+v/YguFZvADsa2b9zKyCT65Ua8vxZlZmZqNI7iv3RpHMA8DXzMzSeRiTDn+ypY5mtg3J1XjtSu8D9zvg621krnb3ndx9p9OPPixSrIiIiIiIiIiIiIiIdAA1mK0alwK/NLOxJN0ktngM2Cq92uuE1V0pMxsMfI/kPmi4+wyS+4td2tZ4hdz9FZL7ez1hZpOA36YvnQecnnZjeAqf3NvsfOAcM3uRpDExy7Q+BH4BPA88DLwKLGpntDeAJ0i6Rzzb3euKZC4h6Spzspm9nD6H5P5zPdN5+C5Jg13U31C3piIiIiIiIiIiIiIiazx92b8S3H1k+u9clr3P1o/S1+cDO7cx/jZ5T7fJG35a3v/TWl5z94sKxu+Z9/8yr6XDZlNwXzN3/23e//u1VrciZV0PXF8wbBrJ/c0Ks1NJ7m/W4ld5+ZZ5uQ64Lm+cI/PyN7n71ekVZncAD7ZTvbHu/o2COhSWX0tytV1hXWv55Gq4duWtc9J70Q2LjisiIiIiIiIiIiIiIp2Uu+uhR6d6AJcDE4HXgd8D1kb2OuC4jq7zCs7nmcqWJtvR01e2c0xf2c4xfWU7x/SV7RzTV7ZzTF/ZzjF9ZTvH9JXtHNNXtnNMX9nOMX1lO8f01+ZsR09/bc/qocea/ujwCujReR7AAJKGqsLHgE5QtwuK1OuCEk7v+SLT23YVT2OcsqXJdvT0le0c01e2c0xf2c4xfWU7x/SV7RzTV7ZzTF/ZzjF9ZTvH9JXtHNNXtnNMX9nOMf21OdvR01/bs3rosaY/1CWjfMzd5wGjO7oexbj7z0nuo7a6prfr6pqWiIiIiIiIiIiIiIh0rLKOroCIiIiIiIiIiIiIiIhIR1KDmUjHuVrZkmU7evrKdo7pK9s5pq9s55i+sp1j+sp2jukr2zmmr2znmL6ynWP6ynaO6SvbOaavbOeY/tqc7ejpr+1ZkTWauXtH10FERERERERERERERESkw+gKMxEREREREREREREREVmnqcFMRERERERERERERERE1mlqMBMRERERERERERGRVcrMKjq6DiIiWajBTGQtYmY9OrJcM9vLzE5P/x9kZhuVoj6d3Zq0HMysm5lt3oHTH2FmB+XVpdcqLr8k+0Re+V1WUTkduh7SOoTXRanWW0cfw0qlFOs363FmVdfBzDbOmC/ZNh5Zv51hHyulUpynS318bk9HT7+z1CGLVVVfM9tt1das3en1NbML2slUmtkYMxvcTq7D19nafryJWFOWgZn1MLOyvOdlZta9I+u0sszsmBKVe24pys0rf+CKjmdm1k5mkJkNauP1KjP7upn90czOau/LfTPb1syOTx/brEi9O5qZHdvRdShUqm03q/SctHP66NNKZniJpn1dhmxJztVm9osVGKfNfQx4YQXKbHc/W5FlEFm/Gcp6cGXGb6PczOsgY/mrbBmIrLXcXQ899FhND2Az4BHg5fT5dsCFrWS7Az8Crkmfbwoc2Up2D+BV4P30+fbAn1rJDgH+BtyXPt8K+NIqKPcnwP+AN9Pnw4CxrWQN+ALw4/T5cGCXVVDfLOUOAi4H7gUebXmsgvWWZTmE5q2E282ngTeAqenz0cBdK7MMMtb1DOBF4J28uj7Sxv6zyveJjGU+DozMe74LMGkVbLdZ1kOWbbwk6yJjNrR8o+urVNvBCizbUq3f6PIKH2ey1CHjMngSeAf4F/BVYNuVnX6p1m/G6WdZBuG6pq//vsjjEuCogtwORR6jgIqV3MaznJ9W+X6evhY9l3To9DtRHUpV3za3c2B8a9txWw/aOTYCGwJXA3cDX06X22+A2cAVBWX9Gdg6/b9Pup1PAT4ETuqAdXYN8CDtv2cs1fEuy3knei7JUuaatgyOB3ql/18I3A7sUCT3HNAz73lP4JkV2f7zyjgm7/9+Gcb7QTuvV6TL9jvp40iKnxcy7b/AAOBrwJXp41xgwGos99PAHGAmMB3Yo40ydyN5P347MAZ4GfiI5BhyWEHWgIuAucA8YEE6nR8XKfcW4AbgLOC/FByP8nJ90um/A9yRZt8BHgN6F2Qfy99XCh6PFGT/B9yV97gz3da/UKQO32zrkWH9vF/wvNh7lI8fBdkvtvVYHdsusDnJ+eOe9HE5sHmR3PC2HgXZLsB1wEJgAjAx3W7+DnRZyf1h/3S7fSV9/AfYbyWXQdY6RJdZqFyy7WMTMtQzy36WZXmF1290eWWZr4zllmrdhpYBcG06rNjjb1nnWQ891sRHh1dADz3WpQfwBMmX7BPyhr3cSvYW4Lt88iVJN2BiK9nnSb6EiJR7H/A50i/6Sd7ATlkF5U5M3zTlZye3kr2K5IPTa+nzfsCLq6C+Wcp9EPgS8Bqwb3ry//UqWG9ZlkNo3kq43bxE8oY0UtdQHVZgWXUpyBZdt6XaJzKWeSjwOknjwM+B8RT58mUFttss6yHLNl6SdZExG1q+0fVVqu1gBZZtqdZvdHlNJHicyVKHLMsgfb0LsCdwAfA+MH91LYMs6zfj9LNsB+G6pq9fTdLQ+LX08Xg6rbuA/8vLPQc0AOPSuteTNAK8CxyyEtt4eLuhBPt5+lr0XNKh0+9EdShVfdvczlnxBrM2j40kX3ZdRHI+/R0wGbgZWK9IWa/k/f914L/p/+vlz+NqWmeTgK+k62LHlkcr2VId77Kcd6LnkixlrmnLYHL6dy/gKeAo4Pli20xw2IN5/7fXsDW+2P+B/afVLMkPHN4gOW/8Dvg/kuPDG8CwaDlFyt2SpKHqOuD8dF+7HpgBbLGayp3cMgzYFXiijXLHAYeQNIguAHZLh29BwXEB+AbwELBR3rCNgQeAbxRk849TFa3NK0nj0eVAWd6wMuBS4A8F2R2LPM4B3qPgfQXJZ9HCx9HAbcCvCrI/yXvMKHj+kwzr6IOC5w0kn22+T9LwdWr+oyD7hyKPP6bz1lTqbRfYPd2+LiLZtz8L/DRdHrsVrtt0G5uS95icjt9ckL0YuJG0sT0d1gv4B3BJQXZCpK5p9lPAVOB0kh82jQb+H8n7uiMKsq+TNAYX+/HUDgXZLPtklmU2ieR9Qf9ijxXcx6YTbOgl236WZRlkWb+h5ZWuw2Nae6zoeoiugxVYt6FlABxb5PENkn18enSZ66HHmvxQP7Iiq1d3d3+hoNeIplayo9z9BDM7CcDda9vqbsLdPyh4ubmV6EB3/7eZ/SAdr8nMWstmKbfB3d3MHNrtHmpXd9/BzCak01jQRtd2WeqbpdwB7v43Mzvf3Z8AnjCzJ1rJZllvWZZDdN5Ktd00ufuidnoxyVqHLHWtd/eGlmza/Ym3UYdS7BPhMt39ATM7m+SDwVxgjLt/1Mrks2y3WdZDlm28VOsiSzbL8o0ea0p1bCzVcSnL+o3OW5bjTJY6hJeBme0F7J0++pJcMfLUSk4fSrN+S7WPZaorsAlwgLs3AZjZVSQ/3jiY5AucFtNIrpp4Jc1tRfKL7EtIfp28TPcvJTpPl2Q/J35c6ujpd5Y6lKq+7W3nG5vZXa2Mi7t/ppWX2js29nf3i9L/HzCzWcDO7l5fpKyGvP8PBm5Ny/yojd2sVOusyd2vam2iRbKlON5lOe9Ey816LluTlkHL8E8BV7n7nWZ2UZFctZnt4O7jAcxsR6C2SC6/q7HjgV+2MU/Wyv8r4xck8/F/y0zI7Ly0LqfmDd7CzCa3Ui939+3yhl0CnO/u/y4o91iSH4fld9u3nZktbqPc3itYbpO7v05SyPPWdjeqFe7+YFrWxe7+XDre60U2my8CB7v73JYB7v6umX2B5Dz6u7xsY16mqY1t9yBgO3fP5eVzZvZDlj2P4+4v5c33viRXUnYFznb3+wqyRT+Dpsfhl0gasVqyP817/bP5zzMqPDYOJdm2TyA5z9wC3ObuC5Yb0f1reXUw4GTgeyQ/+Pl5QbwU2+6PSa40fjwv818ze5Sk4fDwvLpuWzDdkWldD0rrlu8Ykquta/LGX2JmX03n7Ud52fXN7PdF6toy3nl5T78DfNbdJ+UNm2hm40gaG+/NL5fkSqFiG6EDB+Q9z3KuDi8zkgbol9qoQ0u37Fn2sXKSK3gjx8Twfka2ZZBl/UaXVx+SKyZbW1a3FwyLlhtdB1nKhOAycPfbWl63pBv+HwL7AL8iufpVZK2nBjOR1WuumY0ifYNqZseR/BqkmAYz65aXHUXyK/NiPjCzPQBPv/A4j+TKqWKqzWxAXrm7AYtWQbn/NrO/AH3N7AySX01d00q20czK8+owCMi1ks1S3yzltnwwmmlmnyL5Bc4GrWSzrLcsyyE6b6Xabl42s88D5Wa2Kcn6faaVbLQOWer6RPrGt5uZHUxy5db/Wslmnbfothsu08x+RPLL5n1IusV63My+5e73FIln2W6zrIcs23ip1kWWbHT5ZjnWlOrYWKrjUpb1G523LMeZLHXIsgyeIPml9y+Be929oZVclulDadZvqfaxLHWF5EuQHnyyrfQg+XV1s5nlj7dFS2MZgLu/amZj0i8hCsss1Xm6FPs5xI9LHT39zlKHUtW3ve18DskXdlm1e2w0s3588gXQR0B3Sxtv3X1+XnShmR1J0gXjniS9ArQ0gnVrZfqlWmf/s+TLpDvyMwX1bVGq412W80603CxlrmnL4MP0eHcQ8Gsz60rx+7d/HbjVzGakz4eSNBgUausHXYW6mdmYdHpV6f8fH7xbGufSeZialm3AUDN7l08aB/K/EN3N3U9brlLuvzezNwoGTyXp/i5iW3c/rki5t9ny99CZ4u5jSlDuYDP7ZmvP3f23ea/lH6cKGzYL11Fl/hf5eeXNMbPKgsHb2yeNgUayDhezfGNgQ8uPXgrKbCo4jycFmR1K8gV0HfBzd3+sMNOW9P1Bm5G2XjSzKa1kjKSL0/xpzSPpCvfPZrY+cBLwipl9z93/WaTsCuA04FskV7sf5+6F2yKUZtsdVdA40FLmE2Z2dbER0mPRBSRXMf4GOM/dGwtiufyGhLxyl1r6Y6M8tSQNGhHrFTSWtZQ72cyGFAx+290PKMy2Isu5OssyezW4r2fZx2a6+8XBumbZz7IsgyzrN7q83nP3/xecfpZyo+sgS5mQYRmY2ZYk+8wY4DKSxv7WfrQlsvbxTnCZmx56rCsPkl+CPAzUkHwB8DQwopXswSRfSM4huWx6GkX6uU6zA9PMLJI+3G+gSB/xaXYHYCzJB8yxwJskv+BZ4XJJ3nRvmNb5MpJL6A9uYzmcTNIF1XSSX6G9ARy/CuqbpdwjSX4RtA1JF0EvAZ9ZmfW2AsshNG8l3G66p8vpxfTxM6BqJZdBlroayf1GbiXpx/0MwNpYXqt8n8hY5hVAt7znI4CHVsF2m2U9ZNnGS7IuMmZDyze6vkq1HazAsi3V+m133sh4nMlSh4zLoC/JL/d/TXI/jocp6M6kVMtgBfbzUu1j4bqm+S+RfBl0LUk3Ve+S3MepB3BZXu4Wki7z9k0ffwL+TfLL9MJunEp1nl7l+7m3flwa2dmm34nqUKrjeJvbOStwT450vDaPjelyeZdkPyh8vFtQ1mbA/STdLJ6WN/xQ4DerYBkcUmSd7d9Ktt365mWzHG+y1CHLeSdUbsYy17Rl0J3kl/Sbps+HUtClbV62kuTzwLYkXwAXyywk2Wf+l/f/x4+C7GNtPIre9629/S7La21li4zbVjeQ41dTuT9p61GQbQYWA0tIroJanPe8cUXrkGG+3qR4d3k7knZxm5d9Md2ezymSL+xar1i3a6NIulW7cUXng+SzSquPVsbZgeR9wkSSK0m2KpI5J10WV7VWTim3XeClDNvXNiRd/04muXdneRvjTqL1bvAmtTWdlajvSwXPQ8tgFddhhfb1Uhw/0uXfWreUxfazLMsgy/oNLa8s66tU5WZct6FlQPI+amq6rw8qzGaZZz30WFMf5u6ISOmlv+T9lbt/J/0lbZm7L2klWwYcR3Kj991IvgB4zov8gict93p3/0KwDueRXPq/eVruG778r6sylZvmX3L3HQO5MpJ5mg8cmNbhEXdf7hfxGesbLjeLLOstzUeXQ2jeSrzdPODuBwXr2m4dVqCuk919m/amv4Lz1u62m6XMvHG6kdwgutgvKPOnn2U/i66HrPvOKl8XK5Btd/lmPIaV6thYquNS1vUbnbfQcSZLHVbkGJr+8nBfkm4Z9yC5gfy+KzL9vDqs6v28VPtY5uNHOt5QknsAGfCCu88okulGcmXMXmnuaZJGszqSLvqW5s1bqc7Tq3Q/LzJeW+eSDp1+J6pDyc6p6Thb0Mp2bma3u/sx0bLy6hs6Nq4sM+vh7tUFw1ZkGQwgwzoLlBc+3mSpw4os2/bKLdX66uhlYGb925qWp1fEmdkB7v6omRXdzt19mW60LOlSr61yi3apV8jMKltbxmY2wVu5oiC98uzbxV4CLnX3UXnZP7r7uYG6nErSsPnbYi8DX3f3DfPyP3T3wqvDipX7A5IvWUPlRpnZD9y9ra4w87P9SLpPry72MkkDbuEVMJFyl5BcXV+Uu++fl32cT67u8nS6edFPriKyZa80bMnPJbnv18/cfXFeNv+qsU2At/Pmy33ZLjej8/UsSRd6R5Jcpf4v4H5v5WoSM8uR/EhnDstewbZcHUq07S4m+eFRsTI/5+5D8rLNwAfAPRTpstrzuk40s2kkVzEW7QbP8676NLPn3H23QF23Jmngf7KV+u7l7v3y8od42u1oO+XeBljkXG3JFdc3kqzXYnUoXGanuft1gXJzwNJWylxmHzOz/l78iuTCMsfT+pXDwHL7Wej9SroMriG+fmcTWF5mto27vxyY/rPuvnuGcqPr4A8kV0VH1+00AssgzeUfv8gbZ5llJbK2UoOZyGpkZo968BJ7M3vS3fcJZh8APu1td4nVkn3c3fcrQblXAte5+4uB7LPuvnuwDlnqm6Xc3xcZvAgY5+53FmSzrLcsyyE0byXcbu4CTnH3Nt+UZqlDxrreSHLj9PeD+VW+T2Qs89MkV2V0cfeNzGw0cLEXuZdLxu02y3rIso2XZF1kzIaWb8ZjTamOjaU6LmVZv9HlFT7OZKlDxmXwDslVKU+T3Lvs+daWcymWQZqN7uel2sfCdc0bZ32SX3Z/3C26uxf7EiVaXqnO06t8P0+zPy423Au66eno6XeiOpTqOD68lTq8n75+LG109VXYmJBXbpvHxtamWzj9vPz6JFcGTfbk3mSDSbrQO83dhxUpP8syeMTdD2xvWN5r2wBbAVV59f1HkVyW4024DhnPO6Fys5SZ5jv9MihoeBgOLEj/70vyo46N0txP3f0nZnZtkWLcg11smdmGwInuflkbGQP2Bz5Pcrwu7IKtJddqY0Er9cyv8OmR+haUOR64s62Mr8D9sUpZrrvvUIJsPy9yn65WshM80E2amR3s7g8Fy8yUJbmyq1Xu/l6krIJyJwDbk1wB3NLVZf6X5O7LNoKNiNahRNvuNJIrEFsr8/q87KntTP/6tl5vZfpbe17X2e1kxwPfaKcOoQb3gnJD22JeHa5opw4rshxC+1kp9rE0m2XfyXJM2BrYqa1M1uXVMl+renss4boNbeNZ9gWRNY3uYSayek1IP0TeSt4v3lr54uEhM/s2SddM+dliv8yZBoxNy87PFvtl31gz+2ORcscXyWYpd3/g7PQNbDVF3lzneTD9MuZ293Zb7bPUN0u5VSQ3U701fX4s8ArwJTPb392/npfNst6yLIfovJVqu6kDppjZQwXZ84pko3XIUtehJH3jv1CQXa4BagXmbRqxbTdLmReRXBnyeJqZaGYbtVLXLNttlvWQZRsv1brIko0u32nEjzWlOjaW6riUZf1G5y3LcSZLHbIsg00972bc7SjFMoD4+i3VPpalrpjZr0l+BfoKn9yHxSn41bGZ7UlyvClsWCv2a85plOY8XYr9nPzXSc7DLb9m72zT7yx1KNVx/B4+aVSoAjYiaQDfOn39yLzsp1n2PmDO8jexb9HesTF/uvnlDQIGA+UtA83s6yT3zngb6GpmV5BcsfIPkm6Ziml3GZhZFUl3fQNt2fup9QaWa4RLx/kJsB9JY9G9wOEkPxZYrrGIwPFmRepA4LyzAuWGz2VryjLIaxD7M0lXifemzw8nuZ9ZS67ly/aL3X1qwby29t6u5fWBwPEk93han+S+bsVyu5I0kh1N0o3VOcB3Wiu3jcayg6ONCmZ2aoYvRy3acGUZru4qZbnBXNbsIyTdvUVEf23+ayD0RX7WbIYv/cM/ACKZrza3+2XCwUa5LHXIuO0uiGTN7A/u/rXg9MNZ4J/EtxmLNoiZ2W3ufmyw3CxXPlh02WZcDlGl2Mcg276T5Zjwzwz7WXR5OcQbr7KshxKt2+g2nmVfEFmjqMFMZPXqD8wD8n8t3NoXDy2/bDynIFvsC7MZ6aMM6NVOHfZI/+b/ktkL6rQi5R7ezuv5vklyz5ZmM6trqYN/ckPlfFnqm6XcTYADPO1qwsyuIumK4mBgSkE2y3rLshyi81aq7eae9BERrUOWumb9dWkp9oksZTa5+yJb9ubbrb2pz7LdZlkPWbbxUq2LLNno8s1yrCnVsbFUx6Us6zc6b1mOM1nqkGUZdDGzL5F8wZ5/tUGxX+WXYhlAfP2Wah/LUleAzwKbu3vhDcsL/Y3k18gvUaT7oAKlOk+XYj/H3Ze5MbuZXU5yD6DONv3OUoeSHMfdfduCOuwAnJX3+ul5r02IfllPO8fGItMdCXyPpCGjsKu3M0n2l/mWXJn2NrCPuz/XxvQjy+AskqvUhpHsYy0n9cXAla2McxzJ1RcT3P10MxsC/LWVbOR4syJ1iJx3spab5Vy2piyDFju7+9kfh9zvM7NLiuRuY/kv+/5DQaOsmfUiafj6PMn99e4ANnb3DQoLNLOfA58D3ie5d9LFJD1YZP6VfyrLl8LnA9HpZPli+ngg2rDVGcrN1JiQIVuKMkuVrWo/8olSNIJlrEMptt09M0w/SzbLesiyLbb2/nFlZalDluUQVYp9LGu5pTomlGJ5larcUmzjpVq3Ih1ODWYiq1GGLxw+/oVkMPtT+PjDnHt6f5NWsvu39tpKlvuemW1Pci8bgKfcfVIr2fa+1FvR+obLJflFaA8+6SO7BzDM3ZvNbJkvMzOutyzLITRvJdxurjezLiQf/KGNe1dE65Cxrk+kX7jsnA56wd1nt5Ff5ftEljKBl83s80C5mW1Kci+NZ1opN8t2m2U9ZNl3SrIuMmZDyzfjsaZUx8ZSHZeyrN/o8gofZ7LUIeMx9J8kN+U+lOTLwJNp5SqZUiyDNBvdz0u1j2U5fkDS1VEl0F6D2SJ3vy9Yh1Kdp1f5ft6K7hRvVOrQ6XeiOpTsnFow7ngz27m1lzPUIXRsTM+hFwC7Ar8BziuyT9Z5eoWeu79vZm+201gWWgbufgVwhZl9zd3/EKkvUOvuOTNrMrPeJPfuaW2dtXu8WZE6RJZt1nKznMtYQ5ZBnrlmdiFwA8k2/AWSxmcALLmH39ZAH1v2Pma9Kf4l/2zgBeBC4Gl3dzM7upVpn0lyxeZVwN3uXmdmK3MfjM7Q+LKmZbMoxRfppWrc6wyNhlkawUpVh47+kr5U97Xp6IberEqxP5SqESyLUpTbGRoNs4guA93jSdZaajATWY3MbAOSm1XvSXJyeRo4392nF8lWAl8BWu6J8Tjwl2Jf8llyT4F/kvwSGTObC3zRi/QnbGZ9SPr8bin3CZLuSBYVyWYp93zgDD751fMNZnZ1ax+Czewz+fPm7ne3kgvXN0u5wKXAREtuyGzpOL8wsx7AwwVlZllv4eUQnbcSbjf7kfySb1q6DDZMu8N4skg2VIeMdf0ccFlaRwP+YGbfcff/FGZXYN5C226WMoGvkXzBV0/yi+EHgGK/Vs66n+1HcD2k+ei+U5J1kTEbWr4ZjzUlOTamr6/y41LG/Sy6vLIeb7PUIXoM3cTdjzezo9IvR28i2SdWdvql2M/D00/z0e0gy/EDoIbkvPMIeY1mvnzXkI+Z2WUk6zc/V6ybtJKcp0uxn6fZKXzy4bqcpCu+i4vkOnT6nagOpTqOfzPvaRnJFTZzitUhi/aOjen2egFJQ8WlwJfcvbWrKDewZe83Ozj/eZH9JtMycPc/WPCeXMA4M+sLXENyNdRSksaT5WQ53mSpQ5bzTrTcjO+x16hlQNJd4k/4pLvEJ9NhLTYn6Xq0L0m3oy2WkBwnC/0QOJGkEewmM7ulSKbFesAh6fT+z8weA7qZWYWnPVtkVKpGkrElKvfW9iMlLzfTl8hm1r+t1/2T7nWL3uNwLXBKhmxnaKTIsu12tHbvMbuCvpchO61EdbgiGuwE+9i0EpUblWUfyyK8DkQkG/N2b88gIquKJX3530Ty5RYkv3Y82d0PLpL9K8kv0Vu6JjgFaHb3LxfJPgNc4O6Ppc/3A37h7nsUyd4GvFxQ7vbufkyRbJZyJwO7u3t1+rwH8KwXuTeKmf2K5Ne/N6aDTgJecvfvr2R9w+Wm+aEk96Qykl8hz2gll2W9ZVkOoXkr4XbzEvB5d38jfb4ZcLO7L3dvkGgdMtZ1EnCwp7/+NrNBwMPuvn1hdgXmLbTtZikzi4zbbZb1kGXfKcm6yJgNLd+Mx5pSHRtLdVzKsn6jyyt8nMlSh4zL4AV338XMngS+CnxEchxd7oqDUiyDNBvdz0u1j2U6flgrN/r2gm660i9Xi8R8uW7HSnieXuX7eZodkfe0CZhV7Avkjp5+J6pDqY7jPymowzTgNnevS1//H598ebkPBffZ81buNdresdHMmoEPSLrrW66hzJe9x1XR/SUve33hsBVYBvtRcE8udz+urela0o1kb3ef3MrrWY434TpkPO+Eys1SZsF4I+nkyyDKzHZ392cz5DcmOS+cCGxK2ijn7m+2km+5T+FJwF7AI+7++Yx1HO/x++lM4JPjRVFe/B6XkXKfpo1GDS9+X9BVWm70S3cz6++t3E+0lTr0TetgwHBgQfp/X+B9z3gVsZndHt0uS5idQHILgraWbbHuptsrN9P26O5jollW8babdfoZss+RvPdtlRe/r3G7dSC5qKGtdbZdXr7NbcGL3+80Uofp7dSh6HuAdsrsyyrcx9Jybye5irhVK7gMnnP33YLZZpa9N23h9DPvY2m5i0h+FNJauZnWQVpmpm08sgyyLCuRNY2uMBNZvQa5+7V5z6+z5Kbmxexc8AH/0fSLgGJ6tHxZBuDuj6dfhBUzype9mexPzWziKijXWPbLj+Z0WDFHAKPdPQdgZtcDE4BiDVtZ6hsu18yM5BdMG7v7xWY23Mx2cfdiv5bNst6yLIfovJVqu6n8/+ydd9gcVdXAfyeB0HsRBELvvVfBgAhKBxFCkS6IQFDpPYj0DgKCEHoPvYbeQk0joQmG0BVQSj5pAuf749x59+7snZl7991N3sQ9zzPP7sycOXPm9ntqJswAUNW/iVnJhyCWhxRee2l9qKR/YZbuRdCOPlFJMyc4bICCBWtKu02ph5S+0666SMGNrbOUsaZdY2O7xqWU+o39tpRxJoWHlDK4WERmwcJT3QFMDxzdzfdDe+q3XX0shdeggL8Ar18MnoN2zdMt7eeekHN87tkZRcS3LO4p7+8pPLRlHFcXyrMETvf+n1GI1QhVY2Mox2EQYvuL1CexT6mzypxcIrKEqr4qluMt/96VCgSiKeNNSl6wlHknlm4lzUmtDJpYs20lIi8BXwL3OV4OVNWgEFZVxwJ/Av4kIstiirB7gYUL8L/CcqLdLBbKciuP110i2/m4CJwMnqKWz3JxzAAky5G4GTnldwLcBLzn/q+NKTgzL7ttMa/DCUF3GCVCd2BBMMVZrHIN2EBriraLgDtU9R53/jMsxyLuPEpBoapbtwu3DC8HO6vqGMf38Zhh01VYee1Idd7TIkjx3kvxqmlH203xvjknNM75kI13qrqG1IybpgZWAUZhZbMc8CymIE+FQ7FwrlDLiZopEXfEIhX4kHnHzonleHzYnffDPK2TlUVYmb3p/m+Necxm42F/vPGoHX3MXUvpO9kaqbIMUuq3DC8He6jq5S3uYwAXYHNSaR0kQnQbB/Ytw22yrDrQgUkKOgqzDnRgwsLHIrITFs4NbML7VwHudyKysKr+HbosGovC1owVkaOpt0B+swD3SxFZR1WfdHTXxjaJ3aU7CHhWRLLQJ1sClxbggm1sskXUTCV4Kfym0L0A+B5LFH48JsAaTC3vhQ8p9ZZSDrHf1q5284KIXEr9QrxowxvLQwqv94nI/R7udpjQoQja0SdiaGaCw5QFa0q7TakHiG/j7aqLFNzYOksZa9o1NkJ7xqWU+o39ttTxNoWHmakoAxHpBXyuqp9gAoyqJOXtKAOIr9929bEoXkXkRlX9pdSH4usCddbCIrKTql4t9eHyfLyQZXW75ulW93NfyJkHpbENTez39xQeWjqOxyoTVLXQqrkCSsfGJpVgVeAnsU+ps5icXH/AwvOFlIaKrSHzkDLeROcFI23eiaUbQ3NSK4PUNdtPVfUQsXxk72JKmkeo8FoAUNXRwGgsXCOOp6dVdc0C/M+peccBHCsieQW6j+8LhYPzgod7pvvdz+NlCLCSqo5358eRC2soIucR4d2lqid6z+wK9FMXctYJwIdMCLrqvFBihO4kKNe8Z1ZV1X28990rIn749RQFRVtwXZup9BrLlGUONlLV1b3zC0XkWSw0birsnMJDO9puwlx2eSJutBJMnXGTiFwP/NqNB4iFmD3If0fR+s+jtZz7HeI9s7aq+vPbYSLyFF4IZ3V5TkXkLmApVf3Anc8N/DnHQ3Q5eM/8UVXX9dDuFIsskUE7+hgk9IeUMqA2j1XWbxPlFdXHEuge7vCr6iC1P8S28eiy6kAHJlfoKMw60IEJC7sD5wNnYZPaUIqtbQ/G8piMxSan+YHdSugOpLZ4eLwE9zfAFWJ5AMAWNrt2l66qnimWD2wdx+9uqjqigO5JwAg3YQsW7ufwFvCbQnd1VV1JLEQAqvqJWILyEETXW2I5xH5bu9rNbzDrtQMc7uOYIjEEsTyklNXBYhZkWVldrKq3hnCb+LbYtltJMxMcxixYPUhptyn1kNLG21IXifUWW2cpY1i7xsZ2jUsp9Rv1bYnjTAoPUWXghJv7ATeWvLOZ90N76rddfSyW1wHud9MCOhlknmEpFqltmadb3c81MdTOxH5/T+GB1o/jpzc+2QgisgUwr6r+2Z0/i+VaAzhEC3KNkjY2lsHa1SiNkFhnlTm5VHUv95vi9Zky3kTnBSOtbGPpVtKc1MqgiTVb5vn2cyxs5L9FUhxoGmDqapQumAkTDMcoVJrxvOlLff6kb4AFcjgvuN8Ur7EfOn4yIfj07tqEoJtBpdA9UbmWwccichSmMFXMCKXLSCFFON9G3BncvRSPlu9EZEfgevdd/ckZdSQq4lJ4aEfbTVGMR+OmKME8WCLDczTGiMgKOZxs/RfjNZbBdDkDgbWorRPzsEDWZhz8E1gsh9OMAegcIrKQmmctIrIgtbVAW/qYo5uiBMugsgwS6ze1vCr7WJN0S+sglWZsGTTZFzrQgckLVLVzdI7O0UMPYCrMimN5YKoW054Ryz/QKnprADN45zNgSqki/LmBzYEtgLlaxW8sXcwypjcw3J3PgYWDmaDl0Ka6iGo32KK7t3feG5i2le2sgs8Fgam982mwxW63v61N5fUKFsLT5/+V7tZtaj2k9p1W10VqvU3MOmuCbsvHpSbqt/Lbmhhvo3lIGEOPxjZM8wGzZseEKoPEem1bH2tXW2zHkdJu2tnPgVmw/KHrZkdPe39P4SGxfpPn1BJaTwHzeecjgdkwIeZDEc93a12DW5ul4saWASbY9b9vAWC5AN7WZUcBP02trYp4SC3b2G+LpTkploHDiVqzAScDr2Khd6fE9gPPTsi2C9wFzO1dnxu4peCZITSO4/cV4B6JeQUch+VaGwkcUYD7CBZKMzufEnikAHc34C3gcne8Cewygenej4WEXgAzUDgSuL8Ad1jg2gsFuLNiIelGAMOBswmsbYAxufNe+WsTALehnRa1XVdOtwMfAx8Bt1G8zj8ey881g+trv8EMJbrLQ8vbLvB4zLUmcEfGXHPXr8PCyP4YWA9T/l9XgPtUzDV3fWVXBuNcXxiJed2FcM93fWJXYBfMs/q8FpTDxpin2KPuGId5UrW9jzXRH1LKIKV+o8orpY8l0o2qg3a18ZSy6hydY3I7JjoDnaNz/C8dWBiOmb3zWYDLCnB/G8DdtwD3gQBu0abhxADuCS2gOwIQ77wXBZtGLH7/TN75zMCWLeA3he6OmIXbu1gugteAbVtQbynlEPVtbWw3zwDTe+fTA0O7UwaJvL4A9PHO+wDPh3Db1ScSaaYsWFPabUo9pLTxttRFIm5U+cbWV7vaQRNl2676jS2v6HEmhYfEMngzcIydUGWQUr9t7GOx9TUe+Lzo8PDOLTta0MZT5qeW93N3b08shNknmCD1S+Dhnvb+HsRDu8bxRbG8Si8DY7PDu/98Dv98vz+FaLp70WNj2VHULovadZNl0CDgC+AMKjmK6iF6vHH3l8MU9FVKqJR5p/LbYmlOwmUQWrP9tAB3FpyCD5iWbhgjNdN2SRMKv4qnkMcU9a+WvGMlzMt5ALBiCd5reEJrVyavleDPhRmVbFFWXm2kmyJ0j1au+W224n6KcL5duEOx/Wxv12Z2LOtnCe0yRQkWzUM72i4JxoyJuClKsKmB3wG3uuN3eIYbOdyRwDre+VpUKB8wpeVMEfW2FeaNfhawVQlekgGoq6flKTfka3kfS+0PiWWQUr/JBrMxR2J7rKyDdrXxlLLqHJ1jcjsmOgOdo3P8Lx0EPJhC19z1kQm4KXRDuEUCs+7y+2Kbvq2I32i67t4SmJBrP2DJNtZbUTlEfVsb200It+FaCg8t4HVUST10t92ErqW2mdgFa1TdNlEPLS+D1LpoAW472k27yiCF7gSr35RxJoWH1P5QdgAbtrMMUuq3je9PKi8qrLYxYcAuwMXAk8D+7ngcOKs7ZZDabtrRz9310ZhwaaQ7XwK4oae9vwfx0N36LeL3SWAD4EVMuHUcMNC7/0boOXfv7yX3QvxGKxCqvrEAd9cmy+DPWFi3JN4i+Anx0HDNXb8MU/JdQbUSKrpsY7+tVfXVE8vA3StcswHru99oz7nIb2/gsQT3/OyXeCVJtNeYw18HC78L5j23YAFeineXYGHUjnHnfYHVJiRd75kYoXuKcm0tzJDgbXe+PHBBAW6UcL5duKR5jS0GPIRTxGJK6qMKcFOUYCk8tLztkmbMmIIbrQRz+NMAi0e0xRSvsR9geWbvdedLAXuU0J4f+In7Py2eN183ymFaTBF2iTtfFNh0QvWxJvpObBmkKDljveyi+1gi3ag6aFcbTymrztE5Jrejk8OsAx2YsNBLRGZR1U8ARGRWinMJ9hIRUVV1uL0xS9kQfC8ifVX1bYc7P8UxyHuLyFSq+rXDnQbbUHaX7lgROQC40J3vi1ksB78tcK2oHFL4TaELFtv6CYczjYispKrDQ3QT6i2lHGK/rV3t5j/+N4vIyhQnkI/lIYXXj0Rkc1W9w+FugW24iqAdfSKFJthGZwH3TcuLCKp6ZQAvpd0m1UPgWllbaEddpODGlm/KWNOusbFd41JqP4v5tpRxJoWH1DG0DE7BvJ9S3g/tqd929rGU8aM0IbiqXuHo7Ar0U9X/uvOLsFBGIWjXPN2Ofg7wlap+JSK4PvSqiCzeA9/fU3ho1zg+jao+5Hh+CzhORJ7AhJgAz4rIXqp6if+QiOxNcX4pSBsby+Cc/AUROVxVT8pfV9XLvdOUMugH7C0ibwH/wYT1qqrLhZBFZBNgabwcVap6fAA1ZbxZQ1WXKriXh5Syjf22pPqaVMpARObEDOKWxsbDlzEl4oce2npYzrDNAu9R6vOHpcDOjofewCyq+rE774MpxH6nqksCqOp+2a9Y7r0fORpleWT/JCL3eriFuShF5FhgFSx31CAsHOLVBHIEquogRzebow5T1X8UfOMFwPfA+pghyHhgMJafaoLQFcvn9FfMe7GviCwP7K2q+wZ4+DcwQESmV9X/K3h3BmcBG+HybKnqKBFZtwB3ODBeVR8UkWlFZAZVHT+hcFV1HOaJFwOXYLk2/+KefVFErgVOCODugI3B52B94Sl3rQFSeGhH21XV+0RkUczwBMxj7euC96fgfuXWXveo6mtl3yUimwOnYXP+gmL5y45X1c0DdIdh+8cZMY//z0pIX459+5Hu/G9YLsBLAzzsBfwaU1wtDMwDXIQZxuR5iC4H9/5hwJru/F3gJiyMrE+zXX0MIvtDYhlE129CeaX0sRS6UXWQSDO6DFLKqgMdmOxAe4DWrnN0jv+VA/gV5ir9R3e8CuxcgHsaNhlugG0cbgTOKMDNrEmucsdbFFuTHIJZFu+BJY5/kuK45Cl058SSnH6IKaKuBeYswL0MOBNbzCyELZwubwG/KXT/CLyDWd884o6ikEgp9ZZSDlHf1sZ2syrwd0xp+ATwBrByd8ogkdeFsbA9b7u6GAosUtJ/Wt4nEmle5Xi8ADjPHUVh0lLabUo9pLTxttRFIm5U+cbWV7vaQRNl2676jS2v6HEmhYeUMqg6qA+T1vIySOzn7epj0bw6/CirbRJCWCW28ZT5qeX93OHeioW5PA7znLsd24T3qPf3IB7aNY4/5drgLZiX/VZ+G3NtZSi2NjrDHY8CTwM/KGnj0WOjwz+86F4At9LzKbEM5g8dBbgXAVc6msdiXoKXFuCmjDeXAktFfn/KvBP1bYk0J4kywATqbwEDqeWiHIhZ2q8doBnyWgldWwLz/LrbtbPLgU8xBfKSOdztgc+A94HHMAXmu1jfD3qTpBzEe42NxJSlI7xrRV7FKV5jWe41n26RJ2e76D6L5VD1cYvCWKZ4jT0bwwOwF/A8zuMW8/oI5ndsI26K19jzge8a2YK2mOpV09K2S5r3TQru5tha7E13vgJwRwHuMGCmyH4W7TWWUmeuvPrkcEe3oBxeiOwPLe9jmt4fUsogpX5jveyS+lgC3ag6aFcbTymrztE5JrdjojPQOTrH/9rhFkb7YWGWCjeImCBjHyzHxGBgb7wE2gH82YFNMUvJ2St42Bg4HROABAVrzdBNKIPpsCTbL2CLzJOA6brLbwpdN/H3SeA5qt6aKIvYb2tXu5kSWAZYFi8pdzd5SCorzDo0GDKhm99W2XZTaGJCS6niM7VuU+qhib7TlrqIxU0s36ixph3toMmybUf9Jn1byhHDQ2oZVLxveOr721y/Le9jTfC6ALXQRR9TELqIhBBWKWXQZD22tJ/nnlsP24gXzsUT+/09gQfaMI5jCo3pgXkx6+XBmKdPHm99aqFB149sMyljY0q+pxTc2HXFSsAB7vsKFRk44af3Oz0wpAQ/drxZF1OsvIaFxxxNeXjdlLKN/bbYdegkUQaYwrQh3xEm5AvlZgqFQm/IAYcptzcD+mPj8/aYQH8zcgJcYAxOUevq4WvKQ+ttDbzuyuFzXN7LAtxjgTuBv7nzHwJPFeA+538jNr8VCfIvxLzwXnHns1Cc/+9ZzPAjozsHxaFi20U3Reieoly7GRP+D8eE7wcB1wfwRhIvnG8X7mPAapHfdS+m6M3K9hc4pU0AN0URl8JDy9su5nF1iMfrNBQrlVJwU5RgobZYhHsv8MusrWIe40X1+ygwm1cGawCPxfDg6BbxkFIOQ939jIeFs7rJv58W97Em+kNKGaTUb1R5kdDHEulG1UG72nhKWXWOzjG5HROdgc7ROf6XDjfBTeX+/xjbyM4c8dyswHIl99fGCfQwK74zKbaSnQ7o5f4vjglqioSnKXRPxXKyTIktsj8Gdor4tt7AjCX3o/lNpDuYEo+MZustpRxiv62N7WZbnEAJs0a6heI46lE8JJbVAFdWgoVVGU5BQvZ29okEmjcBc0fyl9LPoushsY23pS6arbey8m2mvtrVDiLLdkLUb1l5JY23zfBQVQYR/A/vzvtbWb/t6mMpvDZRfnNhnhGbA3N1twxS2007+rm7fw6wVgSdifr+HsRDu8bxBoVC4BsKj5LnktZsVCjBMGXxWPf7lfd/bAvK4BhMOTPQHaMoFgpngrhnMCHvVMDrBbgpa6s3XBktSLWXW8q8E/VtiTQniTIAXi5pTy97/5cAtsE84bb2jl2BlwLPjvB5LmvHgfNXK9r5G5TkUs7hjiTea+wgLDzYWMxT42lg/wLcFO+uHbFwau8Cf8KUndtOYLopQvcU5drswDWYF/aHWBjA2apoUi6cbxduigfSQsCDwBfAe5h35vwFuClKsFQvqJa2XdK8b1JwU5Rgl2IhK1/EPHrOAy5qQXmthHmDf+Z+/0b5vuAIzAt9Q8yb9U8tKIcNXXv4yPWLccCPI8urW30sRLeiP6SUQUr9xnrZRfexRLpRddCuNp5SVp2jc0xux0RnoHN0jv+lA1soTgEsgm2OzqI4DM+j2KZ/Viy0zDDgzALcF7EF6PLYpngAxRZIwzB37XmwsCq3Ate0gO5I97sVlrh71pIJ+lr3bdO5Rc0HwMEt4DeF7ipuMXM/tjm7g2JX/JR6SymHqG9rZ7txv+tgIXO2IGB9m8JDIq+ZdV0Wx3x5ypO3J31bTNtNpPkI8Elkm0nqZwn1kNLG21IXibhR5RtbX+1qB02UbbvqN7a8oseZFB5SyqDqAG5pZxkk9vN29bFoXh3+vK6tZCERBwPzFuBujnlRnA5sVla3CW08ut3Qhn7ucHcB7sHGpNOAVXri+3sQDyNpzzj+iGvffwSWDtx/E0855f1/kwJllXuucmwkUQnmPTei7H4TZfAKXuJ6zBL7lQLco7EwmtsA/8DGhT8W9Un3GzPeBEOBN1u2qd+WSHOSKAP37bMEnp0VT3HleBoE/Mv9Zse5BBTaeAJCYN/cvTG583eB33tH3XmAdtDLpqAMYj1vBPP42BAbZ04HNiyhG+XdhXnTroUpHH+Leb8WKvvaSDdF6B7rNdYbuDqyHlKE8+3CjfJocd91mtdeqjyQU5Q6KZ5rLW+7pHnfpOCmKMGmxRS8z7vjBLzxN4f7KBFeY67OfofN/0tj3rplxieCKRZvcu19LwqiosSWA9Ynf+n43QSLZFAUsaXlfayJ/pBSBin1W1leJPaxBLrRddCuNp5SVp2jc0xux0RnoHN0jv+lw5u8DsFZSVEcamKE+90TGOj+F1m+ZHSPwcXBplhAkOHuj4v7X8JDCt2X3O8lwMbuf5EgbqT73RGzhp8y4tti+E2h+xJmqd0PC4m0HrBeC+otpRyivq2N7SbDPQnYIbItlPKQyGsmUDkHF6qmCLeJb4tqu4k01wsdLWi3KfXQTN9paV0k4kaVb2x9tasddKNsW12/seUVPc6k8BBbBtjGbX8szNKfMcFWUFDVrjJIqd/E96e0g2he3b0HsHCLU7hjV+CBAN7JmAfY7u54ADipBW08ZX5qeT/PPTMrJsx4iICXysR+fw/ioZ1z6lzYOugpzBupMO9M7EHC2Fh1rxncxDq7F89bD1MG3RXxjqmAmar4JG68uQBT0vfH83LqbtnGfltqfU0KZQD8GhNarwfM4I4fY4qbvQM014xsf3sD0weuLwKcnbt2bNkRoHEOFkorpgxSvMYaQkuWfF+Kd9fTE5Mu6UL3FOXa/USE7CdNON8u3BSvsRTFdIoSLIWHlrdd0rxvUnCjlGCuLT6YULYpXmOPRtLsRYEHYAvK4fFImi3vYyn9oYkySFFyxnrZRfexRLpRddDGNh5dVp2jc0xuxxR0oAMdmJDwXxHpjyVx38xdm7IAdwoRmRuzKjmygu54ETkcC8e0roj0LqErIrImtoHZI3tXC+jeKSKvAl8C+4rIHJjlcAimFJEpgS2B81X1vyKiLeA3RLcAlY9V9dyimzlIqbeUcoj9tna1m/dE5C/AT4BTRGQqbMEZglgeUngdJiJDsDA8h4vIDMD3Jfy2o09E01TVxyre6UNKu02ph5Q23q66SMGNLd+UsaZdY2O7xqWU+o39tpRxJoWHyvYlIksCD2Mb3hHYZnZV4AgRWV9VX+3G+6E99duuPpbCK8AcqjrIO79cRA4M4P0cWEFVvwcQkSuwsj48gNuuebod/dyHRTBPggWwRPE97f09hYe2zamq+g/gXBF5BFPIHYMJQhCR+YFPVfUzd94P6xPjgD+r6jcFZFPGxlR4KgKnsgxE5DxAsbxSL4nIA+58Q0zY2wCuX22C1dUU7hqqemYAPWW8mcbx8VPvmmIhDANslJdtE98WXV+TShmo6sUi8j7Oe9Jdfgk4QVXvDNAcISK/dbhTe3R2z9H9S4h5VX0DODB3bWAItwRmxBQOpWUgNhndgI0bn2NhKY9R1QcK6D4jIquq6vNlLxeRXpiX5yHABti8vqWqvlLwyBAR2QbzJC9aJ7WNrqp+JyJziEifkrEo46E3ptDcsQzPg3HAUyJyB/Af751d7dx914uqugxmgFL2/nbh9gZ+o6o/EZEsVOn4kkdGuG+6KfddoX72W+BiYAkReQ+rw4byS+GhHW3XldcsmIJ5Dax9DVDVj7uJ2xuLIPITqveG34nIFyIyUzZflvDbm5rB5eKOh9dU9b8FjzwlIudj5ebX2fAcD9+LyCgR6auqb1fwEF0ODh4QkYMCPPw7910t7WMer1H9IbEMous3sbyi+1gi3co6SKUZWwYpZdWBDkyWMLE1dp2jc/wvHVji9nOB/u58QeCwAtxtMdfnC9z5QsDgAty5sDAfP3LnfYFfFeCui1n6HerRPbe7dN39WXBJ7jF39B8U4B2AWaHdg03m8wNPtIDfFLpnYpava2LWXitRnF8hut4KymEu796Gqd/WxnYzLbaoWtSdz42X6wMvpE0sD4m89nLlPrM7n43qnGst7RMxNIEn3e94bJOXHWVJ2VPabUo9pLTxttRFFS5eeK/YOoutr3a1gybKtmX1241vixpnUtpYTBlgFp6/DPCzTQmv7SqD2H7erj4Wzau7/yCm2Ortjp2AhwJ4L+LlicI8kYq83No1T7e8n7t7pwCvA/dh3nYz98T39yAe2jWOLwkcB4zBrJF/g5fXFfPG+aH7vwKW7+4PWCjPv5bwGz02uvvnF91r5ogpAywkZuFRQPceTHkxkBJPIYcbPd4lfltl2aZ+W0p9TSpl0ATNmzDl2t9dOQ0BzinBXwi40/WJD4HbgYVyOEO8/4e3uI2neI29DHznvu1FzJO0aC5J8Robjymi/+v+l62H20X3L5i3w9GUhLt0uCkeLceGjgDeNUDfSJrtwk3xGhsUOC4L4KWGb0wKqdrqtkua900K7h2UeNHmcG/EQjFfis3X5xaNS0R6jTncRwJHsLwxQ7bxmMf6HZSnDUgphzcDR0P45Hb0MYeb1B8SyiClfmO97KL6WBN0o+qgXW08paw6R+eY3A5RVTrQgQ70DBCRwaq6jfs/qzZajiyoqm82QfdpVV3T/V9AVcfl7ldaH0bQvUw9a0wRmR64XVU3iKAjmADvW3e+i6peEcnDeaq6v/s/lap+naM7q6r+K/DcIwFyqqrrB3BXVtVhuWubadhStYrf4aq6kvvfkrpoY7vxeW1JGeR4PV5Vj/Hu9Qau1HgLtTztw1X1JPf/Z6p6b+7+Pqp6Ue5aK8trFlX9JBK3q91G4Pr1EN3GI+i2pS58fiNwD1fVk2LrK4Wm+9803RaOS3uo6qW5+yer6mGRn+Q/1/VtFXjRdeDjx7QvEXlNVRcvoBO8164yaFW7aWMfq6svEekLnI8ZaiiWa2CAqr6Ve64/FpbxEUxpty4mdL2+CR5aMk8H6PplFj2Oisg+wM1aYM0sIkur6ks9/f09iIemxnEReQa4DrhJVd8P3H9RVZdz/08HvlfVQ5wF88jsXuC5lq0xA7QryyV1LhORaTBh3GsVdF8s+uYAbuV4JyKHqOqpUvMIqwNVPSBAN6lsY74theakVgYicm6Atc+AF1T1dg9vhKqumH2fmIfx/aH9gMN/BgtFfJ27tD0WVm71PE33v3I+FpGpMY+5Ui83h/tn4PKYPiXmKdoA+TnH4Q7EFBOl3l2p0Ea6x4aua8C7T8zbcSVM8Fvo0ZJ7ZkZDKfSYehjzrn8uR3PzCYh7BpZTKMZrLBpE5OGi9t8dHtrRdkXkaMxrvtT7pgncGzEvnQdyuKFxaZcCXhv2DCLyJ2CmAA/D87gpICLrFfDQEB0lpRwS3t/yPuZwUvpDShmk1G/Ly6tddNvRxlPKqgMdmNygE5KxAx3oWbCQ9/9OJwz8HEBElsIsmJZpgu7U3v/BIrK5qr7n6K6HCfCW7Sbd90TkQlX9jYjMAtxNhft8Bm4D9a13aQBmxRwDa3v/bxGRLTIBN2Z5fxewcuCd/SLpA1zihOWjoUuYeSBmYZoKfmyvVtVFu9qNz2urysDnta/UFCZTYZut7mwYtsW8BgGOFpGvVfVhx++hWA6LvCC9leX1ELZZiIG1q1G6wK+H6DYeAe2qi8L4dQHI6iy2vlJo0h26LRyXfiEiX6nqNY6HC7D8L82A/21lkFIHPn5M+/oPxVB0r11l0Kp2064+VldfamFiGjb5eVDV60TkUUxIIJg3xT+aeD+0aJ4OgF9m0eOoViszryJuHJ3Y7+8pPDQ1jqvqGhV0fWHl+rhwoGphj8qeS1rXpCgHiSuX6DIQkc2A04E+wIIisgJwfEgQB9wrIj9V1SERfMaMd1lIuhci6GUQXbYJ35ZSX5NUGWBj3xJYGwDzgn4J2ENE+qnqge56Fg7tUxFZBvgHFnayCERVr/LOrxaR/XI4qYqhq4BXgY2A47Hwd0VhC/sB+4jIOGzOFWzJ0qDMVNW3RGQlYB3H01MlgvnfYx5F34nIVzUSOmMIWUS29ug+oaq3TUi6mWIsRugOvO+OXlhOu0IQkVUwz5AZ3PlnwO6aMxrEPC1joV24swL/wsboDJRAOFMRWQjLlbeGw3kaOFDDBoIp4RujeaA9bTdTKv829/6Fuol7tzsqQVWvEJE+2HijWJjFolCha7nf43M8hAx2Z8O8r7IyeBIbxxsMuFT1MRGZC1jN4T5fsm6MLgenzN/X4+EJ4CJVzYfzbkcfg4T+kFgG0fVLZHkl9rEUurF1EE3TQWwZpJRVBzoweYH2ADe3ztE5OocduOS67v8mWJic6TFB3UtYTpPu0l0VC2ExF5YnZSQwX3fpuvNTMEHl88A23SiHEU1+217AbVg4iQUwi8aGEDCYAOsKbJP+vPu/bMk7FsIELku6dzxBk67p7aiLCdRuWlIGOZqCJXo/HAuB87tm20y+3WDJh58BfoQlqh0MTBl4ppXl1VS7bUcbn5h1kfhtI1Lqq13toJ31i+VneQDoD1yJxfnvdhtvVR34+DHtC3gXL/yRd/wBeKeAflvKoFX128Y+NiJ3fgVe+D0sPGIoJNJWeOMrMDOW96UZHto1T7dr3hkxKby/h/LQynH8Q0zhdw4W/mdKd31uzEOn6LmkdU3KWBVTLillAAzDLP1HeNdGF+BuhQl4v6Q6JHPUeIcX+izy+6PLNvbbEmlOamXwMDCFdz6Fu9YbeNm7vic2Fq8LjHVtf+8SHk4GDsPmh/mx/FxHY4qDWR3Op5inxZ3e/8IQYdTWQi+63ykpDr02f+gowD0GC2U30B2jgKNiy7ukDC7A+tdu7rgPy204wegCq7hvG+eOUcDKFfRnpDrE4Iu4EMfufB3KQyJvjuWXnKuCbltwE8r2GWBn1w+mwEJCP1uAOyhwFIaWS+BhorfdRH77AMthCvnCcIPYWPQO8Ci2Dngb+FkL3v+AG1sWdMdRwIMFuHu6916OrTXHYUqo7vJwIxZqsp87LsY804vwW9rH3P2o/pBaBrH1m1BW0X2snXXQpjbe0rLqHJ1jUjkmOgOdo3N0jtpBo2BrSyxk02hcHoAW0V3TLVaeA+boDl0sR0F2bINtXi/OrrWC38Rv+y22QR0NrBXA3wLLHbK7m/iXd/9fB7Yoec9iWDz1+4FpelJdTMB20+0ycG1mJe9Y3bWZP1OSR65Jfud0ZTsIswgueq4t5dUu3Ko2PrHrotlvi62vdrWDVn8XTnjmjvmBEZglfJdQrV08pPAaqIeqMfTYsiOH2/YyaEX9trOP5c5HBHBC10bG4CW0xbbP0xN6HJ3Y7+9JPNCmcRwLNfc7YB7v+orARhXPRq9rqtq1G1eOcb/vu//HAMfk8JLLACfIol6pVCQYH4utGcvWEsnjHQn5f1LKNvHbYmlOamXwGvWGBzMBrwbKZcHAsw3XvHtvlhxjHc56ZUeA5nPu93HMqG92CnLUeO39AGD/ovbt8F4BpvbOpwFeKcHfGsvxfAYlRhqYQYB4572AlyYkXdIUW9HKNcyTKeZatHC+jbgLYWuVj6jl0wu2XQKCe+CZsr4Wc6Tw0I62i3mS/h7zaBuMRT+ZuoBmCm60EgzzDl3EO18YN9YEcGfDcpwNxwwbzgFmK8BtyPlGgcEKNt7NlnvPay0oh1GR11rex5roDyllkFK/UeWV2scS6EbVQbvaeEpZdY7OMbkdnZCMHehAzwKRxlj+M2Kb1P1FBG0uXrCIyJ05utNisfwvdXQrw0SF6GLWPj6MwCwjN6M4JEMM3WhcEfl97tn5MGHJGiKyhtbHzz4e2FDrcyCMcjGyb3dHRng09WU2K2aZ+qwrs7oQEmK5PdZQ1aEl/I5rQ120s90klUEMTWzD7MMnwFLuuhIIS5HA7/gcv32wzdwvRETVhYBpU3kl8ZqCm9jGU3hoR10UhSEJwfIi8rl3HqyvRIhuB6l0E3GHOR7E+93EHUo4PEareBiXSHdOr42Vti8N5AcpgbaUQRvqt519zIde4uU6FJFZCYdH7xW41uyavV3z9DcTeRyd2O/vKTy0bU7VQM48VR0RZCJhXSOWeygbD34gIsd49I/PkR7n/f8v0JB7yUEzZTBGRHYAeovIopgQt2j99jowRlW14D40N95Vhj5rcs1Y+m1N0pzUyuBUYKRYeFvBPMhOFJHpgAc9vME0hvq8mYIwvKq6YOh6DqchZ477jvkwRXT+/sViYXKPwrzQpsc8S0I0jsHC/WblM0hEblLVEwLo4zABaha6ayrg7wV0LwAWoZabbR8R2VBVfxtAfw3oS60/zocpsCYYXWC8qj6Rnajqk25tEILLgH0zfBFZBzOyCe1hnhPLx3Qd1ua2Ax514QHRWljAg4EV1YXHc+Hzhrp35aFduNdihgFbufPtgesxo4E8PCIih7n72Xfd7dYhqJfjKDG0XDQPbWq7V2Lerue58/5YiNNtu4l7JtBPVd9wvC+MhaW7N4D7YYbnIPNUDcH1mGJ8G3e+I5Zv6icB3EdEZHvMwwjgFxSHxnsX+7YMxmNKjhCklMMItwZ+BkBEVgeeCuC1o49BWn9IKYOU+o0tr+g+lkg3tg5SaEJ8GaSUVQc6MFlBR2HWgQ5MABCRy1V11wjUQ7FwNz6E4jmnws6YlU0UiMj5wLUVih+AnVV1TLc4C8NTInKiqh4RgXsO9bk2AG51v6EY2lPmlGUAqOo4sUTfPmwa8X6fxvdiyY/XLMHZWgqS0nYD2tVuNqAiDnkZiMicqprfMByqcfkvmoGbVPXESNx8zoxWlFeKQuUcF5N8H0yQMBq4VGt5k3zYALPE9KGsjRczKHKDqm7nTpPqItvIFEG2wVEvN46IzK6qH5c8dnRCncVCSjtIgaLNSQjOUdXL28DDS2L5PYKQCRhVtRCnAK6mvi2Vti8R6Qfsh+VrALMEPl9VH83xUylYbAKS6zfbrOZgvKpmuWta1sdycFPu/AxgqIjcjG2mf4mFkszDCyJyJiaEUsdbs2NUU/O0iKyNebr9R0R2wgTK56jqW2D9XBoT3bdiHP2mJ7y/p/DggxOoz6eqvgC5XXPq0iIy1n89NYWFqurCOfzTE2iP8/6XKcFQ1Su6GBAZ4J/n8PolvD+D/YEjga8xoe/9wB8LcD/ABHr3Ovzsvb4xQTPj3axU5/9JKdsMqr6tGZqTVBmo6qUicg+Wz0aAI1T1fXf7YBFZAlgamCk3r85Ife5HoCtX1g9U9XV3vi3m9QJwv6r+M8SHiMyOCSv7A/NQm1uy+72w0JafYIL0KkOS/pgA+Sv3/MmYt0pI6fA1tm54ACvTDYEnReRcIK/QXw9YJlOIisgV2No0BLMBr4jIc+58VeBpp/jMKzDbRTdF6J6iXFvB/R6bu74W9cr3FOF8u3Bj8ullkK39985d351GZXaKIi6Fh3a03cVVdXnvuUdEZFTB+1NwU5RgL7mx5kbH67bA89m4ovW532ZVVX8sPkFEtiyguzfmLZSVb2/gP87AK28c9h5mzHq742ELrI/83vHgG32llMPqwK9E5G133hfro6Opzz/Xjj4Gaf0hpQxS6je2vFL6WArd2DpIoQnxZZBSVh3owGQFUm4k1oEOdKAVICLDVbVU2Nwk3T2whddp7vw9TLgnwCGqemGTdAdgi+O5Maun61R1ZMUzc2C5XxbAU8ar6u45vN7ALJkQXSxJ7q5YnoklPbx2ldkoYDNVfTt3fX7gTi3wmHJ8/4D6b3s7gDcQs4a8Rbs5wIpIkVWlUJAkOZKu752RKXkU+7Y+qho0pigrg4BAOvOwWRGba/JWVYjIVJiF3QI5msfn8M4t+x4tsOAXkXkwZapP+/EyWmUgIqcDg1T1pYL7s1KzZi9gVffw8G/AhIVPAD8D3lLVAc3yFwMi8raq9g1cr6wLEXnE/Z0aC70xCqvn5bAwFOt4uJth1n/fAt8Bv6xSwFfVl4icioUouij33O+wmPaHNkn396HnPNwzPdzeWHiQeYH7VPUp795RGraSRUTWorFsr/TuD1HVn7r/h6vqSQV0Brm/c2Iby4fdeT/g0byiTESWBS7BhHT3YsL1zLvpOVVdrezbC3jYBAuxdTwm6BBMkXAUsJ+q3lPwXFUZHBN4zEOtEzD4dCv7uViC+fkwrxPBcoJ9gG349tJwovFKKBibPsNC5tweuIeILIUJAwR4SFVf9u7NoqqfiHlAHE3N4ngI8CdV/U+A3tZYXrI5Hc1sfmjwsoudpx3ui1jI4uUwYc2lWPjGbhl8iIhgFtULqerxItIX67/P5fAm6vt7EA+PYrk7psA8Hj8CHlPVhnErdk718KcB+qrqa4F722DhdzLohSl4D8JCRm6Tf6YZSFnricgIVV2xAie6DERkW1W9qeqau54X7GV0gx63VeNduyHl2xJoTlJl4HjYHPMsA+s3d3r3tsDCqG6OeXVlMB64Pr9mEZGLgaHqjGFE5A1sXp0G+FZV9/FwZ8CUDTtg4cxvBbZT1XkL+HxcVdcN3Qvg3gv0V9VP3fnMwNWq2mDkJ40K/TrQeoX0Ldhe7C13Pj9wsqr2D9AtHQPV87BrI91HylF1fQ/3LMwr0VeufYJ5F+aVa6UgIruo6hUiciWWz6dOOA/8zdH0147twj0Zy5Hne7RMhSm7Qh4tZd+1oao+4P4/q6qr5+4/o55BXDM8tKPtisjlwEVa732zi6ruG6CZgnshtrb0lWCv4YzntN4DdlD++XpWa2sst5d8gXqvsaVVNTi+loGILJ3tSYvGZ4+JrnE6sRzyRsl5+NytW1vex9z/lP6QUgYp9Xs5keVV8V1dfSyFbmwdpPIaWwYpZdWBDkxu0FGYdaADEwBE5FXMqirogRJaRIhZNh9HTRCYCcEW8nCeBzbWmpv6CFVdUcxzZUho85UoXJsfU5xtjwnJr8M2kX8L4A7FBP/DMAF59m2DPZztgb9gIVded993FZbA+49+OYgptn5McZmFFDCLYcKcBajfoPubpi2xMC0nUgsdsyqWwPtQVb0tQHd/zArqn8D3NbKNCisxZdR0rgy+pLx8S+tCREY6/q7F4sN/mSuDBqvsmHYTeGYGYF/MIupWVf1DahmIyPc0WonPi1mGBd8vIvdhwuV8mzkjh/cNMAZbqL1Prk1owOJcRE7BFusve7RVc2F7UspLRPbEkpBPgYWZuE5VP8vhhISIfbEY4r19YYmIjFbVZd3/KbAcFqWCw5g2XvF8kcIsqi4c7vWY8H60O18GOEg9L1onaP6lqr7qFuunaomQOaa+RORlzEr5+9yzvbC8Fcs0Sbdsg6VarzT8K7YhfA7z3O0SXBcJfkXkKiyfwcgcDwd4OCPUCYJjBMgichem6PnAnc8N/FkbFWZPYpa7z2CKvt2AzVX17yHhc+QY+igwQFVH5Z5dDjgvVM+RZdAw7mBlvSeWk2D6AN3Yfn4RNrbd785/CmyMjSnn+IKhlD7mBKhLUPMk2wbLwTIfptw9MPBNhRCrPBCR81R1f/f/DcwI5JWI5yrn6TwvYorM99S8NYraeMo4eiE2h6yvqkuKeU0NUdVVe9L7exAP2bpuT8y77FgRebFg/ZEyjm+Gee30UdUFRWQF4PhA3+mFjXUHY/33RPWUvAG60WtM//uK6OVwZw2t/XI4KWXQUJexfbCCh8rxzsONDn2WuH6P+rbU+oqFHlIGJ2Pr+2vcpf6YMcPhObw1VfXpiG8ageVcyjyl/Hn7Sa03GvoSWyccBTypqioiY4vW4iJyNLbGv4H6sJShvc5t7rvqPG9wFv+hMi75psHqlN8i8pijW+fdBXzh6G4eJBKm+7SqrtlOuhG4u2hN6B6tXIugm80LKcL5duE29JF61OK9Xx788UHSlGDRPLSj7YrIK8DiWF4lcN432Byr/lyZiButBIvgtcsQLicnAOc15tGNHntT5qvcujG6HGJ5aEcfc/+j+0MEXb8MUpScLSmvfH21g2472ngr+0IHOjDJgfaARGqdo3NM7gdmrfgw8EjgCCa7xhLI/gzbFM6WHTmcYbnzI7z/zxfQfQNYsolvWBHLe/Jdwf2RETTG4JLiYh4JXwNbFeB+jbl8vxk4gomwMY+X32DhV1bOjgDe8liM52GYh8SVwPIlfL+RL/sWtYvKusCEsQMdn1djiVenKMGvbDce7syYgG8sJlQv/MaqMsAEzPcBy3rX3qxqD5HlNBsWuvARbJO1J+alWPbMa8BUEbSjy8t7ZnHgZExBeC3QrwBvIeCvmBXcbzDBpH9/eNl5s23c9a3QsTLwQXfqwuGOrLqW+m0x9UV5QvmipPBR7aCE7qq58xe9/1MAF2Nho6YCRhTQeAVqSewLcIaH/pfgj8md9wrVYaBe+mHGCmuE3hPZvoKJzMvuxZRBDn8GTND4Jk442p36JZAkPbsWKKOoecThPow3Hrs28TAmAHm5ifYWbEMV7SWYKL3guZEJuI8Bh7vxay73TaOL6p34eWd4/lsJJzSfqO/vQTyMxrz9h+DGI7xxKIebMo4PA2bK8eCPb1NiRjSvYvPYwpF0k9aYWJSEsvtbYuuLjSLpVZaBq6fzMAOgc73jcsxwJfTMKpiX0HAsisCLJfUQPd5hxgw7u7FjCmAnzGO7qbJN/baU+ppUysBvz0Av77x3ro3vBSzq/gvmFf+Ze26lAL3RufNlitod8DvgWWzfcwSmPAzuXRx+yl5nl7Ijpmw8WiO8/+uVHT2NbgRu5VrKL9NW84AZEMXSbBfuhk3WWag9lrbLGB7a0XYxI5WyYxbvmWjciPcf3qa2uHSr22KehxaXQxQP7ehjDjelP6TUw+He/5aUV/672kF3YrTxlL7QOTrHpHZ0cph1oAMTBt7QBMsaB5+palUyzZn8E3V5XZxFcFHOsn9qhCW6ozMlZoW/PZbj5TFMeROCu0Tk51oQjsvBN+piIKvqcBF5U1VvLcB9WSOtjj34ViPCUKp5RvyqDMe3QsJiZX9Whp971g8B86iq3lWAWlkXqvoq5tl1rIhshyn3TgFOK3ikst2I5VP4A2YxeBkWT77q+0rLQFVPF/M8OktE3nE8awXNoSKyrDpPpRLa/wIuAi4SC7/WH4sXf6jWx833YSwm8Pu64H4GMf2sC8RC8i3hjo8x4frvRWRvVd3e4SyJ5Q5ZEaunfTScm2x5Efk8Iw1M487LLLxj2niDJb0HrxZcj6oLB6+IeVpdjdXxTphwzIc5pT7UYd251seRh7j6+kJEFlWXPyQDEVmUnPdlIt06EAuZtz3Wzj7DhIQZ9Mn+uDr9tfM+eRho8IByMAYTtn9Q8tqFxHJ0iPe/C7TRAvtREbmfWuiT7TGFcuBzZKasf6vqI2JekIOxvDF5iGlfDWEBI+7FlAFiYU1/j4WruwITWn5S8khs/f5bRA7FrKXBhYpx/fn7HG7UPOJgHsxSOBsbpwN+qKrfiUh0m/OgaswMwQti4V1voz6vUChESsw8ncF2WDixPVT1H2JhA5uedzz4ryt3BRALE5mvg57w/p7Cw/FY/qknVfV5543zegFuyjj+rap+JhJ04gcTjH4LnI1ZKi8vIstnNwvaF0Ssa8Q8/RfBFBD3l+BdgOWYGgr8UURW04LQrB7ElMH7WEiszanPOTceU3SE4BrMy240xXWVQdR45yAl/0/M+j3126L3BEw6ZeDDzEDmDTNT7t4ATJEINt8vjxk6rYh5vP0oh/+9iMylqv8AUJcb0q1L68pDVc/C1sMLOdq3AT9089Ct2hipY0l1eZ0yEIsW0gBakMfPe67LaywCuuYc9cIdFtCN9u6aEHQjoHBwC8AAbM3RSh7WTnh/u3BPwQwNY8CvswXLECUXWi6Wh3a0XQ1EW8nRHI4ZDSbhRsC2QDB8eoh0JB5Y5J1YHppZM7a6HGJ5aEcfg7T+kAJd9dvC8qr7rnbQnUhtPKUvdKADkxR0FGYd6EDPhUdE5DTMe8EXgvnhG4eIyAmqelTu2eMxa+QQVArXRGRDbIO3KWYheT3waw3kT/FgAHCEExL+l7DQPy9En75CiJ4Kd4rIvpgFrP9t0THcPfAXYGMxAfXdFCQ6z0AaQ8AMEJF1VPWwwDti6mIeTBi+FRYL/HfkEobnIKbdvIXlQRmEhUTZwxeaFdRDZRmo6rvAtmKhnh7AwqmVwTrAri6cx9fU2kwwBIFYIu/+WAiPe6kXBuXhC2CkiDyU4zcf7iOmvLL3nwlshilHTtRavplTROQ1h3MTpmA5Haur74AZs/L126Kq9i7hvwgq27iq9muCbkpd7IZ54Axw548DeQXDJZiXUNF5HmLq6xjgXhE5gVrdr4J5gBzYDbqIhZ/t745vMSu8VVR1XI7eCyKysare59E6XkTep7EMMpgdeFksib3Pg68E28L7f3oBnS5Q1f1EZCtqivmLC4wPTgGWxCz4s2dfFJENsBxZeYgZQxfOK/QcCI0JrTOoLAPXD7fGvPaWVdX/K6DlQ2w/3wFT4t/m+HzSXeuN5WXyIWUeOdW9/1FHd13gRLEcZA9G8N8KmBErh5961xQb0/IQM08bARMK++P725jBRgiix1HM2+VWbD3wJyx/R34NM9Hf34N4uIlayE9UdSwW+jMEKeP4GBHZAejtjA4OwBRTGTyItaPl3VHHFuH2BRXrmkQl2LqY9/93IjItFk60SmFWWQaqOkpExgA/rRLgevCRqobGvRDEjPkZPCIih1Ef+uxuZzyQH3cq14xNfFuKwn2SKAMPTgJGiIUKy8ZnPxzjt6r6X/d/U+BKNQOtB8VypubhNGx++AMWcQNM8Hg6BYp011//BPxJLKdof2z9unAOdSiNQszQtRiIDsOXCEEFXg+m2y7lWgruxIZ28ZqiiEvhoR1tt111m4LbrrbYLmgHD5NaH+sJ/E4q9dAT6qsDHWgLdBRmHejAhIEiwUIZZDlVfO8GBXxPtYOBv4rlMMnyyayA5QTbs4BujHDtCMxz4aBYZZOqlgnDMygToucXk+c4C+xZVPVjABHpA+yKJY9eMkB/F/d7sM8a3V+Av+2OPngeJgXwc2AFdXmWROQKbGMdUpiV1oVY3P8ZsDw7u1Kzku0jxbk8YtrNadTKO19vRYv60jIQka0zgYWq3ikiD9IoEMjDzyruZ7QHYsKMVzCByuEa9tjy4Q7qk7gXQUx5ZTAGOEpVvwjcW839ruqePwjz4oPaQrKyLTpB+5bADqq6SQClso2LyCGqeqr7v60TuGb3TlTVIwJ0o+oCQFW/EssJdY+qvlaAdreqvhBLk4j6UtV7nWfCwUDm/TkG2KbEm6CSrlhep5mwtvULVX1dzPt1XICHnQp4+ysWtiwEx5W93z0ftMAWkfkwhXno/nBgvKo+KCLTisgMqjo+R/faAM1ZgHdUda8AzZgx1Ffu5aFI2XdcyTMZ/AETgB4FHOkp8cs8LqP6uZtD9i+4/UbuPHoeUctpdQ/W/wULi/x+4PlYSN6cqupuscQj52l7QVpuo+hxVFWvEZFhmNe6AFtqwGNkYr+/B/EwBxY6bgHqc+qFckZEj+NYfzgS63PXYl5sJ3j0d02g5UPVGjNFCfaNqn7n+PlCpNgdzoOoMnDvn01E+qjqNxGPHCvmWZ1XzofW98fF8OBgO/e7d+767jSOO1HK8cRvS1G4TzJl4Pi6zhkzrIr1s0OdEjyD78Xyf36C9cU/efemCdC7WkQ+xvrJ0u7yGOAYjfAudeuU0dgeCwAReQH77mlEZEVqY/uMVBudFb4qAbcnCP17gmIrhYenEnAnNmRezL2ANVR1aAnuuAS67SrbdrSFdr2/J/SHmLmr3Ty0ox7a1ccmdrsdl0Azhe7E/q6mPB070IFJAUS107470IF2g9Qn40wJPRFLfyFqm7eXVfXv3aQXCtXVBUVKNCeIXRTPWlBVH49856qq+rx3vj3wFyzE1+vY5vsqTBn4xwKr7ZaBNJn4XUReBH6clZEry0cLLLyraI2jtgjxB+tMaNdyS7x8PSQ811R5uWfnpL7NvJ27/z3m4ZaF3cvKotQjrdUgIg+p6gZV15qg2wdTtO6AhUAdDNyiqnc2Sc8fb/IJhkvrqaouHM7mmNK1j6ouKCIrAMdrvbfQCCxE4XXA9ar6cjPfEnj3HJj31xuq+mmLaN6OhWC6A7hWVYeKyNhQ/xKRdRsI1EBV9YkW8DM7Ft6iPxby71ZVPSiHsxfwayz/z8LOQ+SiQPs8BrhRVV8VkamwPIPLY150O6hqy7ygMuWeqhaFrJtoICKLYUrsBahXOqSGSg7Rngdrkz7d4LwnIsdjCoKhGvDYLjGEyOPtqqqXu//zYjmL1sbGxieBAWoev6Fno+ZpZ4izWZEip1kQkTWwnIPj3fkMwFKq+mxPen8P4mEo1maGYR7LAKjq4BL6leN4BI+/L7uvTUYESJmTROQLagptwYxw3iBi7o+cy/6CefDcgRdONvRtInI1For5JWrh97RAcdkykLTQZ/5z0d+WQHOSKAOxaASFkO0fRGRTbJ/RG7hDVX/trq8HHKJho6WWgoi8ha1vV8H2N5ngczxweYEysormcCxX264RuD9V1aKIJA10Y9f5IrKMupCVMXQxb7prKxQ6qXTPV9Wi0J553BGqumIThpqVNCcyblvkD4ltoV24UW23je9PqYcj1KXN6A4PIrI4ZswcMnirotu1bozhAVgLyx++CKbov1QDxqoJ69YRmBynEJqZnxLrIaUMUuiOxfYYQWhmHHd0o9pjbB2k0HS4UWWQUlYd6MCkBh0Psw50YMKAb/kRHXpCRDbBFGH+pv/4PJ5ayI+x7pl9gQtKaE4N7BGg6294h2GCt5DFStDSXkT2xMI9zQuMBNYAnibsqZM9U5Yr6ChgZVV9w21+n8aEsWXhCBGRZYClct9WFEKplJRHcw7gEBrLLPRtVSFgfF5L60JVF2iC7+h24+GX1UOGk1IGsXxujuXb+iHwISZ0foWa8jeD0lj6BbQXxeoi3xZCbbe0vFw9TQvM7oTNvgXwD3O0+pbx5QvtpBb6dCMs/9RVwGpa4TES0cal4H/oPKMZWxdgoe1WAx517x4pIgv4CE7wsDjWrm4WkW+oKc8a4qvH1JcbY04E/g4sKCK/1orwUDF0VXULEZkJC3M2UEQWAWYWCxX2XI5kyGsoC1s2LyZ4y/OwBqbQWBLzzuwN/Ec9LxUnMN8KU5ouhoVrW0hV5y34tN9idfCs+4bXnYA4D9tR897IPKfmcO+4gkDYwJQxNKTcK8CrLINmIKGf34TlQfwrntKhhG5UGYjIKVgZ1wmQsTClIRiHldW5IjIeU4Q8rqq3u/tXiEihJZs6pXRuwz8I8xDa1p3v5K5tGOA3ZZ5OyhWUMO9cSH2Ysf8ErvWE9/cUHqZV1UMj3x89jovIA8C26gwP3Nx2vapu5FDKvBEL22jEGnMJMcMicEowdx5SgiUJqN37U+ay993Ri/LvBfOKWzaSh1aOd12hzyLX7xlEfVsizUmlDMryuHZ5farqXWLhmOcAdhCRW9z9kdjaJfRdG2FRAOZxuO8Dt6sXpjkR/qWq/URkmzIleCIIEGVIFqssy+iKyB6Yoc5p7sJ7WPsSTMl4oaMbpdTy+H0dOEPM4+8G4DpVHRngN8sbV6nYilWWOXhKPENNEckbau7YxWxxpIY8nNMuXMdHqtfYELEctreotsZivgkeIkh2zQ8N9/DmiIS22y6vxJtE5NwyBHXhwWOVZQ6+EZHlsIgNP8TCz56HyXZWJze+icggiudkVdU93J/LE3gQbI/wX2yd+jNsTTwgjxirqMG8xppaV1TAOW482BNb296nql0eaiJylKqe4Hi9PIHuTdUoXTATlrJhTkzR+LC73g/bKzelMMM8j5/B5pt7MS/pTwBE5DlVXQ2S6gAS23iL8TrQgUkOOgqzDnRgwkAvJ4zo5f33Qyo1THRiIc+mxSbbv2I5Lp7L4YQsgI9wG8oiS52rgFcxIf3x2CagTiCkFcl+C2AAFvbkGbf5WwIYmEeS+FxB36jqG46f4WIh0qqUZccCP8YWdfdgC7wn8XKOpG5IHFyDbeA2xaytdsFygDWAVoeA8aG0LiTSStaHmHbj8GLrIYOqMliiYJNTZgn+R0xg+6BTsPRz/OThYCIsT3MwCFPsnIWVxW4EFomR5bU3liPrh1gYvAw+B/6cw72bRmWzYgKZOalXqNyPbUTWUdU3HT9+u2uAmDZO/aYjvwEp2pDE1gVY3o/PpCI6llq4xoGYEmp5TAD1sIj8Q1XzSZpj6utAYGlV/UjMq/YaqsPxRbUDVf0MuAy4zCmetgPOFpH5VHU+D28z/zkRWQcLa/YBUCScOR/79iy/3a8wDx8fPsTa3VHAk6qqYjnKiuBrVf0mqwMRmYJw3X7jCUY2wgTi3wGvuGfqIHIMTVXuQUQZOAVSqO9MgXkzhtasUfWLtdmiHHN1ENnHMtgSWFxVvw7cawBVzdrYXFjutIMwT8FMiJCFtNwamAu42p33p1joNIeqDvLOLxeRAwtwo+ZpB9G5gmLnnQzdF9ap6vehttgD3t9TeLhLRH6uqvcU3PchZRyfXT0vXVX9xFe6q2pRu6CkfUH1GjNaCaYFyeudcGx7LB9rHqLLIPtGEZlOy3P0AjwjIktpnLd0zJgfC/54Vrl+zyDh26JpMomUgUbmcRXnuebWXZ9jQmmw9nIpufyWInI2NuddCWQevPMCB4jIz1S1QZicAPOKyIyYZ9klmPL8MF8pICKXa1yo1EMxoww/xGMdhPYPEbAzNudu7F37UFXncfvOIXi5XEXkfOLW7js7Rdg5bm+yPTDI0cwMrf7m0Y1SbHn4Uco1sdx/MYaaG+OF1SwCVb1czFOn5bju93sROQMo9BpT1a29098D0wHficiX1PZndUrsFCVYEzxUwaFYbljFDIHupBZdpFlIiQKyQYoSTMwYcAyWOuF90pQRAA1eY6q6hog8i/Wlp7H2Nhwrjx1V9ascibsCZPtie6Y6Iz7Xpyq9xrAye0ydgYSIXErBeqZAFtUFmSyqSoEtIqvmzqOVYGKhgqd1PJ4rIo+pasbX1njhpkVkiKr+1P0/XFVPKuD7RPHSTYjILJmyKgCLquq/ReQuLFrAB+6ZuWmUE6TAZ9j49gxWFk+KyOZqkaSmDD0g4ShR47WWr3MDDzfU1j8DXlDV2zNFbyxeBzowWYKqdo7O0TnafGALy7HAm4FjbMEzL+Z+pweG5HDGY0qMYzCh4bFYLP5jgWML6I7I0Z0SeDiANwVmLXOwOzYBpij5xufd70hgqux/DmcoZol/NLa4AHizgN672OI+O+rOC54ZjSklR7nzHwB35nCGN1F/w/wyc/8fK8BdG5jO/d8JOBOYv5m6wDwWRmOWSg9jXkjZ0VBnCe0muh5iy8DRm7/oKKD5gvsdBfRy/58L4A3ANg3jMEvjFRLqbLR37YlmysvD3b+JtrMAtul5Pf88FgbwFMxj6gHMavqtCnoxbfw7TPgzHlOGfu6d/7c7deGuX4opS17EBGDnYeEAi3juhXm7XAb8A7itmfoi13fz591pB7lnpvP+F7XdDTCrwUeADSvoZWXr952hOZzfYd5iYzCBycIUzA0O/1SH96or21uBPwXwngGWwRS2/wYW9O692mT7+hLLqfYj6ArtXchrbBkEnpkBE56MBc7oTv1iG859gbmBWbOjgGZlGXi49wLTV7VDD/+v2Ph7KzaXrUZgXsW8ziqvuesPYnNNb3fsBDxUgFs5T3u4gwLHZQW4KePoLcAB2Hw3JTa+h8aEifr+HsTDeGwt8JX7Px74vKKfxYzjw4C+3vn8RK6PgLdL7o3IlUNwjRl4rjcmDPSvzYh56J+P5awSLPfaW5hXT3fLYE3g5ex7ME/hCwpwX8HyxbyGzX2j8cazAh6ix7uSchnu/Y8u29hvS6Q5SZVBLF3cWJ+7F7r2twI6ArzeJA8j/PdhisA7XH0lr3k83PE07huy4+Ec7h7Awd75e9TWjL/J4Q7LnR/h/X8+d28AiWv33PMrYjmgv8tdHwMs4v6vhBkzbFVAY3tMsPs+tm7ph+0nbwVWKitfAmukrK6AWfDWEhSsK9qF6z0zEIuOIM20v5KyfzoBt5IHbLwIHcExBAv9OhBTFF2Nha2fIocznvr9TXb+BWYk1SzuN+69h2GK/l38I4c7G6aAegTbx+2JKWdDZbAcplQegylwfoCF4H8XU976uCNz5+8AvSPqYiFsnfk34DeYsZl//wZXnntjhkDnlNCKGn+oyZ9CxzEV/C6FGT68jpszvHt/xZSEB2LrlTNLePPnmSmAi7F11lS4Mda7P6Lqm0L3q3Adzpjcea/8NXd9WWx/9o7jdRbv3nPe/3w76OfKao2S+hiHyQE+Bv7l/r/r2vTKOdyLsYgY+7vjUUzBdwdwdipe5+gck+PR8TDrQAcmAGhzofUyC6IvROSH2KSX9/xaGlPITAcMVEuKvouWWAZj7vUAn4qFnfoHJtTvAve+RzCviRHYZnBT4EwR6aeq7wfovisiM2MLsAdE5BNsg+LDR5il0A8wAe7rFHu8XEK9675/XvTMl2rWbt86S80PaQwf2Tvv4eeDht3aszL7QCzk0vvuO0JwIbC886g5GFMSXAmsV0K3qC7+gG1CvgSux3IZ/V/BezOIaTcp9ZDntagMvtECa/AS+FREpscWYdeIyIeYgqcOVPUcIi1PPfjKWUm+LiL7YQKAUMi6yvISkfVV9WHgPRFpsJTUsLfBopjnURY+4wCtWXdlz43A+tehIrI2ZtXcR0Tuxer64gC/lW1cVRvCAkZAVF042N9929fYZuZ+POu9DETkR+6btsQ2iNdjm8LPAjRj6mvenJVb3bk6q88m6Gb8roVt0KYH+ro+vDemaMlwNnHf/hlwpHoWjyXwhbNoHikip2Lj6nQ+gqqeBZzlPOf6Y+PoD0XkUKwt5Nv4odjGfLTj8R7Hex4GADdj/fwsrXky/hxre3mIGUOPwPrhhcC1zgOn22WQgZtHDsQEFdcCq6rqvwroxtbvLu7XD6mpBMILE1cGXd/lvukh6j2QQm0RTLjSG/gUU2B+rGHr3jlEZCG1cMuIyIJYHYZgd0yhcJb7pqHuWghi5unsG0pDw+YgZt7JYB/gXMybUoGHMC+7HvX+HsRDVahAH1LG8SMxa+XH3Pm6RTwEoMyKvnRd4/rUb7EQQ3dgQsb9MG/LkZjncAZXYUZgT2Pj3cFYeL8tNBCuzXtvbBmcTU1BgaqOkuI8lRsXXA9B9HiXCJXrdw/OJu7bUmhOamVQBVk7HiEia6jqMwAisjoWPiwPX0k4VPOq1Pp/Kuyc4+XnwCBXX/l+Nq3Ee429ofEh0/ch0msMCz3mvzPzQOiFzW/+veS1u4hM6XjZHuflQqMXdEoEkpTw/nPmvGWm98+1FrVlCUyAH6qH/LqiXbgZRHmNZSAWsjYbBx5V1ZB3EqSFb4zh4XsSvMZU9VWc0kVEtsP20adg+ZMznLq5USz6wb7YmriuflNwMcOqbbFIE99iSqbBGvAucmvTi4CLxPLZ9gdeEpFDVTWfr+sS4r3Gps719f8DlsvGhFxfR0SWxOb0FbEy2qdgbbmURniNOVheRD7PXoGFB/ycXP2WyZwk5zXmrs1PXISb1dRFpxHzVr1ALGxufxr7SJ/sj/vuX4vlcH4Y29P5UNWe69gt+F8Ej4rI/dgYp9g49kgA70LiPMdERGbK9s2q+ojrl4MxRXoI7sP2jfc7Aj/F2tuN1MJ6ZrAIsH7WVkTkQmzM3xDbX6bidaADkx9MbI1d5+gc/wsHZgFXeBQ8czQwM6Yw+Qe22Ty+AHcLbHP3C6ot/ffELNjWxSz3PwT2zuFcDhwYePYA4IqI710P2JycZZO7NxMmzHsA87D7BFsUpZRnA2/u+gWuzPbBlEAjsI2nj/M16d5+mzq+l8EWPsOAzQtwM4vVY4A9/GvN1IXDWxATUj+LLXhWKCmbqHaTWg9VZQCc30S/mA6zvpoCE2gfAMwW+WzQ8tS7vyq2SJ4X8woYjIUYSS4vTBkNEd4GrnyuwywndyLCIjD3fC/Mkr7IiyGmjd+FeYBN2+q6wIT9D0bQewcbk/YHfhCBX1lf5Kw880d32oHDfRaYj3rrw7y14PfA29iG/478UUB3fizXyoyYAOBMnHV0RZksi+Xn+nugjTRYLLbiiGlfHu5C2AZ9NCYsPARYrNkyAGZ33zsWE3LN1Ip20+YyiG6LueeWxJSCbwHvBu5v7NrZo+4YB2xUQCtoeR7BQ+E87e7PiwmSPgT+6cp23gLc6PVKAn8T9f09hQdHe3MsXOfpwKYleElzqutzm2LRBGZP4KfMw6x0XQPcjq0x98bWMw9gQvEVArR8z9He2DplhgreossAeNb9jvCuNXgW5Z6ZEwt31RfPQy+H09SYX0DrltiybebbUmhOamUQQfdLbK32Cja3j8PWw98T9gpYCVsnvIwJC4e4Z5+l0XJ/CcwD+W7MY/xyzFDiOWDJAO1Bjt7rWGixGWj05krxGhuRUA4pXmMXACcEaJxASaQBD6/IayyLQvAhtr7aEc/bP4cbHYGESK8xd+/YkuMYDy+lbNuC22R7Pxkzztid2v7v5ALczLP5v9S8soKezQnvr/Qa83DnwYxGn3TtYWcKvPmxefc4bDw4gfI5LxrX4+MgzLBo5xK8lTBF1UgsCsdSAZyRufNCr7GCPl7U12/Cxq3fYoZVZV6MTXutJtRzmddYSqShUBSMY7C95eu561cDGwfw9yQXWQUbh+9w7Sr7H9zHYVE8VgRWxsb6FamW322FGbCdRbHna74tBD3HsL18SHbRF7ikgPYLRdcC730Nb6+FyXledf9HpOJ1js4xOR5ZKJ0OdKADbQQReSRwuavzac4KUHLxw0VkKmBqDXtmZM9Miy1EV1fVoIWso/sLVb2xgt9XVXWJgnuvqeriAbovquoyFXSnVM/LRmq5gvoDdbmCKui8rap9c9cEE2S9484XAGZU1RdzeCNUdcWY9zj83ph30FmR+I9h1j27YRv6j7AFyrI5vKi68PCXxiyVdsYSazc810y7cXil9RBTBiKyGdYG3nLnx2CCw7eAAeo8W3I071fVn5R/ed0zIcvT61T1tgDtk1X14AYi9XhNlVcFze+wTdDdWBiEOlDP80QsefwMqnpzjsaOmIXvA7nrsW18C6yMfoIJV64D7lHVbwp4TqoLEbkD2ziWjUfza6THYWx9pUIqXRF5VlVX98cIERmlqst7OOuV0VDVx/xzx8MVqrpT+heU8noNcLiqvt3Es3ep6qaB67HtaxFMCernE1gO82hYT3MejrFlICL/wcbLQZhwpg40l5Mzpn7FeYdKwDPU0bwlhx9VBs2CiGyKhbJcFxP6Po2FkLwsgDsVJmAC25gG86SJ5XAZiQkb79OChX3sPO3hP4BZP2dW0jthltAbBuhGj6MiMgewF+YV0hXpQlV3z+FN1Pf3IB5OxhTDmedVf0zAfVgOr5k5dR5MseHz8Li7l+UUbHgMmEYDOQVj1jUiMlprFu69sbBBfVW1oc+LyHBVXanoPICfOpfdjClyzscEVQdglu7bB3A3x7zFf4gJ9ecHXlHVpQM8RI/5IvICNuZdq8X5UZpZM1Z+WxM0J6kyiHjvGCzcfBCK1jBiOSjnwfrCuxrIUywij2MC9OkxRcWh1PIAH6iqG+TwewErYIZ7n4rIbMA82dzj9gBXx+5fRORoVf1jJO4bqrpI4HovzFNtIe/adJg3+6pYCEEc388De2ogCkbM2t3tk68DbtZwpA+f3rElt1VVj/dw38X6QQa/98/z64qSd66qqs+7/9H7yHbh5p6L8hoTyzO9gqp+7857Y8Lu5VLf2SwPHv52WEi3U1T1tNy9xzCF8Y1YhIS69pC1DxGZHVOqbYetf84rmXOjcb1nVsLm2w0xI9EzNJe/UUQGYn36FSyKxn0a9uxCRF6l3jvqGkwhEvQaK+GryxvWnY+jNldnv9k7NNd/vwP+4+FMg0VKKPVM9J6fDosasoOqbuJdn58IrzERuR1TOt2By28oImN9Hj3cq7Ex777c9T2BC1U1mMOrClL2cRKW33mojV68riwWVdUHxWRzvfPrGxEZBazrt0G3jxqMKTnrvHVzz84CfFqy1h+CKcavd5e2w9rwxpgBhL+m2gMzUHwUawPrAidiY/Fx2f4qFq8DHZgcoaMw60AHJgCIyGrAO1pLAroLpkwYh000DZsDEXlaVQuT6DqcyzUuAbT/zONaoFDzcAoX7UX3YgS4YqFxbscm2Ef8yT5RwP6OBpRrIjJMVVeueDZ5QyIij2h8EvG5sAXw86r6hIj0BX6sqlcGcEvrQiw82/aYB+E72OLnLm0M3eA/U9luKvhfVFVfD1wvLQO3EVtDLSzoptiGtD+2MN5WVTcKPFOpeHF4Gzpam2DWuddj+V4KE9mLyMPABkULSg8vpp/9vuy+v+F2fbsM9woP9xlgM1X9KPe+ubBwCg18xbRxD3cazDNheyyfyT2YkOKBAG5UXTjcGzEB3APUNl55ZeAdZTRUdfMczcr6SqUZS9fDjREwzqiqnxc83zc0/omF59hMCxSWDqdIOA22KZsph/8wJrB6jvo6aCiDwLvKxveYMfQuzAI9r0hbFcudGVLGxZTBcZSXwfH5i1X1KyIDVfVYERlUQDOkoIgpgxtV9ZciMjrEc5EASkT+jIWKe0LDoY0zvGkxwd78qrqXWIjXxUNCKBERTDm+O5YT7Qbgcg2Hu4pWtIrISFVdoeqaux4974jIUOAJTADVZVSgqoN70vt7EA/RQs7EcfwUTJDyEuZJ4FioHkMq6Fata6KVYM0I9xLLYHbgHKz/CObhM0ADIWCdgGt9zMN6RRHpB/RX1YYwljHjnYe7CGZgtR2QKY6GhMa0mPV76rcl0pykyiDivbeoatCYouQZwcbZebCx/30s54zm8LrmWckppMrafMl7h2Nym1jlS9c7qsYmEbkA+LeqHpW7fgLmebpP4JmFsLQAAC+rhRLL42Rr900xL7zCtbuIFIUXAwrD5Ye+pUux5c6jlWsBWkth6+f+wGequoq7vitmRDGLqn7srvUBdsXCji/p0WgLrvdMlEGFw30R249mCqdZMeVW0XolVhEXa9QxD1aeW2HewjcSSDUgYQUQ1Mb9hRxetJFVIm6KEux7zFstCzHpK6zUL1tpQvlS8M4Go+F2gmuDP8dkGxtjSp1bVPVOd38o5nF0PRZq9XWxMKnBkNQiMhMmA+uPhfqbGYugUBYesorHsjlBVfWJCBrzYeFaT6vCdfh1RuDu2l5YaOtZVXVht3a/SBsNJHbAjCOeyV3vCxytqnu582OAG1X1VTFDrPuw/JbfYkrLBwN8zY55xa6DtcMnMYP6zzDjpDdy+HNjc5pgc1lwbxKL14EOTG7QyWHWgQ5MGLgI27Rmk/pJWKiyFbBEmr8IPBMTP7wZq7AHROQgTKjmC1r9zchMErbIFyy8SgjmxuJ2lwlwl8S+9WjgSiegvk5Vn9W0/FdF5fFMfrMUgHOcwCl6QwIMFYufnS+zkDXYeCyJ7ncishjmIXBdAS9VdfEGFi7mdiwkRl9gX3FpDTRsGVnZbkTkSVVdx/2/SlV39m7fgIUayENVGaiqfuH+bw1cqqrDgGEisi9h+AoYLWbFH1S8ODgCs/I/KHbTjIV8uV1EbsrRviWHF9PPonPIqKcQy4OY1ZkP02pOWeZo/EPMii8EMW08o/MlVl83iFmuXYGFqArlOIutCzDvubsrXr8mpuS9DhOUVMV+j6mvVJqxdDPYBxMwzoOF+BmCl7/MwaO4/iEiD+U2QbcR7jvjgKecINfnwbdynsHRPB4L53aV+74dCbe/wpwBPojIiap6RO7yiJJHYtrXAhrwuFLV5wNtPINxVJfBcUUvlEAeBAel9auqx7rf3YpoByCmDAa43wblYBmo6m8jUQdhypRMyPkuFnanQWDlxq0HsPmkHxaaZl8n3D5MVZ/20GPm6Qw+FpGdqM1f/bG8XCFIyXcyraoeWoHTE97fU3gAEyhlc99MJXgp4/iWmBI26LnYDaha10TlRXHPNJOPM6UMRFV3jKT7X1X9l4j0EpFearlETinAHUfFeOddewM4UkSOxsaTy4DvReQybC3pr3li1u+p35ZCc5IqA6nwXNN0ZdlPsZCEr2P5MsHCti4iIvuq6hAP3W+7+W/uQzoIEFq3lOFnMHUF7sHAX0XkDQJeY6EH1PJrjgVwa/wLAmhHYGNnzNp9GLa3C63rlOI8og2KLWAVj8/CtVJoXSFxnjJfYePxf8Q8vI/D1mzPY2s2JgBuBj+n3qDiCmxd1KAww2QPI5ziJvMSOTxENKAEGyAi64QUcTE8SL3X2K7U5rM+IjKr3z40Pu/7adTkAfl1cn4OTsE9Gmvby7vjRLfvblCCUZyntAG03Oh0jVg65PqIU7KUvbfUQEqKvcYyhfdGWCjIq7DUDfn1dFJedDVjlsuAy6QW4eZsEamLcJOoBAt5OClWf/MS3vdmyqVt3XfOQ2M+uzy+AP0w5eFm2Df78FtMqfSsY/J194155q8N0J4FM67fy7u8HZB5CmcGuXMAi2F7+gaFmZNt7V/wCW8ErvXC6nAKbC5bRF2kgSbxOtCByQo6HmYd6MAEAPHCeolZmH+UCQel2Fp5PJaL4VtsEd0gTJBG9/46CCl0ROTNMGqdy37IGt9HbhA+SoGLu+ZClHn4P8QWKdtj+RCuV9UjvfvNhAN6GVtEvIVtpEMWXtsDf3H38xuSPxaUWTCkpoZd8YdhIbdmwZK5vgB8ERJcVNWFlHtcBDeCke1mhNYsX/PW3l33cnRLy0DMcnEtzPr7TWAbVX3B3XtZVZcK0Ax6Y+WVTm4x/191lsoisji2QXurQPFR1IZVG8N+VZZXKojImtjC+3FV/dApqw4DfpTbDPwNi3P/be75KTGL3UUDtCvbuIf7A+CXWB+bGxO4X6eqIwO4sXWxJWYROFpdQuGCMuiNhYDojyn273bvfqkAv7K+UmnG0vVw11YvzGDoWq7v1PWVkr4TtHAu6L/PqurqRddEZGpMsbcIljvs0nz7yT2bZMkeOYYGwzeV3UspA++ZoHV3Dqe0fiXBO9SjGd3HUsEJRc7DjEf6YJv4/+THGxF5QVVXkZLwoB7ubFiowJ2xPFuXYuFuVgBuUs/KN2WedkKY8zGlnWK5JwZowLglZRwV81oYqqr3hHjpKe/vQTz0x0K61Qk5VfX6AG7UOO5w78W8vxtCqHUHYtaYCbTWV9WH3f8F1QvtLCJbh+b/xDJ4HVuv3AAMVtVPS3h5EBMqnoTlfvsQWFVV1wrgJo13bo2wG7auuR8TUq+Decqt4OFFl23styXSnNTKINpzLQZE5BXgZ9oYZmxBLOy17y20N3BNvn85nvZT1QMT3z0cQOO9xkYBP8aEnA+7/117RQ0rGGO8xkJz6hFYeK68p05LvMZCIJEh4ALPFa4rJNJTRiyU55aq+oZY2L6nMc+UBmF7u3C9Z1K9xubGFGGC5TlsCCfq0Y31bK7kQSK9xhxu6ZpVI0IXSqRhYQhXig2/sve/5eGejwstGPOuEh6ivcbyuFKLdODLghRTrMypAcMTqfAaczjfY57wu2ZzrxSHT2zKa0xEplPncSq5SEMicmfgkS4lWOi7vGfXwXIszwL8KfddM2Bejjtga/1bge1Udd4Seqs7/K2w3HC/xfKdfZLDqwvtLyJTYDnJlsvhRXmO5fYAg7H56y/uPLi/EzPWPojGkN8hmVVUpIFYvA50YLIE7QGJ1DpH55jcD2AMLrEtlkB0Xf9ekzSXJiEBdALdDdtUBk8Hrk0P/ArLv/LPFrxj/tDh3Z/F1cUi7nwl4GsKkrImvHcX7/9w97s/lmsMcklWJ2ZduHbjJ5RtSQJgLKfBG1gy5/u86ysCDzVJc7D7fZxacuBFMMvE87AY3Sc1SfvwhPLK6vE84Nz8kcM/DQvjcR2mhD0WE2IPwHLa+LgnYwKc6bxr02EC71O60cZ/7caF9xzPa7eg3QzGLIgfw4Rlz2FhI2KenQqzKP0I2L8V9dUKmnm6obZf1j9a2HfO8/4PxSyJe2OCrh0xwXp2/wbMg2hvzKPtnArao1ybmDV0NNm+rgP2Cjy7B3BDd8rAve8wx/cwLL/RAt2o3wdcH7wWM5A4wx1/A/5a8ExMGYzHvH6DRwk/L2Bj2AhXx7thm/k83lAs/Fw2nyyMhUAJ0fwbZhE9b+DeoYnl1TBPt+IAlvb+j8c23F+58hpfVmaTw/u7ywNm8LA5Fp55rm7wMNj/j83Zf6FgPmtTORzs/V8wd2/r3Hk7xtvBufPVMA+gsZgH504Fz03n+uwUmKX3AcBsTfLgj/nDsLXMDsBUObxbEulumDuP+rZYmpNiGbhrvVz/eQ/zUh9IYP6LoP06bi+Xu94Hy/XVlj7j3jEcU1hk5yMq8Me5en8zcIyNeN++BdfHY+uQY7C59VgsvN6xWEhmH/fNFB5cu9oM8xY5GAvDHirvoZjg9mhqe4M3S74lal2BRfN4GzOSWMtdC/GZH4teLau3duB6OP0x457LMY+TNzElWwh3bdx+AzOyORNvbZPDfdHvI9ia8cXu8hDZ1r/HDMIeplHGUSjbAJYCjnf99IWKdxTi+vUfwesATLE5DjgFUzI2883v5M7vxAyf8sedmJFVGa0FgAvdt+2fu7ch5t31HraX2AwYV0BnRfdNf8fW0ntghqpV3zInJgMZmv8uD2ct4GXgbXe+PHBBBd11gHsxY+TNCnA2wCKBPEKBDAULn/kYZtycOY8Ex0TgT64cH8K8bWejfKw5FTMgeNWV9a2E1/gvee/+teO3N2ZM95yH9wywDKb8/DfemomC8QEb536Dzf8rZ0cB7mvk5tzu4HWOzjE5Hh0Psw50YAKAiByJWfJ8jIXVW0lV1VkaXqGqazdBczjEx7NPpHt1GY5GJknO0R2hZm0zNbZA648t3u/DrPmGqOp3ZTS6C3kLTXftVVVdort0tWb1OQIL43YWsIeqviRekvtEuh9SS9raABoOMVTJK2b59QdMiHAaZokEZp12qqou3CTdzbCF8iitWSXODUypETlzAjSzNtNVfiLyR2wT91tnITesybKN8rxx33Wsqt4ZY7nuvFNWUtWvxMIrvA8sp+G8cFMAJ2CL8Myqri+mMDtac7HRY78LWyxfh+UZ+b7ikVi6I4ApgeXVwo1Oi+VhKsz15KzmNsH6+gLYZu8yVX2v6JkSWsNVdaVW0szoYpaCawEHYv02gxkxZfryHn6WQF6A31ELtSTAgRrIrRjDgzd+LICFhVwbs6R8ytEd5+77fWEKbGNV2I5F5GtsYxwMc6SJXh+uvH6GbQK/wYRPYCGQ+mDlFbRYjqD7FQl5EGLpunYzBPN6He+uz4B5X23cLE33PxhCU1VPLXg28xx7UZ3FqYgM1ZyHhlgonKMwoc4QrD3sqqqPBmiKtmgx7/r5A5jw4KLcvd9hypqUUILZs9GejiJy6sR8fw/iYSOsLd2cu74j8KEGclFG0ByhNWvlyvmsHSAiX6jqtO5/aT4zacKjN+L9wefEQjOdCeyo5ZbrM1JvtZ3sJZMbQxZSC2/XbSixOI/6tliak1IZSKTnWiTtwzGv/esxxRvAfJjH0o2qelLgmYWwOX1NTBHwNBb+Pel7xXLeTkOi11gk7RSvsb5YW/o7MFAtb3HQ6ySRhx9iQuMPMIMSwQT2cwH91MuXIyK3u3t34Lx7SjxfWp5fyVsHZvB7/zxXXm3BzfGc4jW2PBah4UpMcbK1qq4XwI32bI7hQRK8xtw8uw0WXvN6AnnOPNz5ifQ0jMUVkQHUInPcQEFkjgDt7d0xNbYHu14DuWQLns97jTXUiQ8ajgqwKOZVtTpmGHZFfh8pCV5juefWxsptG8zI+VZVvbgAt9BrzMN5FkvRcYc3x49R1WUCuBtgynEFTgytfURkE/ftnwEnaC5aSA73d1g9TYcZ090APFAwfnyEKYvOxuWQLysvERFsT/9TrC/cjxnnaQ7PX9sUeo6JebZdgSnMzlbVP7rrP8fmsP4BHlJynUdFGojF60AHJkfo5DDrQAcmAKjqn0TkIWzx5YcD6UVxnOEqCIZhbAEI9fG998YskLsLKiLXYrncHscWKTuo6lctoB0LAsyR2xxO758XbUgi6GZwIBYT/lY1ZdlC2IajGfiSmkB6IGbB2V0QzLIqc6N/DFN0ZdBsPOpZndLiPbewfgpAVT8Qkf0wi71U0NwvWML50xztb9zivxmI7T+iLpRDJkh0wiLNhO85+DJr06r6iYi8pgFlmbv/LXCYWILpLIzdG2q5x5oFwTz6hjhe8yEF91PVZuviG3VKbSckKSxDsRwGy2DWgANVdUwT78yRbDlNsPLqg3m7TkH92Pc5jfklL/FwLsnh/7W7zLiN+xYlKF2bX1X9tqQKMni5GaFyCYiq/hNYSyxfVra5vVtd6LRuQFIehEjICqgvpuDL4BtM4dodmmCCND+E5oVOEBBUmAFfiCn6RzqlzAfYpr1GXKQX5sW2NbCGe98AdTk3A7CoWE6fBagIvxIBiuUQahBaYELfF4FkZRFemblxY0fMUvaPYonW5/YEkhP7/T2Fh4HUz80ZPIQprJMVZnj9SVWvEJFpsCTwrzVBqxWQH8Dy51rwP3QeC13Publ8K0x4tjBWrquFHhILsXc8ti773vGqlORWimJGdawT9i2Nl2tKVY9vgpzfxqK/LYHmpFYGw4BPMSOkw7SWr+9Zt05N5fMkp6zZHFOACZZfckdVfbngsWuBP2N1AVYf12FC7RrTxs9IVf2PWN7ElTAP8rfcu9cQC2s3zPtGPzRdXT2kKCiwseYenNeDu9abQP5UNeO3X4jIFlg+ubPyOLnvmgIzsskME18G7tfGUNInAheq6tm55w/Aohp0KfhVdQtPsTVQzPh0ZhFZTRtDwEWvK0RkSo3Lr5Rf+/nnedrtws14ztrNHa7dHCIiXe0mB9+qqrq6O1dVL5Viw4nrRORRakqwQ0sUcTE8vIC1r4+yx/zXYXu77N1nAWeJhTrtDzwkIm9hypKR3nt9ZegvPGXouACP0biqeg6W7zxTgg0SM/YtVIK5bz0FOEVEVsTa0LF4ubPEQgyG6lEwzyWfXoNCrAhEZBlMWbQ0tvbcQ4sNkFd23/SgiIzFyqPSiMLtI59y/fEnjkadwkxE1sL2QdMDfUVkeUyGFMxjrqrv5PYwdTznlGBHlinBMM+7d7Ecs4fm90bqhQ702tdCWPu6DfihiByKyW78+p0LU371x8aBR7C8q1PkxzC3dn/RKf0uKeEV4GtXb/8E+lEzXAaY1uP1WWpjp/8992BjdgjuFMsreSsWRSl7JmRQ8QW2H3koh5s3yI7F60AHJjvoeJh1oAOTKIhZ5N+aWZu0kq5G5LRqhi4mbLqlQNHQdnA83F6Cos1s0PNl5q51WVg1Czkro1bWw8+c0LtlIAlW4wk0M++QqzEvjvewsCoLOoXNzMBjGsjrE0s7BU9EVsFCKM6AbXA+BXZX1WEe/qfUKx3X9c+1It63iFysqr+O/5JGft17Mp5bVhfYoj1LGCyYEO4N91+1Pl/B91j+JwjnK5ixifev0EqaGV2vrOYvEDK0Fdy3PUWJADjblIjId9TKQDCL8y8oKINWjRs+r820n1i60mQehAi6R2KeAbe6W1ti4SMbPAJiabr/QzFh6PVY/fUHfquBnD4Of35sg9wH81CcCQtD80YO73FVXTeSn1HARZggtUvg4I9LseDa4lSqunTB/ZeK7lXR9crsQkzYvr6qLinmiTtEVVeteseEeH8P4qHLCzFAq/BeAg+bAacDfVR1QRFZATi+ao7qLqSsFbz5VLDwSdlcKsA6qjpLE+/3y+BNTFh2o6o+XfHc68CaWqy4bpaHizABWT9M2PgLzHN4j27Sjf62BJqTWhm0zHOtWZBwXtJnVHWN3DXf++cqTMkX9P6JfG/IUM9XmK/v4TblNSYWaWAgsHpovpI0r7HCaB9ihmeLe+dTquc5IzXFVn/AV2xl96PWFWJRPW7HFCOPqCcki10fisiBmlP6tRNX0rzGHsOiuuyG7U0+whRdDVE6pEKBm8qDJHiN5WgvjSlndsbC49/o3UvxNIzGLeAjU4Itp+GcYFNiucC2x8ICPoZ5pt3m4TTrNXYSFm3ANybwlePfYR6vd5NTOjncoFJDKrzGJNHLXdK8xm7GxpzzMcOwAzBvv+09nO8xJdgoAvsjf63STNnm+FkWCwn8Sy2IsiOmNN3U4a2NGafukMO5BvPELI2qI014jnnP3qWqm5bcfzNwWQv6RVSkgVi8DnRgcoSOh1kHOjBpw1ZANsmWJoDuBrRKqy5YkvC9pMArQpvz7koCLUg4DrYhaZKsb9G6JrbRrbOwUtWghVUCtNK6YZRYkuDrsHwen7WQNlBpNZ5KZy8sTvwCwE9V9Qt3fSlM4Ncd2ilwGZbX4QkAsaTCg7BNYgZ576AzEt+xSjVKJUjB/9B5Cs0lK7EcqGqvJt9T+P420IT68vhCRE6j0crdFywtDSysqne487MwpQfA+RqRjLyAhxfc/7Wxdn2DO9+WmpcpoY16BZzT8DITzn/qC4N6AAiANlp3b0+jdXczdP8kFlLkR9hYupuqjmgB3ztgZZyV85PuWiMjIr2xXAY7YeEnC+cizHL/IKwddBleaNhC9FtVvbAJ3oNsYv1gUc15xjrBTXe8XzNY3SkxR0CXJ24f7/7Efn9P4WFqCVsxT4kpypsBf7w7DvM4etTxMFLMor/d0EdE7nC8LOT+Z7zl3+/Pp9lcr7nzVPDLYKGycVBEzlPVLArE3zHjhFaAz8NaqrqcU4IOFJEzgFta8I6Ub4uFSaoMtLWea5ni5XDM4GIOdzlTspysqp8GHntERA6jZlSxHXC3iMzqeMnGdN/75xwNeP9ImtfYoVj+oA/cs7tggvFxWN/3n4v2GhORy1V1V/fcF1iusSKI9hqjfFzNt7n3nAIkU2x9iOXrPU/MKMV/V6zXGNga9xdY+LcrnVD/OlV9NkZZ5uD3WPi2CYUb7TWGffcOmAfSP5yi9LQC3AuB5d0e9mCs/K4EQoqJSh400msMTNGNrf22wBRB12Nrp69yNKM9DVNwPT5CSrCBOZwN3fdsguV2vh74tQYMZquUNgUwCPNUOwszKNiNxn3c7k3QjfEaS/Zy1wqvMQ/2wdbN82BKsSE0eqL1q/6KLhihqp+Hbrh2XgqqOhob2w8vwfkKuBm4WSys+9YBtLmBl0TkOerX7nWGSNqc51gG85Td1IQw9rEKr45irAP/y9BRmHWgA5MufANM5Z1PXYSYCONSkEXkfJy1VgXqzthCNYNWhXpMgSplQcrmxQc/TMDZwEaYFRuqOkpEQpaXvYA1KsptXBOJ+4IrAADTJElEQVS8VME32GIrWxyfJCJPYxvPO7T5cIC+pWBy6CQRmdNten04FMDxdHL+GVd2Ve2uCG6KxPPDuI3PlGXu/U+KSJ23ZJMbIh/yZZAKWYikDJoOYyUiN6jqdu700ExgICKnaC6Hj4icQnNhyrLnZ9dyq/XY+koFn+41mIJiU2wztwu18DEZnIwJejLYCBOuTAscgwnSUuEcrYX73BWzvP6vO78I20g2gFPA/ID6MHx5q8a+IrKEqr4qlv/tPswS+FsR2UFVH0zktSmFq1Rb+tcp9kRkDswi8lzg3LwQLAH8+v0O8+pR99ssdJWBVofQ7AK13H9ziEgfVf2mAj0TgPzWJ0E49FlK+JUq2BnLyXOviJxAfY66w7GQw82A/73/dW1Xoauu/fo4ZiK/v6fwcAtwiVgY3SwfyHTAuUQoE5xifD5VfdG77I/R36rqZznhVtNKdBFZQ1WfiUAdSs2QpEoJNjMwr6r+2b3jOUxRoVTMN07YuQzwXm5t0fVchNGAH7bvcGComBV9d0MS+eNdtub6Qswj5180Kg5jYZzHV8q3RdFkEisDKfBca5IuwI243GHqwtOJyFzArthcs2HgmWwNtXfu+u7Uj+njxXKk7QSs68aHKXPPhAyw/Hr2w/BehK3zcXuQk7AUACtgQvF8uGlU9XYReQATlL8beBfUG4hVwRqZci33nnNFJB8GdiYRCQmgBcsn60OKYitauaaq/8L2pX9x7XBbTLE2JxaK78iIb05ZI7UCN6bddOFi683vRGQxTGB/XQFuiiIumgdVfdPVxzTYemMxzLvJhzew0Me3Y6HR+wL7ZnOVesa1CcrQaNwUJRiW6+9a4KDYNZdEeI15MI2qPiQi4tr2cSLyBF6KhjJlRkCB3OA1pqrfuzVIvi1Mq6r5PRBO2Tpd/jrwjlhYRhUzADoAeKWAtcVVdcccb13pHBykKMEexbwgEZGHVHUD795t2T13fzzFax1V1ZnyF52y9TjM6A5MeRoyvCgzhCsFCXiOiciJqnpEDjVo7Cci66vqwwXjKKp6i4d7o6r+Usx4OuS9t1wKXgc6MDlDJyRjBzrQw0DSEuOOIjIBdNEE6uH6E6k/MS5CfRg21frQawNITI7rnhuhLQoVJs5SswiychCRWcsWtCLyTm7R/PsiXEe3wSNOXPgVqU/oOkoDYQOlwiswt6iblpqVZUP4tZR2k3tHHyy/wPaYUOEhfxEbWwYi8gW18HxZqL6M14VUtWtxHagvwYSSK2LzUrCOYjYZInJuBb+lgh0RWRzb+OzlXcvKdmesHq6jZin8SWgTnbghqgSxUBD7YP1xNHCpNuaAyMr2XSLrouKddUmoveuhEKTNhgjbDNvAfospNH4ZoXyPoXsqMFZVL8pd/x0wV17h5+4NU9WV/W8Rkce0PqzMC6q6infeFVpJRJ5U1XW8e72x5M/zAvdpfS65o1T1hAAPr2Ehr7IxaxbgGfXCEbnr+2Mb5n9SE7Rrvg5E5CVgGSf4+DUmBPgJJqS4QlWTctpUjaElzz2OKeqfx8KqPaFm0enjiPum/bA5TbB2cZ7mPAJE5JiS16nmQhW7eWovYLCjuxVwsaqeF+A1NJ+M95SYs3r1My8mfFsbGxOexPKNBYWNIvIXbON+B/XWp017V0ta+JWtsTwbc2Ll0DCXOLxlMKvyLJTOGOD0fJ15+Cnhm3bExs6VsJA0vwCOUtWbPJyJ+v6ewINY7p8TsDEko9EX82A/Wr2QZN4zj2L5labAhJAfYWGLG+ZxEbkUsxQ/DDNmOgCYUlX3CX1fFYTmhQK8LShRguXK4Clge1V9x52PxCz9pwMG+UIxMeXIeWq5Y2cCnsbmlFmxOb1IMBz1TY7PJ7H5t0u5GRJWOmH0wcD8VOQVFJGjsTFkAyy8qwKXqGrDGCcWAu8PWN65vdw6Y3FVvavZb0uhOamVQTaXe7/TY2Hhf1peOmGQXGjA2HuRtOfCvH+eV9UnnED4x6p6pYezGiVeY7k9X9e+Q0T+DHykqse585GquoKH2+U1FsHnq9g6IqjAye1RC/d5+XsiMqjsvaq6WwGdTLG1PTav1Sm2RGQ2bHzdHlgU8xC5Ts3DoxRce9kaM6acW1V/EPFMcN3cLtyYduPhDsME/rMAz2CRDb7QnOLC4T5GfPjGmLYb8hq7SwN5zEXkOEqMN7QkUoxHo8FDPBZXLJzptVgEltL1rpji6L/qjKDcHvLnwFvqyVVyzzxJzWtsM5zXmKoeG8B9CquzmzE5z3uYN2t+T7AmtsZ+XFU/FJHlsLn9R1ov13gG2ExzijBXh7eqJ5MQkb8BS+X3mmLGKC+r6qK567NjhhA/wcaHIcABoTIs2EcWhmiWnBIsgNs1ngTGluA4JCLHY+kernL87ogpExtyEIvIYGwNmM11OwPLq+rW7n7UHr0MQnzGrqsc7kBVPbZgLFVV3d3DnVstx3zQGFFrBrJReB3owOQMHYVZBzrQw0BqceenxiyaR2ET+XLAs1ovkB1HLfF2HuoEZt4EOiewFrbwAlOSPJpN+g631Jq/QAA0P7YY3t7xXpgc1+G3LBeOExgqVg59gU/c/5mBtzXSPT2/IRGRbPG6OJb4OAsdtBm2KN0zQKMyLreHOxCzortFA4Ox5OL0V/Ae3W4Czy6KbYB3Av6TW2hGlYGI3I2FX3mPsBVSV5sRi0ueb0PzYoqeunab47NykyEi32CL2huB98n1Da158iyHWbP/ELM+Ow+4AEvEfoZa+JCM5iMUgxYIgGJ4vSP/XI6wH5/9BuC/wBOYgvMtVR0Qeq6Z/ltAJ98ffoOFzPCVcGA53Z5SCzWXBGK5D36p5gG1OnCqNpmzI0f3ZUxR9H3uei9qSZnzzzyjqmuIyP2YF8f7wM3qxbOvEJj9TVUX887/iilYn8M2V12C66LxT0R2w6wYsza3HiYIuyKH9wYW1u1fFeXgbyIHY3mS/pLnQSx+/yXYhvteTHD9ibv3nCYq1gp46YONIT/GrO2nV9VZvfu/wwQNv1bVN921hbDQQPfl+uQfAq+YFlMuzKaq0+fe/SKmiPQ9dZ7WgJLXzavzUT+PfIB5f+6l9TkLH8AEK1e5SzsBO6pqyNPAH0/rICQAErPUXYB6YXODECwFXLvZTFWLrH593G21UYnUcM1dT8q/IyJLYMJxwYw0GviZ2O/vQTxMi425AG+o6pciMpWqfh3AHaGqK4rInph32bFSYMzg6B6JJbQX4H7gjyEBZgzErukkTQn2vNbndTtfVfdz/+vyQImXW04svPaPVXVLJwi8NyQwS/kmERmqBbkJA8+Noom8gmJewFNrQZhstw4YBvxKVZcRkWmwcWyFGL5ytDKFWTTNSa0MpGbA9gym+PgXMEZzgt5YEJEhwIOYsck/3bUfYB5mG6rqTzzcGYEfqBPEi8i21EKp3q9N5BEWyzH5E1X9t5jX2PXUvMaWVNVfeLhjgBVU9VsxJdevVfXx7J6/BkoUyo7HDF+K9p1+COuxwEEhMthaL5grKBUkUrElFco1hzM1tmbvjxnC3IeV8xBV/c7hFHmoCOYR1DVntwvXe2Y64Cut9xq7N7R39Pr8/o7eqZJTnnq4KYq4Sh7cvs/3Gqv7Tm3CaEg8IzURuUpVd85/a5O40UowMWOwPVT1dbEwj89h0SqWwvIwNoT4k5px3mh1CkgReUJVfxTAXRXz0poZS8ExI9Z3nvVwTsMiY4zEFDZ3YXu1E4G/+HN60XogdE9ETsaiWIS83D/Wxkgja6tnGBi6JqbYWwvz1PdDv84IbKWecbEkKMFyc3VU7m4J55dsuOauN/QT/5ok7NEdfoPnmIhcpp5Sy13LDOOLDBSaiSTRgQ50IAE6IRk70IEeBqraD0BEshAAo935MuQ2Hqq6QALd3RyduzCLocxCcW7MotPHjRWod3lIuWdOAU6RWnLcY4HUnDvJoE4hJmZhfIdaDGhE5Ge4kCQez6Ubkhzdge6ZIcBKqjrenR9HcYi4UFzu3xbg/h4TEn0nIl86HlRr1v7P4oURKIOUduOu96WWJHs6bEO4RV5ol1AGQzAFVIyn4SFYvRzs8fmmVis2K0NTuPdv677tW8fLYHXCfw8uwQTxT2Mx6odjgu8d8wLDrGwTIYbXNTFLy+uwui4Lz7KUt7G6lJKwQkX9V8zjaXs8ZaUUeyYKjSFVrsWUKSdhlosZjO/Gov1bVX3V8f2sWFz4VoBqTlnmLn4vUpBEEU4Q80z4A6ZAnRH4XQ7nfRFZXXPWySKyBqZg82E1rXmqnQ9cICK3UG6dPUgs11a2YTtMXeinHLyDJU+vgq/dGPBPoB/1Y8G03v8LMUXdM5jS6UkR2VxV/05xeJ9oEMv39yN3zIxt6J/Iof0KEzp2heZUy0GzEza+nOVd7wpN5drMACzE1fWEw1YJ9bkUvqO4v92HWdre7+j/FBsjbqSmVM9gDlX1rTkvl5JcmOopxkRkroK6RUSuwpQkIz2+FcshEsJfhkZv1hDuP/NjfAkcTuM8F7oGcfl3fM+9D/HC/0jYc3Fiv7+n8PBkQNjzNOG1wRRuTfdLTBlWCGr5h46swksAPx9Z6H2ZAUgfdcoyB0+qKf7/JY1hnmbJ0djPO50jh+uHvNwQV0dq4aNi+A+B/+AjYl66d1Id+jQ6r6BbFzyOjYdPaXlO2YVVdTsR6e/e/WXJfFb56iZoTmplcJeIzIzlaRqO81yL4akAtsPWPo+JhXQDm1vvwNadPpyOhR/NPFdOwtZP02AC432gdE8CgNZ7//b2yno7zEt6MDBYTPHsw3WOz4+xkJdZ7t1FaFw7TOv2bZVeY5jSvsFIrAAeI5wDCay+u0ASI3pIWLF1OAUhrB2N9936+RNs/7Un3vgnItdie5PHsfXuDvn9gKMTvU5tF64HjwM/EotG8BDmNbYd5i2TB3EKix2BPdy1oj16SvjGGB6Op9bOp6cEJD5SiD9fLJ0nkztPwb0XK59MCfY0pgTbVERWzSnBZtGad9ou2N53fzEDsWGEc2J9JWa897qI7IcZms4ZwANYQFWfx9IeZHKcbbE9YwabACuq6leuDt4HltOwh11KbtSjMC/3t8TyzYHn5R6gfR6N65L8tT5Y/U+BGVtm8DmNYWJT0gvM6cYQ8f7jzvNrhQy+E/P2z/JL9qc459qXIrKOqj4JpgikPu9i9B7dwcZYOM/aB+WUZQ6WwNpR0EABL0x7yhgaO+8kzk8d6MBkCR2FWQc60HNhCfVC/6jqGBFZwUcoEXZnz4TC8C2QKcsc/BMLz9UMdAnnJC45bl2oRzGLbKgpirobC3lV9cIJqeq9IlIXmqvJDUlf6oUx32CW/w3ghL2hjUoIt4qXZgQhMe1mKKbQuxlTrr0QQbe0DFT1HOAcqXkaDnIb2gZPQ1U93Sn2zhKRdzAlUoy7c+UmwwneLgIuEpF5sAXwSyJyqKpe5aFOpaqXu/+vichBmHKiaLEMgMQnkI/ZEM2FCfb6Y5acd2ObrZcC9LosNdUshst4nBFT0s6DCXIewMLcHYQJ4K/x0EPKhQxe9U+cEOszEcmHM5xeRKbXxvxZMTBnbpFfd54XkiTAFxIIySLmTRnM06e1kE6fQWGy6UOBG0TkckwAB7AytlHeLofbx6P9LfBrsVCCD1MuMPga82iaGlhMRBZTZxnuwVjgUTHPTl9wmS+vA7F+PgdwltY8t35OfRz86VX1Pvf/dLHQPfeJyM7E9c0qeAwTopwE3KPhHF5TaiCPnap+5OaXOnDKh99j4+0VmEI/rxjPYBDwrIjc6s63xDb9IVglN48MEbMG/b2Y94MPHzuFXiZI6o95McTAPRQbRKyCbcAry17Ma+3HmMLsHsy69UnCyrUXxCxhb6O+3fghmX+GWVPPkxNczYgZIYQgJofJMEo8wXE5iyb2+3sQD3NhY/g0OWH2jNQru304HvMUe1JVnxfz0MyPgXdSLgDZvOheBXxE+XySQYoS7FkR2UtV65QcIrI3jQKpT0VkU2yuXRsnEBYLbZkXBGZ0llHVMSW8nuP938H9+gLQOoGVByl5BXcB1sFC650mIl9jIWvzxhoA34h5VKnjf2Gfvg8J3xZNk0msDLQWmnewmLFgoedaDLj55VAC+fPEvMN944lVqc9bNl5V93e4T3o0Z3DXguHBcq/p7Qm7NwB+7d2rk+mo6p9E5CHMiGyIN5f0wrzSfJgH67tFQtlYBVn9gwVhFAvA/9bSHNexii0PP1a5dj+wtzrDwEkERFW/EJE9sJC0pwaUpxkciH33rWqhaxeiFs0gD0mKuCoe1IUDjQTfC3Ug9YaGdWRLaFQpWMrupSjB/GfXx5TzqOo3Yl51ITgQm8MPwLzG+mEGYyGIMdr5Mmv/qvqJWCSMonCUtxCZG9WNM4eJRcNZxF1+Q3O5zqXmNTZHbj83IzmFrFqe78fEwsBWGWenKMEuoTaG+P/B8leGYAdsHjwHq8enqM1xedgHy5c4k3v/vzHP4gyi9+gOeru+VeU59rLGe8dn3xyMCJSjHzXvJM5PHejAZAkdhVkHOtBz4RWxkF5XYxP5TjQmT01JAJ3Bo2Ihx7I8TNtTvGCuApVactxNMYunsuS4mwautRI+FpGjqC+zWOFlGVwFPOeErYrlvymy9D8Vs8j6EtuULQ8cqKpXF+BvjsWHBwuN6edhyC8+66BAmRDTbg7HwimqiEwvItMV1JcPUWWgkZ6Gajl+thXLYfUAxQJAHw4kcpPhlMn9MYXUvdRvwMCs7Hwh5P8By4lb5YaUzZKWQL6SV6ecuw9TTEzl+H1URI7XxtxKy4vI5xkrmBD1cxq9EsHq6hPMKnJPLI9IH8yDcGSOh2a85+6mJvSdGhPyvkaj1WYM5Dc2+fNm4RjgXhE5gVrdr4K1/QNDD4jIgpggaQHqw+Bt7v1/Tsyb7LfUNksvYcnt8yGWXhCRjT1FFKp6vIi8j3l0hXjYE/OWmhdTbq6B1WN+LH/bHX3wFHN5UNVnMAvF/PV7MOWK92qZKRMmquojIrINlvNr1vzzTcBsmKBqXeAAJ0h4WlV9K9WQEi14TywEzdbAxcCyqvp/ZS9X1TPF8jutg7Xb3VR1RAH6v51S+Hp3vh3wiVOA5AUgu2Phd8/C+sRQdy0GynbUYzCF+gclOBn8AptnRqjqbmIhwooEBDNiuTD9HD5KvaDkfUw4tjn14+Z4Gj0uM9gOEzTsoebR0xcnOOp6Sbwn+MR+f0/hYSNsjJkXC/Ps85BPAp/RvwlPkKaqYzElhA+nu9+tsTaWrU36Y7mQmoX/c8KwKkhRgv0OuE1EdqDeQGEqTOntw96Y0G8ubM2VeW9ugM1ZIbjICUEvB65V1U/9m55RTVfdFYGIbKiqD7jTzLPwYJ8cAcWSmhftl9gY9w22Vliy4DXHYWuG+UTkGmxMLVJKxH5bNM1JrQwkzXOtuzCQeoXZFDmDh529/zMHnt9I60OBXSgizwJ+Pp0Ur7Fs/s9fC4XJT/EaC+ZlCkHZ/sXxcqb33/e+3lLL81RFK7YSlWuzA3sVCbsL9lwTG0QivcY8ZcV07nwstkcJ0k1QxFXyIAn5pdULQS4iB2ogR6KDmUVkK0wJPLPUcrULMFM3cFOUYC+KyOmYocYiOCWsmGdrEVR6jUma0c7CUu/dvYB/njOCSfUawynIsmgwF1OvqIc0r7EMvnDr+LwBqj8ORSvBKsaLIKjqOCynXgzuKGwfPqM7/zyHkrJHhwTPsVjQ5qIixcw7KXgd6MBkB50cZh3oQA8FZxH3G2rKlMeBC7U+FnV0Augc7a18uqp6awgvgsfh2CbtOizXT0tiKYsX6jHxuVkx5cy62ILjceD4VvDlFDBZfPHHi4St4mJauzLeEhP4PKJeXG4P92TMCijz+OkPDFPVw9z9DzDBepEFUsMCMabdOLzfYMqD6Rz98cApqnpBsACIKwMJexpep6q3eThba71HwzRYmJ0ya2gkIp+MmCXcppiS8Hos91GDR4A0l5csOoF8DK/u2lRYOI3+mKLmDuAyVX2vhL9SkPq4+L2Bj4G+IeGCiByiLsFxoCwbYqwXvG8lTHixdxVu4NlVNM7DMRnEwtQdDGS5OsYAp6vngZnDH4VtGkfjKUZCQmDXzxbBxpm/lwhgUnkejY0Jz7hxZAlgoKrmvddi6UUJrJxAemxewOaE/ker6l7NvD9Ha0ksJ9uPMGvUt9XL7yQi3wEhxb1gngFTerjfY14F31Iv3AhuTp2S8yVvAzkD5sFVF1rT3Zsdm0cy5dqTmDD0M6wfvZF/phkQkX3z463UvH9mwPLSPEe9d0aD94+4HHNiXoH9sLF8jLp8Tt3gryFsTytAXP6O3LUXVHWVnvT+HsTDNmph12LozgHsRaPSv0GJKyKPq+q6VddiQURuUS8XbgnenNQ8HBuUYBrI7SQi61MzyHhJVR9O5K3QKEjM63h3LKTec1getQdCuBXvaCovr4j8HZujr8UUICM1EE7Yw58NM6QQbJ5o8Mr1cKO+LYVmxbf0qDIQ86DJwgGvgbW5Is+1GD5fLLoFLKaqU3m4ozAhY13YXbHIB/dqLqKGWOSHP1MfHuy3mssZ5+ayzGss8xBZDPMSD0UVifmuERrpxSD1uYJK92pSn7OzwWusSMhd1Y7EcpiWecn6ocd2wdbpMcq1pvidmCAi62FhxJ9S1VNcmz/QV0B5uGtia9zpVbWviCyPrd33DeCOwPJgnYUZgbzk7y1SeZD68MQNXmNFSrGytiC13OxBUM/DMRH3asyb5j0sBOuCasrDmbFcxMt7uNNgRm5zY3u3Ue76Wti+1o9sUvhN+WuublbAvMaP8VDHYzKFTzzc9SiBgj3MNJR4jRVBRX3Mr/EpPYZgKRMOwry3dgE+0lxetFgQkaWx8r7DnZ9FTRF6vj82ish5lI8fob4zFSZfW4D6tVUowkwMv1Fjrojsqp7hjrs2C/CpaliIL5azcnl1eW4d76NUtcF4MmHeicLrQAcmR+gozDrQgR4MbkHTV1VfK7g/nMgE0Lnn5gcWVdUHxRK/947ZTATojMCUIoWgTSirUjZvBc9PrxUeB03QXAcrs0FOKDW9uvBmObyXVHVpEbkEy511n4iM0rDC7EUsKff37rw35iWQ5T1qVvhQ1W6OxKxy91OzMMwEC+cAz6rqCallIDVPw00wwcz1wG0hIVU3vitmk/E9Fq4uW/xnk1wmSK8M+ykia+QVB+56dAL5SF6vwJQ592JhK0sVhoF3TIcpZXdQ1U1K3lO2wUlOlFxFJ/EbRmCWiVnozpdTaZTQngOYH9sMfhqBH0z2nMOZAkukvRvm4dUL8wAZBByp9UnOywTPqqr5HF6IyPOquqqYJe/qqvq11CeWPltVD5SCsGqaU6jkBEAhHgo3e1WbshRwAtHXMOXTE9g4U+ZR1lJw7Wyl7FvEwqW+0EybzdG9AhiQtS9XZmdoOBdB9kxvLJG6v+l+291rRvBxAeZxtD0mtPo/TODc4HUhIvNiOSXWxtrPk47/dz2cG1X1l1IfQtnnoWEMFbPWPgULOytQaFWLmIf7E9R7Qa+rqhv1hPf3IB52UtWri4TDGvB2cEKNJzDL5e883AaFm4i8AmzirQEWxMKlFnn2lIKYR2qZEKrOM0W6qQQr4GEeTHD5opo3wJyYR/GuqvrDkud6Y3PpuZhVvABH5HmueHfdulUi8wqKyABMqTMfFgb5Mcwg6e8B3IdUdYOqaynf1gzNknf1uDIQy+mXGWr0www1Nk79Nkfrn5jn5yf5W8BQv42JheodgI3JmXHZSpiH57maE6SLyALYGjwbm5/CxuYoAXR3QESO1lr4yircrjpO2asl4lYpzKIVW0Xjp4cb9Brr7j50QoNERAoR8wj5BebdnNXhGFVdJoAbrYhL4cHhtaQtiMgPNGBg0QLcZCVYJN3Ma+yXmLIogxkxA67VAs9Mia0VC/f03QURuVhV815jRbj3FY2fbs91COVeYxnuMFVdWZwBqrv2mNYb0aUowe4ETlLVoe78ZcxjblpgG1Xd0sPNlLdrY/NTVhfbYkbLDQYVInIfZjSXX1s1RHoqW+N7OFF9QCyM/42q+qqY4iuLXPQttvd/MPDMkVgby4zhtwRuUNWTArgLUJt3wPYEB6p53yXjdaADkyN0QjJ2oAM9FMRC9Z2GubovKJaH6vicQDQlAXRGdy/MnX5WYGEsdv1FVCi+CmBn6nNy5KEpt3JKNjdl4Bazf8UE8KXWc4l0j8XCuS2OCcanxARdawfQ7xSz7vkS2NctIMu8T2bGYmFDY1iIoGeZiMwHbK+qpwXuxbSbX2HWR118qYXE+SUwCgvXkKdbVQZHYNbBB2mLPA29d6eEpigNGRQJN2IhKvIQSiBfFxYikdedMY+axbAwdV1kKBa09nH0d8A8+QZj/deHlNAQUvA/dJ7x4Hst9cKEQB+FcKtAVVcUkcUxYf/NIvINNeVZ04IisdCGJwJ/x/rBr7NNVwmc49r5EOq9enyr7dMw75+FtOatNCMmBDsd22Rn4Iei6iKHbXbmJRw2513Xxm4DHhCRT7DwcBlkG/XTiQAtsYgWkQO9/4WbMhEJbsoSYVEt8RiYACCZsgxAVb8XU342Ipq1/kE0WpKGQlYtp54yVi1/ROEmWET2xyyr/4ltugVrE8u55x/zcOcCVnP3n9ecp4L3zmx+u8ht6mdU1SJPiEHYOL2tO9/JXdvQwznQ/aaEUD4V2ExV8+F/Q9AfK4NsM/+4u9ZT3t9TeMjCFJflO8zDtBpvof07LATwWHe+APU5l1LBL6vNgDu983zYT5yCrNtKsgzceHYk8AYwlYicg4WyvBLzYAs9sxxm/LAJFhp6M1UdLiI/xELhRivM8NatkpBXUGv5X6d3vBxHbn4Q82ieFphd6vOezAgEFYFV3yYi96TSjICeVga+59qlwP7dnIfuwgzFRgbe9Wjum64WC514AjXF8BjgGFW9N0B7IKbY/cTRmxWb52ND/HYHtsJChyPVET56ufLv5f3vWiuWrP9L93WSkONa08I3+uNnaW60FH57CojnNUbEvldV35H6kJPBvM2aEL4xlQeq28J4D2fa3H7G38OMcu3mOsxA9bMSstG4at5WJweuD8XCbod4XhTL0Zs3EPBlIM2Eet4YGwfK9vQpPISgwbO9CCqMDa7BlE+b4nmNFeBmxoUfiOUGfx8b8304GfueDDaipgQ7hvqwzHNnyjIHnzt5GGLhnv1vuMJd3xXop87QUSxUdj6vYQbzxhha5Nb42VzTtcb34JzAsyEjxe1wYzO1MMdzYHKDK4CGvZla/sp7MSMRpSQEvUaGpYzF60AHJktQ1c7ROTpHDzywxdRMmMdRdu3FHM4YLE4+mFXmuv69ArojsYWXT3d0Dmc+zEPoCUwRMqV377YJ8O3Dm3zuWce7/23BckikOxJbqBfWRQ5/FkyZCbawm6sArz/wFpZj4grgTUwRlt2f1fs/OxZq8XHMg+r0brSb10p4f7UVZVBRnl8ALwaO0SGamOB+F1dWu3jH1lhyZh/3fGCtbtb3OxE4UwEzdYfXRJ42xPLBvYcpKjcDxrWgbQ8P/Q+de9eP9Y4jsbwFU3eXF6/8TsIUXU91g84YYA73fyEsX1bVMycB72LW7Y+44+EczuuY8iX/bG/g9Qr662DehM9gwssqftbDNtZ9vGtNt6EA/be9/y9l34UZVDzivmlJ4LkWvGtqLO/bBa4dX4ZZ7jZLbzzmLTHeOz53Y8u3AfxbMGHPlO4YQMFchhkN/AZTVq2cHSW4s3jns5KbT3P4bwCzRXzfnpgH4+XY3DAO2D2HsxHwi8CzOwIbFtAdWXUt6/fAVQn10XRfDdCaqO/vQTyc4n63TXjmBODnCfhTYWPu8sBULeR9RCvLIvKdL+PWTJjByzdYbsmyZx7HDFemCdzbuZk24/6PxhQKo9z5D4A7C547A1u7voQZ4OyCGWT4OAOw9eHX2PrvTXeMwiIFJH9bMzQnwTIYgOVueQYbS3fDPBYmaNuMLLsRMdfa/e6qd2JzkV/+/jE2pm0U3J+/7GiWbrPlmUJ3Ireb6H0vcDMWDns4Jgc4CDNOC+GuiY2pb7vz5YELustDZFuYsuy+h9cbWwcNwhQUt2EKhtCYF43rPbOoK7OXXZsfW9TGMWOADbC97PyY0n9g0fcB0wCLR3xj5Z6+GR5yz91XcP2OsiPEa54/LIRliPam7ruWwfYbw4DNczgv5M6f8b81d69MrvG3guuvUS9nmaWIDrV8yVVlGbvGPwZYwv2fypXBv4EPschRGZ5f74MxRXRlP3L9dX9gP8xAughvXsxw60PXLwZjysGm8DpH55gcj46HWQc60HPhW1X9TAqSDztISgDt4Gu1UDU43ClotPa6DJsMn8ES+D4mIpup6r+wRVgdOBo/w5KYgi0u79fmc3+UfnQZaKT1XCJ8o6oqIgoWdqIIUSxx732q+p2IHIV535yAxUPP83qds0pdFfvmQ7Xei+C/IvIrzJtoMWyxspCq5q2wfIhpN++KyAaq+lCO9/WBDwqeiS6DCHgTU/hEgVo4jFFiyburQlO8DpwhFobnBix/2shE/ur6g4isr6oPSy1JtH8P9cI2JfKaAlkIr3W0FgazwUKtCci80XxPNNz51KEHtE15HMTC5M2JCdamo0mvNQffqOpH0OU9OVXVA5iV9UJaHipQVTU/XuL6e8N1ABHZALOKVOBEjcyPo4Hwe8D7IvJ6yTOVIUd91rz/33jftREmRPkOeKXIEysRrsKMOjbCcjLsiOUZbApU1U/+jVhOsn0xC/JbA4/sg4UkOwqrh4doTFyewbeqemEkK2cAQ0XkZkf3l8CfSvDfoXhu9uFgYEU35yKWs2coNjdnMJDwOPoQVgahdvaxWKiw69x5fyy0rA99xELWrBUa8zQcpu4FEbmBWm6qBlyJDyc6sd/fU3j4uVtDHE5xwvY8DACOEPPUzSy4VT2v4pL5bOH8fNYNCI6FbYav1Hm3qOrbIvI3DYRW9kFL8rVpetitcd7/L9W8WL8V80D+kOJoC88Ap2pJuDCteWDtr6rnxTAT+W1JNCNgnPe/J5RBpedaO0FENsK8IObB+sT7wO2qel8AvZeIzKL1HmYTSk4T7TWmqgvEEk30GouKKBDhAVcGpeNSCr89CRL2vftgni3zYMZhQzBDphCcja3X7nDvGCUlYcareEjwGgNTwFWGy3Zr1PuB+8Wib/wMi1ZxjliY1h2bwfVgEGYYeBYWznU3iuUT06jqQyIiri0fJyJPkMvV5iDaa4y4PX0zPHSBFntOrYmtWa/D6qSKiRivseydd7m/n2FlG4K6db6qruGdzpnDfV9EVtdcXmKxnI/vE4aTgRFSy2m+HjZHhGAdYFcRyYw2isaE2DV+rOfY12Khjf+JldNBHo1pCYBYiOO9MDmeAFeLhd0MzZuDqI46kYLXgQ5MdtBRmHWgAz0XxojIDkBv52Z/ALlQAGpu1w9RSwCdLUZ7YZYlIXhMRI7AhOMbYkLGO3M4c6hqFuZtfydge1ws3F9emfBDzDLmAyxOv2CWQ2eKSD9VLVqolMHOTTwD8I5YWEZ1C+ID6IZQ1oMbReQvwMxiIS13JxeKz4OjVfUmsXxfG2GL4guBhtxIIrI2Zt1/hyvjQ0TkHG/j+CGWD+wozJpKRWSrCl4r2427druIPEktpOaqWHjFLVpQBlXwTezmOAeVmwxPQDI/thkaJBbKJwvz9zfoince2jwLMFvu2npY6KiQcFoJh21K2RDFwMrY9zwoFkLrelog9FHVaBoiUhrSsNlvE5EfYYL7LTHPsOuB32l5eJUqmFfqQ2LWnWs4D8MoLETqhyV0XxaRX2kuD4vrv6/mrm2CeeB9huU3eyrtE4LwJjZmv5O7Pj/Fm8Ii8Nt/8qYsERZR1W1FZAtVvUJMoXx/d4mKha88EAszey2waqZk8kFVP8T6TxGdw7UW3/9OEdkXUzr5io+GUFOqeqWIvACsj40dW6uXh88XgDoYi4XBuztHO59H5V3May6D8TTW+bSZUjjH0z+k2KBhd8wL9yys/ofSGPJrH0yhOTONY17ReDcj5t330xLcrM9UhROd2O/vKTzch4WTm84TLkJJyN68IrkA1iV9PpsUID/mz1k15ktCCCvXzwcB1+b6dPaMr4B8wY1Nl2BrrP/D1nI+veVU9UVVjVWGoqrnSXxesKhvS6Q5qZXBGZigc3osxOYxOMPCdoOInI0JP6/ExnMw4fEBIvIzVR2QeyTV+KKVMBNWR5lA3A9DrXiKThEpVWRofQjrlJC2sTB1GxVb7eC33RC971XVj7F5LQoSFHExPMyqXo7fCkg2mlUzBH7ZvXdlbHzoLm6KAuorMaO/10VkPywaSF6pk8FxWASDRx0/I8VyRIUgZk8fzUPiPm4uTCHSHzPcvRszQn2p4PETRGQmLPfdediaKBhqUixn6v40hj7335+iBDsUuEFELqc2fq2MKaO2K/jWQWKhCzP5zGFaEPocU7AWgrfWj13jxxopHoh5Oc4BnKU1g9mfU8uNmYc9sBzY/3G4p2DzX0hhNoeqDvLOLxcvXH8TeB3owGQHoo2G0h3oQAd6AIjItJiwNRO+3A/8UVW/Ln4qim4vbDL9KbYovV9VL8nhvISFoPrKu/YTLFfSdKo6t3f9ckzpc3aOxgGOxi7etfmwHEDzYKHJTtNa7Ojb1EvK2uS3zY5Zz/3EfdsQ4ICQoLMJ2htSX2ZBLxFxiVxF5CQsNNe1UpDc1W3ylsdiW1+JeQ9srS7prYj8DhPyTocJg28AHggJczyaUe3GKZF2wHIrCBYK5xq/zpstgyoQkfNVdb8mnhuGCaYf1VrC6q6EwSXPrYiV7XKZgkgsoXUhaNi7p+28RtJeG9vAbIOFyrxVVS9uktZdWNu6TVW/qMD9iBJrw2bKTETewULPXY/l0IpKyh1Bd5ey++pi2OeeeRTri89Tv9HZ3MOZBxMof0m9snkaYCtVfc/D/R4TlI2i2pskCkTkM+BHmstRJSKrAMeq6ma5675Vb90tTBgwhcNbHbNqnAM4W1WzfCY/x0KT5fMrpfL9nKquJiKPYwq/f2ChHpvJcZmN9X/ANsKXAed1R8EqXmJ5MQvSPGgzvEouYb1YXp8Q8TqvTRG5ElgWuB2rvy0wYfPfHP6ZIvI3LFH8t7lnpwReVtVFA/zMGjsfisgeqnppyf0NU+YAMSvuDUTkFI3IszWx39+DeLhdVYsMWUL4m2NKMbD5567c/QGqeo6IrKOqT8bSjXivb4SyLhYSsAuaGe8S39/MmP8kNQ+CzXAeBKra0E/FIjfsho05meLIN1Qr4msBAnkFReQ7zADiOkwI+XLg8TytYwnkBVPVXzT7bYk0J7Uy2BZ4vFXrihQQ83BcLHBdsBBhofF5KWrGFw/FlMeEBql5Y/jQVf8azvVZRTPaa0xEhmPRAApBPYO8vHINC5kG3VCupfDbbijY9w7QgOGQiJyKRTz5klqe2gNV9eoA7s1YDsjzgTUwRc0qqtpgeBTDQ34tVPFN77p3B8FXPohIX2w86o/tla/HlA8NSsMUXIf/FJYH6mbMyOQ94GRVXTyAuyqmgJsZ8xyaEfOafTaA+6yqru7LBor2h7k9vVDb0zfs1WN4aHYfJxaloz8mwzleu+mVLCKjsLx3o6nl+qp7v4ishsk9LiegBFPVvAHGDzCPySxn5EvAn8vGfzFv2kWpN754vAi/hM5wVV0pYY3/DBZ6/Z9YaMiVPWXYq6q6RCOVaF5GY8aDX7nzqbE8yMsGcB/EytePOrGbqm7QDF4HOjA5Qkdh1oEO9FAQkW01Z3UZutYE3QFqXjiF15yiZnh+4eQUD6eq6obetcKJXURe8xeWIvIA9aEeV8Zy+fyrSKmU+G1ra86LI3StCboNwq0igZdTQLyHbRxWxjYmz6nq8gHcbIF1DPCeql4a2lSIyELY4mR7bGF3LKYk+VuAZtPtRkR6YznUrulOGUS8ZzMsxvlb7vwYTPnzFrbJCgmsUzcZU2JeXttjMd0fwwQytzXB7+/L7muj5VgSr82CmPL7J1idNZUYXkS2wMroJ9iG8DrgHg2EJXTtI7M2XI5qa8OY98+vzXkbthykQIka2kCKhS/tUjZrLrxpGb0yuhE8fqmq0xTcGx3aEPUEEJE9sbF/WWzTNT3mjfuXJun9BwvZOYh6Tywg3Ccr6HV7/kmhKyLTqbP+LHiuKoTOQBE5GQtfup/WLEmnw0JPflwwP72OKdkvw0IHN70JkHol47yY9eramHDySWwsf9fDfxnLDXcRZqyRF9T4ngk9/v09iIcu4a1rE6sC2RzeH8srcpiHP1JVV0gRYEby0VYjlFaBiJynqvu7/8NUdWV/7BSRJ1T1RyXP98I8US7EBH2XAeeo6r9FZAlVfVUKvHD8+hWREVg0hf6YEPc/1DzhxxW8ezQm5B6hqss7AeFfNWcokfJtKTQnwTJYBcut9C2WY/TVPE67QMwgbs+AUHc14NKeNFcX1VUGuTpbDcvz+4E73wVbv48DjtMmDBRT5t9ExcvTlHiWQ3woyBzdtqwX2g3e2L8VFtHhd8AjBfvTaEVc5LtT6vgDbGwJepqpUz6IyFDMAPdmbC/yQgnNaFzvmRQlWPTeW0QuxUJnH4b1nQOwvG37VPFUwW8lD6n7OKco28ThL4CF6LxMPeNADzfGayzDfVZVGyLvBPCSlGBOObQItgb7u5YbAe+JhbGeF1sXr4Hlu25G6Z80Joh5yV1OhZFik/KH32NKxVvdpS2ByzVn2O5w+2JK8TWpRZ0YkB8XY/E60IHJEToKsw50oIdCgeKk2wKOArpNL/7Lns3fyxbr3vlOWH6OzYGb2vRt7SqzMmuwjTHvstfFcmktowErdBF5DLPy2w2zyP4I89bLhBt9VfXt3DPL4oQLqrpwJK95L4cZsQXoPJgHw4Pu/GD3/gZr9pQyqAInSFhDVb8QkU0xS8L+wIrAtqq6UcFzlZsMMS+4/tgC/znMgvC2IuG0RIQtihFeN8NrCojlwphBVW/OXd8R+DDUvhLpT4P1w+2xBfE92CaqyJOyJdaG0r4wj+2iu76qPuz+L6iecldEttb6fEUzqurnBXQa+nbk+79W1WA+NhF5Q1UXSaVZ8q67VLXbIYqccPUXqnpjC9jKaB5HcT4SVdXjE+kNBw7SglyFjmhyqLrA2LsmZlU7var2FZHlsSTe+5bQmEsDYWLEwracgFmpvoUJl+Zz9I/WQPgjERFMALY7FhLoBmwj3WB8EfFtXfO7mDHMtViuOrD8BjtqvXHNLzBDmXUw7xQfNFVIMbHf30N5eBFYQVW/d+e9McXCch7+ddgYPwfwd58UPThPT6tA6pWc0R4EDn85bL32c8zS/xqsLnd2guhLVHUvKfDC8es3MDashs2/22IKibUC7888dYcB/TBjgTGqunQAN+rbUmhOKmUgprw9A/gUM1x7CpgFy7Gzs6rmw9u2HJwS6kIsD0+mNJ8P+BzYV1WHtZuHWCiqq64/uToDfqKmHF0XW2PvD6wALKkBT7+I96cowaL3q4m4SV5u3d1XtgokzWvsJVVdWkQuAQar6n0iMkoDCrNW8yBpXmNR5ev6+eOqqmJ5CrVkrxeN6z2TogSLlj9IhNdYM3uYVBlI1T5ORK4AlsEiAl2vqmPKeJIIrzEPdwfMAHgI9RE9GoyGYpRgbj18IjY3vY2lJpkXM6o7smA9PBozMHrGzV1LAANVNRjCsQxE5ENVnVPi8tOm0C2TPxTuddz8sw7Wvh5X1aLwjR3oQAcqoJPDrAMd6GEgIj/DNqLzSH0+hhkxK8lm6WYxqBfKLcRmAIIWYyIyB5Y4dAHqrYV8b5aZCgSM4nj2YUoRmTpb7Kjq1SLyD2yxWJRzpRKcEHItYA6pt8aZkW7keRKR32DhwxaWWox8sDILeq2phbW7xT0/Hebd1B8IKR62w+pkD7W8M32xhWsGt+ESH4vIYFXdRlVHY4vRI3K8prSbq4BPsJjWewGHYLm2tlDVkd0tgwhQrYX/2xqzth0GDBPLHVQE+2ObjK8xK+T7qSXNzeAITGh5kMZZug6iIqmzBhRiERDDawoMJJxz5iHMiqxbCjNV/RITnt/gBGJXYBZqdf1HGq0Nz6V7OW9Sk0q3nK6IPKmq60hj+MJMgOyPY6dTS0Y+mPrE5EdRXxaPUuu/D2l96Irb/GdFZNayj/Ha8l0ispc2htHdAwsR2UqYpxVEVPV7sXwKLVOYqepxRffErIJTQWguV2EqnI3lK7gDQFVHOaFjGdxDfTvDPfstcJiIDMSECQBvuL4cBFVVbKx4QET6AVcD+zpBx2Gq+nTCt/h9pTK/gZqy/2YROVqdNW03YWK/vyfyAGYNn40XMzUgq/YXkbmwOallYRLFvJXnVdU/u/NnMaUcwCGaM/boIXAglqPxAGx+7ofNew3gFDSfYgLBw7QW5vpZsTDJqOpe7rdfxLvz64zngOdE5A/UQmrmoTIvmAcHEvdt0TQnoTI4G/ipqn4k5vVwpqquLWZQdSn1eQbbAk7wu7rra/Ng3/quFufImZhwKCVeYznc3t56ZDvgYlUdDAwWkZETgNeUHNcpVuFTV6P0SPipqh4i5jX2LqZsfgSb2/Nwp4i8iim29nV7/KAHTooiLpKH3lhkgZg1fhBHLK3D9qp6GpgiRkR+IyKHYzIEcev4U1T1Av/ZFFwPDgfyyrG6a83IbNz+90h3FEHKHiaJh4R93M6Y1+9iWO7FLhI07o0AvlLVc4mDZR399akp19SdZ3wGlWAiElKCnYbJJRZS1fHu+RmxPdvpmCdZHr5S1a9EBBGZSs0zOmgsEwGZHC0mPy0S6TlWJn/Iry+962tgkU+Gu/MZJJALzt27AvMU+9SdzwKckZPzReN1oAOTI3QUZh3oQM+D9zHL482pF4COpyB5aiQMBT4AZscsL326LwafMO+jJzAPpKJkv48RFjBCLocF8FcsuWqXtZGqPiiWZ+DUMuYroA+2EJ8CWzBl8DmQbO3owbWYZdVJmKdQBuOLlDFiCY9/jinCNsaE6hcV0B+PhbH5TkQWA5agFh8a6hfIVflzUtrNQlrzYvsr8DHQN1tk5iC5DCJAnIXfF5hC0d+sFG5aIzcZmwL/VRdS0C1+fw68pWEPkeikzq6OLgR+oKrLOMXS5qp6QpO8psC0qvpR4D3/cIrZboFY2ItfYtbdc2Mbwt1yOL614cAqa8NISE0q3XK6qrqO+50hfy8AUvC/6jyvEMvjZvnQQptipdb/9wVuFfMszPr5KtgYuFUx2+UgIieq6hG5y620SHxARA7ClLJdVr3dGEPqQCz3y/ZYfX+GlUnVMwdqLUTJTap6ouNpt+Kn0lnLX1DVdzzBAxTPrYU0cvAtJgxfF0DMc/miAova2TDPp52x3An7Y8q7FbA+v2DFu4rgYzGPcT+/QdAQR1X/KJYLcH7qDXGS80b0oPf3FB5OAkaIeYwI1iYOD7z/H5gAtJVwCPXhz6bCrLenwwxTeqLC7F+q+n+Y0qWq32+rqmNDN1R1a4AC4zEfz1+DnFaAo3hr5Ny9zKDoIhG5j0BeMA8qv01sIDrJCcFiaE4qZdDbWy+9jfUzVPUBETm7jL9Wgivf+TGFmQK9ReSf7vt6ElyEeR7jDDhOouY1djH1+6jeIjKFM9jYAPi1d69ZuZJIZI7rFq07Q5BSJ60y7GoFTOl+f46tcf+dW190gaoeJiKnAJ+7fed/sPyoIUhRxMXw8IHGe/53GZeJhYbcFptP58VT7IjIkVgI5B9n45JYCoNzxPK1ntAkbooCKnrvLWleYyl7oxQeovdxqtqrjN8AnCPmEVXpNYbtVxbSQPh/D1KUYJsCi/ljq6p+Lmb0+yphhdm7zvjiNmyP8glWls1AZggeG3q6bL8ZOxb9HjMOycOF1BvZ/SdwLYPlMiUYgKp+IpZ+pVm8DnRgsoOOwqwDHehhoGZxPgZbrF7RQrpviYVE+E/ChD6tVuSoShEuqupZBddHYAvDpsBZjj0JLFtmjdME3c+cBdqyWhGnWWqhADfCNhVXAatVlM/jwI+cpc5D2IJ3O2DHjAWfnQpeU9pNlyDVbZreLFCWJZVBApyNxQv/HHhFXSx5t/j6II+cuMm4Fwt59bpYgvqnsZBBm4rIqqqaFx5+JRYy7nUxL5j3gDkLXnUJFrbyL+69L4rItZgVZjO8psDUnpCiC8RytQVzWsWAiPwaE3Iujm1ED9HinH+p1oaVoKrfYdar90ktPMijItKtpNKpdF0beFFVl6kiXfC/6rwUV1WjFBVqcfvXEvMOyni9W12YyG7AxuS8VltsOZjR+q3/CqoNAQpBRObH6rU/JsSYH0tKPy6SRNdmU1VPjLX4dO/uCr8pIrOo6icFj+UTYr8jImsB6owrDsByZJTBJRX3L8QEVpnhwc7u2p4B3KexuWlL9XJrYV4bRYYdRTDO+787lt/gLOjKbxBsP2J5trYHXqamLFQaDWx6+vt7Cg9dA7GqXicij2KKKgEO1QKPFjGPoOOoKe2ycbzZPtlH60PdPamW7+ZfrTDqaCH4ktzLneLyeazsn1Dz4m8AVR0rIptguVT88M2+ELgw7xc5L1VVvbYJ3nGGOgvg9vAisoj+f3vnHSZLVa3v9zsEQbKKCSUKKCpIElRMGDEnBFRE8KcoIphzADN6FRUVriQRCZJUREEUkSQ5JxEkKSrq9SpcRQT5fn+s6jM1NdXdVR1mes5Z7/PMM11Vu1btrq6urr3XWt+qDwbq+95sW9L3CdlC+t0759E5uEhTstgvJbK9O1JoA6tOtOzjc4l78vXEcyXEhP+jJO1m+9TZ6EdD2mSNHQWcIekvRPbRWRCfARGsMgg7EnXwyjWuz5D04uIessaAdsfl2GqT5TZu2mSNbUvULv2PpI8QE+ifAup+Ixo74hr2oVHWWME9kl5POIrWI1Q01rb9iMrurwc2ckmir7hHvRq4nNLYrGXbxg4o25cDlxdjwSWJ4NPr6t4rLbLG2oxhWvZh5OO4En2zxkpcTmTD/6mHvTZOMNcFIhTXeu38ie1OkOFeRZDRSsQ5BxZmUfW8h5Tum/fTdBWearsNK8utM8fqmnZbXzln9ymy9epYUB7DKNRO6to2bZckixx5oSfJBFL8wD9Q0tJ9om8GsftPSSvZbjKwOUnSC2z/uFuDNpOMpX2aSD22onhvPWXNBrR7n6TL1b/m0E+IgeNWLuoaSfpKH/Ny1PF6I7Cf7c9XBqcbSbqDeChatngNXR5sW1w3ZbuUbHez2/QcNML2IZJ+QjimLi9t+iP1kdBtZPtWsX198XonYqD39mJy+mJmRtu/g5myRa/vYvv+ti+oDBqr0Ybjkhg8AThQ0u4udPeLSchRSCJ+DviZi7o33XD7aMNGaPQyj63ttrjGO5K2Yrq8rZiZnfPg4v6o0utO21WpQXFxvRZYy5GFsjrwUIdUVbm/pxOO+VGxRK/BoYfMBOvnEJT0HLeow6co4L4SUT/lVY56kTe1cJbBzPfaifhcn3A4dD7bFzPTkVGW3zyN+sjNuvP2FuArRBT974hI3LdV9wNQ1J96CPE7vHphr+7a3NzTa5D8XCGxWMf6dZMKhe19iuM2yhBxkVFS6lfTYICXF/24u1sDRZbD7bavk7QVUYz9Wts/Kh1znMd/HfH7fHhl/ZuIoKMjZ6EPjyKymc+prH8q8HvbnfpjO6pU49Ihq3Zi0fa1itoadd+tg4nJv4vpn+XYhFXKC7Z3Ly3W3u/miIXPZbafVjwbbA48A/iRpOVtz3iWLBzK9yeeEQ4ism6q9+WRZKdKOtn2NjXrDwE2BK5m+oTkjN+1Fu/tPEUw0YUN+jVfzsGuxBjjyYRCxiGldrU1csfAV4haXzeXVyokIn8MPGaW+tGExlljtj8t6TRCieDU0u/JAiIrbSFqkTUmaVXbnaCNtxf34DMlvYR22V9l2ji21Ka/A/Zn5Lhd1thHbR9b/KY+j8jS2Z9QfqnS2BHXsA+NssYK/kTcVz5CBF5YkelWd+wZfbJ9l6QZ45mmbVs6oDo8nzifSwNrSXoCURds0KyxQcZGffswrnFcQZOssQ4PAX4l6UKmZ6OVz1cbJ9g1kl5v+9vllcV95Ff9OuP6QPLTiOeaJuofLyXeU7U+5hq0z1rrljlWd/w6bpS0B/HdhlAmqc0MJxSnfinpuMLeq4FPD9EuSRY50mGWJJPLLcA5xYRsWcKqa9HchvwLuFJRoL5sd4+atnsCH5L0b+Df1DtUymnlu1Jk3/ShidTjIFxanK9jmf7ehp18fxhwtaQLKnbLD3abEhHjP5N0IzGR2y+SVYr6a68lIiop72N7kEjYvtfNgHabnINGSHqdQwf/NkWU+zmFrT8osry+VtmlzSCj/AC5NYXkj+1/1w2ggDWLiaKFskWKKMwZWt+E5NY6nWNIehUzM+LGJTH4ESIC8hZJnUy/1YlJz48OYfc0F5HOkp5SnpwtnHPVz2KkaDwyj4PabXKNlycBqlr11eUDmbo/ll9DTDbW8Q1iEnBrwoF7JxF1PUhdrjY8mpg47zcoHBf70K4O35+JiZaHEJPx19N+Uq2a5bc3gKRTgU08JQGzFzPrWPSS5ux+QPsvTGUQd0XS2wlZ2NuJ30gV/d2wpvl/JK3TcaAoZIa6/a6uq5DGXJPpwSrlCOBOhsiDiQnnTvbiM4lMjbIk0ueBG0sTnZ317yQcvXUZ6jcS0eu1ziKFZNoTgSWLwIpnEd/jd0p6hu33jvP4Bd1qKB1NnIOFmTFj7MOXqWR9FtxVbHsxLJxsPoj2NS7/bvvkHsdvy/mqr6+4K93rbI0NSR+0/dnqetvfKrXZCnhq8bcycBJFxkwNT7a9oaQrbO8t6Yv0mLxUn0wsSbVOduK7/oQu27a0vUG3Y1aO3/S9PRPYtXiu+AdTz/l195p5cQ4KJ8eMukSO+o6jUkrox5JEUESV25jK3pkUWmWN2T6vZt2va+y2yRprXON6TI6tcWW5jRW1yxrrPBe8ENjf9g+K55sZtHHENexD06wxiN+97YkJ/yMlfbfL2/+dpGfZPq3Sn62ZOTZr07ZDEydYh72I55ZfANi+TNKa5QZukTU24Bimbx/GTJOssQ4zyh7U0MYJ9jbgBEm7MCVxvzmhwFLrbG2A3FD9g1AW+JArSjyFo3lfemdezzhuaf9qbe1ym27qMm8hnKsfKfY9jelBEAux/W1JFxFjTgGvsH1N6fir2P7fpu1avMckmTeoxnGfJMkEoNCBnoGHlBxUFHOuszu0/KOkS2331TSWdJntJwx7vBq7h9astoeUFpP09Lr1XSKSOlJHOxBFsy8Dvmf7m13svhs4x/Y+xUTnO7o4L5v2te91I2kZ4oHqUUT9ukNckfrr0tc6u03lPcu2LrG9SfV13XLNvp1BxheIgUt1kPEdYpB2G1FzbS1HFt/KwBmenolRe7xufSg+n28Sk8j/C9wEvLb6gNy0r4MgaVnicwO4oZgAGsbewJ/FKCicmB3nVPmBZCh5kEHsDvA9X7XYPqO23KB0znn5Xirp8up1O2qa3rsn6fiSViLusTsQ34mVgee5lI3Xb7Bpe0bgmCKqeiMX2T/F9/hy24+utNmBiKr/DjEJVJbGq6vZgKbXw+jwd+Ai2z8otbsB2KKYqOuJpGcRNaJuLPqwBrCzIwux2vZyol7NtKwi2xfXtD0JeJMjYwlJDwO+7lJWlaRrgMe5kp2qHhKnko4n6medxvTI4j2K7VcTE0XLEvfx1Yp7+FLApWWb4zh+0eaKLg6DGdvG2Ier6vYttl3pog5p2/6W1n+OCNA5gf71Rvoi6cFELZC7gY6NTYlaZi9zyMnOGk1+vyT9h5De+izwY/eIjJd0vu0tJJ0HvIKoT3eV7XVr2tZmYtl+Y6nNf4g6XXUO9y1tz5gMU8gMfrE8UTXse1NI286g7rlmvpwDRZ2bDxJBFSe7JP0o6RueqoM2NiR9kIjCP5qprINHEo6AY1zjzJ1LJG3JVNZYR8VgPWD5Ie4J08Z6xUT3B4lM3GMrz5vvBC6pPm8ppNo/b/s5pXU/Zbpja1Pgxbb/p/os0dS51ra/k0Ln/q5wkH+WcPB8yPaMrLHiN/02ol7dpoRz9IK658uSE+zOshOs7lpo0gdJdzEza+xG95AALsZcOxDfmXUJJ8v3XDhnJT2WCMA9m+lOkqcAL3UpULFN29I+FxMOgl+Unse7/Z527o2X9mqrmVljJxJj8Nsq7QYZwzTqw7hQyEFvSMgAd8saa2NvNeL55C5qnGDVc1bsszURpCHgalccpC2PXx4f91T/aPO81uC4t9pefdB+N7BfG0zUpW2jeYDZmC9IkrkiM8ySZELxVLT7cp3By4jsHlZMuveVGKh5QHgk8DBX5MHK5ht2o6/U4yB4RFIwNXbPKCYV1rX9M/Wpg+DI0jlHkRL/bOJhf4bDrBgYnqGivoejEPHAzrLCRpPr5jCijtlZhD79Y6kviDutr23OQR96ZWfUZmvUDDK6SVO8iXgvaxL13P5ZrN+AUgaQ2hV1BhZ+Ps8uPq8FxEP8dlQillv0tTUOB9mVxXG+SZeosRa0/ixGicckD9LGrqY7kK8EDnYXB3JxT/wYIT0kQtf9XkJS9ROVto8F1rHdkUfbl5ARBPhal0moexRSfJ0sxlWZkp1alGkdveWQFT4EOKSYrN8e+LKkR9p+ZNGma2Ht4ntax+HABZK+Vyy/jLhnlvkj8KWa10DXmg0QmRaPZipj7ZWEtNgbJT3T9juK9b+lYT0Y26dJWpeQkhTwK3eX+rvX9v5dtlVZs+MsK7idiAyvHH6mlKtD4rTb/eNEpuQu63AxmVaWW4P4HlS/1+M4PkS2w4zfUEkrENHms9GHZXpsqzoSBqlx2ZnM3Ky0rte12xPbfyLqK3YmrGA09RXHyQOJCdOnEfVc7gPOtV2XtX2SIvDmC4RD0HSvL9gkE+taYFdPSUgvRFJV1qnDYcC5isybu6FnNlij9+aob7wJsFXxns7p4SCZL+fgUCLr+HhgF0mvBF5T3Be37GJ3pNj+rKQfEM6WJxX9/B0RZNXX4TnbuHnWWBsaZ425XY3rNvKNY8lymyAaZ40RDtznA/9l+2+KIJj3dGnbRr6xSR+aZo2hQhq9GHN9Gvi0pMcTY6qTgXUAbF8t6XFEwFLHSXImcU+ZJr/Ypm2Jex11vLt1tcxVkl5DyJuuS4zlf1l5X42zxgYcG/Xtw5jpmzUm6WzbW2lmMNsMR2DhENui4gQ7uc4JJmlr2z+3/XOFPPtNpW0Law4PQT/1jzbPa32D+YbqaX+2JRzbTWg6DzD2+YIkmTNs51/+5d8E/hEDrGuAW4vljYBvjMDui4HrgJuK5ScAJ3Zpuz/wdaJ2CISW84U9bF/SsA93Eg8e/wLuKJbvGMF7W4+I2L6qWN4Q+MgI7L6JiJj6TbG8LiFl1639akQW0tM6f7P1GTexCVxZer1kk8+t7TnoY+uSutfdriFiguRiQuLjccN+nqXzshPh7Nqp9PcKog5auW0nWvlrxMBdwO7AzcAPxt3XJudxFDaafBaL4h/wXSJLaFciQ+IrPdq+k5A3W6u0bm1iQuWdlbY/JCYNO8vXEA6SHYHvd7H/WmIi/XfERMF1wLazcA7eULNuFQglglk4/sDXGiHJuGppeY3S64922WdFImq4m81NCMf7HsDGI3yfPweWLC0vWaxbArimtP5gIhL6g0Q9g3cB7+ph98nERNDrO39d2u1F1DN4GPCAzl+Xtl8rrus3FPfGkwnHcLnNhUQQRXXfdYmsuW79XZqYOHocsFRl2z5EMMeFxMT8D4EPE/XeDhj38Yvt7yne75qldWsSErvvnaU+HEVk+FXXvxH4bmXd5wgnwXKldcsV19E+o7p++1zbD+j1N0t9uInItLyJeL7svL6xxz6PIQImjijantHgOPcDVuqx/fzi/3nAw4v211favIqoY1e3/8u6rL+BcMCsRUz2r0HpfjfIeyMCQK4E9i7+LqfBc/MknwPgssryhwnp7weymDzXTMIf8bz09Jr1GwM/7bLPqoRz5ZsUATFEBk65zdXAMpV1zy6ujT/0uRZeV+y/TvVaGKS/c/1HSK3+N/AbIsu+kxHfb7/linPxoy7bLy3+f5ZwNi9cN0wfiOflDxf3nH8B7wfWq7Qpj0uOH+CcLEE4podqS/x+voZQYlkX2I/KM0ip7f2JZ/YLiczeT9dco/cR8x13EnMfnb9RzYH07cN8+gO2Lr1eq7LtFT2umZGMZcvXe8dGZd3lpdeNn9fm+q/b97hL26bzevm7mn+L7N+cdyD/8i//6v+IGkqPrPw4XzUCuxcTWQ5lu1d2advzAaGzb/EweQXwz9LrKwk5otk8Z2cQ+t2jPmeXEZNbTc7ZPoQj5cfERN8P6e6QHPln3MTmIA+Tbc5BA1ud6+TKmmvmHzXtWw8yiMHNcYST4sbOX027TgR+7aRN0eYHwLcIh8oxhMPkDOAJo+jrEJ/1KSOw0eqzWBT/aOFABi4FHlSzflUqgxAqk+XAeaXXZ/c4xqMJPf7dgcfM0jn4GPDo4vX9gNOBvxK1CJ49C8c/oWV7Ec6fvxT9/F+irtnHKu1OBT5dWfdQYlL44z3sb0RkEe5OyDNWt78bWFCz/oFEhmI3u9dRmmQmfot/1bm2Sus/XvfXxebhRBTxN4gJnf2Ar3Zpe1PNXy9nwsuJ+gv7EhI41e3bEJOUbwAeX/ztDPwaeEEXm88gAhXOICK8b6ISVEIEfmxZvF6HcGC9unrOx3X8ot1binb/U/zdArx1Fs/BQ4rP9RdEwfUvFu3PJaSAym2XJJxmfyGe8S4mvg+fo8YZV7r2vkRMrF1U2F+prm3D7+RNlBxUpdc9r7Fx/dFgUoiY4P0xMUH/VGDpHm3PIiYgnw+s0MfuR4mJ41cSGah/AD45gvf08xZtG703IstrmdLyshRBcvP1HBTvqXqv2IlwlNwyS9ffSsX371ele8i1xbqVZ6MP8/GvuOftQ9zvX9n5q7Rp7NiihXNtPv4RTpJXUARtEMEwz+nSdmkiY/4YYlxyKCFlWde2jROsbx8IVZvqfo8HPkMRiFlaf2nd65r9uwUz3sLMYMbGbSvva5FxQI3xGjy7+N9o3EsoBfSd66CFE6zXNVOz3Ci4p/L6fMK52pkXW7VyzMbPa3P9Vz13o2jbxmb+5d98+0tJxiSZYGz/tiIF8J9ubVtQJzHgLm2byIO9qG0HBpB6bMr9bV9QeW89a3M15G7b/+7YlbQk3c/ZywjnSzdJrGmM4zNuYHMjSXcUrwUsWyz3qhvV5hz043RikHRbExseTJriUGKSeV+ihsbO1EsGNCnqvLYL/XFJBxGTkqvbvnNEfR0I288fgZnHjMDGfOeezgvb9/aRX1nK9l+qK23/uZA/K7NCpU1ZCurBdcYL2ZlHE46qa21f26fvo2I7QmYEYmIRYkC4HpE1+bNBDUt6IBGl26kBdi1wlEv1uVyqi9WQdxByY5u7kF5R1LvYX9I7PSXv9BLgOElfsv2uQqamU8fkv7v0d08io/Z44p7xHUnf9PQahOsDl0h6m0OCF0m7Ae8Dvtyj358HLlPUehCRgfyZQuZ14Tl2O0nmzYANbDe5l67Vr02FS4A7XcjwSlqhfN+zfbKklwHvJRyMAFcRE5xXdrH5RUIu9zpYWCfnKKKmSsfuuaXXv6Ekp1t5P2M5fmH7AOAAScsTmZYz7vfj7IOj5teTJT2TyEKDLhKHDinGD0j6BIVcFUWNyx7So4cU/Xx1sbwj8bvZ9rvY6UPba2sSWNc1cppd2ImQLXwl8AVJdwNn2X5ntaHtzr30eEXdoGUcErILkbQFkUWzDhGg8kb3l+r7laQjiUCsco2YOrmppu/tZkJOqiNLdj9ikryOkZ6DDsU991BigvUgwvHxAdun1jRvcg5+SEhnle+ph0m6nQgomA2OIbKHn2H7jwCSHko41o9lpszgIksxdnwTkaW7cO7J9fWl72/7/b3suZ1840GEjOAZpXY/U9To+vwI+junOGTnT4B4XgCeRUgX/rTTRtJzinXPI8ZfhwNPdO8yBo3lG5v0gVBv6NSDOt525/fxSsKpP81kl9dVDieCpc4lPq/3EWO5l9q+bIi25ff14eKvFkk9pZU9YP2uNsx1H2xvVfzvKoFeaX+fpMs70ps9mrYpGdDrmqkud+qh1Q32TGRBYvuvpfVfBb4HPFjSp4ns6I8s3KnF89oE0EY+MSUZk8WedJglyeTyW0lPBixpaUIaahSTp200rns+IEB9YfA6JJ1r+0nFYlUL+v8I6cfNu+zelL9IWocpB9+riKjWYTlD0ocIx9JzCEmrH3ZpeyORtdTEYTaOz7ivTduD1B5rcw76cSoxAfowQg7vqG4DliFY1lHbR8U1upeks5ipsb4XkZX4CwDbl0las9Km7FD5T6GPXjt5Oi7GNSDq9v0tHOXbU6nPtojSxoH87x52qtt+L2kL2+eXV0raEvh9Zd1KRCbjI4kMPwGPl3QrMaC/g/Hy75LD5XnA0bb/A1xbOMcHQtJjiEnDnxDZeSLu8x8qag78akDTrycimBc6L23fqKhlcirhKMf2vyS9HDha0tFE5tI7bH+vh+03Alt0HFWS9iEmWRZOtNp+c3Gf/Zqkqwln4PXAkzy97tc0bB8s6cfEPUfAh2x3roX3dtpJehIhB7Q8sLqkjYhaG7vVmL2KyJpr9FunqOOxAaWaC7a/XdPuTUSNxAcQk/qrAQcQk2Hl93SVpJNs71TZf1vbxzKTpVyqn2r712Vns6S/EpNvRxHZJD0dgaM+frHvi4kM+Vts/5+kjynqIN0C7OlSfYwx9qFTk+N0STe7WU2Osz2z8Pq5FBOVFdax/crS8t6SLqtp1whFjdO/dZwixcTRywiHzNdt97p3joNzem2U9DzgZZJWI54Zf09kGpxS1764v9xF3Of/TQTi1AacFL+fL6Q04S4J2+Vah18nJqDPJBz7+xL33l4sSzxbPrfcNSq1wZq8N0n7FdvuBq6W9NNi+TmEHOxsnIMOu9j+StHvVYkAp0OJe3nrc2D7fV36fwqhPtDp4062q/UpR8WatvepHP+PwOck9XJULIr8gMhO/Bn9gwIb17hu4thq6VwbpL9zSjHOewERlPR8ItDngEqznxDvZytPBRh9pZfdhk6wNn0oT6iv3edtdZ7Jy8/jHRvlZ/LGwYxt2rYcbz2JqDl7FJGFNBeOgznvg6QFxDPT4/o2Dh5G/O5cACwMCquc2zZOsLWLz02l1xTL04J5PEBwj+0jJF1MfA9ESAbPmK+xfTrhlJ4oJL3D9peLxWPLz5CSVrH9v112XXj/7NPuWV3WJ8m8R33GoUmSzBGSHgR8hZCOEDFw3NOlqPwB7d6fiJbqDDZ/AnzKXYreSno0Uw8Ip9U9IDQ87qW2Ny5eX2J7k8q6y21vNIjt0jHWJiJ2n0xEkt1EaJMPNelfPAi+kThnIs7ZQXUTeZKOJ+S8TmN69OseNW1H/hmP8bppfA5a2FyDcMpsT0zeHkVM1A9baBxJ5xAyRMcRE/a3AZ+zvX6l3fm2t6hci1e4VEBe0n+YeqDvFOT9J70z8kaKpD/TY0Bk+4y6/RrYXZGQ/1uNqJ31U0Km5D1E7YeXDtHtRY7KtTBtExFBX574fyLhEP4WkakDkUGyE7CdSxm1kr5KTEC+z0VWQDHh+FnC+dvJXBkLks4D/h9wOyEbuGlpYuVXth/da/8edo8DjrF9TGX9K4m6GK+s37Ov3au6DczL2yS9q1i9FBFRfBYxQQ1QO3kr6Uoic+1fxfIyRO3Ox1farUjU2Ho+8fm/zvaZVXtF20fb/pWkOscFti+ptD+fCFA5sXRfqn3Pkk4napFewPTfnBlOdEkfJ+QANyDk2rYhnCyvqml7GeHYO7/Uhyur56FYf0nVUVO3rlh/CDHZcXix6rVEXbedi+3XEc7JHYiJ0OOIoIrzqrbGcfyizRWEJOQ/Jb2IkC7cgch82db2DMfGGPqwcN+qnZrlhxL38O8Qk5ad34cViZorM76/ks4l6rGdXSw/hcgmeFK1bROKa/bltn+vyNT+GXH/2hC4x/b/G8TuOJD0ZSJ79ttEvUiARxCO+Ott71mzz2+ISdYjifvIZe6SwVU4xf9FZE8sbOMic7Ro0/MzHZSm703STrUGpvo6w5E06nNQanuF7Q2LSfxf2P5e+ZlsXIzqnHexfSrxHTjMkX2ApIcQGWbPsf3scRx3EpF0me0nNGx7J1Ffq+OU7fqcLemXxHV4MSXHlu3ja9o2zhpr09+5QjOzxr5L1Bhds6btxsQ461VEUOfRhHz1Gj3s1znBTrD9w1KbNn3o+ns2KG3uoS3bNh5vFc/pnfOwIVHn9CjbVw/8xloyCX0o+nEE8EH3zhrrtH163frKuf0b8bwuYjzfeb4W4fxdpZ+9OrulfapqR6sT8okz1I40pf4Bof5xVa/jTRqSbrW9emm50fdxHN/bJJlvpMMsSSYUSava/vMY7G7siKxr2n4JQpu5PMDo+zBUY6f8o3s+4dS6sHCcrQqcOuzgWNISjgyg5Yj6BSPJAlJkKPzYDWQWu01C1E0+jIMxXjeNz8GA9jcmJKI29GAZcFV7mxOZdSsTWYwrAp/3zGyfgwnn5gcImaE9iOj/twzbh1EyrgGRpB8wJVPyLGAVQqZkT48+62+xo5ggexvw2GLV1US2xe2VdtcQ1/69lfVLEjXWxiqdqch6+xYR3f9lF3Jakl4A7Gh7hwHtXld1UjfZ1sBu0wFeNaN0Gl0mb99FODU7WWgvA77lqehMFJlsnyBqfHyRCJL4OlG36j22/1SxeaDtNxXOrZpueOtK+zpHfm1QSZOJh1LbK4u+Xmp7o+L6PMj2i2vaTutDcS1e4unBBNsQk2qvJibLOqxIyEQ+scbu/YjvxFbExMeZwDc6vy2Vz291poIqViYCKj5UsjXy4xdtFp7rwrl1nYtskZqJt3H1ofzZT3Me1CzvREzEb0bUWulwJ3HtzshGK5xahxG1liB+B95g+/Jq2yaoFGgi6b+A+2y/TxFsc1n5upkNJD222++jpF/bXq9mvYBf2163ZtuexOf1SKIu1RnAmQ7J0GrbK/q9X0k3Ml3i7L/Ky+XPTNL7bH9eU1lh03ApIGvA97YskW1xXXVbpd1Iz0Gp7aGEw3ct4v60BOE427TUpvE5aMo4nXKSViGeK1/KlATz7URg0ufcPVJ/kUPSp4BfukHWWEu7bRxxbZxrY+nvKJF0H/F+3uCp4KYbbffM3lIERuxAjHcuA75n+5ul7W2cYI37oKlgs3LQIfRwiPajYhN6BDO2bDvQeKv4Xd+BCKT6hKfLeM8Kc9kHST8nFCS6Zo0pAtDeAjyKCKY4uDruKbVt7QQr9lu12N5zLkTS/hRqR7YfU9yzT7W9ealNrfoHMFvqHyNB0m9tP7K03PX5srJfo3ZJsiiTkoxJMrn8UtJNxMPq8bb/NiK7X1JokR9LTD51fQCU9HZCwu52YoAhYqA67MRHX6nHAblJ0inEORulbvRLgC9LOpOIzPtJtwc8R52EpYkIX4iJtnvq2kr6PPAp4C7gFGKi4B22vzNEX8d13TQ+B01RSFA9n5gMfRYx+TJjAntA1rR9ISH32clc2JaIFizzdiLj8m4imvAnTNVymhgc8ninAKeUBkS/kDTsgKiNpEnSEtu3S/oMMTg0Udy8Lpv333XfJ0dNtbE4qSvHOY+p6Mny+h8TmUiD0qv+Vr/aXL0oy2iWEdOlBlvfT2x/SVFjrOPM2Nkzg0y2BZ7pqezlixUyim8BzqMiOWT7TcX/ZzbsRmO5XttnKLJ113VRa4yYcK7jLkf9iHsVGXJ/qva1xBnqL8P7e8JB8xJiIrLDncA7u9hdEviKi+y+YnKqXGdrYTS3Izjn88DnJa1P/FaM+/jFai1PTKg9i5CR7rBMpe24+tBYjsgRkHOYpFfWTQDX4QiI2Ki4DhjBxE85Cn9r4IOF3fvUuy7kuDiceilKgH9JeqJnRpJvzlQtr2nY/grwleK62JmQc34E9d+1kyU91/U1uDqcAby4y3JVZrHz3S87Q7vR6r0p5Ef71XGNTo3+HHR4I5Ele6Mjq/OBhf0ybc5BU8YWNVw4xN5f/E1DIcl46LiOPYHsScgwN8kaq2Z99Kpx3Vi+kQa10Qbp7xyyKfF7+LPC+X403X/3F+KouXqOpD0IJZLtCWWWDm3kGxv3wSMIhBzGZsu2rcZbRZsXMpUV/1UqMrnjZhL6QLPx+2FEmYOzCIWDDYjv2ww8PduspxOsuG98jBjTC1gg6V7C2fuJLn3ZwoXaUWH7f4vn7TKfJH5ztvZM9Y9PM1W3dtKp/tYtqwhUXgAsU7wuP3tf0rJdkiyyZIZZkkwwCkmv7YkI92sIB9cwzpSO3YcS0dDbEVHQ37X9qZp2NxAPFEPJ+RW2pkWmaERSj5VjLEtMOGxPTJScRJyz2noMLW0vRTzcbUdMpP7UNRJDkp5BPBDeTLy3RwI7uUaqqxMdqcjeehkxuXa6h5emHNd10+gcNLDTidx7IRGJdjTwfRc1g0aBWshjzRdqBkQnAofYvm0Im2ORhUroZId9hpj4u5UYcDyCmCj7cNmRLulXxOdanVkW8B2PP8PsXb22u77uTBO7vyPk7GZsIoIDHlmzbWQopC674nqp3C2BqztOY0krEJlCVWd7db8X2T5JNVm+KrIjitfT6lpJ+oxLWVPFusbSuirVGrO9jqI26QG2Z9QUkPQN4EPE78O7iYCCy1ySAiy1nSHDa/vALu99yaYBFAr5z2fb/r9ieXkiqvfJxfKXbPe8Hsd5/GLdLsR5ugP4k+3nF+s3JmQL687tqPvwN5rLEb3O9nckvZv67Js66dHPEFnXfyuWVwHebXug4KViUvVhRC29lwDr2b5HEaD1Q9ubDWJ3UKrPnJVtmwD7AyswJVv4SOLz3s32xTX7fJF47lmeyMg+CzjL9o01bV9OyGMuICYHh55wLybpPmf7vX3atXpvirosWxMZXf2kV8d2DhT11tZguprFmZU2jc5BU3pdI+NEFXmsZAo1yPootW0j3zjxWWODoj5ZY5W2Pb9nGkC+sW0fRoWmZytdQYyHumUrNW5btG803pJ0GPA44GRirD3rUn1z3Qe1yxpb+NtSjJEu6DbmrHOCAbVOMEnvJDL931xy9K5N/Bae4ppahmqgdqQ5Vv9oQ3E/rJvYFyHtv2Sp7S+6tIWS6kXTdkmyKJMOsySZBxSTZ18i6nGNLEpLocn8PqKeTjWqBoV81HOaTgL1Odbjyg9xGpHUY4/jrUJMOI7snGkqI2pn4Km2V61pczFRm+e6Ynk9Qsph05q2V9t+rKQDiWywUzSCWm4l+yO/bpqcgwY2TidqYBxv+6+j6FfJdiN5LLUr6jznjGtApAmoz7aoImlfYtLynSXny4pENP9dLtXJUb1U30LcPDNp0L72ki50dXA6IrsDZYC1PP6/gauAY4hMoGkOSdfX6bkU2MTFA3LhOLqonyO5l7NZLepRtUUtao1V9lsTWNH2FV227+nIKOm6TtIxtl+tkHqsc9TMyEZXjYxW3bomjPP4xcTig4HLPRVZ/DBCsvfWUrux9EEt5Igkvdn2N7t93+q+Z3XOgmGuxWJyazvCaXZMZ2KxmIB9sO2fDGK3ZR8+TnwGAnYFDuhsq7uHaar2m4Df2f5jD9vbEvKDt3drU2p7IxGwdGXnPtKj7UqEksPTilVnEBlef69p+/OmE1RN35sa1HEttR3XOdiHuHauYUouz3XPYS3PwRKObJFu279me/cmttqiqINYu4lwJlczShdZintDo6yxzj1Io69x3ca51ibLbWIonlWeDWzv+tpsjb9nRfvWTrB+fRglkr7L9GylW1xTf3KAto3HWwpZys4Yqnyfm7Ux1Fz3oeW5bVNLrrETrHhuf47tv1RsdC35Iem1xPdhEyLQ+VXARzw9oK3rs+mgz61zgaT7eUwlNZJkUSclGZNkQikmVl9ORHqtQ0gYzqiFMYDdxxAPCK8C/oeIHnt3pU0nuvtGQobgR4RkHTA9WrkYSHyBGJifDHzBReaEpO/bflmxT9lZNi6px84k03bEQ9uFhONkWJsd2cBnAr8ADuphdymX6kDY/nXhZKrjh4rMkruA3YoHu1o5oBZ9Hdd10+Yc9GTMk/9N5bGeRI+izhPIjsSAaD1gD01JXA01IBqlAz6ZwYuIibGFA1jbd0h6K1H/Zc/S+rE6xPrRy3El6R3jsDtLPIyQT9yOiEztSNX2qh+jymd2XxFN2o9e9xB1eV27n+oz4/5OOO5+UFl/t+1/d+4JRV+nTVBLeh6wgu3jOuts3yzptZIeYvunNcfbiQg6KfOGyrp3FP9fVLN/N/4haRMXUi6SNiV+Azt9fQCwO3EvP5jI9HoSIcn2mcpnN/LjF+te58jKvq2YNDwHwPYfJO0OfG3cfSAk9L5r+7cN7K1T/L+mPNnThyXKkyiKDP2BJ/GL78zRNesb18wdATeXXt8D3NKlXWdSfA3i2dXE+bi9zrlTtL0VeIokE9fmBT0cQdcDV/VzFBUcQjj1O89TOxJZyK+oaXupItjnWKbXiJkmvdXmvQFXSXpN0WZdQv71l9VGYz4HLwPWbzih1+gcFNwg6TjgUNvXVDeOy1lW8BCiDlT1t0bUnN9FnG9QZI0REmf/R9T8nJE1BtyjCKjsBKysWuw7gzaOLdsrjKm/c46mZ439i6hHW8fLaP49ayLfOEgfRskGnspWOphQLBlF28bjLdsLBu/+aJiAPrQ5t2U5dRGSf3dQP5Z9PRUnmO0bFTWETwXKWWNLVZ1lRfs/d5uDsX2EIsi5o3b0Ms9UO5ohQ1jq+0QFPUj6qIv605X1KxIZks8orXs3sK+LYLDS+gcSygNvbNMuSRZl0mGWJJPL5cD3iUjTc0do91DCSfBc27/v0qYzsLi1+Fu6+KvjEOB4om7LG4m6Jy92SEd1k3DYk3hoH1rqsYyidtdlRDbBez06ib83EBNBuzYYaFxUPDAeXiy/lumOm4XY/kAR8XeH7f9I+gdRIHwYxnXdvIHm52DOsH05cLmkI4nfuG6F7B/KVFHn19CwqPNcMQEDoqQ9rpssLL7rtZOIRQT/KbbvlPQRIvLxk7M86VzlXcCXB91Z0jMJB0inRtq1wNds/2LonvWh+I05ADigmNDZAbha0vttH95ltxuLCaL9i+XdiOCRfuzaqytdXtctQ9TJejQxKQwR4X018EZJz7T9jlLbM9S/1tjeTK+X1OE0IqhiocNMUueeuJamZ+KuQATZlDmJuEY/ZXvHGvt1vAM4VlLn+eNhhEOzw3cIWZ9NgdcVr/ch7tffYvpv5DiOD3HNd2SM92N6LaxdmO4wG1cfVmOqJulRwLF1E0IFLyjuFx9k6prpx3eA0yQdSlyDuxBR1gNR9HNGdHvx2rbXmbnXaHEpY1SRDVn7fiQ9l5gUvx7oSGw9AniUpN1cqrvVpm2JPxDBZifTJdisxDq2X1la3luRNVrHA4jvYDnDalq9swH6W67jeiQ1dVxn4RzcCCxVbteDvuegxIbEJP9BisyXQ4iMkWHr9TXhJGB5R63AaShkrhYnmtQK6tCmxnVjx1Yb51rL/s4p6pI1xpSEb5k237PGTrCWfRglC2XNHfV+R9I2x1utaXNu2wRptnGC/buHndptCpWlRxO1fK+tcZZB/I51k6TvmpE+RzxV0qdtf7izQpFp/hNm/j6uD1wi6W2FYxxJuxGqU18eoF2SLLKkJGOSTCiSpkW5zyVFdIpdyIpVtlVlhF5HTNq8hJjgmZFqrxFKPVb7OUsD4V59uB/wNqLOg4gBwzfqnExdJsc/5SGKqE7SdTOXqFTI3vZa6lHIXlNFnb9QtJlR1DlJ2iLp+8AJtr9dWf864NVdrsUrbG8oaSuiqPR/AR+yvcVs9LkOSb/1gLXGJL2QcC58AriEuCduQkyA7e5ZqieiqOuzA+F0uRj4Yl3GQdH2wcSk3dbEpM9pRL21P9W0XYKoc7Em0+WFv1Rp15E+LcueUiwvY3upSvufE0Et9xbLSxIRtc8hJM42KLUt1xqDqDV2UMVercRa3TZJawBrEdffB0pN7wSuKP9uS7qKuG9+DJhRV6hL1gfFZMf6xfv/lafX87vB9qOKCc7f2V6ttK36vDGO4z+HiJztyIFd6uk1LarLY+mD7Z8W5+BpxKT/S4mAmKMIaaw7S+2/QNSxW46pawv6ZCArMscX1snzELKJRcRxmQVE1tR7gEsqTqGxU/2cKtuuBbaxfXNl/VrAj12qS9KmbWlbG2nMc4kAr7OL5acQdfKe1Psd1tO2v6rUVKxbNwvn4HhgI+JeW3auzagxOSiSnkZ8d1YGjiMCUW4Ylf2kO2pQK6jSvlGNa7WQb1S72mit+juXSLqOqLHU1wnW5nvWzQnW5bm1cR9GiVpIyrdpm7RjXOdWDWXOa/owrSmVZ2yFDPIPiNqeVxRtHk8EiL90rueRBkVRS+444Ne236XIGO8oP/13TfsnE+OzqwnH4fVEHds/DNIuSRZVMsMsSSaXdSW9h5kTcUMV2CwG43sxFTXWeaBZu6btZkRG2grF8t+BXTy9aPhSkpax/a+if9+R9EciomW5ir3GUo8Dcj9FpP2aTD9nQ+moS3oFEeH+YOJ8dX0ILAYMX6J7RFKZj9o+tpgcfx4xOb4/MMzk+Lium8bnYELYi5Ci/AWA7csUNXsWoplFnb9KfZRykgzC24ATJO1COGlMRD4vS8im1tGZmHghsL/tH0jaa9wd7cMwDvj3EjInl5fWXSbpIiJzZ6wOM0l7E1J51xIZsh/sF6hROMa272Hzg7Y/Wyz+kIi8vpIuslGFzbbSp6sRv5+dOkbLAQ93ZCd2JPReCjzC9teBAyW9CVgV2FTS31ySXyRkZZasvvfCabNspa+3EFJ2TSbs30JE7a/MzAy2blkfFM6hbnVBVismM1cAlpe0pkM+8oHMzHQfx/H3oV1G4Lj68NMi+OUMIotwd8K59Tkia/L+JVvvBd4r6Qe2G2ep2z4FOKVum6Rz2zhtXCgGFA7cHYnv/mXAC7s5p8fMs3psWxL4Xc3624gMjEHbAq2laN8KHFZM4gn4K5HRPwNF/ZavAFsS19a5hDP/piH6W5eVWF037nNwYvHXl4bnoNO2E9CwM/GM90XgCOCpxG/Pei36mAxOm6wxiAnZOyjGMJJWd32N68byjYwvy22uaZM11vh7Rjv5xlaZa6OizXPVAM9gSUPGeG7L8o1lRKgwDNqHTxKlG7b2VH3aJYggsU8TWdfTDziZ6h/TsP0vSS8HjpZ0NPEM/w7b3+uyy1VE6ZLnE+e0mxOsabskWSRJh1mSTC7HEpMiBzE1iToKDiZqOV3cwO4hwG62zwIoHDuHMr3W2EGEg+eMzgrbPyseLj5fsddG6nEQfkAUnf0Zoz1nnwde3C3KEUDSMbZfLelKaiaYXR/dP47J8XFdN33PwYRxr+2/q4s0hKYXdd7bPYo6J8kg2L4N2ELS1sBjiYHGybZP67HbbZL+m5gY36dw6o5dHkbSndQ7xjrRooPy0IqzDADbV0h6yBB2m/JRYjJno+LvM8U9oePwH6Ru5rbEwBrCYTV07c0aPk84Fn9B9PVpRN+XI37fICRRyo69pQkZw+WJ3+myw+wEwqm2uwup4sJW1yABSVsSTs3HFLaXAP7h6fU7zgbOlnSR7YO7vZlOxlTD9347UeMPQibwIIWE6QaEtORCxnR8AetL6kQer1O87mybFlw0xj6U93088VlvR8jRfahup37OspZOsGX6N5lmeyni83oncDYRqf2bNjaGRdLLgEcRWZi9suUOAS4sJpU6NeIeSZzj6mfYpm2nH5sRMoedwDSg/jnQIdm3kULJgT7R7UcSknOdgIvtiUCAcpBVo/5K2gZ4AeGgLtdMXJGo91hm3OfgsMJ50XFgXedSxmWFJuegw/XA6USEfblu2HFFxlkyC7hZrSAA1K7GdRvHVmPnWpv+TgD/JJ4V+maNtfyetXGCNe5DkjRljI64ZxMZkQu//0Uw2oeI4Lc66gKcD2C4AOeRUgpKv4AYH5xFSKu/C6YHpSuUTj4B/DdRA3cj4OuSfg28pwgcbNwuSRZlUpIxSSYUSRfb3nQMds93Q3kvSefYfkq/dUP0pavU44D2psk1jYom71nSw2z/QSFnNYMiar+6z0lEhO6ziYnOu4gi6jPkRFr0dVzXzcg+99lAUUfuNEJS7JVEIfulbL+l2H4fU/INM+quTHDmXDJPkLS17Z8Xr9cqR8BLeoVrpNok3Z+I4rvS9vWSHgY83vU1YiaeXvejcd2rKsfoVkcTqL8vN7B5qafkn/YhJKNG/vkUn/0TiXvSBa7UHJV0oUtyUpK+Znv34vV5trcsbVsS+BTw/4jsMRET3gcTEwEzJs2KLMDtiSCMzYgC7I9yqT5Ci/fSVVqnri2RiSlHPYwlgScAtw0a1TrA8btlgAIDXzdt+7Adcf53ICaOjybqbDapp9fN7sJrd5T9Ldr/jnC0fJkIiJpG3f1ulEj6BhGY8EtiovuHtj/Zo/1jCJnL1Yjvw++AE+uy4SRtQMiM921btL+OyLCblnlavm5Kk1u1uEZxoe75vfpdb/reJG1EfK8+QciJdrgTON32/1ZsjvwclNo+g6ifdzNT96adbM+ogdT0HBTrl7f9f3X9S2aXwln1EKY7T2fcJyTdQGSDNapxrebyja8l7qmbENfaq4CPuCJH2ra/c42knerWu6Z+Y8vvWRv5xsZ9SJK5ptdcUbdtnWcnSZ8lxmdHtnmemg3URQa5g0tZ35J+AOxReSYRoZjwXheqU03bJcmiTDrMkmRCUWQa/YmInis/rP51SLufIyLFT6jYnVE3S9K+hOzPUYRTYTvgf4Hjq/sU0XpvooEcoipSj4TsVFXqcZD39inglx5xXRxJXwEeCnyf6eesbsJ7H9vv77euWF83Of64FlHodX3di/FcN43PwSRQnNsPE3V9RFHI3oV0aJKMm/KEc3XyuddkdBHBuK7tQ4v76vKukZuaD0j6G/WF3wVsZXuV2e0RSHqR7ZOG2L/8ub4c+A6RBXgPQzrcJT3a9q8UNddmUPnNvcH2o7rY+Y3tdWrWL0tk4ADcYPuuHn25yPZmKtU4k/RL209u856K/Vo7aiQtVXXkSXqQa4rAj+P4bY8x6j4QEo9HAUfb7hb13LYPrZx2LR1m36K7fKvrngVHiaKW3EaOSPH7A2eN2yHfoy9n296qT5vGk1ulfT4H/I1wnnaeye9HZFy1fs4rnALftv3aNvs1tN33HJTaXgy8xvZ1xfJ6hHN4xufX5hy0GZck40NdssZck22oljWu2zi2WjjXGvd3ElDDrLGW37NWTrCmfUiSuUbSr4hApKoEjYDvuL4m58gDnCeNzthI0qq2/zxsuyRZVEiHWZJMKJLqJkg9bDRHMRipszujxlWXtrX7SPolkf49TerR9vE1dq8A3ubpUo/fGHYwopAVWw74NzF52ennUNlCkg6tWV07AVQ3yVSecOxxjOWIqPYdbL9wiL6O67ppfA6SJJk+QV6dLO82eV5Mom5G1I5YT9LDgWM9j7I7y0h6eq/tts/otX0cDOsQqXyuNxK1Pq70CB6oJR1o+01NfqclHQH8wvaBFRu7As+wvUON/aWImkkdObIzgAPqJrcknUlMDhwE/BH4A/CGQSYIWjpqziRkD+8HXAq82fbNbe0McfwTbL+ieD2y2p2D9mGUtOxDYwffJNAmKKGPnZNtb1NafigxeX4fkYn1duAVhGzonq7JepT0LGJCrpqdURdk9YCmjq4uz3cl872f86rvrVh3CvAS2//usd+4z8GMZ+Ruz81tzkGbcUkyPtQga0xTGZePBdYH+ta4buvYaupca9LfSUHtssYaf8+KbU0dcY37kCRzTZ+5LWw/s2afiVf/0HRp5Rm4j0Rq02emcQWVJcmkkjXMkmRCsb3WmOzOeBDowbNtN62DdX/XZFF14c6Os6zo09mFs2sobK/Qv9VAdnfu10bSW4HdgLU1Ve8EIovunC77LE3UkHgN8SB2PKGJPUxfx3Xd9D0Hk4CkngWtbb9ktvqSLPa4y+u65Q4vBzYGLgGw/XtJY7mvzQbdHGKSOvVvZt1hxsyo0v47SO+w/eVisSzhdD1w1SicZQC231T8b/I7/U7g+5JeQ3G9EJGv9yOceHXsT9Ql+UaxvGOx7v/VtN2RyJzbvTjWIwl524EonE9dKU2kLws8z/bVkl4F/FTSjrbPY4DPrtKHpwG3276uCNTZErjW9o9K/Sj3c6S1OxX1IGT78Mr6NxH14Y7s9EEz6woa+AtRk+n9A07mStKjgIfYPqey4anA7z1Vd2zHloZbSwyOmEdreq25Tu25GZPo6pLBWbR9QmXdt4jJ++WIc38E8CJC8vCA4n+VnYFHE9+1jhyhqa8XeL6kywjVhZN73Uv6Pd9Jeg5R4652MzPfG4RE6znFs1NHprr6eX2L8Z6DixQy2p3vxesIJ9cMmpwDT6k0tBmXJOPjt4SSSC8GqXG9JxFc1Pde2M25Rn1ttCb9nRS+CDzXlawx4lmgSvV79lq6fM/qnGCSujnB2vQhSeaUlvNgnX3+KelPwFbEc/+9xf9J4i3AVcAxwO9p/7zctP1Qz+FJMt/IDLMkmVBqIsF/Afx3twivFnZXIgYN5QjzT9ieMTgoIjmPAw7pN2GkFnKIaiH12BZJL6F0zjyE9FbJ5iOA/YCnEP09m4iq/V2pzUrAKsBnibpZHe6sRg8Xkxo7EIVjTwe+C+xne80R9HVc103fczAJSPozMdg9CjifyoPdXGS0JIsnmpIjFPBUpqQJu8oRSrrA9hM1JUu3HHButwjg+YSkBwHbEve+1YDv2X7PHPTjibYvaLnPrbZXr1n/LSIb6mT6RMM3PM77bH++eL2tS/VVJH3G9odq9tmaiMoHuNpF3bwu9i+vZoh1WbcEcJjt1w3yPmqOewJTE5APBp4MdPr5TOK3upPVNa0/kh5LTLJ/gKi3NkjW0AnEJOwTiWDBnxDSXCcDTwcutf3emv1GWbP1BGAt4Gmu1G0tnOK/cB8JQUmrAG8Anmx729L6Rk4wSY8DPgd8yPYVlbabAR+3/eIB318viUHb/sQgdlscv3G9Qkn/IZ596yZ+trS9bKntpZ7KKJ12H1D3eidX2n58w36LyOTchbg+vwt8y/avm+xfsXUJUXeo0Xsr9qn93Dy93sm4z8H9gLcRk5Eq+r+/7bt77lhvqyyZOxaZ9qQZg2SNlfbtW+NaLeQbx5XlNte0zM6sfs/OJNRdZnzP1E6+sVXmWpJMApK2BU6xfaekjxD1DT9p+9KathOv/iHpgcQYazvCofdd4HhX6pH22L/R2GiQMVSSzGfSYZYkE4qkg4jIzI5e+I7Af2zXRYK3sXs8EYFStruRa+R/ikmc7YlI0QXAIUQ9jTtq2pblEP8N3aWL+qTD2zXykE1Q1DbYnIh+hZiYvdj2B7rv1cjuT4EjmR79+lrbz+mxz4OBZTrLLsl+SLqPkIl5g4vaRJJu9AiKp47xuml9DuaCYqK345DckBjwHmX76jntWLLYoQHkCCW9B1iXuIY/S0yiHml7v7F0cswUvyEvJ7Jo1yNqK25n+xGz3I8lgBcys5ZNowkwSb+1/cia9X0nm1v2c6C6d23sA9uWHChrA8fV2ZX0EyK7qpdUW9OssfI+JwFvciHjppC2+XrJYXYR8CLbfyzt8wjgJGAdVzLJ1SBrrGh3NfA4IoPtNmC1Imp4KcJh9ria9/V0+tTuVMOssWJdL/mrxhOMNdfGSTR0gkm6qvxeK+0bOznaoOkZmrNK8d3f3vYRpXVXAS+3PSNCvPpdLztwJX3K9kdK27pNTB8I7Gv7mpZ9fSZRE3E54HLgA7bPbbH/pcTzX6P3Vtm2nO1/dNk2lnOgqDG2arVN4di93QPURynOwaOIwC4R5/JuRlBjMmlHHyd6tzp9fWtcD+LYauJcG6S/c42kQ4hrvZw1tqSHVAVp6YgbSx+SZJx0rufiufGzwH8Rz1Fb1LS9jEL9oxQ8MrFOYUmrEfMg7yIUCQ7v0q7R2GjYMVSSzGdSkjFJJpfNPT3q++eSLh+B3XVsl2WV9i4eBGZQRPYdCBxYTEodBewr6TgiCueGUts2smFtpB7b8ALgCbbvA5B0GFEDZSiHGTGgL9fw+pakd9Q1lPRi4EvAw4E/AWsA1zKVAQAhU7E98DNFDZyjgSWG7GOHcV03jc/BXFJcV6cApxTRlDsAv5D0ifnqdEjmJ2WHWDExSL8JQNv/pchAvYOYCPqYp+Sl5iN/Ai4APgKcbduSXj4H/fgh8C/gSqbkwdpQG102hgk0dXldtzwI7wVOL353RPw+dZvUupn+Um0dR0xt1hj18mtrenrNo9uZqpMC8Xv9EKJuWueYvysc0LuXDUn6MkXWWOHg62SNvVPSMzw9a8zF9VeWh4O4HhZU+ljOsvon8Nyyncr7ejdTGd1ljibOwZGldUvVOSYKx3I/+bFO26WYOX5bs+osA7B9kaQ1K6uXqbYrsWyPbcPwLuDLY7INLMxIeRuRvXoi8FPienkPcBlTgVQAezHzM+/w9sryDyQtb/v/Ko6iRwHdssC2AnZSqDTcDd1rKxVR4a8jgptuL45/IiGfeCyRldgU0+69IelJwMHA8sDqkjYCdrW9W6nZuM7BfoQkbJXVgA8RgRZtccvxSDImqr+PapA1RgRm7ubpNa4PZbp0YmP5xpJz7UZiLNDVuTZgf+eatxL3vT0oZY2VG0g6xvarJV1JzbNMl0n/xvKNTfqQJBNIZx7qhURG8w8k7dWl7b+L50dDBJjMRgcHQSE5vQMReHky3b+30HxsNOwYKknmLekwS5LJ5T+S1qlEgo/CyXSXpK1sn13YfQpwV13DUkTJzkRUyReJSYenAj+mNMklScQD9Vq2P6moUfMw16dt31A43fpKPQ7AykBHAnGlEdn8SxFFflSxvAPd60R8iohw/5ntjYuI4R3KDRzp/pcC7y/O/w7A0pJOJmTKvjlEX8d13bQ5B3NK4Sh7IdHHNYGvUj9xmyRjo7gnfoyYpBSwQNK9hPxqrTyZpN2BI+a5k6zMh4jggP2BIyV9d4768Yh+kaCaWTNq4Sa6OBKKaPgPE46nctTloFGng9S9a27cPk3SuoQzVsCv3F327PfF3wKmJiir9naGhRlOG1SzxrrY/UXh3OpIMm9PSBN3bP6sy7H+Dny6svo51GeNfY74jS07zH4k6SzCYXQQcIyk84gssml1WVpGxi9RN6HqkPlZqrL6YOA4SW+1fTNA4dD6erFtIarP3luFkNs5rrK+jRPsQklvsn1g5XhvpPfEyjDMRs2Lwwlp73OJmnzvJSbRX2r7snJD29XzV972/cryx7q0uwF4VRczz2/aaaK/hwMv83SJ64skta5p2+a9FXyZkAc/sWhzeREgV95vXOfg8a7JtLb9E0lfbLB/LQqFhw8RmWZXAJ9zjTJGMjtUs8YkzcgaK9G3xnVLx1br2mgt+zunFL/fXyr+urFn8f9FLUw3doI17EOSTBq3SfpvQhJ5n2LuoFuwyTFF25UV6gG7EAHlE4OkvYnv+LVEwNYHe2XUFvQdG7VslySLHCnJmCQTiqRnEQ/s0yLBbfeSM2xi9wmEXF/HmfS/hDTgjCykIgr9dOBg27+sbPuq7T1Ky/sTUSdb236MotbGqbY3r7HbWOqx5XvbgajPcTpxzp5GPDAcPaTd1YGvAU8iJvh+SdTvuqWm7UW2Nyuyuja2fZ+KukR9jrGAeGjb3vYuQ/R1XNdN43MwlxRZhY8joqqOtn3VHHcpWUyR9E4i6/XNnpJeXZtwHp1ie9+afT5F3BsvIe6LP/Ei8KBWvO8diPe2LuFI/L4HqNMz4PH3AU6zfeqA+9+vzrEk6TpiYn5a1OWg90VFbaV/MOWk+2dnE7CM7aoDZpBjPJmZsirfHtLmNJm/4vfsCneX/ns5U1lZZ9r+XsPjnGx7m+pxJS0D/AF4uO27imCfK21vUNn/ScTE6nmS1iHkQm8lZCl7Rs2qiySmpGuBzbpkjV1o+9GV9W8BPkhk9AD8HzGhv3+lXTmjG+J393+IWmdVucmjgJ93cYI91/Z2pXUPIaRR/82Ug2wzYhL55S5JYY4KdakBOOJjLJSTLD7/vwCr102iS9oC+CawDvHdfaO7SAe2aVuzb1dp7lIbjeoeL+kET0mb7kk8C95JOIg3JiQeT63sc77tLTS9TtmMuoZtbFb26SVP/mvb63XZ7zrb6zd86+X9TiBqJF9MTPK/CFjB9hva2kpGg6QrgLd5etbYN+omYNWixnXVsUWNfGON/Sa10Rr3d67QAFljkvax/f5+68bZhySZFCTdnwjsuNL29UWg1+O7/Z4p1D+eSzyP/2TSAhsV6gk3MhUE3/lO9spwbzQ2GnYMlSTzmXSYJckEU0S7NIkEH8T2igC9nFQqJFga2rvE9iZNBt2V/TpSjysTUdPTpB7bUjzwbE6cs/PHMfnT5/g/A15G6GE/iJAk29z2k7u0X42Z2Qln1rVt0YexXTeTTvHA2Jm4LP/AZe2KZFZR1FJ5ju2/VNavSgQTbNxlPxGDsp2JiexjiKCF34y5yyNHIdn1ENvnlNZtSGQ1PN32qKRo+/Xj5URtoAV0qWUj6aO2P1mz74rAibafUbPtbNtbja3jI0bS4cTE/2VMZR67HPxSavtDZk6C/R24CPhv2/8qtf0a4QgtZ43dYHuGBFzRfg1gXds/KyYtFmZpKeRkancDTrL9sJKdfQgpyGUI+cNHA52ssRttv6WLrdaUn20q699DSEHWZY39wvYXuthbnhiHDS33NYgTTJH93nFoXm3759U2LfvQM0PT9lhVTaoOzW4OzmLbRYTT8kzgJcD/s/28YduW9nkJocgwTZrb9mNr2q5HyEauyfTnwBm1fIu+HErUtfzfPn243PZGkp5HZIp8FDi0ek4Uag9fIgKitiQySjazvf2gNpueA4U83tdt/7iy7zbAHmXneJtzIOky208oLQ9d/zEZHEnn2H5Kv3XF+sY1rls64ho719r0d66Q9DDbfyh+S2fg+oDOGd8DVeowtXGCDdKHJJkkinvGurYPLcZmy7sIcKy066h/9PzdnUu6fQ87dLkn9B0btWmXJIsiKcmYJBOKpLcRP85XFMurSHqj7aF0wSV9Bvi87b917ALv9vS6BPtRPCjH3O106ibXgHuKqN7OfqvSRedYLaQeW763lxNR1icWyytLepnrZWja2D2MyKb6W7G8CvBF12eCvZSI7nknIVG5ElBb56aY7NsOuIbS5CUVeaiWfR3XddPmHMwZtrvJKSTJbLNU1VkGUcdMM6Xaytst6Y9EDad7CRm24yT91Pb7xtfdsfBlQhprIbavkPR+4OOz2I8vEtmxV7p7pNhTJX3a9oc7KyQ9FPgJ3SVdPy7pIOA0ptdFmVQJ2M0I6cQm0XI3AqsyJcO7HVP1xg4kai4BYHt3Tc8a+6a7ZI0p5GzeDDyAcN6tBhxAOJ0ALgTOgFoZv5XLC7bfr/qssYOoyBZK+ivxOXaysdpGDP6obqWj7uD/AWcUTjDonjX2YiLz7hbb/yfpY5JeCdxC/L7eVGr71V6dKT+H2b4deHLFCfajOieYpK1t/9z26ZJurhzzFYNeu577ulEbSeoEgAlYtlium9hZ4Kno8GMlfbCH3TZtO3ySPtLcJY4lrv+D6C+f3VFmuLDkODq1y7Xc+f68gHBqXa66B3p4C/AV4nv4O+BUwhlWR1Ob0OwcvBM4SdKrme7ofRLd5eOanAMVz6idvi1RXrb9V5LZ5AKFnFk5a+wXneAIl7LGaFfjuq98Y4kmtdEG6e+c4Kk6oLu5JmsMeH9p+a3AbsDahZOxwwrAOUynsXxjmz4kyaQh6ePE7836xL1gKcIpVOcYfyjxmzOx6h9dHGIvsn1Sj92ajI3atEuSRY7MMEuSCaUaIVmsu9RdshJa2J1hoyYyd6deNmwfVmP3tcSgYhNC8vFVwEdsH1vTtrHUYxtm+ZzV2lULyQuFnNeGHm3m4JyfgyRJ+mY4dJN32wPYiZATO4iQLbxHIXF3ve11xtrpEaOKVF9l20IJtVnox0+AbdxDdk8h63cc8Gvb71LU+joZ+ILt/+6yz3eIrKarmQoQ8aQFEnSQdCyRufGHBm3PtP20unWSrnYlW0Y9ssYq7S4DnkhkgHey0ctyelcRWVHX1+z7W9uPbPyGp+97HbAfU7UtjwOOsn3eIPa6HKNn1lgxWbmlo87ai4jMnh0IabttXcpcqjyH7U3FwVx+Dus4wYrXa/VygpXvPTXPfotFJk7xDPqe0qr/Ki9XzlfjtqV9GktzS7rY9qYt+7+AmNDuSKEfAnyl7AhSSHquBqwFbAQsQWQ8blqxtartPzc8biObRdtG50ChiPAaStmORPbYv+hBr3Mg6eZiXZ0zz7bXbvJ+k9GgdlljNxH35r41rtVOvnEsWW5zTd09WzOzxlYiAq8+C3yg1PTObs7jlmPZvn1IkkmjeBbdGLik9Cza9botgkPmlfpHv2e6JmOjNu2SZFEkM8ySZHJZIE3VNlBkZfUsVNyQJVSqxyJpWeB+5QZVh5iiFofdQ57R9hGSLiaixEUUMO822Nmwm61BnWUFddlFo7jPLZC0iotUfEkP6GH3OcyMqtumZh1EBP9SlDITRsC4rps25yBJkukZD2VEqaZLhQcBr6hGChaTjW0Ktk8K3d4nRI2u2eIPRIT4yUzPBPtS6fW/FFlSR0s6moimfId719faaLacfiPiQcA1ki5g+nl4SU3bVSWt7qLmkKKO5YOKbf8uN1T/rLEyd9v+t4rEFElLMl36aS+6F16fJvGodllj/7D9NeBrxXvZHviGpJWJepcf6rFv+ZjTHL1qkTVGPEd16tK9gphsuRi4WNJuTG9Ydoi9oy5QqcR/EcFKEJPF5QmSjzA9Q1JdXtctzxvaOA2JDMYXd1k2089Xm7Yd/lY4T88EjpD0JyJbuNzfBxQvf6hQBjiB6d/JbhPZGxITdi8gPusjgK2AnwNPKDV9Y7F8Y+GgfWCxX5VfFk6K7wLHu1AR6EJTm9DgHBTv825Jj7b97sr77Fpbqd85sL1mj/eQzD5tssY2JO7NBxdO0V41rp9Q/K9mqz+Z+G6WHVvjynKbE9Qia8z23wkJyh2KfTt1BZdXlF6YUVuRBmPZNn1Ikgnk37YtqTNfslyvxkXb+ab+0e+Zru/YqGW7JFnkyMnOJJlcfgIcI+kA4uH+LcApI7D7HeC0IlLUwC5ERtgMJD0OOJyYBJOkPwOvt311F9vXA3dQ3FvKk23F8iBSj224SNKXiNohJibXuhZ/bsEXiUmF4wq7rwY+XW4w4MDhn8BlkqpyXsOch3FdN33PQZIkU3iA+ly2PwbTJjQ662/tF209oVwo6U22DyyvlPRGRnNvbspNxd/SdAkgkPSu4uUFwPuAs4C1Ouu7DAzPk7SB7WtG3+WxsFeLtu8Gzpb0G2LQvRawWzGpUH1meBtF1hiAo4D6g7vYPUPShwjJvOcQv5s/7Gy0fVyX/fBMeeU/E/XYPgF8u/h96pY1tvCho3gu+TzweUnrExO0Uw2lV3TpgghpnjKfJqTnKJzar2Mqa+wAoFzvSoUT4Z+EM7EsldzLudxPDqSNE8xdXjc5ziTT2Glou5uTZwZt2pZ4KfAvpktzf6LS5mLifHc+n3dXts/IgiqC0v4GHAx8wFPqBOdLmpYpUwRZ3A5sUDila7G9rqQnEt+BD0u6hnBQfKembSObBU3OQYfGgWZNzkHhgPuVutRD9ARI6i1m3FDcm/tmjTmycw8EDtRUjet9i/2rNa7bOLaeUPxv4lxr3N855EgiA75N1tiLiazmaXUFgXJdwTZj2dZ9SJIJ4pjCib5yEfS1C3HvmYFmqn+81yX1D2LMMIns2md737FRy3ZJssiRkoxJMqEUP8JvBp5NDKhPBQ4aRdSbpOeX7dr+SZd2vwQ+bPv0YvkZwGdsP7mm7duJgcjtRB2GTt2IsixEa6nHNhQTeR8l3hvEOfu07X8MY7ewvQExoBJwWnmCVFEb4T7aS17Uno9hzsOYr5ue58ATXAw3SeYD3SY0XJG/my9IegjwPSIjqVyjZmlCdu+Pc9W3Kop6Bl2xPaMWpaRriYyqm4ighxm/e5OGGkonFm3vR0hOCviVu8ikSTrf9hYqZHqLyfRL6s5D8Rv1RkLaRkQtiANL299V3adM2XGp6fKCnayx7YlaZ9OyxiR9yXZP26W29xBZK3WDpFe5VK9L0uW2NypeHwJcZ3ufav+K5V2Imn53AH+y/fxi/cbAf9muy8hrIqvTWGZR0t+IrB8RdWM7NVMFbGV7lW7HmWRUkohWRS66ulxavxLx3NqRHj0D+ESRkTFw29I+K1IKTq17FlSoPOxGZEiZcNQfYPuumrZr276x2/EqbWtr5Lo+m7Szz4OI35/X1gV8DGiz6zkoTc6vA5QdISsA59h+XY29vudA0jdtv1nTpfUWfpc9QZJ6iwMKlZJO7bmeWWOaWeP6cKZqXH/G9nqltm3kG5doOg5q099JoS7IqqbN5cQYblpdQdtvLrVpLd/Ypg9JMkkUQVvlZ9Gfdmn3CUIRoK5O2GMmybFeuoeuyfTf3swGS5IBSIdZksxTJB1v+5VjsHuu7ScVrxdOBJW2z1hXrL8B2ML2/7Q4Vl+px1EiaT/bb+/fsrXd6qTdDLoNHCQtDXQGgNfZvmfU/ascb1zXzWJR+yRJxkmTCY35SPE+FtaocSGdNovH3wz4MOGALA8gh3JsFc6nGdQNqicBlaQTba+jqNN2QJ2jpnCmvQtYw/abirbru6aAuKTPE1kfrycyu3cDrrH94Zq2e9r+Srd1ku4jssY60i/TMqTKjssejpD1ge3rnJxNKLJYdrJ9Vc22aXXUiij8JxNZYzcBr7R9UbHtGtsbVPZfDXgwcLmLehCSHgYs5ekZ+XcyNcl//8I+TDllVyy1/RsNnWCSnt7rvds+o9f2SaWN07C0/njgKqYyJnckZFZnZBi2bLsrkU11F1O1tOya2lmSjiEcqEcUq3YAVrb96i7v84VERkh5YnpG5pYa1sgtHFovJxwE6xABDsc4pEIHslm07XsOBp2c73cOFBlzt3YCMorgtFcCNwN79Zv4T8aHprLGViacXdOyxtSixnVLR1xj51qb/s41bYKs1KK2YmmfJo64RSrQK1k8kLQ7cIRbBPvOB6ewpB8T2d1XMlVbuVvQX6Ox0bjGUEkyH0hJxiSZv4yraHVZFuhGSR8lIvwgpIZumrkLAL8lNNL7ovZSj6NiRnHnEVGe0PsRUzI7yxAyVtdRkrxYuFNk7B1GDOIFPFLSTrbPrLYdIeO6buZt7ZMkmSDusf0/khZIWmD79CKqf17jyFI+vW/D8XEE8F4qA8gykr7ay4BrpHJt31JEcz6E+fFM3UY68VAiK/BJxfLvgGOBGQ4zYrL7jcT53RX4sSsynCV2Ar5SWfeG0rpNiEnQFxbHP4rIaK6L8Ku9pmxfB0ybHFDUjNod+D0h5/ah4r1dS2QulCdN3kE4Mep4eWX5y4SD7w5ikrDjLNuYqPtQ7sPrHFJ3tynk484p+vuHYvLma6X3sALNeWnp9X9VtlWXNwe+a/u3LezPB9aWdCLxLNJ5TbG8Vpd91qkEEO0t6bIRtH0P8Fjbf2nQ7/UrQWinFxPaM1BIbd8feCYhC/UqQkK2jqY1ci8Hvk9ky53bp22burt9z4GL2kqSPgL80VHP7BnAhpK+7Zp6ag3PwQEUShOFw+OzhCP/CcA3i32SWaIma+yLTGWN/ZipoEFoUePa7eQbG9dGa9nfueZThCTwtCCrLm3/pgZ1BaG7E4yasWzLPiTJpPBQQjb+EuJ+8JMuz5ltvw9zzSNaOLL6jo1atkuSRY75MLhPkqSecaWHlu3uQkw6dWo/nEmlwLem5JNuJAqC/oj+BUG/CbzL06UeDySitOcjZamXx5c3KGoodNOQ/iLw3GJyD0nrEQO+TcfUT5id6yZJksFoPKGRtOLPtk/s0+YtRAbJMYRTpW8QgKZLEXcGkSYm5yaRu23/W0UNUYV0Yrd79zq2t5O0A4Dtu6Sa4qPB24sMsbK04rRMssLOa4i6cOXPYgVgYWa67csIB9QHJD2ZmHjbT9L7q5+hG0osFnyHGOxvSgT/XAnsQ9RP+hYlp5Pts7oZ6TjESsuHSPoJRdZYadMfqTwvERl7ndpQ+zG9ztYulBxmLWnjBFuNqEd6E/G8cWxDx86kU+c0dGW5yl2StrJ9NkDhxJwhhThA298wlRHYj0slbemi7p6kLehe9/bJtjeUdIXtvSV9kVJttgpNa+Su3W2SsOhPWZmhTd3dNufgeGAzSY8inNknEvWRXlDTtsk5WKKURbYd8E3bxwPH93ByJuPjeiK44QuVrLHjCkcXGqDGdRvHVkvnWt/+ThBtgqxeStyzynUFu2Vht3GCLZKBXsmije2PFEHhzyXuIV8rMr4Ptv2bSvP55BQ+WdJzbZ/aoG2TsVGbdkmyyJEOsyRJZiBpGWLy8FHEpNK73V0qsBMFfWvx16Qg6HIdZxmA7V8o6o8tcti+RNLmXTYv1XGWFW1/LWmpWepakiSTR92Exgy5raQ1H5d0EFCd6C1PtD4M2JaYYL0X+C5wfB+5lj2JDJHGUsRzzBmSPgQsq6jdsBvwwy5t/62or9SZxFyH7pkl/bLGAH5JZFw9iJjY7HAncEXVoKRVgY2BxxPZbX+qadMma+zhtl9QOP1+Z/sZxfqzqpPo5cnbOiqyYI2zxpjuhK3OCg+Tpd3YCWb7nUWg09OIjIuPFhlNRwHfc5d6dvOAlYnI6q8DSLoAWJX4HN/fZZ+3AocppAEF/JW4bodt+0Hi8zif/o6lLYDXS+pIO60OXCvpSmbWQ+w46P4p6eGEo7lb9tyJxV9PejnLCsrKDI1sFrQ5B/fZvlfSK4Av295P0qVd7DY5B0tIWtL2vcCzCBnaDjn3Mfs0yRq7qG57Hxo7tsaV5TYBtAmy+pjt9xPBPYcBnbqEdffHNk6wDPRK5iW2LemPRIDTvYRE8HGSfmr7faWm88kpfB7wvSKT9h5qZLxLNBkbtWmXJIsc+dCYJPOXcUngiXiQvocoPr4N8BhComgGrmgiK+ohuM+kSxupx1EyznMWL6Yy7iA09TcB/txlv4skHczUeXgtIUE1TsZ+DpIkGQzb/yhe3ldk6/5PgwnNpD87A48m5MTKmWALB3uF0+sA4ABFnakdgKuLzKbDqaexFPGEUJZOfDPwI9sHdWm7F3AKIRV8BDFxXs0wb5Q1Bgvrut3ClMRjLZJ2JpyWyxA1Y15te4azrKBx1hiwQNIqRd+Wl7Sm7ZslPZCZQT7lydu9iSzCbrTJGnOX13XLjWnrBCvuKWcQDtTdCfm6zxHX//0H7ccc8z7ivXdYGtgMWI6QFz22ukORzbhR8dxKnTzbIG2B/wZ+TjP5ouf32V7mJEkrA18ALiGumVrpU9uHacQ1clvabHMO7inuJa8HXlys6xY81uQcHEVc238hHGxnARQZbPPpfj2vaZM1Zvuw8jY1q3HdxrE1liy3CaBN1thzmOkc26ZmHbRzgrXpQ5JMBJL2IIK9/kLI+77X9j2Fo+l64pmiw3xyCn+ReM6+ssH4se/YqGW7JFnkUM7DJMlkIelbtt/QoF3TdOu2x38ccJQLaUGFZNMFrimYXtlvM2JSopNx9ndgF9cXDl+FeJjeqlh1JrB3n0j+Xsf+jO0PNWj3Btvfaml7AXCF7cf1aPOAjvyLpPLE2r1EfbLjbf+rZr/7ETVltiIcTmcC33CDguqDMsbrZuE5SJKkHZK2JCas/wp8knCiP4hwur/e9ilz2L15j6QrXZHL7dF2E8JZ9hwigOGLtq+ptOkERjwWWJ+oXdlPinjOkPRSumffvM/2cV32eyAhQyPgvGrWkqQ1iOyOzxLOuA53Er+bMyYUimt9PyIQZ2lgCeAfnehXSfcRk+ydjJtpAxXbLynZusz2E0pZY6tVt5WWdyDqjUFk1r21sL0B8fzxzS7n4FLbG9dtq26vtq1Z/idwA3E+1yleUyyvbXskmfZFNkXHCba+7VonmKTHE06m7QgH51G2vzyKPsw2ki60vXlp+Wu2dy9en2d7y9K2nlKe5e9vm7alfX5pe6wS48Xz4zKOOmB1259BpUYusJNb1siVdEnn+b+NzTbnQNIGhKrFubaPkrQWsJ3tz/XZr+s5KO4zDwNO7QSiKGTPl7d9SZN+JcMhaade26tOsmKfaTWuiYDDaTWu1SIDuLTP8n2cbwP1d66RtE+RNdZ1naS3Er95axNSqR1WAM6x/boau8sRTrAFTDnBvlM3zmvShySZNCR9gpBfvKVm22NsX1tarvs+HOEJVJdQSIRvY7tvrbGmY6M2Y6gkWdRIh1mSTBjlwemI7b4ReIDtLxTLtxEPyyImzPbv1ocmfZJ0BfA2F7U/JG1FOH82LLWpSj0eMmzEa9P+DWn/COCDtm/t23iOKM5/7SZmyvq0sXsnUwPTTsiliQzlpW1npnKSDImkiwg5uZWIGo/b2D5P0qOJSeyuE/ZJfyQdCOxbdXxV2uwNvIiQ8zsaOKXO4VO07ZVxZNsTJaMp6Rxgexc1rhQShFsDywOH2n5WzT6nVdfXrRugLxcRTppjiQyg1wOPsv3hYvvTa3Zb+Btk+4ySrSuApxPPMlcCG3kqa+ws2xtUjr1EYePeIhjoCcBttv/Qo789ny8qDoWez06Fg7ErdRM3bennBJO0brF9B+A/xLV+lO0bhz32XCLpBtuP6rLtN7bXKS33+v5OU05o07a0z6eJbMofMt2RPlRQkaSziMCqs4iJ7q5KDpIuBl7jSo1c261q5FYcwo1tTsI5SCaLJlljkn4JfNjTa1x/pux8bePYGsS51qa/c03d75Oivl957L0SITU3I7Cl2/exjROsSR+SZFKR9GBC0QCAfnM9kh7EBKt/SPoW4Rw/mT6BfE3GRm3aJcmiSE50JsnkcX9JG0O9xN0QkZFvYbr0y59sr1Y4sU4F9i9t20hSR25GRL2TO6CnDvKdHWdZ0c+zC2dLmcZSjy1ZQpG11u2cDZv59DBCmusCoCOZVo1071nXodL2GNuvVlGjoqbtIIOM+wpbRxITFN2K0bfC9grl5WIAuRuwK/C9URwjSRKWdJH5KekTts8DsP0r1UgDJa3ZCthJUePpbuoDCT4K3AhsVPx9pjj3M9p2JsklbWt7mtSbpG3H+UYGZOmOs6zg7OJ38a+q1A8tngnuDzyo8ru6IvDwOuPqkzVWxfYNkpaw/R/g0GKStMPKNK9F9VngV8XrXYCDJC3MGqs57n9U1AktnKEXFcd4kLvU/GrAowvHnYB1SsErIiYtyscf2iFWRxcn2HO7OMF+QkjWbWf7ynH0Z444X9KbbE+T55O0K3BBeV3p+9s3M75N2xKvKf5/sGyKyvUwADsR97JXAl+QdDfhGH5nTdtGNXIlPc72VT2OWa5D2KbubuNzUFy/nyW+t+WJy7rz1eYcJBNANWtM0oyssRJ9a1y7nXxj69poLfs7J5SzxioBkytQ1NHs4MjA/Luk6u/n8orMuzoHQV/5xjZ9SJJJQ9KLgS8Rz7V/AtYgAuYeW2rTVf1D0qSqf9xU/C3NTLnxKk3GRm3aJckiR2aYJcmEUTiZLqTe+WPbWw9o9+JyFKikD9n+TPF6mpTNgPb3JSbZjiIGxdsB/wscX3T8EpVSutVQ6rHhse8GbqP7ORtqkqJLxDuVSPc/E/VsjgLOr/al0vZhtv/QLdp80Em1IhtlB6IGxDWE8+xUd8mSaGl7ZcK5+frC7r6eQCmCJJmPtMlSSdrT5F47SPZPl8jqifu8Wmbf7Enc6x/O9N/VO4ADbX+txkbPrLFK2zMJucCDiELrfwDeYHujYntdNtyzKGpReWbWW6OsMUnPJCY77gdcCrzZ9s3Ftup3rpxZfX/gn51NVIKGBrxuXkHUWntwYbNXMFJfJN1IPHscvYg5wRpTRIl/n5jM6QSWbUp83i+zfXvNPtcDlxFy4if3ihhv07ZBX59j+6cD7vswIqvyqcAzgVttz6iDJukQ4hou1wpewna1DuHZxKTat4Ajbf+tx7Eb2Wz4Phaeg6IPHwf2JZ5fdya+07XZfU3PQTIZNMkaK7X9HvH9LV9jm9l+WU3bvvKNNfuMJMttrhkwa6wTpCnCMb0WUYew7CBoLN84SB+SZFJQ1HndGviZ7Y2LZ8QdbL+51GaRVv9oOg816vmqJJlPpMMsSSYM9amXMYTd2gkzRY2uG0bgVDq9x2bb3npcE8HjOmeVY6wBrGv7Z5LuT0wS3FnavgQRkbcDsCFR0+aoPgO3sem+S9oO+DqwjwsZzgHtPAh4N+EAPQTYz11qZiRJMhiS/kNkrwpYlukT9MvY7hbFnzSkuEc/hJK6QpfI6vI+L7J9Us36bYAXAK8GvlvatCKwge0njqTTI0IhK/yLLtk3z7C9Q80+b7e9X0P7F9neTCUZJnWpYVT8lt5OTNK/k5iI+IbtG4rtjWtRldos5Yq8czVrTNKFhGPuakmvIib5diwmP8b+DFHp2w3Ai12qkTGLxy47Ayle/wU4HXj/fA+EkbQ1UxHiV9v+eY+2Ipy3uwBPJL7L37L962HaNujjQM++kn5DfFZHEkoNl7lLnRLNrJF7BrC/a2rkKjK8dgG2JbLxDq1z6LWx2eC9lINELra9aSWo7izbTx3mHCSTgaTLOwERvdYV6xvXuG7piGvsXGvT37lG0up16/s92xT7bgLsanvX0rpBHHED9yFJ5orSc+vlwMa275N0Qfn5XaVauJKutf2Y0rZZfW5siqTNgA8TGXPl8U5tNljTsdEgY6gkWRRIh1mSTBhjdJh9A/ir7Y9U1n8KeJDttwxpvyOv1KtNZ1IYpk8MDxtdPdaHFklvAt5M1IBbp5hcOMBdarkUkwo7AF8APtFt0rFLdsLAuu+SViOi/F9OZPcdA3yvVyRlA5v/IAaWhwIzakW4RhM7SZJkkpD0diKD4XZCvhYayIl0m9iWtBGRyfQJ4GOlTXcCp9dN7s0lg2TfFPs9GViT6QPkb9e065k1Vmq3BHBYOUq9xlabbLg2WWPTJjwlPRY4gZgU/OiwwTttssYknWP7KcMcr2JvKCdYMUn9BuDJtidRUnTsFNfSd4hMxsuBD9g+d9i2XfYf6JlVkf25FfBIQor0DOBM278ptVkVWNWVWiOFw+B223/uYnsJ4GXAV4lsUgEfsn3CoDb7vJeF50CRVfpU4Djg50Rm6+dsrz/IOUgmCzXIGtMANa5bOuLGkuU216hB1lif/bs94zR2gg3bhySZCyT9jPjN+ywhs/gnYHNPr5c479Q/JF0HvJe4jy4MJnG92kGjsdGgY6gkWRRIh1mSTBiSPmr7k2OwuxwxmbU5McCHmPC7EPh/wzhVCvs3EYPdQ2Y7alrSG4iBzSqdiHJJSxMTQO8sRwQNaP8yIqL4/NIAf2EkbKnd/YAXEs6yNYETifNxW6VdY8mLFn08o9j/GOJzmBYJ2C0ysIHdveheMNu2PzGI3SRJktmiyOjZom32TL+J7brMpkmmZfbN4cA6hAxdJxjGtveoadsza6zS9idEdtW/uxy3cTZcm6wxhbTOi2z/sbTuEcBJwDqu1OtsS5OsscKpBiEn91CmnJgA2D5hmD5UjtXaCTapE0DjQtIDicnwHYnr92Diue0JwLG21xqkbYPjDnWeJS1PyBa+h6j3t0Rp29FE1tcZlX2eB+xk+zWV9RsWtl4I/BQ42CGh/nDgXNtrtLXZ8D2UJyI3J2rHrEzUiVkJ+LyLWp5tz0EyWTTJGpP0XabXuL7Z9jv62G0j3ziWLLdJoy5rrLTtXaXFBcAmwANtP6+m7cBOsF59SJJJoZgXu4v4LryW+N05ojxO0DxU/5B0tu2t+rdsPjYadAyVJIsC6TBLkgmjMog81/aTRmx/baYmzK4ZVVSmQhd+e2IAu4CQ7zva9h2jsN/n2NsD/0081FwP7EUMoC4EPmn7ku57N7J/vu0tOhNwijopl5QjayQdBjwOOJl4312LqGsMuu+SbmbKsVW+sXei3IctNl93zM1tXzhqu0mSJKNEIRn8HLes5yjpibYv6LF9XeI+vgExqQTAOO63s42kawl5yZ4DhSZZY5X2/01M1J3IVMb5wmzlNtlwbbLGJD0b+LPtTsBQZ/1KwO62P92k/z3eV9+sMUmH9ths27sM04cux2zknJG0FHDx4hQxLOnXxLPiobZ/V9n2ftv7DNK2wXEHlWT8IjGJvzxwLuFcOMv2jaU2V3eb1JZ0le3HVdadCRwIHGf7rsq2HW0f3tZmw/cytnOQTAZtssY0QI3rNo6tcWW5TSI9ssbKNQHvBW4Gjrf9rwY2WznBFrfgi2R+oyg/8T/9nnfnA5KeRQRun0afgKymY6NBx1BJsiiQDrMkmTA0XaakZ3T7CI61m+1vjMHu04gC9CsT2U6frIs0H+HxriIm0m4oHurPBba3/b0R2f888Dfg9cDbieywa2x/uNTmPqYm/+ocVl3lJosJwvJk68RqQkvagHCM7gD83fZmc9ylJEmSWkoR1Y8F1idqS5YHkDMkZQsH0AuZKUVY1/ZsQqZkX+DFRMCIbH+82na+IelYYA/bf2jQtmfWWKVt7bmxvXelXd9suHFnjTVhNrPG2lLnBCv1t8wqRJ3Ss70YZY1LUtMJsjZtG9g6wXbd59Bvv20J+cFaCdWiza9tr9dl23WukTlscNxx2Fx4DiStR0hIVWuubF2zX99zkEwGbbLGqg6WXg6XQRxb48pym2vaZI2N4FjdHHGz1ockGRZJWwKfI5R4Pkk40R9EXLuvt33KHHZvaCR9B3g0cDXT5RN3KbVpNDYaZAyVJIsaS/ZvkiTJLLOgeLBfUHqtzsYhMpDeVbP6Q8XAY+gfvdIk487EROMXgSOIugQ/BmoH2yPi3x2HnENK5qZROcsKPgC8kRiY7Qr82BW5KNsL2hqV9GLgS8DDCe3sNQhZmta674WjsCvDZNkp5LZ2KP7uJfq5mYtaMUmSJBNKx2Fya/G3dPEH3aVmfwj8i4r+fxeWtX1aMZl+C7CXpLMIJ9p850HANZIuYPoA+SU1bW8GzpFUmzVWpuoY60bhIOsqGVnwAaII+UKHme3fSXo6sHuT4wBIOtn2Nk3bV3hx6fU/geeWlk1kvPU69tCR+H2cYMdV1r+4smzgf4Cv2P7RMP2Yh6wr6T3MdI7PcNS0aVs4cg8FjqzLdmnrLJO0oe0rbB/boPn1kl5g+8cVG9sAM7KwGmbJtrJZbGtzDo4FDiAy3WprIbc8B8lksEEpa+xgoGu2NrCRpI4iiYBli+W6oMPDmO7YegzwjjqjNc61d/dwrrXp76RQDgq5l5jQPr7coPhd7krdb3oXJ1i3WoV9+5AkE8TXgA8REow/B7ZxyHg/mgj2ntcOM2AjV0qG1NB0bDTIGCpJFinSYZYkk8dKwMVMOcnKjg4Tda8GYW/CcXV1yfYSTH/QHYbriQLzX7D9y9L644qMs3Hy4MrD/fLl5RFEwLzd9leIwTwAkvYs1g3Dp4AtgZ85pB6fSTilBuEi4rPtDGhU2magbgKoL4pC2SsBRwOvsn194ZC8ecB+JkmSzAod54ykbasTrUWmQh2PcHNZun9JWkBMKO8O3AY8eOAOTxZ7tWj7++JvAX2eKST9kJkD7b8Tv2H/3UQeqoPtn3VZ/3dgmsRij6ASEXWoBsL2zoPuWzr+sDR2go2gv4sSHUfNQXRx1AzYtiNPfmHJcXTqEBlqlyrqBB8FHGX7mh5t3wmcJOnVxFgCYDPgScCLatofylSW7DOLflevybY2od05uNf2/j3eE7Q7B8lksNAxZfteqfutzu1q0LVxbDV2rrXp76TQMADlScBvie/O+TT7zWnsBGsaBJMkE8KStk8FkPQJF7Uybf9qPnznG3CepA16/UY2HRsNOIZKkkWKlGRMksUESasT2Uy/IWQo/inpRo+o1oqk5W3/3yhsDXDsXtH8HlZiqC4CfBRymZIusr2ZpMuBjW3fJ+kC208cwNY7gVcSE49HA98bxech6QfAxkS9mSNt/3KU102SJMm46XIP7yYvtA9wWmdA3cXe4bZ3lPQ+4BuE/PAnieCCz3cG4MlMJH0FWJWYvIPIgvojUVB9Rds7jug407LGFMXbz6B+snBL28uO4rjFsRpnjUn6lO2PjOrYDY731V7bbe8xW32ZayRdbHvTUbct7bOAcCjtT2SrHkI4MVspRUi6FNiRCKjajsjiPIqol3tzTfv7Aa8h6upCBFMdWeeM7rwvTa8hdZbtpw5qs7Jf13Mg6QFFsz2IYK8TmJ7N+teSnVbnIJl7intuJ+NYxD3+nzSQqu9jt418Y+PaaOPq7zhokzVWKMA8h/jubEg4v46yffVs9SFJJoXy/aLNvWS+oKg/vA5wE/F72rl/zQgEbDo2ajOGSpJFjcwwS5IJY1zSeo66WK+S9FLgp5L2HcROFUn7UUSL10XmzMbkS6/oNknvGNSupB2ICYK1KgODFYno7WH5m6TlCS39IyT9iYjka43tfYF9Ja1FUexV0i3AZ2xfNmgHbb9U0kqEM25vSY8CVpb0RNvzQa4kSZLFlEIy7AXAahVHwYp0v9eeB3yvmOi9h/rJsk0VUrWvJTKP/wm8e9T9nwsknW17K0l30rAeZ8ussY1tl7POfyjpTNtPk9RqAq9l1ti1wK62r6+x89s2x23StaYNR+Esa+kEu7j0em8WDfnQVpQcNT+U9DZ6O2oat60cY0Miw+oFRGbGEUT9pJ/TPqPRtq8CPgx8WNITiQyusyT91vaTK43vlvRo29PuSZL2sf3+iu1GWbItbXa29TsHFxP3jc73pXoPLQdmtToHydzTMmusDW3kG8eV5TbXNM4as/0fQmbulMLxvQPwiyK7Zr9y25ZOsEEy15JkrtmodL9YtnIvWab7bvOG5/dr0HRsNOAYKkkWKTLDLEkmDEmn16xe+EV1fW2Ftse4PzFRskVl4moQWzv12m77sGHsD4ukW22vPuC+awBrEfUdPlDadCdwhe2hHhYkLQfcRchYvZbITvhO2+jjGruPJSYSdgTeZ/uYYexVbD+YiO7dAXik7UeOynaSJMkokbQRMTH7CeBjpU13Aqe7praOpBuBlwFXustDsqQ9gLcSE7q3UUzUMTVht1hl4LbJGiuiX59XBPF0st9Psb1B28ztNlljkl5FfKbX1dh5me3vNz1ug361zhorZ0IMcLzyc9gMJ1i357BRZMrPRxTSfmVHzbTvefn726ZtaZ+Lgb8BBwPH2767tO0Et69hVvs5KWb/n2b7jJptdRHhV1SjzCVtTjiTVyayZFckpNVnZMk2tVmsb3wOJC0L7EY400zI5x1g+65hzkGSzKessTa0zRorHGUvLNqvSaiGHGL7tkq7P9PDCVb+no0rcy1JkuEovpsPYXq91VtL2xuNjQYZQyXJokY6zJJkwiiiJn9r+w/F8k5Eds/NwF6DOlMkfcv2G0bVzx7HWYEYhMyJPGOVIvJ0KKdOx7HlkExcD3g0cLK7F45uandGZG6vaN0+ttYmnGQvJQY7RwMnuUU9mAGOuW5dtH6SJMkkIWmppvdrST8hioDf16Dt/rbfOnQH5zmdDLG6dZKutv3Y0voXELWgfkNMxq1FTJb/AniT7S+3OO5VwMu7ZY1NWkCHpG6OEhEOglVHcIzGTrDFXVKniaNmwLZr275xhP18je0jG7Z9a9HPdYAbSptWAM6x/bq2fW1rs6ndUttjgDuIDDSICfiVbb+61KbxOUiSxYlS1tgXgLqsscMIKdWTCQnTq3rYGsgJ1q8PSZLMDpLeTgRN3U7IIEN3ScZGY6M2Y6gkWdRIh1mSTBiSLgGe7dD3fxrh+Hg7EeHxGNuvGtTuOCdGJD0OOBx4ADH582fg9XMdaTZMhlnJxsXAU4FVCLmui4B/2n7tkHYbR+s2sHUfcAXwA2LioRoF/aUB+3i27a2K14dXMgUW68m2JEnmB5LWJTKFN6AkudIlO+RbRObYyUyXXhvoHro40DZrrJhcezTxrPCrQQM72mSNSXpXL1vj+HyrWWOS7iGcAnWDr1fZXmEEx2xTP22x/g1v4qgZpG3R/oXAY5l+vxmqnm4TFBLaq1CjjFAXcCfpTGA14EJCHvws21cOY7O0X6NzIOly2xv1W5ckyRQtssbuYyrLrpHMcsl+TydY0z4kSTI7SLqBUJDqWzqk6diozRgqSRY1soZZkkweS5QGoNsB37R9PHC8pMuGsHt/SRtTL100cG20Et8E3mX7dABJzyBqu4y9roBm1lpZuImQ3xj6ELb/KemNwH62P68oQD6Ysalo3bUlXVHatAJwzoBmP8HUOVh+0L7VsFzp9WMr21KvPkmS+cChRMTlvsAzibo63e5fNxV/Sxd/SX/eDZwtaVrWWJGdPU0OUCEJ/S5gDdtvkrSupPVtn9T2oLaP67Ht+5VV/wVcxpQjdCS/X32yxh5aWXcF8F91Ef6Snj2K/vSj8rx0f02v3zFvJcoGZP2KU+Z0SZcP21bSAcD9iXvNQcCrgLHUfJV0su1tOsu2/w78XdJHgD86ao89A9hQ0rdt/628f5EFujSwOfAM4EeSlrf9gEFtFv1qcw4ulbSlCxlISVvQ4lm4eg6SZFGnkjW2d6+sMdsLWtquOsG+StRuHLgPSZLMGr8lagg3oenYqM0YKkkWKTLDLEkmjEJi6AmO4sS/At5s+8zONtuPG9DunUQEad0PnD1kbbRFOUK0cI7tRjwovNH21dXI8Zb2BorWnQvKEejVaPTFPTo9SZL5gaSLbW9avm9LOsv2U+e6b4sKTbPGJH0XuJjIQH9cIXV3ru0nDHDMxlljkp5AyBY/vzj+UcBpHnIg1CZrTNJTgVtcqiVR2raZ7YsG7MM0JxhRowcWTydYY4ps0gMqjpqdbO82ZNsrbG9Y+r88cILt5w7Yz27PWSKktx9Ws89lwGbEhPdPiMyP9W2/oNJuK0JB4alEHbPLiCyzo6jQ1GbRtvE5KDJU1wc634vVibpq91FISQ1yDpJkUWXQrLEGdtvIN46lD0mStKf0PPxY4vf0R/RRyWg6NsoxVLI4kxlmSTJ5HAWcIekvwF1EnQQkPYrmESN13DCsU6wPN0r6KCHLCPA6Ikp/UWBP4IPA9wpn2drA6YMaK0XrVmuVLV9E9s6YTOuHpK/2OeYebW0WrCzp5cCC4nUnml7ASgPaTJIkmU3+JWkBcL2k3YHbgAfXNZS0GfBhYA2mF8xuLZW7uNAya2wd29tJ2gHA9l2SBo1UbZw1Zvuyou0HJD2ZiJ7fT9L7bZ844PGhRdaY7bN69G8gZ1mx79BSjospWwCvlzTNUSPpSmbW/GjTtlPX7J+SHg78D5F1OSgXAmdQf32v3GWf+4rAu1cAX7a9XxdlhDMImfHPAj+2/e8e/WhqE9qdg+f3OGaHQc5BkiyStM0aa8GOhBNsPWCP0k/zDCfYGPuQJEl7Os+BtxZ/ZZWMboFhTcdGjcdQSbKokQ6zJJkwbH9a0mnAw4BTS9HPC4haZpPKLsDeTMk2nEmkbM97igy/M0vLNwKDOqDK/Ih4iBGhCb0WcB0zpQ+bcHHp9d5E6vwoOAN4Sen1i0vbzpzZPEmSZDLQVN3FHxCZN3sAnwS2BnbqstsRwHuBK5kqmJ305lDiN+hJxfLvgGOBOofZv4usMgNIWodSFGxLNiGyxl5Iw6wxSasCGwOPL/r5pwGP3eEdRG2rOl5eOfZ+dJ+4GCawJRmMJo6aQdqeJGllovbPJcRnfmCL/atcC+xq+/rqBkm/7bLPPYVT+vVMPbctVdPugcBTgKcRE+T3ERmfHx3CJrQ4B7Zv6WKjzCDnIEmSFqQTLEnmJ7b3BpC0re1jy9skbVtZbjQ2GnAMlSSLFCnJmCSLCZI+avuTY7C7DPAW4FHEBOMhtu8Z9XHmkmKC7X3MLF4+0oy9QnJmV9u7DmnnUtsbj6hPD7F9+yhsJUmSzCaSrgG2IaTDnkElO6FOAlfS2ba3mpUOLiJIusj2ZuXfnm6SzJKeS2TwbQCcSkzW7+yi/ukQfehkjT0bmJE1Jmlnoi7sMsBxwDG2h3WWte1jeYJhRmCL7cNIFikKqdJlCmWBQW28CrjS9nU1217mmfX6kLQB8Wx+ru2jJK0FbGf7czVtHwM8nZBlfDJwq+2nD2Ozst+cnIMkSZIkWZyoK5dRU1Kj0dhokDFUkixqpMMsSRYTKrWozrX9pH77NLT7XeAeQjpyG+Bm2+8Yhe1JQdKpwHeB9xCTBTsBf7ZdlVQcxbGGrgs2ChslW38kHKFHAccPM+GRJEkym0jaA3grsDYhISKmsnpte+2afZ5FOF5OY7r+/4yi90kg6ZfAs4BzbG9SZI0dZfuJXdo/ENiS+BzOs/2XIY+/KvBqYFvieeSjnVpTpTb3Eb9lHUm9aQMg2y9hAAbNGhtlYEsyWUg6i8jAP4v4Ttw5x13qiqTfEMoGZxP9Pb+PLGNTu/PmHCRJkiTJfEbSNsALiGfh75Y2rQhsUH4ebzo2GmQMlSSLGukwS5LFhErk9ygzkMoFQJcELhiVs2ZSKBU7vaJTp0LSGXURuC3tvqu0uICQl3qg7ecNaXeUDrMliIj97YkHsXMJ59mJtu/qtW+SJMkkIGl/229t2PY7wKOBq5mSZLTtXcbVv/lOm6wxSafZfla/dQ2P2zhrTFLd73VnECTbZ7Q9fmF3oKyxUf5OJ5NFUed2KyJja0vC8X6W7XcOaG8L4JvAOoTT9422r+mzz7pEXbINmK6MsHal3QLbjaRnm9os2o70HJTs7klIwN4JHETIq37A9qnD2E2SJEmS+YqkjYAnAJ8APlbadCdwuu3/rdmn0diozRgqSRY1soZZkiw+LJC0CuGY6bxemFo9RFr1QvnFohj4cL2cICStbvtWpt7jHyS9EPg98IgRHGKF0ut7iZpmxw9iSNKdTE3+3V9Sp6bKjELNbbD9H+AnwE8kLU1kEW4PfKWY5HztIHaTJElmi5YDvY06QSBJM2yfKuliprLG9qxmjRXyzfcHHlR5/lgRePiAhz6Yqayx5wHPLT+DVLLGVgYeYfvrRX8uAFYlfjcHzhYvO8QkvSNlFRPbN0q6C/h38fdM4DFDmPw6oXBwJlFTdl/ieu/FoYTzdt/i+DtTkVOS9DzgZZJWI74Hvwd+YPuUQW12GMM56LCL7a8UfV+16MOhhKM+SZIkSRY7bF8OXC7pyKalUZqOjdJZlizOZIZZkiwmSLqZiJavG9wOnFYt6T/APzqLwLLAPxnSUTMJdCLAJb2IkJV5JLAfMcG3d7VGylwiaanZqB1XRBjvALwO+EdKSiVJsigh6UBg334ZHMkUTbLGisyQdxDOsY60C8AdwIG2vzbAcRtnjUk6B9je9m+L5csIGcnlgEMHyXCr6U/PrLFqYAvxrASLwPNSMkUhc/gX4Eji2fGypllcXexV64/0zU4sKSOUVSDOsv3U4vWXgfWAbwO/K3Z7BPB64Hrbe7a1WWk70nNQsnuF7Q0lfQX4he3vpbxpkiRJkrTLBE+SpD+ZYZYkiwm21xyT3SXGYXdCEIDtk4rlvxNRssMZlXo62gaspXI+Iek4ciStTshe7UBMLh4NvNT2teM4XpIkyRyyFbCTpJsIGbGOM2PDue3W5NEma8z2V4jM5Lfb3m9EXViZ5lljS3ecZQVn2/4f4H8kLTei/vTE9gr9WyWLAF8l7iM7EJKBZ0g60/ZvBrS3sqRXdFvuUl/xX5IWANdL2p1wUj+4tP0Ftter7lTUJf41MMNh1sBmmVGfgw4XF3WF1wI+KGkFpqRzkyRJkmRxpnEmeJIk/ckMsyRZTJDU05li+5LZ6st8QdKfCOdQLbb3GNDun4HfErXAzqfyIDNILZVxRdhK+iWwGlEf5ijbF436GEmSJJOCpDXq1tu+Zbb7MukMmjUm6cnAmpQC92x/e4DjN84ak3SD7Ud1sfMb2+u0PX6xb2aNJbVIWp6YrHoP4dgdKMBM0qE9NtfWV5S0OXAt4VT+JLAS8Hnb5xXbrwD+n+0LKvs9ETi4Tpa2n80ufR/JOSjZW0DUabnR9t8kPRBYzfYVw9hNkiRJkvlOm0zwJEn6kxlmSbL48MWadWWP+daz1ZF5xF3AxWOw+1DgOUTk7WuI2mVH2b56CJurSnpXt422vzSg3Q8CZ9q2pOUlLWf7H333SpIkmYfYvkXSEsBDyOfkngySNSbpcGAd4DLgPx1ThDRcW9pkjZ0v6U22D6z0Z1fgAgYks8aSKpK+SGRXLQ+cC3yMkCUcCNs7D7DPhcXL/yMcVlXeAOxfZGh1JBkfSTi73zCgzYWM+hyU+nCfpNuBDSTl/TlJkiRJpmiTCZ4kSR8ywyxJFhOKqNHf2v5DsbwT8ErgZmAv23+dw+5NJE3qRIzgGPcjHGdfAD4xqFSVpD8A+9O9APveQ/TxrYTjbLnC/p3APra/MajNJEmSSUTS2wk5k9uZkvpKScY+NM0ak3QtsIFHMABpkzUm6cHA9wmZzU5G/abA/YCX2b592P4kCYCkbYlAo5FeU5JWIu5NTytWnUE8N/69pu16wHuBNZj+ndy60u6hhIqAgN/Z/mOP4zeyWbQd1znYh5AIv4aSw31AKfMkSZIkmfdIOtz2jpLeB3yDFpngSZJ0Jx1mSbKYIOkS4Nm2/yrpaYTU4NsJaZPH2H7VXPZvEpF0nu0tx2T7fsALCWfZmsCJwCG2bxvQ3lice5I+DDwF2N32jcW6tYGvAOfb/tSoj5kkSTJXSLoB2KLIVEoa0C1rrE62WNKxwB6d4J0hj3sE8IsuWWPPsL1DzT5bA48tFq+2/fNh+5EkHSQJeCLhhDLwe+CCETmIjweuAg4rVu0IbGT7FTVtLwcOIFQSOt9JbF9catOqr01sDmK3DZKuAza0ffewtpIkSZJkUUDSNcA2xHzSM5hZ7iMD45NkANJhliSLCZIut71R8frrwJ9t71UsX2b7CXPYvYlH0mrMjKo9c0BbhwGPA04GjrZ91Qj6V1vDTNIjiRovXxjQ7nXEhMy/KuuXBS6vKxqfJEkyX5F0OvAc2/fOdV/mC22yxorz+wRCBnHhpPcgGSKZNZZMEpKeS0R2X0/IIAE8AngUsJvtU4e0P+NZvdvze6eOySj72s/moHbbIOlkYFvb/zeMnSRJkiRZVJC0B/BWYG2magq789/22nPYvSSZt6T2d5IsPiwhacliEvBZwJtL2/Je0INuEjDAQA4zIir4H8B6wB4RjBuHIh5qVhzA5rNK/X0QsC2RvfYI4IQB+wnRoX/VrLtL0n117ZMkSeYbpRqQNwK/kPQjpjt0Bq0DuThwFVGbs0nW2F6jOqjtPwFPrmSN/SizxpI54iuEksPN5ZWS1gJ+DDxmSPt3SdrK9tmF3acQtXbLx3pA8fKHkt5GPP+V72OdKPPGfW1hs5XdAfkncJmk0yp9mJHNmiRJkiSLA7a/CnxV0v623zrX/UmSRYWcJE+SxYejgDMk/YUYYJ8FIOlRwIz6B8k0XgasPyoJGNsLRmGnwj2SXg+8hnDEfQ9Y2/YjhrT7O0nPsn1aeWUxQTm0pFaSJMmEsELx/9bib+niDyJAIunOg4BrJPXNGrN9xqgPXjjI0kmWzDVLAr+rWX8bsNQI7L8VOKyoZSbgr8AbKm0uZiqqHODdle2dKPM2fW1qs63dQTix+EuSJEmSpEQ6y5JktKTDLEkWE2x/uojIfBhwakk6aQFRyyzpzo3EQH+Sayb8iZC4+ghwtm1LevkI7O4B/EDS2UxNmmxO1DV76QjsJ0mSzDm29waQtK3tY8vbJG07N72aN+zVr4Gks21vJelOpjsgh8msTpJJ4hDgQklHA78t1j0S2B44eFjjti8DNpK0YrF8R02btWChbPZuwFbE9+0sov5Y6762sNnK7iDYPkzS0kRgGMB1tu8Z1m6SJEmSJEmSlMkaZkmSJH0oCq1vBEysBIykdxITEssBRwLfBX46Cs1qScsQmWuPJSY3rwaOqJNqTJIkmc9IusT2Jv3WJUmSVJG0AfASYDXieel3wIm2rxnC5rt6ba+Ti5V0DHAHcESxagdgZduvLrV5DBH41KivTWwW7UZ+Dkq2nwEcBtxc2H4ksNOgNYWTJEmSJEmSpI50mCVJkvRB0k51620fNtt96YektYlJjO2BdYGPA9+z/esRH2cJYHvbR/RtnCRJMuFI2gZ4AfBqIuCgw4rABrafOCcdm2AyayxJxo+kj/fa3smOrexzue2N+q1r2Y+R2xygDxcDr7F9XbG8HnCU7U1nqw9JkiRJkiTJok9KMiZJkvRhPkjASFrd9q22bwQ+DXxa0uMJ59nJwDoD2l0ReBsRKfwD4GfF8nuBy5iKNE6SJJnP/B64iMiMuLi0/k7gnXPSownH9lbF/xX6tU2SRR1JDyWClO4DPkbInb8C+BWwp+2B6r6W5GIfYPuvDXe7VNKWts8r9t0COKfJjpJOtr3NIDbHdQ5KLNVxlgHY/rWkUdRGS5IkSZIkSZKFZIZZkiRJH+aDBExZMkzS8bZfOSK7PwD+FzgXeBawCrA0MfFx2SiOkSRJMilIWmrSAiKSJJl8JJ0C/IiQxn4NEVB0FCF7+GzbQ9V9lXQ9Eah0KHCyewziJV0LrA/cWqxaHbiWcGQZeEO3XYGTbD+srU3bG87COTik6P/hxarXAUvY3nkYu0mSJEmSJElSJh1mSZIkfZgPEjCSLrW9cfX1COxeafvxxeslgL8Aq9u+cxT2kyRJJglJ6wKfBTYAlumsH0U9yCRJFl0qz2G32l69tO0y208Y0r6AZwO7AE8kpGO/VSe5LWmNPuZuBM4gHGRVtrS9bFubtm+ZhXNwP0LlYKui72cA+9u+u+eOSZIkSZIkSdKClGRMkiTpz3yQgHGX18OyMNPC9n8k3ZTOsiRJFmEOJSTF9gWeCexM/aRykiRJmQWl19/usW0gioyynwI/lfRM4DvAbpIuBz5g+9xS21t62SqyxXa1fX3Ntt92OX5PmwVjOQeSVgVWtX0N8KXiD0mPI+pM/nlQ20mSJEmSJElSZeiH9yRJksWAiyQdLOkZxd+BTK9xMwlsJOkOSXcCGxav75B0p6Q7RmG3YntYu0mSJJPIsrZPI1QYbrG9F7D1HPcpSZLJ5weSlgew/ZHOSkmPAmZkgbVF0gMl7SnpIuA9RH2wBwHvBo5saW4vus8DvH3gTo7vHOwHrFqzfjXgK0PYTZIkSZIkSZIZpCRjkiRJH2okYM4EvpESMEmSJIsWks4BngocB/wcuA34nO3157RjSZIs1kj6NVG761Dbv6tse7/tfeamZ+NH0tW2H9tl21W2HzfbfUqSJEmSJEkWXTLDLEmSpA+277b9JduvsP1y2/suLs4ySctIeoekr0l6s6SU8k2SZJFD0uHFyx8A9wf2ADYFdgR2mqt+JUkyP5C0haTLJf2fpHMlbTDiQ6xv+5NVZxnAoM6yImNtRQUHS7pE0nOH7egY7PaSQZ80ifQkSZIkSZJknpMOsyRJki5IOqb4f6WkK6p/c92/WeIwYDPgSuAFwBfntjtJkiRjYVNJawCvJSZg/0lInf0/RiCnliTJIs/XCanEBxI1tvYdsf11JX1T0qmSft75G9LmLrbvAJ5LSB7uDHxu6J6O3u71kl5QXSlpG+DGIewmSZIkSZIkyQwyUyBJkqQ7exb/XzSnvZhbNrD9eABJBwMXzHF/kiRJxsEBwCnA2kSNSgEu/V977rqWJMk8YIHtnxavj5X0wRHbP5a4Tx0E/GdENlX8fwEh9Xi5JPXaYY7svhM4SdKrmaohvBnwJBbvZ/QkSZIkSZJkDKTDLEmSpAu2/1C83M32+8vbJO0DvH/mXosc93Re2L53NPMoSZIkk4XtrwJflbS/7bfOdX+SJJl3rCzpFd2WbZ8wpP17be8/pI0qF0s6FVgL+KCkFYD7Js2u7V9LejzwGqBTr+wMYFfb/xq6t0mSJEmSJElSQrbnug9JkiQTjaRLbG9SWXeF7Q3nqk+zhaT/AP/oLALLElJlAmx7xbnqW5IkSZIkySQg6dAem217lwHtPqB4uQfwZ+AEYGEdXdt/HcRuYXsB8ATgRtt/k/RAYDXbQ8mOj9HuPnUBbNV1SZIkSZIkSTIM6TBLkiTpgqS3ArsRUly/KW1aATjH9uvmpGNJkiRJkiTJIo+km5iSh6V4vRDbQ8nFSloNWIOS8oztM4exOS67i3MAW5IkSZIkSTJ7pMMsSZKkC5JWAlYBPgt8oLTpzmEiepMkSZIkSZJFj+LZ8ePA04pVZwCfsP33Ie0uSwRxbUU4zc4CDrB91xA29wG2A65hqi6abb9kyL6O1G4pgG0d4IbSpgxgS5IkSZIkSUZOOsySJEkaIunBwDKdZdu3zmF3kiRJkiRJkglC0vHAVcBhxaodgY1sv6L7Xo3sHgPcARxRrNoBWNn2q4eweR2woe27+zaeQ7sZwJYkSZIkSZLMJkv2b5IkSbJ4I+nFwJeAhwN/IiRmrgUeO5f9SpIkSZIkSSaKdWy/srS8t6TLRmB3fdsblZZPl3T5kDZvBJaiVBNtRIzUbpGd93dJHwH+aPtuSc8ANpT0bdt/G8VxkiRJkiRJkgTSYZYkSdKETwFbAj+zvbGkZxKRvUmSJEmSJEnS4S5JW9k+G0DSU4CBZRNLXCppS9vnFXa3AM4Z0uY/gcsknUbJuWV7jwm1ezywmaRHAQcDJwJHAi8Y0m6SJEmSJEmSLCQdZkmSJP25x/b/SFogaYHt04v6DEmSJEmSJEnS4a3AYYWMoIC/Am8Ygd0tgNdL6siBrw5cK+lKoj7YhgPYPLH4GzXjsnuf7XslvQL4su39JF06huMkSZIkSZIkizHpMEuSJOnP3yQtD5wJHCHpT8C9c9ynJEmSJEmSZIKwfRmwkaQVi+U7RmT6+SOysxDbh0laGlivWHWd7Xsm1S5wj6QdgNcDLy7WLTUCu0mSJEmSJEmyENme6z4kSZJMNJKWI+R0FgCvBVYCvpOFxpMkSZIkSRJJ7+q13faXZqsvTSnqgB0G3Exkwz0S2Mn2mRNqdwPgLcC5to+StBawne3PDWM3SZIkSZIkScqkwyxJkqQPkvax/f5+65IkSZIkSZLFD0kf77Xd9t6z1ZemSLoYeI3t64rl9YCjbG86iXaTJEmSJEmSZDZIh1mSJEkfJF1ie5PKuisGrBeRJEmSJEmSLIJIesB8USCoe5YdxfPtGO2uC3wW2ABYprPe9trD2E2SJEmSJEmSMlnDLEmSpAuS3grsBqwt6YrSphWAc+amV0mSJEmSJMmEcr6ky4BDgZM92dGpF0k6GDi8WH4dcPEE2z0U+DiwL/BMYGdC8jFJkiRJkiRJRkZmmCVJknRB0krAKkQ06wdKm+6cL9HDSZIkSZIkyewgScCzgV2AJwLfBb5l+9dz2rEaJN0PeBuwFeF4OgPY3/bdE2r3YtubSrrS9uOLdWfZfuowdpMkSZIkSZKkTDrMkiRJ+iBp9br1tm+d7b4kSZIkSZIkk4+kZwLfAZYDLgc+YPvcue0VSFoVWNX2NZX1jwNut/3nSbJbsnMO8FTgOODnwG3A52yvP4zdJEmSJEmSJCmzYK47kCRJMg/4EXBS8f804Ebg5DntUZIkSZIkSTJRSHqgpD0lXQS8B3g78CDg3cCRc9q5KfYDVq1ZvxrwlQm02+EdwP2BPYBNgR2BnUZgN0mSJEmSJEkWkhlmSZIkLZG0CbCr7V3nui9JkiRJkiTJZCDp10TtrkNt/66y7f2295mbnk3rx9W2H9tl21W2HzdJdpMkSZIkSZJkNllyrjuQJEky37B9iaTN57ofSZIkSZIkyUSxvrtEpE6Cs6xgqQG3zZVdACStB7wXWIPSPIbtrYe1nSRJkiRJkiQd0mGWJEnSB0nvKi0uADYBhqrDkCRJkiRJkixyrCvpPcCaTK5T53pJL7D94/JKSdsQsuOTZrfDscABwIHAf0ZgL0mSJEmSJElmkJKMSZIkfZD08dLivcDNwPG2/zU3PUqSJEmSJEkmDUmXE06diyk5dWxfPGedqlBkap0E/JLoJ8BmwJOAF9n+9STZLdm/2Pamw9hIkiRJkiRJkn6kwyxJkiRJkiRJkiRJhmS+OHUk3Q94DdCpK3Y1cOSwwWDjsCvpAcXLPQiFhxOAuzvbbf91UNtJkiRJkiRJUiUdZkmSJF2QdGKv7bZfMlt9SZIkSZIkSSaT+ejUkbSP7ff3WzfXdiXdBBhQsWraBIbttQfqaJIkSZIkSZLUkA6zJEmSLkj6M/Bb4CjgfKYG6gDYPmMu+pUkSZIkSZJMDvPRqSPpEtubVNZdYXvDCbW7LLAbsBVxfs8CDrB91zB2kyRJkiRJkqRMOsySJEm6IGkJ4DnADsCGwI+Ao2xfPacdS5IkSZIkSSaO+eDUkfRWoo/rADeUNq0AnGP7dZNkt2T/GOAO4Ihi1Q7AyrZfPYzdJEmSJEmSJCmTDrMkSZIGFDUZdgC+AHzC9n5z3KUkSZIkSZJkgpgPTh1JKwGrAJ8FPlDadOcw0pHjsluyf7ntjfqtS5IkSZIkSZJhWHKuO5AkSTLJFI6yFxITHmsCXyXqUiRJkiRJkiRJmfUrDpzTJV0+Z72pwfbfgb9L+gjwR9t3S3oGsKGkb9v+2yTZLXGppC1tnwcgaQvgnCFtJkmSJEmSJMk0Fsx1B5IkSSYVSYcBvwQ2Afa2vbntT9q+bY67liRJkiRJkkwel0rasrMw4U6d44H/SHoUcDCwFnDkBNvdAvilpJsl3QycCzxd0pWSrhiB/SRJkiRJkiRJScYkSZJuSLoP+EexWL5ZCrDtFWe/V0mSJEmSJMkkIulaYH3g1mLV6sC1wH3Es+OGc9W3KpIusb2JpPcBd9neT9KltjeeULtr9Npu+5Zh7CdJkiRJkiQJpCRjkiRJV2xnFm6SJEmSJEnSlOfPdQdacI+kHYDXAy8u1i01qXbTIZYkSZIkSZLMBukwS5IkSZIkSZIkSZIhmWdOnZ2BtwCftn2TpLWA70yw3SRJkiRJkiQZOynJmCRJkiRJkiRJkiRJkiRJkiRJkizWZIZZkiRJkiRJkiRJkixGSFoX+CywAbBMZ73ttSfRbpIkSZIkSZLMBlmfJ0mSJEmSJEmSJEkWLw4F9gfuBZ4JfBs4fILtJkmSJEmSJMnYSYdZkiRJkiRJkiRJkixeLGv7NKJMwy229wK2nmC7SZIkSZIkSTJ2UpIxSZIkSZIkSZIkSRYv/iVpAXC9pN2B24AHT7DdJEmSJEmSJBk7sj3XfUiSJEmSJEmSJEmSZJaQtDlwLbAy8ElgJeDzts+bRLtJkiRJkiRJMhukwyxJkiRJkiRJkiRJkiRJkiRJkiRZrElJxiRJkiRJkiRJkiRZjJC0HvBeYA1K8wK2h6o3Ni67SZIkSZIkSTIbZIZZkiRJkiRJkiRJkixGSLocOAC4GPhPZ73tiyfRbpIkSZIkSZLMBukwS5IkSZIkSZIkSZLFCEkX2950vthNkiRJkiRJktkgHWZJkiRJkiRJkiRJshgg6QHFyz2APwMnAHd3ttv+6yTZTZIkSZIkSZLZJB1mSZIkSZIkSZIkSbIYIOkmwICKVdMmBGyvPUl2kyRJkiRJkmQ2SYdZkiRJkiRJkiRJkixGSFoW2A3YinBunQUcYPuuSbSbJEmSJEmSJLNBOsySJEmSJEmSJEmSZDFC0jHAHcARxaodgJVtv3oS7SZJkiRJkiTJbJAOsyRJkiRJkiRJkiRZjJB0ue2N+q2bFLtJkiRJkiRJMhssmOsOJEmSJEmSJEmSJEkyq1wqacvOgqQtgHMm2G6SJEmSJEmSjJ3MMEuSJEmSJEmSJEmSxQhJ1wLrA7cWq1YHrgXuA2x7w0mymyRJkiRJkiSzQTrMkiRJkiRJkiRJkmQxQtIavbbbvmWS7CZJkiRJkiTJbJAOsyRJkiRJkiRJkiRJkiRJkiRJkmSxJmuYJUmSJEmSJEmSJEmSJEmSJEmSJIs16TBLkiRJkiRJkiRJkiRJkiRJkiRJFmvSYZYkSZIkSZIkSZIkSZIkSZIkSZIs1qTDLEmSJEmSJEmSJEmSJEmSJEmSJFmsSYdZkiRJkiRJkiRJkiRJkiRJkiRJsljz/wE1KaiIf1Q2bQAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(30,20))\n", - "sns.heatmap(df.corr(), fmt=\".2g\", cmap=\"coolwarm\")" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "#Give variant IDs and add the variant info columns\n", - "#df = df.reset_index(drop=True)\n", - "#df['ID'] = [f'var_{num}' for num in range(len(df))]\n", - "#print('NAs filled!')\n", - "df = pd.concat([var.reset_index(drop=True), df.reset_index(drop=True)], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['#chr', 'pos(1-based)', 'ref', 'alt', 'aaref', 'aaalt', 'genename', 'Ensembl_geneid', 'Ensembl_transcriptid', 'Ensembl_proteinid', 'Uniprot_acc', 'clinvar_review', 'Interpro_domain', 'SIFT_score', 'SIFT_converted_rankscore', 'SIFT4G_score', 'SIFT4G_converted_rankscore', 'Polyphen2_HDIV_score', 'Polyphen2_HDIV_rankscore', 'Polyphen2_HVAR_score', 'Polyphen2_HVAR_rankscore', 'LRT_score', 'LRT_converted_rankscore', 'LRT_Omega', 'MutationTaster_converted_rankscore', 'MutationAssessor_score', 'MutationAssessor_rankscore', 'FATHMM_score', 'FATHMM_converted_rankscore', 'PROVEAN_score', 'PROVEAN_converted_rankscore', 'VEST4_score', 'VEST4_rankscore', 'MetaSVM_score', 'MetaSVM_rankscore', 'MetaLR_score', 'MetaLR_rankscore', 'Reliability_index', 'MetaRNN_score', 'MetaRNN_rankscore', 'M-CAP_score', 'M-CAP_rankscore', 'REVEL_score', 'REVEL_rankscore', 'MutPred_score', 'MutPred_rankscore', 'MVP_score', 'MVP_rankscore', 'MPC_score', 'MPC_rankscore', 'PrimateAI_score', 'PrimateAI_rankscore', 'DEOGEN2_score', 'DEOGEN2_rankscore', 'BayesDel_addAF_score', 'BayesDel_addAF_rankscore', 'BayesDel_noAF_score', 'BayesDel_noAF_rankscore', 'ClinPred_score', 'ClinPred_rankscore', 'LIST-S2_score', 'LIST-S2_rankscore', 'CADD_raw', 'CADD_raw_rankscore', 'CADD_phred', 'CADD_raw_hg19', 'CADD_raw_rankscore_hg19', 'CADD_phred_hg19', 'DANN_score', 'DANN_rankscore', 'fathmm-MKL_coding_score', 'fathmm-MKL_coding_rankscore', 'fathmm-XF_coding_score', 'fathmm-XF_coding_rankscore', 'Eigen-raw_coding', 'Eigen-raw_coding_rankscore', 'Eigen-phred_coding', 'Eigen-PC-raw_coding', 'Eigen-PC-raw_coding_rankscore', 'Eigen-PC-phred_coding', 'GenoCanyon_score', 'GenoCanyon_rankscore', 'integrated_fitCons_score', 'integrated_fitCons_rankscore', 'integrated_confidence_value', 'GM12878_fitCons_score', 'GM12878_fitCons_rankscore', 'GM12878_confidence_value', 'H1-hESC_fitCons_score', 'H1-hESC_fitCons_rankscore', 'H1-hESC_confidence_value', 'HUVEC_fitCons_score', 'HUVEC_fitCons_rankscore', 'HUVEC_confidence_value', 'LINSIGHT', 'LINSIGHT_rankscore', 'GERP++_NR', 'GERP++_RS', 'GERP++_RS_rankscore', 'phyloP100way_vertebrate', 'phyloP100way_vertebrate_rankscore', 'phyloP30way_mammalian', 'phyloP30way_mammalian_rankscore', 'phyloP17way_primate', 'phyloP17way_primate_rankscore', 'phastCons100way_vertebrate', 'phastCons100way_vertebrate_rankscore', 'phastCons30way_mammalian', 'phastCons30way_mammalian_rankscore', 'phastCons17way_primate', 'phastCons17way_primate_rankscore', 'SiPhy_29way_logOdds', 'SiPhy_29way_logOdds_rankscore', 'bStatistic', 'bStatistic_converted_rankscore', '1000Gp3_AF', '1000Gp3_AFR_AF', '1000Gp3_EUR_AF', '1000Gp3_AMR_AF', '1000Gp3_EAS_AF', '1000Gp3_SAS_AF', 'TWINSUK_AF', 'ALSPAC_AF', 'UK10K_AF', 'ESP6500_AA_AF', 'ESP6500_EA_AF', 'ExAC_AF', 'ExAC_Adj_AF', 'ExAC_AFR_AF', 'ExAC_AMR_AF', 'ExAC_EAS_AF', 'ExAC_FIN_AF', 'ExAC_NFE_AF', 'ExAC_SAS_AF', 'ExAC_nonTCGA_AF', 'ExAC_nonTCGA_Adj_AF', 'ExAC_nonTCGA_AFR_AF', 'ExAC_nonTCGA_AMR_AF', 'ExAC_nonTCGA_EAS_AF', 'ExAC_nonTCGA_FIN_AF', 'ExAC_nonTCGA_NFE_AF', 'ExAC_nonTCGA_SAS_AF', 'ExAC_nonpsych_AF', 'ExAC_nonpsych_Adj_AF', 'ExAC_nonpsych_AFR_AF', 'ExAC_nonpsych_AMR_AF', 'ExAC_nonpsych_EAS_AF', 'ExAC_nonpsych_FIN_AF', 'ExAC_nonpsych_NFE_AF', 'ExAC_nonpsych_SAS_AF', 'gnomAD_exomes_AF', 'gnomAD_exomes_AFR_AF', 'gnomAD_exomes_AMR_AF', 'gnomAD_exomes_ASJ_AF', 'gnomAD_exomes_EAS_AF', 'gnomAD_exomes_FIN_AF', 'gnomAD_exomes_NFE_AF', 'gnomAD_exomes_SAS_AF', 'gnomAD_exomes_POPMAX_AF', 'gnomAD_exomes_controls_AF', 'gnomAD_exomes_non_neuro_AF', 'gnomAD_exomes_non_cancer_AF', 'gnomAD_exomes_non_topmed_AF', 'gnomAD_exomes_controls_AFR_AF', 'gnomAD_exomes_controls_AMR_AF', 'gnomAD_exomes_controls_ASJ_AF', 'gnomAD_exomes_controls_EAS_AF', 'gnomAD_exomes_controls_FIN_AF', 'gnomAD_exomes_controls_NFE_AF', 'gnomAD_exomes_controls_SAS_AF', 'gnomAD_exomes_controls_POPMAX_AF', 'gnomAD_exomes_non_neuro_AFR_AF', 'gnomAD_exomes_non_neuro_AMR_AF', 'gnomAD_exomes_non_neuro_ASJ_AF', 'gnomAD_exomes_non_neuro_EAS_AF', 'gnomAD_exomes_non_neuro_FIN_AF', 'gnomAD_exomes_non_neuro_NFE_AF', 'gnomAD_exomes_non_neuro_SAS_AF', 'gnomAD_exomes_non_neuro_POPMAX_AF', 'gnomAD_exomes_non_cancer_AFR_AF', 'gnomAD_exomes_non_cancer_AMR_AF', 'gnomAD_exomes_non_cancer_ASJ_AF', 'gnomAD_exomes_non_cancer_EAS_AF', 'gnomAD_exomes_non_cancer_FIN_AF', 'gnomAD_exomes_non_cancer_NFE_AF', 'gnomAD_exomes_non_cancer_SAS_AF', 'gnomAD_exomes_non_cancer_POPMAX_AF', 'gnomAD_exomes_non_topmed_AFR_AF', 'gnomAD_exomes_non_topmed_AMR_AF', 'gnomAD_exomes_non_topmed_ASJ_AF', 'gnomAD_exomes_non_topmed_EAS_AF', 'gnomAD_exomes_non_topmed_FIN_AF', 'gnomAD_exomes_non_topmed_NFE_AF', 'gnomAD_exomes_non_topmed_SAS_AF', 'gnomAD_exomes_non_topmed_POPMAX_AF', 'gnomAD_genomes_AF', 'gnomAD_genomes_POPMAX_AF', 'gnomAD_genomes_AFR_AF', 'gnomAD_genomes_AMI_AF', 'gnomAD_genomes_AMR_AF', 'gnomAD_genomes_ASJ_AF', 'gnomAD_genomes_EAS_AF', 'gnomAD_genomes_FIN_AF', 'gnomAD_genomes_MID_AF', 'gnomAD_genomes_NFE_AF', 'gnomAD_genomes_SAS_AF', 'gnomAD_genomes_controls_and_biobanks_AF', 'gnomAD_genomes_non_neuro_AF', 'gnomAD_genomes_non_cancer_AF', 'gnomAD_genomes_non_topmed_AF', 'gnomAD_genomes_controls_and_biobanks_AFR_AF', 'gnomAD_genomes_controls_and_biobanks_AMI_AF', 'gnomAD_genomes_controls_and_biobanks_AMR_AF', 'gnomAD_genomes_controls_and_biobanks_ASJ_AF', 'gnomAD_genomes_controls_and_biobanks_EAS_AF', 'gnomAD_genomes_controls_and_biobanks_FIN_AF', 'gnomAD_genomes_controls_and_biobanks_MID_AF', 'gnomAD_genomes_controls_and_biobanks_NFE_AF', 'gnomAD_genomes_controls_and_biobanks_SAS_AF', 'gnomAD_genomes_non_neuro_AFR_AF', 'gnomAD_genomes_non_neuro_AMI_AF', 'gnomAD_genomes_non_neuro_AMR_AF', 'gnomAD_genomes_non_neuro_ASJ_AF', 'gnomAD_genomes_non_neuro_EAS_AF', 'gnomAD_genomes_non_neuro_FIN_AF', 'gnomAD_genomes_non_neuro_MID_AF', 'gnomAD_genomes_non_neuro_NFE_AF', 'gnomAD_genomes_non_neuro_SAS_AF', 'gnomAD_genomes_non_cancer_AFR_AF', 'gnomAD_genomes_non_cancer_AMI_AF', 'gnomAD_genomes_non_cancer_AMR_AF', 'gnomAD_genomes_non_cancer_ASJ_AF', 'gnomAD_genomes_non_cancer_EAS_AF', 'gnomAD_genomes_non_cancer_FIN_AF', 'gnomAD_genomes_non_cancer_MID_AF', 'gnomAD_genomes_non_cancer_NFE_AF', 'gnomAD_genomes_non_cancer_SAS_AF', 'gnomAD_genomes_non_topmed_AFR_AF', 'gnomAD_genomes_non_topmed_AMI_AF', 'gnomAD_genomes_non_topmed_AMR_AF', 'gnomAD_genomes_non_topmed_ASJ_AF', 'gnomAD_genomes_non_topmed_EAS_AF', 'gnomAD_genomes_non_topmed_FIN_AF', 'gnomAD_genomes_non_topmed_MID_AF', 'gnomAD_genomes_non_topmed_NFE_AF', 'gnomAD_genomes_non_topmed_SAS_AF', 'cds_strand_+', 'SIFT_pred_D', 'SIFT_pred_T', 'SIFT4G_pred_D', 'SIFT4G_pred_T', 'Polyphen2_HDIV_pred_B', 'Polyphen2_HDIV_pred_D', 'Polyphen2_HDIV_pred_P', 'Polyphen2_HVAR_pred_B', 'Polyphen2_HVAR_pred_D', 'Polyphen2_HVAR_pred_P', 'LRT_pred_D', 'LRT_pred_N', 'LRT_pred_U', 'MutationAssessor_pred_H', 'MutationAssessor_pred_L', 'MutationAssessor_pred_M', 'MutationAssessor_pred_N', 'FATHMM_pred_D', 'FATHMM_pred_T', 'PROVEAN_pred_D', 'PROVEAN_pred_N', 'MetaSVM_pred_D', 'MetaSVM_pred_T', 'MetaLR_pred_D', 'MetaLR_pred_T', 'MetaRNN_pred_D', 'MetaRNN_pred_T', 'M-CAP_pred_D', 'M-CAP_pred_T', 'PrimateAI_pred_D', 'PrimateAI_pred_T', 'DEOGEN2_pred_D', 'DEOGEN2_pred_T', 'BayesDel_addAF_pred_D', 'BayesDel_addAF_pred_T', 'BayesDel_noAF_pred_D', 'BayesDel_noAF_pred_T', 'ClinPred_pred_D', 'ClinPred_pred_T', 'LIST-S2_pred_D', 'LIST-S2_pred_T', 'fathmm-MKL_coding_pred_D', 'fathmm-MKL_coding_pred_N', 'fathmm-XF_coding_pred_D', 'fathmm-XF_coding_pred_N']\n" - ] - } - ], - "source": [ - "train_columns = df.columns.values.tolist()\n", - "print(train_columns)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Data shape (After filtering) = (287358, 292)\n", - "Class shape= (287358,)\n" - ] - } - ], - "source": [ - "print('\\nData shape (After filtering) =', df.shape)\n", - "print('Class shape=', y.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "# Write it to a file\n", - "df.to_csv('../processed/train_no_filter_data-dbnsfp.csv', index=False)\n", - "y.to_csv('../processed/train_no_filter_data-y-dbnsfp.csv', index=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Preparing Testing data" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Data shape = (49338, 268)\n" - ] - } - ], - "source": [ - "df = test_df\n", - "print('\\nData shape =', df.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Pathogenic/Likely_pathogenic', 'Benign/Likely_benign']" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "config_dict['Clinsig_test']" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "clinvar_clnsig:\n", - " Benign/Likely_benign 26904\n", - "Pathogenic/Likely_pathogenic 22434\n", - "Name: clinvar_clnsig, dtype: int64\n" - ] - } - ], - "source": [ - "df= df.loc[df['clinvar_clnsig'].isin(config_dict['Clinsig_test'])]\n", - "print('\\nclinvar_clnsig:\\n', df['clinvar_clnsig'].value_counts())" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Impact (Class):\n", - " low_impact 26904\n", - "high_impact 22434\n", - "Name: clinvar_clnsig, dtype: int64\n" - ] - } - ], - "source": [ - "#Convert classes from HGMD and ClinVar to either \"high_impact\" or \"Low_impact\"\n", - "y = df.clinvar_clnsig.str.replace(r'Pathogenic/Likely_pathogenic','high_impact')\n", - "y = y.str.replace(r'Benign/Likely_benign','low_impact')\n", - "print('\\nImpact (Class):\\n', y.value_counts())" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "df.drop('clinvar_clnsig', axis=1, inplace=True)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Drop variant info columns so we can perform one-hot encoding\n", - "var = df[config_dict['var']]\n", - "df = df.drop(config_dict['var'], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['MutationAssessor_pred',\n", - " 'Polyphen2_HDIV_pred',\n", - " 'ClinPred_pred',\n", - " 'MetaRNN_pred',\n", - " 'MetaLR_pred',\n", - " 'BayesDel_noAF_pred',\n", - " 'DEOGEN2_pred',\n", - " 'PROVEAN_pred',\n", - " 'fathmm-MKL_coding_pred',\n", - " 'SIFT_pred',\n", - " 'cds_strand',\n", - " 'SIFT4G_pred',\n", - " 'MetaSVM_pred',\n", - " 'PrimateAI_pred',\n", - " 'fathmm-XF_coding_pred',\n", - " 'M-CAP_pred',\n", - " 'BayesDel_addAF_pred',\n", - " 'LRT_pred',\n", - " 'FATHMM_pred',\n", - " 'Polyphen2_HVAR_pred',\n", - " 'LIST-S2_pred']" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Double check if there are any columns with weird formatting as categorical before performing one-hot encoding\n", - "num_cols = df._get_numeric_data().columns\n", - "\n", - "list(set(df.columns) - set(num_cols))" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "#Perform one-hot encoding\n", - "df = pd.get_dummies(df, prefix_sep='_')" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "df[config_dict['allele_freq_columns']] = df[config_dict['allele_freq_columns']].fillna(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 233/233 [00:00<00:00, 3002.30it/s]\n" - ] - } - ], - "source": [ - "\n", - "\n", - "for key in tqdm(median_scores.keys()):\n", - " if key in df.columns:\n", - " df[key] = (\n", - " df[key]\n", - " .fillna(median_scores[key])\n", - " .astype(\"float32\")\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 292/292 [00:00<00:00, 1976.95it/s]\n" - ] - } - ], - "source": [ - "\n", - "df2 = pd.DataFrame()\n", - "for key in tqdm(train_columns):\n", - " if key in df.columns:\n", - " df2[key] = df[key]\n", - " else:\n", - " df2[key] = 0" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Data shape (nsSNV) = (49338, 292)\n" - ] - } - ], - "source": [ - "print('\\nData shape (nsSNV) =', df2.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "#chr 49338\n", - "pos(1-based) 49338\n", - "ref 49338\n", - "alt 49338\n", - "aaref 49338\n", - "aaalt 49338\n", - "genename 49338\n", - "Ensembl_geneid 49338\n", - "Ensembl_transcriptid 49338\n", - "Ensembl_proteinid 49338\n", - "Uniprot_acc 49338\n", - "clinvar_review 49338\n", - "Interpro_domain 49338\n", - "SIFT_score 0\n", - "SIFT_converted_rankscore 0\n", - "SIFT4G_score 0\n", - "SIFT4G_converted_rankscore 0\n", - "Polyphen2_HDIV_score 0\n", - "Polyphen2_HDIV_rankscore 0\n", - "Polyphen2_HVAR_score 0\n", - "Polyphen2_HVAR_rankscore 0\n", - "LRT_score 0\n", - "LRT_converted_rankscore 0\n", - "LRT_Omega 0\n", - "MutationTaster_converted_rankscore 0\n", - "MutationAssessor_score 0\n", - "MutationAssessor_rankscore 0\n", - "FATHMM_score 0\n", - "FATHMM_converted_rankscore 0\n", - "PROVEAN_score 0\n", - "PROVEAN_converted_rankscore 0\n", - "VEST4_score 0\n", - "VEST4_rankscore 0\n", - "MetaSVM_score 0\n", - "MetaSVM_rankscore 0\n", - "MetaLR_score 0\n", - "MetaLR_rankscore 0\n", - "Reliability_index 0\n", - "MetaRNN_score 0\n", - "MetaRNN_rankscore 0\n", - "M-CAP_score 0\n", - "M-CAP_rankscore 0\n", - "REVEL_score 0\n", - "REVEL_rankscore 0\n", - "MutPred_score 0\n", - "MutPred_rankscore 0\n", - "MVP_score 0\n", - "MVP_rankscore 0\n", - "MPC_score 0\n", - "MPC_rankscore 0\n", - "PrimateAI_score 0\n", - "PrimateAI_rankscore 0\n", - "DEOGEN2_score 0\n", - "DEOGEN2_rankscore 0\n", - "BayesDel_addAF_score 0\n", - "BayesDel_addAF_rankscore 0\n", - "BayesDel_noAF_score 0\n", - "BayesDel_noAF_rankscore 0\n", - "ClinPred_score 0\n", - "ClinPred_rankscore 0\n", - "LIST-S2_score 0\n", - "LIST-S2_rankscore 0\n", - "CADD_raw 0\n", - "CADD_raw_rankscore 0\n", - "CADD_phred 0\n", - "CADD_raw_hg19 0\n", - "CADD_raw_rankscore_hg19 0\n", - "CADD_phred_hg19 0\n", - "DANN_score 0\n", - "DANN_rankscore 0\n", - "fathmm-MKL_coding_score 0\n", - "fathmm-MKL_coding_rankscore 0\n", - "fathmm-XF_coding_score 0\n", - "fathmm-XF_coding_rankscore 0\n", - "Eigen-raw_coding 0\n", - "Eigen-raw_coding_rankscore 0\n", - "Eigen-phred_coding 0\n", - "Eigen-PC-raw_coding 0\n", - "Eigen-PC-raw_coding_rankscore 0\n", - "Eigen-PC-phred_coding 0\n", - "GenoCanyon_score 0\n", - "GenoCanyon_rankscore 0\n", - "integrated_fitCons_score 0\n", - "integrated_fitCons_rankscore 0\n", - "integrated_confidence_value 0\n", - "GM12878_fitCons_score 0\n", - "GM12878_fitCons_rankscore 0\n", - "GM12878_confidence_value 0\n", - "H1-hESC_fitCons_score 0\n", - "H1-hESC_fitCons_rankscore 0\n", - "H1-hESC_confidence_value 0\n", - "HUVEC_fitCons_score 0\n", - "HUVEC_fitCons_rankscore 0\n", - "HUVEC_confidence_value 0\n", - "LINSIGHT 0\n", - "LINSIGHT_rankscore 0\n", - "GERP++_NR 0\n", - "GERP++_RS 0\n", - "GERP++_RS_rankscore 0\n", - "phyloP100way_vertebrate 0\n", - "phyloP100way_vertebrate_rankscore 0\n", - "phyloP30way_mammalian 0\n", - "phyloP30way_mammalian_rankscore 0\n", - "phyloP17way_primate 0\n", - "phyloP17way_primate_rankscore 0\n", - "phastCons100way_vertebrate 0\n", - "phastCons100way_vertebrate_rankscore 0\n", - "phastCons30way_mammalian 0\n", - "phastCons30way_mammalian_rankscore 0\n", - "phastCons17way_primate 0\n", - "phastCons17way_primate_rankscore 0\n", - "SiPhy_29way_logOdds 0\n", - "SiPhy_29way_logOdds_rankscore 0\n", - "bStatistic 0\n", - "bStatistic_converted_rankscore 0\n", - "1000Gp3_AF 0\n", - "1000Gp3_AFR_AF 0\n", - "1000Gp3_EUR_AF 0\n", - "1000Gp3_AMR_AF 0\n", - "1000Gp3_EAS_AF 0\n", - "1000Gp3_SAS_AF 0\n", - "TWINSUK_AF 0\n", - "ALSPAC_AF 0\n", - "UK10K_AF 0\n", - "ESP6500_AA_AF 0\n", - "ESP6500_EA_AF 0\n", - "ExAC_AF 0\n", - "ExAC_Adj_AF 0\n", - "ExAC_AFR_AF 0\n", - "ExAC_AMR_AF 0\n", - "ExAC_EAS_AF 0\n", - "ExAC_FIN_AF 0\n", - "ExAC_NFE_AF 0\n", - "ExAC_SAS_AF 0\n", - "ExAC_nonTCGA_AF 0\n", - "ExAC_nonTCGA_Adj_AF 0\n", - "ExAC_nonTCGA_AFR_AF 0\n", - "ExAC_nonTCGA_AMR_AF 0\n", - "ExAC_nonTCGA_EAS_AF 0\n", - "ExAC_nonTCGA_FIN_AF 0\n", - "ExAC_nonTCGA_NFE_AF 0\n", - "ExAC_nonTCGA_SAS_AF 0\n", - "ExAC_nonpsych_AF 0\n", - "ExAC_nonpsych_Adj_AF 0\n", - "ExAC_nonpsych_AFR_AF 0\n", - "ExAC_nonpsych_AMR_AF 0\n", - "ExAC_nonpsych_EAS_AF 0\n", - "ExAC_nonpsych_FIN_AF 0\n", - "ExAC_nonpsych_NFE_AF 0\n", - "ExAC_nonpsych_SAS_AF 0\n", - "gnomAD_exomes_AF 0\n", - "gnomAD_exomes_AFR_AF 0\n", - "gnomAD_exomes_AMR_AF 0\n", - "gnomAD_exomes_ASJ_AF 0\n", - "gnomAD_exomes_EAS_AF 0\n", - "gnomAD_exomes_FIN_AF 0\n", - "gnomAD_exomes_NFE_AF 0\n", - "gnomAD_exomes_SAS_AF 0\n", - "gnomAD_exomes_POPMAX_AF 0\n", - "gnomAD_exomes_controls_AF 0\n", - "gnomAD_exomes_non_neuro_AF 0\n", - "gnomAD_exomes_non_cancer_AF 0\n", - "gnomAD_exomes_non_topmed_AF 0\n", - "gnomAD_exomes_controls_AFR_AF 0\n", - "gnomAD_exomes_controls_AMR_AF 0\n", - "gnomAD_exomes_controls_ASJ_AF 0\n", - "gnomAD_exomes_controls_EAS_AF 0\n", - "gnomAD_exomes_controls_FIN_AF 0\n", - "gnomAD_exomes_controls_NFE_AF 0\n", - "gnomAD_exomes_controls_SAS_AF 0\n", - "gnomAD_exomes_controls_POPMAX_AF 0\n", - "gnomAD_exomes_non_neuro_AFR_AF 0\n", - "gnomAD_exomes_non_neuro_AMR_AF 0\n", - "gnomAD_exomes_non_neuro_ASJ_AF 0\n", - "gnomAD_exomes_non_neuro_EAS_AF 0\n", - "gnomAD_exomes_non_neuro_FIN_AF 0\n", - "gnomAD_exomes_non_neuro_NFE_AF 0\n", - "gnomAD_exomes_non_neuro_SAS_AF 0\n", - "gnomAD_exomes_non_neuro_POPMAX_AF 0\n", - "gnomAD_exomes_non_cancer_AFR_AF 0\n", - "gnomAD_exomes_non_cancer_AMR_AF 0\n", - "gnomAD_exomes_non_cancer_ASJ_AF 0\n", - "gnomAD_exomes_non_cancer_EAS_AF 0\n", - "gnomAD_exomes_non_cancer_FIN_AF 0\n", - "gnomAD_exomes_non_cancer_NFE_AF 0\n", - "gnomAD_exomes_non_cancer_SAS_AF 0\n", - "gnomAD_exomes_non_cancer_POPMAX_AF 0\n", - "gnomAD_exomes_non_topmed_AFR_AF 0\n", - "gnomAD_exomes_non_topmed_AMR_AF 0\n", - "gnomAD_exomes_non_topmed_ASJ_AF 0\n", - "gnomAD_exomes_non_topmed_EAS_AF 0\n", - "gnomAD_exomes_non_topmed_FIN_AF 0\n", - "gnomAD_exomes_non_topmed_NFE_AF 0\n", - "gnomAD_exomes_non_topmed_SAS_AF 0\n", - "gnomAD_exomes_non_topmed_POPMAX_AF 0\n", - "gnomAD_genomes_AF 0\n", - "gnomAD_genomes_POPMAX_AF 0\n", - "gnomAD_genomes_AFR_AF 0\n", - "gnomAD_genomes_AMI_AF 0\n", - "gnomAD_genomes_AMR_AF 0\n", - "gnomAD_genomes_ASJ_AF 0\n", - "gnomAD_genomes_EAS_AF 0\n", - "gnomAD_genomes_FIN_AF 0\n", - "gnomAD_genomes_MID_AF 0\n", - "gnomAD_genomes_NFE_AF 0\n", - "gnomAD_genomes_SAS_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_AF 0\n", - "gnomAD_genomes_non_neuro_AF 0\n", - "gnomAD_genomes_non_cancer_AF 0\n", - "gnomAD_genomes_non_topmed_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_AFR_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_AMI_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_AMR_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_ASJ_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_EAS_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_FIN_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_MID_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_NFE_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_SAS_AF 0\n", - "gnomAD_genomes_non_neuro_AFR_AF 0\n", - "gnomAD_genomes_non_neuro_AMI_AF 0\n", - "gnomAD_genomes_non_neuro_AMR_AF 0\n", - "gnomAD_genomes_non_neuro_ASJ_AF 0\n", - "gnomAD_genomes_non_neuro_EAS_AF 0\n", - "gnomAD_genomes_non_neuro_FIN_AF 0\n", - "gnomAD_genomes_non_neuro_MID_AF 0\n", - "gnomAD_genomes_non_neuro_NFE_AF 0\n", - "gnomAD_genomes_non_neuro_SAS_AF 0\n", - "gnomAD_genomes_non_cancer_AFR_AF 0\n", - "gnomAD_genomes_non_cancer_AMI_AF 0\n", - "gnomAD_genomes_non_cancer_AMR_AF 0\n", - "gnomAD_genomes_non_cancer_ASJ_AF 0\n", - "gnomAD_genomes_non_cancer_EAS_AF 0\n", - "gnomAD_genomes_non_cancer_FIN_AF 0\n", - "gnomAD_genomes_non_cancer_MID_AF 0\n", - "gnomAD_genomes_non_cancer_NFE_AF 0\n", - "gnomAD_genomes_non_cancer_SAS_AF 0\n", - "gnomAD_genomes_non_topmed_AFR_AF 0\n", - "gnomAD_genomes_non_topmed_AMI_AF 0\n", - "gnomAD_genomes_non_topmed_AMR_AF 0\n", - "gnomAD_genomes_non_topmed_ASJ_AF 0\n", - "gnomAD_genomes_non_topmed_EAS_AF 0\n", - "gnomAD_genomes_non_topmed_FIN_AF 0\n", - "gnomAD_genomes_non_topmed_MID_AF 0\n", - "gnomAD_genomes_non_topmed_NFE_AF 0\n", - "gnomAD_genomes_non_topmed_SAS_AF 0\n", - "cds_strand_+ 0\n", - "SIFT_pred_D 0\n", - "SIFT_pred_T 0\n", - "SIFT4G_pred_D 0\n", - "SIFT4G_pred_T 0\n", - "Polyphen2_HDIV_pred_B 0\n", - "Polyphen2_HDIV_pred_D 0\n", - "Polyphen2_HDIV_pred_P 0\n", - "Polyphen2_HVAR_pred_B 0\n", - "Polyphen2_HVAR_pred_D 0\n", - "Polyphen2_HVAR_pred_P 0\n", - "LRT_pred_D 0\n", - "LRT_pred_N 0\n", - "LRT_pred_U 0\n", - "MutationAssessor_pred_H 0\n", - "MutationAssessor_pred_L 0\n", - "MutationAssessor_pred_M 0\n", - "MutationAssessor_pred_N 0\n", - "FATHMM_pred_D 0\n", - "FATHMM_pred_T 0\n", - "PROVEAN_pred_D 0\n", - "PROVEAN_pred_N 0\n", - "MetaSVM_pred_D 0\n", - "MetaSVM_pred_T 0\n", - "MetaLR_pred_D 0\n", - "MetaLR_pred_T 0\n", - "MetaRNN_pred_D 0\n", - "MetaRNN_pred_T 0\n", - "M-CAP_pred_D 0\n", - "M-CAP_pred_T 0\n", - "PrimateAI_pred_D 0\n", - "PrimateAI_pred_T 0\n", - "DEOGEN2_pred_D 0\n", - "DEOGEN2_pred_T 0\n", - "BayesDel_addAF_pred_D 0\n", - "BayesDel_addAF_pred_T 0\n", - "BayesDel_noAF_pred_D 0\n", - "BayesDel_noAF_pred_T 0\n", - "ClinPred_pred_D 0\n", - "ClinPred_pred_T 0\n", - "LIST-S2_pred_D 0\n", - "LIST-S2_pred_T 0\n", - "fathmm-MKL_coding_pred_D 0\n", - "fathmm-MKL_coding_pred_N 0\n", - "fathmm-XF_coding_pred_D 0\n", - "fathmm-XF_coding_pred_N 0\n", - "dtype: int64" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Check how many columns are null\n", - "df2.isnull().sum(axis = 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "df2 = df2.drop(config_dict['var'], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Data shape (nsSNV) = (49338, 279)\n" - ] - } - ], - "source": [ - "print('\\nData shape (nsSNV) =', df2.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "df = df2\n", - "del df2" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.shape[0] == var.shape[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.concat([var.reset_index(drop=True), df.reset_index(drop=True)], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Data shape (nsSNV) = (49338, 292)\n" - ] - } - ], - "source": [ - "print('\\nData shape (nsSNV) =', df.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "#chr 0\n", - "pos(1-based) 0\n", - "ref 0\n", - "alt 0\n", - "aaref 2076\n", - "aaalt 2076\n", - "genename 0\n", - "Ensembl_geneid 0\n", - "Ensembl_transcriptid 0\n", - "Ensembl_proteinid 0\n", - "Uniprot_acc 847\n", - "clinvar_review 0\n", - "Interpro_domain 34463\n", - "SIFT_score 0\n", - "SIFT_converted_rankscore 0\n", - "SIFT4G_score 0\n", - "SIFT4G_converted_rankscore 0\n", - "Polyphen2_HDIV_score 0\n", - "Polyphen2_HDIV_rankscore 0\n", - "Polyphen2_HVAR_score 0\n", - "Polyphen2_HVAR_rankscore 0\n", - "LRT_score 0\n", - "LRT_converted_rankscore 0\n", - "LRT_Omega 0\n", - "MutationTaster_converted_rankscore 0\n", - "MutationAssessor_score 0\n", - "MutationAssessor_rankscore 0\n", - "FATHMM_score 0\n", - "FATHMM_converted_rankscore 0\n", - "PROVEAN_score 0\n", - "PROVEAN_converted_rankscore 0\n", - "VEST4_score 0\n", - "VEST4_rankscore 0\n", - "MetaSVM_score 0\n", - "MetaSVM_rankscore 0\n", - "MetaLR_score 0\n", - "MetaLR_rankscore 0\n", - "Reliability_index 0\n", - "MetaRNN_score 0\n", - "MetaRNN_rankscore 0\n", - "M-CAP_score 0\n", - "M-CAP_rankscore 0\n", - "REVEL_score 0\n", - "REVEL_rankscore 0\n", - "MutPred_score 0\n", - "MutPred_rankscore 0\n", - "MVP_score 0\n", - "MVP_rankscore 0\n", - "MPC_score 0\n", - "MPC_rankscore 0\n", - "PrimateAI_score 0\n", - "PrimateAI_rankscore 0\n", - "DEOGEN2_score 0\n", - "DEOGEN2_rankscore 0\n", - "BayesDel_addAF_score 0\n", - "BayesDel_addAF_rankscore 0\n", - "BayesDel_noAF_score 0\n", - "BayesDel_noAF_rankscore 0\n", - "ClinPred_score 0\n", - "ClinPred_rankscore 0\n", - "LIST-S2_score 0\n", - "LIST-S2_rankscore 0\n", - "CADD_raw 0\n", - "CADD_raw_rankscore 0\n", - "CADD_phred 0\n", - "CADD_raw_hg19 0\n", - "CADD_raw_rankscore_hg19 0\n", - "CADD_phred_hg19 0\n", - "DANN_score 0\n", - "DANN_rankscore 0\n", - "fathmm-MKL_coding_score 0\n", - "fathmm-MKL_coding_rankscore 0\n", - "fathmm-XF_coding_score 0\n", - "fathmm-XF_coding_rankscore 0\n", - "Eigen-raw_coding 0\n", - "Eigen-raw_coding_rankscore 0\n", - "Eigen-phred_coding 0\n", - "Eigen-PC-raw_coding 0\n", - "Eigen-PC-raw_coding_rankscore 0\n", - "Eigen-PC-phred_coding 0\n", - "GenoCanyon_score 0\n", - "GenoCanyon_rankscore 0\n", - "integrated_fitCons_score 0\n", - "integrated_fitCons_rankscore 0\n", - "integrated_confidence_value 0\n", - "GM12878_fitCons_score 0\n", - "GM12878_fitCons_rankscore 0\n", - "GM12878_confidence_value 0\n", - "H1-hESC_fitCons_score 0\n", - "H1-hESC_fitCons_rankscore 0\n", - "H1-hESC_confidence_value 0\n", - "HUVEC_fitCons_score 0\n", - "HUVEC_fitCons_rankscore 0\n", - "HUVEC_confidence_value 0\n", - "LINSIGHT 0\n", - "LINSIGHT_rankscore 0\n", - "GERP++_NR 0\n", - "GERP++_RS 0\n", - "GERP++_RS_rankscore 0\n", - "phyloP100way_vertebrate 0\n", - "phyloP100way_vertebrate_rankscore 0\n", - "phyloP30way_mammalian 0\n", - "phyloP30way_mammalian_rankscore 0\n", - "phyloP17way_primate 0\n", - "phyloP17way_primate_rankscore 0\n", - "phastCons100way_vertebrate 0\n", - "phastCons100way_vertebrate_rankscore 0\n", - "phastCons30way_mammalian 0\n", - "phastCons30way_mammalian_rankscore 0\n", - "phastCons17way_primate 0\n", - "phastCons17way_primate_rankscore 0\n", - "SiPhy_29way_logOdds 0\n", - "SiPhy_29way_logOdds_rankscore 0\n", - "bStatistic 0\n", - "bStatistic_converted_rankscore 0\n", - "1000Gp3_AF 0\n", - "1000Gp3_AFR_AF 0\n", - "1000Gp3_EUR_AF 0\n", - "1000Gp3_AMR_AF 0\n", - "1000Gp3_EAS_AF 0\n", - "1000Gp3_SAS_AF 0\n", - "TWINSUK_AF 0\n", - "ALSPAC_AF 0\n", - "UK10K_AF 0\n", - "ESP6500_AA_AF 0\n", - "ESP6500_EA_AF 0\n", - "ExAC_AF 0\n", - "ExAC_Adj_AF 0\n", - "ExAC_AFR_AF 0\n", - "ExAC_AMR_AF 0\n", - "ExAC_EAS_AF 0\n", - "ExAC_FIN_AF 0\n", - "ExAC_NFE_AF 0\n", - "ExAC_SAS_AF 0\n", - "ExAC_nonTCGA_AF 0\n", - "ExAC_nonTCGA_Adj_AF 0\n", - "ExAC_nonTCGA_AFR_AF 0\n", - "ExAC_nonTCGA_AMR_AF 0\n", - "ExAC_nonTCGA_EAS_AF 0\n", - "ExAC_nonTCGA_FIN_AF 0\n", - "ExAC_nonTCGA_NFE_AF 0\n", - "ExAC_nonTCGA_SAS_AF 0\n", - "ExAC_nonpsych_AF 0\n", - "ExAC_nonpsych_Adj_AF 0\n", - "ExAC_nonpsych_AFR_AF 0\n", - "ExAC_nonpsych_AMR_AF 0\n", - "ExAC_nonpsych_EAS_AF 0\n", - "ExAC_nonpsych_FIN_AF 0\n", - "ExAC_nonpsych_NFE_AF 0\n", - "ExAC_nonpsych_SAS_AF 0\n", - "gnomAD_exomes_AF 0\n", - "gnomAD_exomes_AFR_AF 0\n", - "gnomAD_exomes_AMR_AF 0\n", - "gnomAD_exomes_ASJ_AF 0\n", - "gnomAD_exomes_EAS_AF 0\n", - "gnomAD_exomes_FIN_AF 0\n", - "gnomAD_exomes_NFE_AF 0\n", - "gnomAD_exomes_SAS_AF 0\n", - "gnomAD_exomes_POPMAX_AF 0\n", - "gnomAD_exomes_controls_AF 0\n", - "gnomAD_exomes_non_neuro_AF 0\n", - "gnomAD_exomes_non_cancer_AF 0\n", - "gnomAD_exomes_non_topmed_AF 0\n", - "gnomAD_exomes_controls_AFR_AF 0\n", - "gnomAD_exomes_controls_AMR_AF 0\n", - "gnomAD_exomes_controls_ASJ_AF 0\n", - "gnomAD_exomes_controls_EAS_AF 0\n", - "gnomAD_exomes_controls_FIN_AF 0\n", - "gnomAD_exomes_controls_NFE_AF 0\n", - "gnomAD_exomes_controls_SAS_AF 0\n", - "gnomAD_exomes_controls_POPMAX_AF 0\n", - "gnomAD_exomes_non_neuro_AFR_AF 0\n", - "gnomAD_exomes_non_neuro_AMR_AF 0\n", - "gnomAD_exomes_non_neuro_ASJ_AF 0\n", - "gnomAD_exomes_non_neuro_EAS_AF 0\n", - "gnomAD_exomes_non_neuro_FIN_AF 0\n", - "gnomAD_exomes_non_neuro_NFE_AF 0\n", - "gnomAD_exomes_non_neuro_SAS_AF 0\n", - "gnomAD_exomes_non_neuro_POPMAX_AF 0\n", - "gnomAD_exomes_non_cancer_AFR_AF 0\n", - "gnomAD_exomes_non_cancer_AMR_AF 0\n", - "gnomAD_exomes_non_cancer_ASJ_AF 0\n", - "gnomAD_exomes_non_cancer_EAS_AF 0\n", - "gnomAD_exomes_non_cancer_FIN_AF 0\n", - "gnomAD_exomes_non_cancer_NFE_AF 0\n", - "gnomAD_exomes_non_cancer_SAS_AF 0\n", - "gnomAD_exomes_non_cancer_POPMAX_AF 0\n", - "gnomAD_exomes_non_topmed_AFR_AF 0\n", - "gnomAD_exomes_non_topmed_AMR_AF 0\n", - "gnomAD_exomes_non_topmed_ASJ_AF 0\n", - "gnomAD_exomes_non_topmed_EAS_AF 0\n", - "gnomAD_exomes_non_topmed_FIN_AF 0\n", - "gnomAD_exomes_non_topmed_NFE_AF 0\n", - "gnomAD_exomes_non_topmed_SAS_AF 0\n", - "gnomAD_exomes_non_topmed_POPMAX_AF 0\n", - "gnomAD_genomes_AF 0\n", - "gnomAD_genomes_POPMAX_AF 0\n", - "gnomAD_genomes_AFR_AF 0\n", - "gnomAD_genomes_AMI_AF 0\n", - "gnomAD_genomes_AMR_AF 0\n", - "gnomAD_genomes_ASJ_AF 0\n", - "gnomAD_genomes_EAS_AF 0\n", - "gnomAD_genomes_FIN_AF 0\n", - "gnomAD_genomes_MID_AF 0\n", - "gnomAD_genomes_NFE_AF 0\n", - "gnomAD_genomes_SAS_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_AF 0\n", - "gnomAD_genomes_non_neuro_AF 0\n", - "gnomAD_genomes_non_cancer_AF 0\n", - "gnomAD_genomes_non_topmed_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_AFR_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_AMI_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_AMR_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_ASJ_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_EAS_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_FIN_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_MID_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_NFE_AF 0\n", - "gnomAD_genomes_controls_and_biobanks_SAS_AF 0\n", - "gnomAD_genomes_non_neuro_AFR_AF 0\n", - "gnomAD_genomes_non_neuro_AMI_AF 0\n", - "gnomAD_genomes_non_neuro_AMR_AF 0\n", - "gnomAD_genomes_non_neuro_ASJ_AF 0\n", - "gnomAD_genomes_non_neuro_EAS_AF 0\n", - "gnomAD_genomes_non_neuro_FIN_AF 0\n", - "gnomAD_genomes_non_neuro_MID_AF 0\n", - "gnomAD_genomes_non_neuro_NFE_AF 0\n", - "gnomAD_genomes_non_neuro_SAS_AF 0\n", - "gnomAD_genomes_non_cancer_AFR_AF 0\n", - "gnomAD_genomes_non_cancer_AMI_AF 0\n", - "gnomAD_genomes_non_cancer_AMR_AF 0\n", - "gnomAD_genomes_non_cancer_ASJ_AF 0\n", - "gnomAD_genomes_non_cancer_EAS_AF 0\n", - "gnomAD_genomes_non_cancer_FIN_AF 0\n", - "gnomAD_genomes_non_cancer_MID_AF 0\n", - "gnomAD_genomes_non_cancer_NFE_AF 0\n", - "gnomAD_genomes_non_cancer_SAS_AF 0\n", - "gnomAD_genomes_non_topmed_AFR_AF 0\n", - "gnomAD_genomes_non_topmed_AMI_AF 0\n", - "gnomAD_genomes_non_topmed_AMR_AF 0\n", - "gnomAD_genomes_non_topmed_ASJ_AF 0\n", - "gnomAD_genomes_non_topmed_EAS_AF 0\n", - "gnomAD_genomes_non_topmed_FIN_AF 0\n", - "gnomAD_genomes_non_topmed_MID_AF 0\n", - "gnomAD_genomes_non_topmed_NFE_AF 0\n", - "gnomAD_genomes_non_topmed_SAS_AF 0\n", 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"ClinPred_pred_D 0\n", - "ClinPred_pred_T 0\n", - "LIST-S2_pred_D 0\n", - "LIST-S2_pred_T 0\n", - "fathmm-MKL_coding_pred_D 0\n", - "fathmm-MKL_coding_pred_N 0\n", - "fathmm-XF_coding_pred_D 0\n", - "fathmm-XF_coding_pred_N 0\n", - "dtype: int64" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Check how many columns are null\n", - "df.isnull().sum(axis = 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [], - "source": [ - "# Write it to a file\n", - "df.to_csv('../processed/test_no_filter_data-dbnsfp.csv', index=False)\n", - "y.to_csv('../processed/test_no_filter_data-y-dbnsfp.csv', index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 159, - "metadata": {}, - "outputs": [], - "source": [ - "df = df1" - ] - }, - { - "cell_type": "code", - "execution_count": 160, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " SIFT MutationAssessor CADD DANN DEOGEN2 LRT M-CAP \\\n", - "0 NaN NaN 29.700001 0.998092 0.595316 0.003497 0.01394 \n", - "1 0.0 NaN 29.700001 0.998092 NaN 0.003497 0.01394 \n", - "2 0.0 2.255 29.700001 0.998092 0.062945 0.003497 0.01394 \n", - "3 NaN NaN 29.700001 0.998092 0.150567 0.003497 0.01394 \n", - "4 NaN NaN 29.700001 0.998092 0.085518 0.003497 0.01394 \n", - "\n", - " MetaLR MetaSVM MetaRNN ClinPred MutPred VEST4 PrimateAI \\\n", - "0 0.1527 -0.858 0.710692 0.996041 NaN NaN 0.905049 \n", - "1 0.1527 -0.858 0.710692 0.996041 NaN NaN 0.905049 \n", - "2 0.1527 -0.858 0.710692 0.996041 NaN 0.909 0.905049 \n", - "3 0.1527 -0.858 0.710692 0.996041 NaN 0.890 0.905049 \n", - "4 0.1527 -0.858 0.710692 0.996041 NaN 0.908 0.905049 \n", - "\n", - " clinvar_clnsig \n", - "0 low_impact \n", - "1 low_impact \n", - "2 low_impact \n", - "3 low_impact \n", - "4 low_impact " - ] - }, - "execution_count": 160, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "benchmark_columns = ['SIFT_score','MutationAssessor_score','CADD_phred','DANN_score','DEOGEN2_score','LRT_score','M-CAP_score','MetaLR_score','MetaSVM_score','MetaRNN_score','ClinPred_score','MutPred_score','VEST4_score','PrimateAI_score']\n", - "benchmark_df = df[benchmark_columns]\n", - "benchmark_df.columns = ['SIFT','MutationAssessor','CADD','DANN','DEOGEN2','LRT','M-CAP','MetaLR','MetaSVM','MetaRNN','ClinPred','MutPred','VEST4','PrimateAI']\n", - "benchmark_df = pd.concat([benchmark_df.reset_index(drop=True), y.reset_index(drop=True)], axis=1)\n", - "benchmark_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 161, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1544830, 15)" - ] - }, - "execution_count": 161, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "benchmark_df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [], - "source": [ - "# Write it to a file\n", - "benchmark_df.to_csv('../processed/benchmark_filtered_95_data-dbnsfp.csv', index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "def test_parsing(dataframe, config_dict, train_columns, median_scores):\n", - " # Drop variant info columns so we can perform one-hot encoding\n", - " var = dataframe[config_dict['var']]\n", - " dataframe = dataframe.drop(config_dict['var'], axis=1)\n", - " #dataframe['DEOGEN2_score'] = [np.max([float(item.replace('.', '0')) if item == '.' else float(item) for item in i]) if type(i) is list else i for i in dataframe['DEOGEN2_score'].str.split('&')]\n", - " #Perform one-hot encoding\n", - " dataframe = pd.get_dummies(dataframe, prefix_sep='_')\n", - " dataframe[config_dict['allele_freq_columns']] = dataframe[config_dict['allele_freq_columns']].fillna(0)\n", - " \n", - " for key in tqdm(median_scores.keys()):\n", - " if key in dataframe.columns:\n", - " dataframe[key] = (\n", - " dataframe[key]\n", - " .fillna(median_scores[key])\n", - " .astype(\"float32\")\n", - " )\n", - " \n", - " df2 = pd.DataFrame()\n", - " for key in tqdm(train_columns):\n", - " if key in dataframe.columns:\n", - " df2[key] = dataframe[key]\n", - " else:\n", - " df2[key] = 0\n", - " \n", - " del dataframe\n", - "\n", - " \n", - " df2 = df2.drop(config_dict['var'], axis=1)\n", - " df2 = pd.concat([var.reset_index(drop=True), df2.reset_index(drop=True)], axis=1)\n", - " return df2" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "df=df1" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 233/233 [00:01<00:00, 220.49it/s]\n", - "100%|██████████| 292/292 [00:02<00:00, 131.74it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Data shape (nsSNV) = (1521194, 292)\n" - ] - } - ], - "source": [ - "df2 = test_parsing(df, config_dict, train_columns, median_scores)\n", - "print('\\nData shape (nsSNV) =', df2.shape)\n", - "# Write it to a file\n", - "df2.to_csv('../processed/all_data_no_filter-dbnsfp.csv', index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python [conda env:.conda-training]", - "language": "python", - "name": "conda-env-.conda-training-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/dbNSFP/array_script.job b/src/dbNSFP/array_script.job deleted file mode 100644 index a2f0aa1..0000000 --- a/src/dbNSFP/array_script.job +++ /dev/null @@ -1,22 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=gene -#SBATCH --ntasks=1 -#SBATCH --mem=5G -#SBATCH --partition=express -#SBATCH --mail-type=FAIL -#SBATCH --mail-user=tmamidi@uab.edu -#SBATCH --output=../logs/%x_%A_%a.log -#SBATCH --array=3-5 - -module reset -module load Anaconda3/2020.02 -source activate training - -n=$SLURM_ARRAY_TASK_ID #19622 -FILES=(/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/dbnsfp_genes/*) -gene=${FILES[$SLURM_ARRAY_TASK_ID]} - -echo "${gene##*/}" - - -python predictions.py -i /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/dbnsfp_genes/${gene##*/}/dbNSFP_${gene##*/}_variants.tsv.gz --ditto /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/dbnsfp_predictions/${gene##*/}_ditto_predictions.csv.gz diff --git a/src/dbNSFP/extract_genes.py b/src/dbNSFP/extract_genes.py deleted file mode 100644 index 4a0995f..0000000 --- a/src/dbNSFP/extract_genes.py +++ /dev/null @@ -1,32 +0,0 @@ -import gzip -import gc -import os - -def extract_variants(input): - print(f"Writing variants from {input} ...") - - #with gzip.open(output, "wt") as out: - with gzip.open(input, "rt") as vcffp: - for cnt, line in enumerate(vcffp): - if not line.startswith("#"): - line = line.rstrip("\n") - cols = line.split("\t") - gene = cols[12] - if not os.path.exists(f"/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/dbnsfp_genes/{gene}"): - os.makedirs(f"/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/dbnsfp_genes/{gene}") - - if os.path.isfile(f"/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/dbnsfp_genes/{gene}/dbNSFP_{gene}_variants.tsv.gz"): - with gzip.open(f"/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/dbnsfp_genes/{gene}/dbNSFP_{gene}_variants.tsv.gz", "at") as out: - out.write(line + "\n") - else: - with gzip.open(f"/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/dbnsfp_genes/{gene}/dbNSFP_{gene}_variants.tsv.gz", "wt") as out: - out.write(line + "\n") - - return None - -if __name__ == "__main__": - - extract_variants("/data/project/worthey_lab/temp_datasets_central/tarun/dbNSFP/v4.3_20220319/dbNSFP4.3a_variant.complete.parsed.sorted.tsv.gz") - - gc.collect() - diff --git a/src/dbNSFP/predictions.py b/src/dbNSFP/predictions.py deleted file mode 100644 index c827431..0000000 --- a/src/dbNSFP/predictions.py +++ /dev/null @@ -1,118 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -#python predictions.py -i /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/dbnsfp_genes/${gene##*/}/dbNSFP_${gene##*/}_variants.tsv.gz --ditto /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/dbnsfp_predictions/${gene##*/}_ditto_predictions.csv.gz - -import pandas as pd -import yaml -import warnings -warnings.simplefilter("ignore") -from joblib import load, dump -from tqdm import tqdm -import argparse -import shap -import numpy as np -import functools -print = functools.partial(print, flush=True) - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--input", - "-i", - type=str, - required=True, - help="Input csv file with path for filtering and predictions", - ) - parser.add_argument( - "--ditto", - type=str, - default="ditto_predictions.csv.gz", - help="Output file with path (default:ditto_predictions.csv.gz)", - ) - - - args = parser.parse_args() - - print("Loading data and Ditto model....") - - with open( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/configs/col_config.yaml" - ) as fh: - config_dict = yaml.safe_load(fh) - - - def parse_and_predict(dataframe, config_dict, explainer): - dataframe.columns = config_dict["raw_cols"] - var = dataframe[config_dict['ditto_info']] - dataframe = dataframe[config_dict["columns"]] - # Drop variant info columns so we can perform one-hot encoding - dataframe = dataframe.drop(config_dict['var'], axis=1) - dataframe = dataframe.replace(['.','-'], np.nan) - - for key in tqdm(dataframe.columns): - try: - dataframe[key] = ( - dataframe[key] - .astype("float32") - ) - except: - dataframe[key] = dataframe[key] - - #Perform one-hot encoding - dataframe = pd.get_dummies(dataframe, prefix_sep='_') - dataframe[config_dict['allele_freq_columns']] = dataframe[config_dict['allele_freq_columns']].fillna(0) - - for key in tqdm(config_dict['nssnv_median'].keys()): - if key in dataframe.columns: - dataframe[key] = ( - dataframe[key] - .fillna(config_dict['nssnv_median'][key]) - .astype("float32") - ) - - df2 = pd.DataFrame() - for key in tqdm(config_dict['nssnv_columns']): - if key in dataframe.columns: - df2[key] = dataframe[key] - else: - df2[key] = 0 - - del dataframe - - - df2 = df2.drop(config_dict['var'], axis=1) - X_test = df2.values - y_score = clf.predict_proba(X_test) - del X_test - pred = pd.DataFrame(y_score, columns=["Ditto_Benign", "Ditto_Deleterious"]) - - ditto_scores = pd.concat([var, pred], axis=1) - ditto_scores.to_csv(args.ditto, index=False, - compression="gzip") - - del df2 - - return None - - - with open( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/models_custom/dbnsfp/StackingClassifier_dbnsfp.joblib", - "rb", - ) as f: - clf = load(f) - - X_train = pd.read_csv('/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_custom_data-dbnsfp.csv') - X_train = X_train.drop(config_dict['var'], axis=1) - X_train = X_train.values - background = shap.kmeans(X_train, 10) - explainer = shap.KernelExplainer(clf.predict_proba, background) - del background, X_train - - - print('Processing data...') - df = pd.read_csv(args.input, sep='\t', header=None) - - parse_and_predict(df, config_dict, explainer) - - diff --git a/src/pkd/array_script.job b/src/pkd/array_script.job deleted file mode 100644 index 179eff2..0000000 --- a/src/pkd/array_script.job +++ /dev/null @@ -1,22 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=gene -#SBATCH --ntasks=1 -#SBATCH --mem=10G -#SBATCH --partition=short -#SBATCH --mail-type=FAIL -#SBATCH --mail-user=tmamidi@uab.edu -#SBATCH --output=../logs/%x_%A_%a.log -#SBATCH --array=0-2 - -module reset -module load Anaconda3/2020.02 -source activate training - -n=$SLURM_ARRAY_TASK_ID #19622 -FILES=(/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/dbnsfp_genes/*) -gene=${FILES[$SLURM_ARRAY_TASK_ID]} - -echo "${gene##*/}" - - -python predictions.py -i /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/dbnsfp_genes/${gene##*/}/dbNSFP_${gene##*/}_variants.tsv.gz --filter /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/dbnsfp_genes/${gene##*/}/${gene##*/}-procesed-dbnsfp.csv.gz --ditto /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/dbnsfp_predictions/${gene##*/}_ditto_predictions.csv.gz --shapley /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/dbnsfp_genes/${gene##*/}/${gene##*/}_shapley.joblib diff --git a/src/pkd/extract_pkd.py b/src/pkd/extract_pkd.py deleted file mode 100644 index 29ee117..0000000 --- a/src/pkd/extract_pkd.py +++ /dev/null @@ -1,42 +0,0 @@ -import gzip -import gc -import os -import ray -# Start Ray. -ray.init(ignore_reinit_error=True) - -@ray.remote # (num_cpus=9) -def extract_variants(input, output, gene): - print(f"Writing {gene} variants from {input} to {output}...") - if not os.path.exists(f"/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/Ditto/{gene}"): - os.makedirs(f"/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/Ditto/{gene}") - pkd1 = 0 - with gzip.open(output, "wt") as out: - with gzip.open(input, "rt") as vcffp: - for cnt, line in enumerate(vcffp): - if not line.startswith("#"): - line = line.rstrip("\n") - cols = line.split("\t") - if cols[12] == gene:#12 - pkd1 = pkd1 + 1 - out.write(line + "\n") - #elif cols[12] == 'PKD2': - # pkd2 = pkd2 + 1 - # out.write(line + "\n") - else: - out.write(line) - - print(f"{gene} variants: {pkd1}\n")#PKD2 variants: {pkd2}\n") - return None - -if __name__ == "__main__": - - gene_list = ["PIBF1","KIAA0753","AHI1","ATXN10","B9D1","BCAR1","CC2D2A","CCP110","CEP97","DCTN1","DCTN2","IFT88","INVS","KIF3A","MKS1","NPHP3","PIBF1","KIAA0753","PCNT","PDE6D","RPGR","RPGRIP1","RPGRIP1L","TMEM216","TMEM67","UNC119B", "OFD1", "CEP89","CEP164","CC2D2A", "B9D2", "TCTN1"] - remote_ml = [ - extract_variants.remote("/data/project/worthey_lab/temp_datasets_central/tarun/dbNSFP/v4.3_20220319/dbNSFP4.3a_variant.complete.parsed.sorted.tsv.gz",f"/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/Ditto/{gene}/dbNSFP_{gene}_variants.tsv.gz", gene) - - for gene in gene_list - ] - ray.get(remote_ml) - gc.collect() - diff --git a/src/pkd/merge.py b/src/pkd/merge.py deleted file mode 100644 index 38d9e2e..0000000 --- a/src/pkd/merge.py +++ /dev/null @@ -1,40 +0,0 @@ -import pandas as pd -import ray -# Start Ray. -ray.init(ignore_reinit_error=True) -import gc - -raw_cols = ['#chr', 'pos(1-based)', 'ref', 'alt', 'aaref', 'aaalt', 'rs_dbSNP', 'hg19_chr', 'hg19_pos(1-based)', 'hg18_chr', 'hg18_pos(1-based)', 'aapos', 'genename', 'Ensembl_geneid', 'Ensembl_transcriptid', 'Ensembl_proteinid', 'Uniprot_acc', 'Uniprot_entry', 'HGVSc_ANNOVAR', 'HGVSp_ANNOVAR', 'HGVSc_snpEff', 'HGVSp_snpEff', 'HGVSc_VEP', 'HGVSp_VEP', 'APPRIS', 'GENCODE_basic', 'TSL', 'VEP_canonical', 'cds_strand', 'refcodon', 'codonpos', 'codon_degeneracy', 'Ancestral_allele', 'AltaiNeandertal', 'Denisova', 'VindijiaNeandertal', 'ChagyrskayaNeandertal', 'SIFT_score', 'SIFT_converted_rankscore', 'SIFT_pred', 'SIFT4G_score', 'SIFT4G_converted_rankscore', 'SIFT4G_pred', 'Polyphen2_HDIV_score', 'Polyphen2_HDIV_rankscore', 'Polyphen2_HDIV_pred', 'Polyphen2_HVAR_score', 'Polyphen2_HVAR_rankscore', 'Polyphen2_HVAR_pred', 'LRT_score', 'LRT_converted_rankscore', 'LRT_pred', 'LRT_Omega', 'MutationTaster_score', 'MutationTaster_converted_rankscore', 'MutationTaster_pred', 'MutationTaster_model', 'MutationTaster_AAE', 'MutationAssessor_score', 'MutationAssessor_rankscore', 'MutationAssessor_pred', 'FATHMM_score', 'FATHMM_converted_rankscore', 'FATHMM_pred', 'PROVEAN_score', 'PROVEAN_converted_rankscore', 'PROVEAN_pred', 'VEST4_score', 'VEST4_rankscore', 'MetaSVM_score', 'MetaSVM_rankscore', 'MetaSVM_pred', 'MetaLR_score', 'MetaLR_rankscore', 'MetaLR_pred', 'Reliability_index', 'MetaRNN_score', 'MetaRNN_rankscore', 'MetaRNN_pred', 'M-CAP_score', 'M-CAP_rankscore', 'M-CAP_pred', 'REVEL_score', 'REVEL_rankscore', 'MutPred_score', 'MutPred_rankscore', 'MutPred_protID', 'MutPred_AAchange', 'MutPred_Top5features', 'MVP_score', 'MVP_rankscore', 'MPC_score', 'MPC_rankscore', 'PrimateAI_score', 'PrimateAI_rankscore', 'PrimateAI_pred', 'DEOGEN2_score', 'DEOGEN2_rankscore', 'DEOGEN2_pred', 'BayesDel_addAF_score', 'BayesDel_addAF_rankscore', 'BayesDel_addAF_pred', 'BayesDel_noAF_score', 'BayesDel_noAF_rankscore', 'BayesDel_noAF_pred', 'ClinPred_score', 'ClinPred_rankscore', 'ClinPred_pred', 'LIST-S2_score', 'LIST-S2_rankscore', 'LIST-S2_pred', 'Aloft_Fraction_transcripts_affected', 'Aloft_prob_Tolerant', 'Aloft_prob_Recessive', 'Aloft_prob_Dominant', 'Aloft_pred', 'Aloft_Confidence', 'CADD_raw', 'CADD_raw_rankscore', 'CADD_phred', 'CADD_raw_hg19', 'CADD_raw_rankscore_hg19', 'CADD_phred_hg19', 'DANN_score', 'DANN_rankscore', 'fathmm-MKL_coding_score', 'fathmm-MKL_coding_rankscore', 'fathmm-MKL_coding_pred', 'fathmm-MKL_coding_group', 'fathmm-XF_coding_score', 'fathmm-XF_coding_rankscore', 'fathmm-XF_coding_pred', 'Eigen-raw_coding', 'Eigen-raw_coding_rankscore', 'Eigen-phred_coding', 'Eigen-PC-raw_coding', 'Eigen-PC-raw_coding_rankscore', 'Eigen-PC-phred_coding', 'GenoCanyon_score', 'GenoCanyon_rankscore', 'integrated_fitCons_score', 'integrated_fitCons_rankscore', 'integrated_confidence_value', 'GM12878_fitCons_score', 'GM12878_fitCons_rankscore', 'GM12878_confidence_value', 'H1-hESC_fitCons_score', 'H1-hESC_fitCons_rankscore', 'H1-hESC_confidence_value', 'HUVEC_fitCons_score', 'HUVEC_fitCons_rankscore', 'HUVEC_confidence_value', 'LINSIGHT', 'LINSIGHT_rankscore', 'GERP++_NR', 'GERP++_RS', 'GERP++_RS_rankscore', 'phyloP100way_vertebrate', 'phyloP100way_vertebrate_rankscore', 'phyloP30way_mammalian', 'phyloP30way_mammalian_rankscore', 'phyloP17way_primate', 'phyloP17way_primate_rankscore', 'phastCons100way_vertebrate', 'phastCons100way_vertebrate_rankscore', 'phastCons30way_mammalian', 'phastCons30way_mammalian_rankscore', 'phastCons17way_primate', 'phastCons17way_primate_rankscore', 'SiPhy_29way_pi', 'SiPhy_29way_logOdds', 'SiPhy_29way_logOdds_rankscore', 'bStatistic', 'bStatistic_converted_rankscore', '1000Gp3_AC', '1000Gp3_AF', '1000Gp3_AFR_AC', '1000Gp3_AFR_AF', '1000Gp3_EUR_AC', '1000Gp3_EUR_AF', '1000Gp3_AMR_AC', '1000Gp3_AMR_AF', '1000Gp3_EAS_AC', '1000Gp3_EAS_AF', '1000Gp3_SAS_AC', '1000Gp3_SAS_AF', 'TWINSUK_AC', 'TWINSUK_AF', 'ALSPAC_AC', 'ALSPAC_AF', 'UK10K_AC', 'UK10K_AF', 'ESP6500_AA_AC', 'ESP6500_AA_AF', 'ESP6500_EA_AC', 'ESP6500_EA_AF', 'ExAC_AC', 'ExAC_AF', 'ExAC_Adj_AC', 'ExAC_Adj_AF', 'ExAC_AFR_AC', 'ExAC_AFR_AF', 'ExAC_AMR_AC', 'ExAC_AMR_AF', 'ExAC_EAS_AC', 'ExAC_EAS_AF', 'ExAC_FIN_AC', 'ExAC_FIN_AF', 'ExAC_NFE_AC', 'ExAC_NFE_AF', 'ExAC_SAS_AC', 'ExAC_SAS_AF', 'ExAC_nonTCGA_AC', 'ExAC_nonTCGA_AF', 'ExAC_nonTCGA_Adj_AC', 'ExAC_nonTCGA_Adj_AF', 'ExAC_nonTCGA_AFR_AC', 'ExAC_nonTCGA_AFR_AF', 'ExAC_nonTCGA_AMR_AC', 'ExAC_nonTCGA_AMR_AF', 'ExAC_nonTCGA_EAS_AC', 'ExAC_nonTCGA_EAS_AF', 'ExAC_nonTCGA_FIN_AC', 'ExAC_nonTCGA_FIN_AF', 'ExAC_nonTCGA_NFE_AC', 'ExAC_nonTCGA_NFE_AF', 'ExAC_nonTCGA_SAS_AC', 'ExAC_nonTCGA_SAS_AF', 'ExAC_nonpsych_AC', 'ExAC_nonpsych_AF', 'ExAC_nonpsych_Adj_AC', 'ExAC_nonpsych_Adj_AF', 'ExAC_nonpsych_AFR_AC', 'ExAC_nonpsych_AFR_AF', 'ExAC_nonpsych_AMR_AC', 'ExAC_nonpsych_AMR_AF', 'ExAC_nonpsych_EAS_AC', 'ExAC_nonpsych_EAS_AF', 'ExAC_nonpsych_FIN_AC', 'ExAC_nonpsych_FIN_AF', 'ExAC_nonpsych_NFE_AC', 'ExAC_nonpsych_NFE_AF', 'ExAC_nonpsych_SAS_AC', 'ExAC_nonpsych_SAS_AF', 'gnomAD_exomes_flag', 'gnomAD_exomes_AC', 'gnomAD_exomes_AN', 'gnomAD_exomes_AF', 'gnomAD_exomes_nhomalt', 'gnomAD_exomes_AFR_AC', 'gnomAD_exomes_AFR_AN', 'gnomAD_exomes_AFR_AF', 'gnomAD_exomes_AFR_nhomalt', 'gnomAD_exomes_AMR_AC', 'gnomAD_exomes_AMR_AN', 'gnomAD_exomes_AMR_AF', 'gnomAD_exomes_AMR_nhomalt', 'gnomAD_exomes_ASJ_AC', 'gnomAD_exomes_ASJ_AN', 'gnomAD_exomes_ASJ_AF', 'gnomAD_exomes_ASJ_nhomalt', 'gnomAD_exomes_EAS_AC', 'gnomAD_exomes_EAS_AN', 'gnomAD_exomes_EAS_AF', 'gnomAD_exomes_EAS_nhomalt', 'gnomAD_exomes_FIN_AC', 'gnomAD_exomes_FIN_AN', 'gnomAD_exomes_FIN_AF', 'gnomAD_exomes_FIN_nhomalt', 'gnomAD_exomes_NFE_AC', 'gnomAD_exomes_NFE_AN', 'gnomAD_exomes_NFE_AF', 'gnomAD_exomes_NFE_nhomalt', 'gnomAD_exomes_SAS_AC', 'gnomAD_exomes_SAS_AN', 'gnomAD_exomes_SAS_AF', 'gnomAD_exomes_SAS_nhomalt', 'gnomAD_exomes_POPMAX_AC', 'gnomAD_exomes_POPMAX_AN', 'gnomAD_exomes_POPMAX_AF', 'gnomAD_exomes_POPMAX_nhomalt', 'gnomAD_exomes_controls_AC', 'gnomAD_exomes_controls_AN', 'gnomAD_exomes_controls_AF', 'gnomAD_exomes_controls_nhomalt', 'gnomAD_exomes_non_neuro_AC', 'gnomAD_exomes_non_neuro_AN', 'gnomAD_exomes_non_neuro_AF', 'gnomAD_exomes_non_neuro_nhomalt', 'gnomAD_exomes_non_cancer_AC', 'gnomAD_exomes_non_cancer_AN', 'gnomAD_exomes_non_cancer_AF', 'gnomAD_exomes_non_cancer_nhomalt', 'gnomAD_exomes_non_topmed_AC', 'gnomAD_exomes_non_topmed_AN', 'gnomAD_exomes_non_topmed_AF', 'gnomAD_exomes_non_topmed_nhomalt', 'gnomAD_exomes_controls_AFR_AC', 'gnomAD_exomes_controls_AFR_AN', 'gnomAD_exomes_controls_AFR_AF', 'gnomAD_exomes_controls_AFR_nhomalt', 'gnomAD_exomes_controls_AMR_AC', 'gnomAD_exomes_controls_AMR_AN', 'gnomAD_exomes_controls_AMR_AF', 'gnomAD_exomes_controls_AMR_nhomalt', 'gnomAD_exomes_controls_ASJ_AC', 'gnomAD_exomes_controls_ASJ_AN', 'gnomAD_exomes_controls_ASJ_AF', 'gnomAD_exomes_controls_ASJ_nhomalt', 'gnomAD_exomes_controls_EAS_AC', 'gnomAD_exomes_controls_EAS_AN', 'gnomAD_exomes_controls_EAS_AF', 'gnomAD_exomes_controls_EAS_nhomalt', 'gnomAD_exomes_controls_FIN_AC', 'gnomAD_exomes_controls_FIN_AN', 'gnomAD_exomes_controls_FIN_AF', 'gnomAD_exomes_controls_FIN_nhomalt', 'gnomAD_exomes_controls_NFE_AC', 'gnomAD_exomes_controls_NFE_AN', 'gnomAD_exomes_controls_NFE_AF', 'gnomAD_exomes_controls_NFE_nhomalt', 'gnomAD_exomes_controls_SAS_AC', 'gnomAD_exomes_controls_SAS_AN', 'gnomAD_exomes_controls_SAS_AF', 'gnomAD_exomes_controls_SAS_nhomalt', 'gnomAD_exomes_controls_POPMAX_AC', 'gnomAD_exomes_controls_POPMAX_AN', 'gnomAD_exomes_controls_POPMAX_AF', 'gnomAD_exomes_controls_POPMAX_nhomalt', 'gnomAD_exomes_non_neuro_AFR_AC', 'gnomAD_exomes_non_neuro_AFR_AN', 'gnomAD_exomes_non_neuro_AFR_AF', 'gnomAD_exomes_non_neuro_AFR_nhomalt', 'gnomAD_exomes_non_neuro_AMR_AC', 'gnomAD_exomes_non_neuro_AMR_AN', 'gnomAD_exomes_non_neuro_AMR_AF', 'gnomAD_exomes_non_neuro_AMR_nhomalt', 'gnomAD_exomes_non_neuro_ASJ_AC', 'gnomAD_exomes_non_neuro_ASJ_AN', 'gnomAD_exomes_non_neuro_ASJ_AF', 'gnomAD_exomes_non_neuro_ASJ_nhomalt', 'gnomAD_exomes_non_neuro_EAS_AC', 'gnomAD_exomes_non_neuro_EAS_AN', 'gnomAD_exomes_non_neuro_EAS_AF', 'gnomAD_exomes_non_neuro_EAS_nhomalt', 'gnomAD_exomes_non_neuro_FIN_AC', 'gnomAD_exomes_non_neuro_FIN_AN', 'gnomAD_exomes_non_neuro_FIN_AF', 'gnomAD_exomes_non_neuro_FIN_nhomalt', 'gnomAD_exomes_non_neuro_NFE_AC', 'gnomAD_exomes_non_neuro_NFE_AN', 'gnomAD_exomes_non_neuro_NFE_AF', 'gnomAD_exomes_non_neuro_NFE_nhomalt', 'gnomAD_exomes_non_neuro_SAS_AC', 'gnomAD_exomes_non_neuro_SAS_AN', 'gnomAD_exomes_non_neuro_SAS_AF', 'gnomAD_exomes_non_neuro_SAS_nhomalt', 'gnomAD_exomes_non_neuro_POPMAX_AC', 'gnomAD_exomes_non_neuro_POPMAX_AN', 'gnomAD_exomes_non_neuro_POPMAX_AF', 'gnomAD_exomes_non_neuro_POPMAX_nhomalt', 'gnomAD_exomes_non_cancer_AFR_AC', 'gnomAD_exomes_non_cancer_AFR_AN', 'gnomAD_exomes_non_cancer_AFR_AF', 'gnomAD_exomes_non_cancer_AFR_nhomalt', 'gnomAD_exomes_non_cancer_AMR_AC', 'gnomAD_exomes_non_cancer_AMR_AN', 'gnomAD_exomes_non_cancer_AMR_AF', 'gnomAD_exomes_non_cancer_AMR_nhomalt', 'gnomAD_exomes_non_cancer_ASJ_AC', 'gnomAD_exomes_non_cancer_ASJ_AN', 'gnomAD_exomes_non_cancer_ASJ_AF', 'gnomAD_exomes_non_cancer_ASJ_nhomalt', 'gnomAD_exomes_non_cancer_EAS_AC', 'gnomAD_exomes_non_cancer_EAS_AN', 'gnomAD_exomes_non_cancer_EAS_AF', 'gnomAD_exomes_non_cancer_EAS_nhomalt', 'gnomAD_exomes_non_cancer_FIN_AC', 'gnomAD_exomes_non_cancer_FIN_AN', 'gnomAD_exomes_non_cancer_FIN_AF', 'gnomAD_exomes_non_cancer_FIN_nhomalt', 'gnomAD_exomes_non_cancer_NFE_AC', 'gnomAD_exomes_non_cancer_NFE_AN', 'gnomAD_exomes_non_cancer_NFE_AF', 'gnomAD_exomes_non_cancer_NFE_nhomalt', 'gnomAD_exomes_non_cancer_SAS_AC', 'gnomAD_exomes_non_cancer_SAS_AN', 'gnomAD_exomes_non_cancer_SAS_AF', 'gnomAD_exomes_non_cancer_SAS_nhomalt', 'gnomAD_exomes_non_cancer_POPMAX_AC', 'gnomAD_exomes_non_cancer_POPMAX_AN', 'gnomAD_exomes_non_cancer_POPMAX_AF', 'gnomAD_exomes_non_cancer_POPMAX_nhomalt', 'gnomAD_exomes_non_topmed_AFR_AC', 'gnomAD_exomes_non_topmed_AFR_AN', 'gnomAD_exomes_non_topmed_AFR_AF', 'gnomAD_exomes_non_topmed_AFR_nhomalt', 'gnomAD_exomes_non_topmed_AMR_AC', 'gnomAD_exomes_non_topmed_AMR_AN', 'gnomAD_exomes_non_topmed_AMR_AF', 'gnomAD_exomes_non_topmed_AMR_nhomalt', 'gnomAD_exomes_non_topmed_ASJ_AC', 'gnomAD_exomes_non_topmed_ASJ_AN', 'gnomAD_exomes_non_topmed_ASJ_AF', 'gnomAD_exomes_non_topmed_ASJ_nhomalt', 'gnomAD_exomes_non_topmed_EAS_AC', 'gnomAD_exomes_non_topmed_EAS_AN', 'gnomAD_exomes_non_topmed_EAS_AF', 'gnomAD_exomes_non_topmed_EAS_nhomalt', 'gnomAD_exomes_non_topmed_FIN_AC', 'gnomAD_exomes_non_topmed_FIN_AN', 'gnomAD_exomes_non_topmed_FIN_AF', 'gnomAD_exomes_non_topmed_FIN_nhomalt', 'gnomAD_exomes_non_topmed_NFE_AC', 'gnomAD_exomes_non_topmed_NFE_AN', 'gnomAD_exomes_non_topmed_NFE_AF', 'gnomAD_exomes_non_topmed_NFE_nhomalt', 'gnomAD_exomes_non_topmed_SAS_AC', 'gnomAD_exomes_non_topmed_SAS_AN', 'gnomAD_exomes_non_topmed_SAS_AF', 'gnomAD_exomes_non_topmed_SAS_nhomalt', 'gnomAD_exomes_non_topmed_POPMAX_AC', 'gnomAD_exomes_non_topmed_POPMAX_AN', 'gnomAD_exomes_non_topmed_POPMAX_AF', 'gnomAD_exomes_non_topmed_POPMAX_nhomalt', 'gnomAD_genomes_flag', 'gnomAD_genomes_AC', 'gnomAD_genomes_AN', 'gnomAD_genomes_AF', 'gnomAD_genomes_nhomalt', 'gnomAD_genomes_POPMAX_AC', 'gnomAD_genomes_POPMAX_AN', 'gnomAD_genomes_POPMAX_AF', 'gnomAD_genomes_POPMAX_nhomalt', 'gnomAD_genomes_AFR_AC', 'gnomAD_genomes_AFR_AN', 'gnomAD_genomes_AFR_AF', 'gnomAD_genomes_AFR_nhomalt', 'gnomAD_genomes_AMI_AC', 'gnomAD_genomes_AMI_AN', 'gnomAD_genomes_AMI_AF', 'gnomAD_genomes_AMI_nhomalt', 'gnomAD_genomes_AMR_AC', 'gnomAD_genomes_AMR_AN', 'gnomAD_genomes_AMR_AF', 'gnomAD_genomes_AMR_nhomalt', 'gnomAD_genomes_ASJ_AC', 'gnomAD_genomes_ASJ_AN', 'gnomAD_genomes_ASJ_AF', 'gnomAD_genomes_ASJ_nhomalt', 'gnomAD_genomes_EAS_AC', 'gnomAD_genomes_EAS_AN', 'gnomAD_genomes_EAS_AF', 'gnomAD_genomes_EAS_nhomalt', 'gnomAD_genomes_FIN_AC', 'gnomAD_genomes_FIN_AN', 'gnomAD_genomes_FIN_AF', 'gnomAD_genomes_FIN_nhomalt', 'gnomAD_genomes_MID_AC', 'gnomAD_genomes_MID_AN', 'gnomAD_genomes_MID_AF', 'gnomAD_genomes_MID_nhomalt', 'gnomAD_genomes_NFE_AC', 'gnomAD_genomes_NFE_AN', 'gnomAD_genomes_NFE_AF', 'gnomAD_genomes_NFE_nhomalt', 'gnomAD_genomes_SAS_AC', 'gnomAD_genomes_SAS_AN', 'gnomAD_genomes_SAS_AF', 'gnomAD_genomes_SAS_nhomalt', 'gnomAD_genomes_controls_and_biobanks_AC', 'gnomAD_genomes_controls_and_biobanks_AN', 'gnomAD_genomes_controls_and_biobanks_AF', 'gnomAD_genomes_controls_and_biobanks_nhomalt', 'gnomAD_genomes_non_neuro_AC', 'gnomAD_genomes_non_neuro_AN', 'gnomAD_genomes_non_neuro_AF', 'gnomAD_genomes_non_neuro_nhomalt', 'gnomAD_genomes_non_cancer_AC', 'gnomAD_genomes_non_cancer_AN', 'gnomAD_genomes_non_cancer_AF', 'gnomAD_genomes_non_cancer_nhomalt', 'gnomAD_genomes_non_topmed_AC', 'gnomAD_genomes_non_topmed_AN', 'gnomAD_genomes_non_topmed_AF', 'gnomAD_genomes_non_topmed_nhomalt', 'gnomAD_genomes_controls_and_biobanks_AFR_AC', 'gnomAD_genomes_controls_and_biobanks_AFR_AN', 'gnomAD_genomes_controls_and_biobanks_AFR_AF', 'gnomAD_genomes_controls_and_biobanks_AFR_nhomalt', 'gnomAD_genomes_controls_and_biobanks_AMI_AC', 'gnomAD_genomes_controls_and_biobanks_AMI_AN', 'gnomAD_genomes_controls_and_biobanks_AMI_AF', 'gnomAD_genomes_controls_and_biobanks_AMI_nhomalt', 'gnomAD_genomes_controls_and_biobanks_AMR_AC', 'gnomAD_genomes_controls_and_biobanks_AMR_AN', 'gnomAD_genomes_controls_and_biobanks_AMR_AF', 'gnomAD_genomes_controls_and_biobanks_AMR_nhomalt', 'gnomAD_genomes_controls_and_biobanks_ASJ_AC', 'gnomAD_genomes_controls_and_biobanks_ASJ_AN', 'gnomAD_genomes_controls_and_biobanks_ASJ_AF', 'gnomAD_genomes_controls_and_biobanks_ASJ_nhomalt', 'gnomAD_genomes_controls_and_biobanks_EAS_AC', 'gnomAD_genomes_controls_and_biobanks_EAS_AN', 'gnomAD_genomes_controls_and_biobanks_EAS_AF', 'gnomAD_genomes_controls_and_biobanks_EAS_nhomalt', 'gnomAD_genomes_controls_and_biobanks_FIN_AC', 'gnomAD_genomes_controls_and_biobanks_FIN_AN', 'gnomAD_genomes_controls_and_biobanks_FIN_AF', 'gnomAD_genomes_controls_and_biobanks_FIN_nhomalt', 'gnomAD_genomes_controls_and_biobanks_MID_AC', 'gnomAD_genomes_controls_and_biobanks_MID_AN', 'gnomAD_genomes_controls_and_biobanks_MID_AF', 'gnomAD_genomes_controls_and_biobanks_MID_nhomalt', 'gnomAD_genomes_controls_and_biobanks_NFE_AC', 'gnomAD_genomes_controls_and_biobanks_NFE_AN', 'gnomAD_genomes_controls_and_biobanks_NFE_AF', 'gnomAD_genomes_controls_and_biobanks_NFE_nhomalt', 'gnomAD_genomes_controls_and_biobanks_SAS_AC', 'gnomAD_genomes_controls_and_biobanks_SAS_AN', 'gnomAD_genomes_controls_and_biobanks_SAS_AF', 'gnomAD_genomes_controls_and_biobanks_SAS_nhomalt', 'gnomAD_genomes_non_neuro_AFR_AC', 'gnomAD_genomes_non_neuro_AFR_AN', 'gnomAD_genomes_non_neuro_AFR_AF', 'gnomAD_genomes_non_neuro_AFR_nhomalt', 'gnomAD_genomes_non_neuro_AMI_AC', 'gnomAD_genomes_non_neuro_AMI_AN', 'gnomAD_genomes_non_neuro_AMI_AF', 'gnomAD_genomes_non_neuro_AMI_nhomalt', 'gnomAD_genomes_non_neuro_AMR_AC', 'gnomAD_genomes_non_neuro_AMR_AN', 'gnomAD_genomes_non_neuro_AMR_AF', 'gnomAD_genomes_non_neuro_AMR_nhomalt', 'gnomAD_genomes_non_neuro_ASJ_AC', 'gnomAD_genomes_non_neuro_ASJ_AN', 'gnomAD_genomes_non_neuro_ASJ_AF', 'gnomAD_genomes_non_neuro_ASJ_nhomalt', 'gnomAD_genomes_non_neuro_EAS_AC', 'gnomAD_genomes_non_neuro_EAS_AN', 'gnomAD_genomes_non_neuro_EAS_AF', 'gnomAD_genomes_non_neuro_EAS_nhomalt', 'gnomAD_genomes_non_neuro_FIN_AC', 'gnomAD_genomes_non_neuro_FIN_AN', 'gnomAD_genomes_non_neuro_FIN_AF', 'gnomAD_genomes_non_neuro_FIN_nhomalt', 'gnomAD_genomes_non_neuro_MID_AC', 'gnomAD_genomes_non_neuro_MID_AN', 'gnomAD_genomes_non_neuro_MID_AF', 'gnomAD_genomes_non_neuro_MID_nhomalt', 'gnomAD_genomes_non_neuro_NFE_AC', 'gnomAD_genomes_non_neuro_NFE_AN', 'gnomAD_genomes_non_neuro_NFE_AF', 'gnomAD_genomes_non_neuro_NFE_nhomalt', 'gnomAD_genomes_non_neuro_SAS_AC', 'gnomAD_genomes_non_neuro_SAS_AN', 'gnomAD_genomes_non_neuro_SAS_AF', 'gnomAD_genomes_non_neuro_SAS_nhomalt', 'gnomAD_genomes_non_cancer_AFR_AC', 'gnomAD_genomes_non_cancer_AFR_AN', 'gnomAD_genomes_non_cancer_AFR_AF', 'gnomAD_genomes_non_cancer_AFR_nhomalt', 'gnomAD_genomes_non_cancer_AMI_AC', 'gnomAD_genomes_non_cancer_AMI_AN', 'gnomAD_genomes_non_cancer_AMI_AF', 'gnomAD_genomes_non_cancer_AMI_nhomalt', 'gnomAD_genomes_non_cancer_AMR_AC', 'gnomAD_genomes_non_cancer_AMR_AN', 'gnomAD_genomes_non_cancer_AMR_AF', 'gnomAD_genomes_non_cancer_AMR_nhomalt', 'gnomAD_genomes_non_cancer_ASJ_AC', 'gnomAD_genomes_non_cancer_ASJ_AN', 'gnomAD_genomes_non_cancer_ASJ_AF', 'gnomAD_genomes_non_cancer_ASJ_nhomalt', 'gnomAD_genomes_non_cancer_EAS_AC', 'gnomAD_genomes_non_cancer_EAS_AN', 'gnomAD_genomes_non_cancer_EAS_AF', 'gnomAD_genomes_non_cancer_EAS_nhomalt', 'gnomAD_genomes_non_cancer_FIN_AC', 'gnomAD_genomes_non_cancer_FIN_AN', 'gnomAD_genomes_non_cancer_FIN_AF', 'gnomAD_genomes_non_cancer_FIN_nhomalt', 'gnomAD_genomes_non_cancer_MID_AC', 'gnomAD_genomes_non_cancer_MID_AN', 'gnomAD_genomes_non_cancer_MID_AF', 'gnomAD_genomes_non_cancer_MID_nhomalt', 'gnomAD_genomes_non_cancer_NFE_AC', 'gnomAD_genomes_non_cancer_NFE_AN', 'gnomAD_genomes_non_cancer_NFE_AF', 'gnomAD_genomes_non_cancer_NFE_nhomalt', 'gnomAD_genomes_non_cancer_SAS_AC', 'gnomAD_genomes_non_cancer_SAS_AN', 'gnomAD_genomes_non_cancer_SAS_AF', 'gnomAD_genomes_non_cancer_SAS_nhomalt', 'gnomAD_genomes_non_topmed_AFR_AC', 'gnomAD_genomes_non_topmed_AFR_AN', 'gnomAD_genomes_non_topmed_AFR_AF', 'gnomAD_genomes_non_topmed_AFR_nhomalt', 'gnomAD_genomes_non_topmed_AMI_AC', 'gnomAD_genomes_non_topmed_AMI_AN', 'gnomAD_genomes_non_topmed_AMI_AF', 'gnomAD_genomes_non_topmed_AMI_nhomalt', 'gnomAD_genomes_non_topmed_AMR_AC', 'gnomAD_genomes_non_topmed_AMR_AN', 'gnomAD_genomes_non_topmed_AMR_AF', 'gnomAD_genomes_non_topmed_AMR_nhomalt', 'gnomAD_genomes_non_topmed_ASJ_AC', 'gnomAD_genomes_non_topmed_ASJ_AN', 'gnomAD_genomes_non_topmed_ASJ_AF', 'gnomAD_genomes_non_topmed_ASJ_nhomalt', 'gnomAD_genomes_non_topmed_EAS_AC', 'gnomAD_genomes_non_topmed_EAS_AN', 'gnomAD_genomes_non_topmed_EAS_AF', 'gnomAD_genomes_non_topmed_EAS_nhomalt', 'gnomAD_genomes_non_topmed_FIN_AC', 'gnomAD_genomes_non_topmed_FIN_AN', 'gnomAD_genomes_non_topmed_FIN_AF', 'gnomAD_genomes_non_topmed_FIN_nhomalt', 'gnomAD_genomes_non_topmed_MID_AC', 'gnomAD_genomes_non_topmed_MID_AN', 'gnomAD_genomes_non_topmed_MID_AF', 'gnomAD_genomes_non_topmed_MID_nhomalt', 'gnomAD_genomes_non_topmed_NFE_AC', 'gnomAD_genomes_non_topmed_NFE_AN', 'gnomAD_genomes_non_topmed_NFE_AF', 'gnomAD_genomes_non_topmed_NFE_nhomalt', 'gnomAD_genomes_non_topmed_SAS_AC', 'gnomAD_genomes_non_topmed_SAS_AN', 'gnomAD_genomes_non_topmed_SAS_AF', 'gnomAD_genomes_non_topmed_SAS_nhomalt', 'clinvar_id', 'clinvar_clnsig', 'clinvar_trait', 'clinvar_review', 'clinvar_hgvs', 'clinvar_var_source', 'clinvar_MedGen_id', 'clinvar_OMIM_id', 'clinvar_Orphanet_id', 'Interpro_domain', 'GTEx_V8_gene', 'GTEx_V8_tissue', 'Geuvadis_eQTL_target_gene'] - -@ray.remote # (num_cpus=9) -def gene_integration(gene,raw_cols): - ditto = pd.read_csv(f'/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/Ditto/{gene}/dbnsfp_{gene}_ditto_predictions.csv.gz') - #ditto['#chr'] = ditto['#chr'].astype('int64') - ditto['pos(1-based)'] = ditto['pos(1-based)'].astype('int64') - dbnsfp = pd.read_csv(f'/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/Ditto/{gene}/dbNSFP_{gene}_variants.tsv.gz', header=None, sep='\t', names=raw_cols) - #dbnsfp['#chr'] = dbnsfp['#chr'].astype('int64') - dbnsfp['pos(1-based)'] = dbnsfp['pos(1-based)'].astype('int64') - dbnsfp = dbnsfp[['#chr','pos(1-based)','ref','alt', 'aapos', 'aaref', 'aaalt',"CADD_phred", - "gnomAD_genomes_AF","HGVSc_VEP","HGVSp_VEP","Ensembl_transcriptid"]] - ditto = ditto.merge(dbnsfp, on=['#chr','pos(1-based)','ref','alt','Ensembl_transcriptid'], how='left') - - ditto = ditto[['#chr', 'pos(1-based)', 'ref', 'alt', 'cds_strand', 'genename', - 'Ensembl_geneid', 'Ensembl_transcriptid', 'Ensembl_proteinid', 'Uniprot_acc', - 'clinvar_clnsig', 'clinvar_review', 'Interpro_domain', - 'Ditto_Deleterious', 'aapos', - 'aaref', 'aaalt', 'CADD_phred', 'gnomAD_genomes_AF', 'HGVSc_VEP', - 'HGVSp_VEP']] - - ditto.to_csv(f'/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/Ditto/{gene}/{gene}_Integrated.csv.gz', index=False) - ditto.to_csv(f'/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/ciliopathy_genes/{gene}_Integrated.csv', index=False) - -if __name__ == "__main__": - - gene_list = ["AHI1","DCTN1","LRPPRC","PCNT","STOM","ATXN10","CEP97","DCTN2","MKS1","PDE6D","TBC1D4","B9D1","CFAP52","EIF5B","PIBF1","TCTN1","B9D2","COX6C","IFT88","MYL6","TMEM216","BCAR1","INPP5E","MYL6B","TMEM67","CALM1","INVS","PTPN11","TTC21B","CC2D2A","IQCB1","NME7","RAC1","TTC26","CCDC65","IQGAP1","NPHP1","RIBC1","UNC119B","CCP110","IQGAP2","NPHP3","RPGR","CEP164","IQGAP3","NPHP4","RPGRIP1","WDR35","CEP290","KIAA0753","RPGRIP1L","ZMYND12","CEP89","KIF3A","OFD1","SDCCAG8"] - remote_ml = [ - gene_integration.remote(gene,raw_cols) - - for gene in gene_list - ] - ray.get(remote_ml) - gc.collect() diff --git a/src/pkd/model.job b/src/pkd/model.job deleted file mode 100644 index ce4a9e7..0000000 --- a/src/pkd/model.job +++ /dev/null @@ -1,28 +0,0 @@ -#!/bin/bash -# -#SBATCH --job-name=extract_vars -#SBATCH --output=extract_vars.out -# -# Number of tasks needed for this job. Generally, used with MPI jobs -#SBATCH --ntasks=1 -#SBATCH --partition=long -# -# Number of CPUs allocated to each task. -#SBATCH --cpus-per-task=10 -# -# Mimimum memory required per allocated CPU in MegaBytes. -#SBATCH --mem=20G -# -# Send mail to the email address when the job fails -#SBATCH --mail-type=FAIL -#SBATCH --mail-user=tmamidi@uab.edu - -#Set your environment here -module load Anaconda3/2020.02 -source activate training - -#Run your commands here -#python extract_pkd.py -python merge.py - - diff --git a/src/pkd/predictions.py b/src/pkd/predictions.py deleted file mode 100644 index 1061653..0000000 --- a/src/pkd/predictions.py +++ /dev/null @@ -1,135 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -#for gene in ../data/processed/dbnsfp_genes/* ; do python slurm-launch.py --exp-name ${gene##*/}-predictions --command "python pkd/predictions.py -i /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/dbnsfp_genes/${gene##*/}/dbNSFP_${gene##*/}_variants.tsv.gz --filter /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/dbnsfp_genes/${gene##*/}/${gene##*/}-procesed-dbnsfp.csv.gz --ditto /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/dbnsfp_predictions/${gene##*/}_ditto_predictions.csv.gz --shapley /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/dbnsfp_genes/${gene##*/}/${gene##*/}_shapley.joblib" --partition short --mem 5G ; done - -import pandas as pd -import yaml -import warnings -warnings.simplefilter("ignore") -from joblib import load, dump -from tqdm import tqdm -import argparse -import shap -import numpy as np -import functools -print = functools.partial(print, flush=True) - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--input", - "-i", - type=str, - required=True, - help="Input csv file with path for filtering and predictions", - ) - parser.add_argument( - "--filter", - type=str, - default="filter.csv.gz", - help="Output file with path (default:filter.csv.gz)", - ) - parser.add_argument( - "--ditto", - type=str, - default="ditto_predictions.csv.gz", - help="Output file with path (default:ditto_predictions.csv.gz)", - ) - parser.add_argument( - "--shapley", - type=str, - default="shapley.csv.gz", - help="Output file with path (default:shapley.csv.gz)", - ) - - args = parser.parse_args() - - print("Loading data and Ditto model....") - - with open( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/configs/col_config.yaml" - ) as fh: - config_dict = yaml.safe_load(fh) - - - def parse_and_predict(dataframe, config_dict, explainer): - dataframe.columns = config_dict["raw_cols"] - var = dataframe[config_dict['ditto_info']] - dataframe = dataframe[config_dict["columns"]] - # Drop variant info columns so we can perform one-hot encoding - dataframe = dataframe.drop(config_dict['var'], axis=1) - dataframe = dataframe.replace(['.','-'], np.nan) - - for key in tqdm(dataframe.columns): - try: - dataframe[key] = ( - dataframe[key] - .astype("float32") - ) - except: - dataframe[key] = dataframe[key] - - #Perform one-hot encoding - dataframe = pd.get_dummies(dataframe, prefix_sep='_') - dataframe[config_dict['allele_freq_columns']] = dataframe[config_dict['allele_freq_columns']].fillna(0) - - for key in tqdm(config_dict['nssnv_median'].keys()): - if key in dataframe.columns: - dataframe[key] = ( - dataframe[key] - .fillna(config_dict['nssnv_median'][key]) - .astype("float32") - ) - - df2 = pd.DataFrame() - for key in tqdm(config_dict['nssnv_columns']): - if key in dataframe.columns: - df2[key] = dataframe[key] - else: - df2[key] = 0 - - del dataframe - - - df2 = df2.drop(config_dict['var'], axis=1) - X_test = df2.values - y_score = clf.predict_proba(X_test) - del X_test - pred = pd.DataFrame(y_score, columns=["Ditto_Benign", "Ditto_Deleterious"]) - - ditto_scores = pd.concat([var, pred], axis=1) - ditto_scores.to_csv(args.ditto, index=False, - compression="gzip") - df3 = pd.concat([var.reset_index(drop=True), df2.reset_index(drop=True)], axis=1) - df3.to_csv(args.filter, index=False, - compression='gzip') - del df3 - shapley_values = explainer.shap_values(df2) - with open(args.shapley, "wb") as f: - dump([explainer.expected_value,shapley_values], f, compress="lz4") - del df2 - - return None - - - with open( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/models_custom/dbnsfp/StackingClassifier_dbnsfp.joblib", - "rb", - ) as f: - clf = load(f) - - X_train = pd.read_csv('/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_custom_data-dbnsfp.csv') - X_train = X_train.drop(config_dict['var'], axis=1) - X_train = X_train.values - background = shap.kmeans(X_train, 10) - explainer = shap.KernelExplainer(clf.predict_proba, background) - del background, X_train - - - print('Processing data...') - df = pd.read_csv(args.input, sep='\t', header=None) - - parse_and_predict(df, config_dict, explainer) - - diff --git a/src/Ditto/predict.py b/src/predict/predict.py similarity index 100% rename from src/Ditto/predict.py rename to src/predict/predict.py diff --git a/src/streamlit.py b/src/streamlit.py deleted file mode 100644 index da0b973..0000000 --- a/src/streamlit.py +++ /dev/null @@ -1,121 +0,0 @@ -import streamlit as st -import pandas as pd -import yaml -import warnings -warnings.simplefilter("ignore") -from joblib import load -import shap -import numpy as np -import matplotlib.pyplot as plt -import gzip -from PIL import Image -#import re - -# Config the whole app -st.set_page_config( - page_title="DITTO", page_icon="🧊", layout="wide", #initial_sidebar_state="expanded", -) - -st.write( - "", - unsafe_allow_html=True, -) -st.write( - "", - unsafe_allow_html=True, -) - - -@st.cache(allow_output_mutation=True) -def load_data(): - #with gzip.open('./data/processed/ditto_predictions.csv.gz', 'rt') as fp: - - pred = pd.read_csv('./data/processed/ditto_predictions.csv.gz', usecols=['#chr', 'pos(1-based)', 'ref', 'alt', 'genename', 'Ensembl_transcriptid', 'Ditto_Deleterious']) - pred.columns = ['Chromosome', 'position', 'ref_allele', 'alt_allele', 'Gene', 'Transcript', 'Ditto'] - pred.position = pred.position.astype('int64') - - with open( - "./data/processed/StackingClassifier_dbnsfp.joblib", - "rb", - ) as f: - clf = load(f) - - X_train = pd.read_csv('./data/processed/train_custom_data-dbnsfp.csv.gz') - X_train = X_train.drop(['#chr','pos(1-based)','ref','alt','cds_strand','genename','Ensembl_transcriptid','clinvar_clnsig','Ensembl_geneid','Ensembl_proteinid','Uniprot_acc','clinvar_review','Interpro_domain'], axis=1) - X_train = X_train.values - background = shap.kmeans(X_train, 10) - explainer = shap.KernelExplainer(clf.predict_proba, background) - del clf,X_train,background - - image1 = Image.open("./data/processed/StackingClassifier_dbnsfp_features.jpg") - - - return pred, explainer, image1 - -def main(): - - col1, col2, col3 = st.columns(3) - - pred, explainer, image1 = load_data() - col1.subheader("Variant details:") - col3.subheader("DITTO overall feature importances") - col3.image(image1, caption="DITTO overall feature importances generated using SHAP") - - method = col1.radio( - "Search method:", - ('Variant', 'Gene' )) - - if method == 'Variant': - chr = col1.selectbox('Please select a chromosome:',options = pred.Chromosome.unique(), help = '1-22,X,Y,M') - pos = col1.selectbox('Please select a position:',options = pred[pred.Chromosome==chr]['position'].unique()) - ref = col1.selectbox('Please select a Reference allele:',options = pred[(pred.Chromosome==chr) & (pred.position==pos)]['ref_allele'].unique()) - alt = col1.selectbox('Please select an Alternate allele:',options = pred[(pred.Chromosome==chr) & (pred.position==pos) & (pred.ref_allele==ref)]['alt_allele'].unique()) - trans = col1.selectbox('Please select a Transcript:',options = pred[(pred.Chromosome==chr) & (pred.position==pos) & (pred.ref_allele==ref) & (pred.alt_allele==alt)]['Transcript'].unique()) - ditto = pred[(pred.Chromosome==chr) & (pred.position==pos) & (pred.ref_allele==ref) & (pred.alt_allele==alt) & (pred.Transcript==trans)] - else: - gene = col1.selectbox('Please select a Gene:',options = pred.Gene.unique()) - trans = col1.selectbox('Please select a Transcript:',options = pred[pred.Gene==gene]['Transcript'].unique()) - pos = col1.selectbox('Please select a Position:',options = pred[(pred.Gene==gene) & (pred.Transcript==trans)]['position'].unique()) - ref = col1.selectbox('Please select a Reference allele:',options = pred[(pred.Gene==gene) & (pred.Transcript==trans) & (pred.position==pos)]['ref_allele'].unique()) - alt = col1.selectbox('Please select an Alternate allele:',options = pred[(pred.Gene==gene) & (pred.Transcript==trans) & (pred.position==pos) & (pred.ref_allele==ref)]['alt_allele'].unique()) - ditto = pred[(pred.Gene==gene) & (pred.Transcript==trans) & (pred.position==pos) & (pred.ref_allele==ref) & (pred.alt_allele==alt)] - - row_idx = ditto.index.values[0] - col2.subheader(f"Ditto damage predictions")#\n{row_idx}") - col2.subheader(f"Ditto score = {ditto['Ditto'].values[0]}")#\n{row_idx}") - - annots = pd.read_csv('./data/processed/all_data_custom-dbnsfp.csv.gz',skiprows=range(1,row_idx+1), nrows=1) - original = pd.read_csv('./data/processed/dbNSFP_clinvar_variants_parsed.tsv.gz', sep='\t',skiprows=range(1,row_idx+1), nrows=1) - #col2.write(f"Chromosome = {annots['#chr'].values[0]}") - #col2.write(f"Pos(1-based) = {annots['pos(1-based)'].values[0]}") - #col2.write(f"Reference allele = {annots['ref'].values[0]}") - #col2.write(f"Alternate allele = {annots['alt'].values[0]}") - #col2.write(f"Strand = {annots['cds_strand'].values[0]}") - #col2.write(f"Gene name = {annots['genename'].values[0]}") - #col2.write(f"Transcript = {annots['Ensembl_transcriptid'].values[0]}") - col2.write(f"Clinvar significance = {annots['clinvar_clnsig'].values[0]}") - annots = annots.drop(['#chr','pos(1-based)','ref','alt','cds_strand','genename','Ensembl_transcriptid','clinvar_clnsig','Ensembl_geneid','Ensembl_proteinid','Uniprot_acc','clinvar_review','Interpro_domain'], axis=1) - col1.subheader("All dbNSFP annotations:") - col1.write(original) - shap_values1 = explainer.shap_values(annots.iloc[0,:]) - - - # NOW CHANGED: SET UP THE WORKAROUND - class helper_object(): - """ - This wraps the shap object. - It takes as input i, which indicates the index of the observation to be explained. - """ - def __init__(self, i): - self.base_values = explainer.expected_value[1] - self.data = annots.iloc[0,:] - self.feature_names = list(annots.columns) - self.values = shap_values1[1] - - plt.figure() - shap.waterfall_plot(helper_object(1), 20) - #shap.force_plot(explainer.expected_value[1], shap_values1[1], annots.iloc[0,:], matplotlib = True, show = True) - col2.pyplot(plt) - -if __name__ == "__main__": - main() diff --git a/src/training/data-prep/extract_class.py b/src/training/data-prep/extract_class.py deleted file mode 100644 index 4bbe418..0000000 --- a/src/training/data-prep/extract_class.py +++ /dev/null @@ -1,35 +0,0 @@ -import os -import gzip - -os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/interim/" -) -vcf = "merged_sig_norm.vcf.gz" -vcf1 = "merged_sig_norm_vep-annotated.tsv" -output = "merged_sig_norm_class_vep-annotated.tsv" - -print("Collecting variant class...") -class_dict = dict() -with gzip.open(vcf, "rt") as vcffp: - for cnt, line in enumerate(vcffp): - if not line.startswith("#"): - line = line.rstrip("\n") - cols = line.split("\t") - var_info = cols[0] + "\t" + cols[1] + "\t" + cols[3] + "\t" + cols[4] - # hgmd_class = cols[7].split(";")[0].split("=")[1] - class_dict[var_info] = cols[5] - -# print(class_dict) -print("Writing variant class...") -with open(output, "w") as out: - with open(vcf1, "rt") as vcffp: - for cnt, line in enumerate(vcffp): - if not line.startswith("Chromosome"): - line = line.rstrip("\n") - cols = line.split("\t") - var_info = cols[0] + "\t" + cols[1] + "\t" + cols[2] + "\t" + cols[3] - new_line = line + "\t" + class_dict[var_info] - out.write(new_line + "\n") - # print(line+"\t"+class_dict[var_info]) - else: - out.write(line.rstrip("\n") + "\thgmd_class\n") diff --git a/src/training/data-prep/extract_dbNSFP_clinvar_variants.py b/src/training/data-prep/extract_dbNSFP_clinvar_variants.py deleted file mode 100644 index 98c98fb..0000000 --- a/src/training/data-prep/extract_dbNSFP_clinvar_variants.py +++ /dev/null @@ -1,25 +0,0 @@ -import gzip - -input = "/data/project/worthey_lab/temp_datasets_central/tarun/dbNSFP/v4.3_20220319/dbNSFP4.3a_variant.complete.bgz" -output = "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/interim/dbNSFP_clinvar_variants.tsv.gz" - -clinvar = {} - -print("Writing dbNSFP clinvar variants...") -with gzip.open(output, "wt") as out: - with gzip.open(input, "rt") as vcffp: - for cnt, line in enumerate(vcffp): - if not line.startswith("#"): - line = line.rstrip("\n") - cols = line.split("\t") - if cols[631] != ".": - #print(cols[631]) - out.write(line + "\n") - if cols[631] not in clinvar.keys(): - clinvar[cols[631]] = 0 - else: - clinvar[cols[631]] = clinvar[cols[631]]+1 - else: - out.write(line) - -print(clinvar) diff --git a/src/training/data-prep/extract_gnomad_snv.py b/src/training/data-prep/extract_gnomad_snv.py deleted file mode 100644 index 33139f1..0000000 --- a/src/training/data-prep/extract_gnomad_snv.py +++ /dev/null @@ -1,17 +0,0 @@ -import gzip - -input = "/data/project/worthey_lab/temp_datasets_central/mana/gnomad/v3.0/data/gnomad.genomes.r3.0.sites.vcf.bgz" -output = "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/interim/gnomad_snv.vcf" - -print("Writing gnomad SNV variants...") -with open(output, "w") as out: - with gzip.open(input, "rt") as vcffp: - for cnt, line in enumerate(vcffp): - if not line.startswith("#"): - line = line.rstrip("\n") - cols = line.split("\t") - if len(cols[3]) <2 and len(cols[4]) <2 and cols[7].split(';')[4] == 'variant_type=snv': - out.write(line + "\n") - # print(line+"\t"+class_dict[var_info]) - else: - out.write(line) diff --git a/src/training/data-prep/extract_variants.py b/src/training/data-prep/extract_variants.py deleted file mode 100644 index 466a868..0000000 --- a/src/training/data-prep/extract_variants.py +++ /dev/null @@ -1,67 +0,0 @@ -import os -import gzip -import yaml -import re - -regex = re.compile("[@_!#$%^&*()<>?/\|}{~:]") - -os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/interim/" -) -vcf = "merged_norm.vcf.gz" -output = "merged_sig_norm.vcf" - -with open("../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - -cln = hgmd = 0 -print("Collecting variant class...") -with open(output, "w") as out: - with gzip.open(vcf, "rt") as vcffp: # gzip. - for cnt, line in enumerate(vcffp): - if not line.startswith("#"): - line = line.rstrip("\n") - cols = line.split("\t") - if ( - (len(cols[3]) < 30000) - and (len(cols[4]) < 30000) - and (regex.search(cols[3]) == None) - and (regex.search(cols[4]) == None) - ): - var_info = ( - cols[0] - + "\t" - + cols[1] - + "\t" - + cols[2] - + "\t" - + cols[3] - + "\t" - + cols[4] - ) - if "CLASS" in cols[7]: - var_class = cols[7].split(";")[0].split("=")[1] - if var_class in config_dict["ClinicalSignificance"]: - hgmd = hgmd + 1 - new_line = var_info + "\t" + var_class - out.write(new_line + "\n") - elif "CLNSIG" in line: - var_class = cols[7].split(";CLN")[5].split("=")[1] - var_sub = cols[7].split(";CLN")[4].split("=")[1] - if (var_class in config_dict["ClinicalSignificance"]) and ( - var_sub in config_dict["CLNREVSTAT"] - ): - cln = cln + 1 - new_line = var_info + "\t" + var_class - out.write(new_line + "\n") - # class_dict[var_info] = var_class - else: - pass - else: - pass - else: - pass - else: - out.write(line) - -print(f"Clinvar variants: {cln}\nHGMD variants: {hgmd}\n") diff --git a/src/training/data-prep/filter.py b/src/training/data-prep/filter.py deleted file mode 100644 index 0a3d346..0000000 --- a/src/training/data-prep/filter.py +++ /dev/null @@ -1,380 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -# python slurm-launch.py --exp-name no_null --command "python training/data-prep/filter.py --var-tag no_null_nssnv --cutoff 1" --mem 50G - -import pandas as pd - -pd.set_option("display.max_rows", None) -import numpy as np -from tqdm import tqdm -import seaborn as sns -import yaml -import os -import argparse -import matplotlib.pyplot as plt - -# from sklearn.linear_model import LinearRegression -# from sklearn.experimental import enable_iterative_imputer -# from sklearn.impute import IterativeImputer -# import pickle - - -def get_col_configs(config_f): - with open(config_f) as fh: - config_dict = yaml.safe_load(fh) - - # print(config_dict) - return config_dict - - -def extract_col(config_dict, df, stats, list_tag): - print("Extracting columns and rows according to config file !....") - df = df[config_dict["columns"]] - if "non_snv" in stats: - # df= df.loc[df['hgmd_class'].isin(config_dict['Clinsig_train'])] - df = df[ - (df["Alternate Allele"].str.len() > 1) - | (df["Reference Allele"].str.len() > 1) - ] - print("\nData shape (non-snv) =", df.shape, file=open(stats, "a")) - else: - # df= df.loc[df['hgmd_class'].isin(config_dict['Clinsig_train'])] - df = df[ - (df["Alternate Allele"].str.len() < 2) - & (df["Reference Allele"].str.len() < 2) - ] - if "protein" in stats: - df = df[df["BIOTYPE"] == "protein_coding"] - else: - pass - print("\nData shape (snv) =", df.shape, file=open(stats, "a")) - # df = df[config_dict['Consequence']] - df = df.loc[df["Consequence"].isin(config_dict["Consequence"])] - print("\nData shape (nsSNV) =", df.shape, file=open(stats, "a")) - if "train" in stats: - df = df.loc[df["hgmd_class"].isin(config_dict["Clinsig_train"])] - else: - df = df.loc[df["hgmd_class"].isin(config_dict["Clinsig_test"])] - - if "train" in stats: - print("Dropping empty columns and rows along with duplicate rows...") - # df.dropna(axis=1, thresh=(df.shape[1]*0.3), inplace=True) #thresh=(df.shape[0]/4) - df.dropna( - axis=0, thresh=(df.shape[1] * list_tag[1]), inplace=True - ) # thresh=(df.shape[1]*0.3), how='all', - df.drop_duplicates() - df.dropna(axis=1, how="all", inplace=True) # thresh=(df.shape[0]/4) - print("\nhgmd_class:\n", df["hgmd_class"].value_counts(), file=open(stats, "a")) - print( - "\nclinvar_CLNSIG:\n", - df["clinvar_CLNSIG"].value_counts(), - file=open(stats, "a"), - ) - print( - "\nclinvar_CLNREVSTAT:\n", - df["clinvar_CLNREVSTAT"].value_counts(), - file=open(stats, "a"), - ) - print("\nConsequence:\n", df["Consequence"].value_counts(), file=open(stats, "a")) - print("\nIMPACT:\n", df["IMPACT"].value_counts(), file=open(stats, "a")) - print("\nBIOTYPE:\n", df["BIOTYPE"].value_counts(), file=open(stats, "a")) - # df = df.drop(['CLNVC','MC'], axis=1) - # CLNREVSTAT, CLNVC, MC - return df - - -def fill_na(df, config_dict, column_info, stats, list_tag): # (config_dict,df): - - var = df[config_dict["var"]] - df = df.drop(config_dict["var"], axis=1) - print("parsing difficult columns......") - # df['GERP'] = [np.mean([float(item.replace('.', '0')) if item == '.' else float(item) for item in i]) if type(i) is list else i for i in df['GERP'].str.split('&')] - if "nssnv" in stats: - # df['MutationTaster_score'] = [np.mean([float(item.replace('.', '0')) if item == '.' else float(item) for item in i]) if type(i) is list else i for i in df['MutationTaster_score'].str.split('&')] - # else: - for col in tqdm(config_dict["col_conv"]): - df[col] = [ - np.mean( - [ - float(item.replace(".", "0")) if item == "." else float(item) - for item in i.split("&") - ] - ) - if "&" in str(i) - else i - for i in df[col] - ] - df[col] = df[col].astype("float64") - if "train" in stats: - fig = plt.figure(figsize=(20, 15)) - sns.heatmap(df.corr(), fmt=".2g", cmap="coolwarm") # annot = True, - plt.savefig( - f"train_{list_tag[0]}/correlation_filtered_raw_{list_tag[0]}.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - print("One-hot encoding...") - df = pd.get_dummies(df, prefix_sep="_") - print(df.columns.values.tolist(), file=open(column_info, "w")) - # df.head(2).to_csv(column_info, index=False) - # lr = LinearRegression() - # imp= IterativeImputer(estimator=lr, verbose=2, max_iter=10, tol=1e-10, imputation_order='roman') - print("Filling NAs ....") - # df = imp.fit_transform(df) - # df = pd.DataFrame(df, columns = columns) - - if list_tag[2] == 1: - print("Including AF columns...") - df1 = df[config_dict["gnomad_columns"]] - df1 = df1.fillna(list_tag[3]) - - if list_tag[4] == 1: - df = df.drop(config_dict["gnomad_columns"], axis=1) - df = df.fillna(df.median()) - if "train" in stats: - print("\nColumns:\t", df.columns.values.tolist(), file=open(stats, "a")) - print( - "\nMedian values:\t", - df.median().values.tolist(), - file=open(stats, "a"), - ) - else: - pass - else: - print("Excluding AF columns...") - if list_tag[4] == 1: - df = df.drop(config_dict["gnomad_columns"], axis=1) - df1 = df.fillna(df.median()) - if "train" in stats: - print("\nColumns:\t", df.columns.values.tolist(), file=open(stats, "a")) - print( - "\nMedian values:\t", - df.median().values.tolist(), - file=open(stats, "a"), - ) - else: - df1 = pd.DataFrame() - - if "non_nssnv" in stats: - for key in tqdm(config_dict["non_nssnv_columns"]): - if key in df.columns: - df1[key] = ( - df[key] - .fillna(config_dict["non_nssnv_columns"][key]) - .astype("float64") - ) - else: - df1[key] = config_dict["non_nssnv_columns"][key] - else: - for key in tqdm(config_dict["nssnv_columns"]): - if key in df.columns: - df1[key] = ( - df[key].fillna(config_dict["nssnv_columns"][key]).astype("float64") - ) - else: - df1[key] = config_dict["nssnv_columns"][key] - df = df1 - df = df.drop(df.std()[(df.std() == 0)].index, axis=1) - del df1 - df = df.reset_index(drop=True) - print(df.columns.values.tolist(), file=open(column_info, "a")) - if "train" in stats: - fig = plt.figure(figsize=(20, 15)) - sns.heatmap(df.corr(), fmt=".2g", cmap="coolwarm") # annot = True, - plt.savefig( - f"train_{list_tag[0]}/correlation_before_{list_tag[0]}.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - - # Create correlation matrix - corr_matrix = df.corr().abs() - - # Select upper triangle of correlation matrix - upper = corr_matrix.where( - np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool) - ) - - # Find features with correlation greater than 0.90 - to_drop = [column for column in upper.columns if any(upper[column] > 0.90)] - print( - f"Correlated columns being dropped: {to_drop}", file=open(column_info, "a") - ) - - # Drop features - df.drop(to_drop, axis=1, inplace=True) - df = df.reset_index(drop=True) - print(df.columns.values.tolist(), file=open(column_info, "a")) - # df.dropna(axis=1, how='all', inplace=True) - df["ID"] = [f"var_{num}" for num in range(len(df))] - print("NAs filled!") - df = pd.concat([var.reset_index(drop=True), df], axis=1) - return df - - -def main(df, config_f, stats, column_info, null_info, list_tag): - # read QA config file - config_dict = get_col_configs(config_f) - print("Config file loaded!") - # read clinvar data - - print("\nData shape (Before filtering) =", df.shape, file=open(stats, "a")) - df = extract_col(config_dict, df, stats, list_tag) - print("Columns extracted! Extracting class info....") - df.isnull().sum(axis=0).to_csv(null_info) - # print('\n Unique Impact (Class):\n', df.hgmd_class.unique(), file=open("./data/processed/stats1.csv", "a")) - df["hgmd_class"] = ( - df["hgmd_class"] - .str.replace(r"DFP", "high_impact") - .str.replace(r"DM\?", "high_impact") - .str.replace(r"DM", "high_impact") - ) - df["hgmd_class"] = ( - df["hgmd_class"] - .str.replace(r"Pathogenic/Likely_pathogenic", "high_impact") - .str.replace(r"Likely_pathogenic", "high_impact") - .str.replace(r"Pathogenic", "high_impact") - ) - df["hgmd_class"] = ( - df["hgmd_class"] - .str.replace(r"DP", "low_impact") - .str.replace(r"FP", "low_impact") - ) - df["hgmd_class"] = ( - df["hgmd_class"] - .str.replace(r"Benign/Likely_benign", "low_impact") - .str.replace(r"Likely_benign", "low_impact") - .str.replace(r"Benign", "low_impact") - ) - df.drop_duplicates() - df.dropna(axis=1, how="all", inplace=True) - y = df["hgmd_class"] - class_dummies = pd.get_dummies(df["hgmd_class"]) - # del class_dummies[class_dummies.columns[-1]] - print("\nImpact (Class):\n", y.value_counts(), file=open(stats, "a")) - # y = df.hgmd_class - df = df.drop("hgmd_class", axis=1) - df = fill_na(df, config_dict, column_info, stats, list_tag) - - if "train" in stats: - var = df[config_dict["ML_VAR"]] - df = df.drop(config_dict["ML_VAR"], axis=1) - df = pd.concat([class_dummies.reset_index(drop=True), df], axis=1) - fig = plt.figure(figsize=(20, 15)) - sns.heatmap(df.corr(), fmt=".2g", cmap="coolwarm") - plt.savefig( - f"train_{list_tag[0]}/correlation_after_{list_tag[0]}.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - df = pd.concat([var, df], axis=1) - df = df.drop(["high_impact", "low_impact"], axis=1) - return df, y - - -if __name__ == "__main__": - - parser = argparse.ArgumentParser() - parser.add_argument( - "--var-tag", - "-v", - type=str, - required=True, - default="nssnv", - help="The tag used when generating train/test data. Default:'nssnv'", - ) - parser.add_argument( - "--cutoff", - type=float, - default=0.5, - help=f"Cutoff to include at least __% of data for all rows. Default:0.5 (i.e. 50%)", - ) - parser.add_argument( - "--af-columns", - "-af", - type=int, - default=0, - help=f"To include columns with Allele frequencies or not. Default:0", - ) - parser.add_argument( - "--af-values", - "-afv", - type=float, - default=0, - help=f"value to impute nulls for allele frequency columns. Default:0", - ) - parser.add_argument( - "--other-values", - "-otv", - type=int, - default=0, - help=f"Impute other columns with either custom defined values (0) or median (1). Default:0", - ) - - args = parser.parse_args() - list_tag = [ - args.var_tag, - args.cutoff, - args.af_columns, - args.af_values, - args.other_values, - ] - print(list_tag) - var = list_tag[0] - - if not os.path.exists( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ): - os.makedirs( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - print("Loading data...") - var_f = pd.read_csv( - "../../interim/merged_sig_norm_class_vep-annotated.tsv", sep="\t" - ) - print("Data Loaded !....") - config_f = "../../../configs/columns_config.yaml" - - # variants = ['train_non_snv','train_snv','train_snv_protein_coding','test_snv','test_non_snv','test_snv_protein_coding'] - variants = ["train_" + var, "test_" + var] - # variants = ['train_'+var] - for var in variants: - if not os.path.exists(var): - os.makedirs(var) - stats = var + "/stats_" + var + ".csv" - print( - "Filtering " - + var - + " variants with at-least " - + str(list_tag[1] * 100) - + " percent data for each variant...", - file=open(stats, "w"), - ) - # print("Filtering "+var+" variants with at-least 50 percent data for each variant...") - column_info = var + "/" + var + "_columns.csv" - null_info = var + "/Nulls_" + var + ".csv" - df, y = main(var_f, config_f, stats, column_info, null_info, list_tag) - if "train" in stats: - train_columns = df.columns.values.tolist() - else: - df1 = pd.DataFrame() - for key in tqdm(train_columns): - if key in df.columns: - df1[key] = df[key] - else: - df1[key] = 0 - df = df1 - del df1 - - print("\nData shape (After filtering) =", df.shape, file=open(stats, "a")) - print("Class shape=", y.shape, file=open(stats, "a")) - print("writing to csv...") - df.to_csv(var + "/" + "merged_data-" + var + ".csv", index=False) - y.to_csv(var + "/" + "merged_data-y-" + var + ".csv", index=False) - del df, y diff --git a/src/training/data-prep/parse_clinvar.py b/src/training/data-prep/parse_clinvar.py deleted file mode 100644 index 779ff98..0000000 --- a/src/training/data-prep/parse_clinvar.py +++ /dev/null @@ -1,26 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -# module load Anaconda3/2020.02 -# source activate envi -# python /data/project/worthey_lab/projects/experimental_pipelines/annovar_vcf_annotation/Annovar_Tarun.py /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/external/clinvar.vcf /data/scratch/tmamidi/ /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/interim/ /data/project/worthey_lab/tools/annovar/annovar_hg19_db - -import allel - -# print(allel.__version__) - -import os - -os.chdir("/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/") -# print(os.listdir()) - -print("Converting vcf.....") -# df = allel.vcf_to_dataframe('./interim/try.vcf', fields='*') -df = allel.vcf_to_dataframe("./interim/clinvar.out.hg19_multianno.vcf", fields="*") -# df.head(2) -# df.SIFT_score.unique() -print("vcf converted to dataframe.\nWriting it to a csv file.....") -df.to_csv("./external/clinvar.out.hg19_multianno.csv", index=False) -print("vcf to csv conversion completed!") -# df.to_csv("./external/clinvar.out.hg19_multianno.csv", index=False) -# print(df.head(20)) diff --git a/src/training/data-prep/parse_dbNSFP.py b/src/training/data-prep/parse_dbNSFP.py deleted file mode 100644 index ad45d3f..0000000 --- a/src/training/data-prep/parse_dbNSFP.py +++ /dev/null @@ -1,79 +0,0 @@ -#python src/training/data-prep/parse_dbNSFP.py -i /data/project/worthey_lab/temp_datasets_central/tarun/dbNSFP/v4.3_20220319/dbNSFP4.3a_variant.complete.bgz -o /data/project/worthey_lab/temp_datasets_central/tarun/dbNSFP/v4.3_20220319/dbNSFP4.3a_variant.complete.parsed.bgz - -from pathlib import Path -import argparse -import os -import gzip - -def parse_n_print(vcf, outfile): - with gzip.open(outfile, "wt") if outfile.suffix == ".gz" else outfile.open('w')as out: - print("Parsing variants...") - with gzip.open(vcf, 'rt') if (vcf.suffix == ".bgz" or vcf.suffix == ".gz") else vcf.open('r') as vcffp: - for cnt, line in enumerate(vcffp): - if not line.startswith("#"): - line = line.rstrip("\n") - cols = line.split("\t") - transcripts = cols[14].split(";") - if len(transcripts) > 1: - for idx in range(len(transcripts)): - col_list = [] - for col in cols: - if ';' in col: - if len(col.split(';'))==len(transcripts): - col_list.append(col.split(';')[idx]) - else: - col_list.append(col) - else: - col_list.append(col) - out.write("\t".join(col_list) + "\n") - else: - out.write(line + "\n") - else: - out.write(line) - - -def is_valid_output_file(p, arg): - if os.access(Path(os.path.expandvars(arg)).parent, os.W_OK): - return os.path.expandvars(arg) - else: - p.error(f"Output file {arg} can't be accessed or is invalid!") - - -def is_valid_file(p, arg): - if not Path(os.path.expandvars(arg)).is_file(): - p.error("The file '%s' does not exist!" % arg) - else: - return os.path.expandvars(arg) - - -if __name__ == "__main__": - PARSER = argparse.ArgumentParser( - description="Simple parser for converting an annotated VCF file produced by VEP into a columnar format", - formatter_class=argparse.ArgumentDefaultsHelpFormatter - ) - - PARSER.add_argument( - "-i", - "--input", - help="File path to the input dbNSFP file to parse", - required=True, - type=lambda x: is_valid_file(PARSER, x), - metavar="\b" - ) - - OPTIONAL_ARGS = PARSER.add_argument_group("Override Args") - PARSER.add_argument( - "-o", - "--output", - help="File path to the desired output file (default is to use input file location and name but with *.tsv.gz extension)", - required=False, - type=lambda x: is_valid_output_file(PARSER, x), - metavar="\b" - ) - - ARGS = PARSER.parse_args() - - inputf = Path(ARGS.input) - outputf = Path(ARGS.output) if ARGS.output else inputf.parent / inputf.stem.rstrip(".bgz") + ".tsv.gz" - - parse_n_print(inputf, outputf) diff --git a/src/training/training/Tuning/NN.py b/src/training/training/NN.py similarity index 100% rename from src/training/training/Tuning/NN.py rename to src/training/training/NN.py diff --git a/src/training/training/Tuning/ABC.py b/src/training/training/Tuning/ABC.py deleted file mode 100644 index 0634538..0000000 --- a/src/training/training/Tuning/ABC.py +++ /dev/null @@ -1,312 +0,0 @@ -import numpy as np -import pandas as pd -import ray -import time -import argparse -import pickle -from joblib import dump -import yaml - -# from ray import tune -from ray.tune import Trainable, run -from hyperopt import hp -from ray.tune.suggest.hyperopt import HyperOptSearch - -# from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import cross_validate, StratifiedKFold -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) -from sklearn.tree import DecisionTreeClassifier -from sklearn.ensemble import AdaBoostClassifier -import os -import gc -import shap -from joblib import dump, load -import matplotlib.pyplot as plt -import warnings - -warnings.simplefilter("ignore") -import functools - -print = functools.partial(print, flush=True) - - -TUNE_STATE_REFRESH_PERIOD = 10 # Refresh resources every 10 s - - -def f_unpack_dict(dct): - """ - Unpacks all sub-dictionaries in given dictionary recursively. There should be no duplicated keys - across all nested subdictionaries, or some instances will be lost without warning - - Source: https://www.kaggle.com/fanvacoolt/tutorial-on-hyperopt - - Parameters: - ---------------- - dct : dictionary to unpack - - Returns: - ---------------- - : unpacked dictionary - """ - - res = {} - for (k, v) in dct.items(): - if isinstance(v, dict): - res = {**res, **f_unpack_dict(v)} - else: - res[k] = v - - return res - - -class stacking( - Trainable -): # https://docs.ray.io/en/master/hp/examples/pbt_hp_cifar10_with_keras.html - def setup(self, config, x_train, x_test, y_train, y_test): - self.x_train = x_train - self.x_test = x_test - self.y_train = y_train - self.y_test = y_test - self.config = f_unpack_dict(config) - self.model = AdaBoostClassifier( - algorithm=self.config.get("algorithm", "SAMME"), - base_estimator=DecisionTreeClassifier( - max_depth=self.config.get("max_depth", 1) - ), - learning_rate=self.config.get("learning_rate", 1), - n_estimators=self.config.get("n_estimators", 5), - ) - - def reset_config(self, new_config): - self.algorithm = new_config["algorithm"] - self.max_depth = new_config["max_depth"] - self.learning_rate = new_config["learning_rate"] - self.n_estimators = new_config["n_estimators"] - self.config = new_config - return True - - def step(self): - score = cross_validate( - self.model, self.x_train, self.y_train, cv=10, n_jobs=20, verbose=0 - ) - testing_score = np.mean(score["test_score"]) - # testing_score = self.model.fit(self.x_train, self.y_train).accuracy_score(self.x_test, self.y_test) - return {"mean_accuracy": testing_score} - - def save_checkpoint(self, checkpoint_dir): - file_path = checkpoint_dir + "/model" - pickle.dump(self.model, open(file_path, "wb")) - return file_path - - def load_checkpoint(self, path): - # See https://stackoverflow.com/a/42763323 - del self.model - self.model = pickle.load(open(path, "rb")) - - def cleanup(self): - # If need, save your model when exit. - # saved_path = self.model.save(self.logdir) - # print('save model at: ', saved_path) - pass - - -def results(config, x_train, x_test, y_train, y_test, var, output, feature_names): - start1 = time.perf_counter() - config = f_unpack_dict(config) - # self.x_train, self.x_test, self.y_train, self.y_test, self.feature_names = self._read_data(config) - clf_name = "AdaBoost" - clf = AdaBoostClassifier( - algorithm=config.get("algorithm", "SAMME"), - base_estimator=DecisionTreeClassifier(max_depth=config.get("max_depth", 1)), - learning_rate=config.get("learning_rate", 1), - n_estimators=config.get("n_estimators", 5), - ) - # score = cross_validate(clf, x_train, y_train, cv=StratifiedKFold(n_splits=5,shuffle=True,random_state=42), return_train_score=True, return_estimator=True, n_jobs=-1, verbose=0) - clf.fit(x_train, y_train) - train_score = clf.score(x_train, y_train) - with open(f"../tuning/{var}/{clf_name}_tuned_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # training_score = np.mean(score['train_score']) - # testing_score = np.mean(score['test_score']) - y_score = clf.predict(x_test) - prc = precision_score(y_test, y_score, average="weighted") - recall = recall_score(y_test, y_score, average="weighted") - roc_auc = roc_auc_score(y_test, y_score) - accuracy = accuracy_score(y_test, y_score) - matrix = confusion_matrix(y_test, y_score) - finish = (time.perf_counter() - start1) / 60 - # with open(output, 'a') as f: - # f.write(f"AdaBoostClassifier: \nCross_validate(avg_train_score): {training_score}\nCross_validate(avg_test_score): {testing_score}\nPrecision: {prc}\nRecall: {recall}\nROC_AUC: {roc_auc}\nAccuracy: {accuracy}\nTime(in min): {finish}\nConfusion Matrix:\n{matrix}") - - print( - f"Model\ttrain_score\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - print( - f"{clf_name}\t{train_score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del y_test - # explain all the predictions in the test set - background = shap.kmeans(x_train, 10) - explainer = shap.KernelExplainer(clf.predict, background) - del clf, x_train, y_train, background - background = x_test[np.random.choice(x_test.shape[0], 10000, replace=False)] - del x_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"../tuning/{var}/{clf_name}_tuned_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, feature_names - print(f"tuning and testing done!") - return None - - -def wrap_trainable(trainable, x_train, x_test, y_train, y_test): - x_train_id = ray.put(x_train) - x_test_id = ray.put(x_test) - y_train_id = ray.put(y_train) - y_test_id = ray.put(y_test) - - class _Wrapped(trainable): - def setup(self, config): - x_train = ray.get(x_train_id) - x_test = ray.get(x_test_id) - y_train = ray.get(y_train_id) - y_test = ray.get(y_test_id) - - super(_Wrapped, self).setup(config, x_train, x_test, y_train, y_test) - - return _Wrapped - - -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - # Load data - # print(f'\nUsing merged_data-train_{var}..', file=open(output, 'a')) - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - X_train.replace([np.inf, -np.inf], np.nan, inplace=True) - X_train.fillna(0, inplace=True) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - return X_train, X_test, Y_train, Y_test, feature_names - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--var-tag", - "-v", - type=str, - default="nssnv", - help="Var tag used while filtering or untuned models. (Default: nssnv)", - ) - - args = parser.parse_args() - - var = args.var_tag - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - with open("../../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - start = time.perf_counter() - if not os.path.exists("../tuning/" + var): - os.makedirs("../tuning/" + var) - output = "../tuning/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, 'w')) - print("Working with " + var + " dataset...") - x_train, x_test, y_train, y_test, feature_names = data_parsing( - var, config_dict, output - ) - config = { - "n_estimators": hp.randint("n_estimators", 1, 500), - "algorithm": hp.choice("algorithm", ["SAMME", "SAMME.R"]), - "learning_rate": hp.loguniform("learning_rate", 0.00001, 1.0), - "max_depth": hp.randint("max_depth", 2, 500), - } - hyperopt_search = HyperOptSearch(config, metric="mean_accuracy", mode="max") - # scheduler = AsyncHyperBandScheduler() - analysis = run( - wrap_trainable(stacking, x_train, x_test, y_train, y_test), - name=f"AdaBoost_{var}", - verbose=1, - # scheduler=scheduler, - search_alg=hyperopt_search, - reuse_actors=True, - local_dir="../ray_results", - max_failures=2, - # resources_per_trial={ - # 'cpu': 10, - # 'gpu': 1 - # }, - # global_checkpoint_period=np.inf, # Do not save checkpoints based on time interval - checkpoint_freq=50, # Save checkpoint every time the checkpoint_score_attr improves - checkpoint_at_end=True, - keep_checkpoints_num=2, # Keep only the best checkpoint - checkpoint_score_attr="mean_accuracy", # Metric used to compare checkpoints - metric="mean_accuracy", - mode="max", - stop={ - "training_iteration": 1, - }, - num_samples=1000, - # fail_fast=True, - queue_trials=True, - ) - finish = (time.perf_counter() - start) / 120 - # ttime = (finish- start)/120 - print(f"Total time in min: {finish}") - config = analysis.best_config - print( - f"AdaBoost_{var}: {config}", file=open(f"../tuning/tuned_parameters.csv", "a") - ) - results(config, x_train, x_test, y_train, y_test, var, output, feature_names) - gc.collect() diff --git a/src/training/training/Tuning/BRF.py b/src/training/training/Tuning/BRF.py deleted file mode 100644 index 135d4bb..0000000 --- a/src/training/training/Tuning/BRF.py +++ /dev/null @@ -1,329 +0,0 @@ -import numpy as np -import pandas as pd -import ray -import time -import argparse -import pickle -from joblib import dump -import yaml - -# from ray import tune -from ray.tune import Trainable, run -from hyperopt import hp -from ray.tune.suggest.hyperopt import HyperOptSearch - -# from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import cross_validate, StratifiedKFold -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) -from imblearn.ensemble import BalancedRandomForestClassifier -import os -import gc -import shap -from joblib import dump, load -import matplotlib.pyplot as plt -import warnings - -warnings.simplefilter("ignore") -import functools - -print = functools.partial(print, flush=True) - - -TUNE_STATE_REFRESH_PERIOD = 10 # Refresh resources every 10 s - - -def f_unpack_dict(dct): - """ - Unpacks all sub-dictionaries in given dictionary recursively. There should be no duplicated keys - across all nested subdictionaries, or some instances will be lost without warning - - Source: https://www.kaggle.com/fanvacoolt/tutorial-on-hyperopt - - Parameters: - ---------------- - dct : dictionary to unpack - - Returns: - ---------------- - : unpacked dictionary - """ - - res = {} - for (k, v) in dct.items(): - if isinstance(v, dict): - res = {**res, **f_unpack_dict(v)} - else: - res[k] = v - - return res - - -class stacking( - Trainable -): # https://docs.ray.io/en/master/hp/examples/pbt_hp_cifar10_with_keras.html - def setup(self, config, x_train, x_test, y_train, y_test): - self.x_train = x_train - self.x_test = x_test - self.y_train = y_train - self.y_test = y_test - self.config = f_unpack_dict(config) - self.model = BalancedRandomForestClassifier( - random_state=42, - n_estimators=self.config.get("n_estimators", 100), - criterion=self.config.get("criterion", "gini"), - max_depth=self.config.get("max_depth", 2), - min_samples_split=self.config.get("min_samples_split", 2), - min_samples_leaf=self.config.get("min_samples_leaf", 1), - max_features=self.config.get("max_features", "sqrt"), - oob_score=self.config.get("oob_score", False), - class_weight=self.config.get("class_weight", "balanced"), - n_jobs=20, - ) - - def reset_config(self, new_config): - self.n_estimators = new_config["n_estimators"] - self.max_depth = new_config["max_depth"] - self.min_samples_split = new_config["min_samples_split"] - self.min_samples_leaf = new_config["min_samples_leaf"] - self.criterion = new_config["criterion"] - self.max_features = new_config["max_features"] - self.class_weight = new_config["class_weight"] - self.config = new_config - return True - - def step(self): - score = cross_validate( - self.model, self.x_train, self.y_train, cv=10, n_jobs=20, verbose=0 - ) - testing_score = np.mean(score["test_score"]) - # testing_score = self.model.fit(self.x_train, self.y_train).accuracy_score(self.x_test, self.y_test) - return {"mean_accuracy": testing_score} - - def save_checkpoint(self, checkpoint_dir): - file_path = checkpoint_dir + "/model" - pickle.dump(self.model, open(file_path, "wb")) - return file_path - - def load_checkpoint(self, path): - # See https://stackoverflow.com/a/42763323 - del self.model - self.model = pickle.load(open(path, "rb")) - - def cleanup(self): - # If need, save your model when exit. - # saved_path = self.model.save(self.logdir) - # print('save model at: ', saved_path) - pass - - -def results(config, x_train, x_test, y_train, y_test, var, output, feature_names): - start1 = time.perf_counter() - config = f_unpack_dict(config) - # self.x_train, self.x_test, self.y_train, self.y_test, self.feature_names = self._read_data(config) - clf_name = "BalancedRF" - clf = BalancedRandomForestClassifier( - random_state=42, - n_estimators=config.get("n_estimators", 100), - criterion=config.get("criterion", "gini"), - max_depth=config.get("max_depth", 2), - min_samples_split=config.get("min_samples_split", 2), - min_samples_leaf=config.get("min_samples_leaf", 1), - max_features=config.get("max_features", "sqrt"), - oob_score=config.get("oob_score", False), - class_weight=config.get("class_weight", "balanced"), - n_jobs=-1, - ) - # score = cross_validate(clf, x_train, y_train, cv=StratifiedKFold(n_splits=5,shuffle=True,random_state=42), return_train_score=True, return_estimator=True, n_jobs=-1, verbose=0) - clf.fit(x_train, y_train) - train_score = clf.score(x_train, y_train) - with open(f"../tuning/{var}/{clf_name}_tuned_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # training_score = np.mean(score['train_score']) - # testing_score = np.mean(score['test_score']) - y_score = clf.predict(x_test) - prc = precision_score(y_test, y_score, average="weighted") - recall = recall_score(y_test, y_score, average="weighted") - roc_auc = roc_auc_score(y_test, y_score) - accuracy = accuracy_score(y_test, y_score) - matrix = confusion_matrix(y_test, y_score) - finish = (time.perf_counter() - start1) / 60 - # with open(output, 'a') as f: - # f.write(f"AdaBoostClassifier: \nCross_validate(avg_train_score): {training_score}\nCross_validate(avg_test_score): {testing_score}\nPrecision: {prc}\nRecall: {recall}\nROC_AUC: {roc_auc}\nAccuracy: {accuracy}\nTime(in min): {finish}\nConfusion Matrix:\n{matrix}") - - print( - f"Model\ttrain_score\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - print( - f"{clf_name}\t{train_score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del y_test - # explain all the predictions in the test set - background = shap.kmeans(x_train, 10) - explainer = shap.KernelExplainer(clf.predict, background) - del clf, x_train, y_train, background - background = x_test[np.random.choice(x_test.shape[0], 10000, replace=False)] - del x_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"../tuning/{var}/{clf_name}_tuned_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, feature_names - print(f"tuning and testing done!") - return None - - -def wrap_trainable(trainable, x_train, x_test, y_train, y_test): - x_train_id = ray.put(x_train) - x_test_id = ray.put(x_test) - y_train_id = ray.put(y_train) - y_test_id = ray.put(y_test) - - class _Wrapped(trainable): - def setup(self, config): - x_train = ray.get(x_train_id) - x_test = ray.get(x_test_id) - y_train = ray.get(y_train_id) - y_test = ray.get(y_test_id) - - super(_Wrapped, self).setup(config, x_train, x_test, y_train, y_test) - - return _Wrapped - - -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - # Load data - # print(f'\nUsing merged_data-train_{var}..', file=open(output, 'a')) - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - X_train.replace([np.inf, -np.inf], np.nan, inplace=True) - X_train.fillna(0, inplace=True) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - return X_train, X_test, Y_train, Y_test, feature_names - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--var-tag", - "-v", - type=str, - default="nssnv", - help="Var tag used while filtering or untuned models. (Default: nssnv)", - ) - - args = parser.parse_args() - - var = args.var_tag - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - with open("../../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - start = time.perf_counter() - if not os.path.exists("../tuning/" + var): - os.makedirs("../tuning/" + var) - output = "../tuning/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, 'w')) - print("Working with " + var + " dataset...") - x_train, x_test, y_train, y_test, feature_names = data_parsing( - var, config_dict, output - ) - config = { - "n_estimators": hp.randint("n_estimators", 1, 500), - "min_samples_split": hp.randint("min_samples_split", 2, 100), - "min_samples_leaf": hp.randint("min_samples_leaf", 1, 100), - "criterion": hp.choice("criterion", ["gini", "entropy"]), - "max_features": hp.choice("max_features", ["sqrt", "log2"]), - "class_weight": hp.choice("class_weight", ["balanced", "balanced_subsample"]), - # "oob_score" : hp.choice("oob_score", [True, False]), - "max_depth": hp.randint("max_depth", 2, 500), - } - hyperopt_search = HyperOptSearch(config, metric="mean_accuracy", mode="max") - # scheduler = AsyncHyperBandScheduler() - analysis = run( - wrap_trainable(stacking, x_train, x_test, y_train, y_test), - name=f"BalancedRF_{var}", - verbose=1, - # scheduler=scheduler, - search_alg=hyperopt_search, - reuse_actors=True, - local_dir="../ray_results", - max_failures=2, - # resources_per_trial={ - # 'cpu': 10, - # 'gpu': 1 - # }, - # global_checkpoint_period=np.inf, # Do not save checkpoints based on time interval - checkpoint_freq=50, # Save checkpoint every time the checkpoint_score_attr improves - checkpoint_at_end=True, - keep_checkpoints_num=2, # Keep only the best checkpoint - checkpoint_score_attr="mean_accuracy", # Metric used to compare checkpoints - metric="mean_accuracy", - mode="max", - stop={ - "training_iteration": 1, - }, - num_samples=1000, - # fail_fast=True, - queue_trials=True, - ) - finish = (time.perf_counter() - start) / 120 - # ttime = (finish- start)/120 - print(f"Total time in min: {finish}") - config = analysis.best_config - print( - f"BalancedRF_{var}: {config}", - file=open(f"../tuning/tuned_parameters.csv", "a"), - ) - results(config, x_train, x_test, y_train, y_test, var, output, feature_names) - gc.collect() diff --git a/src/training/training/Tuning/DT.py b/src/training/training/Tuning/DT.py deleted file mode 100644 index f324d9c..0000000 --- a/src/training/training/Tuning/DT.py +++ /dev/null @@ -1,325 +0,0 @@ -import numpy as np -import pandas as pd -import ray -import time -import argparse -import pickle -from joblib import dump -import yaml - -# from ray import tune -from ray.tune import Trainable, run -from hyperopt import hp -from ray.tune.suggest.hyperopt import HyperOptSearch - -# from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import cross_validate, StratifiedKFold -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) -from sklearn.tree import DecisionTreeClassifier -import os -import gc -import shap -from joblib import dump, load -import matplotlib.pyplot as plt -import warnings - -warnings.simplefilter("ignore") -import functools - -print = functools.partial(print, flush=True) - - -TUNE_STATE_REFRESH_PERIOD = 10 # Refresh resources every 10 s - - -def f_unpack_dict(dct): - """ - Unpacks all sub-dictionaries in given dictionary recursively. There should be no duplicated keys - across all nested subdictionaries, or some instances will be lost without warning - - Source: https://www.kaggle.com/fanvacoolt/tutorial-on-hyperopt - - Parameters: - ---------------- - dct : dictionary to unpack - - Returns: - ---------------- - : unpacked dictionary - """ - - res = {} - for (k, v) in dct.items(): - if isinstance(v, dict): - res = {**res, **f_unpack_dict(v)} - else: - res[k] = v - - return res - - -class stacking( - Trainable -): # https://docs.ray.io/en/master/hp/examples/pbt_hp_cifar10_with_keras.html - def setup(self, config, x_train, x_test, y_train, y_test): - self.x_train = x_train - self.x_test = x_test - self.y_train = y_train - self.y_test = y_test - self.config = f_unpack_dict(config) - self.model = DecisionTreeClassifier( - random_state=42, - criterion=self.config.get("criterion", "gini"), - splitter=self.config.get("splitter", "best"), - max_depth=self.config.get("max_depth", 2), - min_samples_split=self.config.get("min_samples_split", 2), - min_samples_leaf=self.config.get("min_samples_leaf", 1), - max_features=self.config.get("max_features", "sqrt"), - class_weight=self.config.get("class_weight", "balanced"), - ) - - def reset_config(self, new_config): - self.splitter = new_config["splitter"] - self.max_depth = new_config["max_depth"] - self.min_samples_split = new_config["min_samples_split"] - self.min_samples_leaf = new_config["min_samples_leaf"] - self.criterion = new_config["criterion"] - self.max_features = new_config["max_features"] - self.class_weight = new_config["class_weight"] - self.config = new_config - return True - - def step(self): - score = cross_validate( - self.model, self.x_train, self.y_train, cv=10, n_jobs=-1, verbose=0 - ) - testing_score = np.mean(score["test_score"]) - # testing_score = self.model.fit(self.x_train, self.y_train).accuracy_score(self.x_test, self.y_test) - return {"mean_accuracy": testing_score} - - def save_checkpoint(self, checkpoint_dir): - file_path = checkpoint_dir + "/model" - pickle.dump(self.model, open(file_path, "wb")) - return file_path - - def load_checkpoint(self, path): - # See https://stackoverflow.com/a/42763323 - del self.model - self.model = pickle.load(open(path, "rb")) - - def cleanup(self): - # If need, save your model when exit. - # saved_path = self.model.save(self.logdir) - # print('save model at: ', saved_path) - pass - - -def results(config, x_train, x_test, y_train, y_test, var, output, feature_names): - start1 = time.perf_counter() - config = f_unpack_dict(config) - # self.x_train, self.x_test, self.y_train, self.y_test, self.feature_names = self._read_data(config) - clf_name = "DecisionTree" - clf = DecisionTreeClassifier( - random_state=42, - criterion=config.get("criterion", "gini"), - splitter=config.get("splitter", "best"), - max_depth=config.get("max_depth", 2), - min_samples_split=config.get("min_samples_split", 2), - min_samples_leaf=config.get("min_samples_leaf", 1), - max_features=config.get("max_features", "sqrt"), - class_weight=config.get("class_weight", "balanced"), - ) - # score = cross_validate(clf, x_train, y_train, cv=StratifiedKFold(n_splits=5,shuffle=True,random_state=42), return_train_score=True, return_estimator=True, n_jobs=-1, verbose=0) - clf.fit(x_train, y_train) - train_score = clf.score(x_train, y_train) - with open(f"../tuning/{var}/{clf_name}_tuned_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # training_score = np.mean(score['train_score']) - # testing_score = np.mean(score['test_score']) - y_score = clf.predict(x_test) - prc = precision_score(y_test, y_score, average="weighted") - recall = recall_score(y_test, y_score, average="weighted") - roc_auc = roc_auc_score(y_test, y_score) - accuracy = accuracy_score(y_test, y_score) - matrix = confusion_matrix(y_test, y_score) - finish = (time.perf_counter() - start1) / 60 - # with open(output, 'a') as f: - # f.write(f"AdaBoostClassifier: \nCross_validate(avg_train_score): {training_score}\nCross_validate(avg_test_score): {testing_score}\nPrecision: {prc}\nRecall: {recall}\nROC_AUC: {roc_auc}\nAccuracy: {accuracy}\nTime(in min): {finish}\nConfusion Matrix:\n{matrix}") - - print( - f"Model\ttrain_score\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - print( - f"{clf_name}\t{train_score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del y_test - # explain all the predictions in the test set - background = shap.kmeans(x_train, 10) - explainer = shap.KernelExplainer(clf.predict, background) - del clf, x_train, y_train, background - background = x_test[np.random.choice(x_test.shape[0], 10000, replace=False)] - del x_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"../tuning/{var}/{clf_name}_tuned_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, feature_names - print(f"tuning and testing done!") - return None - - -def wrap_trainable(trainable, x_train, x_test, y_train, y_test): - x_train_id = ray.put(x_train) - x_test_id = ray.put(x_test) - y_train_id = ray.put(y_train) - y_test_id = ray.put(y_test) - - class _Wrapped(trainable): - def setup(self, config): - x_train = ray.get(x_train_id) - x_test = ray.get(x_test_id) - y_train = ray.get(y_train_id) - y_test = ray.get(y_test_id) - - super(_Wrapped, self).setup(config, x_train, x_test, y_train, y_test) - - return _Wrapped - - -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - # Load data - # print(f'\nUsing merged_data-train_{var}..', file=open(output, 'a')) - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - X_train.replace([np.inf, -np.inf], np.nan, inplace=True) - X_train.fillna(0, inplace=True) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - return X_train, X_test, Y_train, Y_test, feature_names - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--var-tag", - "-v", - type=str, - default="nssnv", - help="Var tag used while filtering or untuned models. (Default: nssnv)", - ) - - args = parser.parse_args() - - var = args.var_tag - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - with open("../../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - start = time.perf_counter() - if not os.path.exists("../tuning/" + var): - os.makedirs("../tuning/" + var) - output = "../tuning/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, 'w')) - print("Working with " + var + " dataset...") - x_train, x_test, y_train, y_test, feature_names = data_parsing( - var, config_dict, output - ) - config = { - "splitter": hp.choice("splitter", ["best", "random"]), - "min_samples_split": hp.randint("min_samples_split", 2, 100), - "min_samples_leaf": hp.randint("min_samples_leaf", 1, 100), - "criterion": hp.choice("criterion", ["gini", "entropy"]), - "max_features": hp.choice("max_features", ["sqrt", "log2"]), - "class_weight": hp.choice("class_weight", ["balanced"]), - # "oob_score" : hp.choice("oob_score", [True, False]), - "max_depth": hp.randint("max_depth", 2, 500), - } - hyperopt_search = HyperOptSearch(config, metric="mean_accuracy", mode="max") - # scheduler = AsyncHyperBandScheduler() - analysis = run( - wrap_trainable(stacking, x_train, x_test, y_train, y_test), - name=f"DecisionTree_{var}", - verbose=1, - # scheduler=scheduler, - search_alg=hyperopt_search, - reuse_actors=True, - local_dir="../ray_results", - max_failures=2, - # resources_per_trial={ - # 'cpu': 10, - # 'gpu': 1 - # }, - # global_checkpoint_period=np.inf, # Do not save checkpoints based on time interval - checkpoint_freq=50, # Save checkpoint every time the checkpoint_score_attr improves - checkpoint_at_end=True, - keep_checkpoints_num=2, # Keep only the best checkpoint - checkpoint_score_attr="mean_accuracy", # Metric used to compare checkpoints - metric="mean_accuracy", - mode="max", - stop={ - "training_iteration": 1, - }, - num_samples=1000, - # fail_fast=True, - queue_trials=True, - ) - finish = (time.perf_counter() - start) / 120 - # ttime = (finish- start)/120 - print(f"Total time in min: {finish}") - config = analysis.best_config - print( - f"DecisionTree_{var}: {config}", - file=open(f"../tuning/tuned_parameters.csv", "a"), - ) - results(config, x_train, x_test, y_train, y_test, var, output, feature_names) - gc.collect() diff --git a/src/training/training/Tuning/ET.py b/src/training/training/Tuning/ET.py deleted file mode 100644 index dc9bfbe..0000000 --- a/src/training/training/Tuning/ET.py +++ /dev/null @@ -1,328 +0,0 @@ -import numpy as np -import pandas as pd -import ray -import time -import argparse -import pickle -from joblib import dump -import yaml - -# from ray import tune -from ray.tune import Trainable, run -from hyperopt import hp -from ray.tune.suggest.hyperopt import HyperOptSearch - -# from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import cross_validate, StratifiedKFold -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) -from sklearn.ensemble import ExtraTreesClassifier -import os -import gc -import shap -from joblib import dump, load -import matplotlib.pyplot as plt -import warnings - -warnings.simplefilter("ignore") -import functools - -print = functools.partial(print, flush=True) - - -TUNE_STATE_REFRESH_PERIOD = 10 # Refresh resources every 10 s - - -def f_unpack_dict(dct): - """ - Unpacks all sub-dictionaries in given dictionary recursively. There should be no duplicated keys - across all nested subdictionaries, or some instances will be lost without warning - - Source: https://www.kaggle.com/fanvacoolt/tutorial-on-hyperopt - - Parameters: - ---------------- - dct : dictionary to unpack - - Returns: - ---------------- - : unpacked dictionary - """ - - res = {} - for (k, v) in dct.items(): - if isinstance(v, dict): - res = {**res, **f_unpack_dict(v)} - else: - res[k] = v - - return res - - -class stacking( - Trainable -): # https://docs.ray.io/en/master/hp/examples/pbt_hp_cifar10_with_keras.html - def setup(self, config, x_train, x_test, y_train, y_test): - self.x_train = x_train - self.x_test = x_test - self.y_train = y_train - self.y_test = y_test - self.config = f_unpack_dict(config) - self.model = ExtraTreesClassifier( - random_state=42, - n_estimators=self.config.get("n_estimators", 100), - criterion=self.config.get("criterion", "gini"), - max_depth=self.config.get("max_depth", 2), - min_samples_split=self.config.get("min_samples_split", 2), - min_samples_leaf=self.config.get("min_samples_leaf", 1), - max_features=self.config.get("max_features", "sqrt"), - oob_score=self.config.get("oob_score", False), - class_weight=self.config.get("class_weight", "balanced"), - n_jobs=20, - ) - - def reset_config(self, new_config): - self.n_estimators = new_config["n_estimators"] - # self.max_depth = new_config['max_depth'] - self.min_samples_split = new_config["min_samples_split"] - self.min_samples_leaf = new_config["min_samples_leaf"] - self.criterion = new_config["criterion"] - self.max_features = new_config["max_features"] - self.class_weight = new_config["class_weight"] - self.config = new_config - return True - - def step(self): - score = cross_validate( - self.model, self.x_train, self.y_train, cv=10, n_jobs=20, verbose=0 - ) - testing_score = np.mean(score["test_score"]) - # testing_score = self.model.fit(self.x_train, self.y_train).accuracy_score(self.x_test, self.y_test) - return {"mean_accuracy": testing_score} - - def save_checkpoint(self, checkpoint_dir): - file_path = checkpoint_dir + "/model" - pickle.dump(self.model, open(file_path, "wb")) - return file_path - - def load_checkpoint(self, path): - # See https://stackoverflow.com/a/42763323 - del self.model - self.model = pickle.load(open(path, "rb")) - - def cleanup(self): - # If need, save your model when exit. - # saved_path = self.model.save(self.logdir) - # print('save model at: ', saved_path) - pass - - -def results(config, x_train, x_test, y_train, y_test, var, output, feature_names): - start1 = time.perf_counter() - config = f_unpack_dict(config) - # self.x_train, self.x_test, self.y_train, self.y_test, self.feature_names = self._read_data(config) - clf_name = "ExtraTrees" - clf = ExtraTreesClassifier( - random_state=42, - n_estimators=config.get("n_estimators", 100), - criterion=config.get("criterion", "gini"), - min_samples_split=config.get("min_samples_split", 2), - min_samples_leaf=config.get("min_samples_leaf", 1), - max_features=config.get("max_features", "sqrt"), - oob_score=config.get("oob_score", False), - class_weight=config.get("class_weight", "balanced"), - n_jobs=-1, - ) - # score = cross_validate(clf, x_train, y_train, cv=StratifiedKFold(n_splits=5,shuffle=True,random_state=42), return_train_score=True, return_estimator=True, n_jobs=-1, verbose=0) - clf.fit(x_train, y_train) - train_score = clf.score(x_train, y_train) - with open(f"../tuning/{var}/{clf_name}_tuned_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # training_score = np.mean(score['train_score']) - # testing_score = np.mean(score['test_score']) - y_score = clf.predict(x_test) - prc = precision_score(y_test, y_score, average="weighted") - recall = recall_score(y_test, y_score, average="weighted") - roc_auc = roc_auc_score(y_test, y_score) - accuracy = accuracy_score(y_test, y_score) - matrix = confusion_matrix(y_test, y_score) - finish = (time.perf_counter() - start1) / 60 - # with open(output, 'a') as f: - # f.write(f"AdaBoostClassifier: \nCross_validate(avg_train_score): {training_score}\nCross_validate(avg_test_score): {testing_score}\nPrecision: {prc}\nRecall: {recall}\nROC_AUC: {roc_auc}\nAccuracy: {accuracy}\nTime(in min): {finish}\nConfusion Matrix:\n{matrix}") - - print( - f"Model\ttrain_score\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - print( - f"{clf_name}\t{train_score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del y_test - # explain all the predictions in the test set - background = shap.kmeans(x_train, 10) - explainer = shap.KernelExplainer(clf.predict, background) - del clf, x_train, y_train, background - background = x_test[np.random.choice(x_test.shape[0], 10000, replace=False)] - del x_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"../tuning/{var}/{clf_name}_tuned_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, feature_names - print(f"tuning and testing done!") - return None - - -def wrap_trainable(trainable, x_train, x_test, y_train, y_test): - x_train_id = ray.put(x_train) - x_test_id = ray.put(x_test) - y_train_id = ray.put(y_train) - y_test_id = ray.put(y_test) - - class _Wrapped(trainable): - def setup(self, config): - x_train = ray.get(x_train_id) - x_test = ray.get(x_test_id) - y_train = ray.get(y_train_id) - y_test = ray.get(y_test_id) - - super(_Wrapped, self).setup(config, x_train, x_test, y_train, y_test) - - return _Wrapped - - -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - # Load data - # print(f'\nUsing merged_data-train_{var}..', file=open(output, 'a')) - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - X_train.replace([np.inf, -np.inf], np.nan, inplace=True) - X_train.fillna(0, inplace=True) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - return X_train, X_test, Y_train, Y_test, feature_names - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--var-tag", - "-v", - type=str, - default="nssnv", - help="Var tag used while filtering or untuned models. (Default: nssnv)", - ) - - args = parser.parse_args() - - var = args.var_tag - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - with open("../../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - start = time.perf_counter() - if not os.path.exists("../tuning/" + var): - os.makedirs("../tuning/" + var) - output = "../tuning/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, 'w')) - print("Working with " + var + " dataset...") - x_train, x_test, y_train, y_test, feature_names = data_parsing( - var, config_dict, output - ) - config = { - "n_estimators": hp.randint("n_estimators", 1, 500), - "min_samples_split": hp.randint("min_samples_split", 2, 100), - "min_samples_leaf": hp.randint("min_samples_leaf", 1, 100), - "criterion": hp.choice("criterion", ["gini", "entropy"]), - "max_features": hp.choice("max_features", ["sqrt", "log2"]), - "class_weight": hp.choice("class_weight", ["balanced", "balanced_subsample"]), - # "oob_score" : hp.choice("oob_score", [True, False]), - # "max_depth" : hp.randint("max_depth", 2, 500) - } - hyperopt_search = HyperOptSearch(config, metric="mean_accuracy", mode="max") - # scheduler = AsyncHyperBandScheduler() - analysis = run( - wrap_trainable(stacking, x_train, x_test, y_train, y_test), - name=f"ExtraTrees_{var}", - verbose=1, - # scheduler=scheduler, - search_alg=hyperopt_search, - reuse_actors=True, - local_dir="../ray_results", - max_failures=2, - # resources_per_trial={ - # 'cpu': 10, - # 'gpu': 1 - # }, - # global_checkpoint_period=np.inf, # Do not save checkpoints based on time interval - checkpoint_freq=50, # Save checkpoint every time the checkpoint_score_attr improves - checkpoint_at_end=True, - keep_checkpoints_num=2, # Keep only the best checkpoint - checkpoint_score_attr="mean_accuracy", # Metric used to compare checkpoints - metric="mean_accuracy", - mode="max", - stop={ - "training_iteration": 1, - }, - num_samples=1000, - # fail_fast=True, - queue_trials=True, - ) - finish = (time.perf_counter() - start) / 120 - # ttime = (finish- start)/120 - print(f"Total time in min: {finish}") - config = analysis.best_config - print( - f"ExtraTrees_{var}: {config}", - file=open(f"../tuning/tuned_parameters.csv", "a"), - ) - results(config, x_train, x_test, y_train, y_test, var, output, feature_names) - gc.collect() diff --git a/src/training/training/Tuning/GBC.py b/src/training/training/Tuning/GBC.py deleted file mode 100644 index 4e0bec3..0000000 --- a/src/training/training/Tuning/GBC.py +++ /dev/null @@ -1,332 +0,0 @@ -import numpy as np -import pandas as pd -import ray -import time -import argparse -import pickle -from joblib import dump -import yaml - -# from ray import tune -from ray.tune import Trainable, run -from hyperopt import hp -from ray.tune.suggest.hyperopt import HyperOptSearch - -# from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import cross_validate, StratifiedKFold -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) -from sklearn.ensemble import GradientBoostingClassifier -import os -import gc -import shap -from joblib import dump, load -import matplotlib.pyplot as plt -import warnings - -warnings.simplefilter("ignore") -import functools - -print = functools.partial(print, flush=True) - - -TUNE_STATE_REFRESH_PERIOD = 10 # Refresh resources every 10 s - - -def f_unpack_dict(dct): - """ - Unpacks all sub-dictionaries in given dictionary recursively. There should be no duplicated keys - across all nested subdictionaries, or some instances will be lost without warning - - Source: https://www.kaggle.com/fanvacoolt/tutorial-on-hyperopt - - Parameters: - ---------------- - dct : dictionary to unpack - - Returns: - ---------------- - : unpacked dictionary - """ - - res = {} - for (k, v) in dct.items(): - if isinstance(v, dict): - res = {**res, **f_unpack_dict(v)} - else: - res[k] = v - - return res - - -class stacking( - Trainable -): # https://docs.ray.io/en/master/hp/examples/pbt_hp_cifar10_with_keras.html - def setup(self, config, x_train, x_test, y_train, y_test): - self.x_train = x_train - self.x_test = x_test - self.y_train = y_train - self.y_test = y_test - self.config = f_unpack_dict(config) - self.model = GradientBoostingClassifier( - random_state=42, - loss=self.config.get("loss", 100), - learning_rate=self.config.get("learning_rate", 0.1), - n_estimators=self.config.get("n_estimators", 100), - subsample=self.config.get("subsample", 1), - criterion=self.config.get("criterion", "friedman_mse"), - min_samples_split=self.config.get("min_samples_split", 2), - min_samples_leaf=self.config.get("min_samples_leaf", 1), - max_depth=self.config.get("max_depth", 2), - max_features=self.config.get("max_features", "sqrt"), - ) - - def reset_config(self, new_config): - self.loss = new_config["loss"] - self.learning_rate = new_config["learning_rate"] - self.n_estimators = new_config["n_estimators"] - self.subsample = new_config["subsample"] - self.criterion = new_config["criterion"] - self.min_samples_split = new_config["min_samples_split"] - self.min_samples_leaf = new_config["min_samples_leaf"] - self.max_depth = new_config["max_depth"] - self.max_features = new_config["max_features"] - self.config = new_config - return True - - def step(self): - score = cross_validate( - self.model, self.x_train, self.y_train, cv=10, n_jobs=20, verbose=0 - ) - testing_score = np.mean(score["test_score"]) - # testing_score = self.model.fit(self.x_train, self.y_train).accuracy_score(self.x_test, self.y_test) - return {"mean_accuracy": testing_score} - - def save_checkpoint(self, checkpoint_dir): - file_path = checkpoint_dir + "/model" - pickle.dump(self.model, open(file_path, "wb")) - return file_path - - def load_checkpoint(self, path): - # See https://stackoverflow.com/a/42763323 - del self.model - self.model = pickle.load(open(path, "rb")) - - def cleanup(self): - # If need, save your model when exit. - # saved_path = self.model.save(self.logdir) - # print('save model at: ', saved_path) - pass - - -def results(config, x_train, x_test, y_train, y_test, var, output, feature_names): - start1 = time.perf_counter() - config = f_unpack_dict(config) - # self.x_train, self.x_test, self.y_train, self.y_test, self.feature_names = self._read_data(config) - clf_name = "GradientBoost" - clf = GradientBoostingClassifier( - random_state=42, - loss=config.get("loss", 100), - learning_rate=config.get("learning_rate", 0.1), - n_estimators=config.get("n_estimators", 100), - subsample=config.get("subsample", 1), - criterion=config.get("criterion", "friedman_mse"), - min_samples_split=config.get("min_samples_split", 2), - min_samples_leaf=config.get("min_samples_leaf", 1), - max_depth=config.get("max_depth", 2), - max_features=config.get("max_features", "sqrt"), - ) - # score = cross_validate(clf, x_train, y_train, cv=StratifiedKFold(n_splits=5,shuffle=True,random_state=42), return_train_score=True, return_estimator=True, n_jobs=-1, verbose=0) - clf.fit(x_train, y_train) - train_score = clf.score(x_train, y_train) - with open(f"../tuning/{var}/{clf_name}_tuned_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # training_score = np.mean(score['train_score']) - # testing_score = np.mean(score['test_score']) - y_score = clf.predict(x_test) - prc = precision_score(y_test, y_score, average="weighted") - recall = recall_score(y_test, y_score, average="weighted") - roc_auc = roc_auc_score(y_test, y_score) - accuracy = accuracy_score(y_test, y_score) - matrix = confusion_matrix(y_test, y_score) - finish = (time.perf_counter() - start1) / 60 - # with open(output, 'a') as f: - # f.write(f"AdaBoostClassifier: \nCross_validate(avg_train_score): {training_score}\nCross_validate(avg_test_score): {testing_score}\nPrecision: {prc}\nRecall: {recall}\nROC_AUC: {roc_auc}\nAccuracy: {accuracy}\nTime(in min): {finish}\nConfusion Matrix:\n{matrix}") - - print( - f"Model\ttrain_score\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - print( - f"{clf_name}\t{train_score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del y_test - # explain all the predictions in the test set - background = shap.kmeans(x_train, 10) - explainer = shap.KernelExplainer(clf.predict, background) - del clf, x_train, y_train, background - background = x_test[np.random.choice(x_test.shape[0], 10000, replace=False)] - del x_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"../tuning/{var}/{clf_name}_tuned_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, feature_names - print(f"tuning and testing done!") - return None - - -def wrap_trainable(trainable, x_train, x_test, y_train, y_test): - x_train_id = ray.put(x_train) - x_test_id = ray.put(x_test) - y_train_id = ray.put(y_train) - y_test_id = ray.put(y_test) - - class _Wrapped(trainable): - def setup(self, config): - x_train = ray.get(x_train_id) - x_test = ray.get(x_test_id) - y_train = ray.get(y_train_id) - y_test = ray.get(y_test_id) - - super(_Wrapped, self).setup(config, x_train, x_test, y_train, y_test) - - return _Wrapped - - -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - # Load data - # print(f'\nUsing merged_data-train_{var}..', file=open(output, 'a')) - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - X_train.replace([np.inf, -np.inf], np.nan, inplace=True) - X_train.fillna(0, inplace=True) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - return X_train, X_test, Y_train, Y_test, feature_names - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--var-tag", - "-v", - type=str, - default="nssnv", - help="Var tag used while filtering or untuned models. (Default: nssnv)", - ) - - args = parser.parse_args() - - var = args.var_tag - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - with open("../../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - start = time.perf_counter() - if not os.path.exists("../tuning/" + var): - os.makedirs("../tuning/" + var) - output = "../tuning/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, 'w')) - print("Working with " + var + " dataset...") - x_train, x_test, y_train, y_test, feature_names = data_parsing( - var, config_dict, output - ) - config = { - "loss": hp.choice("loss", ["deviance", "exponential"]), - "learning_rate": hp.loguniform("learning_rate", 0.00001, 1.0), - "n_estimators": hp.randint("n_estimators", 1, 500), - "subsample": hp.uniform("subsample", 0.1, 1.0), - "criterion": hp.choice("criterion", ["friedman_mse", "mse"]), - "min_samples_split": hp.randint("min_samples_split", 2, 100), - "min_samples_leaf": hp.randint("min_samples_leaf", 1, 100), - "max_depth": hp.randint("max_depth", 2, 500), - "max_features": hp.choice("max_features", ["sqrt", "log2"]), - } - hyperopt_search = HyperOptSearch(config, metric="mean_accuracy", mode="max") - # scheduler = AsyncHyperBandScheduler() - analysis = run( - wrap_trainable(stacking, x_train, x_test, y_train, y_test), - name=f"GradientBoost_{var}", - verbose=1, - # scheduler=scheduler, - search_alg=hyperopt_search, - reuse_actors=True, - local_dir="../ray_results", - max_failures=2, - # resources_per_trial={ - # 'cpu': 10, - # 'gpu': 1 - # }, - # global_checkpoint_period=np.inf, # Do not save checkpoints based on time interval - checkpoint_freq=50, # Save checkpoint every time the checkpoint_score_attr improves - checkpoint_at_end=True, - keep_checkpoints_num=2, # Keep only the best checkpoint - checkpoint_score_attr="mean_accuracy", # Metric used to compare checkpoints - metric="mean_accuracy", - mode="max", - stop={ - "training_iteration": 1, - }, - num_samples=1000, - # fail_fast=True, - queue_trials=True, - ) - finish = (time.perf_counter() - start) / 120 - # ttime = (finish- start)/120 - print(f"Total time in min: {finish}") - config = analysis.best_config - print( - f"GradientBoost_{var}: {config}", - file=open(f"../tuning/tuned_parameters.csv", "a"), - ) - results(config, x_train, x_test, y_train, y_test, var, output, feature_names) - gc.collect() diff --git a/src/training/training/Tuning/GNB.py b/src/training/training/Tuning/GNB.py deleted file mode 100644 index 08f6eb9..0000000 --- a/src/training/training/Tuning/GNB.py +++ /dev/null @@ -1,294 +0,0 @@ -import numpy as np -import pandas as pd -import ray -import time -import argparse -import pickle -from joblib import dump -import yaml - -# from ray import tune -from ray.tune import Trainable, run -from hyperopt import hp -from ray.tune.suggest.hyperopt import HyperOptSearch - -# from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import cross_validate, StratifiedKFold -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) -from sklearn.naive_bayes import GaussianNB -import os -import gc -import shap -from joblib import dump, load -import matplotlib.pyplot as plt -import warnings - -warnings.simplefilter("ignore") -import functools - -print = functools.partial(print, flush=True) - - -TUNE_STATE_REFRESH_PERIOD = 10 # Refresh resources every 10 s - - -def f_unpack_dict(dct): - """ - Unpacks all sub-dictionaries in given dictionary recursively. There should be no duplicated keys - across all nested subdictionaries, or some instances will be lost without warning - - Source: https://www.kaggle.com/fanvacoolt/tutorial-on-hyperopt - - Parameters: - ---------------- - dct : dictionary to unpack - - Returns: - ---------------- - : unpacked dictionary - """ - - res = {} - for (k, v) in dct.items(): - if isinstance(v, dict): - res = {**res, **f_unpack_dict(v)} - else: - res[k] = v - - return res - - -class stacking( - Trainable -): # https://docs.ray.io/en/master/hp/examples/pbt_hp_cifar10_with_keras.html - def setup(self, config, x_train, x_test, y_train, y_test): - self.x_train = x_train - self.x_test = x_test - self.y_train = y_train - self.y_test = y_test - self.config = f_unpack_dict(config) - self.model = GaussianNB(var_smoothing=self.config.get("var_smoothing", 1e-09)) - - def reset_config(self, new_config): - score = cross_validate( - self.model, self.x_train, self.y_train, cv=10, n_jobs=-1, verbose=0 - ) - testing_score = np.mean(score["test_score"]) - return True - - def step(self): - score = cross_validate( - self.model, self.x_train, self.y_train, cv=5, n_jobs=-1, verbose=0 - ) - testing_score = np.max(score["test_score"]) - # testing_score = self.model.fit(self.x_train, self.y_train).accuracy_score(self.x_test, self.y_test) - return {"mean_accuracy": testing_score} - - def save_checkpoint(self, checkpoint_dir): - file_path = checkpoint_dir + "/model" - pickle.dump(self.model, open(file_path, "wb")) - return file_path - - def load_checkpoint(self, path): - # See https://stackoverflow.com/a/42763323 - del self.model - self.model = pickle.load(open(path, "rb")) - - def cleanup(self): - # If need, save your model when exit. - # saved_path = self.model.save(self.logdir) - # print('save model at: ', saved_path) - pass - - -def results(config, x_train, x_test, y_train, y_test, var, output, feature_names): - start1 = time.perf_counter() - config = f_unpack_dict(config) - # self.x_train, self.x_test, self.y_train, self.y_test, self.feature_names = self._read_data(config) - clf_name = "GaussianNB" - clf = GaussianNB(var_smoothing=config.get("var_smoothing", 1e-09)) - # score = cross_validate(clf, x_train, y_train, cv=StratifiedKFold(n_splits=5,shuffle=True,random_state=42), return_train_score=True, return_estimator=True, n_jobs=-1, verbose=0) - clf.fit(x_train, y_train) - train_score = clf.score(x_train, y_train) - with open(f"../tuning/{var}/{clf_name}_tuned_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # training_score = np.mean(score['train_score']) - # testing_score = np.mean(score['test_score']) - y_score = clf.predict(x_test) - prc = precision_score(y_test, y_score, average="weighted") - recall = recall_score(y_test, y_score, average="weighted") - roc_auc = roc_auc_score(y_test, y_score) - accuracy = accuracy_score(y_test, y_score) - matrix = confusion_matrix(y_test, y_score) - finish = (time.perf_counter() - start1) / 60 - # with open(output, 'a') as f: - # f.write(f"AdaBoostClassifier: \nCross_validate(avg_train_score): {training_score}\nCross_validate(avg_test_score): {testing_score}\nPrecision: {prc}\nRecall: {recall}\nROC_AUC: {roc_auc}\nAccuracy: {accuracy}\nTime(in min): {finish}\nConfusion Matrix:\n{matrix}") - - print( - f"Model\ttrain_score\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - print( - f"{clf_name}\t{train_score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del y_test - # explain all the predictions in the test set - background = shap.kmeans(x_train, 10) - explainer = shap.KernelExplainer(clf.predict, background) - del clf, x_train, y_train, background - background = x_test[np.random.choice(x_test.shape[0], 10000, replace=False)] - del x_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"../tuning/{var}/{clf_name}_tuned_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, feature_names - print(f"tuning and testing done!") - return None - - -def wrap_trainable(trainable, x_train, x_test, y_train, y_test): - x_train_id = ray.put(x_train) - x_test_id = ray.put(x_test) - y_train_id = ray.put(y_train) - y_test_id = ray.put(y_test) - - class _Wrapped(trainable): - def setup(self, config): - x_train = ray.get(x_train_id) - x_test = ray.get(x_test_id) - y_train = ray.get(y_train_id) - y_test = ray.get(y_test_id) - - super(_Wrapped, self).setup(config, x_train, x_test, y_train, y_test) - - return _Wrapped - - -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - # Load data - # print(f'\nUsing merged_data-train_{var}..', file=open(output, 'a')) - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - X_train.replace([np.inf, -np.inf], np.nan, inplace=True) - X_train.fillna(0, inplace=True) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - return X_train, X_test, Y_train, Y_test, feature_names - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--var-tag", - "-v", - type=str, - default="nssnv", - help="Var tag used while filtering or untuned models. (Default: nssnv)", - ) - - args = parser.parse_args() - - var = args.var_tag - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - with open("../../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - start = time.perf_counter() - if not os.path.exists("../tuning/" + var): - os.makedirs("../tuning/" + var) - output = "../tuning/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, 'w')) - print("Working with " + var + " dataset...") - x_train, x_test, y_train, y_test, feature_names = data_parsing( - var, config_dict, output - ) - config = {"var_smoothing": hp.loguniform("var_smoothing", 1e-11, 1e-1)} - hyperopt_search = HyperOptSearch(config, metric="mean_accuracy", mode="max") - # scheduler = AsyncHyperBandScheduler() - analysis = run( - wrap_trainable(stacking, x_train, x_test, y_train, y_test), - name=f"GaussianNB_{var}", - verbose=1, - # scheduler=scheduler, - search_alg=hyperopt_search, - reuse_actors=True, - local_dir="../ray_results", - max_failures=2, - # resources_per_trial={ - # 'cpu': 10, - # 'gpu': 1 - # }, - # global_checkpoint_period=np.inf, # Do not save checkpoints based on time interval - checkpoint_freq=50, # Save checkpoint every time the checkpoint_score_attr improves - checkpoint_at_end=True, - keep_checkpoints_num=2, # Keep only the best checkpoint - checkpoint_score_attr="mean_accuracy", # Metric used to compare checkpoints - metric="mean_accuracy", - mode="max", - stop={ - "training_iteration": 1, - }, - num_samples=1000, - # fail_fast=True, - queue_trials=True, - ) - finish = (time.perf_counter() - start) / 120 - # ttime = (finish- start)/120 - print(f"Total time in min: {finish}") - config = analysis.best_config - print( - f"GaussianNB_{var}: {config}", - file=open(f"../tuning/tuned_parameters.csv", "a"), - ) - results(config, x_train, x_test, y_train, y_test, var, output, feature_names) - gc.collect() diff --git a/src/training/training/Tuning/LDA.py b/src/training/training/Tuning/LDA.py deleted file mode 100644 index c847d4b..0000000 --- a/src/training/training/Tuning/LDA.py +++ /dev/null @@ -1,317 +0,0 @@ -import numpy as np -import pandas as pd -import ray -import time -import argparse -import pickle -from joblib import dump -import yaml - -# from ray import tune -from ray.tune import Trainable, run -from hyperopt import hp -from ray.tune.suggest.hyperopt import HyperOptSearch - -# from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import cross_validate, StratifiedKFold -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) -from sklearn.discriminant_analysis import LinearDiscriminantAnalysis -import os -import gc -import shap -from joblib import dump, load -import matplotlib.pyplot as plt -import warnings - -warnings.simplefilter("ignore") -import functools - -print = functools.partial(print, flush=True) - - -TUNE_STATE_REFRESH_PERIOD = 10 # Refresh resources every 10 s - - -def f_unpack_dict(dct): - """ - Unpacks all sub-dictionaries in given dictionary recursively. There should be no duplicated keys - across all nested subdictionaries, or some instances will be lost without warning - - Source: https://www.kaggle.com/fanvacoolt/tutorial-on-hyperopt - - Parameters: - ---------------- - dct : dictionary to unpack - - Returns: - ---------------- - : unpacked dictionary - """ - - res = {} - for (k, v) in dct.items(): - if isinstance(v, dict): - res = {**res, **f_unpack_dict(v)} - else: - res[k] = v - - return res - - -class stacking( - Trainable -): # https://docs.ray.io/en/master/hp/examples/pbt_hp_cifar10_with_keras.html - def setup(self, config, x_train, x_test, y_train, y_test): - self.x_train = x_train - self.x_test = x_test - self.y_train = y_train - self.y_test = y_test - self.config = f_unpack_dict(config) - self.model = LinearDiscriminantAnalysis( - solver=self.config.get("lda_solver", "svd"), - shrinkage=self.config.get("lda_shrinkage", None), - ) - - def reset_config(self, new_config): - self.lda_solver = new_config["lda_solver"] - # self.lda_shrinkage = new_config['lda_shrinkage'] - self.config = new_config - return True - - def step(self): - score = cross_validate( - self.model, self.x_train, self.y_train, cv=10, n_jobs=-1, verbose=0 - ) - testing_score = np.mean(score["test_score"]) - # testing_score = self.model.fit(self.x_train, self.y_train).accuracy_score(self.x_test, self.y_test) - return {"mean_accuracy": testing_score} - - def save_checkpoint(self, checkpoint_dir): - file_path = checkpoint_dir + "/model" - pickle.dump(self.model, open(file_path, "wb")) - return file_path - - def load_checkpoint(self, path): - # See https://stackoverflow.com/a/42763323 - del self.model - self.model = pickle.load(open(path, "rb")) - - def cleanup(self): - # If need, save your model when exit. - # saved_path = self.model.save(self.logdir) - # print('save model at: ', saved_path) - pass - - -def results(config, x_train, x_test, y_train, y_test, var, output, feature_names): - start1 = time.perf_counter() - config = f_unpack_dict(config) - # self.x_train, self.x_test, self.y_train, self.y_test, self.feature_names = self._read_data(config) - clf_name = "LDA" - clf = LinearDiscriminantAnalysis( - solver=config.get("lda_solver", "svd"), - shrinkage=config.get("lda_shrinkage", None), - ) - # score = cross_validate(clf, x_train, y_train, cv=StratifiedKFold(n_splits=5,shuffle=True,random_state=42), return_train_score=True, return_estimator=True, n_jobs=-1, verbose=0) - clf.fit(x_train, y_train) - train_score = clf.score(x_train, y_train) - with open(f"../tuning/{var}/{clf_name}_tuned_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # training_score = np.mean(score['train_score']) - # testing_score = np.mean(score['test_score']) - y_score = clf.predict(x_test) - prc = precision_score(y_test, y_score, average="weighted") - recall = recall_score(y_test, y_score, average="weighted") - roc_auc = roc_auc_score(y_test, y_score) - accuracy = accuracy_score(y_test, y_score) - matrix = confusion_matrix(y_test, y_score) - finish = (time.perf_counter() - start1) / 60 - # with open(output, 'a') as f: - # f.write(f"AdaBoostClassifier: \nCross_validate(avg_train_score): {training_score}\nCross_validate(avg_test_score): {testing_score}\nPrecision: {prc}\nRecall: {recall}\nROC_AUC: {roc_auc}\nAccuracy: {accuracy}\nTime(in min): {finish}\nConfusion Matrix:\n{matrix}") - - print( - f"Model\ttrain_score\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - print( - f"{clf_name}\t{train_score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del y_test - # explain all the predictions in the test set - background = shap.kmeans(x_train, 10) - explainer = shap.KernelExplainer(clf.predict, background) - del clf, x_train, y_train, background - background = x_test[np.random.choice(x_test.shape[0], 10000, replace=False)] - del x_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"../tuning/{var}/{clf_name}_tuned_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, feature_names - print(f"tuning and testing done!") - return None - - -def wrap_trainable(trainable, x_train, x_test, y_train, y_test): - x_train_id = ray.put(x_train) - x_test_id = ray.put(x_test) - y_train_id = ray.put(y_train) - y_test_id = ray.put(y_test) - - class _Wrapped(trainable): - def setup(self, config): - x_train = ray.get(x_train_id) - x_test = ray.get(x_test_id) - y_train = ray.get(y_train_id) - y_test = ray.get(y_test_id) - - super(_Wrapped, self).setup(config, x_train, x_test, y_train, y_test) - - return _Wrapped - - -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - # Load data - # print(f'\nUsing merged_data-train_{var}..', file=open(output, 'a')) - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - X_train.replace([np.inf, -np.inf], np.nan, inplace=True) - X_train.fillna(0, inplace=True) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - return X_train, X_test, Y_train, Y_test, feature_names - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--var-tag", - "-v", - type=str, - default="nssnv", - help="Var tag used while filtering or untuned models. (Default: nssnv)", - ) - - args = parser.parse_args() - - var = args.var_tag - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - with open("../../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - start = time.perf_counter() - if not os.path.exists("../tuning/" + var): - os.makedirs("../tuning/" + var) - output = "../tuning/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, 'w')) - print("Working with " + var + " dataset...") - x_train, x_test, y_train, y_test, feature_names = data_parsing( - var, config_dict, output - ) - config = { - "lda_solver": hp.choice( - "lda_solver", - [ - {"lda_solver": "svd"}, - { - "lda_solver": "lsqr", - "lda_shrinkage": hp.choice( - "shrinkage_type_lsqr", - ["auto", hp.uniform("shrinkage_value_lsqr", 0, 1)], - ), - }, - { - "lda_solver": "eigen", - "lda_shrinkage": hp.choice( - "shrinkage_type_eigen", - ["auto", hp.uniform("shrinkage_value_eigen", 0, 1)], - ), - }, - ], - ), - } - hyperopt_search = HyperOptSearch(config, metric="mean_accuracy", mode="max") - # scheduler = AsyncHyperBandScheduler() - analysis = run( - wrap_trainable(stacking, x_train, x_test, y_train, y_test), - name=f"LDA_{var}", - verbose=1, - # scheduler=scheduler, - search_alg=hyperopt_search, - reuse_actors=True, - local_dir="../ray_results", - max_failures=2, - # resources_per_trial={ - # 'cpu': 10, - # 'gpu': 1 - # }, - # global_checkpoint_period=np.inf, # Do not save checkpoints based on time interval - checkpoint_freq=50, # Save checkpoint every time the checkpoint_score_attr improves - checkpoint_at_end=True, - keep_checkpoints_num=2, # Keep only the best checkpoint - checkpoint_score_attr="mean_accuracy", # Metric used to compare checkpoints - metric="mean_accuracy", - mode="max", - stop={ - "training_iteration": 1, - }, - num_samples=1000, - # fail_fast=True, - queue_trials=True, - ) - finish = (time.perf_counter() - start) / 120 - # ttime = (finish- start)/120 - print(f"Total time in min: {finish}") - config = analysis.best_config - print(f"LDA_{var}: {config}", file=open(f"../tuning/tuned_parameters.csv", "a")) - results(config, x_train, x_test, y_train, y_test, var, output, feature_names) - gc.collect() diff --git a/src/training/training/Tuning/RF.py b/src/training/training/Tuning/RF.py deleted file mode 100644 index 08cb85a..0000000 --- a/src/training/training/Tuning/RF.py +++ /dev/null @@ -1,329 +0,0 @@ -import numpy as np -import pandas as pd -import ray -import time -import argparse -import pickle -from joblib import dump -import yaml - -# from ray import tune -from ray.tune import Trainable, run -from hyperopt import hp -from ray.tune.suggest.hyperopt import HyperOptSearch - -# from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import cross_validate, StratifiedKFold -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) -from sklearn.ensemble import RandomForestClassifier -import os -import gc -import shap -from joblib import dump, load -import matplotlib.pyplot as plt -import warnings - -warnings.simplefilter("ignore") -import functools - -print = functools.partial(print, flush=True) - - -TUNE_STATE_REFRESH_PERIOD = 10 # Refresh resources every 10 s - - -def f_unpack_dict(dct): - """ - Unpacks all sub-dictionaries in given dictionary recursively. There should be no duplicated keys - across all nested subdictionaries, or some instances will be lost without warning - - Source: https://www.kaggle.com/fanvacoolt/tutorial-on-hyperopt - - Parameters: - ---------------- - dct : dictionary to unpack - - Returns: - ---------------- - : unpacked dictionary - """ - - res = {} - for (k, v) in dct.items(): - if isinstance(v, dict): - res = {**res, **f_unpack_dict(v)} - else: - res[k] = v - - return res - - -class stacking( - Trainable -): # https://docs.ray.io/en/master/hp/examples/pbt_hp_cifar10_with_keras.html - def setup(self, config, x_train, x_test, y_train, y_test): - self.x_train = x_train - self.x_test = x_test - self.y_train = y_train - self.y_test = y_test - self.config = f_unpack_dict(config) - self.model = RandomForestClassifier( - random_state=42, - n_estimators=self.config.get("n_estimators", 100), - criterion=self.config.get("criterion", "gini"), - max_depth=self.config.get("max_depth", 2), - min_samples_split=self.config.get("min_samples_split", 2), - min_samples_leaf=self.config.get("min_samples_leaf", 1), - max_features=self.config.get("max_features", "sqrt"), - oob_score=self.config.get("oob_score", False), - class_weight=self.config.get("class_weight", "balanced"), - n_jobs=20, - ) - - def reset_config(self, new_config): - self.n_estimators = new_config["n_estimators"] - self.max_depth = new_config["max_depth"] - self.min_samples_split = new_config["min_samples_split"] - self.min_samples_leaf = new_config["min_samples_leaf"] - self.criterion = new_config["criterion"] - self.max_features = new_config["max_features"] - self.class_weight = new_config["class_weight"] - self.config = new_config - return True - - def step(self): - score = cross_validate( - self.model, self.x_train, self.y_train, cv=10, n_jobs=20, verbose=0 - ) - testing_score = np.mean(score["test_score"]) - # testing_score = self.model.fit(self.x_train, self.y_train).accuracy_score(self.x_test, self.y_test) - return {"mean_accuracy": testing_score} - - def save_checkpoint(self, checkpoint_dir): - file_path = checkpoint_dir + "/model" - pickle.dump(self.model, open(file_path, "wb")) - return file_path - - def load_checkpoint(self, path): - # See https://stackoverflow.com/a/42763323 - del self.model - self.model = pickle.load(open(path, "rb")) - - def cleanup(self): - # If need, save your model when exit. - # saved_path = self.model.save(self.logdir) - # print('save model at: ', saved_path) - pass - - -def results(config, x_train, x_test, y_train, y_test, var, output, feature_names): - start1 = time.perf_counter() - config = f_unpack_dict(config) - # self.x_train, self.x_test, self.y_train, self.y_test, self.feature_names = self._read_data(config) - clf_name = "RandomForest" - clf = RandomForestClassifier( - random_state=42, - n_estimators=config.get("n_estimators", 100), - criterion=config.get("criterion", "gini"), - max_depth=config.get("max_depth", 2), - min_samples_split=config.get("min_samples_split", 2), - min_samples_leaf=config.get("min_samples_leaf", 1), - max_features=config.get("max_features", "sqrt"), - oob_score=config.get("oob_score", False), - class_weight=config.get("class_weight", "balanced"), - n_jobs=-1, - ) - # score = cross_validate(clf, x_train, y_train, cv=StratifiedKFold(n_splits=5,shuffle=True,random_state=42), return_train_score=True, return_estimator=True, n_jobs=-1, verbose=0) - clf.fit(x_train, y_train) - train_score = clf.score(x_train, y_train) - with open(f"../tuning/{var}/{clf_name}_tuned_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # training_score = np.mean(score['train_score']) - # testing_score = np.mean(score['test_score']) - y_score = clf.predict(x_test) - prc = precision_score(y_test, y_score, average="weighted") - recall = recall_score(y_test, y_score, average="weighted") - roc_auc = roc_auc_score(y_test, y_score) - accuracy = accuracy_score(y_test, y_score) - matrix = confusion_matrix(y_test, y_score) - finish = (time.perf_counter() - start1) / 60 - # with open(output, 'a') as f: - # f.write(f"AdaBoostClassifier: \nCross_validate(avg_train_score): {training_score}\nCross_validate(avg_test_score): {testing_score}\nPrecision: {prc}\nRecall: {recall}\nROC_AUC: {roc_auc}\nAccuracy: {accuracy}\nTime(in min): {finish}\nConfusion Matrix:\n{matrix}") - - print( - f"Model\ttrain_score\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - print( - f"{clf_name}\t{train_score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del y_test - # explain all the predictions in the test set - background = shap.kmeans(x_train, 10) - explainer = shap.KernelExplainer(clf.predict, background) - del clf, x_train, y_train, background - background = x_test[np.random.choice(x_test.shape[0], 10000, replace=False)] - del x_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"../tuning/{var}/{clf_name}_tuned_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, feature_names - print(f"tuning and testing done!") - return None - - -def wrap_trainable(trainable, x_train, x_test, y_train, y_test): - x_train_id = ray.put(x_train) - x_test_id = ray.put(x_test) - y_train_id = ray.put(y_train) - y_test_id = ray.put(y_test) - - class _Wrapped(trainable): - def setup(self, config): - x_train = ray.get(x_train_id) - x_test = ray.get(x_test_id) - y_train = ray.get(y_train_id) - y_test = ray.get(y_test_id) - - super(_Wrapped, self).setup(config, x_train, x_test, y_train, y_test) - - return _Wrapped - - -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - # Load data - # print(f'\nUsing merged_data-train_{var}..', file=open(output, 'a')) - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - X_train.replace([np.inf, -np.inf], np.nan, inplace=True) - X_train.fillna(0, inplace=True) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - return X_train, X_test, Y_train, Y_test, feature_names - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--var-tag", - "-v", - type=str, - default="nssnv", - help="Var tag used while filtering or untuned models. (Default: nssnv)", - ) - - args = parser.parse_args() - - var = args.var_tag - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - with open("../../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - start = time.perf_counter() - if not os.path.exists("../tuning/" + var): - os.makedirs("../tuning/" + var) - output = "../tuning/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, 'w')) - print("Working with " + var + " dataset...") - x_train, x_test, y_train, y_test, feature_names = data_parsing( - var, config_dict, output - ) - config = { - "n_estimators": hp.randint("n_estimators", 1, 500), - "min_samples_split": hp.randint("min_samples_split", 2, 100), - "min_samples_leaf": hp.randint("min_samples_leaf", 1, 100), - "criterion": hp.choice("criterion", ["gini", "entropy"]), - "max_features": hp.choice("max_features", ["sqrt", "log2"]), - "class_weight": hp.choice("class_weight", ["balanced", "balanced_subsample"]), - # "oob_score" : hp.choice("oob_score", [True, False]), - "max_depth": hp.randint("max_depth", 2, 500), - } - hyperopt_search = HyperOptSearch(config, metric="mean_accuracy", mode="max") - # scheduler = AsyncHyperBandScheduler() - analysis = run( - wrap_trainable(stacking, x_train, x_test, y_train, y_test), - name=f"RandomForest_{var}", - verbose=1, - # scheduler=scheduler, - search_alg=hyperopt_search, - reuse_actors=True, - local_dir="../ray_results", - max_failures=2, - # resources_per_trial={ - # 'cpu': 10, - # 'gpu': 1 - # }, - # global_checkpoint_period=np.inf, # Do not save checkpoints based on time interval - checkpoint_freq=50, # Save checkpoint every time the checkpoint_score_attr improves - checkpoint_at_end=True, - keep_checkpoints_num=2, # Keep only the best checkpoint - checkpoint_score_attr="mean_accuracy", # Metric used to compare checkpoints - metric="mean_accuracy", - mode="max", - stop={ - "training_iteration": 1, - }, - num_samples=1000, - # fail_fast=True, - queue_trials=True, - ) - finish = (time.perf_counter() - start) / 120 - # ttime = (finish- start)/120 - print(f"Total time in min: {finish}") - config = analysis.best_config - print( - f"RandomForest_{var}: {config}", - file=open(f"../tuning/tuned_parameters.csv", "a"), - ) - results(config, x_train, x_test, y_train, y_test, var, output, feature_names) - gc.collect() diff --git a/src/training/training/Tuning/stacking.py b/src/training/training/Tuning/stacking.py deleted file mode 100644 index c8cc3f0..0000000 --- a/src/training/training/Tuning/stacking.py +++ /dev/null @@ -1,625 +0,0 @@ -import numpy as np -import pandas as pd -import ray -import time -import argparse -import pickle -from joblib import dump -import yaml - -# from ray import tune -from ray.tune import Trainable, run -from hyperopt import hp -from ray.tune.suggest.hyperopt import HyperOptSearch - -# from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import cross_validate # , StratifiedKFold -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) -from sklearn.tree import DecisionTreeClassifier -from sklearn.linear_model import LogisticRegression # SGDClassifier, -from sklearn.ensemble import ( - RandomForestClassifier, - AdaBoostClassifier, - GradientBoostingClassifier, - ExtraTreesClassifier, - StackingClassifier, -) -from sklearn.naive_bayes import GaussianNB -from imblearn.ensemble import BalancedRandomForestClassifier -from sklearn.discriminant_analysis import LinearDiscriminantAnalysis -from ray.tune.schedulers import AsyncHyperBandScheduler -import os -import gc -import shap -from joblib import dump, load -import matplotlib.pyplot as plt -import warnings - -warnings.simplefilter("ignore") -import functools - -print = functools.partial(print, flush=True) - -TUNE_STATE_REFRESH_PERIOD = 10 # Refresh resources every 10 s - - -def f_unpack_dict(dct): - """ - Unpacks all sub-dictionaries in given dictionary recursively. There should be no duplicated keys - across all nested subdictionaries, or some instances will be lost without warning - - Source: https://www.kaggle.com/fanvacoolt/tutorial-on-hyperopt - - Parameters: - ---------------- - dct : dictionary to unpack - - Returns: - ---------------- - : unpacked dictionary - """ - - res = {} - for (k, v) in dct.items(): - if isinstance(v, dict): - res = {**res, **f_unpack_dict(v)} - else: - res[k] = v - - return res - - -class stacking( - Trainable -): # https://docs.ray.io/en/master/hp/examples/pbt_hp_cifar10_with_keras.html - def setup(self, config, x_train, x_test, y_train, y_test): - self.x_train = x_train - self.x_test = x_test - self.y_train = y_train - self.y_test = y_test - self.config = f_unpack_dict(config) - self.model = StackingClassifier( - estimators=[ - ( - "dt", - DecisionTreeClassifier( - random_state=42, - criterion=self.config.get("dt__criterion", "gini"), - splitter=self.config.get("dt__splitter", "best"), - max_depth=self.config.get("dt__max_depth", 2), - min_samples_split=self.config.get("dt__min_samples_split", 2), - min_samples_leaf=self.config.get("dt__min_samples_leaf", 1), - max_features=self.config.get("dt__max_features", "sqrt"), - class_weight=self.config.get("dt__class_weight", "balanced"), - ), - ), - ( - "rf", - RandomForestClassifier( - random_state=42, - n_estimators=self.config.get("rf__n_estimators", 100), - criterion=self.config.get("rf__criterion", "gini"), - max_depth=self.config.get("rf__max_depth", 2), - min_samples_split=self.config.get("rf__min_samples_split", 2), - min_samples_leaf=self.config.get("rf__min_samples_leaf", 1), - max_features=self.config.get("rf__max_features", "sqrt"), - oob_score=self.config.get("rf__oob_score", False), - class_weight=self.config.get("rf__class_weight", "balanced"), - n_jobs=20, - ), - ), - ( - "brf", - BalancedRandomForestClassifier( - random_state=42, - n_estimators=self.config.get("brf__n_estimators", 100), - criterion=self.config.get("brf__criterion", "gini"), - max_depth=self.config.get("brf__max_depth", 2), - min_samples_split=self.config.get("brf__min_samples_split", 2), - min_samples_leaf=self.config.get("brf__min_samples_leaf", 1), - max_features=self.config.get("brf__max_features", "sqrt"), - oob_score=self.config.get("brf__oob_score", False), - class_weight=self.config.get("brf__class_weight", "balanced"), - n_jobs=20, - ), - ), - ( - "ada", - AdaBoostClassifier( - algorithm=self.config.get("ada__algorithm", "SAMME"), - base_estimator=DecisionTreeClassifier( - max_depth=self.config.get("ada__max_depth", 1) - ), - learning_rate=self.config.get("ada__learning_rate", 1), - n_estimators=self.config.get("ada__n_estimators", 5), - ), - ), - # ('et', ExtraTreesClassifier(random_state=42, n_estimators=self.config.get('et__n_estimators', 100), criterion=self.config.get('et__criterion','gini'), max_depth=self.config.get('et__max_depth', 2), min_samples_split=self.config.get('et__min_samples_split',2), min_samples_leaf=self.config.get('et__min_samples_leaf',1), max_features=self.config.get('et__max_features','sqrt'), oob_score=self.config.get('et__oob_score',False), class_weight=self.config.get('et__class_weight','balanced'), n_jobs = 20)), - ( - "gnb", - GaussianNB( - var_smoothing=self.config.get("gnb__var_smoothing", 1e-09) - ), - ), - # ('lda', LinearDiscriminantAnalysis(solver=self.config.get('lda__solver', 'svd'), shrinkage=self.config.get('lda__shrinkage', None))), - ( - "gbc", - GradientBoostingClassifier( - random_state=42, - loss=self.config.get("gbc__loss", 100), - learning_rate=self.config.get("gbc__learning_rate", 0.1), - n_estimators=self.config.get("gbc__n_estimators", 100), - subsample=self.config.get("gbc__subsample", 1), - criterion=self.config.get("gbc__criterion", "friedman_mse"), - min_samples_split=self.config.get("gbc__min_samples_split", 2), - min_samples_leaf=self.config.get("gbc__min_samples_leaf", 1), - max_depth=self.config.get("gbc__max_depth", 2), - max_features=self.config.get("gbc__max_features", "sqrt"), - ), - ), - ], - cv=5, - stack_method="predict_proba", - n_jobs=-1, - passthrough=False, - final_estimator=LogisticRegression( - C=self.config.get("lr__C", 1), - penalty=self.config.get("lr__penalty", "l2"), - solver=self.config.get("lr__solver", "lbfgs"), - max_iter=self.config.get("lr__max_iter", 100), - l1_ratio=self.config.get("lr__l1_ratio", 0), - tol=self.config.get("lr__tol", 1e-4), - n_jobs=-1, - ), - verbose=0, - ) # .set_params(**f_unpack_dict(config)) - - def reset_config(self, new_config): - self.dt__criterion = new_config["dt__criterion"] - self.dt__splitter = new_config["dt__splitter"] - self.dt__max_depth = new_config["dt__max_depth"] - self.dt__min_samples_split = new_config["dt__min_samples_split"] - self.dt__min_samples_leaf = new_config["dt__min_samples_leaf"] - self.dt__max_features = new_config["dt__max_features"] - self.dt__class_weight = new_config["dt__class_weight"] - self.rf__n_estimators = new_config["rf__n_estimators"] - self.rf__criterion = new_config["rf__criterion"] - self.rf__max_depth = new_config["rf__max_depth"] - self.rf__min_samples_split = new_config["rf__min_samples_split"] - self.rf__min_samples_leaf = new_config["rf__min_samples_leaf"] - self.rf__max_features = new_config["rf__max_features"] - # self.rf__oob_score = new_config['rf__oob_score'] - self.rf__class_weight = new_config["rf__class_weight"] - self.brf__n_estimators = new_config["brf__n_estimators"] - self.brf__criterion = new_config["brf__criterion"] - self.brf__max_depth = new_config["brf__max_depth"] - self.brf__min_samples_split = new_config["brf__min_samples_split"] - self.brf__min_samples_leaf = new_config["brf__min_samples_leaf"] - self.brf__max_features = new_config["brf__max_features"] - # self.brf__oob_score = new_config['brf__oob_score'] - self.brf__class_weight = new_config["brf__class_weight"] - self.ada__algorithm = new_config["ada__algorithm"] - self.ada__max_depth = new_config["ada__max_depth"] - self.ada__learning_rate = new_config["ada__learning_rate"] - self.ada__n_estimators = new_config["ada__n_estimators"] - # self.et__n_estimators = new_config['et__n_estimators'] - # self.et__min_samples_split = new_config['et__min_samples_split'] - # self.et__min_samples_leaf = new_config['et__min_samples_leaf'] - # self.et__criterion = new_config['et__criterion'] - # self.et__max_features = new_config['et__max_features'] - # self.et__class_weight = new_config['et__class_weight'] - self.gnb__var_smoothing = new_config["gnb__var_smoothing"] - # self.lda__solver = new_config['lda__solver'] - # self.lda_shrinkage = new_config['lda_shrinkage'] - self.gbc__loss = new_config["gbc__loss"] - self.gbc__learning_rate = new_config["gbc__learning_rate"] - self.gbc__n_estimators = new_config["gbc__n_estimators"] - self.gbc__subsample = new_config["gbc__subsample"] - self.gbc__criterion = new_config["gbc__criterion"] - self.gbc__min_samples_split = new_config["gbc__min_samples_split"] - self.gbc__min_samples_leaf = new_config["gbc__min_samples_leaf"] - self.gbc__max_depth = new_config["gbc__max_depth"] - self.gbc__max_features = new_config["gbc__max_features"] - self.lr__C = new_config["lr__C"] - self.lr__solver = new_config["lr__solver"] - self.lr__penalty = new_config["lr__penalty"] - self.lr__tol = new_config["lr__tol"] - self.lr__l1_ratio = new_config["lr__l1_ratio"] - self.lr__max_iter = new_config["lr__max_iter"] - self.config = new_config - return True - - def step(self): - # score = cross_validate(self.model, self.x_train, self.y_train, cv=3, n_jobs=-1, verbose=0) - # testing_score = np.max(score['test_score']) - testing_score = self.model.fit(self.x_train, self.y_train).score( - self.x_test, self.y_test - ) - return {"mean_accuracy": testing_score} - - def save_checkpoint(self, checkpoint_dir): - file_path = checkpoint_dir + "/model" - pickle.dump(self.model, open(file_path, "wb")) - return file_path - - def load_checkpoint(self, path): - # See https://stackoverflow.com/a/42763323 - del self.model - self.model = pickle.load(open(path, "rb")) - - def cleanup(self): - # If need, save your model when exit. - # saved_path = self.model.save(self.logdir) - # print('save model at: ', saved_path) - pass - - -def results(config, x_train, x_test, y_train, y_test, var, output, feature_names): - start1 = time.perf_counter() - config = f_unpack_dict(config) - # self.x_train, self.x_test, self.y_train, self.y_test, self.feature_names = self._read_data(config) - clf = StackingClassifier( - estimators=[ - ( - "dt", - DecisionTreeClassifier( - random_state=42, - criterion=config.get("dt__criterion", "gini"), - splitter=config.get("dt__splitter", "best"), - max_depth=config.get("dt__max_depth", 2), - min_samples_split=config.get("dt__min_samples_split", 2), - min_samples_leaf=config.get("dt__min_samples_leaf", 1), - max_features=config.get("dt__max_features", "sqrt"), - class_weight=config.get("dt__class_weight", "balanced"), - ), - ), - ( - "rf", - RandomForestClassifier( - random_state=42, - n_estimators=config.get("rf__n_estimators", 100), - criterion=config.get("rf__criterion", "gini"), - max_depth=config.get("rf__max_depth", 2), - min_samples_split=config.get("rf__min_samples_split", 2), - min_samples_leaf=config.get("rf__min_samples_leaf", 1), - max_features=config.get("rf__max_features", "sqrt"), - oob_score=config.get("rf__oob_score", False), - class_weight=config.get("rf__class_weight", "balanced"), - n_jobs=-1, - ), - ), - ( - "brf", - BalancedRandomForestClassifier( - random_state=42, - n_estimators=config.get("brf__n_estimators", 100), - criterion=config.get("brf__criterion", "gini"), - max_depth=config.get("brf__max_depth", 2), - min_samples_split=config.get("brf__min_samples_split", 2), - min_samples_leaf=config.get("brf__min_samples_leaf", 1), - max_features=config.get("brf__max_features", "sqrt"), - oob_score=config.get("brf__oob_score", False), - class_weight=config.get("brf__class_weight", "balanced"), - n_jobs=-1, - ), - ), - ( - "ada", - AdaBoostClassifier( - algorithm=config.get("ada__algorithm", "SAMME"), - base_estimator=DecisionTreeClassifier( - max_depth=config.get("ada__max_depth", 1) - ), - learning_rate=config.get("ada__learning_rate", 1), - n_estimators=config.get("ada__n_estimators", 5), - ), - ), - # ('et', ExtraTreesClassifier(random_state=42, n_estimators=config.get('et__n_estimators', 100), criterion=config.get('et__criterion','gini'), max_depth=config.get('et__max_depth', 2), min_samples_split=config.get('et__min_samples_split',2), min_samples_leaf=config.get('et__min_samples_leaf',1), max_features=config.get('et__max_features','sqrt'), oob_score=config.get('et__oob_score',False), class_weight=config.get('et__class_weight','balanced'), n_jobs = -1)), - ("gnb", GaussianNB(var_smoothing=config.get("gnb__var_smoothing", 1e-09))), - # ('lda', LinearDiscriminantAnalysis(solver=config.get('lda__solver', 'svd'), shrinkage=config.get('lda__shrinkage', None))), - ( - "gbc", - GradientBoostingClassifier( - random_state=42, - loss=config.get("gbc__loss", 100), - learning_rate=config.get("gbc__learning_rate", 0.1), - n_estimators=config.get("gbc__n_estimators", 100), - subsample=config.get("gbc__subsample", 1), - criterion=config.get("gbc__criterion", "friedman_mse"), - min_samples_split=config.get("gbc__min_samples_split", 2), - min_samples_leaf=config.get("gbc__min_samples_leaf", 1), - max_depth=config.get("gbc__max_depth", 2), - max_features=config.get("gbc__max_features", "sqrt"), - ), - ), - ], - cv=5, - stack_method="predict_proba", - n_jobs=-1, - passthrough=False, - final_estimator=LogisticRegression( - C=config.get("lr__C", 1), - penalty=config.get("lr__penalty", "l2"), - solver=config.get("lr__solver", "lbfgs"), - max_iter=config.get("lr__max_iter", 100), - l1_ratio=config.get("lr__l1_ratio", 0), - tol=config.get("lr__tol", 1e-4), - n_jobs=-1, - ), - verbose=0, - ).fit(x_train, y_train) - # score = cross_validate(clf, x_train, y_train, cv=StratifiedKFold(n_splits=5,shuffle=True,random_state=42), return_train_score=True, return_estimator=True, n_jobs=-1, verbose=0) - clf_name = "StackingClassifier" - train_score = clf.score(x_train, y_train) - with open(f"../tuning/{var}/{clf_name}_tuned_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # training_score = np.mean(score['train_score']) - # testing_score = np.mean(score['test_score']) - y_score = clf.predict(x_test) - prc = precision_score(y_test, y_score, average="weighted") - recall = recall_score(y_test, y_score, average="weighted") - roc_auc = roc_auc_score(y_test, y_score) - accuracy = score(y_test, y_score) - matrix = confusion_matrix(y_test, y_score) - finish = (time.perf_counter() - start1) / 60 - # print(f'RandomForestClassifier: \nCross_validate(avg_train_score): {training_score}\nCross_validate(avg_test_score): {testing_score}\nPrecision: {prc}\nRecall: {recall}\nROC_AUC: {roc_auc}\nAccuracy: {accuracy}\nTime(in min): {finish}\nConfusion Matrix:\n{matrix}', file=open(output, 'a')) - - print( - "Model\ttrain_score\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - print( - f"{clf_name}\t{train_score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del y_test - # explain all the predictions in the test set - background = shap.kmeans(x_train, 10) - explainer = shap.KernelExplainer(clf.predict, background) - del clf, x_train, y_train, background - background = x_test[np.random.choice(x_test.shape[0], 10000, replace=False)] - del x_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"../tuning/{var}/{clf_name}_tuned_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, feature_names - print(f"training and testing done!") - return None - - -def wrap_trainable(trainable, x_train, x_test, y_train, y_test): - x_train_id = ray.put(x_train) - x_test_id = ray.put(x_test) - y_train_id = ray.put(y_train) - y_test_id = ray.put(y_test) - - class _Wrapped(trainable): - def setup(self, config): - x_train = ray.get(x_train_id) - x_test = ray.get(x_test_id) - y_train = ray.get(y_train_id) - y_test = ray.get(y_test_id) - - super(_Wrapped, self).setup(config, x_train, x_test, y_train, y_test) - - return _Wrapped - - -def data_parsing(var, config_dict, output): - # Load data - print(f"\nUsing merged_data-train_{var}..", file=open(output, "a")) - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - X_train.replace([np.inf, -np.inf], np.nan, inplace=True) - X_train.fillna(0, inplace=True) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - return X_train, X_test, Y_train, Y_test, feature_names - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--var-tag", - "-v", - type=str, - required=True, - default="nssnv", - help="The tag used when generating train/test data. Default:'nssnv'", - ) - - args = parser.parse_args() - - var = args.var_tag - - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - with open("../../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - # variants = ['non_snv','snv','snv_protein_coding'] - # for var in variants: - - start = time.perf_counter() - if not os.path.exists("../tuning/" + var): - os.makedirs("../tuning/" + var) - output = "../tuning/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, 'w')) - print("Working with " + var + " dataset...") - - x_train, x_test, y_train, y_test, feature_names = data_parsing( - var, config_dict, output - ) - - config = { - # DecisionTree - https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier - "dt__criterion": hp.choice("dt__criterion", ["gini", "entropy"]), - "dt__splitter": hp.choice("dt__splitter", ["best", "random"]), - "dt__max_depth": hp.randint("dt__max_depth", 2, 500), - "dt__min_samples_split": hp.randint("dt__min_samples_split", 2, 100), - "dt__min_samples_leaf": hp.randint("dt__min_samples_leaf", 1, 100), - "dt__max_features": hp.choice("dt__max_features", ["sqrt", "log2"]), - "dt__class_weight": hp.choice("dt__class_weight", ["balanced"]), - # RandomForest - https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html?highlight=randomforestclassifier#sklearn.ensemble.RandomForestClassifier - "rf__n_estimators": hp.randint("rf__n_estimators", 1, 500), - "rf__criterion": hp.choice("rf__criterion", ["gini", "entropy"]), - "rf__max_depth": hp.randint("rf__max_depth", 2, 500), - "rf__min_samples_split": hp.randint("rf__min_samples_split", 2, 100), - "rf__min_samples_leaf": hp.randint("rf__min_samples_leaf", 1, 100), - "rf__max_features": hp.choice("rf__max_features", ["sqrt", "log2"]), - #'rf__oob_score' : hp.choice('rf__oob_score', [True, False]), - "rf__class_weight": hp.choice( - "rf__class_weight", ["balanced", "balanced_subsample"] - ), - # BalancedRandomForest - https://imbalanced-learn.org/dev/references/generated/imblearn.ensemble.BalancedRandomForestClassifier.html - "brf__n_estimators": hp.randint("brf__n_estimators", 1, 500), - "brf__criterion": hp.choice("brf__criterion", ["gini", "entropy"]), - "brf__max_depth": hp.randint("brf__max_depth", 2, 500), - "brf__min_samples_split": hp.randint("brf__min_samples_split", 2, 100), - "brf__min_samples_leaf": hp.randint("brf__min_samples_leaf", 1, 100), - "brf__max_features": hp.choice("brf__max_features", ["sqrt", "log2"]), - #'brf__oob_score' : hp.choice('brf__oob_score', [True, False]), - "brf__class_weight": hp.choice( - "brf__class_weight", ["balanced", "balanced_subsample"] - ), - # AdaBoost - - "ada__n_estimators": hp.randint("ada__n_estimators", 1, 500), - "ada__algorithm": hp.choice("ada__algorithm", ["SAMME", "SAMME.R"]), - "ada__learning_rate": hp.loguniform("ada__learning_rate", 0.00001, 1.0), - "ada__max_depth": hp.randint("ada__max_depth", 2, 500), - ##ExtraTrees - - # "et__n_estimators" : hp.randint("et__n_estimators", 1, 500), - # "et__min_samples_split" : hp.randint("et__min_samples_split", 2, 100), - # "et__min_samples_leaf" : hp.randint("et__min_samples_leaf", 1, 100), - # "et__criterion" : hp.choice("et__criterion", ["gini", "entropy"]), - # "et__max_features" : hp.choice("et__max_features", ["sqrt", "log2"]), - # "et__class_weight" : hp.choice("et__class_weight", ["balanced", "balanced_subsample"]), - # GaussianNB - https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB - "gnb__var_smoothing": hp.loguniform("gnb__var_smoothing", 1e-11, 1e-1), - ##LinearDiscriminantAnalysis - https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis - # 'lda__solver': hp.choice('lda__solver', [ - # {'lda__solver':'svd'}, - # {'lda__solver':'lsqr','lda__shrinkage':hp.choice('shrinkage_type_lsqr', ['auto', hp.uniform('shrinkage_value_lsqr', 0, 1)])} - # ,{'lda__solver':'eigen','lda__shrinkage':hp.choice('shrinkage_type_eigen', ['auto', hp.uniform('shrinkage_value_eigen', 0, 1)])} - # ]), - # GradientBoostingClassifier - https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier - "gbc__loss": hp.choice("gbc__loss", ["deviance", "exponential"]), - "gbc__learning_rate": hp.loguniform("gbc__learning_rate", 0.01, 1.0), - "gbc__n_estimators": hp.randint("gbc__n_estimators", 1, 200), - "gbc__subsample": hp.uniform("gbc__subsample", 0.1, 1.0), - "gbc__criterion": hp.choice("gbc__criterion", ["friedman_mse", "mse"]), - "gbc__min_samples_split": hp.randint("gbc__min_samples_split", 2, 100), - "gbc__min_samples_leaf": hp.randint("gbc__min_samples_leaf", 1, 100), - "gbc__max_depth": hp.randint("gbc__max_depth", 2, 200), - "gbc__max_features": hp.choice("gbc__max_features", ["sqrt", "log2"]), - # LogisticRegression - https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic#sklearn.linear_model.LogisticRegression; https://github.com/hyperopt/hyperopt/issues/304 - "lr__C": hp.uniform("lr__C", 0.0, 100.0), - "lr__solver": hp.choice( - "lr__solver", - [ - { - "lr__solver": "newton-cg", - "lr__penalty": hp.choice("p_newton", ["none", "l2"]), - }, - { - "lr__solver": "lbfgs", - "lr__penalty": hp.choice("p_lbfgs", ["none", "l2"]), - }, - { - "lr__solver": "liblinear", - "lr__penalty": hp.choice("p_lib", ["l1", "l2"]), - }, - { - "lr__solver": "sag", - "lr__penalty": hp.choice("p_sag", ["l2", "none"]), - }, - { - "lr__solver": "saga", - "lr__penalty": "elasticnet", - "lr__l1_ratio": hp.uniform("lr__l1_ratio", 0, 1), - }, - ], - ), - "lr__tol": hp.loguniform("lr__tol", 1e-13, 1e-1), - "lr__max_iter": hp.randint("lr__max_iter", 2, 100), - } - - hyperopt_search = HyperOptSearch(config, metric="mean_accuracy", mode="max") - scheduler = AsyncHyperBandScheduler() - - analysis = run( - wrap_trainable(stacking, x_train, x_test, y_train, y_test), - name=f"StackingClassifier_{var}", - verbose=1, - scheduler=scheduler, - search_alg=hyperopt_search, - reuse_actors=True, - local_dir="./ray_results", - max_failures=2, - # resources_per_trial={ - # 'cpu': 10, - # 'gpu': 1 - # }, - # global_checkpoint_period=np.inf, # Do not save checkpoints based on time interval - checkpoint_freq=50, # Save checkpoint every time the checkpoint_score_attr improves - checkpoint_at_end=True, - keep_checkpoints_num=2, # Keep only the best checkpoint - checkpoint_score_attr="mean_accuracy", # Metric used to compare checkpoints - metric="mean_accuracy", - mode="max", - stop={ - "training_iteration": 1, - }, - num_samples=1500, - # fail_fast=True, - queue_trials=True, - ) - - finish = (time.perf_counter() - start) / 120 - # ttime = (finish- start)/120 - print(f"Total time in min: {finish}") - config = analysis.best_config - print( - f"StackingClassifier_{var}: {config}", - file=open(f"../tuning/tuned_parameters.csv", "a"), - ) - results(config, x_train, x_test, y_train, y_test, var, output, feature_names) - gc.collect() diff --git a/src/training/training/stacking.py b/src/training/training/stacking.py deleted file mode 100644 index 4d03b9a..0000000 --- a/src/training/training/stacking.py +++ /dev/null @@ -1,209 +0,0 @@ -# python slurm-launch.py --exp-name Training --command "python training/training/stacking.py" --partition express --mem 50G - -# from numpy import mean -import numpy as np -import pandas as pd -import time -import argparse -import ray - -# Start Ray. -ray.init(ignore_reinit_error=True) -import warnings - -warnings.simplefilter("ignore") -from joblib import dump, load -import shap - -# from sklearn.preprocessing import StandardScaler -# from sklearn.feature_selection import RFE -# from sklearn.preprocessing import MinMaxScaler -from sklearn.model_selection import train_test_split, cross_validate -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - average_precision_score, - recall_score, -) - -# from sklearn.multiclass import OneVsRestClassifier -from sklearn.tree import DecisionTreeClassifier -from sklearn.linear_model import LogisticRegression -from sklearn.ensemble import ( - RandomForestClassifier, - AdaBoostClassifier, - GradientBoostingClassifier, - ExtraTreesClassifier, - StackingClassifier, -) -from sklearn.naive_bayes import GaussianNB -from imblearn.ensemble import BalancedRandomForestClassifier -from sklearn.neural_network import MLPClassifier -from sklearn.discriminant_analysis import LinearDiscriminantAnalysis -import matplotlib.pyplot as plt -import yaml -import functools - -print = functools.partial(print, flush=True) -import gc -import os - -os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" -) - -TUNE_STATE_REFRESH_PERIOD = 10 # Refresh resources every 10 s - -def data_parsing(var, config_dict): - # Load data - # print(f'\nUsing merged_data-train_{var}..', file=open(output, "a")) - X_train = pd.read_csv(f"train_custom_data-{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["var"], axis=1) - feature_names = X_train.columns.tolist() - #X_train = X_train.sample(frac=1).reset_index(drop=True) - X_train = X_train.values - Y_train = pd.read_csv(f"train_custom_data-y-{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_custom_data-{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["var"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_custom_data-y-{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - background = shap.kmeans(X_train, 10) - return X_train, X_test, Y_train, Y_test, background, feature_names - - -# @ray.remote #(num_cpus=9) -def classifier( - clf, var, X_train, X_test, Y_train, Y_test, background, feature_names, output -): - start = time.perf_counter() - # score = cross_validate(clf, X_train, Y_train, cv=10, return_train_score=True, return_estimator=True, n_jobs=-1, verbose=0, scoring=('roc_auc','neg_log_loss')) - # clf = score['estimator'][np.argmin(score['test_neg_log_loss'])] - # y_score = cross_val_predict(clf, X_train, Y_train, cv=5, n_jobs=-1, verbose=0) - # class_weights = class_weight.compute_class_weight('balanced', np.unique(Y_train), Y_train) - clf.fit(X_train, Y_train) # , class_weight=class_weights) - clf_name = "StackingClassifier" - with open(f"./models_custom/{var}/{clf_name}_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # del clf - # with open(f"./models_custom/{var}/{clf_name}_{var}.joblib", 'rb') as f: - # clf = load(f) - train_score = clf.score(X_train, Y_train) - y_score = clf.predict(X_test) - prc = precision_score(Y_test, y_score, average="weighted") - recall = recall_score(Y_test, y_score, average="weighted") - roc_auc = roc_auc_score(Y_test, y_score) - prc_auc = average_precision_score(Y_test, y_score, average="weighted") - # roc_auc = roc_auc_score(Y_test, np.argmax(y_score, axis=1)) - accuracy = accuracy_score(Y_test, y_score) - # score = clf.score(X_train, Y_train) - # matrix = confusion_matrix(np.argmax(Y_test, axis=1), np.argmax(y_score, axis=1)) - matrix = confusion_matrix(Y_test, y_score) - finish = (time.perf_counter() - start) / 60 - with open(output, "a") as f: - f.write( - f"{clf_name}\t{train_score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}\n" - ) - - # explain all the predictions in the test set - # background = shap.kmeans(X_train, 6) - explainer = shap.KernelExplainer(clf.predict, background) - del clf, X_train - background = X_test[np.random.choice(X_test.shape[0], 10000, replace=False)] - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, max_display = 50, show=False) - # shap.plots.waterfall(shap_values[0], max_display=15) - plt.savefig( - f"./models_custom/{var}/{clf_name}_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - return None - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--var-tag", - "-v", - type=str, - #required=True, - default="dbnsfp", - help="The tag used when generating train/test data. Default:'dbnsfp'", - ) - - args = parser.parse_args() - - # Classifiers I wish to use - classifiers = StackingClassifier( - estimators=[ - #("DecisionTree", DecisionTreeClassifier(class_weight="balanced")), - #( - # "RandomForest", - # RandomForestClassifier(class_weight="balanced", n_jobs=-1), - #), - ("BalancedRF", BalancedRandomForestClassifier()), - #("AdaBoost", AdaBoostClassifier()), - ("ExtraTrees", ExtraTreesClassifier(class_weight='balanced', n_jobs=-1)), - #("GaussianNB", GaussianNB()), - ("LDA", LinearDiscriminantAnalysis()), - ("GradientBoost", GradientBoostingClassifier()), - ("MLP", MLPClassifier()) - ], - cv=5, - stack_method="predict_proba", - n_jobs=-1, - passthrough=False, - final_estimator=LogisticRegression(n_jobs=-1), - verbose=1, - ) - - with open("../../configs/col_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - var = args.var_tag - if not os.path.exists("./models_custom/" + var): - os.makedirs("./models_custom/" + var) - output = "./models_custom/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, "a")) - print("Working with " + var + " dataset...") - X_train, X_test, Y_train, Y_test, background, feature_names = data_parsing( - var, config_dict - ) - with open(output, "a") as f: - f.write( - "Model\ttrain_score\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]\n" - ) - classifier( - classifiers, - var, - X_train, - X_test, - Y_train, - Y_test, - background, - feature_names, - output, - ) - gc.collect() diff --git a/src/training/training/stacking_LC.py b/src/training/training/stacking_LC.py deleted file mode 100644 index b7e50bd..0000000 --- a/src/training/training/stacking_LC.py +++ /dev/null @@ -1,196 +0,0 @@ -# python slurm-launch.py --exp-name Training --command "python training/training/stacking_LC.py" --partition short --mem 50G - -# from numpy import mean -import numpy as np -import pandas as pd -import time -import argparse -import warnings - -warnings.simplefilter("ignore") - -from sklearn.model_selection import learning_curve -from sklearn.preprocessing import label_binarize - -from sklearn.tree import DecisionTreeClassifier -from sklearn.linear_model import LogisticRegression -from sklearn.ensemble import ( - RandomForestClassifier, - AdaBoostClassifier, - GradientBoostingClassifier, - ExtraTreesClassifier, - StackingClassifier, -) -from sklearn.naive_bayes import GaussianNB -from imblearn.ensemble import BalancedRandomForestClassifier -from sklearn.neural_network import MLPClassifier -from sklearn.discriminant_analysis import LinearDiscriminantAnalysis -import matplotlib.pyplot as plt -import yaml -import functools - -print = functools.partial(print, flush=True) -import gc -import os - -os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" -) - - -def data_parsing(var, config_dict): - # Load data - # print(f'\nUsing merged_data-train_{var}..', file=open(output, "a")) - X_train = pd.read_csv(f"train_custom_data-{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["var"], axis=1) - #X_train = X_train.sample(frac=1).reset_index(drop=True) - X_train = X_train.values - Y_train = pd.read_csv(f"train_custom_data-y-{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - return X_train, Y_train - - -def classifier( - clf, var, X, y -): - _, axes = plt.subplots(1, 3, figsize=(20, 5)) - - axes[0].set_title("Learning Curves") - axes[0].set_xlabel("Training examples") - axes[0].set_ylabel("Score") - - train_sizes, train_scores, test_scores, fit_times, _ = learning_curve( - clf, - X, - y, - cv=5, - n_jobs=-1, - scoring="neg_mean_squared_error", - return_times=True, - train_sizes=np.linspace(0.001, 1.0, 10), - ) - train_scores_mean = np.mean(train_scores, axis=1) - train_scores_std = np.std(train_scores, axis=1) - test_scores_mean = np.mean(test_scores, axis=1) - test_scores_std = np.std(test_scores, axis=1) - fit_times_mean = np.mean(fit_times, axis=1) - fit_times_std = np.std(fit_times, axis=1) - - # Plot learning curve - axes[0].grid() - axes[0].fill_between( - train_sizes, - train_scores_mean - train_scores_std, - train_scores_mean + train_scores_std, - alpha=0.1, - color="r", - ) - axes[0].fill_between( - train_sizes, - test_scores_mean - test_scores_std, - test_scores_mean + test_scores_std, - alpha=0.1, - color="g", - ) - axes[0].plot( - train_sizes, train_scores_mean, "o-", color="r", label="Training score" - ) - axes[0].plot( - train_sizes, test_scores_mean, "o-", color="g", label="Cross-validation score" - ) - axes[0].legend(loc="best") - - # Plot n_samples vs fit_times - axes[1].grid() - axes[1].plot(train_sizes, fit_times_mean, "o-") - axes[1].fill_between( - train_sizes, - fit_times_mean - fit_times_std, - fit_times_mean + fit_times_std, - alpha=0.1, - ) - axes[1].set_xlabel("Training examples") - axes[1].set_ylabel("fit_times") - axes[1].set_title("Scalability of the model") - - # Plot fit_time vs score - fit_time_argsort = fit_times_mean.argsort() - fit_time_sorted = fit_times_mean[fit_time_argsort] - test_scores_mean_sorted = test_scores_mean[fit_time_argsort] - test_scores_std_sorted = test_scores_std[fit_time_argsort] - axes[2].grid() - axes[2].plot(fit_time_sorted, test_scores_mean_sorted, "o-") - axes[2].fill_between( - fit_time_sorted, - test_scores_mean_sorted - test_scores_std_sorted, - test_scores_mean_sorted + test_scores_std_sorted, - alpha=0.1, - ) - axes[2].set_xlabel("fit_times") - axes[2].set_ylabel("Score") - axes[2].set_title("Performance of the model") - plt.savefig( "./models_custom/" + var + "/Stacking_LC_" + var +".png") - plt.close() - - return None - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--var-tag", - "-v", - type=str, - #required=True, - default="dbnsfp", - help="The tag used when generating train/test data. Default:'dbnsfp'", - ) - - args = parser.parse_args() - - # Classifiers I wish to use - classifiers = StackingClassifier( - estimators=[ - #("DecisionTree", DecisionTreeClassifier(class_weight="balanced")), - #( - # "RandomForest", - # RandomForestClassifier(class_weight="balanced", n_jobs=-1), - #), - ("BalancedRF", BalancedRandomForestClassifier()), - #("AdaBoost", AdaBoostClassifier()), - ("ExtraTrees", ExtraTreesClassifier(class_weight='balanced', n_jobs=-1)), - #("GaussianNB", GaussianNB()), - ("LDA", LinearDiscriminantAnalysis()), - ("GradientBoost", GradientBoostingClassifier()), - ("MLP", MLPClassifier()) - ], - cv=5, - stack_method="predict_proba", - n_jobs=-1, - passthrough=False, - final_estimator=LogisticRegression(n_jobs=-1), - verbose=1, - ) - - with open("../../configs/col_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - var = args.var_tag - if not os.path.exists("./models_custom/" + var): - os.makedirs("./models_custom/" + var) - # print('Working with '+var+' dataset...', file=open(output, "a")) - print("Working with " + var + " dataset...") - X_train,Y_train = data_parsing( - var, config_dict - ) - - classifier( - classifiers, - var, - X_train, - Y_train, - ) - gc.collect() diff --git a/src/training/training/stacking_LC_error.py b/src/training/training/stacking_LC_error.py deleted file mode 100644 index c4ffdc3..0000000 --- a/src/training/training/stacking_LC_error.py +++ /dev/null @@ -1,188 +0,0 @@ -# python slurm-launch.py --exp-name Learning_curve --command "python training/training/stacking_LC_error.py" --partition short --mem 50G - -# from numpy import mean -import numpy as np -import pandas as pd -import time -import argparse -import ray - -# Start Ray. -ray.init(ignore_reinit_error=True) -import warnings - -warnings.simplefilter("ignore") - -from sklearn.model_selection import learning_curve -from sklearn.preprocessing import label_binarize - -from sklearn.tree import DecisionTreeClassifier -from sklearn.linear_model import LogisticRegression -from sklearn.ensemble import ( - RandomForestClassifier, - AdaBoostClassifier, - GradientBoostingClassifier, - ExtraTreesClassifier, - StackingClassifier, -) -from sklearn.naive_bayes import GaussianNB -from imblearn.ensemble import BalancedRandomForestClassifier -from sklearn.neural_network import MLPClassifier -from sklearn.discriminant_analysis import LinearDiscriminantAnalysis -import matplotlib.pyplot as plt -import yaml -import functools - -print = functools.partial(print, flush=True) -import gc -import os - -os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" -) - -TUNE_STATE_REFRESH_PERIOD = 10 # Refresh resources every 10 s - -def data_parsing(var, config_dict): - # Load data - # print(f'\nUsing merged_data-train_{var}..', file=open(output, "a")) - X_train = pd.read_csv(f"train_custom_data-{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["var"], axis=1) - #X_train = X_train.sample(frac=1).reset_index(drop=True) - X_train = X_train.values - Y_train = pd.read_csv(f"train_custom_data-y-{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - - return X_train, Y_train - - - -# @ray.remote #(num_cpus=9) -def classifier( - clf, var, X, y -): - _, axes = plt.subplots(1, 3, figsize=(20, 5)) - - axes[0].set_title("Learning Curves") - axes[0].set_xlabel("Training examples") - axes[0].set_ylabel("Score") - - train_sizes, train_scores, test_scores, fit_times, _ = learning_curve( - clf, - X, - y, - cv=5, - n_jobs=-1, - scoring="neg_mean_squared_error", - return_times=True, - train_sizes=np.linspace(0.001, 1.0, 10), - ) - test_scores_mean = np.mean(test_scores, axis=1) - test_scores_std = np.std(test_scores, axis=1) - fit_times_mean = np.mean(fit_times, axis=1) - fit_times_std = np.std(fit_times, axis=1) - - # Plot learning curve - axes[0].grid() - axes[0].plot(train_sizes, -train_scores.mean(1), "o--", color="r", label="Training score") - axes[0].plot(train_sizes, -test_scores.mean(1), "o--", color="g", label="Cross-validation score") - axes[0].set_xlabel("Train size") - axes[0].set_ylabel("Neg Mean Squared Error") - axes[0].set_title("Learning curves") - axes[0].legend(loc="best") - - # Plot n_samples vs fit_times - axes[1].grid() - axes[1].plot(train_sizes, fit_times_mean, "o-") - axes[1].fill_between( - train_sizes, - fit_times_mean - fit_times_std, - fit_times_mean + fit_times_std, - alpha=0.1, - ) - axes[1].set_xlabel("Training examples") - axes[1].set_ylabel("fit_times") - axes[1].set_title("Scalability of the model") - - # Plot fit_time vs score - fit_time_argsort = fit_times_mean.argsort() - fit_time_sorted = fit_times_mean[fit_time_argsort] - test_scores_mean_sorted = test_scores_mean[fit_time_argsort] - test_scores_std_sorted = test_scores_std[fit_time_argsort] - axes[2].grid() - axes[2].plot(fit_time_sorted, test_scores_mean_sorted, "o-") - axes[2].fill_between( - fit_time_sorted, - test_scores_mean_sorted - test_scores_std_sorted, - test_scores_mean_sorted + test_scores_std_sorted, - alpha=0.1, - ) - axes[2].set_xlabel("fit_times") - axes[2].set_ylabel("Neg Mean Squared Error") - axes[2].set_title("Performance of the model") - plt.savefig( "./models_custom/" + var + "/Stacking_LC_error_" + var +".png") - plt.close() - - return None - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--var-tag", - "-v", - type=str, - #required=True, - default="dbnsfp", - help="The tag used when generating train/test data. Default:'dbnsfp'", - ) - - args = parser.parse_args() - - # Classifiers I wish to use - classifiers = StackingClassifier( - estimators=[ - #("DecisionTree", DecisionTreeClassifier(class_weight="balanced")), - #( - # "RandomForest", - # RandomForestClassifier(class_weight="balanced", n_jobs=-1), - #), - ("BalancedRF", BalancedRandomForestClassifier()), - #("AdaBoost", AdaBoostClassifier()), - ("ExtraTrees", ExtraTreesClassifier(class_weight='balanced', n_jobs=-1)), - #("GaussianNB", GaussianNB()), - ("LDA", LinearDiscriminantAnalysis()), - ("GradientBoost", GradientBoostingClassifier()), - ("MLP", MLPClassifier()) - ], - cv=5, - stack_method="predict_proba", - n_jobs=-1, - passthrough=False, - final_estimator=LogisticRegression(n_jobs=-1), - verbose=1, - ) - - with open("../../configs/col_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - var = args.var_tag - if not os.path.exists("./models_custom/" + var): - os.makedirs("./models_custom/" + var) - # print('Working with '+var+' dataset...', file=open(output, "a")) - print("Working with " + var + " dataset...") - X_train,Y_train = data_parsing( - var, config_dict - ) - - classifier( - classifiers, - var, - X_train, - Y_train, - ) - gc.collect() diff --git a/src/training/training/temp_files/ABC.py b/src/training/training/temp_files/ABC.py deleted file mode 100644 index bcbe534..0000000 --- a/src/training/training/temp_files/ABC.py +++ /dev/null @@ -1,191 +0,0 @@ -# for FILE in Tuning/*.py; do python slurm-launch.py --exp-name $FILE --command "python $FILE --vtype snv_protein_coding" ; done - - -import numpy as np -import pandas as pd -import time -import ray -from ray import tune -import argparse -from tune_sklearn import TuneSearchCV -import warnings - -warnings.simplefilter("ignore") -from joblib import dump, load -import shap - -# from sklearn.preprocessing import StandardScaler -# from sklearn.feature_selection import RFE -# from sklearn.preprocessing import MinMaxScaler -from sklearn.model_selection import cross_validate, StratifiedKFold -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) - -# from sklearn.multiclass import OneVsRestClassifier -from sklearn.ensemble import AdaBoostClassifier -from sklearn.tree import DecisionTreeClassifier -import matplotlib.pyplot as plt -import yaml -import functools - -print = functools.partial(print, flush=True) -import os -import gc - -TUNE_STATE_REFRESH_PERIOD = 10 # Refresh resources every 10 s - - -def data_parsing(var, config_dict, output): - # Load data - # print(f'\nUsing merged_data-train_{var}..') - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - # background = shap.kmeans(X_train, 10) - return X_train, X_test, Y_train, Y_test, feature_names - - -def tuning(var, X_train, X_test, Y_train, Y_test, feature_names, output): - model = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1)) - clf_name = "AdaBoostClassifier" - config = { - "n_estimators": tune.randint(1, 300), - "algorithm": tune.choice(["SAMME", "SAMME.R"]), - "learning_rate": tune.uniform(0.0001, 2.0), - "max_depth": tune.randint(1, 200), - } - start = time.perf_counter() - clf = TuneSearchCV( - model, - param_distributions=config, - n_trials=150, - early_stopping=False, - max_iters=1, # max_iters specifies how many times tune-sklearn will be given the decision to start/stop training a model. Thus, if you have early_stopping=False, you should set max_iters=1 (let sklearn fit the entire estimator). - search_optimization="bayesian", - n_jobs=10, - refit=True, - cv=StratifiedKFold(n_splits=5, shuffle=True, random_state=42), - verbose=0, - # loggers = "tensorboard", - random_state=42, - local_dir="../ray_results", - ) - clf.fit(X_train, Y_train) - print( - f"{model}_{var}:{clf.best_params_}", - file=open("../tuning/tuned_parameters.csv", "a"), - ) - clf = clf.best_estimator_ - - score = clf.score(X_train, Y_train) - with open(f"../tuning/{var}/{clf_name}_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # with open(f"./models/{var}/{clf_name}_{var}.joblib", 'rb') as f: - # clf = load(f) - y_score = clf.predict(X_test) - prc = precision_score(Y_test, y_score, average="weighted") - recall = recall_score(Y_test, y_score, average="weighted") - roc_auc = roc_auc_score(Y_test, y_score) - accuracy = accuracy_score(Y_test, y_score) - matrix = confusion_matrix(Y_test, y_score) - finish = (time.perf_counter() - start) / 60 - print( - "Model\tScore\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - print( - f"{clf_name}\t{score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del Y_test - # explain all the predictions in the test set - background = shap.kmeans(X_train, 10) - del X_train, Y_train - explainer = shap.KernelExplainer(clf.predict, background) - del clf, background - background = X_test[np.random.choice(X_test.shape[0], 10, replace=False)] - del X_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"../tuning/{var}/{clf_name}_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, feature_names - print(f"{model} tuning and testing done!") - return None - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--var-tag", - "-v", - type=str, - default="nssnv", - help="Var tag used while filtering or untuned models. (Default: nssnv)", - ) - - args = parser.parse_args() - - var = args.var_tag - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - - with open("../../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - ##variants = ['snv','non_snv','snv_protein_coding'] - - if not os.path.exists("../tuning/" + var): - os.makedirs("../tuning/" + var) - output = "../tuning/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, "w")) - print("Working with " + var + " dataset...") - X_train, X_test, Y_train, Y_test, feature_names = data_parsing( - var, config_dict, output - ) - tuning(var, X_train, X_test, Y_train, Y_test, feature_names, output) - gc.collect() diff --git a/src/training/training/temp_files/BRF.py b/src/training/training/temp_files/BRF.py deleted file mode 100644 index 109a53e..0000000 --- a/src/training/training/temp_files/BRF.py +++ /dev/null @@ -1,195 +0,0 @@ -# from numpy import mean -import numpy as np -import pandas as pd -import time -import ray -from ray import tune -import argparse -from tune_sklearn import TuneSearchCV -import warnings - -warnings.simplefilter("ignore") -from joblib import dump, load -import shap - -# from sklearn.preprocessing import StandardScaler -# from sklearn.feature_selection import RFE -# from sklearn.preprocessing import MinMaxScaler -from sklearn.model_selection import cross_validate, StratifiedKFold -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) - -# from sklearn.multiclass import OneVsRestClassifier -from imblearn.ensemble import BalancedRandomForestClassifier -import matplotlib.pyplot as plt -import yaml -import functools - -print = functools.partial(print, flush=True) -import os -import gc - - -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - # Load data - print(f"\nUsing merged_data-train_{var}..") - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - # background = shap.kmeans(X_train, 10) - return X_train, X_test, Y_train, Y_test, feature_names - - -def tuning(var, X_train, X_test, Y_train, Y_test, feature_names, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - model = BalancedRandomForestClassifier(n_jobs=10) - config = { - "n_estimators": tune.randint(1, 200), - "min_samples_split": tune.randint(2, 100), - "min_samples_leaf": tune.randint(1, 100), - "criterion": tune.choice(["gini", "entropy"]), - "max_features": tune.choice(["sqrt", "log2"]), - "class_weight": tune.choice(["balanced", "balanced_subsample"]), - # "oob_score" : tune.choice([True, False]), - "max_depth": tune.randint(2, 200), - } - start = time.perf_counter() - clf = TuneSearchCV( - model, - param_distributions=config, - n_trials=300, - early_stopping=False, - max_iters=1, # max_iters specifies how many times tune-sklearn will be given the decision to start/stop training a model. Thus, if you have early_stopping=False, you should set max_iters=1 (let sklearn fit the entire estimator). - search_optimization="bayesian", - n_jobs=10, - refit=True, - cv=StratifiedKFold(n_splits=5, shuffle=True, random_state=42), - verbose=0, - # loggers = "tensorboard", - random_state=42, - local_dir="./ray_results", - ) - clf.fit(X_train, Y_train) - print( - f"{model}_{var}:{clf.best_params_}", - file=open("tuning/tuned_parameters.csv", "a"), - ) - clf = clf.best_estimator_ - - score = clf.score(X_train, Y_train) - clf_name = str(type(model)).split("'")[1] # .split(".")[3] - with open(f"./tuning/{var}/{model}_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # with open(f"./models/{var}/{clf_name}_{var}.joblib", 'rb') as f: - # clf = load(f) - y_score = clf.predict(X_test) - prc = precision_score(Y_test, y_score, average="weighted") - recall = recall_score(Y_test, y_score, average="weighted") - roc_auc = roc_auc_score(Y_test, y_score) - accuracy = accuracy_score(Y_test, y_score) - matrix = confusion_matrix(Y_test, y_score) - finish = (time.perf_counter() - start) / 60 - print( - "Model\tScore\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - print( - f"{clf_name}\t{score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del Y_test - # explain all the predictions in the test set - background = shap.kmeans(X_train, 10) - del X_train, Y_train - explainer = shap.KernelExplainer(clf.predict, background) - del clf, background - background = X_test[np.random.choice(X_test.shape[0], 1000, replace=False)] - del X_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"./tuning/{var}/{clf_name}_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, feature_names - print(f"{model} training and testing done!") - return None - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--vtype", - type=str, - default="non_snv", - help="Type of variation/s (without spaces between) to tune the classifier (like: snv,non_snv,snv_protein_coding). (Default: non_snv)", - ) - - args = parser.parse_args() - - var = args.vtype - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - - with open("../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - ##variants = ['snv','non_snv','snv_protein_coding'] - - if not os.path.exists("tuning/" + var): - os.makedirs("./tuning/" + var) - output = "tuning/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, "w")) - print("Working with " + var + " dataset...") - X_train, X_test, Y_train, Y_test, feature_names = data_parsing( - var, config_dict, output - ) - tuning(var, X_train, X_test, Y_train, Y_test, feature_names, output) - gc.collect() diff --git a/src/training/training/temp_files/DT.py b/src/training/training/temp_files/DT.py deleted file mode 100644 index f24c552..0000000 --- a/src/training/training/temp_files/DT.py +++ /dev/null @@ -1,192 +0,0 @@ -# from numpy import mean -import numpy as np -import pandas as pd -import time -import ray -from ray import tune -import argparse -from tune_sklearn import TuneSearchCV -import warnings - -warnings.simplefilter("ignore") -from joblib import dump, load -import shap - -# from sklearn.preprocessing import StandardScaler -# from sklearn.feature_selection import RFE -# from sklearn.preprocessing import MinMaxScaler -from sklearn.model_selection import cross_validate, StratifiedKFold -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) - -# from sklearn.multiclass import OneVsRestClassifier -from sklearn.tree import DecisionTreeClassifier -import matplotlib.pyplot as plt -import yaml -import functools - -print = functools.partial(print, flush=True) -import os -import gc - - -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - # Load data - print(f"\nUsing merged_data-train_{var}..") - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - # background = shap.kmeans(X_train, 10) - return X_train, X_test, Y_train, Y_test, feature_names - - -def tuning(var, X_train, X_test, Y_train, Y_test, feature_names, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - model = DecisionTreeClassifier() - config = { - "min_samples_split": tune.randint(2, 100), - "min_samples_leaf": tune.randint(1, 100), - "criterion": tune.choice(["gini", "entropy"]), - "max_features": tune.choice(["sqrt", "log2"]), - "class_weight": tune.choice([None, "balanced"]), - } - start = time.perf_counter() - clf = TuneSearchCV( - model, - param_distributions=config, - n_trials=300, - early_stopping=False, - max_iters=1, # max_iters specifies how many times tune-sklearn will be given the decision to start/stop training a model. Thus, if you have early_stopping=False, you should set max_iters=1 (let sklearn fit the entire estimator). - search_optimization="bayesian", - n_jobs=30, - refit=True, - cv=StratifiedKFold(n_splits=5, shuffle=True, random_state=42), - verbose=0, - # loggers = "tensorboard", - random_state=42, - local_dir="./ray_results", - ) - clf.fit(X_train, Y_train) - print( - f"{model}_{var}:{clf.best_params_}", - file=open("tuning/tuned_parameters.csv", "a"), - ) - clf = clf.best_estimator_ - - score = clf.score(X_train, Y_train) - clf_name = str(type(model)).split("'")[1] # .split(".")[3] - with open(f"./tuning/{var}/{model}_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # with open(f"./models/{var}/{clf_name}_{var}.joblib", 'rb') as f: - # clf = load(f) - y_score = clf.predict(X_test) - prc = precision_score(Y_test, y_score, average="weighted") - recall = recall_score(Y_test, y_score, average="weighted") - roc_auc = roc_auc_score(Y_test, y_score) - accuracy = accuracy_score(Y_test, y_score) - matrix = confusion_matrix(Y_test, y_score) - finish = (time.perf_counter() - start) / 60 - print( - "Model\tScore\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - print( - f"{clf_name}\t{score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del Y_test - # explain all the predictions in the test set - background = shap.kmeans(X_train, 10) - del X_train, Y_train - explainer = shap.KernelExplainer(clf.predict, background) - del clf, background - background = X_test[np.random.choice(X_test.shape[0], 1000, replace=False)] - del X_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"./tuning/{var}/{clf_name}_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, feature_names - print(f"{model} training and testing done!") - return None - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--vtype", - type=str, - default="non_snv", - help="Type of variation/s (without spaces between) to tune the classifier (like: snv,non_snv,snv_protein_coding). (Default: non_snv)", - ) - - args = parser.parse_args() - - var = args.vtype - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - - with open("../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - ##variants = ['snv','non_snv','snv_protein_coding'] - - if not os.path.exists("tuning/" + var): - os.makedirs("./tuning/" + var) - output = "tuning/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, "w")) - print("Working with " + var + " dataset...") - X_train, X_test, Y_train, Y_test, feature_names = data_parsing( - var, config_dict, output - ) - tuning(var, X_train, X_test, Y_train, Y_test, feature_names, output) - gc.collect() diff --git a/src/training/training/temp_files/ET.py b/src/training/training/temp_files/ET.py deleted file mode 100644 index 6f5d732..0000000 --- a/src/training/training/temp_files/ET.py +++ /dev/null @@ -1,196 +0,0 @@ -# from numpy import mean -import numpy as np -import pandas as pd -import time -import ray -from ray import tune -import argparse -from tune_sklearn import TuneSearchCV -import warnings - -warnings.simplefilter("ignore") -from joblib import dump, load -import shap - -# from sklearn.preprocessing import StandardScaler -# from sklearn.feature_selection import RFE -# from sklearn.preprocessing import MinMaxScaler -from sklearn.model_selection import cross_validate, StratifiedKFold -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) - -# from sklearn.multiclass import OneVsRestClassifier -from sklearn.ensemble import ExtraTreesClassifier -import matplotlib.pyplot as plt -import yaml -import functools - -print = functools.partial(print, flush=True) -import os -import gc - - -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - # Load data - print(f"\nUsing merged_data-train_{var}..") - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - # background = shap.kmeans(X_train, 10) - return X_train, X_test, Y_train, Y_test, feature_names - - -def tuning(var, X_train, X_test, Y_train, Y_test, feature_names, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - model = ExtraTreesClassifier() - config = { # bootstrap = True, - # warm_start=True, - # oob_score=True): { - "n_estimators": tune.randint(1, 200), - "min_samples_split": tune.randint(2, 100), - "min_samples_leaf": tune.randint(1, 100), - "criterion": tune.choice(["gini", "entropy"]), - "max_features": tune.choice(["sqrt", "log2"]), - # "oob_score" : tune.choice([True, False]), - "class_weight": tune.choice([None, "balanced", "balanced_subsample"]), - } - start = time.perf_counter() - clf = TuneSearchCV( - model, - param_distributions=config, - n_trials=300, - early_stopping=False, - max_iters=1, # max_iters specifies how many times tune-sklearn will be given the decision to start/stop training a model. Thus, if you have early_stopping=False, you should set max_iters=1 (let sklearn fit the entire estimator). - search_optimization="bayesian", - n_jobs=30, - refit=True, - cv=StratifiedKFold(n_splits=5, shuffle=True, random_state=42), - verbose=0, - # loggers = "tensorboard", - random_state=42, - local_dir="./ray_results", - ) - clf.fit(X_train, Y_train) - print( - f"{model}_{var}:{clf.best_params_}", - file=open("tuning/tuned_parameters.csv", "a"), - ) - clf = clf.best_estimator_ - - score = clf.score(X_train, Y_train) - clf_name = str(type(model)).split("'")[1] # .split(".")[3] - with open(f"./tuning/{var}/{model}_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # with open(f"./models/{var}/{clf_name}_{var}.joblib", 'rb') as f: - # clf = load(f) - y_score = clf.predict(X_test) - prc = precision_score(Y_test, y_score, average="weighted") - recall = recall_score(Y_test, y_score, average="weighted") - roc_auc = roc_auc_score(Y_test, y_score) - accuracy = accuracy_score(Y_test, y_score) - matrix = confusion_matrix(Y_test, y_score) - finish = (time.perf_counter() - start) / 60 - print( - "Model\tScore\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - print( - f"{clf_name}\t{score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del Y_test - # explain all the predictions in the test set - background = shap.kmeans(X_train, 10) - del X_train, Y_train - explainer = shap.KernelExplainer(clf.predict, background) - del clf, background - background = X_test[np.random.choice(X_test.shape[0], 1000, replace=False)] - del X_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"./tuning/{var}/{clf_name}_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, feature_names - print(f"{model} training and testing done!") - return None - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--vtype", - type=str, - default="non_snv", - help="Type of variation/s (without spaces between) to tune the classifier (like: snv,non_snv,snv_protein_coding). (Default: non_snv)", - ) - - args = parser.parse_args() - - var = args.vtype - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - - with open("../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - ##variants = ['snv','non_snv','snv_protein_coding'] - - if not os.path.exists("tuning/" + var): - os.makedirs("./tuning/" + var) - output = "tuning/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, "w")) - print("Working with " + var + " dataset...") - X_train, X_test, Y_train, Y_test, feature_names = data_parsing( - var, config_dict, output - ) - tuning(var, X_train, X_test, Y_train, Y_test, feature_names, output) - gc.collect() diff --git a/src/training/training/temp_files/Explain.ipynb b/src/training/training/temp_files/Explain.ipynb deleted file mode 100644 index f5ba11c..0000000 --- a/src/training/training/temp_files/Explain.ipynb +++ /dev/null @@ -1,430 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - " Visualization omitted, Javascript library not loaded!
\n", - " Have you run `initjs()` in this notebook? If this notebook was from another\n", - " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", - " this notebook on github the Javascript has been stripped for security. If you are using\n", - " JupyterLab this error is because a JupyterLab extension has not yet been written.\n", - "
\n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#For 1 variant\n", - "shap_values = explainer.shap_values(x_test.iloc[0,:])\n", - "shap.force_plot(explainer.expected_value[1], shap_values[1], x_test.iloc[0,:])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/training/training/temp_files/GBC.py b/src/training/training/temp_files/GBC.py deleted file mode 100644 index bf57e91..0000000 --- a/src/training/training/temp_files/GBC.py +++ /dev/null @@ -1,195 +0,0 @@ -# from numpy import mean -import numpy as np -import pandas as pd -import time -import ray -from ray import tune -import argparse -from tune_sklearn import TuneSearchCV -import warnings - -warnings.simplefilter("ignore") -from joblib import dump, load -import shap - -# from sklearn.preprocessing import StandardScaler -# from sklearn.feature_selection import RFE -# from sklearn.preprocessing import MinMaxScaler -from sklearn.model_selection import cross_validate, StratifiedKFold -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) - -# from sklearn.multiclass import OneVsRestClassifier -from sklearn.ensemble import GradientBoostingClassifier -import matplotlib.pyplot as plt -import yaml -import functools - -print = functools.partial(print, flush=True) -import os -import gc - - -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - # Load data - print(f"\nUsing merged_data-train_{var}..") - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - # background = shap.kmeans(X_train, 10) - return X_train, X_test, Y_train, Y_test, feature_names - - -def tuning(var, X_train, X_test, Y_train, Y_test, feature_names, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - model = GradientBoostingClassifier() - config = { - "n_estimators": tune.randint(1, 200), - "min_samples_split": tune.randint(2, 100), - "min_samples_leaf": tune.randint(1, 100), - "max_features": tune.choice(["sqrt", "log2"]), - "max_features": tune.randint(1, 10), - "subsample": tune.uniform(0.1, 1.0), - "learning_rate": tune.loguniform(0.01, 1.0), - "max_depth": tune.randint(2, 200), - } - start = time.perf_counter() - clf = TuneSearchCV( - model, - param_distributions=config, - n_trials=300, - early_stopping=False, - max_iters=1, # max_iters specifies how many times tune-sklearn will be given the decision to start/stop training a model. Thus, if you have early_stopping=False, you should set max_iters=1 (let sklearn fit the entire estimator). - search_optimization="bayesian", - n_jobs=30, - refit=True, - cv=StratifiedKFold(n_splits=5, shuffle=True, random_state=42), - verbose=0, - # loggers = "tensorboard", - random_state=42, - local_dir="./ray_results", - ) - clf.fit(X_train, Y_train) - print( - f"{model}_{var}:{clf.best_params_}", - file=open("tuning/tuned_parameters.csv", "a"), - ) - clf = clf.best_estimator_ - - score = clf.score(X_train, Y_train) - clf_name = str(type(model)).split("'")[1] # .split(".")[3] - with open(f"./tuning/{var}/{model}_{var}_tsv.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # with open(f"./models/{var}/{clf_name}_{var}.joblib", 'rb') as f: - # clf = load(f) - y_score = clf.predict(X_test) - prc = precision_score(Y_test, y_score, average="weighted") - recall = recall_score(Y_test, y_score, average="weighted") - roc_auc = roc_auc_score(Y_test, y_score) - accuracy = accuracy_score(Y_test, y_score) - matrix = confusion_matrix(Y_test, y_score) - finish = (time.perf_counter() - start) / 60 - print( - "Model\tScore\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - print( - f"{clf_name}\t{score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del Y_test - # explain all the predictions in the test set - background = shap.kmeans(X_train, 10) - del X_train, Y_train - explainer = shap.KernelExplainer(clf.predict, background) - del clf, background - background = X_test[np.random.choice(X_test.shape[0], 1000, replace=False)] - del X_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"./tuning/{var}/{clf_name}_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, feature_names - print(f"{model} training and testing done!") - return None - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--vtype", - type=str, - default="non_snv", - help="Type of variation to tune the classifier (like: snv,non_snv,snv_protein_coding). (Default: non_snv)", - ) - - args = parser.parse_args() - - var = args.vtype - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - - with open("../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - ##variants = ['snv','non_snv','snv_protein_coding'] - - if not os.path.exists("tuning/" + var): - os.makedirs("./tuning/" + var) - output = "tuning/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, "w")) - print("Working with " + var + " dataset...") - X_train, X_test, Y_train, Y_test, feature_names = data_parsing( - var, config_dict, output - ) - tuning(var, X_train, X_test, Y_train, Y_test, feature_names, output) - gc.collect() diff --git a/src/training/training/temp_files/GNB.py b/src/training/training/temp_files/GNB.py deleted file mode 100644 index e1f36c1..0000000 --- a/src/training/training/temp_files/GNB.py +++ /dev/null @@ -1,209 +0,0 @@ -# from numpy import mean -import numpy as np -import pandas as pd -import time -import ray -from ray import tune -import argparse -from tune_sklearn import TuneSearchCV -import warnings - -warnings.simplefilter("ignore") -from joblib import dump, load -import shap - -# from sklearn.preprocessing import StandardScaler -# from sklearn.feature_selection import RFE -# from sklearn.preprocessing import MinMaxScaler -from sklearn.model_selection import cross_validate, StratifiedKFold -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) - -# from sklearn.multiclass import OneVsRestClassifier -from sklearn.linear_model import SGDClassifier -import matplotlib.pyplot as plt -import yaml -import functools - -print = functools.partial(print, flush=True) -import os -import gc - - -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - # Load data - print(f"\nUsing merged_data-train_{var}..") - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - # background = shap.kmeans(X_train, 10) - return X_train, X_test, Y_train, Y_test, feature_names - - -def tuning(var, X_train, X_test, Y_train, Y_test, feature_names, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - model = SGDClassifier(n_jobs=1) - config = { - "loss": tune.choice( - [ - "squared_hinge", - "hinge", - "log", - "modified_huber", - "perceptron", - "squared_loss", - "huber", - "epsilon_insensitive", - "squared_epsilon_insensitive", - ] - ), - "penalty": tune.choice(["l2", "l1", "elasticnet"]), - "alpha": tune.loguniform(1e-9, 1e-1), - "epsilon": tune.uniform(1e-9, 1e-1), - "fit_intercept": tune.choice([True, False]), - "learning_rate": tune.choice( - ["constant", "optimal", "invscaling", "adaptive"] - ), #'optimal', - "class_weight": tune.choice(["balanced"]), - "eta0": tune.uniform(0.01, 0.9), - } - start = time.perf_counter() - clf = TuneSearchCV( - model, - param_distributions=config, - n_trials=300, - early_stopping=False, - max_iters=1, # max_iters specifies how many times tune-sklearn will be given the decision to start/stop training a model. Thus, if you have early_stopping=False, you should set max_iters=1 (let sklearn fit the entire estimator). - search_optimization="bayesian", - n_jobs=30, - refit=True, - cv=StratifiedKFold(n_splits=5, shuffle=True, random_state=42), - verbose=0, - # loggers = "tensorboard", - random_state=42, - local_dir="./ray_results", - ) - clf.fit(X_train, Y_train) - print( - f"{model}_{var}:{clf.best_params_}", - file=open("tuning/tuned_parameters.csv", "a"), - ) - clf = clf.best_estimator_ - - score = clf.score(X_train, Y_train) - clf_name = str(type(model)).split("'")[1] # .split(".")[3] - with open(f"./tuning/{var}/{model}_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # with open(f"./models/{var}/{clf_name}_{var}.joblib", 'rb') as f: - # clf = load(f) - y_score = clf.predict(X_test) - prc = precision_score(Y_test, y_score, average="weighted") - recall = recall_score(Y_test, y_score, average="weighted") - roc_auc = roc_auc_score(Y_test, y_score) - accuracy = accuracy_score(Y_test, y_score) - matrix = confusion_matrix(Y_test, y_score) - finish = (time.perf_counter() - start) / 60 - print( - "Model\tScore\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - print( - f"{clf_name}\t{score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del Y_test - # explain all the predictions in the test set - background = shap.kmeans(X_train, 10) - del X_train, Y_train - explainer = shap.KernelExplainer(clf.predict, background) - del clf, background - background = X_test[np.random.choice(X_test.shape[0], 1000, replace=False)] - del X_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"./tuning/{var}/{clf_name}_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, feature_names - print(f"{model} training and testing done!") - return None - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--vtype", - type=str, - default="non_snv", - help="Type of variation/s (without spaces between) to tune the classifier (like: snv,non_snv,snv_protein_coding). (Default: non_snv)", - ) - - args = parser.parse_args() - - var = args.vtype - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - - with open("../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - ##variants = ['snv','non_snv','snv_protein_coding'] - - if not os.path.exists("tuning/" + var): - os.makedirs("./tuning/" + var) - output = "tuning/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, "w")) - print("Working with " + var + " dataset...") - X_train, X_test, Y_train, Y_test, feature_names = data_parsing( - var, config_dict, output - ) - tuning(var, X_train, X_test, Y_train, Y_test, feature_names, output) - gc.collect() diff --git a/src/training/training/temp_files/K-chooser.ipynb b/src/training/training/temp_files/K-chooser.ipynb deleted file mode 100644 index fb39000..0000000 --- a/src/training/training/temp_files/K-chooser.ipynb +++ /dev/null @@ -1,966 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "from matplotlib import colors\n", - "\n", - "#from sklearnex import patch_sklearn\n", - "#patch_sklearn()\n", - "\n", - "from skimage.color import rgb2gray, rgb2hsv, hsv2rgb\n", - "from skimage.io import imread, imshow\n", - "from sklearn.cluster import KMeans, MiniBatchKMeans\n", - "\n", - "# requires v0.22.0<=sklearn<=v0.23.2\n", - "from sklearn import cluster, datasets, mixture\n", - "from sklearn.neighbors import kneighbors_graph\n", - "from sklearn.preprocessing import StandardScaler\n", - "\n", - "import yellowbrick" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 3, - "source": [ - "\n", - "df_tissue = pd.read_csv('/Users/tarunmamidi/Documents/Development/ditto-1/data/train_F_3_0_1_nssnv/merged_data-train_F_3_0_1_nssnv.csv')\n", - "df_tissue.head()" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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SYMBOLFeatureConsequenceclinvar_CLNREVSTATclinvar_CLNSIGChromosomePositionAlternate AlleleReference AlleleID...integrated_fitCons_scorephastCons100way_vertebratephastCons30way_mammalianphyloP100way_vertebratephyloP30way_mammalianIMPACT_HIGHBIOTYPE_protein_codingBIOTYPE_polymorphic_pseudogeneBIOTYPE_IG_C_geneBIOTYPE_non_stop_decay
0SAMD11ENST00000342066missense_variantcriteria_provided&_single_submitterLikely_benignchr1930248AGvar_0...0.597741.00.0632.4860.9850.01.00.00.00.0
1SAMD11ENST00000420190missense_variantcriteria_provided&_single_submitterLikely_benignchr1930248AGvar_1...0.597741.00.0632.4860.9850.01.00.00.00.0
2SAMD11ENST00000437963missense_variantcriteria_provided&_single_submitterLikely_benignchr1930248AGvar_2...0.597741.00.0632.4860.9850.01.00.00.00.0
3SAMD11ENST00000616016missense_variantcriteria_provided&_single_submitterLikely_benignchr1930248AGvar_3...0.597741.00.0632.4860.9850.01.00.00.00.0
4SAMD11ENST00000616125missense_variantcriteria_provided&_single_submitterLikely_benignchr1930248AGvar_4...0.597741.00.0632.4860.9850.01.00.00.00.0
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" - ], - "text/plain": [ - " SYMBOL Feature Consequence \\\n", - "0 SAMD11 ENST00000342066 missense_variant \n", - "1 SAMD11 ENST00000420190 missense_variant \n", - "2 SAMD11 ENST00000437963 missense_variant \n", - "3 SAMD11 ENST00000616016 missense_variant \n", - "4 SAMD11 ENST00000616125 missense_variant \n", - "\n", - " clinvar_CLNREVSTAT clinvar_CLNSIG Chromosome Position \\\n", - "0 criteria_provided&_single_submitter Likely_benign chr1 930248 \n", - "1 criteria_provided&_single_submitter Likely_benign chr1 930248 \n", - "2 criteria_provided&_single_submitter Likely_benign chr1 930248 \n", - "3 criteria_provided&_single_submitter Likely_benign chr1 930248 \n", - "4 criteria_provided&_single_submitter Likely_benign chr1 930248 \n", - "\n", - " Alternate Allele Reference Allele ID ... integrated_fitCons_score \\\n", - "0 A G var_0 ... 0.59774 \n", - "1 A G var_1 ... 0.59774 \n", - "2 A G var_2 ... 0.59774 \n", - "3 A G var_3 ... 0.59774 \n", - "4 A G var_4 ... 0.59774 \n", - "\n", - " phastCons100way_vertebrate phastCons30way_mammalian \\\n", - "0 1.0 0.063 \n", - "1 1.0 0.063 \n", - "2 1.0 0.063 \n", - "3 1.0 0.063 \n", - "4 1.0 0.063 \n", - "\n", - " phyloP100way_vertebrate phyloP30way_mammalian IMPACT_HIGH \\\n", - "0 2.486 0.985 0.0 \n", - "1 2.486 0.985 0.0 \n", - "2 2.486 0.985 0.0 \n", - "3 2.486 0.985 0.0 \n", - "4 2.486 0.985 0.0 \n", - "\n", - " BIOTYPE_protein_coding BIOTYPE_polymorphic_pseudogene BIOTYPE_IG_C_gene \\\n", - "0 1.0 0.0 0.0 \n", - "1 1.0 0.0 0.0 \n", - "2 1.0 0.0 0.0 \n", - "3 1.0 0.0 0.0 \n", - "4 1.0 0.0 0.0 \n", - "\n", - " BIOTYPE_non_stop_decay \n", - "0 0.0 \n", - "1 0.0 \n", - "2 0.0 \n", - "3 0.0 \n", - "4 0.0 \n", - "\n", - "[5 rows x 40 columns]" - ] - }, - "metadata": {}, - "execution_count": 3 - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 4, - "source": [ - "df_tissue = df_tissue.drop(['SYMBOL','Feature',\t'Consequence',\t'clinvar_CLNREVSTAT',\t'clinvar_CLNSIG',\t'Chromosome',\t'Position',\t'Alternate Allele'\t,'Reference Allele',\t'ID'], axis=1)\n", - "df_tissue.head()" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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gnomADv3_AFSIFTPolyPhenCADD_PHREDMetaSVM_scoreFATHMM_scoreMutationAssessor_scorePROVEAN_scoreVEST4_scoreGERP...integrated_fitCons_scorephastCons100way_vertebratephastCons30way_mammalianphyloP100way_vertebratephyloP30way_mammalianIMPACT_HIGHBIOTYPE_protein_codingBIOTYPE_polymorphic_pseudogeneBIOTYPE_IG_C_geneBIOTYPE_non_stop_decay
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" - ], - "text/plain": [ - " gnomADv3_AF SIFT PolyPhen CADD_PHRED MetaSVM_score FATHMM_score \\\n", - "0 0.003488 0.01 0.014 21.9 -1.0342 -1.26 \n", - "1 0.003488 0.02 0.168 21.9 -1.0342 -1.26 \n", - "2 0.003488 0.02 0.014 21.9 -1.0342 -1.26 \n", - "3 0.003488 0.00 0.747 21.9 -1.0342 -1.26 \n", - "4 0.003488 0.00 0.031 21.9 -1.0342 -1.26 \n", - "\n", - " MutationAssessor_score PROVEAN_score VEST4_score GERP ... \\\n", - "0 2.015 -3.25 0.261 4.12 ... \n", - "1 2.215 -2.88 0.762 4.12 ... \n", - "2 2.215 -3.66 0.762 4.12 ... \n", - "3 2.215 -2.88 0.459 4.12 ... \n", - "4 2.215 -2.88 0.265 4.12 ... \n", - "\n", - " integrated_fitCons_score phastCons100way_vertebrate \\\n", - "0 0.59774 1.0 \n", - "1 0.59774 1.0 \n", - "2 0.59774 1.0 \n", - "3 0.59774 1.0 \n", - "4 0.59774 1.0 \n", - "\n", - " phastCons30way_mammalian phyloP100way_vertebrate phyloP30way_mammalian \\\n", - "0 0.063 2.486 0.985 \n", - "1 0.063 2.486 0.985 \n", - "2 0.063 2.486 0.985 \n", - "3 0.063 2.486 0.985 \n", - "4 0.063 2.486 0.985 \n", - "\n", - " IMPACT_HIGH BIOTYPE_protein_coding BIOTYPE_polymorphic_pseudogene \\\n", - "0 0.0 1.0 0.0 \n", - "1 0.0 1.0 0.0 \n", - "2 0.0 1.0 0.0 \n", - "3 0.0 1.0 0.0 \n", - "4 0.0 1.0 0.0 \n", - "\n", - " BIOTYPE_IG_C_gene BIOTYPE_non_stop_decay \n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "\n", - "[5 rows x 30 columns]" - ] - }, - "metadata": {}, - "execution_count": 4 - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Gap statistic" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 4, - "source": [ - "# Gap Statistic for K means\n", - "def optimalK(data, nrefs=3, maxClusters=15):\n", - " \"\"\"\n", - " Calculates KMeans optimal K using Gap Statistic \n", - " Params:\n", - " data: ndarry of shape (n_samples, n_features)\n", - " nrefs: number of sample reference datasets to create\n", - " maxClusters: Maximum number of clusters to test for\n", - " Returns: (gaps, optimalK)\n", - " \"\"\"\n", - " gaps = np.zeros((len(range(1, maxClusters)),))\n", - " resultsdf = pd.DataFrame({'clusterCount':[], 'gap':[]})\n", - " for gap_index, k in enumerate(range(1, maxClusters)):\n", - " # Holder for reference dispersion results\n", - " refDisps = np.zeros(nrefs)\n", - " # For n references, generate random sample and perform kmeans getting resulting dispersion of each loop\n", - " for i in range(nrefs):\n", - " \n", - " # Create new random reference set\n", - " randomReference = np.random.random_sample(size=data.shape)\n", - " \n", - " # Fit to it\n", - " km = MiniBatchKMeans(k, batch_size=6144)\n", - " # km = KMeans(k)\n", - " km.fit(randomReference)\n", - " \n", - " refDisp = km.inertia_\n", - " refDisps[i] = refDisp\n", - " # Fit cluster to original data and create dispersion\n", - " km = MiniBatchKMeans(k, batch_size=6144)\n", - " #km= KMeans(k)\n", - " km.fit(data)\n", - " \n", - " origDisp = km.inertia_\n", - " # Calculate gap statistic\n", - " gap = np.log(np.mean(refDisps)) - np.log(origDisp)\n", - " # Assign this loop's gap statistic to gaps\n", - " gaps[gap_index] = gap\n", - " \n", - " resultsdf = resultsdf.append({'clusterCount':k, 'gap':gap}, ignore_index=True)\n", - " return (gaps.argmax() + 1, resultsdf)\n", - "\n", - "# run function\n", - "score_g, df = optimalK(df_tissue, nrefs=5, maxClusters=30)\n", - "\n", - "plt.plot(df['clusterCount'], df['gap'], linestyle='--', marker='o', color='b');\n", - "plt.xlabel('K');\n", - "plt.ylabel('Gap Statistic');\n", - "plt.title('Gap Statistic vs. K');\n", - "print(score_g)\n", - "plt.show()" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "29\n" - ] - }, - { - "output_type": "display_data", - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {} - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Elbow" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 5, - "source": [ - "# Import ElbowVisualizer\n", - "from yellowbrick.cluster import KElbowVisualizer\n", - "model = MiniBatchKMeans(batch_size=6144)\n", - "# k is range of number of clusters.\n", - "visualizer = KElbowVisualizer(model, k=(2,30), timings= True)\n", - "visualizer.fit(df_tissue) # Fit data to visualizer\n", - "visualizer.show() # Finalize and render figure" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {} - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Silhouette " - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": null, - "source": [ - "# Taking a long time\n", - "# Import ElbowVisualizer\n", - "from yellowbrick.cluster import KElbowVisualizer\n", - "model = MiniBatchKMeans(batch_size=6144)\n", - "# k is range of number of clusters.\n", - "visualizer = KElbowVisualizer(model, k=(2,30),metric='silhouette', timings= True)\n", - "visualizer.fit(df_tissue) # Fit the data to the visualizer\n", - "visualizer.show() # Finalize and render the figure" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Calinski Harabasz" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 7, - "source": [ - "# Import ElbowVisualizer\n", - "from yellowbrick.cluster import KElbowVisualizer\n", - "model = MiniBatchKMeans(batch_size=6144)\n", - "# k is range of number of clusters.\n", - "visualizer = KElbowVisualizer(model, k=(2,30),metric='calinski_harabasz', timings= True)\n", - "visualizer.fit(df_tissue) # Fit the data to the visualizer\n", - "visualizer.show() # Finalize and render the figure" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {} - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 7 - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Davies Bouldin \n", - "minimum is best" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 8, - "source": [ - "from sklearn.metrics import davies_bouldin_score\n", - "def get_kmeans_score(data, center):\n", - " '''\n", - " returns the kmeans score regarding Davies Bouldin for points to centers\n", - " INPUT:\n", - " data - the dataset you want to fit kmeans to\n", - " center - the number of centers you want (the k value)\n", - " OUTPUT:\n", - " score - the Davies Bouldin score for the kmeans model fit to the data\n", - " '''\n", - " #instantiate kmeans\n", - " kmeans = MiniBatchKMeans(n_clusters=center, batch_size=6144)\n", - " # Then fit the model to your data using the fit method\n", - " model = kmeans.fit_predict(data)\n", - " \n", - " # Calculate Davies Bouldin score\n", - " score = davies_bouldin_score(data, model)\n", - " \n", - " return score\n", - "scores = []\n", - "centers = list(range(2,30))\n", - "for center in centers:\n", - " scores.append(get_kmeans_score(df_tissue, center))\n", - " \n", - "plt.plot(centers, scores, linestyle='--', marker='o', color='b');\n", - "plt.xlabel('K');\n", - "plt.ylabel('Davies Bouldin score');\n", - "plt.title('Davies Bouldin score vs. K');\n", - "# want minimum\n", - "plt.show()" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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" - ] - }, - "metadata": {} - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# Dendogram for Heirarchical Clustering" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 9, - "source": [ - "# wanted 17Tb of memory!\n", - "import scipy.cluster.hierarchy as shc\n", - "from matplotlib import pyplot\n", - "pyplot.figure(figsize=(10, 7)) \n", - "pyplot.title(\"Dendrograms\") \n", - "dend = shc.dendrogram(shc.linkage(df_tissue, method='ward'))" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "# BIC for GMM\n", - "minimum is best" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 5, - "source": [ - "from sklearn.mixture import GaussianMixture\n", - "n_components = range(1, 30)\n", - "covariance_type = ['spherical', 'tied', 'diag', 'full']\n", - "score=[]\n", - "for cov in covariance_type:\n", - " for n_comp in n_components:\n", - " gmm=GaussianMixture(n_components=n_comp,covariance_type=cov)\n", - " gmm.fit(df_tissue)\n", - " score.append((cov,n_comp,gmm.bic(df_tissue)))\n", - "score" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": null, - "source": [ - "score_df = pd.DataFrame(score)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": null, - "source": [ - "score_df.sort_values(by=2, ascending=True)" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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Thus, if you have early_stopping=False, you should set max_iters=1 (let sklearn fit the entire estimator). - search_optimization="bayesian", - n_jobs=30, - refit=True, - cv=StratifiedKFold(n_splits=5, shuffle=True, random_state=42), - verbose=0, - # loggers = "tensorboard", - random_state=42, - local_dir="./ray_results", - ) - clf.fit(X_train, Y_train) - print( - f"{model}_{var}:{clf.best_params_}", - file=open("tuning/tuned_parameters.csv", "a"), - ) - clf = clf.best_estimator_ - - score = clf.score(X_train, Y_train) - clf_name = str(type(model)).split("'")[1] # .split(".")[3] - with open(f"./tuning/{var}/{model}_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # with open(f"./models/{var}/{clf_name}_{var}.joblib", 'rb') as f: - # clf = load(f) - y_score = clf.predict(X_test) - prc = precision_score(Y_test, y_score, average="weighted") - recall = recall_score(Y_test, y_score, average="weighted") - roc_auc = roc_auc_score(Y_test, y_score) - accuracy = accuracy_score(Y_test, y_score) - matrix = confusion_matrix(Y_test, y_score) - finish = (time.perf_counter() - start) / 60 - print( - "Model\tScore\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - print( - f"{clf_name}\t{score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del Y_test - # explain all the predictions in the test set - background = shap.kmeans(X_train, 10) - del X_train, Y_train - explainer = shap.KernelExplainer(clf.predict, background) - del clf, background - background = X_test[np.random.choice(X_test.shape[0], 1000, replace=False)] - del X_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"./tuning/{var}/{clf_name}_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, feature_names - print(f"{model} training and testing done!") - return None - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--vtype", - type=str, - default="non_snv", - help="Type of variation/s (without spaces between) to tune the classifier (like: snv,non_snv,snv_protein_coding). (Default: non_snv)", - ) - - args = parser.parse_args() - - var = args.vtype - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - - with open("../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - ##variants = ['snv','non_snv','snv_protein_coding'] - - if not os.path.exists("tuning/" + var): - os.makedirs("./tuning/" + var) - output = "tuning/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, "w")) - print("Working with " + var + " dataset...") - X_train, X_test, Y_train, Y_test, feature_names = data_parsing( - var, config_dict, output - ) - tuning(var, X_train, X_test, Y_train, Y_test, feature_names, output) - gc.collect() diff --git a/src/training/training/temp_files/MLP.py b/src/training/training/temp_files/MLP.py deleted file mode 100644 index 71ff17b..0000000 --- a/src/training/training/temp_files/MLP.py +++ /dev/null @@ -1,332 +0,0 @@ -import numpy as np -import pandas as pd -import ray -import time -import argparse -import pickle -from joblib import dump -import yaml - -# from ray import tune -from ray.tune import Trainable, run -from hyperopt import hp -import random -from ray.tune.suggest.hyperopt import HyperOptSearch - -# from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import cross_validate, StratifiedKFold -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) - -# from sklearn.neural_network import MLPClassifier -import os -import gc -import shap -from joblib import dump, load -import matplotlib.pyplot as plt -import tensorflow as tf -import tensorflow.keras as keras -from tensorflow.keras.models import Sequential -from tensorflow.keras.layers import Dense, Dropout, Activation - -try: - tf.get_logger().setLevel("INFO") -except Exception as exc: - print(exc) -import warnings - -warnings.simplefilter("ignore") -import functools - -print = functools.partial(print, flush=True) - - -TUNE_STATE_REFRESH_PERIOD = 10 # Refresh resources every 10 s - - -def f_unpack_dict(dct): - """ - Unpacks all sub-dictionaries in given dictionary recursively. There should be no duplicated keys - across all nested subdictionaries, or some instances will be lost without warning - - Source: https://www.kaggle.com/fanvacoolt/tutorial-on-hyperopt - - Parameters: - ---------------- - dct : dictionary to unpack - - Returns: - ---------------- - : unpacked dictionary - """ - - res = {} - for (k, v) in dct.items(): - if isinstance(v, dict): - res = {**res, **f_unpack_dict(v)} - else: - res[k] = v - - return res - - -class stacking( - Trainable -): # https://docs.ray.io/en/master/hp/examples/pbt_hp_cifar10_with_keras.html - def setup(self, config, x_train, x_test, y_train, y_test): - self.x_train = x_train - self.x_test = x_test - self.y_train = y_train - self.y_test = y_test - self.config = f_unpack_dict(config) - self.model = MLPClassifier() - - def reset_config(self, new_config): - self.hidden_layer_sizes = new_config["hidden_layer_sizes"] - self.activation = new_config["activation"] - self.solver = new_config["solver"] - self.alpha = new_config["alpha"] - self.learning_rate = new_config["learning_rate"] - self.tol = new_config["tol"] - self.epsilon = new_config["epsilon"] - self.max_iter = new_config["max_iter"] - # self.n_layers = new_config['n_layers'] - # self.n_neurons = new_config['n_neurons'] - self.config = new_config - return True - - def step(self): - score = cross_validate( - self.model, self.x_train, self.y_train, cv=5, n_jobs=-1, verbose=0 - ) - testing_score = np.max(score["test_score"]) - # testing_score = self.model.fit(self.x_train, self.y_train).accuracy_score(self.x_test, self.y_test) - return {"mean_accuracy": testing_score} - - def save_checkpoint(self, checkpoint_dir): - file_path = checkpoint_dir + "/model" - pickle.dump(self.model, open(file_path, "wb")) - return file_path - - def load_checkpoint(self, path): - # See https://stackoverflow.com/a/42763323 - del self.model - self.model = pickle.load(open(path, "rb")) - - def cleanup(self): - # If need, save your model when exit. - # saved_path = self.model.save(self.logdir) - # print('save model at: ', saved_path) - pass - - -def results(config, x_train, x_test, y_train, y_test, var, output, feature_names): - start1 = time.perf_counter() - config = f_unpack_dict(config) - # self.x_train, self.x_test, self.y_train, self.y_test, self.feature_names = self._read_data(config) - clf_name = "MLPClassifier" - clf = MLPClassifier() - # score = cross_validate(clf, x_train, y_train, cv=StratifiedKFold(n_splits=5,shuffle=True,random_state=42), return_train_score=True, return_estimator=True, n_jobs=-1, verbose=0) - clf.fit(x_train, y_train) - train_score = clf.score(x_train, y_train) - with open(f"../tuning/{var}/{clf_name}_tuned_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # training_score = np.mean(score['train_score']) - # testing_score = np.mean(score['test_score']) - y_score = clf.predict(x_test) - prc = precision_score(y_test, y_score, average="weighted") - recall = recall_score(y_test, y_score, average="weighted") - roc_auc = roc_auc_score(y_test, y_score) - accuracy = accuracy_score(y_test, y_score) - matrix = confusion_matrix(y_test, y_score) - finish = (time.perf_counter() - start1) / 60 - # with open(output, 'a') as f: - # f.write(f"AdaBoostClassifier: \nCross_validate(avg_train_score): {training_score}\nCross_validate(avg_test_score): {testing_score}\nPrecision: {prc}\nRecall: {recall}\nROC_AUC: {roc_auc}\nAccuracy: {accuracy}\nTime(in min): {finish}\nConfusion Matrix:\n{matrix}") - - print( - f"Model\ttrain_score\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - print( - f"{clf_name}\t{train_score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del y_test - # explain all the predictions in the test set - background = shap.kmeans(x_train, 10) - explainer = shap.KernelExplainer(clf.predict, background) - del clf, x_train, y_train, background - background = x_test[np.random.choice(x_test.shape[0], 10000, replace=False)] - del x_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"../tuning/{var}/{clf_name}_tuned_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, feature_names - print(f"tuning and testing done!") - return None - - -def wrap_trainable(trainable, x_train, x_test, y_train, y_test): - x_train_id = ray.put(x_train) - x_test_id = ray.put(x_test) - y_train_id = ray.put(y_train) - y_test_id = ray.put(y_test) - - class _Wrapped(trainable): - def setup(self, config): - x_train = ray.get(x_train_id) - x_test = ray.get(x_test_id) - y_train = ray.get(y_train_id) - y_test = ray.get(y_test_id) - - super(_Wrapped, self).setup(config, x_train, x_test, y_train, y_test) - - return _Wrapped - - -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - # Load data - # print(f'\nUsing merged_data-train_{var}..', file=open(output, 'a')) - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - X_train.replace([np.inf, -np.inf], np.nan, inplace=True) - X_train.fillna(0, inplace=True) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - return X_train, X_test, Y_train, Y_test, feature_names - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--var-tag", - "-v", - type=str, - default="nssnv", - help="Var tag used while filtering or untuned models. (Default: nssnv)", - ) - - args = parser.parse_args() - - var = args.var_tag - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - with open("../../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - start = time.perf_counter() - if not os.path.exists("../tuning/" + var): - os.makedirs("../tuning/" + var) - output = "../tuning/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, 'w')) - print("Working with " + var + " dataset...") - x_train, x_test, y_train, y_test, feature_names = data_parsing( - var, config_dict, output - ) - config = { # https://stackoverflow.com/questions/61502257/optimize-hyperparameters-hidden-layer-size-mlpclassifier-with-skopt - # "hidden_layer_sizes": hp.sample_from(lambda list: [list.append(tune.randint(1, 100)) for i in range(tune.randint(1, 50))]), - "activation": hp.choice("activation", ["identity", "logistic", "tanh", "relu"]), - "solver": hp.choice("solver", ["lbfgs", "sgd", "adam"]), - "alpha": hp.loguniform("alpha", 1e-9, 1e-1), - "learning_rate": hp.choice( - "learning_rate", ["constant", "adaptive", "invscaling"] - ), - "tol": hp.loguniform("tol", 1e-9, 1e-1), - "epsilon": hp.uniform("epsilon", 1e-9, 1e-1), - "max_iter": hp.randint("max_iter", 10, 1000), - # "n_neurons": hp.randint("n_neurons", 1, 300), - # "n_layers": hp.randint("n_layers", 1, 50) - } - # create the hidden layers as a tuple with length n_layers and n_neurons per layer - # config["hidden_layer_sizes"] = tuple([hp.randint("n_neurons", 1, 300) for i in range(hp.randint("n_layers", 1, 50))]) - - # the parameters are deleted to avoid an error from the MLPRegressor - # config.pop('n_neurons_per_layer') - # config.pop('n_hidden_layer') - - hyperopt_search = HyperOptSearch(config, metric="mean_accuracy", mode="max") - # scheduler = AsyncHyperBandScheduler() - analysis = run( - wrap_trainable(stacking, x_train, x_test, y_train, y_test), - name=f"MLPClassifier_{var}", - verbose=1, - # scheduler=scheduler, - search_alg=hyperopt_search, - reuse_actors=True, - local_dir="../ray_results", - max_failures=2, - # resources_per_trial={ - # 'cpu': 10, - # 'gpu': 1 - # }, - # global_checkpoint_period=np.inf, # Do not save checkpoints based on time interval - checkpoint_freq=20, # Save checkpoint every time the checkpoint_score_attr improves - checkpoint_at_end=True, - keep_checkpoints_num=2, # Keep only the best checkpoint - checkpoint_score_attr="mean_accuracy", # Metric used to compare checkpoints - metric="mean_accuracy", - mode="max", - stop={ - "training_iteration": 1, - }, - num_samples=5, - # fail_fast=True, - queue_trials=True, - ) - finish = (time.perf_counter() - start) / 120 - # ttime = (finish- start)/120 - print(f"Total time in min: {finish}") - config = analysis.best_config - print( - f"MLPClassifier_{var}: {config}", - file=open(f"../tuning/tuned_parameters.csv", "a"), - ) - results(config, x_train, x_test, y_train, y_test, var, output, feature_names) - gc.collect() diff --git a/src/training/training/temp_files/RF.py b/src/training/training/temp_files/RF.py deleted file mode 100644 index ca99574..0000000 --- a/src/training/training/temp_files/RF.py +++ /dev/null @@ -1,195 +0,0 @@ -# from numpy import mean -import numpy as np -import pandas as pd -import time -import ray -from ray import tune -import argparse -from tune_sklearn import TuneSearchCV -import warnings - -warnings.simplefilter("ignore") -from joblib import dump, load -import shap - -# from sklearn.preprocessing import StandardScaler -# from sklearn.feature_selection import RFE -# from sklearn.preprocessing import MinMaxScaler -from sklearn.model_selection import cross_validate, StratifiedKFold -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) - -# from sklearn.multiclass import OneVsRestClassifier -from sklearn.ensemble import RandomForestClassifier -import matplotlib.pyplot as plt -import yaml -import functools - -print = functools.partial(print, flush=True) -import os -import gc - - -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - # Load data - print(f"\nUsing merged_data-train_{var}..") - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - # background = shap.kmeans(X_train, 10) - return X_train, X_test, Y_train, Y_test, feature_names - - -def tuning(var, X_train, X_test, Y_train, Y_test, feature_names, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - model = RandomForestClassifier(n_jobs=10) - config = { - "n_estimators": tune.randint(1, 200), - "min_samples_split": tune.randint(2, 10), - "min_samples_leaf": tune.randint(1, 10), - "criterion": tune.choice(["gini", "entropy"]), - "max_features": tune.choice(["sqrt", "log2"]), - "class_weight": tune.choice(["balanced", "balanced_subsample"]), - # "oob_score" : tune.choice([True, False]), - "max_depth": tune.randint(2, 200), - } - start = time.perf_counter() - clf = TuneSearchCV( - model, - param_distributions=config, - n_trials=300, - early_stopping=False, - max_iters=1, # max_iters specifies how many times tune-sklearn will be given the decision to start/stop training a model. Thus, if you have early_stopping=False, you should set max_iters=1 (let sklearn fit the entire estimator). - search_optimization="bayesian", - n_jobs=10, - refit=True, - cv=StratifiedKFold(n_splits=5, shuffle=True, random_state=42), - verbose=0, - # loggers = "tensorboard", - random_state=42, - local_dir="./ray_results", - ) - clf.fit(X_train, Y_train) - print( - f"{model}_{var}:{clf.best_params_}", - file=open("tuning/tuned_parameters.csv", "a"), - ) - clf = clf.best_estimator_ - - score = clf.score(X_train, Y_train) - clf_name = str(type(model)).split("'")[1] # .split(".")[3] - with open(f"./tuning/{var}/{model}_{var}_tsv.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # with open(f"./models/{var}/{clf_name}_{var}.joblib", 'rb') as f: - # clf = load(f) - y_score = clf.predict(X_test) - prc = precision_score(Y_test, y_score, average="weighted") - recall = recall_score(Y_test, y_score, average="weighted") - roc_auc = roc_auc_score(Y_test, y_score) - accuracy = accuracy_score(Y_test, y_score) - matrix = confusion_matrix(Y_test, y_score) - finish = (time.perf_counter() - start) / 60 - print( - "Model\tScore\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - print( - f"{clf_name}\t{score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del Y_test - # explain all the predictions in the test set - background = shap.kmeans(X_train, 10) - del X_train, Y_train - explainer = shap.KernelExplainer(clf.predict, background) - del clf, background - background = X_test[np.random.choice(X_test.shape[0], 1000, replace=False)] - del X_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"./tuning/{var}/{clf_name}_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, feature_names - print(f"{model} training and testing done!") - return None - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--vtype", - type=str, - default="non_snv", - help="Type of variation/s (without spaces between) to tune the classifier (like: snv,non_snv,snv_protein_coding). (Default: non_snv)", - ) - - args = parser.parse_args() - - var = args.vtype - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - - with open("../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - ##variants = ['snv','non_snv','snv_protein_coding'] - - if not os.path.exists("tuning/" + var): - os.makedirs("./tuning/" + var) - output = "tuning/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, "w")) - print("Working with " + var + " dataset...") - X_train, X_test, Y_train, Y_test, feature_names = data_parsing( - var, config_dict, output - ) - tuning(var, X_train, X_test, Y_train, Y_test, feature_names, output) - gc.collect() diff --git a/src/training/training/temp_files/Tune_RF.py b/src/training/training/temp_files/Tune_RF.py deleted file mode 100644 index db61b7d..0000000 --- a/src/training/training/temp_files/Tune_RF.py +++ /dev/null @@ -1,310 +0,0 @@ -import numpy as np -import pandas as pd -import ray -import time -import argparse -import pickle -from joblib import dump, load -import yaml -from ray import tune -from ray.tune import Trainable, run -from ray.tune.suggest.skopt import SkOptSearch -from ray.tune.suggest import ConcurrencyLimiter -from ray.tune.schedulers import AsyncHyperBandScheduler -from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import train_test_split, cross_validate, StratifiedKFold -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) -from sklearn.ensemble import RandomForestClassifier -import os -import gc -import shap -from joblib import dump, load -import matplotlib.pyplot as plt -import warnings - -warnings.simplefilter("ignore") -import functools - -print = functools.partial(print, flush=True) - -TUNE_STATE_REFRESH_PERIOD = 10 # Refresh resources every 10 s - - -class RF_PB2( - Trainable -): # https://docs.ray.io/en/master/tune/examples/pbt_tune_cifar10_with_keras.html - def setup(self, config, x_train, x_test, y_train, y_test): - self.x_train = x_train - self.x_test = x_test - self.y_train = y_train - self.y_test = y_test - self.model = RandomForestClassifier( - random_state=42, - n_estimators=self.config.get("n_estimators", 100), - max_depth=self.config.get("max_depth", 2), - min_samples_split=self.config.get("min_samples_split", 2), - min_samples_leaf=self.config.get("min_samples_leaf", 1), - criterion=self.config.get("criterion", "gini"), - max_features=self.config.get("max_features", "sqrt"), - class_weight=self.config.get("class_weight", "balanced"), - n_jobs=-1, - ) - - def reset_config(self, new_config): - self.n_estimators = new_config["n_estimators"] - self.min_samples_split = new_config["min_samples_split"] - self.min_samples_leaf = new_config["min_samples_leaf"] - self.criterion = new_config["criterion"] - self.max_features = new_config["max_features"] - self.class_weight = new_config["class_weight"] - # self.oob_score = new_config["oob_score"] - self.max_depth = new_config["max_depth"] - self.config = new_config - return True - - def step(self): - score = cross_validate( - self.model, self.x_train, self.y_train, cv=3, n_jobs=-1, verbose=0 - ) - testing_score = np.max(score["test_score"]) - # print(accuracy) - return {"mean_accuracy": testing_score} - - def save_checkpoint(self, checkpoint_dir): - file_path = checkpoint_dir + "/model" - pickle.dump(self.model, open(file_path, "wb")) - return file_path - - def load_checkpoint(self, path): - # See https://stackoverflow.com/a/42763323 - del self.model - self.model = pickle.load(open(path, "rb")) - - def cleanup(self): - # If need, save your model when exit. - # saved_path = self.model.save(self.logdir) - # print("save model at: ", saved_path) - pass - - -def results(config, x_train, x_test, y_train, y_test, var, output, feature_names): - start1 = time.perf_counter() - # self.x_train, self.x_test, self.y_train, self.y_test, self.feature_names = self._read_data(config) - clf = RandomForestClassifier( - random_state=42, - n_estimators=config.get("n_estimators", 100), - max_depth=config.get("max_depth", 2), - min_samples_split=config.get("min_samples_split", 2), - min_samples_leaf=config.get("min_samples_leaf", 1), - criterion=config.get("criterion", "gini"), - max_features=config.get("max_features", "sqrt"), - class_weight=config.get("class_weight", "balanced"), - n_jobs=-1, - ) - score = cross_validate( - clf, - x_train, - y_train, - cv=StratifiedKFold(n_splits=5, shuffle=True, random_state=42), - return_train_score=True, - return_estimator=True, - n_jobs=-1, - verbose=0, - ) - clf = score["estimator"][np.argmax(score["test_score"])] - with open(f"./tuning/{var}/RandomForestClassifier_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - training_score = np.mean(score["train_score"]) - testing_score = np.mean(score["test_score"]) - y_score = clf.predict(x_test) - prc = precision_score(y_test, y_score, average="weighted") - recall = recall_score(y_test, y_score, average="weighted") - roc_auc = roc_auc_score(y_test, y_score) - accuracy = accuracy_score(y_test, y_score) - matrix = confusion_matrix(y_test, y_score) - finish = (time.perf_counter() - start1) / 60 - # print(f'RandomForestClassifier: \nCross_validate(avg_train_score): {training_score}\nCross_validate(avg_test_score): {testing_score}\nPrecision: {prc}\nRecall: {recall}\nROC_AUC: {roc_auc}\nAccuracy: {accuracy}\nTime(in min): {finish}\nConfusion Matrix:\n{matrix}', file=open(output, "a")) - clf_name = str(type(clf)).split("'")[1] # .split(".")[3] - print( - "Model\tCross_validate(avg_train_score)\tCross_validate(avg_test_score)\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - print( - f"{clf_name}\t{training_score}\t{testing_score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del y_test - # explain all the predictions in the test set - background = shap.kmeans(x_train, 10) - explainer = shap.KernelExplainer(clf.predict, background) - del clf, x_train, y_train, background - background = x_test[np.random.choice(x_test.shape[0], 1000, replace=False)] - del x_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"./tuning/{var}/RandomForestClassifier_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, feature_names - print(f"training and testing done!") - return None - - -def wrap_trainable(trainable, x_train, x_test, y_train, y_test): - x_train_id = ray.put(x_train) - x_test_id = ray.put(x_test) - y_train_id = ray.put(y_train) - y_test_id = ray.put(y_test) - - class _Wrapped(trainable): - def setup(self, config): - x_train = ray.get(x_train_id) - x_test = ray.get(x_test_id) - y_train = ray.get(y_train_id) - y_test = ray.get(y_test_id) - - super(_Wrapped, self).setup(config, x_train, x_test, y_train, y_test) - - return _Wrapped - - -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - # Load data - print(f"\nUsing merged_data-train_{var}..", file=open(output, "a")) - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - X_train.replace([np.inf, -np.inf], np.nan, inplace=True) - X_train.fillna(0, inplace=True) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - return X_train, X_test, Y_train, Y_test, feature_names - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--vtype", - type=str, - default="non_snv", - help="Type of variation/s (without spaces between) to tune the classifier (like: snv,non_snv,snv_protein_coding). (Default: non_snv)", - ) - - args = parser.parse_args() - - variants = args.vtype.split(",") - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - with open("../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - # variants = ['non_snv','snv','snv_protein_coding'] - for var in variants: - - start = time.perf_counter() - if not os.path.exists("tuning/" + var): - os.makedirs("./tuning/" + var) - output = "tuning/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, "w")) - print("Working with " + var + " dataset...") - - x_train, x_test, y_train, y_test, feature_names = data_parsing( - var, config_dict, output - ) - - skopt_search = SkOptSearch(metric="mean_accuracy", mode="max") - skopt_search = ConcurrencyLimiter(skopt_search, max_concurrent=10) - scheduler = AsyncHyperBandScheduler() - - analysis = run( - wrap_trainable(RF_PB2, x_train, x_test, y_train, y_test), - name=f"RandomForestClassifier_PB2_{var}", - verbose=1, - scheduler=scheduler, - search_alg=skopt_search, - reuse_actors=True, - local_dir="./ray_results", - max_failures=2, - # resources_per_trial={ - # "cpu": 10, - # "gpu": 1 - # }, - # global_checkpoint_period=np.inf, # Do not save checkpoints based on time interval - checkpoint_freq=20, # Save checkpoint every time the checkpoint_score_attr improves - checkpoint_at_end=True, - keep_checkpoints_num=1, # Keep only the best checkpoint - checkpoint_score_attr="mean_accuracy", # Metric used to compare checkpoints - metric="mean_accuracy", - mode="max", - stop={ - "training_iteration": 100, - }, - num_samples=5, - # fail_fast=True, - queue_trials=True, - config={ # https://www.geeksforgeeks.org/hyperparameters-of-random-forest-classifier/ - "n_estimators": tune.randint(1, 200), - "min_samples_split": tune.randint(2, 10), - "min_samples_leaf": tune.randint(1, 10), - "criterion": tune.choice(["gini", "entropy"]), - "max_features": tune.choice(["sqrt", "log2"]), - "class_weight": tune.choice(["balanced", "balanced_subsample"]), - # "oob_score" : tune.choice([True, False]), - "max_depth": tune.randint(2, 200), - }, - ) - finish = (time.perf_counter() - start) / 120 - # ttime = (finish- start)/120 - print(f"Total time in min: {finish}") - config = analysis.best_config - print( - f"RandomForestClassifier_{var}: {config}", - file=open(f"tuning/tuned_parameters.csv", "a"), - ) - results(config, x_train, x_test, y_train, y_test, var, output, feature_names) - gc.collect() diff --git a/src/training/training/temp_files/Tune_RF_PB2.py b/src/training/training/temp_files/Tune_RF_PB2.py deleted file mode 100644 index 1416f2c..0000000 --- a/src/training/training/temp_files/Tune_RF_PB2.py +++ /dev/null @@ -1,306 +0,0 @@ -import numpy as np -import pandas as pd -import ray -import time -import argparse -import pickle -import yaml -from ray import tune -from ray.tune import Trainable, run -from ray.tune.schedulers.pb2 import PB2 -from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import train_test_split, cross_validate -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) -from sklearn.ensemble import RandomForestClassifier -import os -import gc -import shap -from joblib import dump, load -import matplotlib.pyplot as plt -import warnings - -warnings.simplefilter("ignore") -import functools - -print = functools.partial(print, flush=True) - -TUNE_STATE_REFRESH_PERIOD = 10 # Refresh resources every 10 s - - -class RF_PB2( - Trainable -): # https://docs.ray.io/en/master/tune/examples/pbt_tune_cifar10_with_keras.html - def setup(self, config, x_train, x_test, y_train, y_test): - self.x_train = x_train - self.x_test = x_test - self.y_train = y_train - self.y_test = y_test - self.model = RandomForestClassifier( - random_state=42, - n_estimators=self.config.get("n_estimators", 100), - oob_score=self.config.get("oob_score", False), - min_samples_split=self.config.get("min_samples_split", 2), - min_samples_leaf=self.config.get("min_samples_leaf", 1), - criterion=self.config.get("criterion", "gini"), - max_features=self.config.get("max_features", "sqrt"), - class_weight=self.config.get("class_weight", "balanced"), - n_jobs=-1, - ) - - def reset_config(self, new_config): - self.n_estimators = new_config["n_estimators"] - self.min_samples_split = new_config["min_samples_split"] - self.min_samples_leaf = new_config["min_samples_leaf"] - self.criterion = new_config["criterion"] - self.max_features = new_config["max_features"] - self.class_weight = new_config["class_weight"] - self.oob_score = new_config["oob_score"] - self.config = new_config - return True - - def step(self): - score = cross_validate( - self.model, self.x_train, self.y_train, cv=3, n_jobs=-1, verbose=0 - ) - testing_score = np.mean(score["test_score"]) - # print(accuracy) - return {"mean_accuracy": testing_score} - - def save_checkpoint(self, checkpoint_dir): - file_path = checkpoint_dir + "/model" - pickle.dump(self.model, open(file_path, "wb")) - return file_path - - def load_checkpoint(self, path): - # See https://stackoverflow.com/a/42763323 - del self.model - self.model = pickle.load(open(path, "rb")) - - def cleanup(self): - # If need, save your model when exit. - # saved_path = self.model.save(self.logdir) - # print("save model at: ", saved_path) - pass - - -def results(config, x_train, x_test, y_train, y_test, var, output, feature_names): - start1 = time.perf_counter() - # self.x_train, self.x_test, self.y_train, self.y_test, self.feature_names = self._read_data(config) - clf = RandomForestClassifier( - random_state=42, - n_estimators=config.get("n_estimators", 100), - oob_score=config.get("oob_score", False), - min_samples_split=config.get("min_samples_split", 2), - min_samples_leaf=config.get("min_samples_leaf", 1), - criterion=config.get("criterion", "gini"), - max_features=config.get("max_features", "sqrt"), - class_weight=config.get("class_weight", "balanced"), - n_jobs=-1, - ) - score = cross_validate( - clf, - x_train, - y_train, - cv=10, - return_train_score=True, - return_estimator=True, - n_jobs=-1, - verbose=0, - ) - clf = score["estimator"][np.argmax(score["test_score"])] - training_score = np.mean(score["train_score"]) - testing_score = np.mean(score["test_score"]) - y_score = clf.predict(x_test) - prc = precision_score(y_test, y_score, average="weighted") - recall = recall_score(y_test, y_score, average="weighted") - roc_auc = roc_auc_score(y_test, y_score) - accuracy = accuracy_score(y_test, y_score) - matrix = confusion_matrix(y_test, y_score) - finish = (time.perf_counter() - start1) / 60 - print( - f"RandomForestClassifier: \nCross_validate(avg_train_score): {training_score}\nCross_validate(avg_test_score): {testing_score}\nPrecision: {prc}\nRecall: {recall}\nROC_AUC: {roc_auc}\nAccuracy: {accuracy}\nTime(in min): {finish}\nConfusion Matrix:\n{matrix}", - file=open(output, "a"), - ) - # explain all the predictions in the test set - background = shap.kmeans(x_train, 10) - explainer = shap.KernelExplainer(clf.predict, background) - with open(f"./tuning/{var}/RandomForestClassifier_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - del clf, x_train, y_train, background - background = x_test[np.random.choice(x_test.shape[0], 1000, replace=False)] - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"./tuning/{var}/RandomForestClassifier_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - return None - - -def wrap_trainable(trainable, x_train, x_test, y_train, y_test): - x_train_id = ray.put(x_train) - x_test_id = ray.put(x_test) - y_train_id = ray.put(y_train) - y_test_id = ray.put(y_test) - - class _Wrapped(trainable): - def setup(self, config): - x_train = ray.get(x_train_id) - x_test = ray.get(x_test_id) - y_train = ray.get(y_train_id) - y_test = ray.get(y_test_id) - - super(_Wrapped, self).setup(config, x_train, x_test, y_train, y_test) - - return _Wrapped - - -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - # Load data - print(f"\nUsing merged_data-train_{var}..", file=open(output, "a")) - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - X_train.replace([np.inf, -np.inf], np.nan, inplace=True) - X_train.fillna(0, inplace=True) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - return X_train, X_test, Y_train, Y_test, feature_names - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--vtype", - type=str, - default="non_snv", - help="Type of variation/s (without spaces between) to tune the classifier (like: snv,non_snv,snv_protein_coding). (Default: non_snv)", - ) - - args = parser.parse_args() - - variants = args.vtype.split(",") - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - with open("../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - # variants = ['non_snv','snv','snv_protein_coding'] - for var in variants: - - pbt = PB2( - time_attr="training_iteration", - # metric="mean_accuracy", - # mode="max", - perturbation_interval=20, - # resample_probability=0.25, - quantile_fraction=0.25, # copy bottom % with top % - log_config=True, - # Specifies the search space for these hyperparams - hyperparam_bounds={ - "n_estimators": [50, 200], - "min_samples_split": [2, 6], - "min_samples_leaf": [1, 4], - }, - ) - - start = time.perf_counter() - if not os.path.exists("tuning/" + var): - os.makedirs("./tuning/" + var) - output = f"tuning/{var}/RandomForestClassifier_{var}_.csv" - print("Working with " + var + " dataset...", file=open(output, "w")) - print("Working with " + var + " dataset...") - - x_train, x_test, y_train, y_test, feature_names = data_parsing( - var, config_dict, output - ) - - analysis = run( - wrap_trainable(RF_PB2, x_train, x_test, y_train, y_test), - name=f"RandomForestClassifier_PB2_{var}", - verbose=1, - scheduler=pbt, - reuse_actors=True, - local_dir="./ray_results", - max_failures=2, - resources_per_trial={ - "cpu": 10, - # "gpu": 1 - }, - # global_checkpoint_period=np.inf, # Do not save checkpoints based on time interval - checkpoint_freq=20, # Save checkpoint every time the checkpoint_score_attr improves - checkpoint_at_end=True, - keep_checkpoints_num=2, # Keep only the best checkpoint - checkpoint_score_attr="mean_accuracy", # Metric used to compare checkpoints - metric="mean_accuracy", - mode="max", - stop={ - "training_iteration": 100, - }, - num_samples=10, - # fail_fast=True, - queue_trials=True, - config={ # https://www.geeksforgeeks.org/hyperparameters-of-random-forest-classifier/ - "n_estimators": tune.randint(10, 200), - "min_samples_split": tune.randint(1, 10), - "min_samples_leaf": tune.randint(2, 10), - "criterion": tune.choice(["gini", "entropy"]), - "max_features": tune.choice([None, "sqrt", "log2"]), - "class_weight": tune.choice([None, "balanced", "balanced_subsample"]), - "oob_score": tune.choice([True, False]), - }, - ) - finish = (time.perf_counter() - start) / 120 - # ttime = (finish- start)/120 - print(f"Total time in min: {finish}") - config = analysis.best_config - print( - f"RandomForestClassifier_{var}: {config}", - file=open(f"tuning/tuned_parameters.csv", "a"), - ) - results(config, x_train, x_test, y_train, y_test, var, output, feature_names) - gc.collect() diff --git a/src/training/training/temp_files/Tune_RF_PBT.py b/src/training/training/temp_files/Tune_RF_PBT.py deleted file mode 100644 index af84fcc..0000000 --- a/src/training/training/temp_files/Tune_RF_PBT.py +++ /dev/null @@ -1,241 +0,0 @@ -# from numpy import mean -import numpy as np -import pandas as pd -import time -import ray -import sys -import argparse -import pickle -from ray import tune -from ray.tune import Trainable -from ray.tune.schedulers import PopulationBasedTraining -from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import label_binarize -from sklearn.metrics import average_precision_score -from sklearn.metrics import confusion_matrix -from sklearn.tree import DecisionTreeClassifier -from sklearn.ensemble import ( - RandomForestClassifier, - AdaBoostClassifier, - GradientBoostingClassifier, - BaggingClassifier, - ExtraTreesClassifier, -) -from imblearn.ensemble import BalancedRandomForestClassifier -from imblearn.ensemble import EasyEnsembleClassifier - -import os - -TUNE_STATE_REFRESH_PERIOD = 10 # Refresh resources every 10 s - - -class Ditto( - Trainable -): # https://docs.ray.io/en/master/tune/examples/pbt_tune_cifar10_with_keras.html - def _read_data(self): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - X = pd.read_csv("clinvar-md.csv") - var = X[["AAChange.refGene", "ID"]] - X = X.drop(["AAChange.refGene", "ID"], axis=1) - X = X.values - # X[1] - # var - y = pd.read_csv("clinvar-y-md.csv") - # Y = pd.get_dummies(y) - Y = label_binarize( - y.values, classes=["Benign", "Pathogenic"] - ) #'Benign', 'Likely_benign', 'Uncertain_significance', 'Likely_pathogenic', 'Pathogenic' - X_train, X_test, Y_train, Y_test = train_test_split( - X, Y, test_size=0.30, random_state=42 - ) - scaler = StandardScaler().fit(X_train) - X_train = scaler.transform(X_train) - X_test = scaler.transform(X_test) - - return (X_train, Y_train), (X_test, Y_test) - - def setup(self, config): - self.train_data, self.test_data = self._read_data() - x_train = self.train_data[0] - model = RandomForestClassifier( - random_state=42, - n_estimators=self.config.get("n_estimators", 100), - min_samples_split=self.config.get("min_samples_split", 2), - min_samples_leaf=self.config.get("min_samples_leaf", 1), - max_features=self.config.get("max_features", "sqrt"), - n_jobs=-1, - ) - # model = RandomForestClassifier(config) - self.model = model - - def reset_config(self, new_config): - self.n_estimators = new_config["n_estimators"] - self.min_samples_split = new_config["min_samples_split"] - self.min_samples_leaf = new_config["min_samples_leaf"] - self.max_features = new_config["max_features"] - self.config = new_config - return True - - def step(self): - x_train, y_train = self.train_data - # x_train, y_train = x_train[:NUM_SAMPLES], y_train[:NUM_SAMPLES] - x_test, y_test = self.test_data - # x_test, y_test = x_test[:NUM_SAMPLES], y_test[:NUM_SAMPLES] - self.model.fit(x_train, y_train.ravel()) - y_score = self.model.predict_proba(x_test) - accuracy = average_precision_score( - y_test, np.argmax(y_score, axis=1), average=None - ) - print(accuracy) - return {"mean_accuracy": accuracy} - - def save_checkpoint(self, checkpoint_dir): - file_path = checkpoint_dir + "/model" - pickle.dump(self.model, open(file_path, "wb")) - return file_path - - def load_checkpoint(self, path): - # See https://stackoverflow.com/a/42763323 - del self.model - self.model = pickle.load(open(path, "rb")) - - def cleanup(self): - # If need, save your model when exit. - # saved_path = self.model.save(self.logdir) - # print("save model at: ", saved_path) - pass - - def results(self, config): - X_train, Y_train = self.train_data - # x_train, y_train = x_train[:NUM_SAMPLES], y_train[:NUM_SAMPLES] - X_test, Y_test = self.test_data - # x_test, y_test = x_test[:NUM_SAMPLES], y_test[:NUM_SAMPLES] - - start = time.perf_counter() - clf = RandomForestClassifier( - random_state=42, - n_estimators=config["n_estimators"], - min_samples_split=config["min_samples_split"], - min_samples_leaf=config["min_samples_leaf"], - max_features=config["max_features"], - n_jobs=-1, - ) - - clf.fit(X_train, Y_train) - y_score = clf.predict_proba(X_test) - prc = average_precision_score(Y_test, np.argmax(y_score, axis=1), average=None) - prc_micro = average_precision_score( - Y_test, np.argmax(y_score, axis=1), average="micro" - ) - score = clf.score(X_train, Y_train) - # matrix = confusion_matrix(np.argmax(Y_test, axis=1), np.argmax(y_score, axis=1)) - matrix = confusion_matrix(Y_test, np.argmax(y_score, axis=1)) - finish = (time.perf_counter() - start) / 60 - list1 = [clf, prc, prc_micro, score, matrix, finish] - print( - "Model\tprecision_score\taverage_precision_score\tTrain_score\tTime(min)\tConfusion_matrix[Benign, Pathogenic]" - ) - print(f"{clf}\n{prc}\t{prc_micro}\t{score}\t{finish}\n{matrix}") - return clf - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - args, _ = parser.parse_known_args() - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init() - - classifiers = [ - ( - "RandomForest(sklearn)", - RandomForestClassifier( - random_state=42, - n_estimators=100, - min_samples_split=2, - min_samples_leaf=1, - max_features="sqrt", - n_jobs=-1, - ), - { # https://www.geeksforgeeks.org/hyperparameters-of-random-forest-classifier/ - "n_estimators": tune.randint(50, 200), - "min_samples_split": tune.randint(2, 6), - "min_samples_leaf": tune.randint(1, 4), - "max_features": tune.choice(["sqrt", "log2"]), - }, - { - "n_estimators": tune.randint(50, 200), - "min_samples_split": tune.randint(2, 6), - "min_samples_leaf": tune.randint(1, 4), - }, - ), - # ('BalancedRandomForest(imblearn)', BalancedRandomForestClassifier(random_state=42, n_estimators=300, max_depth=4, min_samples_split=2, max_features='sqrt'), - # { - # 'n_estimators' : [100, 200, 300], - # 'max_depth' : [2, 3, 4], - # 'min_samples_split' : [2, 3], - # 'max_features' : ["sqrt", "log2"] - # }), - ##('imb_rus', RUSBoostClassifier(random_state=0)), #this one never seems to have a good result on the test set, I think it's overfitting due to boosting - # ('EasyEnsembleClassifier(imblearn)', EasyEnsembleClassifier(random_state=42, n_estimators=50), - # { - # 'n_estimators' : [10, 20, 30, 40, 50] - # }) - ] - - pbt = PopulationBasedTraining( - time_attr="training_iteration", - # metric="mean_accuracy", - # mode="max", - perturbation_interval=20, - resample_probability=0.25, - quantile_fraction=0.25, # copy bottom % with top % - # Specifies the search space for these hyperparams - hyperparam_mutations=classifiers[0][3], - ) - - analysis = tune.run( - Ditto, - name="pbt_test", - verbose=0, - scheduler=pbt, - reuse_actors=True, - checkpoint_freq=20, - resources_per_trial={ - # "cpu": 1, - "gpu": 1 - }, - metric="mean_accuracy", - mode="max", - stop={ - "training_iteration": 100, - }, - num_samples=4, - fail_fast=True, - config=classifiers[0][2], - ) - - print("Model: ", classifiers[0][0]) - # config = analysis.best_config - print("Best hyperparameters found were: ", analysis.best_config) - - clf = Ditto().results(analysis.best_config) - pickle.dump(clf, open("Randomforest.pkl", "wb")) - - -# Classifiers I wish to use -# classifiers = [ -# DecisionTreeClassifier(), -# RandomForestClassifier(random_state=42), -# params) -# GradientBoostingClassifier(), -# ExtraTreesClassifier(), -# BalancedRandomForestClassifier() -# ] diff --git a/src/training/training/temp_files/Tune_hp_stacking.py b/src/training/training/temp_files/Tune_hp_stacking.py deleted file mode 100644 index 5a296d1..0000000 --- a/src/training/training/temp_files/Tune_hp_stacking.py +++ /dev/null @@ -1,617 +0,0 @@ -import numpy as np -import pandas as pd -import ray -import time -import argparse -import pickle -from joblib import dump -import yaml - -# from ray import tune -from ray.tune import Trainable, run -from hyperopt import hp -from ray.tune.suggest.hyperopt import HyperOptSearch - -# from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import cross_validate # , StratifiedKFold -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) -from sklearn.tree import DecisionTreeClassifier -from sklearn.linear_model import LogisticRegression # SGDClassifier, -from sklearn.ensemble import ( - RandomForestClassifier, - GradientBoostingClassifier, - StackingClassifier, -) -from sklearn.naive_bayes import GaussianNB -from imblearn.ensemble import BalancedRandomForestClassifier -from sklearn.discriminant_analysis import LinearDiscriminantAnalysis -from sklearn.neighbors import KNeighborsClassifier -import os -import gc -import shap -from joblib import dump, load -import matplotlib.pyplot as plt -import warnings - -warnings.simplefilter("ignore") -import functools - -print = functools.partial(print, flush=True) - -TUNE_STATE_REFRESH_PERIOD = 10 # Refresh resources every 10 s - - -def f_unpack_dict(dct): - """ - Unpacks all sub-dictionaries in given dictionary recursively. There should be no duplicated keys - across all nested subdictionaries, or some instances will be lost without warning - - Source: https://www.kaggle.com/fanvacoolt/tutorial-on-hyperopt - - Parameters: - ---------------- - dct : dictionary to unpack - - Returns: - ---------------- - : unpacked dictionary - """ - - res = {} - for (k, v) in dct.items(): - if isinstance(v, dict): - res = {**res, **f_unpack_dict(v)} - else: - res[k] = v - - return res - - -class stacking( - Trainable -): # https://docs.ray.io/en/master/hp/examples/pbt_hp_cifar10_with_keras.html - def setup(self, config, x_train, x_test, y_train, y_test): - self.x_train = x_train - self.x_test = x_test - self.y_train = y_train - self.y_test = y_test - self.config = f_unpack_dict(config) - self.model = StackingClassifier( - estimators=[ - ( - "rf", - RandomForestClassifier( - random_state=42, - n_estimators=self.config.get("rf__n_estimators", 100), - criterion=self.config.get("rf__criterion", "gini"), - max_depth=self.config.get("rf__max_depth", 2), - min_samples_split=self.config.get("rf__min_samples_split", 2), - min_samples_leaf=self.config.get("rf__min_samples_leaf", 1), - max_features=self.config.get("rf__max_features", "sqrt"), - oob_score=self.config.get("rf__oob_score", False), - class_weight=self.config.get("rf__class_weight", "balanced"), - n_jobs=-1, - ), - ), - # ('knn', KNeighborsClassifier(n_neighbors=self.config.get('knn__n_neighbors', 1), weights=self.config.get('knn__weights', 'uniform'), algorithm=self.config.get('knn__algorithm', 'auto'), p=config.get('knn__p', 1), metric=self.config.get('knn__metric', 'minkowski'), n_jobs = -1)), #leaf_size=self.config.get('leaf_size', 30), - ( - "gbc", - GradientBoostingClassifier( - random_state=42, - loss=self.config.get("gbc__loss", 100), - learning_rate=self.config.get("gbc__learning_rate", 0.1), - n_estimators=self.config.get("gbc__n_estimators", 100), - subsample=self.config.get("gbc__subsample", 1), - criterion=self.config.get("gbc__criterion", "friedman_mse"), - min_samples_split=self.config.get("gbc__min_samples_split", 2), - min_samples_leaf=self.config.get("gbc__min_samples_leaf", 1), - max_depth=self.config.get("gbc__max_depth", 2), - max_features=self.config.get("gbc__max_features", "sqrt"), - ), - ), - ( - "dt", - DecisionTreeClassifier( - random_state=42, - criterion=self.config.get("dt__criterion", "gini"), - splitter=self.config.get("dt__splitter", "best"), - max_depth=self.config.get("dt__max_depth", 2), - min_samples_split=self.config.get("dt__min_samples_split", 2), - min_samples_leaf=self.config.get("dt__min_samples_leaf", 1), - max_features=self.config.get("dt__max_features", "sqrt"), - class_weight=self.config.get("dt__class_weight", "balanced"), - ), - ), - ( - "gnb", - GaussianNB( - var_smoothing=self.config.get("gnb__var_smoothing", 1e-09) - ), - ), - ( - "brf", - BalancedRandomForestClassifier( - random_state=42, - n_estimators=self.config.get("brf__n_estimators", 100), - criterion=self.config.get("brf__criterion", "gini"), - max_depth=self.config.get("brf__max_depth", 2), - min_samples_split=self.config.get("brf__min_samples_split", 2), - min_samples_leaf=self.config.get("brf__min_samples_leaf", 1), - max_features=self.config.get("brf__max_features", "sqrt"), - oob_score=self.config.get("brf__oob_score", False), - class_weight=self.config.get("brf__class_weight", "balanced"), - n_jobs=-1, - ), - ), - ( - "lda", - LinearDiscriminantAnalysis( - solver=self.config.get("lda_solver", "svd"), - shrinkage=self.config.get("lda_shrinkage", None), - ), - ), - ], - cv=5, - stack_method="predict_proba", - n_jobs=-1, - passthrough=False, - final_estimator=LogisticRegression( - C=self.config.get("lr__C", 1), - penalty=self.config.get("lr__penalty", "l2"), - solver=self.config.get("lr__solver", "lbfgs"), - max_iter=self.config.get("lr__max_iter", 100), - l1_ratio=self.config.get("lr__l1_ratio", 0), - tol=self.config.get("lr__tol", 1e-4), - n_jobs=-1, - ), - verbose=0, - ) # .set_params(**f_unpack_dict(config)) - - def reset_config(self, new_config): - self.rf__n_estimators = new_config["rf__n_estimators"] - self.rf__criterion = new_config["rf__criterion"] - self.rf__max_depth = new_config["rf__max_depth"] - self.rf__min_samples_split = new_config["rf__min_samples_split"] - self.rf__min_samples_leaf = new_config["rf__min_samples_leaf"] - self.rf__max_features = new_config["rf__max_features"] - # self.rf__oob_score = new_config['rf__oob_score'] - self.rf__class_weight = new_config["rf__class_weight"] - self.knn__n_neighbors = new_config["knn__n_neighbors"] - self.knn__weights = new_config["knn__weights"] - self.knn__algorithm = new_config["knn__algorithm"] - self.knn__p = new_config["knn__p"] - self.knn__metric = new_config["knn__metric"] - self.gbc__loss = new_config["gbc__loss"] - self.gbc__learning_rate = new_config["gbc__learning_rate"] - self.gbc__n_estimators = new_config["gbc__n_estimators"] - self.gbc__subsample = new_config["gbc__subsample"] - self.gbc__criterion = new_config["gbc__criterion"] - self.gbc__min_samples_split = new_config["gbc__min_samples_split"] - self.gbc__min_samples_leaf = new_config["gbc__min_samples_leaf"] - self.gbc__max_depth = new_config["gbc__max_depth"] - self.gbc__max_features = new_config["gbc__max_features"] - self.dt__criterion = new_config["dt__criterion"] - self.dt__splitter = new_config["dt__splitter"] - self.dt__max_depth = new_config["dt__max_depth"] - self.dt__min_samples_split = new_config["dt__min_samples_split"] - self.dt__min_samples_leaf = new_config["dt__min_samples_leaf"] - self.dt__max_features = new_config["dt__max_features"] - self.dt__class_weight = new_config["dt__class_weight"] - self.gnb__var_smoothing = new_config["gnb__var_smoothing"] - self.brf__n_estimators = new_config["brf__n_estimators"] - self.brf__criterion = new_config["brf__criterion"] - self.brf__max_depth = new_config["brf__max_depth"] - self.brf__min_samples_split = new_config["brf__min_samples_split"] - self.brf__min_samples_leaf = new_config["brf__min_samples_leaf"] - self.brf__max_features = new_config["brf__max_features"] - # self.brf__oob_score = new_config['brf__oob_score'] - self.brf__class_weight = new_config["brf__class_weight"] - self.lda_solver = new_config["lda_solver"] - self.lda_shrinkage = new_config["lda_shrinkage"] - self.lr__C = new_config["lr__C"] - self.lr__solver = new_config["lr__solver"] - self.lr__penalty = new_config["lr__penalty"] - self.lr__tol = new_config["lr__tol"] - self.lr__l1_ratio = new_config["lr__l1_ratio"] - self.lr__max_iter = new_config["lr__max_iter"] - self.config = new_config - return True - - def step(self): - # score = cross_validate(self.model, self.x_train, self.y_train, cv=3, n_jobs=-1, verbose=0) - # testing_score = np.max(score['test_score']) - testing_score = self.model.fit(self.x_train, self.y_train).accuracy_score( - self.x_test, self.y_test - ) - return {"mean_accuracy": testing_score} - - def save_checkpoint(self, checkpoint_dir): - file_path = checkpoint_dir + "/model" - pickle.dump(self.model, open(file_path, "wb")) - return file_path - - def load_checkpoint(self, path): - # See https://stackoverflow.com/a/42763323 - del self.model - self.model = pickle.load(open(path, "rb")) - - def cleanup(self): - # If need, save your model when exit. - # saved_path = self.model.save(self.logdir) - # print('save model at: ', saved_path) - pass - - -def results(config, x_train, x_test, y_train, y_test, var, output, feature_names): - start1 = time.perf_counter() - config = f_unpack_dict(config) - # self.x_train, self.x_test, self.y_train, self.y_test, self.feature_names = self._read_data(config) - clf = StackingClassifier( - estimators=[ - ( - "rf", - RandomForestClassifier( - random_state=42, - n_estimators=config.get("rf__n_estimators", 100), - criterion=config.get("rf__criterion", "gini"), - max_depth=config.get("rf__max_depth", 2), - min_samples_split=config.get("rf__min_samples_split", 2), - min_samples_leaf=config.get("rf__min_samples_leaf", 1), - max_features=config.get("rf__max_features", "sqrt"), - oob_score=config.get("rf__oob_score", False), - class_weight=config.get("rf__class_weight", "balanced"), - n_jobs=-1, - ), - ), - # ('knn', KNeighborsClassifier(n_neighbors=config.get('knn__n_neighbors', 1), weights=config.get('knn__weights', 'uniform'), algorithm=config.get('knn__algorithm', 'auto'), p=config.get('knn__p', 1), metric=config.get('knn__metric', 'minkowski'), n_jobs = -1)), #leaf_size=config.get('leaf_size', 30), - ( - "gbc", - GradientBoostingClassifier( - random_state=42, - loss=config.get("gbc__loss", 100), - learning_rate=config.get("gbc__learning_rate", 0.1), - n_estimators=config.get("gbc__n_estimators", 100), - subsample=config.get("gbc__subsample", 1), - criterion=config.get("gbc__criterion", "friedman_mse"), - min_samples_split=config.get("gbc__min_samples_split", 2), - min_samples_leaf=config.get("gbc__min_samples_leaf", 1), - max_depth=config.get("gbc__max_depth", 2), - max_features=config.get("gbc__max_features", "sqrt"), - ), - ), - ( - "dt", - DecisionTreeClassifier( - random_state=42, - criterion=config.get("dt__criterion", "gini"), - splitter=config.get("dt__splitter", "best"), - max_depth=config.get("dt__max_depth", 2), - min_samples_split=config.get("dt__min_samples_split", 2), - min_samples_leaf=config.get("dt__min_samples_leaf", 1), - max_features=config.get("dt__max_features", "sqrt"), - class_weight=config.get("dt__class_weight", "balanced"), - ), - ), - ("gnb", GaussianNB(var_smoothing=config.get("gnb__var_smoothing", 1e-09))), - ( - "brf", - BalancedRandomForestClassifier( - random_state=42, - n_estimators=config.get("brf__n_estimators", 100), - criterion=config.get("brf__criterion", "gini"), - max_depth=config.get("brf__max_depth", 2), - min_samples_split=config.get("brf__min_samples_split", 2), - min_samples_leaf=config.get("brf__min_samples_leaf", 1), - max_features=config.get("brf__max_features", "sqrt"), - oob_score=config.get("brf__oob_score", False), - class_weight=config.get("brf__class_weight", "balanced"), - n_jobs=-1, - ), - ), - ( - "lda", - LinearDiscriminantAnalysis( - solver=config.get("lda_solver", "svd"), - shrinkage=config.get("lda_shrinkage", None), - ), - ), - ], - cv=5, - stack_method="predict_proba", - n_jobs=-1, - passthrough=False, - final_estimator=LogisticRegression( - C=config.get("lr__C", 1), - penalty=config.get("lr__penalty", "l2"), - solver=config.get("lr__solver", "lbfgs"), - max_iter=config.get("lr__max_iter", 100), - l1_ratio=config.get("lr__l1_ratio", 0), - tol=config.get("lr__tol", 1e-4), - n_jobs=-1, - ), - verbose=0, - ).fit(x_train, y_train) - # score = cross_validate(clf, x_train, y_train, cv=StratifiedKFold(n_splits=5,shuffle=True,random_state=42), return_train_score=True, return_estimator=True, n_jobs=-1, verbose=0) - - train_score = clf.score(x_train, y_train) - with open(f"./tuning/{var}/StackingClassifier_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # training_score = np.mean(score['train_score']) - # testing_score = np.mean(score['test_score']) - y_score = clf.predict(x_test) - prc = precision_score(y_test, y_score, average="weighted") - recall = recall_score(y_test, y_score, average="weighted") - roc_auc = roc_auc_score(y_test, y_score) - accuracy = accuracy_score(y_test, y_score) - matrix = confusion_matrix(y_test, y_score) - finish = (time.perf_counter() - start1) / 60 - # print(f'RandomForestClassifier: \nCross_validate(avg_train_score): {training_score}\nCross_validate(avg_test_score): {testing_score}\nPrecision: {prc}\nRecall: {recall}\nROC_AUC: {roc_auc}\nAccuracy: {accuracy}\nTime(in min): {finish}\nConfusion Matrix:\n{matrix}', file=open(output, 'a')) - clf_name = str(type(clf)).split("'")[1] # .split('.')[3] - print( - "Model\ttrain_score\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - print( - f"{clf_name}\t{train_score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del y_test - # explain all the predictions in the test set - background = shap.kmeans(x_train, 10) - explainer = shap.KernelExplainer(clf.predict, background) - del clf, x_train, y_train, background - background = x_test[np.random.choice(x_test.shape[0], 1000, replace=False)] - del x_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"./tuning/{var}/StackingClassifier_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, feature_names - print(f"training and testing done!") - return None - - -def wrap_trainable(trainable, x_train, x_test, y_train, y_test): - x_train_id = ray.put(x_train) - x_test_id = ray.put(x_test) - y_train_id = ray.put(y_train) - y_test_id = ray.put(y_test) - - class _Wrapped(trainable): - def setup(self, config): - x_train = ray.get(x_train_id) - x_test = ray.get(x_test_id) - y_train = ray.get(y_train_id) - y_test = ray.get(y_test_id) - - super(_Wrapped, self).setup(config, x_train, x_test, y_train, y_test) - - return _Wrapped - - -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - # Load data - print(f"\nUsing merged_data-train_{var}..", file=open(output, "a")) - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - X_train.replace([np.inf, -np.inf], np.nan, inplace=True) - X_train.fillna(0, inplace=True) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - return X_train, X_test, Y_train, Y_test, feature_names - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--vtype", - type=str, - default="non_snv", - help="Type of variation/s (without spaces between) to hp the classifier (like: snv,non_snv,snv_protein_coding). (Default: non_snv)", - ) - - args = parser.parse_args() - - variants = args.vtype.split(",") - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - with open("../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - # variants = ['non_snv','snv','snv_protein_coding'] - for var in variants: - - start = time.perf_counter() - if not os.path.exists("tuning/" + var): - os.makedirs("./tuning/" + var) - output = "tuning/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, 'w')) - print("Working with " + var + " dataset...") - - x_train, x_test, y_train, y_test, feature_names = data_parsing( - var, config_dict, output - ) - - config = { - # RandomForest - https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html?highlight=randomforestclassifier#sklearn.ensemble.RandomForestClassifier - "rf__n_estimators": hp.randint("rf__n_estimators", 1, 200), - "rf__criterion": hp.choice("rf__criterion", ["gini", "entropy"]), - "rf__max_depth": hp.randint("rf__max_depth", 2, 200), - "rf__min_samples_split": hp.randint("rf__min_samples_split", 2, 100), - "rf__min_samples_leaf": hp.randint("rf__min_samples_leaf", 1, 100), - "rf__max_features": hp.choice("rf__max_features", ["sqrt", "log2"]), - #'rf__oob_score' : hp.choice('rf__oob_score', [True, False]), - "rf__class_weight": hp.choice( - "rf__class_weight", ["balanced", "balanced_subsample"] - ), - # GradientBoostingClassifier - https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier - "gbc__loss": hp.choice("gbc__loss", ["deviance", "exponential"]), - "gbc__learning_rate": hp.loguniform("gbc__learning_rate", 0.01, 1.0), - "gbc__n_estimators": hp.randint("gbc__n_estimators", 1, 200), - "gbc__subsample": hp.uniform("gbc__subsample", 0.1, 1.0), - "gbc__criterion": hp.choice("gbc__criterion", ["friedman_mse", "mse"]), - "gbc__min_samples_split": hp.randint("gbc__min_samples_split", 2, 100), - "gbc__min_samples_leaf": hp.randint("gbc__min_samples_leaf", 1, 100), - "gbc__max_depth": hp.randint("gbc__max_depth", 2, 200), - "gbc__max_features": hp.choice("gbc__max_features", ["sqrt", "log2"]), - # DecisionTree - https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier - "dt__criterion": hp.choice("dt__criterion", ["gini", "entropy"]), - "dt__splitter": hp.choice("dt__splitter", ["best", "random"]), - "dt__max_depth": hp.randint("dt__max_depth", 2, 200), - "dt__min_samples_split": hp.randint("dt__min_samples_split", 2, 100), - "dt__min_samples_leaf": hp.randint("dt__min_samples_leaf", 1, 100), - "dt__max_features": hp.choice("dt__max_features", ["sqrt", "log2"]), - "dt__class_weight": hp.choice("dt__class_weight", ["balanced"]), - # GaussianNB - https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB - "gnb__var_smoothing": hp.loguniform("gnb__var_smoothing", 1e-11, 1e-1), - # BalancedRandomForest - https://imbalanced-learn.org/dev/references/generated/imblearn.ensemble.BalancedRandomForestClassifier.html - "brf__n_estimators": hp.randint("brf__n_estimators", 1, 200), - "brf__criterion": hp.choice("brf__criterion", ["gini", "entropy"]), - "brf__max_depth": hp.randint("brf__max_depth", 2, 200), - "brf__min_samples_split": hp.randint("brf__min_samples_split", 2, 10), - "brf__min_samples_leaf": hp.randint("brf__min_samples_leaf", 1, 10), - "brf__max_features": hp.choice("brf__max_features", ["sqrt", "log2"]), - #'brf__oob_score' : hp.choice('brf__oob_score', [True, False]), - "brf__class_weight": hp.choice( - "brf__class_weight", ["balanced", "balanced_subsample"] - ), - # LinearDiscriminantAnalysis - https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis - "lda_solver": hp.choice( - "lda_solver", - [ - {"lda_solver": "svd"}, - { - "lda_solver": "lsqr", - "lda_shrinkage": hp.choice( - "shrinkage_type_lsqr", - ["auto", hp.uniform("shrinkage_value_lsqr", 0, 1)], - ), - }, - { - "lda_solver": "eigen", - "lda_shrinkage": hp.choice( - "shrinkage_type_eigen", - ["auto", hp.uniform("shrinkage_value_eigen", 0, 1)], - ), - }, - ], - ), - # LogisticRegression - https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic#sklearn.linear_model.LogisticRegression; https://github.com/hyperopt/hyperopt/issues/304 - "lr__C": hp.uniform("lr__C", 0.0, 100.0), - "lr__solver": hp.choice( - "lr__solver", - [ - { - "lr__solver": "newton-cg", - "lr__penalty": hp.choice("p_newton", ["none", "l2"]), - }, - { - "lr__solver": "lbfgs", - "lr__penalty": hp.choice("p_lbfgs", ["none", "l2"]), - }, - { - "lr__solver": "liblinear", - "lr__penalty": hp.choice("p_lib", ["l1", "l2"]), - }, - { - "lr__solver": "sag", - "lr__penalty": hp.choice("p_sag", ["l2", "none"]), - }, - { - "lr__solver": "saga", - "lr__penalty": "elasticnet", - "lr__l1_ratio": hp.uniform("lr__l1_ratio", 0, 1), - }, - ], - ), - "lr__tol": hp.loguniform("lr__tol", 1e-13, 1e-1), - "lr__max_iter": hp.randint("lr__max_iter", 2, 100), - } - hyperopt_search = HyperOptSearch(config, metric="mean_accuracy", mode="max") - scheduler = AsyncHyperBandScheduler() - - analysis = run( - wrap_trainable(stacking, x_train, x_test, y_train, y_test), - name=f"StackingClassifier_{var}", - verbose=1, - scheduler=scheduler, - search_alg=hyperopt_search, - reuse_actors=True, - local_dir="./ray_results", - max_failures=2, - # resources_per_trial={ - # 'cpu': 10, - # 'gpu': 1 - # }, - # global_checkpoint_period=np.inf, # Do not save checkpoints based on time interval - checkpoint_freq=20, # Save checkpoint every time the checkpoint_score_attr improves - checkpoint_at_end=True, - keep_checkpoints_num=2, # Keep only the best checkpoint - checkpoint_score_attr="mean_accuracy", # Metric used to compare checkpoints - metric="mean_accuracy", - mode="max", - stop={ - "training_iteration": 10, - }, - num_samples=300, - # fail_fast=True, - queue_trials=True, - ) - - finish = (time.perf_counter() - start) / 120 - # ttime = (finish- start)/120 - print(f"Total time in min: {finish}") - config = analysis.best_config - print( - f"StackingClassifier_{var}: {config}", - file=open(f"tuning/tuned_parameters.csv", "a"), - ) - results(config, x_train, x_test, y_train, y_test, var, output, feature_names) - gc.collect() diff --git a/src/training/training/temp_files/Tune_models.py b/src/training/training/temp_files/Tune_models.py deleted file mode 100644 index 2aac804..0000000 --- a/src/training/training/temp_files/Tune_models.py +++ /dev/null @@ -1,327 +0,0 @@ -# from numpy import mean -import numpy as np -import pandas as pd -import time -import ray -from ray import tune -from tune_sklearn import TuneSearchCV - -# Start Ray. -ray.init(ignore_reinit_error=True) -import warnings - -warnings.simplefilter("ignore") -import joblib -from joblib import dump, load -import shap - -# from sklearn.preprocessing import StandardScaler -# from sklearn.feature_selection import RFE -# from sklearn.preprocessing import MinMaxScaler -from sklearn.model_selection import cross_validate -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) - -# from sklearn.multiclass import OneVsRestClassifier -from sklearn.tree import DecisionTreeClassifier -from sklearn.linear_model import SGDClassifier -from sklearn.ensemble import ( - RandomForestClassifier, - AdaBoostClassifier, - GradientBoostingClassifier, - ExtraTreesClassifier, -) -from sklearn.naive_bayes import GaussianNB -from imblearn.ensemble import BalancedRandomForestClassifier -from sklearn.neural_network import MLPClassifier -from sklearn.discriminant_analysis import LinearDiscriminantAnalysis -import matplotlib.pyplot as plt -import yaml -from functools import partial - -print = partial(print, flush=True) -import os -from ray.util.multiprocessing import Pool - -print( - f"""This cluster consists of - {len(ray.nodes())} nodes in total - {ray.cluster_resources()['CPU']} CPU resources in total -""" -) - - -@ray.remote -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - # Load data - print(f"\nUsing merged_data-train_{var}..", file=open(output, "a")) - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - # background = shap.kmeans(X_train, 10) - return X_train, X_test, Y_train, Y_test, feature_names - - -@ray.remote -def classifier(clf, model, var, X_train, X_test, Y_train, Y_test, feature_names): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - start = time.perf_counter() - # score = cross_validate(clf, X_train, Y_train, cv=10, return_train_score=True, return_estimator=True, n_jobs=-1, verbose=0, scoring=('roc_auc','neg_log_loss')) - # clf = score['estimator'][np.argmin(score['test_neg_log_loss'])] - # y_score = cross_val_predict(clf, X_train, Y_train, cv=5, n_jobs=-1, verbose=0) - # class_weights = class_weight.compute_class_weight('balanced', np.unique(Y_train), Y_train) - # clf.fit(X_train, Y_train) #, class_weight=class_weights) - # clf_name = str(type(clf)).split("'")[1] #.split(".")[3] - with open(f"./tuning/{var}/{model}_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # del clf - # with open(f"./models/{var}/{clf_name}_{var}.joblib", 'rb') as f: - # clf = load(f) - y_score = clf.predict(X_test) - prc = precision_score(Y_test, y_score, average="weighted") - recall = recall_score(Y_test, y_score, average="weighted") - roc_auc = roc_auc_score(Y_test, y_score) - # roc_auc = roc_auc_score(Y_test, np.argmax(y_score, axis=1)) - accuracy = accuracy_score(Y_test, y_score) - score = clf.score(X_train, Y_train) - # matrix = confusion_matrix(np.argmax(Y_test, axis=1), np.argmax(y_score, axis=1)) - matrix = confusion_matrix(Y_test, y_score) - - # explain all the predictions in the test set - # background = shap.kmeans(X_train, 6) - # explainer = shap.KernelExplainer(clf.predict, background) - del clf, X_train - # background = X_test[np.random.choice(X_test.shape[0], 1000, replace=False)] - # shap_values = explainer.shap_values(background) - # plt.figure() - # shap.summary_plot(shap_values, background, feature_names, show=False) - ##shap.plots.waterfall(shap_values[0], max_display=15) - # plt.savefig(f"./models/{var}/{clf_name}_{var}_features.pdf", format='pdf', dpi=1000, bbox_inches='tight') - finish = (time.perf_counter() - start) / 60 - list1 = [model, prc, recall, roc_auc, accuracy, score, finish, matrix] - # list1 = [clf_name, np.mean(score['train_roc_auc']), np.mean(score['test_roc_auc']),np.mean(score['train_neg_log_loss']), np.mean(score['test_neg_log_loss']), prc, recall, roc_auc, accuracy, finish, matrix] - # pickle.dump(clf, open("./models/"+var+"/"+clf_name+"_"+var+".pkl", 'wb')) - return list1 - - -def tuning(var, X_train, X_test, Y_train, Y_test, feature_names, output, models): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - for model, config in models.items(): - # return model, config, len(X_train), len(Y_train) - hyperopt_tune_search = TuneSearchCV( - model, - param_distributions=config, - n_trials=10, - early_stopping=False, - max_iters=1, # max_iters specifies how many times tune-sklearn will be given the decision to start/stop training a model. Thus, if you have early_stopping=False, you should set max_iters=1 (let sklearn fit the entire estimator). - search_optimization="bayesian", - n_jobs=-1, - refit=True, - cv=5, - verbose=1, - # loggers = "tensorboard", - random_state=42, - local_dir="./ray_results", - ) - hyperopt_tune_search.fit(X_train, Y_train) - best_model = hyperopt_tune_search.best_estimator_ - print( - f"{model}_{var}:{hyperopt_tune_search.best_params_}", - file=open("tuning/tuned_parameters.csv", "a"), - ) - list1 = ray.get( - classifier.remote( - best_model, model, var, X_train, X_test, Y_train, Y_test, feature_names - ) - ) - # print(f'{list1[0]}\t{list1[1]}\t{list1[2]}\t{list1[3]}\t{list1[4]}\t{list1[5]}\t{list1[6]}\t{list1[7]}\t{list1[8]}\t{list1[9]}\n{list1[10]}', file=open(output, "a")) - print( - f"{list1[0]}\t{list1[1]}\t{list1[2]}\t{list1[3]}\t{list1[4]}\t{list1[5]}\t{list1[6]}\n{list1[7]}", - file=open(output, "a"), - ) - del best_config, best_model - return model - - -if __name__ == "__main__": - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - - # Classifiers I wish to use - classifiers = [ - { - ExtraTreesClassifier(): { # bootstrap = True, - # warm_start=True, - # oob_score=True): { - "n_estimators": tune.randint(0, 200), - "min_samples_split": tune.randint(2, 100), - "min_samples_leaf": tune.randint(1, 100), - "criterion": tune.choice(["gini", "entropy"]), - "max_features": tune.choice(["sqrt", "log2"]), - # "oob_score" : tune.choice([True, False]), - "class_weight": tune.choice([None, "balanced", "balanced_subsample"]), - } - }, - { - DecisionTreeClassifier(): { - "min_samples_split": tune.randint(2, 100), - "min_samples_leaf": tune.randint(1, 100), - "criterion": tune.choice(["gini", "entropy"]), - "max_features": tune.choice(["sqrt", "log2"]), - "class_weight": tune.choice([None, "balanced"]), - } - }, - { - SGDClassifier(n_jobs=-1): { - "loss": tune.choice( - [ - "squared_hinge", - "hinge", - "log", - "modified_huber", - "perceptron", - "squared_loss", - "huber", - "epsilon_insensitive", - "squared_epsilon_insensitive", - ] - ), - "penalty": tune.choice(["l2", "l1", "elasticnet"]), - "alpha": tune.loguniform(1e-9, 1e-1), - "epsilon": tune.uniform(1e-9, 1e-1), - "fit_intercept": tune.choice([True, False]), - "learning_rate": tune.choice( - ["constant", "optimal", "invscaling", "adaptive"] - ), - "class_weight": tune.choice([None, "balanced"]), - } - }, - { - RandomForestClassifier(n_jobs=-1): { - "n_estimators": tune.randint(0, 200), - "min_samples_split": tune.randint(2, 100), - "min_samples_leaf": tune.randint(1, 100), - "criterion": tune.choice(["gini", "entropy"]), - "max_features": tune.choice(["sqrt", "log2"]), - "class_weight": tune.choice([None, "balanced", "balanced_subsample"]), - # "oob_score" : tune.choice([True, False]), - "max_depth": tune.randint(0, 200), - } - }, - { - AdaBoostClassifier(): { - "n_estimators": tune.randint(0, 200), - "algorithm": tune.choice(["SAMME", "SAMME.R"]), - } - }, - { - BalancedRandomForestClassifier(): { - "n_estimators": tune.randint(0, 200), - "min_samples_split": tune.randint(2, 100), - "min_samples_leaf": tune.randint(1, 100), - "criterion": tune.choice(["gini", "entropy"]), - "max_features": tune.choice(["sqrt", "log2"]), - "class_weight": tune.choice([None, "balanced", "balanced_subsample"]), - # "oob_score" : tune.choice([True, False]), - "max_depth": tune.randint(0, 200), - } - }, - # {GaussianNB(): { - # #"var_smoothing": tune.loguniform(0.01, 1.0), - # }}, - { - LinearDiscriminantAnalysis(): { - "solver": tune.choice(["svd", "lsqr", "eigen"]) - } - }, - { - GradientBoostingClassifier(): { - "n_estimators": tune.randint(0, 200), - "min_samples_split": tune.randint(2, 100), - "min_samples_leaf": tune.randint(1, 100), - "max_features": tune.choice(["sqrt", "log2"]), - "max_features": tune.randint(1, 10), - "subsample": tune.uniform(0.0, 1.0), - "learning_rate": tune.loguniform(0.01, 1.0), - "max_depth": tune.randint(2, 200), - } - }, - # {MLPClassifier(): { - # "hidden_layer_sizes": tune.sample_from(lambda _: [tune.randint(1, 100) for i in range(tune.randint(1, 50))]), - # "activation": tune.choice(['identity', 'logistic', 'tanh', 'relu']), - # "solver": tune.choice(['lbfgs', 'sgd', 'adam']), - # 'alpha': tune.loguniform(1e-9, 1e-1), - # 'learning_rate': tune.choice(['constant','adaptive','invscaling']), - # 'tol': tune.loguniform(1e-9, 1e-1), - # 'epsilon': tune.uniform(1e-9, 1e-1), - # "max_iter": tune.randint(10, 300), - # }}, - ] - - with open("../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - ##variants = ['snv','non_snv','snv_protein_coding'] #'snv', - variants = ["non_snv"] - for var in variants: - if not os.path.exists("tuning/" + var): - os.makedirs("./tuning/" + var) - output = "tuning/" + var + "/ML_results_" + var + ".csv" - print("Working with " + var + " dataset...", file=open(output, "w")) - print("Working with " + var + " dataset...") - X_train, X_test, Y_train, Y_test, feature_names = ray.get( - data_parsing.remote(var, config_dict, output) - ) - # #print('Model\tCross_validate(avg_train_roc_auc)\tCross_validate(avg_test_roc_auc)\tCross_validate(avg_train_neg_log_loss)\tCross_validate(avg_test_neg_log_loss)\tPrecision(test_data)\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]', file=open(output, "a")) #\tConfusion_matrix[low_impact, high_impact] - print( - "Model\tPrecision(test_data)\tRecall\troc_auc\tAccuracy\tScore\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - pool = Pool(ray_address="auto") - func = partial( - tuning, var, X_train, X_test, Y_train, Y_test, feature_names, output - ) - for models in pool.map(func, classifiers): - # for model, config in zip(classifiers.keys(), classifiers.values()): - # best_config, hyperopt_tune_search = ray.get(tuning.remote(model,config, X_train, Y_train)) - # print(models[0], models[1], models[2], models[3]) - # best_config, best_model, model= models[0], models[1], models[2] - print(f"{models} training and testing done!") diff --git a/src/training/training/temp_files/Tune_models1.py b/src/training/training/temp_files/Tune_models1.py deleted file mode 100644 index 0358f8e..0000000 --- a/src/training/training/temp_files/Tune_models1.py +++ /dev/null @@ -1,363 +0,0 @@ -# from numpy import mean -import numpy as np -import pandas as pd -import time -import ray -from ray import tune -import argparse -from tune_sklearn import TuneSearchCV -import warnings - -warnings.simplefilter("ignore") -import joblib -from joblib import dump, load -import shap - -# from sklearn.preprocessing import StandardScaler -# from sklearn.feature_selection import RFE -# from sklearn.preprocessing import MinMaxScaler -from sklearn.model_selection import cross_validate, StratifiedKFold -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) - -# from sklearn.multiclass import OneVsRestClassifier -from sklearn.tree import DecisionTreeClassifier -from sklearn.linear_model import SGDClassifier -from sklearn.ensemble import ( - RandomForestClassifier, - AdaBoostClassifier, - GradientBoostingClassifier, - ExtraTreesClassifier, -) - -# from sklearn.naive_bayes import GaussianNB -from imblearn.ensemble import BalancedRandomForestClassifier -from sklearn.neural_network import MLPClassifier - -# from sklearn.discriminant_analysis import LinearDiscriminantAnalysis -import matplotlib.pyplot as plt -import yaml -import functools - -print = functools.partial(print, flush=True) -import os - -# import gc - -# print(f'''This cluster consists of -# {len(ray.nodes())} nodes in total -# {ray.cluster_resources()['CPU']} CPU resources in total -#''') - - -@ray.remote -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - # Load data - print(f"\nUsing merged_data-train_{var}..", file=open(output, "a")) - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - # background = shap.kmeans(X_train, 10) - return X_train, X_test, Y_train, Y_test, feature_names - - -@ray.remote -def classifier( - clf, model, var, X_train, X_test, Y_train, Y_test, feature_names, output -): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - start = time.perf_counter() - # score = cross_validate(clf, X_train, Y_train, cv=10, return_train_score=True, return_estimator=True, n_jobs=-1, verbose=0, scoring=('roc_auc','neg_log_loss')) - # clf = score['estimator'][np.argmin(score['test_neg_log_loss'])] - # y_score = cross_val_predict(clf, X_train, Y_train, cv=5, n_jobs=-1, verbose=0) - # class_weights = class_weight.compute_class_weight('balanced', np.unique(Y_train), Y_train) - clf.fit(X_train, Y_train) # , class_weight=class_weights) - score = clf.score(X_train, Y_train) - clf_name = str(type(model)).split("'")[1] # .split(".")[3] - with open(f"./tuning/{var}/{model}_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # with open(f"./models/{var}/{clf_name}_{var}.joblib", 'rb') as f: - # clf = load(f) - y_score = clf.predict(X_test) - prc = precision_score(Y_test, y_score, average="weighted") - recall = recall_score(Y_test, y_score, average="weighted") - roc_auc = roc_auc_score(Y_test, y_score) - # roc_auc = roc_auc_score(Y_test, np.argmax(y_score, axis=1)) - accuracy = accuracy_score(Y_test, y_score) - # matrix = confusion_matrix(np.argmax(Y_test, axis=1), np.argmax(y_score, axis=1)) - matrix = confusion_matrix(Y_test, y_score) - finish = (time.perf_counter() - start) / 60 - # list1 = [clf_name, score, prc, recall, roc_auc, accuracy, finish, matrix] - print( - f"{clf_name}\t{score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del Y_test - # explain all the predictions in the test set - background = shap.kmeans(X_train, 10) - del X_train, Y_train - explainer = shap.KernelExplainer(clf.predict, background) - del clf, background - background = X_test[np.random.choice(X_test.shape[0], 1000, replace=False)] - del X_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - # shap.plots.waterfall(shap_values[0], max_display=15) - plt.savefig( - f"./tuning/{var}/{clf_name}_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, model, feature_names - # list1 = [clf_name, np.mean(score['train_roc_auc']), np.mean(score['test_roc_auc']),np.mean(score['train_neg_log_loss']), np.mean(score['test_neg_log_loss']), prc, recall, roc_auc, accuracy, finish, matrix] - # pickle.dump(clf, open("./models/"+var+"/"+clf_name+"_"+var+".pkl", 'wb')) - return list1 - - -@ray.remote -def tuning(models, var, X_train, X_test, Y_train, Y_test, feature_names, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - for model, config in models.items(): - # return model, config, len(X_train), len(Y_train) - skopt_tune_search = TuneSearchCV( - model, - param_distributions=config, - n_trials=200, - early_stopping=False, - max_iters=1, # max_iters specifies how many times tune-sklearn will be given the decision to start/stop training a model. Thus, if you have early_stopping=False, you should set max_iters=1 (let sklearn fit the entire estimator). - search_optimization="bayesian", - n_jobs=50, - refit=True, - cv=StratifiedKFold(n_splits=5, shuffle=True, random_state=42), - verbose=0, - # loggers = "tensorboard", - random_state=42, - local_dir="./ray_results", - ) - skopt_tune_search.fit(X_train, Y_train) - best_model = skopt_tune_search.best_estimator_ - print( - f"{model}_{var}:{skopt_tune_search.best_params_}", - file=open("tuning/tuned_parameters.csv", "a"), - ) - ray.get( - classifier.remote( - best_model, - model, - var, - X_train, - X_test, - Y_train, - Y_test, - feature_names, - output, - ) - ) - # print(f'{list1[0]}\t{list1[1]}\t{list1[2]}\t{list1[3]}\t{list1[4]}\t{list1[5]}\t{list1[6]}\t{list1[7]}\t{list1[8]}\t{list1[9]}\n{list1[10]}', file=open(output, "a")) - print(f"{model} training and testing done!") - del best_model, config, skopt_tune_search, model, list1 - return None - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--vtype", - type=str, - default="non_snv", - help="Type of variation/s (without spaces between) to tune the classifier (like: snv,non_snv,snv_protein_coding). (Default: non_snv)", - ) - - args = parser.parse_args() - - variants = args.vtype.split(",") - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init(ignore_reinit_error=True) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - - # Classifiers I wish to use - classifiers = [ - { - ExtraTreesClassifier(): { # bootstrap = True, - # warm_start=True, - # oob_score=True): { - "n_estimators": tune.randint(1, 200), - "min_samples_split": tune.randint(2, 100), - "min_samples_leaf": tune.randint(1, 100), - "criterion": tune.choice(["gini", "entropy"]), - "max_features": tune.choice(["sqrt", "log2"]), - # "oob_score" : tune.choice([True, False]), - "class_weight": tune.choice([None, "balanced", "balanced_subsample"]), - } - }, - { - DecisionTreeClassifier(): { - "min_samples_split": tune.randint(2, 100), - "min_samples_leaf": tune.randint(1, 100), - "criterion": tune.choice(["gini", "entropy"]), - "max_features": tune.choice(["sqrt", "log2"]), - "class_weight": tune.choice([None, "balanced"]), - } - }, - { - SGDClassifier(n_jobs=-1): { - "loss": tune.choice( - [ - "squared_hinge", - "hinge", - "log", - "modified_huber", - "perceptron", - "squared_loss", - "huber", - "epsilon_insensitive", - "squared_epsilon_insensitive", - ] - ), - "penalty": tune.choice(["l2", "l1", "elasticnet"]), - "alpha": tune.loguniform(1e-9, 1e-1), - "epsilon": tune.uniform(1e-9, 1e-1), - "fit_intercept": tune.choice([True, False]), - "learning_rate": tune.choice( - ["constant", "optimal", "invscaling", "adaptive"] - ), #'optimal', - "class_weight": tune.choice([None, "balanced"]), - "eta0": tune.uniform(0.01, 0.9), - } - }, - { - RandomForestClassifier(n_jobs=-1): { - "n_estimators": tune.randint(1, 200), - "min_samples_split": tune.randint(2, 100), - "min_samples_leaf": tune.randint(1, 100), - "criterion": tune.choice(["gini", "entropy"]), - "max_features": tune.choice(["sqrt", "log2"]), - "class_weight": tune.choice([None, "balanced", "balanced_subsample"]), - # "oob_score" : tune.choice([True, False]), - "max_depth": tune.randint(2, 200), - } - }, - { - AdaBoostClassifier(): { - "n_estimators": tune.randint(1, 200), - "algorithm": tune.choice(["SAMME", "SAMME.R"]), - } - }, - { - BalancedRandomForestClassifier(n_jobs=-1): { - "n_estimators": tune.randint(1, 200), - "min_samples_split": tune.randint(2, 100), - "min_samples_leaf": tune.randint(1, 100), - "criterion": tune.choice(["gini", "entropy"]), - "max_features": tune.choice(["sqrt", "log2"]), - "class_weight": tune.choice([None, "balanced", "balanced_subsample"]), - # "oob_score" : tune.choice([True, False]), - "max_depth": tune.randint(2, 200), - } - }, - { - GradientBoostingClassifier(): { - "n_estimators": tune.randint(1, 200), - "min_samples_split": tune.randint(2, 100), - "min_samples_leaf": tune.randint(1, 100), - "max_features": tune.choice(["sqrt", "log2"]), - "max_features": tune.randint(1, 10), - "subsample": tune.uniform(0.1, 1.0), - "learning_rate": tune.loguniform(0.01, 1.0), - "max_depth": tune.randint(2, 200), - } - }, - { - MLPClassifier(): { - # "hidden_layer_sizes": tune.sample_from(lambda list: [list.append(tune.randint(1, 100)) for i in range(tune.randint(1, 50))]), - "activation": tune.choice(["identity", "logistic", "tanh", "relu"]), - "solver": tune.choice(["lbfgs", "sgd", "adam"]), - "alpha": tune.loguniform(1e-9, 1e-1), - "learning_rate": tune.choice(["constant", "adaptive", "invscaling"]), - "tol": tune.loguniform(1e-9, 1e-1), - "epsilon": tune.uniform(1e-9, 1e-1), - "max_iter": tune.randint(10, 300), - } - }, - # {GaussianNB(): { - # #"var_smoothing": tune.loguniform(0.01, 1.0), - # }}, - # {LinearDiscriminantAnalysis(): { - # #"solver": tune.choice(['svd', 'lsqr', 'eigen']) - # }}, - ] - - with open("../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - ##variants = ['snv','non_snv','snv_protein_coding'] #'snv', - # variants = ['snv'] - for var in variants: - if not os.path.exists("tuning/" + var): - os.makedirs("./tuning/" + var) - output = "tuning/" + var + "/ML_results_" + var + ".csv" - print("Working with " + var + " dataset...", file=open(output, "w")) - print("Working with " + var + " dataset...") - X_train, X_test, Y_train, Y_test, feature_names = ray.get( - data_parsing.remote(var, config_dict, output) - ) - # #print('Model\tCross_validate(avg_train_roc_auc)\tCross_validate(avg_test_roc_auc)\tCross_validate(avg_train_neg_log_loss)\tCross_validate(avg_test_neg_log_loss)\tPrecision(test_data)\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]', file=open(output, "a")) #\tConfusion_matrix[low_impact, high_impact] - print( - "Model\tScore\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - for model in classifiers: - ray.get( - tuning.remote( - model, var, X_train, X_test, Y_train, Y_test, feature_names, output - ) - ) - - # gc.collect() diff --git a/src/training/training/temp_files/Tune_models_copy.py b/src/training/training/temp_files/Tune_models_copy.py deleted file mode 100644 index 9503546..0000000 --- a/src/training/training/temp_files/Tune_models_copy.py +++ /dev/null @@ -1,250 +0,0 @@ -# from numpy import mean -import numpy as np -import pandas as pd -import time - -# import ray -## Start Ray. -# ray.init(ignore_reinit_error=True) -import warnings - -warnings.simplefilter("ignore") -import joblib -from ray.util.joblib import register_ray -from joblib import dump, load -import shap - -# from sklearn.preprocessing import StandardScaler -# from sklearn.feature_selection import RFE -# from sklearn.preprocessing import MinMaxScaler -from sklearn.model_selection import cross_validate -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) - -# from sklearn.multiclass import OneVsRestClassifier -from sklearn.tree import DecisionTreeClassifier -from sklearn.linear_model import SGDClassifier -from sklearn.ensemble import RandomForestClassifier -from sklearn.ensemble import AdaBoostClassifier -from sklearn.ensemble import GradientBoostingClassifier -from sklearn.ensemble import ExtraTreesClassifier -from sklearn.naive_bayes import GaussianNB -from imblearn.ensemble import BalancedRandomForestClassifier -from sklearn.neural_network import MLPClassifier -from sklearn.discriminant_analysis import LinearDiscriminantAnalysis -from skopt import BayesSearchCV -from skopt.space import Real, Categorical, Integer -import matplotlib.pyplot as plt -import yaml -import functools - -print = functools.partial(print, flush=True) -import os - -os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" -) - -##Patch to fix the version of sklearn. Refer this - https://github.com/scikit-optimize/scikit-optimize/issues/978 -# def bayes_search_CV_init(self, estimator, search_spaces, optimizer_kwargs=None, -# n_iter=50, scoring=None, fit_params=None, n_jobs=1, -# n_points=1, iid=True, refit=True, cv=None, verbose=0, -# pre_dispatch='2*n_jobs', random_state=None, -# error_score='raise', return_train_score=False): -# -# self.search_spaces = search_spaces -# self.n_iter = n_iter -# self.n_points = n_points -# self.random_state = random_state -# self.optimizer_kwargs = optimizer_kwargs -# self._check_search_space(self.search_spaces) -# self.fit_params = fit_params -# -# super(BayesSearchCV, self).__init__( -# estimator=estimator, scoring=scoring, -# n_jobs=n_jobs, refit=refit, cv=cv, verbose=verbose, -# pre_dispatch=pre_dispatch, error_score=error_score, -# return_train_score=return_train_score) -# -# BayesSearchCV.__init__ = bayes_search_CV_init - -# @ray.remote -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - # Load data - print(f"\nUsing merged_data-train_{var}..", file=open(output, "a")) - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - # background = shap.kmeans(X_train, 10) - return X_train, X_test, Y_train, Y_test, feature_names - - -# @ray.remote -def classifier(clf, model, var, X_train, X_test, Y_train, Y_test, feature_names): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - start = time.perf_counter() - # score = cross_validate(clf, X_train, Y_train, cv=10, return_train_score=True, return_estimator=True, n_jobs=-1, verbose=0, scoring=('roc_auc','neg_log_loss')) - # clf = score['estimator'][np.argmin(score['test_neg_log_loss'])] - # y_score = cross_val_predict(clf, X_train, Y_train, cv=5, n_jobs=-1, verbose=0) - # class_weights = class_weight.compute_class_weight('balanced', np.unique(Y_train), Y_train) - # clf.fit(X_train, Y_train) #, class_weight=class_weights) - # clf_name = str(type(clf)).split("'")[1] #.split(".")[3] - with open(f"./tuning/{var}/{model}_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # del clf - # with open(f"./models/{var}/{clf_name}_{var}.joblib", 'rb') as f: - # clf = load(f) - y_score = clf.predict(X_test) - prc = precision_score(Y_test, y_score, average="weighted") - recall = recall_score(Y_test, y_score, average="weighted") - roc_auc = roc_auc_score(Y_test, y_score) - # roc_auc = roc_auc_score(Y_test, np.argmax(y_score, axis=1)) - accuracy = accuracy_score(Y_test, y_score) - score = clf.score(X_train, Y_train) - # matrix = confusion_matrix(np.argmax(Y_test, axis=1), np.argmax(y_score, axis=1)) - matrix = confusion_matrix(Y_test, y_score) - - # explain all the predictions in the test set - # background = shap.kmeans(X_train, 6) - # explainer = shap.KernelExplainer(clf.predict, background) - del clf, X_train - # background = X_test[np.random.choice(X_test.shape[0], 1000, replace=False)] - # shap_values = explainer.shap_values(background) - # plt.figure() - # shap.summary_plot(shap_values, background, feature_names, show=False) - ##shap.plots.waterfall(shap_values[0], max_display=15) - # plt.savefig(f"./models/{var}/{clf_name}_{var}_features.pdf", format='pdf', dpi=1000, bbox_inches='tight') - finish = (time.perf_counter() - start) / 60 - list1 = [model, prc, recall, roc_auc, accuracy, score, finish, matrix] - # list1 = [clf_name, np.mean(score['train_roc_auc']), np.mean(score['test_roc_auc']),np.mean(score['train_neg_log_loss']), np.mean(score['test_neg_log_loss']), prc, recall, roc_auc, accuracy, finish, matrix] - # pickle.dump(clf, open("./models/"+var+"/"+clf_name+"_"+var+".pkl", 'wb')) - return list1 - - -if __name__ == "__main__": - # Classifiers I wish to use - classifiers = { - ExtraTreesClassifier(): { # bootstrap = True, - # warm_start=True, - # oob_score=True): { - "n_estimators": Integer(50, 200), - "min_samples_split": Integer(2, 100), - "min_samples_leaf": Integer(1, 100), - "criterion": Categorical(["gini", "entropy"]), - "max_features": Categorical(["sqrt", "log2"]), - "class_weight": Categorical([None, "balanced", "balanced_subsample"]), - }, - DecisionTreeClassifier(): { - "min_samples_split": Integer(2, 100), - "min_samples_leaf": Integer(1, 100), - "criterion": Categorical(["gini", "entropy"]), - "max_features": Categorical(["sqrt", "log2"]), - "class_weight": Categorical([None, "balanced"]), - }, - SGDClassifier(): { - "loss": Categorical(["squared_hinge", "hinge"]), - "alpha": Real(1e-4, 1e-1), - "epsilon": Real(1e-2, 1e-1), - }, - RandomForestClassifier(n_jobs=-1): { - "n_estimators": Integer(10, 200), - "min_samples_split": Integer(2, 100), - "min_samples_leaf": Integer(1, 100), - "criterion": Categorical(["gini", "entropy"]), - "max_features": Categorical(["sqrt", "log2"]), - "class_weight": Categorical(["balanced", "balanced_subsample"]), - "oob_score": Categorical([True, False]), - "max_depth": Integer(1, 200), - }, - # AdaBoostClassifier(), - # BalancedRandomForestClassifier(), - # GaussianNB(), - # LinearDiscriminantAnalysis(), - GradientBoostingClassifier(): { - "n_estimators": Integer(10, 200), - "max_depth": Integer(1, 200), - "learning_rate": (0.01, 1.0, "log-uniform"), - "min_samples_split": Integer(2, 100), - "min_samples_leaf": Integer(1, 100), - "max_features": Integer(1, 10), - "subsample": Real(0.0, 1.0), - "max_features": Categorical(["sqrt", "log2"]), - }, - # MLPClassifier() - } - - with open("../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - # variants = ['snv','non_snv','snv_protein_coding'] #'snv', - variants = ["non_snv"] - for var in variants: - if not os.path.exists("tuning/" + var): - os.makedirs("./tuning/" + var) - output = "tuning/" + var + "/ML_results_" + var + "_.csv" - print("Working with " + var + " dataset...", file=open(output, "w")) - print("Working with " + var + " dataset...") - # X_train, X_test, Y_train, Y_test, feature_names = ray.get(data_parsing.remote(var,config_dict,output)) - X_train, X_test, Y_train, Y_test, feature_names = data_parsing( - var, config_dict, output - ) - # print('Model\tCross_validate(avg_train_roc_auc)\tCross_validate(avg_test_roc_auc)\tCross_validate(avg_train_neg_log_loss)\tCross_validate(avg_test_neg_log_loss)\tPrecision(test_data)\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]', file=open(output, "a")) #\tConfusion_matrix[low_impact, high_impact] - print( - "Model\tPrecision(test_data)\tRecall\troc_auc\tAccuracy\tScore\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - for model, config in zip(classifiers.keys(), classifiers.values()): - clf = BayesSearchCV( - model, config, n_jobs=-1, cv=10, random_state=42, n_iter=200, refit=True - ) - register_ray() - with joblib.parallel_backend("ray"): - clf.fit(X_train, Y_train) - print( - f"{model}_{var}:{clf.best_params_}", - file=open("tuning/tuned_parameters.csv", "a"), - ) - list1 = classifier( - clf, model, var, X_train, X_test, Y_train, Y_test, feature_names - ) - # print(f'{list1[0]}\t{list1[1]}\t{list1[2]}\t{list1[3]}\t{list1[4]}\t{list1[5]}\t{list1[6]}\t{list1[7]}\t{list1[8]}\t{list1[9]}\n{list1[10]}', file=open(output, "a")) - print( - f"{list1[0]}\t{list1[1]}\t{list1[2]}\t{list1[3]}\t{list1[4]}\t{list1[5]}\t{list1[6]}\n{list1[7]}", - file=open(output, "a"), - ) - print(f"{model} training and testing done!") - del clf diff --git a/src/training/training/temp_files/Tune_stacking.py b/src/training/training/temp_files/Tune_stacking.py deleted file mode 100644 index 2347664..0000000 --- a/src/training/training/temp_files/Tune_stacking.py +++ /dev/null @@ -1,548 +0,0 @@ -import numpy as np -import pandas as pd -import ray -import time -import argparse -import pickle -from joblib import dump -import yaml -from ray import tune -from ray.tune import Trainable, run -from ray.tune.suggest.skopt import SkOptSearch -from ray.tune.suggest import ConcurrencyLimiter -from ray.tune.schedulers import AsyncHyperBandScheduler - -# from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import cross_validate # , StratifiedKFold -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - recall_score, -) -from sklearn.tree import DecisionTreeClassifier -from sklearn.linear_model import LogisticRegression # SGDClassifier, -from sklearn.ensemble import ( - RandomForestClassifier, - GradientBoostingClassifier, - StackingClassifier, -) -from sklearn.naive_bayes import GaussianNB -from imblearn.ensemble import BalancedRandomForestClassifier -from sklearn.discriminant_analysis import LinearDiscriminantAnalysis -from sklearn.neighbors import KNeighborsClassifier -import os -import gc -import shap -from joblib import dump, load -import matplotlib.pyplot as plt -import warnings - -warnings.simplefilter("ignore") -import functools - -print = functools.partial(print, flush=True) - -TUNE_STATE_REFRESH_PERIOD = 10 # Refresh resources every 10 s - - -class stacking( - Trainable -): # https://docs.ray.io/en/master/tune/examples/pbt_tune_cifar10_with_keras.html - def setup(self, config, x_train, x_test, y_train, y_test): - self.x_train = x_train - self.x_test = x_test - self.y_train = y_train - self.y_test = y_test - self.model = StackingClassifier( - estimators=[ - ( - "rf", - RandomForestClassifier( - random_state=42, - n_estimators=self.config.get("rf_n_estimators", 100), - criterion=self.config.get("rf_criterion", "gini"), - max_depth=self.config.get("rf_max_depth", 2), - min_samples_split=self.config.get("rf_min_samples_split", 2), - min_samples_leaf=self.config.get("rf_min_samples_leaf", 1), - max_features=self.config.get("rf_max_features", "sqrt"), - oob_score=self.config.get("rf_oob_score", False), - class_weight=self.config.get("rf_class_weight", "balanced"), - n_jobs=-1, - ), - ), - ( - "knn", - KNeighborsClassifier( - n_neighbors=self.config.get("knn_n_neighbors", 1), - weights=self.config.get("knn_weights", "uniform"), - algorithm=self.config.get("knn_algorithm", "auto"), - metric=self.config.get("knn_metric", "minkowski"), - n_jobs=-1, - ), - ), # leaf_size=self.config.get("leaf_size", 30), - ( - "gbc", - GradientBoostingClassifier( - random_state=42, - loss=self.config.get("gbc_loss", 100), - learning_rate=self.config.get("gbc_learning_rate", 0.1), - n_estimators=self.config.get("gbc_n_estimators", 100), - subsample=self.config.get("gbc_subsample", 1), - criterion=self.config.get("gbc_criterion", "friedman_mse"), - min_samples_split=self.config.get("gbc_min_samples_split", 2), - min_samples_leaf=self.config.get("gbc_min_samples_leaf", 1), - max_depth=self.config.get("gbc_max_depth", 2), - max_features=self.config.get("gbc_max_features", "sqrt"), - ), - ), - ( - "dt", - DecisionTreeClassifier( - random_state=42, - criterion=self.config.get("dt_criterion", "gini"), - splitter=self.config.get("dt_splitter", "best"), - max_depth=self.config.get("dt_max_depth", 2), - min_samples_split=self.config.get("dt_min_samples_split", 2), - min_samples_leaf=self.config.get("dt_min_samples_leaf", 1), - max_features=self.config.get("dt_max_features", "sqrt"), - class_weight=self.config.get("dt_class_weight", "balanced"), - ), - ), - # ('sgd', SGDClassifier(random_state=42, loss=self.config.get("sgd_loss", "hinge"), penalty=self.config.get("sgd_penalty", "l2"), alpha=self.config.get("sgd_alpha", 0.0001), max_iter=self.config.get("sgd_max_iter", 1000), epsilon=self.config.get("sgd_epsilon", 0.1), learning_rate = self.config.get("sgd_learning_rate", "optimal"), eta0 = self.config.get("sgd_eta0", 0.0), power_t = self.config.get("sgd_power_t", 0.5), class_weight=self.config.get("sgd_class_weight","balanced"), n_jobs = -1)), - ( - "gnb", - GaussianNB(var_smoothing=self.config.get("var_smoothing", 1e-09)), - ), - ( - "brf", - BalancedRandomForestClassifier( - random_state=42, - n_estimators=self.config.get("brf_n_estimators", 100), - criterion=self.config.get("brf_criterion", "gini"), - max_depth=self.config.get("brf_max_depth", 2), - min_samples_split=self.config.get("brf_min_samples_split", 2), - min_samples_leaf=self.config.get("brf_min_samples_leaf", 1), - max_features=self.config.get("brf_max_features", "sqrt"), - oob_score=self.config.get("brf_oob_score", False), - class_weight=self.config.get("brf_class_weight", "balanced"), - n_jobs=-1, - ), - ), - ( - "lda", - LinearDiscriminantAnalysis( - solver=self.config.get("lda_solver", "svd"), - shrinkage=self.config.get("lda_shrinkage", None), - ), - ), - ], - cv=3, - stack_method="predict_proba", - n_jobs=-1, - passthrough=False, - final_estimator=LogisticRegression( - C=self.config.get("lr_C", 1), - penalty=self.config.get("lr_penalty", "l2"), - solver=self.config.get("lr_solver", "lbfgs"), - max_iter=self.config.get("lr_max_iter", 100), - n_jobs=-1, - ), - verbose=0, - ) - - def reset_config(self, new_config): - self.n_estimators = new_config["rf_n_estimators"] - self.n_neighbors = new_config["n_neighbors"] - self.C = new_config["C"] - self.config = new_config - return True - - def step(self): - score = cross_validate( - self.model, self.x_train, self.y_train, cv=3, n_jobs=-1, verbose=0 - ) - testing_score = np.max(score["test_score"]) - # print(accuracy) - return {"mean_accuracy": testing_score} - - def save_checkpoint(self, checkpoint_dir): - file_path = checkpoint_dir + "/model" - pickle.dump(self.model, open(file_path, "wb")) - return file_path - - def load_checkpoint(self, path): - # See https://stackoverflow.com/a/42763323 - del self.model - self.model = pickle.load(open(path, "rb")) - - def cleanup(self): - # If need, save your model when exit. - # saved_path = self.model.save(self.logdir) - # print("save model at: ", saved_path) - pass - - -def results(config, x_train, x_test, y_train, y_test, var, output, feature_names): - start1 = time.perf_counter() - # self.x_train, self.x_test, self.y_train, self.y_test, self.feature_names = self._read_data(config) - clf = StackingClassifier( - estimators=[ - ( - "rf", - RandomForestClassifier( - random_state=42, - n_estimators=config.get("rf_n_estimators", 100), - criterion=config.get("rf_criterion", "gini"), - max_depth=config.get("rf_max_depth", 2), - min_samples_split=config.get("rf_min_samples_split", 2), - min_samples_leaf=config.get("rf_min_samples_leaf", 1), - max_features=config.get("rf_max_features", "sqrt"), - oob_score=config.get("rf_oob_score", False), - class_weight=config.get("rf_class_weight", "balanced"), - n_jobs=-1, - ), - ), - ( - "knn", - KNeighborsClassifier( - n_neighbors=config.get("knn_n_neighbors", 1), - weights=config.get("knn_weights", "uniform"), - algorithm=config.get("knn_algorithm", "auto"), - metric=config.get("knn_metric", "minkowski"), - n_jobs=-1, - ), - ), # leaf_size=config.get("leaf_size", 30), - ( - "gbc", - GradientBoostingClassifier( - random_state=42, - loss=config.get("gbc_loss", 100), - learning_rate=config.get("gbc_learning_rate", 0.1), - n_estimators=config.get("gbc_n_estimators", 100), - subsample=config.get("gbc_subsample", 1), - criterion=config.get("gbc_criterion", "friedman_mse"), - min_samples_split=config.get("gbc_min_samples_split", 2), - min_samples_leaf=config.get("gbc_min_samples_leaf", 1), - max_depth=config.get("gbc_max_depth", 2), - max_features=config.get("gbc_max_features", "sqrt"), - ), - ), - ( - "dt", - DecisionTreeClassifier( - random_state=42, - criterion=config.get("dt_criterion", "gini"), - splitter=config.get("dt_splitter", "best"), - max_depth=config.get("dt_max_depth", 2), - min_samples_split=config.get("dt_min_samples_split", 2), - min_samples_leaf=config.get("dt_min_samples_leaf", 1), - max_features=config.get("dt_max_features", "sqrt"), - class_weight=config.get("dt_class_weight", "balanced"), - ), - ), - # ('sgd', SGDClassifier(random_state=42, loss=config.get("sgd_loss", "hinge"), penalty=config.get("sgd_penalty", "l2"), alpha=config.get("sgd_alpha", 0.0001), max_iter=config.get("sgd_max_iter", 1000), epsilon=config.get("sgd_epsilon", 0.1), learning_rate = config.get("sgd_learning_rate", "optimal"), eta0 = config.get("sgd_eta0", 0.0), power_t = config.get("sgd_power_t", 0.5), class_weight=config.get("sgd_class_weight","balanced"), n_jobs = -1)), - ("gnb", GaussianNB(var_smoothing=config.get("var_smoothing", 1e-09))), - ( - "brf", - BalancedRandomForestClassifier( - random_state=42, - n_estimators=config.get("brf_n_estimators", 100), - criterion=config.get("brf_criterion", "gini"), - max_depth=config.get("brf_max_depth", 2), - min_samples_split=config.get("brf_min_samples_split", 2), - min_samples_leaf=config.get("brf_min_samples_leaf", 1), - max_features=config.get("brf_max_features", "sqrt"), - oob_score=config.get("brf_oob_score", False), - class_weight=config.get("brf_class_weight", "balanced"), - n_jobs=-1, - ), - ), - ( - "lda", - LinearDiscriminantAnalysis( - solver=config.get("lda_solver", "svd"), - shrinkage=config.get("lda_shrinkage", None), - ), - ), - ], - cv=3, - stack_method="predict_proba", - n_jobs=-1, - passthrough=False, - final_estimator=LogisticRegression( - C=config.get("lr_C", 1), - penalty=config.get("lr_penalty", "l2"), - solver=config.get("lr_solver", "lbfgs"), - max_iter=config.get("lr_max_iter", 100), - n_jobs=-1, - ), - verbose=0, - ).fit(x_train, y_train) - # score = cross_validate(clf, x_train, y_train, cv=StratifiedKFold(n_splits=5,shuffle=True,random_state=42), return_train_score=True, return_estimator=True, n_jobs=-1, verbose=0) - - train_score = clf.score(x_train, y_train) - with open(f"./tuning/{var}/StackingClassifier_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # training_score = np.mean(score['train_score']) - # testing_score = np.mean(score['test_score']) - y_score = clf.predict(x_test) - prc = precision_score(y_test, y_score, average="weighted") - recall = recall_score(y_test, y_score, average="weighted") - roc_auc = roc_auc_score(y_test, y_score) - accuracy = accuracy_score(y_test, y_score) - matrix = confusion_matrix(y_test, y_score) - finish = (time.perf_counter() - start1) / 60 - # print(f'RandomForestClassifier: \nCross_validate(avg_train_score): {training_score}\nCross_validate(avg_test_score): {testing_score}\nPrecision: {prc}\nRecall: {recall}\nROC_AUC: {roc_auc}\nAccuracy: {accuracy}\nTime(in min): {finish}\nConfusion Matrix:\n{matrix}', file=open(output, "a")) - clf_name = str(type(clf)).split("'")[1] # .split(".")[3] - print( - "Model\ttrain_score\tPrecision\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]", - file=open(output, "a"), - ) # \tConfusion_matrix[low_impact, high_impact] - print( - f"{clf_name}\t{train_score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\t{finish}\n{matrix}", - file=open(output, "a"), - ) - del y_test - # explain all the predictions in the test set - background = shap.kmeans(x_train, 10) - explainer = shap.KernelExplainer(clf.predict, background) - del clf, x_train, y_train, background - background = x_test[np.random.choice(x_test.shape[0], 1000, replace=False)] - del x_test - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - plt.savefig( - f"./tuning/{var}/StackingClassifier_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del background, explainer, feature_names - print(f"training and testing done!") - return None - - -def wrap_trainable(trainable, x_train, x_test, y_train, y_test): - x_train_id = ray.put(x_train) - x_test_id = ray.put(x_test) - y_train_id = ray.put(y_train) - y_test_id = ray.put(y_test) - - class _Wrapped(trainable): - def setup(self, config): - x_train = ray.get(x_train_id) - x_test = ray.get(x_test_id) - y_train = ray.get(y_train_id) - y_test = ray.get(y_test_id) - - super(_Wrapped, self).setup(config, x_train, x_test, y_train, y_test) - - return _Wrapped - - -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - # Load data - print(f"\nUsing merged_data-train_{var}..", file=open(output, "a")) - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) - X_train.replace([np.inf, -np.inf], np.nan, inplace=True) - X_train.fillna(0, inplace=True) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - return X_train, X_test, Y_train, Y_test, feature_names - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--smoke-test", action="store_true", help="Finish quickly for testing" - ) - parser.add_argument( - "--vtype", - type=str, - default="non_snv", - help="Type of variation/s (without spaces between) to tune the classifier (like: snv,non_snv,snv_protein_coding). (Default: non_snv)", - ) - parser.add_argument( - "--cpus", type=int, default=10, help="Number of CPUs needed. (Default: 10)" - ) - parser.add_argument( - "--gpus", type=int, default=0, help="Number of GPUs needed. (Default: 0)" - ) - parser.add_argument( - "--mem", - type=int, - default=100 * 1024 * 1024 * 1024, - help="Memory needed in bytes. (Default: 100*1024*1024*1024 (100GB))", - ) - - args = parser.parse_args() - - variants = args.vtype.split(",") - - if args.smoke_test: - ray.init(num_cpus=2) # force pausing to happen for test - else: - ray.init( - ignore_reinit_error=True, - num_cpus=args.cpus, - num_gpus=args.gpus, - _memory=args.mem, - ) - - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - with open("../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - # variants = ['non_snv','snv','snv_protein_coding'] - for var in variants: - - start = time.perf_counter() - if not os.path.exists("tuning/" + var): - os.makedirs("./tuning/" + var) - output = "tuning/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, "w")) - print("Working with " + var + " dataset...") - - x_train, x_test, y_train, y_test, feature_names = data_parsing( - var, config_dict, output - ) - - skopt_search = SkOptSearch(metric="mean_accuracy", mode="max") - skopt_search = ConcurrencyLimiter(skopt_search, max_concurrent=10) - scheduler = AsyncHyperBandScheduler() - - analysis = run( - wrap_trainable(stacking, x_train, x_test, y_train, y_test), - name=f"StackingClassifier_{var}", - verbose=1, - scheduler=scheduler, - search_alg=skopt_search, - reuse_actors=True, - local_dir="./ray_results", - max_failures=2, - # resources_per_trial={ - # "cpu": 10, - # "gpu": 1 - # }, - # global_checkpoint_period=np.inf, # Do not save checkpoints based on time interval - checkpoint_freq=20, # Save checkpoint every time the checkpoint_score_attr improves - checkpoint_at_end=True, - keep_checkpoints_num=2, # Keep only the best checkpoint - checkpoint_score_attr="mean_accuracy", # Metric used to compare checkpoints - metric="mean_accuracy", - mode="max", - stop={ - "training_iteration": 10000, - }, - num_samples=10, - # fail_fast=True, - queue_trials=True, - config={ - # RandomForest - https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html?highlight=randomforestclassifier#sklearn.ensemble.RandomForestClassifier - "rf_n_estimators": tune.randint(1, 200), - "rf_criterion": tune.choice(["gini", "entropy"]), - "rf_max_depth": tune.randint(2, 200), - "rf_min_samples_split": tune.randint(2, 10), - "rf_min_samples_leaf": tune.randint(1, 10), - "rf_max_features": tune.choice(["sqrt", "log2"]), - "rf_oob_score": tune.choice([True, False]), - "rf_class_weight": tune.choice(["balanced", "balanced_subsample"]), - # KNeighborsClassifier - https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html?highlight=kn#sklearn.neighbors.KNeighborsClassifier - "knn_n_neighbors": tune.randint(1, 10), - "knn_weights": tune.choice(["uniform", "distance"]), - "knn_algorithm": tune.choice(["auto", "ball_tree", "kd_tree", "brute"]), - "knn_metric": tune.choice(["minkowski", "chebyshev"]), - # GradientBoostingClassifier - https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier - "gbc_loss": tune.choice(["deviance", "exponential"]), - "gbc_learning_rate": tune.loguniform(0.01, 1.0), - "gbc_n_estimators": tune.randint(1, 200), - "gbc_subsample": tune.uniform(0.1, 1.0), - "gbc_criterion": tune.choice(["friedman_mse", "mse"]), - "gbc_min_samples_split": tune.randint(2, 100), - "gbc_min_samples_leaf": tune.randint(1, 100), - "gbc_max_depth": tune.randint(2, 200), - "gbc_max_features": tune.choice(["sqrt", "log2"]), - # DecisionTree - https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier - "dt_criterion": tune.choice(["gini", "entropy"]), - "dt_splitter": tune.choice(["best", "random"]), - "dt_max_depth": tune.randint(2, 200), - "dt_min_samples_split": tune.randint(2, 100), - "dt_min_samples_leaf": tune.randint(1, 100), - "dt_max_features": tune.choice(["sqrt", "log2"]), - "dt_class_weight": tune.choice([None, "balanced"]), - # StochasticGradientDescent - https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier - # "sgd_loss": tune.choice(['squared_hinge', 'hinge', 'log', 'modified_huber', 'perceptron', 'squared_loss', 'huber', 'epsilon_insensitive', 'squared_epsilon_insensitive']), - # "sgd_penalty" : tune.choice(['l2', 'l1', 'elasticnet']), - # "sgd_alpha" : tune.loguniform(1e-9, 1e-1), - # "sgd_max_iter" : tune.randint(2, 1000), - # "sgd_epsilon" : tune.uniform(1e-9, 1e-1), - # "sgd_learning_rate" : tune.choice(['constant', 'optimal', 'invscaling', 'adaptive']), #'optimal', - # "sgd_eta0" : tune.uniform(0.01, 0.9), - # "sgd_power_t" : tune.uniform(0.1, 0.9), - # "sgd_class_weight" : tune.choice(["balanced"]), - # GaussianNB - https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB - "var_smoothing": tune.loguniform(1e-11, 1e-1), - # BalancedRandomForest - https://imbalanced-learn.org/dev/references/generated/imblearn.ensemble.BalancedRandomForestClassifier.html - "brf_n_estimators": tune.randint(1, 200), - "brf_criterion": tune.choice(["gini", "entropy"]), - "brf_max_depth": tune.randint(2, 200), - "brf_min_samples_split": tune.randint(2, 10), - "brf_min_samples_leaf": tune.randint(1, 10), - "brf_max_features": tune.choice(["sqrt", "log2"]), - "brf_oob_score": tune.choice([True, False]), - "brf_class_weight": tune.choice(["balanced", "balanced_subsample"]), - # LinearDiscriminantAnalysis - https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis - "lda_solver": tune.choice(["svd", "lsqr", "eigen"]), - # "lda_shrinkage" : tune.choice(["auto", None]), - # LogisticRegression - https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic#sklearn.linear_model.LogisticRegression - "lr_C": tune.uniform(0.1, 10.0), - "lr_penalty": tune.choice(["l2"]), - "lr_solver": tune.choice( - ["newton-cg", "lbfgs", "sag"] - ), # "liblinear", "sag", "saga"]), - "lr_max_iter": tune.randint(2, 100), - }, - ) - - finish = (time.perf_counter() - start) / 120 - # ttime = (finish- start)/120 - print(f"Total time in min: {finish}") - config = analysis.best_config - print( - f"StackingClassifier_{var}: {config}", - file=open(f"tuning/tuned_parameters.csv", "a"), - ) - results(config, x_train, x_test, y_train, y_test, var, output, feature_names) - gc.collect() diff --git a/src/training/training/temp_files/filter.py b/src/training/training/temp_files/filter.py deleted file mode 100644 index 39007c6..0000000 --- a/src/training/training/temp_files/filter.py +++ /dev/null @@ -1,101 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -import pandas as pd - -pd.set_option("display.max_rows", 200) -import numpy as np - -# from tqdm import tqdm -import yaml -import os - -# from sklearn.linear_model import LinearRegression -# from sklearn.experimental import enable_iterative_imputer -# from sklearn.impute import IterativeImputer -import sys - - -def get_col_configs(config_f): - - with open(config_f) as fh: - config_dict = yaml.safe_load(fh) - - # print(config_dict) - return config_dict - - -def filter(config_dict, df): - print("Extracting columns and rows according to config file !....") - df = df[config_dict["columns"]] - var = df[["AAChange.refGene", "ID"]] - df = df.drop(["AAChange.refGene", "ID", "CLNSIG"], axis=1) - df.replace(".", np.nan, inplace=True) - # df = df.dropna(axis=1, how='all').dropna(axis=0, how='all') - df.to_csv("./data/interim/temp1.csv", index=False) - df = pd.read_csv("./data/interim/temp1.csv") - os.remove("./data/interim/temp1.csv") - df = pd.get_dummies(df, prefix_sep="_") - df1 = pd.DataFrame() - for key in config_dict["Fill_NAs"]: - if key in df.columns: - df1[key] = df[key] - else: - df1[key] = 0 - - df = df1 - del df1 - # columns = df.columns - # lr = LinearRegression() - # imp= IterativeImputer(estimator=lr, verbose=2, max_iter=3, tol=1e-10, imputation_order='roman') - print("Filling NAs using median values...") - # df1 = imp.fit_transform(df) - # filehandler = open('./data/processed/imputer.pkl', 'wb') - # pickle.dump(imp, filehandler) - # df = pd.DataFrame(df, columns = columns) - df["is_snp"] = df["is_snp"].map({False: 0, True: 1}) - df = df.fillna(df.median()) - # for key in tqdm(config_dict['Fill_NAs']): - # if key in df.columns: - # df1[key] = df[key].fillna(config_dict['Fill_NAs'][key]).astype('float64') - # else: - # df1[key] = config_dict['Fill_NAs'][key] - print("NAs filled!") - for i in df.columns: - if type(df[i]) == np.object: - print(i) - sys.exit("object columns identified") - df = pd.concat([var.reset_index(drop=True), df], axis=1) - return df - - -def main(var_f, config_f): - # read QA config file - config_dict = get_col_configs(config_f) - print("Config file loaded!\nNow loading data.....") - # read clinvar data - df = pd.read_csv(var_f) - print("Data Loaded!") - df = filter(config_dict, df) - print("Data filtered!") - # df.isnull().sum(axis = 0).to_csv('./data/processed/SL135596-NA-count-6-class.csv') - # y = df.CLNSIG.str.replace(r'/Likely_pathogenic','').str.replace(r'/Likely_benign','') - # y = y.str.replace(r'Likely_benign','Benign').str.replace(r'Likely_pathogenic','Pathogenic') - # df = df.drop('CLNSIG', axis=1) - - # print dataframe shape - # df.dtypes.to_csv('../../data/interim/head.csv') - print("Data shape=", df.shape) - # print('Class shape=', y.shape) - df.to_csv("./data/processed/sample1-filtered.csv", index=False) # sample1-filtered. - # y.to_csv('./data/processed/sample1-y.csv', index=False) - return None - - -if __name__ == "__main__": - - os.chdir("/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/") - var_f = "./data/interim/filtered_sample.csv" - config_f = "./configs/col_config.yaml" - - main(var_f, config_f) diff --git a/src/training/training/temp_files/model1.job b/src/training/training/temp_files/model1.job deleted file mode 100644 index 497ad5a..0000000 --- a/src/training/training/temp_files/model1.job +++ /dev/null @@ -1,94 +0,0 @@ -#!/bin/bash -# -#SBATCH --job-name=Ditto -#SBATCH --output=NN.out -# -### Modify this according to your Ray workload. -#SBATCH --nodes=1 -#SBATCH --exclusive -# -# Number of tasks needed for this job. Generally, used with MPI jobs -#SBATCH --ntasks=1 -#SBATCH --partition=pascalnodes -# -# Time format = HH:MM:SS, DD-HH:MM:SS -#SBATCH --time=11:59:58 -# -# Number of CPUs allocated to each task. -#SBATCH --cpus-per-task=2 -# -# Mimimum memory required per allocated CPU in MegaBytes. -#SBATCH --mem=50G -# -### Similarly, you can also specify the number of GPUs per node. -### Modify this according to your Ray workload. Sometimes this -### should be 'gres' instead. -#SBATCH --gres=gpu:4 -# -# Send mail to the email address when the job fails -#SBATCH --mail-type=FAIL -#SBATCH --mail-user=tmamidi@uab.edu - -#Set your environment here -module load Anaconda3/2020.02 -conda activate training -module load cuda10.1/toolkit/10.1.243 -#export CUDA_VISIBLE_DEVICES=0,1 -#module load cuDNN/7.6.2.24-CUDA-10.1.243 - -## ===== DO NOT CHANGE THINGS HERE UNLESS YOU KNOW WHAT YOU ARE DOING ===== -## This script is a modification to the implementation suggest by gregSchwartz18 here: -## https://github.com/ray-project/ray/issues/826#issuecomment-522116599 -#redis_password=$(uuidgen) -#export redis_password -# -## Getting the node names -#nodes=$(scontrol show hostnames "$SLURM_JOB_NODELIST") # Getting the node names -#nodes_array=($nodes) -# -#head_node=${nodes_array[0]} -#head_node_ip=$(srun --nodes=1 --ntasks=1 -w "$head_node" hostname --ip-address) # making redis-address -# -## if we detect a space character in the head node IP, we'll -## convert it to an ipv4 address. This step is optional. -#if [[ "$head_node_ip" == *" "* ]]; then -#IFS=' ' read -ra ADDR <<<"$head_node_ip" -#if [[ ${#ADDR[0]} -gt 16 ]]; then -# head_node_ip=${ADDR[1]} -#else -# head_node_ip=${ADDR[0]} -#fi -#echo "IPV6 address detected. We split the IPV4 address as $head_node_ip" -#fi -# -#port=6379 -#ip_head=$head_node_ip:$port -#export ip_head -#echo "IP Head: $ip_head" -# -#echo "Starting HEAD at $head_node" -#srun --nodes=1 --ntasks=1 -w "$head_node" \ -# ray start --head --node-ip-address="$head_node_ip" --port=$port \ -# --num-cpus "${SLURM_CPUS_PER_TASK}" --num-gpus "${SLURM_GPUS_PER_TASK}" --block & -# -## optional, though may be useful in certain versions of Ray < 1.0. -#sleep 10 -# -## number of nodes other than the head node -#worker_num=$((SLURM_JOB_NUM_NODES - 1)) -# -#for ((i = 1; i <= worker_num; i++)); do -# node_i=${nodes_array[$i]} -# echo "Starting WORKER $i at $node_i" -# srun --nodes=1 --ntasks=1 -w "$node_i" \ -# ray start --address "$ip_head" \ -# --num-cpus "${SLURM_CPUS_PER_TASK}" --num-gpus "${SLURM_GPUS_PER_TASK}" --block & -# sleep 5 -#done - - -#Run your commands here -#python Tune_RF_PB2.py -#python ML_models.py -#python Ditto.py -python optuna-tpe-2.ipy diff --git a/src/training/training/temp_files/optuna-model.py b/src/training/training/temp_files/optuna-model.py deleted file mode 100644 index dd98ead..0000000 --- a/src/training/training/temp_files/optuna-model.py +++ /dev/null @@ -1,179 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Fri Oct 2 23:42:18 2020 - -@author: tarunmamidi -""" - -import pandas as pd -import numpy as np - -np.random.seed(5) -import tensorflow as tf -import tensorflow.keras as keras - -try: - tf.get_logger().setLevel("INFO") -except Exception as exc: - print(exc) -import warnings - -warnings.simplefilter("ignore") -from tensorflow.keras.models import Sequential -from tensorflow.keras.layers import Dense, Dropout, Activation -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import label_binarize -from sklearn.preprocessing import StandardScaler -from sklearn.metrics import average_precision_score -from sklearn.metrics import confusion_matrix -import shap - -shap.initjs() -import os - -os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" -) - -# n_columns = 112 - -print("Loading data....\n") - -x = pd.read_csv("clinvar-md.csv") -var = x[["AAChange.refGene", "ID"]] -X = x.drop(["AAChange.refGene", "ID"], axis=1) -features = X.columns.tolist() -# var.to_csv('/Users/tarunmamidi/Documents/Development/ditto/tune/variant_ID.csv') -X = X.values -y = pd.read_csv("clinvar-y-md.csv") -Y = pd.get_dummies(y) -# Y = label_binarize(y, classes=['Benign', 'Pathogenic']) -train_x, test_x, train_y, test_y = train_test_split( - X, Y, test_size=0.30, random_state=42 -) -scaler = StandardScaler() -train_x = scaler.fit_transform(train_x) -test_x = scaler.transform(test_x) -# n_columns = train_x.shape[1] -# n_columns = 141 -print("Data loaded and scaled.\n") - -# 4-class -# parameters = {'n_layers': 2, 'activation': 'sigmoid', 'n_units_l0': 42, 'kernel_initializer_l0': 'he_normal', 'activation_l0': 'sigmoid', 'dropout_l0': 0.11794277735529887, 'n_units_l1': 14, 'kernel_initializer_l1': 'zero', 'activation_l1': 'softmax', 'dropout_l1': 0.44728594031480523, 'kernel_initializer': 'he_normal', 'optimizer': 'Adam', 'batch_size': 658} -# 6-class - LR -# parameters = {'n_layers': 5, 'activation': 'relu', 'n_units_l0': 168, 'kernel_initializer_l0': 'glorot_uniform', 'activation_l0': 'relu', 'dropout_l0': 0.12849446365413872, 'n_units_l1': 299, 'kernel_initializer_l1': 'uniform', 'activation_l1': 'elu', 'dropout_l1': 0.2952461200802723, 'n_units_l2': 280, 'kernel_initializer_l2': 'normal', 'activation_l2': 'softplus', 'dropout_l2': 0.8957821599147904, 'n_units_l3': 14, 'kernel_initializer_l3': 'normal', 'activation_l3': 'linear', 'dropout_l3': 0.13794225422380274, 'n_units_l4': 169, 'kernel_initializer_l4': 'normal', 'activation_l4': 'softsign', 'dropout_l4': 0.6453839014735023, 'kernel_initializer': 'uniform', 'optimizer': 'Adamax', 'batch_size': 610} -# 6-class - median -parameters = { - "n_layers": 2, - "activation": "elu", - "n_units_l0": 215, - "kernel_initializer_l0": "glorot_normal", - "activation_l0": "hard_sigmoid", - "dropout_l0": 0.7248274834825591, - "n_units_l1": 35, - "kernel_initializer_l1": "lecun_uniform", - "activation_l1": "elu", - "dropout_l1": 0.4279528310227717, - "kernel_initializer": "normal", - "optimizer": "Adamax", - "batch_size": 497, -} - - -def tune_data(config): - # Clear clutter from previous TensorFlow graphs. - tf.keras.backend.clear_session() - - model = Sequential() - model.add( - Dense(X.shape[1], input_shape=(X.shape[1],), activation=config["activation"]) - ) - for i in range(config["n_layers"]): - model.add( - Dense( - config["n_units_l{}".format(i)], - name="dense_l{}".format(i), - kernel_initializer=config["kernel_initializer_l{}".format(i)], - activation=config["activation_l{}".format(i)], - ) - ) - model.add(Dropout(config["dropout_l{}".format(i)])) - model.add( - Dense( - units=Y.shape[1], - name="dense_last", - kernel_initializer=config["kernel_initializer"], - activation="sigmoid", - ) - ) - model.compile( - loss="binary_crossentropy", optimizer=config["optimizer"], metrics=["accuracy"] - ) - model.summary() - # Train the model - model.fit(train_x, train_y, verbose=2, batch_size=config["batch_size"], epochs=150) - # Evaluate the model accuracy on the validation set. - # score = model.evaluate(test_x, test_y, verbose=0) - return model - - -model = tune_data(parameters) -results = model.evaluate(test_x, test_y) -y_score = model.predict(test_x) -prc = average_precision_score(test_y, y_score, average=None) -prc_micro = average_precision_score(test_y, y_score, average="micro") -# matrix = confusion_matrix(np.argmax(test_y, axis=1), np.argmax(y_score, axis=1)) -matrix = confusion_matrix(np.argmax(test_y.values, axis=1), np.argmax(y_score, axis=1)) -print( - f"Test loss: {results[0]}\nTest accuracy: {results[1]}\nOverall precision score: {prc_micro}\nPrecision score: {prc}\nConfusion matrix:\n{matrix}" -) # ,, file=open("Ditto-v0.csv", "a") - -# Calling `save('my_model')` creates a SavedModel folder `my_model`. -model.save("my_model") -model.save_weights("weights.h5") - -# from tensorflow import keras -# my_model = keras.models.load_model('my_model') -# my_model.load_weights("weights.h5") - -# test_x = scaler.transform(X) -# y_score = model.predict(test_x) -# pred = pd.DataFrame(y_score, columns = ['pred_Benign','pred_VUS', 'pred_Pathogenic']) -# var = var.to_frame() -# classified = pd.concat([var.reset_index(drop=True), pred], axis=1) -# overall = classified.merge(df1,on='CLNHGVS') -# overall.to_csv('predicted_results.csv', index=False) -test_y = test_y.sort_index(ascending=True) -mis = np.where(np.argmax(test_y.values, axis=1) != np.argmax(y_score, axis=1))[ - 0 -].tolist() -var = var.loc[var.index.isin(test_y.index)] -var = pd.concat([var, test_y], axis=1) - -pred = pd.DataFrame(y_score, columns=["pred_Benign", "pred_Pathogenic"]) -var = pd.concat([var.reset_index(drop=True), pred], axis=1) -var = var.loc[var.index.isin(mis)] -# var = pd.concat([var.reset_index(drop=True), x], axis=1) -misclass = var.merge(x, on="ID") - -# true = test_y -# pred = pred.loc[pred.index.isin(mis)] -# true = true.loc[true.index.isin(mis)] -# misclass = var.merge(true, how='outer', left_index=True, right_index=True).merge(pred, how='outer', left_index=True, right_index=True) -misclass.to_csv( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/misclassified_pb-6.csv", - index=False, -) - -x = scaler.fit_transform(X) -background = shap.kmeans(train_x, 6) -explainer = shap.KernelExplainer(model.predict, background) -print("base value =", explainer.expected_value) -background = x[np.random.choice(x.shape[0], 1000, replace=False)] -shap_values = explainer.shap_values(background) # , nsamples=500 -shap.summary_plot(shap_values, x, features, show=False) - -import matplotlib.pyplot as pl - -pl.savefig("summary_plot.pdf", format="pdf", dpi=1000, bbox_inches="tight") diff --git a/src/training/training/temp_files/optuna-tpe-2.ipy b/src/training/training/temp_files/optuna-tpe-2.ipy deleted file mode 100644 index 6f226a1..0000000 --- a/src/training/training/temp_files/optuna-tpe-2.ipy +++ /dev/null @@ -1,222 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Oct 1 01:11:09 2020 - -@author: tarunmamidi -""" -import time -import os -import numpy as np; np.random.seed(5) -import optuna -from optuna.integration import TFKerasPruningCallback -from optuna.integration.tensorboard import TensorBoardCallback -from optuna.samplers import TPESampler -import tensorflow as tf -import tensorflow.keras as keras -#import ray -# Start Ray. -#ray.init(ignore_reinit_error=True) -try: - tf.get_logger().setLevel('INFO') -except Exception as exc: - print(exc) -import warnings -warnings.simplefilter("ignore") -#import ray -from tensorflow.keras.models import Sequential -from tensorflow.keras.layers import Dense, Dropout, Activation -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import label_binarize -#from sklearn.preprocessing import StandardScaler -from sklearn.metrics import precision_score, roc_auc_score, accuracy_score, confusion_matrix, recall_score -import pandas as pd -import yaml -import matplotlib.pyplot as plt -import shap -#from joblib import dump, load - - -#EPOCHS = 150 -class Objective(object): - def __init__(self, train_x,test_x, train_y, test_y): - - self.train_x = train_x - self.test_x = test_x - self.train_y = train_y - self.test_y = test_y - #self.var = var - #self.x = x - #self.n_columns = 112 - #self.CLASS = 2 - - def __call__(self, config): - # Clear clutter from previous TensorFlow graphs. - tf.keras.backend.clear_session() - - # Metrics to be monitored by Optuna. - if tf.__version__ >= "2": - monitor = "val_accuracy" - else: - monitor = "val_acc" - n_layers = config.suggest_int('n_layers', 1, 30) - model = Sequential() - model.add(Dense(self.train_x.shape[1], input_shape=(self.train_x.shape[1],), activation=config.suggest_categorical("activation", ['tanh', 'softmax', 'elu', 'softplus', 'softsign', 'relu', 'sigmoid', 'hard_sigmoid', 'linear']))) - for i in range(n_layers): - num_hidden = config.suggest_int("n_units_l{}".format(i), 1, 200) - model.add(Dense(num_hidden, name = "dense_l{}".format(i), kernel_initializer=config.suggest_categorical("kernel_initializer_l{}".format(i),['uniform', 'lecun_uniform', 'normal', 'zero', 'glorot_normal', 'glorot_uniform', 'he_normal', 'he_uniform']), activation=config.suggest_categorical("activation_l{}".format(i), ['tanh', 'softmax', 'elu', 'softplus', 'softsign', 'relu', 'sigmoid', 'hard_sigmoid', 'linear']))) - model.add(Dropout( config.suggest_float("dropout_l{}".format(i), 0.0, 0.9), name = "dropout_l{}".format(i))) - model.add(Dense(units = self.train_y.shape[1], name = "dense_last", kernel_initializer=config.suggest_categorical("kernel_initializer",['uniform', 'lecun_uniform', 'normal', 'zero', 'glorot_normal', 'glorot_uniform', 'he_normal', 'he_uniform']), activation = 'sigmoid')) - model.compile(loss='binary_crossentropy', optimizer=config.suggest_categorical("optimizer",['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam']), metrics=['accuracy']) - #model.summary() - # Create callbacks for early stopping and pruning. - callbacks = [ - tf.keras.callbacks.EarlyStopping(patience=10), - TFKerasPruningCallback(config, monitor), - ] - - # Train the model - model.fit( - self.train_x, self.train_y, - validation_data=(self.test_x, self.test_y), - verbose=0, - shuffle=True, - callbacks=callbacks, - batch_size=config.suggest_int('batch_size', 100, 1000), - epochs=150) - - - # Evaluate the model accuracy on the validation set. - score = model.evaluate(self.test_x, self.test_y, verbose=0) - return score[1] - - def tuned_run(self, config): - # Clear clutter from previous TensorFlow graphs. - print('running tuned params\n') - tf.keras.backend.clear_session() - model = Sequential() - model.add(Dense(self.train_x.shape[1], input_shape=(self.train_x.shape[1],), activation=config["activation"])) - for i in range(config['n_layers']): - model.add(Dense(config['n_units_l{}'.format(i)], name = "dense_l{}".format(i), kernel_initializer=config["kernel_initializer_l{}".format(i)], activation = config["activation_l{}".format(i)])) - model.add(Dropout( config["dropout_l{}".format(i)])) - model.add(Dense(units = self.train_y.shape[1], name = "dense_last", kernel_initializer=config["kernel_initializer"], activation = 'sigmoid')) - model.compile(loss='binary_crossentropy', optimizer=config["optimizer"], metrics=['accuracy']) - #model.summary() - # Train the model - model.fit( - self.train_x, self.train_y, - verbose=2, - batch_size=config['batch_size'], - epochs=500) - # Evaluate the model accuracy on the validation set. - #score = model.evaluate(test_x, test_y, verbose=0) - return model - - def show_result(self, study,var, output, feature_names): - pruned_trials = [t for t in study.trials if t.state == optuna.trial.TrialState.PRUNED] - complete_trials = [t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE] - print("Study statistics: ", file=open(output, "a")) - print(" Number of finished trials: ", len(study.trials), file=open(output, "a")) - print(" Number of pruned trials: ", len(pruned_trials), file=open(output, "a")) - print(" Number of complete trials: ", len(complete_trials), file=open(output, "a")) - print("Best trial:", file=open(output, "a")) - trial = study.best_trial - print(" Value: ", trial.value, file=open(output, "a")) - print(" Params: ", file=open(output, "a")) - for key, value in trial.params.items(): - print(" {}: {}".format(key, value), file=open(output, "a")) - model = self.tuned_run(trial.params) - print('ran tuned model\n', file=open(output, "a")) - results = model.evaluate(self.test_x, self.test_y) - y_score = model.predict(self.test_x) - prc = precision_score(self.test_y, y_score.round(), average="weighted") - recall = recall_score(self.test_y, y_score.round(), average="weighted") - roc_auc = roc_auc_score(self.test_y, y_score.round()) - accuracy = accuracy_score(self.test_y, y_score.round()) - #prc_micro = average_precision_score(self.test_y, y_score, average='micro') - matrix = confusion_matrix(np.argmax(self.test_y.values,axis=1), np.argmax(y_score, axis=1)) - print(f'Neural_Network_{var} results:\nstorage ="sqlite:///tuning/{var}/Neural_network_{var}.db"\nTest loss: {results[0]}\nTest accuracy: {results[1]}\nPrecision score: {prc}\nRecall {recall}\nroc_auc: {roc_auc}\nAccuracy: {accuracy}\nConfusion matrix:\n{matrix}\n', file=open(output, "a")) - # Calling `save('my_model')` creates a SavedModel folder `my_model`. - model.save(f"tuning/{var}/Neural_network/Neural_network_{var}") - model.save_weights(f"tuning/{var}/Neural_network/weights.h5") - - # explain all the predictions in the test set - background = shap.kmeans(self.train_x, 10) - explainer = shap.KernelExplainer(model.predict, background) - background = self.test_x[np.random.choice(self.test_x.shape[0], 1000, replace=False)] - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - #shap.plots.beeswarm(shap_vals, feature_names) - #shap.plots.waterfall(shap_values[1], max_display=10) - plt.savefig(f"./tuning/{var}/Neural_network_{var}_features.pdf", format='pdf', dpi=1000, bbox_inches='tight') - del background,shap_values, model, study - return None - - - -#@ray.remote -def data_parsing(var,config_dict,output): - os.chdir('/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/') - #Load data - print(f'\nUsing merged_data-train_{var}..', file=open(output, "a")) - X_train = pd.read_csv(f'train_{var}/merged_data-train_{var}.csv') - #var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict['ML_VAR'], axis=1) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f'train_{var}/merged_data-y-train_{var}.csv') - #Y_train = pd.get_dummies(Y_train) - Y_train = label_binarize(Y_train.values, classes=['low_impact', 'high_impact']).ravel() - - X_test = pd.read_csv(f'test_{var}/merged_data-test_{var}.csv') - #var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict['ML_VAR'], axis=1) - #feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f'test_{var}/merged_data-y-test_{var}.csv') - print('Data Loaded!') - #Y_test = pd.get_dummies(Y_test) - Y_test = label_binarize(Y_test.values, classes=['low_impact', 'high_impact']).ravel() - print(f'Shape: {Y_test.shape}') - #scaler = StandardScaler().fit(X_train) - #X_train = scaler.transform(X_train) - #X_test = scaler.transform(X_test) - # explain all the predictions in the test set - #background = shap.kmeans(X_train, 10) - return X_train, X_test, Y_train, Y_test, feature_names - -if __name__ == "__main__": - os.chdir('/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/') - with open("../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - variants = ['non_snv']#,'snv_protein_coding'] #'non_snv', - for var in variants: - start = time.perf_counter() - if not os.path.exists('tuning/'+var): - os.makedirs('./tuning/'+var) - output = f"tuning/{var}/Neural_network_{var}_.csv" - print('Working with '+var+' dataset...', file=open(output, "w")) - print('Working with '+var+' dataset...') - #X_train, X_test, Y_train, Y_test, feature_names = ray.get(data_parsing.remote(var,config_dict,output)) - X_train, X_test, Y_train, Y_test, feature_names = data_parsing(var,config_dict,output) - #print('Model\tCross_validate(avg_train_score)\tCross_validate(avg_test_score)\tPrecision(test_data)\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]', file=open(output, "a")) #\tConfusion_matrix[low_impact, high_impact] - #list1 = ray.get(classifier.remote(classifiers,var, X_train, X_test, Y_train, Y_test,background,feature_names)) - #print(f'{list1[0]}\t{list1[1]}\t{list1[2]}\t{list1[3]}\t{list1[4]}\t{list1[5]}\t{list1[6]}\t{list1[7]}\n{list1[8]}', file=open(output, "a")) - #print(f'training and testing done!') - - print('Starting Objective...') - objective = Objective(X_train, X_test, Y_train, Y_test) - tensorboard_callback = TensorBoardCallback(f"tuning/{var}/Neural_network_{var}_logs/", metric_name="accuracy") - study = optuna.create_study(sampler=TPESampler(**TPESampler.hyperopt_parameters()), study_name= f"Neural_network_{var}", storage = f"sqlite:///tuning/{var}/Neural_network_{var}.db", #study_name= "Ditto3", - direction="maximize", pruner=optuna.pruners.MedianPruner(n_startup_trials=50), load_if_exists=True #, pruner=optuna.pruners.MedianPruner(n_startup_trials=150) - ) - #study = optuna.load_study(study_name= "Ditto_all", sampler=TPESampler(**TPESampler.hyperopt_parameters()),storage ="sqlite:///Ditto_all.db") # study_name= "Ditto3", - study.optimize(objective, n_trials=100, callbacks=[tensorboard_callback], n_jobs = -1, gc_after_trial=True) #, n_jobs = -1 timeout=600, - finish = (time.perf_counter()- start)/120 - #ttime = (finish- start)/120 - print(f'Total time in hrs: {finish}') - objective.show_result(study, var, output, feature_names) - del X_train, X_test, Y_train, Y_test, feature_names - diff --git a/src/training/training/temp_files/optuna-tpe-stacking_results.ipy b/src/training/training/temp_files/optuna-tpe-stacking_results.ipy deleted file mode 100644 index 4a4ebe2..0000000 --- a/src/training/training/temp_files/optuna-tpe-stacking_results.ipy +++ /dev/null @@ -1,199 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Oct 1 01:11:09 2020 - -@author: tarunmamidi -""" -import time -import os -import numpy as np; np.random.seed(5) -import argparse -import yaml -import optuna -#from optuna.integration import TFKerasPruningCallback -#from optuna.integration.tensorboard import TensorBoardCallback -from optuna.samplers import TPESampler -import logging -#import tensorflow as tf -#import tensorflow.keras as keras -#try: -# tf.get_logger().setLevel('INFO') -#except Exception as exc: -# print(exc) -import warnings -warnings.simplefilter("ignore") -#import ray -#from tensorflow.keras.models import Sequential -#from tensorflow.keras.layers import Dense, Dropout, Activation -#from sklearn.model_selection import train_test_split -from sklearn.preprocessing import label_binarize -#from sklearn.preprocessing import StandardScaler -from sklearn.metrics import precision_score, roc_auc_score, accuracy_score, confusion_matrix, recall_score -from sklearn.tree import DecisionTreeClassifier -from sklearn.linear_model import LogisticRegression #SGDClassifier, -from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, StackingClassifier -from sklearn.naive_bayes import GaussianNB -from imblearn.ensemble import BalancedRandomForestClassifier -from sklearn.discriminant_analysis import LinearDiscriminantAnalysis -from sklearn.neighbors import KNeighborsClassifier -import pandas as pd -import yaml -import matplotlib.pyplot as plt -from joblib import dump -import shap -import functools -print = functools.partial(print, flush=True) -#from joblib import dump, load - - -#EPOCHS = 150 -class Objective(object): - def __init__(self, train_x,test_x, train_y, test_y): - - self.train_x = train_x - self.test_x = test_x - self.train_y = train_y - self.test_y = test_y - #self.var = var - #self.x = x - #self.n_columns = 112 - #self.CLASS = 2 - - def tuned_run(self, param): - model = StackingClassifier(estimators = [ - ('rf', RandomForestClassifier(random_state=42, n_estimators=param["rf_n_estimators"], criterion=param["rf_criterion"], max_depth=param["rf_max_depth"], min_samples_split=param["rf_min_samples_split"], min_samples_leaf=param["rf_min_samples_leaf"], max_features=param["rf_max_features"], oob_score=param["rf_oob_score"], class_weight=param["rf_class_weight"], n_jobs = -1)), - ('knn', KNeighborsClassifier(n_neighbors=param["knn_n_neighbors"], weights=param["knn_weights"], algorithm=param["knn_algorithm"], p=param["knn_p"], metric=param["knn_metric"], n_jobs = -1)), #leaf_size=leaf_size", 30), - ('gbc', GradientBoostingClassifier(random_state=42, loss=param["gbc_loss"], learning_rate = param["gbc_learning_rate"], n_estimators=param["gbc_n_estimators"], subsample=param["gbc_subsample"], criterion=param["gbc_criterion"], min_samples_split=param["gbc_min_samples_split"], min_samples_leaf=param["gbc_min_samples_leaf"], max_depth=param["gbc_max_depth"], max_features=param["gbc_max_features"])), - ('dt', DecisionTreeClassifier(random_state=42, criterion=param["dt_criterion"], splitter=param["dt_splitter"], max_depth=param["dt_max_depth"], min_samples_split=param["dt_min_samples_split"], min_samples_leaf=param["dt_min_samples_leaf"], max_features=param["dt_max_features"], class_weight=param["dt_class_weight"])), - ('gnb', GaussianNB(var_smoothing=param["var_smoothing"])), - ('brf', BalancedRandomForestClassifier(random_state=42, n_estimators=param["brf_n_estimators"], criterion=param["brf_criterion"], max_depth=param["brf_max_depth"], min_samples_split=param["brf_min_samples_split"], min_samples_leaf=param["brf_min_samples_leaf"], max_features=param["brf_max_features"], oob_score=param["brf_oob_score"], class_weight=param["brf_class_weight"], n_jobs = -1)), - ('lda', LinearDiscriminantAnalysis(solver=param["lda_solver"], shrinkage=param["lda_shrinkage"])) - ], - cv = 3, - stack_method = "predict_proba", - n_jobs=-1, - passthrough=False, - final_estimator= LogisticRegression(C=param["lr_C"], penalty=param["lr_penalty"], solver=param["lr_solver"], max_iter=param["lr_max_iter"], l1_ratio=param["lr_l1_ratio"], tol=param["lr_tol"], n_jobs = -1), - verbose=0).fit(self.train_x, self.train_y) - return model - - def show_result(self, study,var, output, feature_names): - pruned_trials = [t for t in study.trials if t.state == optuna.trial.TrialState.PRUNED] - complete_trials = [t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE] - print("Study statistics: ") - print(" Number of finished trials: ", len(study.trials)) - print(" Number of pruned trials: ", len(pruned_trials)) - print(" Number of complete trials: ", len(complete_trials)) - print("Best trial:") - trial = study.best_trial - print(" Value: ", trial.value) - print(f"Parameters: {trial.params}", file=open(output, "a")) - model = self.tuned_run(trial.params) - print('ran tuned model\n') - with open(f"./tuning/{var}/StackingClassifier_{var}.joblib", 'wb') as f: - dump(model, f, compress='lz4') - train_score = model.score(self.train_x, self.train_y) - y_score = model.predict(self.test_x) - prc = precision_score(self.test_y,y_score, average="weighted") - recall = recall_score(self.test_y,y_score, average="weighted") - roc_auc = roc_auc_score(self.test_y, y_score) - accuracy = accuracy_score(self.test_y, y_score) - matrix = confusion_matrix(self.test_y, y_score) - #print(f'RandomForestClassifier: \nCross_validate(avg_train_score): {training_score}\nCross_validate(avg_test_score): {testing_score}\nPrecision: {prc}\nRecall: {recall}\nROC_AUC: {roc_auc}\nAccuracy: {accuracy}\nTime(in min): {finish}\nConfusion Matrix:\n{matrix}', file=open(output, "a")) - clf_name = str(type(model)).split("'")[1] #.split(".")[3] - print('Model\ttrain_score\tPrecision\tRecall\troc_auc\tAccuracy\tConfusion_matrix[low_impact, high_impact]', file=open(output, "a")) #\tConfusion_matrix[low_impact, high_impact] - print(f'{clf_name}\t{train_score}\t{prc}\t{recall}\t{roc_auc}\t{accuracy}\n{matrix}', file=open(output, "a")) - - # explain all the predictions in the test set - background = shap.kmeans(self.train_x, 10) - explainer = shap.KernelExplainer(model.predict, background) - background = self.test_x[np.random.choice(self.test_x.shape[0], 1000, replace=False)] - shap_values = explainer.shap_values(background) - plt.figure() - shap.summary_plot(shap_values, background, feature_names, show=False) - #shap.plots.beeswarm(shap_vals, feature_names) - #shap.plots.waterfall(shap_values[1], max_display=10) - plt.savefig(f"./tuning/{var}/StackingClassifier_{var}_features.pdf", format='pdf', dpi=1000, bbox_inches='tight') - del background,shap_values, model, study - return None - - - -def data_parsing(var,config_dict,output): - os.chdir('/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/') - #Load data - print(f'\nUsing merged_data-train_{var}..', file=open(output, "a")) - X_train = pd.read_csv(f'train_{var}/merged_data-train_{var}.csv') - #var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict['ML_VAR'], axis=1) - X_train.replace([np.inf, -np.inf], np.nan, inplace=True) - X_train.fillna(0, inplace=True) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f'train_{var}/merged_data-y-train_{var}.csv') - Y_train = label_binarize(Y_train.values, classes=['low_impact', 'high_impact']).ravel() - - X_test = pd.read_csv(f'test_{var}/merged_data-test_{var}.csv') - #var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict['ML_VAR'], axis=1) - #feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f'test_{var}/merged_data-y-test_{var}.csv') - print('Data Loaded!') - #Y = pd.get_dummies(y) - Y_test = label_binarize(Y_test.values, classes=['low_impact', 'high_impact']).ravel() - - #scaler = StandardScaler().fit(X_train) - #X_train = scaler.transform(X_train) - #X_test = scaler.transform(X_test) - # explain all the predictions in the test set - return X_train, X_test, Y_train, Y_test,feature_names - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--vtype", - type=str, - default="non_snv", - help="Type of variation/s (without spaces between) to hp the classifier (like: snv,non_snv,snv_protein_coding). (Default: non_snv)") - parser.add_argument( - "--cpus", - type=int, - default=10, - help="Number of CPUs needed. (Default: 10)") - parser.add_argument( - "--gpus", - type=int, - default=0, - help="Number of GPUs needed. (Default: 0)") - parser.add_argument( - "--mem", - type=int, - default=100*1024*1024*1024, - help="Memory needed in bytes. (Default: 100*1024*1024*1024 (100GB))") - - args = parser.parse_args() - - variants = args.vtype.split(',') - - os.chdir('/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/') - with open("../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - #variants = ['non_snv','snv','snv_protein_coding'] - for var in variants: - start = time.perf_counter() - if not os.path.exists('tuning/'+var): - os.makedirs('./tuning/'+var) - output = "tuning/"+var+"/ML_results_"+var+".csv" - print('Working with '+var+' dataset...') - X_train, X_test, Y_train, Y_test, feature_names = data_parsing(var,config_dict,output) - - print('Starting Objective...') - objective = Objective(X_train, X_test, Y_train, Y_test) - - study = optuna.load_study(study_name= f"StackingClassifier_{var}", storage =f"sqlite:///tuning/{var}/StackingClassifier_{var}.db") - - objective.show_result(study, var, output, feature_names) - del X_train, X_test, Y_train, Y_test, feature_names diff --git a/src/training/training/temp_files/optuna-tpe-stacking_training.ipy b/src/training/training/temp_files/optuna-tpe-stacking_training.ipy deleted file mode 100644 index 0bd7cfd..0000000 --- a/src/training/training/temp_files/optuna-tpe-stacking_training.ipy +++ /dev/null @@ -1,245 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Oct 1 01:11:09 2020 - -@author: tarunmamidi -""" -import time -import os -import numpy as np; np.random.seed(5) -import argparse -import yaml -import optuna -#from optuna.integration import TFKerasPruningCallback -#from optuna.integration.tensorboard import TensorBoardCallback -from optuna.samplers import TPESampler -import logging -import sys -#import tensorflow as tf -#import tensorflow.keras as keras -#try: -# tf.get_logger().setLevel('INFO') -#except Exception as exc: -# print(exc) -import warnings -warnings.simplefilter("ignore") -#import ray -#from tensorflow.keras.models import Sequential -#from tensorflow.keras.layers import Dense, Dropout, Activation -#from sklearn.model_selection import train_test_split -from sklearn.preprocessing import label_binarize -from sklearn.tree import DecisionTreeClassifier -from sklearn.linear_model import LogisticRegression #SGDClassifier, -from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, StackingClassifier -from sklearn.naive_bayes import GaussianNB -from imblearn.ensemble import BalancedRandomForestClassifier -from sklearn.discriminant_analysis import LinearDiscriminantAnalysis -from sklearn.neighbors import KNeighborsClassifier -import pandas as pd -import yaml -import functools -print = functools.partial(print, flush=True) -#from joblib import dump, load - - -#EPOCHS = 150 -class Objective(object): - def __init__(self, train_x,test_x, train_y, test_y): - - self.train_x = train_x - self.test_x = test_x - self.train_y = train_y - self.test_y = test_y - #self.var = var - #self.x = x - #self.n_columns = 112 - #self.CLASS = 2 - - def __call__(self, config): - param = { - #RandomForest - https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html?highlight=randomforestclassifier#sklearn.ensemble.RandomForestClassifier - "rf_n_estimators" : config.suggest_int('rf_n_estimators', 1, 200), - "rf_criterion" : config.suggest_categorical('rf_criterion', ["gini", "entropy"]), - "rf_max_depth" : config.suggest_int('rf_max_depth', 2, 200), - "rf_min_samples_split" : config.suggest_int('rf_min_samples_split', 2, 10), - "rf_min_samples_leaf" : config.suggest_int('rf_min_samples_leaf', 1, 10), - "rf_max_features" : config.suggest_categorical('rf_max_features', ["sqrt", "log2"]), - "rf_oob_score" : config.suggest_categorical('rf_oob_score', [True, False]), - "rf_class_weight" : config.suggest_categorical('rf_class_weight', [None, "balanced", "balanced_subsample"]), - #KNeighborsClassifier - https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html?highlight=kn#sklearn.neighbors.KNeighborsClassifier - "knn_n_neighbors" : config.suggest_int('knn_n_neighbors', 1, 10), - "knn_weights" : config.suggest_categorical('knn_weights', ['uniform', 'distance']), - "knn_algorithm" : config.suggest_categorical('knn_algorithm', ['auto', 'ball_tree', 'kd_tree', 'brute']), - "knn_p" : config.suggest_int('knn_p', 1, 5), - "knn_metric" : config.suggest_categorical('knn_metric', ['minkowski', 'chebyshev']), - #GradientBoostingClassifier - https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier - "gbc_loss" : config.suggest_categorical('gbc_loss', ["deviance", "exponential"]), - "gbc_learning_rate": config.suggest_float('gbc_learning_rate', 0.01, 1.0, log = True), - "gbc_n_estimators" : config.suggest_int('gbc_n_estimators', 1, 200), - "gbc_subsample" : config. suggest_float('gbc_subsample', 0.1, 1.0), - "gbc_criterion" : config.suggest_categorical('gbc_criterion', ["friedman_mse", "mse"]), - "gbc_min_samples_split" : config.suggest_int('gbc_min_samples_split', 2, 100), - "gbc_min_samples_leaf" : config.suggest_int('gbc_min_samples_leaf', 1, 100), - "gbc_max_depth" : config.suggest_int('gbc_max_depth', 2, 200), - "gbc_max_features" : config.suggest_categorical('gbc_max_features', ["sqrt", "log2"]), - #DecisionTree - https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier - "dt_criterion" : config.suggest_categorical('dt_criterion', ["gini", "entropy"]), - "dt_splitter" : config.suggest_categorical('dt_splitter', ["best", "random"]), - "dt_max_depth" : config.suggest_int('dt_max_depth', 2, 200), - "dt_min_samples_split" : config.suggest_int('dt_min_samples_split', 2, 100), - "dt_min_samples_leaf" : config.suggest_int('dt_min_samples_leaf', 1, 100), - "dt_max_features" : config.suggest_categorical('dt_max_features', ["sqrt", "log2"]), - "dt_class_weight" : config.suggest_categorical('dt_class_weight', [None, "balanced"]), - #GaussianNB - https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB - "var_smoothing" : config.suggest_float('var_smoothing', 1e-11, 1e-1, log = True), - #BalancedRandomForest - https://imbalanced-learn.org/dev/references/generated/imblearn.ensemble.BalancedRandomForestClassifier.html - "brf_n_estimators" : config.suggest_int('brf_n_estimators', 1, 200), - "brf_criterion" : config.suggest_categorical('brf_criterion', ["gini", "entropy"]), - "brf_max_depth" : config.suggest_int('brf_max_depth', 2, 200), - "brf_min_samples_split" : config.suggest_int('brf_min_samples_split', 2, 10), - "brf_min_samples_leaf" : config.suggest_int('brf_min_samples_leaf', 1, 10), - "brf_max_features" : config.suggest_categorical('brf_max_features', ["sqrt", "log2"]), - "brf_oob_score" : config.suggest_categorical('brf_oob_score', [True, False]), - "brf_class_weight" : config.suggest_categorical('brf_class_weight', ["balanced", "balanced_subsample"]), - #LinearDiscriminantAnalysis - https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis - 'lda_solver': config.suggest_categorical('lda_solver', ['svd', 'lsqr', 'eigen']), - - #LogisticRegression - https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic#sklearn.linear_model.LogisticRegression; https://github.com/hyperopt/hyperopt/issues/304 - "lr_C" : config. suggest_float('lr_C', 0.0, 10.0), - "lr_solver": config.suggest_categorical('lr__solver',['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']), - "lr_tol": config.suggest_float('lr_tol',1e-11,1e-1, log = True), - "lr_l1_ratio": config.suggest_float('lr_l1_ratio',0,1), - "lr_max_iter" : config.suggest_int('lr_max_iter', 2, 100), - } - - if param["lr_solver"] == 'newton-cg': - param["lr_penalty"] = config.suggest_categorical('p_newton',['none','l2']) - elif param["lr_solver"] == 'lbfgs': - param["lr_penalty"] = config.suggest_categorical('p_lbfgs',['none','l2']) - elif param["lr_solver"] == 'liblinear': - param["lr_penalty"] =config.suggest_categorical('p_lib',['l1','l2']) - elif param["lr_solver"] == 'sag': - param["lr_penalty"] = config.suggest_categorical('p_sag',['l2','none']) - elif param["lr_solver"] == 'saga': - param["lr_penalty"] ='elasticnet' - if param['lda_solver'] =='lsqr': - param['lda_shrinkage']= config.suggest_categorical('shrinkage_type_lsqr', ['auto', 'float']) - if param['lda_shrinkage'] == 'float': - param['lda_shrinkage']=config.suggest_float('shrinkage_value_lsqr', 0, 1) - elif param['lda_solver'] =='eigen': - param['lda_shrinkage']= config.suggest_categorical('shrinkage_type_eigen', ['auto', 'float']) - if param['lda_shrinkage'] == 'float': - param['lda_shrinkage']=config.suggest_float('shrinkage_value_lsqr', 0, 1) - else: - param['lda_shrinkage']= None - - - model = StackingClassifier(estimators = [ - ('rf', RandomForestClassifier(random_state=42, n_estimators=param["rf_n_estimators"], criterion=param["rf_criterion"], max_depth=param["rf_max_depth"], min_samples_split=param["rf_min_samples_split"], min_samples_leaf=param["rf_min_samples_leaf"], max_features=param["rf_max_features"], oob_score=param["rf_oob_score"], class_weight=param["rf_class_weight"], n_jobs = -1)), - ('knn', KNeighborsClassifier(n_neighbors=param["knn_n_neighbors"], weights=param["knn_weights"], algorithm=param["knn_algorithm"], p=param["knn_p"], metric=param["knn_metric"], n_jobs = -1)), #leaf_size=leaf_size", 30), - ('gbc', GradientBoostingClassifier(random_state=42, loss=param["gbc_loss"], learning_rate = param["gbc_learning_rate"], n_estimators=param["gbc_n_estimators"], subsample=param["gbc_subsample"], criterion=param["gbc_criterion"], min_samples_split=param["gbc_min_samples_split"], min_samples_leaf=param["gbc_min_samples_leaf"], max_depth=param["gbc_max_depth"], max_features=param["gbc_max_features"])), - ('dt', DecisionTreeClassifier(random_state=42, criterion=param["dt_criterion"], splitter=param["dt_splitter"], max_depth=param["dt_max_depth"], min_samples_split=param["dt_min_samples_split"], min_samples_leaf=param["dt_min_samples_leaf"], max_features=param["dt_max_features"], class_weight=param["dt_class_weight"])), - ('gnb', GaussianNB(var_smoothing=param["var_smoothing"])), - ('brf', BalancedRandomForestClassifier(random_state=42, n_estimators=param["brf_n_estimators"], criterion=param["brf_criterion"], max_depth=param["brf_max_depth"], min_samples_split=param["brf_min_samples_split"], min_samples_leaf=param["brf_min_samples_leaf"], max_features=param["brf_max_features"], oob_score=param["brf_oob_score"], class_weight=param["brf_class_weight"], n_jobs = -1)), - ('lda', LinearDiscriminantAnalysis(solver=param["lda_solver"], shrinkage=param["lda_shrinkage"])) - ], - cv = 3, - stack_method = "predict_proba", - n_jobs=-1, - passthrough=False, - final_estimator= LogisticRegression(C=param["lr_C"], penalty=param["lr_penalty"], solver=param["lr_solver"], max_iter=param["lr_max_iter"], l1_ratio=param["lr_l1_ratio"], tol=param["lr_tol"], n_jobs = -1), - verbose=0).fit(self.train_x, self.train_y) - - # Evaluate the model accuracy on the validation set. - score = model.score(self.train_x, self.train_y) - return score - - -def data_parsing(var,config_dict,output): - os.chdir('/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/') - #Load data - print(f'\nUsing merged_data-train_{var}..', file=open(output, "a")) - X_train = pd.read_csv(f'train_{var}/merged_data-train_{var}.csv') - #var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict['ML_VAR'], axis=1) - X_train.replace([np.inf, -np.inf], np.nan, inplace=True) - X_train.fillna(0, inplace=True) - feature_names = X_train.columns.tolist() - X_train = X_train.values - Y_train = pd.read_csv(f'train_{var}/merged_data-y-train_{var}.csv') - Y_train = label_binarize(Y_train.values, classes=['low_impact', 'high_impact']).ravel() - - X_test = pd.read_csv(f'test_{var}/merged_data-test_{var}.csv') - #var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict['ML_VAR'], axis=1) - #feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f'test_{var}/merged_data-y-test_{var}.csv') - print('Data Loaded!') - #Y = pd.get_dummies(y) - Y_test = label_binarize(Y_test.values, classes=['low_impact', 'high_impact']).ravel() - - #scaler = StandardScaler().fit(X_train) - #X_train = scaler.transform(X_train) - #X_test = scaler.transform(X_test) - # explain all the predictions in the test set - return X_train, X_test, Y_train, Y_test,feature_names - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--vtype", - type=str, - default="non_snv", - help="Type of variation/s (without spaces between) to hp the classifier (like: snv,non_snv,snv_protein_coding). (Default: non_snv)") - parser.add_argument( - "--cpus", - type=int, - default=10, - help="Number of CPUs needed. (Default: 10)") - parser.add_argument( - "--gpus", - type=int, - default=0, - help="Number of GPUs needed. (Default: 0)") - parser.add_argument( - "--mem", - type=int, - default=100*1024*1024*1024, - help="Memory needed in bytes. (Default: 100*1024*1024*1024 (100GB))") - - args = parser.parse_args() - - variants = args.vtype.split(',') - - os.chdir('/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/') - with open("../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - #variants = ['non_snv','snv','snv_protein_coding'] - for var in variants: - start = time.perf_counter() - if not os.path.exists('tuning/'+var): - os.makedirs('./tuning/'+var) - output = "tuning/"+var+"/ML_results_"+var+".csv" - print('Working with '+var+' dataset...') - X_train, X_test, Y_train, Y_test, feature_names = data_parsing(var,config_dict,output) - - print('Starting Objective...') - objective = Objective(X_train, X_test, Y_train, Y_test) - # Add stream handler of stdout to show the messages - optuna.logging.get_logger("optuna").addHandler(logging.StreamHandler(sys.stdout)) - #options = {'pool_size': my_pool_size, 'max_overflow': my_max_overflow} - #storage = optuna.storages.RDBStorage(f'postgresql+pg8000://tmamidi@localhost/StackingClassifier_{var}') - study = optuna.create_study(sampler=TPESampler(**TPESampler.hyperopt_parameters()), study_name= f"StackingClassifier_{var}", storage = f"sqlite:///tuning/{var}/StackingClassifier_{var}.db", #study_name= "Ditto3", - direction="maximize", pruner=optuna.pruners.HyperbandPruner(), load_if_exists=True #, pruner=optuna.pruners.MedianPruner(n_startup_trials=150) - ) - #study = optuna.load_study(study_name= f"StackingClassifier_{var}", storage =f"mysql:///tuning/{var}/Stacking_Classifier_{var}.db", pruner=optuna.pruners.HyperbandPruner(), sampler=TPESampler(**TPESampler.hyperopt_parameters())) - study.optimize(objective, n_trials=500, n_jobs = -1, gc_after_trial=True) #, n_jobs = -1 timeout=600,callbacks=[tensorboard_callback], - finish = (time.perf_counter()- start)/120 - #ttime = (finish- start)/120 - print(f'Total time in hrs: {finish}') - #objective.show_result(study, var, output, feature_names) - del X_train, X_test, Y_train, Y_test, feature_names - #print('Training done!', file=open(f"tuning/"+var+"/.done_"+var+".csv", "a")) diff --git a/src/training/training/temp_files/parse_vcf.py b/src/training/training/temp_files/parse_vcf.py deleted file mode 100644 index f01d0d0..0000000 --- a/src/training/training/temp_files/parse_vcf.py +++ /dev/null @@ -1,26 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -# module load Anaconda3/2020.02 -# source activate envi -# python /data/project/worthey_lab/projects/experimental_pipelines/annovar_vcf_annotation/Annovar_Tarun.py /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/external/filtered_SL156674.vcf /data/scratch/tmamidi/ /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/interim/ /data/project/worthey_lab/tools/annovar/annovar_hg19_db - -import allel - -# print(allel.__version__) - -import os - -os.chdir("/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/") -# print(os.listdir()) - -print("Converting vcf.....") -# df = allel.vcf_to_dataframe('./external/clinvar.out.hg19_multianno.vcf', fields='*') -df = allel.vcf_to_dataframe("./interim/SL212589.out.hg19_multianno.vcf", fields="*") -# df.head(2) -# df.SIFT_score.unique() -df["ID"] = [f"var_{num}" for num in range(len(df))] -print("vcf converted to dataframe.\nWriting it to a csv file.....") -df.to_csv("./interim/filtered_sample.csv", index=False) -print("vcf to csv conversion completed!") -# df.to_csv("./external/clinvar.out.hg19_multianno.csv", index=False) -# print(df.head(20)) diff --git a/src/training/training/temp_files/predict.py b/src/training/training/temp_files/predict.py deleted file mode 100644 index 6baa205..0000000 --- a/src/training/training/temp_files/predict.py +++ /dev/null @@ -1,81 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -import pandas as pd -from tensorflow import keras -from sklearn.preprocessing import StandardScaler -import os -import yaml -import pickle - -os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" -) - -print("Loading data....") - -with open("SL212589_genes.yaml") as fh: - config_dict = yaml.safe_load(fh) - -X = pd.read_csv("sample1-filtered.csv") -print("Data Loaded!") -# overall.loc[:, overall.columns.str.startswith('CLN')] -var = X[["AAChange.refGene", "ID"]] -X = X.drop(["AAChange.refGene", "ID"], axis=1) -X = X.values -scaler = StandardScaler() -X = scaler.fit_transform(X) -print("Data scaled and Loading Ditto....!") -# y = pd.read_csv('clinvar-y-3.csv') -model = keras.models.load_model("my_model") -model.load_weights("weights.h5") -print("Ditto Loaded!\nRunning predictions.....") - - -# SL135596 MSTO1 MYOPATHY, MITOCHONDRIAL, AND ATAXIA MSTO1(NM_018116.3,c.676C>T,p.Gln226Ter,Likely Pathogenic); MSTO1(NM_018116.3,c.971C>T,p.Thr324Ile,VUS) - -y_score = model.predict(X) -print("Predictions finished!Sorting ....") -pred = pd.DataFrame(y_score, columns=["pred_Benign", "pred_Pathogenic"]) - -overall = pd.concat([var, pred], axis=1) -# classified1 = pd.concat([y.reset_index(drop=True), classified], axis=1) -del X -X = pd.read_csv("../interim/filtered_sample.csv") - -overall = overall.merge(X, on="ID") -overall["hazel"] = X["Gene.refGene"].map(config_dict) -del X -overall["hazel"] = overall["hazel"].fillna(0) -overall["HD"] = (overall["pred_Pathogenic"] + overall["hazel"]) / 2 -overall.drop_duplicates(inplace=True) -overall = overall.reset_index(drop=True) -overall = overall.sort_values(["HD", "pred_Pathogenic"], ascending=[False, False]) -overall.head(500).to_csv("predicted_results_500_SL212589.csv", index=False) -overall = overall.sort_values(["CHROM", "POS"]) -# columns = overall.columns -print("writing to database...") -# overall.head(500).to_csv('predicted_results_500_SL212589.tsv', sep='\t', index=False) -overall.to_csv("predicted_results_SL212589.tsv", sep="\t", index=False) -# overall.loc[:, overall.columns.str.startswith('CLNS')] - -# df1 = overall.iloc[:, :90] -# df2 = overall.iloc[:, 90:] -# df2 = pd.concat([df1['ID'], df2], axis=1) -# del overall -# store = pd.HDFStore("predicted_results_SL212589.h5") -# store.append("SL212589", overall, min_itemsize={"values": 100}, data_columns=columns) -# overall.to_hdf("predicted_results_SL212589.h5", "SL212589", format="table", mode="w") -# pd.read_hdf("store_tl.h5", "table", where=["index>2"]) -# from sqlalchemy import event -# engine1 = create_engine('sqlite:///SL212589_1.db', echo=True, pool_pre_ping=True) -# engine = create_engine('sqlite:///SL212589.db', echo=True, pool_pre_ping=True) -# sqlite_connection = engine.connect() -# sqlite_connection1 = engine1.connect() -# sqlite_table = 'SL212589' -# overall.to_sql(sqlite_table, sqlite_connection, index=False, if_exists="append", chunksize=10000, method='multi') #chunksize=10000, -# -# df1.to_sql(sqlite_table, sqlite_connection, if_exists='fail') -# df2.to_sql(sqlite_table, sqlite_connection1, if_exists='fail') -# sqlite_connection.close() -print("Database storage complete!") diff --git a/src/training/training/temp_files/snakemake_template.smk b/src/training/training/temp_files/snakemake_template.smk deleted file mode 100644 index 0ab3490..0000000 --- a/src/training/training/temp_files/snakemake_template.smk +++ /dev/null @@ -1,38 +0,0 @@ -from pathlib import Path - - -WORKFLOW_PATH = Path(workflow.basedir).parent - -configfile: WORKFLOW_PATH / "configs/some_workflow_config.yaml" -SAMPLE_LIST = config["samples"] - - -RAW_DIR = Path("data/raw") -EXTERNAL_DIR = Path("data/external") -PROCESSED_DIR = Path("data/processed") - - - -wildcard_constraints: - sample="|".join(SAMPLE_LIST) #"TRAIN_12|TRAIN_13" - - -rule all: - input: - # define target files here - - -rule some_name: - input: - # input file - output: - # output file - PROCESSED_DIR / "some_outfile.txt" - message: - "some message" - conda: - str(WORKFLOW_PATH / "configs/envs/bcftools.yaml") - shell: - r""" - sopme command - """ diff --git a/workflow/Snakefile b/workflow/Snakefile deleted file mode 100644 index bd1dcd0..0000000 --- a/workflow/Snakefile +++ /dev/null @@ -1,140 +0,0 @@ -from pathlib import Path - -WORKFLOW_PATH = Path(workflow.basedir).parent - -configfile: "/data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/metadata/train_test_metadata_original.json" -PROCESSED_DIR = Path("data/processed/trial/filter_vcf_by_DP8_AB_hpo_removed") -EXOMISER_DIR = Path("/data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/exomiser/hpo_nonGeneticHPOsRemoved") -ANNOTATED_VCF_DIR = Path("/data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/filter_vcf_by_DP8_AB") - -TRAIN_TEST = list(config.keys()) -SAMPLES = {} -SAMPLES['train'] = list(config['train'].keys()) -SAMPLES['test'] = list(config['test'].keys()) - -wildcard_constraints: - #sample="|".join(SAMPLE_LIST) #"TRAIN_12|TRAIN_13" - train_test = '|'.join(TRAIN_TEST), - sample = '|'.join(SAMPLES['train'] + SAMPLES['test']), - - -rule all: - input: - #TODO: specify all important output files here - #[PROCESSED_DIR / dataset_type / f"{sample}_vep-annotated_filtered.vcf.gz" for dataset_type in TRAIN_TEST for sample in SAMPLES[dataset_type] if config[dataset_type][sample]["affected_status"]=="Affected"], - [PROCESSED_DIR / dataset_type / sample / "ditto_predictions.csv" for dataset_type in TRAIN_TEST for sample in SAMPLES[dataset_type] if "PROBAND" in sample], - [PROCESSED_DIR / dataset_type / sample / "ditto_predictions_100.csv" for dataset_type in TRAIN_TEST for sample in SAMPLES[dataset_type] if "PROBAND" in sample], - [PROCESSED_DIR / dataset_type / sample / "combined_predictions.csv" for dataset_type in TRAIN_TEST for sample in SAMPLES[dataset_type] if "PROBAND" in sample], - [PROCESSED_DIR / dataset_type / sample / "combined_predictions_100.csv" for dataset_type in TRAIN_TEST for sample in SAMPLES[dataset_type] if "PROBAND" in sample], - [PROCESSED_DIR / dataset_type / sample / "combined_predictions_1000.csv" for dataset_type in TRAIN_TEST for sample in SAMPLES[dataset_type] if "PROBAND" in sample], - #expand(str(PROCESSED_DIR / "train/CAGI6_RGP_{sample}_PROBAND/predictions.csv"), sample=SAMPLE_LIST), - - -rule filter_variants: - input: - ANNOTATED_VCF_DIR / "{train_test}" / "{sample}_vep-annotated.vcf.gz" - output: - PROCESSED_DIR / "{train_test}" / "{sample}_vep-annotated_filtered.vcf.gz" - message: - "Filter variants from vcf using BCFTools: {wildcards.sample}" - conda: - str(WORKFLOW_PATH / "configs/envs/testing.yaml") - # threads: 2 - shell: - r""" - bcftools annotate \ - -e'ALT="*"' \ - {input} \ - -Oz \ - -o {output} - """ - - -rule parse_annotated_vars: - input: - PROCESSED_DIR / "{train_test}" / "{sample}_vep-annotated_filtered.vcf.gz" - output: - PROCESSED_DIR / "{train_test}" / "{sample}_vep-annotated_filtered.tsv" - message: - "Parse variants from annotated vcf to tsv: {wildcards.sample}" - conda: - str(WORKFLOW_PATH / "configs/envs/testing.yaml") - shell: - r""" - python annotation_parsing/parse_annotated_vars.py \ - -i {input} \ - -o {output} - """ - -rule ditto_filter: - input: - PROCESSED_DIR / "{train_test}" / "{sample}_vep-annotated_filtered.tsv" - output: - col = PROCESSED_DIR / "{train_test}" / "{sample}/columns.csv", - data = PROCESSED_DIR / "{train_test}" / "{sample}" / "data.csv", - nulls = PROCESSED_DIR / "{train_test}" / "{sample}/Nulls.csv", - stats = PROCESSED_DIR / "{train_test}" / "{sample}/stats_nssnv.csv", - plot = PROCESSED_DIR / "{train_test}" / "{sample}/correlation_plot.pdf", - message: - "Filter variants from annotated tsv for predictions: {wildcards.sample}" - conda: - str(WORKFLOW_PATH / "configs/envs/testing.yaml") - params: - outdir = lambda wildcards, output: Path(output['data']).parent - shell: - r""" - python src/Ditto/filter.py \ - -i {input} \ - -O {params.outdir} - """ - - -rule ditto_predict: - input: - data = PROCESSED_DIR / "{train_test}" / "{sample}/data.csv", - output: - pred = PROCESSED_DIR / "{train_test}" / "{sample}" / "ditto_predictions.csv", - pred_100 = PROCESSED_DIR / "{train_test}" / "{sample}" / "ditto_predictions_100.csv" - message: - "Run Ditto predictions: {wildcards.sample}" - params: - sample_name = lambda wildcards: f"{wildcards.sample}", - conda: - str(WORKFLOW_PATH / "configs/envs/testing.yaml") - shell: - r""" - python src/Ditto/predict.py \ - -i {input.data} \ - --sample {params.sample_name} \ - -o {output.pred} \ - -o100 {output.pred_100} \ - """ - -rule combine_scores: - input: - raw = PROCESSED_DIR / "{train_test}" / "{sample}_vep-annotated_filtered.tsv", - ditto = PROCESSED_DIR / "{train_test}" / "{sample}" / "ditto_predictions.csv", - exomiser = EXOMISER_DIR / "{train_test}" / "{sample}", - output: - pred = PROCESSED_DIR / "{train_test}" / "{sample}" / "combined_predictions.csv", - pred_100 = PROCESSED_DIR / "{train_test}" / "{sample}" / "combined_predictions_100.csv", - pred_1000 = PROCESSED_DIR / "{train_test}" / "{sample}" / "combined_predictions_1000.csv" - message: - "Combine Ditto predictions with Exomiser: {wildcards.sample}" - params: - sample_name = lambda wildcards: f"{wildcards.sample}", - conda: - str(WORKFLOW_PATH / "configs/envs/testing.yaml") - #params: - # variant= lambda wildcards: --variant str('Chr' + str(config[f"{wildcards.train_test}"][f"{wildcards.sample}"]["solves"][0]["Chrom"]) + ',' + str(config[f"{wildcards.train_test}"][f"{wildcards.sample}"]["solves"][0]["Pos"]) + ',' + config[f"{wildcards.train_test}"][f"{wildcards.sample}"]["solves"][0]["Ref"] + ',' + config[f"{wildcards.train_test}"][f"{wildcards.sample}"]["solves"][0]["Alt"]) if 'TRAIN' in {wildcards.sample} - shell: - r""" - python src/Ditto/combine_scores.py \ - --raw {input.raw} \ - --ditto {input.ditto} \ - -ep {input.exomiser} \ - --sample {params.sample_name} \ - -o {output.pred} \ - -o100 {output.pred_100} \ - -o1000 {output.pred_1000} \ - """ diff --git a/workflow/Snakefile1 b/workflow/Snakefile1 deleted file mode 100644 index b9ba341..0000000 --- a/workflow/Snakefile1 +++ /dev/null @@ -1,50 +0,0 @@ -from pathlib import Path - -WORKFLOW_PATH = Path(workflow.basedir).parent - -configfile: "/data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/metadata/train_test_metadata_original.json" -PROCESSED_DIR = Path("/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/debugged/annotated_vcf") -HAZEL_DIR = Path("/data/project/worthey_lab/projects/experimental_pipelines/tarun/uab-meter/data/processed/fixed") -ANNOTATED_VCF_DIR = Path("/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/debugged/annotated_vcf") - -#TRAIN_TEST = list(config.keys()) -SAMPLES = {} -SAMPLES['train'] = list(config['train'].keys()) -#SAMPLES['test'] = list(config['test'].keys()) - - -wildcard_constraints: - sample = "|".join(SAMPLES['train']), #"TRAIN_12|TRAIN_13" - train_test = "train" - #train_test = '|'.join(TRAIN_TEST), - #sample = '|'.join(SAMPLES['train'] + SAMPLES['test']), - - -rule all: - input: - [PROCESSED_DIR / "train" / sample / "Ditto_Hazel_fixed.csv" for sample in SAMPLES['train'] if "PROBAND" in sample], - [PROCESSED_DIR / "train" / sample / "Ditto_Hazel_fixed_100.csv" for sample in SAMPLES['train'] if "PROBAND" in sample], - #expand(str(PROCESSED_DIR / "train/CAGI6_RGP_{sample}_PROBAND/predictions.csv"), sample=SAMPLE_LIST), - -rule combine_scores: - input: - raw = PROCESSED_DIR / "train" / "{sample}_vep-annotated_filtered.tsv", - ditto = PROCESSED_DIR / "train" / "{sample}" / "ditto_predictions.csv", - exomiser = HAZEL_DIR / "{sample}" / "Hazel_{sample}.csv" , - output: - pred = PROCESSED_DIR / "train" / "{sample}" / "Ditto_Hazel_fixed.csv", - pred_100 = PROCESSED_DIR / "train" / "{sample}" / "Ditto_Hazel_fixed_100.csv", - message: - "Combine Ditto predictions with Hazel: {wildcards.sample}" - params: - sample_name = lambda wildcards: f"{wildcards.sample}", - shell: - r""" - python /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/src/cohort/combine_scores.py \ - --raw {input.raw} \ - --ditto {input.ditto} \ - --hazel {input.exomiser} \ - --sample {params.sample_name} \ - -o {output.pred} \ - -o100 {output.pred_100} \ - """ From 1c6d0d60693112b6450d928a4deb158de722564f Mon Sep 17 00:00:00 2001 From: Brandon Wilk Date: Tue, 18 Apr 2023 11:08:46 -0500 Subject: [PATCH 03/13] inital data survey for opencravat parsing --- src/annotation_parsing/parse_annotated_vars.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/src/annotation_parsing/parse_annotated_vars.py b/src/annotation_parsing/parse_annotated_vars.py index 3ed4147..ff36cdd 100644 --- a/src/annotation_parsing/parse_annotated_vars.py +++ b/src/annotation_parsing/parse_annotated_vars.py @@ -4,6 +4,17 @@ import gzip +# Example fields for parsing and normalizing: +# 'all_mappings': 'ENST00000253952.9:THOC6:Q86W42:missense_variant:p.Val234Leu:c.700G>C; ENST00000326266.13:THOC6:Q86W42:missense_variant:p.Val234Leu:c.700G>C; ENST00000389347.4:BICDL2:A1A5D9:2kb_downstream_variant::c.*936C>G; ENST00000572449.6:BICDL2:A1A5D9:2kb_downstream_variant::c.*936C>G; ENST00000573514.5:BICDL2:A1A5D9:2kb_downstream_variant::c.*936C>G; ENST00000574549.5:THOC6:Q86W42:missense_variant:p.Val210Leu:c.628G>C; ENST00000575576.5:THOC6:Q86W42:missense_variant:p.Val210Leu:c.628G>C; ENST00000642419.1:BICDL2::2kb_downstream_variant::c.*936C>G' +# 'chasmplus.all': '[["ENST00000574549.5", 0.064, 0.314], ["ENST00000575576.5", 0.064, 0.314], ["NM_001142350.1", 0.055, 0.358], ["NM_024339.3", 0.047, 0.405]]' +# 'biogrid.acts': 'EFTUD2;PPP2R1A;RRP9;SNRNP200;THOC1;THOC7;TPR;TRIM55;U2AF1;U2AF2;UTRN;VDAC2;ZC3H15;ZCCHC8;ZNF326' +# 'clinvar.sig_conf': 'Pathogenic(1)|Likely pathogenic(2)|Uncertain significance(3)' +# 'clinvar.disease_refs': 'MONDO:MONDO:0013362,MedGen:C3150939,OMIM:613680,Orphanet:ORPHA363444|MeSH:D030342,MedGen:C0950123|MedGen:CN517202' +# 'funseq2.all': '[["", "", "", "", "", "", "4"]]' +# 'intact.intact': 'GABARAPL2[20562859]|NUDC[25036637]|JUN[25609649]|THOC1[19165146;26496610]|THOC2[19165146;26496610]|DDX41[25920683]|THOC5[26496610]|ESR2[21182203]|GABARAP[20562859]|THOC7[26496610]|PLEKHA7[28877994]|BCLAF1[26496610]|MAP1LC3A[20562859]|ID1[26496610]|ABI1[26496610]|NCBP3[26496610;26382858]|' + +# TODO create config for field mappings and parsing logic needed for various field types from examples above + def parse_n_print(vcf, outfile): # collect header information for the annotated information as well as the sample itself print("Collecting header info...") From 81c61ee8578e08e4054a073286e5e76877b818f4 Mon Sep 17 00:00:00 2001 From: Brandon Wilk Date: Tue, 18 Apr 2023 12:01:54 -0500 Subject: [PATCH 04/13] working config generation --- opencravat_2.3.0_config.json | 4335 +++++++++++++++++ .../parse_annotated_vars.py | 344 +- 2 files changed, 4541 insertions(+), 138 deletions(-) create mode 100644 opencravat_2.3.0_config.json diff --git a/opencravat_2.3.0_config.json b/opencravat_2.3.0_config.json new file mode 100644 index 0000000..22b79b0 --- /dev/null +++ b/opencravat_2.3.0_config.json @@ -0,0 +1,4335 @@ +[ + { + "col_num": "0", + "col_id": "uid", + "description": "UID", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "1", + "col_id": "chrom", + "description": "Chrom", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "2", + "col_id": "pos", + "description": "Position", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "3", + "col_id": "ref_base", + "description": "Ref", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "4", + "col_id": "alt_base", + "description": "Alt", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "5", + "col_id": "note", + "description": "Note", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "6", + "col_id": "coding", + "description": "Coding", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "7", + "col_id": "hugo", + "description": "Gene", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "8", + "col_id": "transcript", + "description": "Transcript", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "9", + "col_id": "so", + "description": "Sequence", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "10", + "col_id": "cchange", + "description": "cDNA", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "11", + "col_id": "achange", + "description": "Protein", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "12", + "col_id": "all_mappings", + "description": "All", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "13", + "col_id": "numsample", + "description": "Sample", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "14", + "col_id": "samples", + "description": "Samples", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "15", + "col_id": "tags", + "description": "Tags", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "16", + "col_id": "hg19.chrom", + "description": "Chrom", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "17", + "col_id": "hg19.pos", + "description": "Position", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "18", + "col_id": "thousandgenomes.af", + "description": "AF", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "19", + "col_id": "thousandgenomes.afr_af", + "description": "AFR", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "20", + "col_id": "thousandgenomes.amr_af", + "description": "AMR", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "21", + "col_id": "thousandgenomes.eas_af", + "description": "EAS", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "22", + "col_id": "thousandgenomes.eur_af", + "description": "EUR", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "23", + "col_id": "thousandgenomes.sas_af", + "description": "SAS", + "parse": 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+ "col_id": "gnomad_gene.oe_lof", + "description": "Obv/Exp", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "594", + "col_id": "gnomad_gene.oe_mis", + "description": "Obv/Exp", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "595", + "col_id": "gnomad_gene.oe_syn", + "description": "Obv/Exp", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "596", + "col_id": "gnomad_gene.lof_z", + "description": "LoF", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "597", + "col_id": "gnomad_gene.mis_z", + "description": "Mis", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "598", + "col_id": "gnomad_gene.syn_z", + "description": "Syn", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "599", + "col_id": "gnomad_gene.pLI", + "description": "pLI", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "600", + "col_id": "gnomad_gene.pRec", + "description": "pRec", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "601", + "col_id": "gnomad_gene.pNull", + "description": "pNull", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "602", + "col_id": "gnomad_gene.all", + "description": "All", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "603", + "col_id": "gnomad3.af", + "description": "Global", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "604", + "col_id": "gnomad3.af_afr", + "description": "African", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "605", + "col_id": "gnomad3.af_asj", + "description": "Ashkenazi", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "606", + "col_id": "gnomad3.af_eas", + "description": "East", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "607", + "col_id": "gnomad3.af_fin", + "description": "Finnish", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "608", + "col_id": "gnomad3.af_lat", + "description": "Latino", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "609", + "col_id": "gnomad3.af_nfe", + "description": "Non-Fin", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "610", + "col_id": "gnomad3.af_oth", + "description": "Other", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "611", + "col_id": "gnomad3.af_sas", + "description": "South", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "612", + "col_id": "mirbase.transcript", + "description": "Transcript", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "613", + "col_id": "mirbase.id", + "description": "Accession", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "614", + "col_id": "mirbase.name", + "description": "Name", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "615", + "col_id": "mirbase.derives_from", + "description": "Derives", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "616", + "col_id": "ncrna.ncrnaclass", + "description": "Class", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "617", + "col_id": "ncrna.ncrnaname", + "description": "Name", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "618", + "col_id": "phi.phi", + "description": "P(HI)", + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + } +] diff --git a/src/annotation_parsing/parse_annotated_vars.py b/src/annotation_parsing/parse_annotated_vars.py index ff36cdd..4e42262 100644 --- a/src/annotation_parsing/parse_annotated_vars.py +++ b/src/annotation_parsing/parse_annotated_vars.py @@ -1,7 +1,7 @@ from pathlib import Path import argparse import os -import gzip +import json # Example fields for parsing and normalizing: @@ -15,142 +15,181 @@ # TODO create config for field mappings and parsing logic needed for various field types from examples above -def parse_n_print(vcf, outfile): - # collect header information for the annotated information as well as the sample itself - print("Collecting header info...") - output_header = list() - vcf_header = list() - with gzip.open(vcf, 'rt') if vcf.suffix == ".gz" else vcf.open('r') as vcffp: - for cnt, line in enumerate(vcffp): - line = line.rstrip("\n") - if line.startswith("#"): - if "ID=CSQ" in line: - output_header = ["Chromosome", "Position", "Reference Allele", "Alternate Allele"] + \ - line.replace(" Allele|"," VEP_Allele_Identifier|").split("Format: ")[1].rstrip(">").rstrip('"').split("|") - elif line.startswith("#CHROM"): - vcf_header = line.split("\t") - else: - break - for idx, sample in enumerate(vcf_header): - if idx > 8: - output_header.append(f"{sample} allele depth") - output_header.append(f"{sample} total depth") - output_header.append(f"{sample} allele percent reads") - - with open(outfile, "w") as out: - out.write("\t".join(output_header) + "\n") - print("Parsing variants...") - with gzip.open(vcf, 'rt') if vcf.suffix == ".gz" else vcf.open('r') as vcffp: - for cnt, line in enumerate(vcffp): - if not line.startswith("#"): - line = line.rstrip("\n") - cols = line.split("\t") - csq = parse_csq(next(filter(lambda info: info.startswith("CSQ="),cols[7].split(";"))).replace("CSQ=","")) - #print(line, file=open("var_info.txt", "w")) - #var_info = parse_var_info(vcf_header, cols) - alt_alleles = cols[4].split(",") - alt2csq = format_alts_for_csq_lookup(cols[3], alt_alleles) - for alt_allele in alt_alleles: - possible_alt_allele4lookup = alt2csq[alt_allele] - if possible_alt_allele4lookup not in csq.keys(): - possible_alt_allele4lookup = alt_allele - try: - write_parsed_variant( - out, - vcf_header, - cols[0], - cols[1], - cols[3], - alt_allele, - csq[possible_alt_allele4lookup] - #,var_info[alt_allele] - ) - except KeyError: - print("Variant annotation matching based on allele failed!") - print(line) - print(csq) - print(alt2csq) - raise SystemExit(1) - - -def write_parsed_variant(out_fp, vcf_header, chr, pos, ref, alt, annots):#, var_info): - var_list = [chr, pos, ref, alt] - for annot_info in annots: - full_fmt_list = var_list + annot_info - #for idx, sample in enumerate(vcf_header): - # if idx > 8: - # full_fmt_list.append(str(var_info[sample]["alt_depth"])) - # full_fmt_list.append(str(var_info[sample]["total_depth"])) - # full_fmt_list.append(str(var_info[sample]["prct_reads"])) - - out_fp.write("\t".join(full_fmt_list) + "\n") - - -def format_alts_for_csq_lookup(ref, alt_alleles): - alt2csq = dict() - dels = list() - for alt in alt_alleles: - if len(ref) == len(alt): - alt2csq[alt] = alt - elif alt.startswith(ref): - alt2csq[alt] = alt[1:] - else: - dels.append(alt) - - if len(dels) > 0: - min_length = min([len(alt) for alt in dels]) - for alt in dels: - if min_length == len(alt): - alt2csq[alt] = "-" - else: - alt2csq[alt] = alt[1:] +# list of dictionaries (that looks like a list of lists, can have empty values), this will require mapping configuration - return alt2csq +# list -def parse_csq(csq): - csq_allele_dict = dict() - for annot in csq.split(","): - parsed_annot = annot.split("|") - if parsed_annot[0] not in csq_allele_dict: - csq_allele_dict[parsed_annot[0]] = list() - csq_allele_dict[parsed_annot[0]].append(parsed_annot) +# lists that don't need parsing - return csq_allele_dict - -def parse_var_info(headers, cols): - if len(cols) < 9: - return {alt_allele: dict() for alt_allele in cols[4].split(",")} - else: - ad_index = cols[8].split(":").index("AD") - parsed_alleles = dict() - for alt_index, alt_allele in enumerate(cols[4].split(",")): - allele_dict = dict() - for col_index, col in enumerate(cols): - if col_index > 8: - ad_info = col.split(":")[ad_index] - alt_depth = 0 - total_depth = 0 - prct_reads = 0 - sample = headers[col_index] - if ad_info != ".": - ad_info = ad_info.replace(".", "0").split(",") - alt_depth = int(ad_info[alt_index + 1]) - total_depth = sum([int(dp) for dp in ad_info]) - prct_reads = (alt_depth / total_depth) * 100 - - allele_dict[sample] = { - "alt_depth": alt_depth, - "total_depth": total_depth, - "prct_reads": prct_reads - } - - parsed_alleles[alt_allele] = allele_dict - - return parsed_alleles +def create_data_config(annot_csv, outfile = None): + #Column description. Column 0 uid=UID + #Column description. Column 1 chrom=Chrom + #Column description. Column 2 pos=Position + #Column description. Column 3 ref_base=Ref Base + #Column description. Column 4 alt_base=Alt Base + columns = list() + with open(annot_csv) as csvfp: + cntr = 0 + for line in csvfp: + if line.startswith("#Column description. Column"): + line = line.replace("#Column description. Column ","").strip() + info = line.replace("="," ").split(" ") + columns.append({ + "col_num": info[0], + "col_id": info[1], + "description": info[2], + "parse": "true|false", + "parse_type": "list-o-dicts,list,none" + }) + if cntr > 2000: + break + else: + cntr += 1 + + with open(outfile, "wt") as ofp: + json.dump(columns,ofp) + + +# def parse_n_print(vcf, outfile): +# # collect header information for the annotated information as well as the sample itself +# print("Collecting header info...") +# output_header = list() +# vcf_header = list() +# with gzip.open(vcf, 'rt') if vcf.suffix == ".gz" else vcf.open('r') as vcffp: +# for cnt, line in enumerate(vcffp): +# line = line.rstrip("\n") +# if line.startswith("#"): +# if "ID=CSQ" in line: +# output_header = ["Chromosome", "Position", "Reference Allele", "Alternate Allele"] + \ +# line.replace(" Allele|"," VEP_Allele_Identifier|").split("Format: ")[1].rstrip(">").rstrip('"').split("|") +# elif line.startswith("#CHROM"): +# vcf_header = line.split("\t") +# else: +# break + +# for idx, sample in enumerate(vcf_header): +# if idx > 8: +# output_header.append(f"{sample} allele depth") +# output_header.append(f"{sample} total depth") +# output_header.append(f"{sample} allele percent reads") + +# with open(outfile, "w") as out: +# out.write("\t".join(output_header) + "\n") +# print("Parsing variants...") +# with gzip.open(vcf, 'rt') if vcf.suffix == ".gz" else vcf.open('r') as vcffp: +# for cnt, line in enumerate(vcffp): +# if not line.startswith("#"): +# line = line.rstrip("\n") +# cols = line.split("\t") +# csq = parse_csq(next(filter(lambda info: info.startswith("CSQ="),cols[7].split(";"))).replace("CSQ=","")) +# #print(line, file=open("var_info.txt", "w")) +# #var_info = parse_var_info(vcf_header, cols) +# alt_alleles = cols[4].split(",") +# alt2csq = format_alts_for_csq_lookup(cols[3], alt_alleles) +# for alt_allele in alt_alleles: +# possible_alt_allele4lookup = alt2csq[alt_allele] +# if possible_alt_allele4lookup not in csq.keys(): +# possible_alt_allele4lookup = alt_allele +# try: +# write_parsed_variant( +# out, +# vcf_header, +# cols[0], +# cols[1], +# cols[3], +# alt_allele, +# csq[possible_alt_allele4lookup] +# #,var_info[alt_allele] +# ) +# except KeyError: +# print("Variant annotation matching based on allele failed!") +# print(line) +# print(csq) +# print(alt2csq) +# raise SystemExit(1) + + +# def write_parsed_variant(out_fp, vcf_header, chr, pos, ref, alt, annots):#, var_info): +# var_list = [chr, pos, ref, alt] +# for annot_info in annots: +# full_fmt_list = var_list + annot_info +# #for idx, sample in enumerate(vcf_header): +# # if idx > 8: +# # full_fmt_list.append(str(var_info[sample]["alt_depth"])) +# # full_fmt_list.append(str(var_info[sample]["total_depth"])) +# # full_fmt_list.append(str(var_info[sample]["prct_reads"])) + +# out_fp.write("\t".join(full_fmt_list) + "\n") + + +# def format_alts_for_csq_lookup(ref, alt_alleles): +# alt2csq = dict() +# dels = list() +# for alt in alt_alleles: +# if len(ref) == len(alt): +# alt2csq[alt] = alt +# elif alt.startswith(ref): +# alt2csq[alt] = alt[1:] +# else: +# dels.append(alt) + +# if len(dels) > 0: +# min_length = min([len(alt) for alt in dels]) +# for alt in dels: +# if min_length == len(alt): +# alt2csq[alt] = "-" +# else: +# alt2csq[alt] = alt[1:] + +# return alt2csq + + +# def parse_csq(csq): +# csq_allele_dict = dict() +# for annot in csq.split(","): +# parsed_annot = annot.split("|") +# if parsed_annot[0] not in csq_allele_dict: +# csq_allele_dict[parsed_annot[0]] = list() + +# csq_allele_dict[parsed_annot[0]].append(parsed_annot) + +# return csq_allele_dict + + +# def parse_var_info(headers, cols): +# if len(cols) < 9: +# return {alt_allele: dict() for alt_allele in cols[4].split(",")} +# else: +# ad_index = cols[8].split(":").index("AD") +# parsed_alleles = dict() +# for alt_index, alt_allele in enumerate(cols[4].split(",")): +# allele_dict = dict() +# for col_index, col in enumerate(cols): +# if col_index > 8: +# ad_info = col.split(":")[ad_index] +# alt_depth = 0 +# total_depth = 0 +# prct_reads = 0 +# sample = headers[col_index] +# if ad_info != ".": +# ad_info = ad_info.replace(".", "0").split(",") +# alt_depth = int(ad_info[alt_index + 1]) +# total_depth = sum([int(dp) for dp in ad_info]) +# prct_reads = (alt_depth / total_depth) * 100 + +# allele_dict[sample] = { +# "alt_depth": alt_depth, +# "total_depth": total_depth, +# "prct_reads": prct_reads +# } + +# parsed_alleles[alt_allele] = allele_dict + +# return parsed_alleles def is_valid_output_file(p, arg): @@ -162,39 +201,68 @@ def is_valid_output_file(p, arg): def is_valid_file(p, arg): if not Path(os.path.expandvars(arg)).is_file(): - p.error("The file '%s' does not exist!" % arg) + p.error(f"The file {arg} does not exist!") else: return os.path.expandvars(arg) if __name__ == "__main__": + EXECUTIONS = [ + "config", + "parse", + ] + PARSER = argparse.ArgumentParser( - description="Simple parser for converting an annotated VCF file produced by VEP into a columnar format", + description="Simple parser for creating data model, data parsing config, and data parsing of annotations from OpenCravat", formatter_class=argparse.ArgumentDefaultsHelpFormatter ) PARSER.add_argument( "-i", - "--input_vcf", - help="File path to the input VEP annotated VCF file to parse", + "--input_csv", + help="File path to the CSV file of annotated variants from OpenCravat", required=True, type=lambda x: is_valid_file(PARSER, x), metavar="\b" ) + PARSER.add_argument( + "-e", + "--exec", + help="Determine what should be done: create a new data config file or parse the annotations from the OpenCravat CSV file", + required=True, + choices=EXECUTIONS, + metavar="\b" + ) + OPTIONAL_ARGS = PARSER.add_argument_group("Override Args") PARSER.add_argument( "-o", "--output", - help="File path to the desired output file (default is to use input VCF location and name but with *.tsv extension)", + help="Output from parsing", required=False, type=lambda x: is_valid_output_file(PARSER, x), metavar="\b" ) + PARSER.add_argument( + "-v", + "--version", + help="Verison of OpenCravat used to generate the config file (only required during config parsing)", + required=False, + type=str, + metavar="\b" + ) + ARGS = PARSER.parse_args() - inputf = Path(ARGS.input_vcf) - outputf = Path(ARGS.output) if ARGS.output else inputf.parent / inputf.stem.rstrip(".vcf") + ".tsv" + if ARGS.exec == "config" and not ARGS.version: + print("Version of OpenCravat must be specified when creating a config from their data for tracking purposes") + raise SystemExit(1) - parse_n_print(inputf, outputf) + if ARGS.exec == "config": + create_data_config(ARGS.input_csv, f"opencravat_{ARGS.version}_config.json") + else: + # TODO parsing method lolz + print("TODO") + From 80c67332de94b6f4ec9e55f98d832585bfd5afc5 Mon Sep 17 00:00:00 2001 From: Brandon Wilk Date: Tue, 18 Apr 2023 13:01:31 -0500 Subject: [PATCH 05/13] adding list o list parsing and some updated config logic --- opencravat_test.test_config.json | 1 + .../parse_annotated_vars.py | 188 +++++------------- 2 files changed, 46 insertions(+), 143 deletions(-) create mode 100644 opencravat_test.test_config.json diff --git a/opencravat_test.test_config.json b/opencravat_test.test_config.json new file mode 100644 index 0000000..9991029 --- /dev/null +++ b/opencravat_test.test_config.json @@ -0,0 +1 @@ +[{"col_num": "0", "col_id": "uid", "description": "UID", "parse": true, "parse_type": "list-o-dicts,list,none"}, {"col_num": "1", "col_id": "chrom", "description": "Chrom", "parse": true, "parse_type": "list-o-dicts,list,none"}, {"col_num": "2", "col_id": "pos", "description": "Position", "parse": true, "parse_type": "list-o-dicts,list,none"}, {"col_num": "3", "col_id": "ref_base", "description": "Ref", "parse": true, "parse_type": "list-o-dicts,list,none"}, {"col_num": "4", "col_id": "alt_base", "description": "Alt", "parse": true, "parse_type": "list-o-dicts,list,none"}, {"col_num": "5", "col_id": "note", "description": "Note", "parse": true, "parse_type": "list-o-dicts,list,none"}, {"col_num": "6", "col_id": "coding", "description": "Coding", "parse": true, "parse_type": "list-o-dicts,list,none"}, {"col_num": "7", "col_id": "hugo", "description": "Gene", "parse": true, "parse_type": "list-o-dicts,list,none"}, {"col_num": "8", "col_id": "transcript", "description": "Transcript", "parse": true, "parse_type": "list-o-dicts,list,none"}, {"col_num": "9", "col_id": "so", "description": "Sequence", "parse": true, "parse_type": "list-o-dicts,list,none"},{"col_num": "12", "col_id": "all_mappings", "description": "All", "parse": true, "parse_type": "list-o-dicts,list,none"},{"col_num": "85", "col_id": "chasmplus.all", "description": "All", "parse": true, "parse_type": "list-o-dicts,list,none"},{"col_num": "69", "col_id": "biogrid.acts", "description": "Interactors", "parse": true, "parse_type": "list-o-dicts,list,none"},{"col_num": "270", "col_id": "clinvar.sig_conf", "description": "Significance", "parse": true, "parse_type": "list-o-dicts,list,none"},{"col_num": "266", "col_id": "clinvar.disease_refs", "description": "Disease", "parse": true, "parse_type": "list-o-dicts,list,none"},{"col_num": "337", "col_id": "funseq2.all", "description": "All", "parse": true, "parse_type": "list-o-dicts,list,none"},{"col_num": "381", "col_id": "intact.intact", "description": "Raw", "parse": true, "parse_type": "list-o-dicts,list,none"}] \ No newline at end of file diff --git a/src/annotation_parsing/parse_annotated_vars.py b/src/annotation_parsing/parse_annotated_vars.py index 4e42262..87f2c42 100644 --- a/src/annotation_parsing/parse_annotated_vars.py +++ b/src/annotation_parsing/parse_annotated_vars.py @@ -2,6 +2,7 @@ import argparse import os import json +import csv # Example fields for parsing and normalizing: @@ -42,8 +43,9 @@ def create_data_config(annot_csv, outfile = None): "col_num": info[0], "col_id": info[1], "description": info[2], - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": True, + "parse_type": "list-o-dicts,list,none", + "separator": ";" }) if cntr > 2000: break @@ -54,142 +56,37 @@ def create_data_config(annot_csv, outfile = None): json.dump(columns,ofp) -# def parse_n_print(vcf, outfile): -# # collect header information for the annotated information as well as the sample itself -# print("Collecting header info...") -# output_header = list() -# vcf_header = list() -# with gzip.open(vcf, 'rt') if vcf.suffix == ".gz" else vcf.open('r') as vcffp: -# for cnt, line in enumerate(vcffp): -# line = line.rstrip("\n") -# if line.startswith("#"): -# if "ID=CSQ" in line: -# output_header = ["Chromosome", "Position", "Reference Allele", "Alternate Allele"] + \ -# line.replace(" Allele|"," VEP_Allele_Identifier|").split("Format: ")[1].rstrip(">").rstrip('"').split("|") -# elif line.startswith("#CHROM"): -# vcf_header = line.split("\t") -# else: -# break - -# for idx, sample in enumerate(vcf_header): -# if idx > 8: -# output_header.append(f"{sample} allele depth") -# output_header.append(f"{sample} total depth") -# output_header.append(f"{sample} allele percent reads") - -# with open(outfile, "w") as out: -# out.write("\t".join(output_header) + "\n") -# print("Parsing variants...") -# with gzip.open(vcf, 'rt') if vcf.suffix == ".gz" else vcf.open('r') as vcffp: -# for cnt, line in enumerate(vcffp): -# if not line.startswith("#"): -# line = line.rstrip("\n") -# cols = line.split("\t") -# csq = parse_csq(next(filter(lambda info: info.startswith("CSQ="),cols[7].split(";"))).replace("CSQ=","")) -# #print(line, file=open("var_info.txt", "w")) -# #var_info = parse_var_info(vcf_header, cols) -# alt_alleles = cols[4].split(",") -# alt2csq = format_alts_for_csq_lookup(cols[3], alt_alleles) -# for alt_allele in alt_alleles: -# possible_alt_allele4lookup = alt2csq[alt_allele] -# if possible_alt_allele4lookup not in csq.keys(): -# possible_alt_allele4lookup = alt_allele -# try: -# write_parsed_variant( -# out, -# vcf_header, -# cols[0], -# cols[1], -# cols[3], -# alt_allele, -# csq[possible_alt_allele4lookup] -# #,var_info[alt_allele] -# ) -# except KeyError: -# print("Variant annotation matching based on allele failed!") -# print(line) -# print(csq) -# print(alt2csq) -# raise SystemExit(1) - - -# def write_parsed_variant(out_fp, vcf_header, chr, pos, ref, alt, annots):#, var_info): -# var_list = [chr, pos, ref, alt] -# for annot_info in annots: -# full_fmt_list = var_list + annot_info -# #for idx, sample in enumerate(vcf_header): -# # if idx > 8: -# # full_fmt_list.append(str(var_info[sample]["alt_depth"])) -# # full_fmt_list.append(str(var_info[sample]["total_depth"])) -# # full_fmt_list.append(str(var_info[sample]["prct_reads"])) - -# out_fp.write("\t".join(full_fmt_list) + "\n") - - -# def format_alts_for_csq_lookup(ref, alt_alleles): -# alt2csq = dict() -# dels = list() -# for alt in alt_alleles: -# if len(ref) == len(alt): -# alt2csq[alt] = alt -# elif alt.startswith(ref): -# alt2csq[alt] = alt[1:] -# else: -# dels.append(alt) - -# if len(dels) > 0: -# min_length = min([len(alt) for alt in dels]) -# for alt in dels: -# if min_length == len(alt): -# alt2csq[alt] = "-" -# else: -# alt2csq[alt] = alt[1:] - -# return alt2csq - - -# def parse_csq(csq): -# csq_allele_dict = dict() -# for annot in csq.split(","): -# parsed_annot = annot.split("|") -# if parsed_annot[0] not in csq_allele_dict: -# csq_allele_dict[parsed_annot[0]] = list() - -# csq_allele_dict[parsed_annot[0]].append(parsed_annot) - -# return csq_allele_dict - - -# def parse_var_info(headers, cols): -# if len(cols) < 9: -# return {alt_allele: dict() for alt_allele in cols[4].split(",")} -# else: -# ad_index = cols[8].split(":").index("AD") -# parsed_alleles = dict() -# for alt_index, alt_allele in enumerate(cols[4].split(",")): -# allele_dict = dict() -# for col_index, col in enumerate(cols): -# if col_index > 8: -# ad_info = col.split(":")[ad_index] -# alt_depth = 0 -# total_depth = 0 -# prct_reads = 0 -# sample = headers[col_index] -# if ad_info != ".": -# ad_info = ad_info.replace(".", "0").split(",") -# alt_depth = int(ad_info[alt_index + 1]) -# total_depth = sum([int(dp) for dp in ad_info]) -# prct_reads = (alt_depth / total_depth) * 100 - -# allele_dict[sample] = { -# "alt_depth": alt_depth, -# "total_depth": total_depth, -# "prct_reads": prct_reads -# } - -# parsed_alleles[alt_allele] = allele_dict - -# return parsed_alleles +def parse_list_of_dicts(data_value): + list_of_dicts = list() + if data_value.startswith("[["): + # parse list of dicts that uses json formatting + for sublist in json.loads(data_value): + sublist_dict = dict() + list_of_dicts.append(sublist_dict) + for index, value in enumerate(sublist): + sublist_dict[index] = value + else: + for sublist in data_value.split(";"): + sublist_dict = dict() + list_of_dicts.append(sublist_dict) + for index, value in enumerate(sublist.trim().split(":")): + sublist_dict[index] = value + + +def parse_annotations(annot_csv, data_config_file, outfile): + # reading data config for determination of parsing + data_config = list() + with open(data_config_file, "rt") as dcfp: + # parse and filter for column configs that needing parsing + data_config = [filter(lambda colconf: colconf["parse"], json.load(dcfp))] + + with open(outfile, "w", newline="") as paserdcsv: + csvwriter = csv.DictWriter(paserdcsv, fieldnames=[colconf["col_id"] for colconf in data_config]) + csvwriter.writeheader() + with open(annot_csv, "r", newline="") as csvfile: + reader = csv.DictReader(filter(lambda row: row[0]!='#', csvfile)) + for row in reader: + # TODO add parsing logic with the mix of config info and data column def is_valid_output_file(p, arg): @@ -240,7 +137,6 @@ def is_valid_file(p, arg): "-o", "--output", help="Output from parsing", - required=False, type=lambda x: is_valid_output_file(PARSER, x), metavar="\b" ) @@ -249,11 +145,18 @@ def is_valid_file(p, arg): "-v", "--version", help="Verison of OpenCravat used to generate the config file (only required during config parsing)", - required=False, type=str, metavar="\b" ) + PARSER.add_argument( + "-c", + "--config", + help="File path to the data config JSON file that determines how to parse annotated variants from OpenCravat", + type=lambda x: is_valid_file(PARSER, x), + metavar="\b" + ) + ARGS = PARSER.parse_args() if ARGS.exec == "config" and not ARGS.version: @@ -263,6 +166,5 @@ def is_valid_file(p, arg): if ARGS.exec == "config": create_data_config(ARGS.input_csv, f"opencravat_{ARGS.version}_config.json") else: - # TODO parsing method lolz - print("TODO") - + outfile = ARGS.outfile if ARGS.outfile else f"{Path(ARGS.input_csv).stem}.csv" + parse_annotations(ARGS.input_csv, ARGS.config, outfile) From c02044f47b90b68b190d960b2b3ccc1a55611772 Mon Sep 17 00:00:00 2001 From: Brandon Wilk Date: Tue, 18 Apr 2023 16:13:01 -0500 Subject: [PATCH 06/13] added new design logic for parsing and noramlization of data around variant + transcript --- src/annotation_parsing/parse_annotated_vars.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/src/annotation_parsing/parse_annotated_vars.py b/src/annotation_parsing/parse_annotated_vars.py index 87f2c42..25d2fec 100644 --- a/src/annotation_parsing/parse_annotated_vars.py +++ b/src/annotation_parsing/parse_annotated_vars.py @@ -80,13 +80,21 @@ def parse_annotations(annot_csv, data_config_file, outfile): # parse and filter for column configs that needing parsing data_config = [filter(lambda colconf: colconf["parse"], json.load(dcfp))] + # the column "all_mappings" is the key split-by column to separate results on a per variant + transcript with open(outfile, "w", newline="") as paserdcsv: csvwriter = csv.DictWriter(paserdcsv, fieldnames=[colconf["col_id"] for colconf in data_config]) csvwriter.writeheader() with open(annot_csv, "r", newline="") as csvfile: reader = csv.DictReader(filter(lambda row: row[0]!='#', csvfile)) for row in reader: - # TODO add parsing logic with the mix of config info and data column + for column in data_config: + # TODO rewrite parsing and configs to focus on "all_mappings" column + if column["parse_type"] == "list-o-dicts": + parse_list_of_dicts(row[column["col_id"]]) + elif column["parse_type"] == "list": + row[column["col_id"]].split(column["separator"]) + else: + row[column["col_id"]] def is_valid_output_file(p, arg): From ab3492b06868c4768a82f26b0864111d32c8d752 Mon Sep 17 00:00:00 2001 From: Tarun Mamidi Date: Wed, 19 Apr 2023 23:25:37 -0500 Subject: [PATCH 07/13] selected required columns and parsing style --- opencravat_2.3.0_config.json | 1466 +++++++++++++++++----------------- 1 file changed, 733 insertions(+), 733 deletions(-) diff --git a/opencravat_2.3.0_config.json b/opencravat_2.3.0_config.json index 22b79b0..ee32106 100644 --- a/opencravat_2.3.0_config.json +++ b/opencravat_2.3.0_config.json @@ -3,2919 +3,2919 @@ "col_num": "0", "col_id": "uid", "description": "UID", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "1", "col_id": "chrom", "description": "Chrom", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "2", "col_id": "pos", "description": "Position", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "3", "col_id": "ref_base", "description": "Ref", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "4", "col_id": "alt_base", "description": "Alt", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "5", "col_id": "note", "description": "Note", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "6", "col_id": "coding", "description": "Coding", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "7", "col_id": "hugo", "description": "Gene", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "false", + "parse_type": "none" }, { "col_num": "8", "col_id": "transcript", "description": "Transcript", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "9", "col_id": "so", "description": "Sequence", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "10", "col_id": "cchange", "description": "cDNA", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "11", "col_id": "achange", "description": "Protein", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "12", "col_id": "all_mappings", "description": "All", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "list" }, { "col_num": "13", "col_id": "numsample", "description": "Sample", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "14", "col_id": "samples", "description": "Samples", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "15", "col_id": "tags", "description": "Tags", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "16", "col_id": "hg19.chrom", "description": "Chrom", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "17", "col_id": "hg19.pos", "description": "Position", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "18", "col_id": "thousandgenomes.af", "description": "AF", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "19", "col_id": "thousandgenomes.afr_af", "description": "AFR", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "20", "col_id": "thousandgenomes.amr_af", "description": "AMR", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "21", "col_id": "thousandgenomes.eas_af", "description": "EAS", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "22", "col_id": "thousandgenomes.eur_af", "description": "EUR", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "23", "col_id": "thousandgenomes.sas_af", "description": "SAS", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "24", "col_id": "thousandgenomes_ad_mixed_american.mxl_af", "description": "MXL", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "25", "col_id": "thousandgenomes_ad_mixed_american.pur_af", "description": "PUR", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "26", "col_id": "thousandgenomes_ad_mixed_american.clm_af", "description": "CLM", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "27", "col_id": "thousandgenomes_ad_mixed_american.pel_af", "description": "PEL", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "28", "col_id": "thousandgenomes_african.yri_af", "description": "YRI", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "29", "col_id": "thousandgenomes_african.lwk_af", "description": "LWK", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "30", "col_id": "thousandgenomes_african.gwd_af", "description": "GWD", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "31", "col_id": "thousandgenomes_african.msl_af", "description": "MSL", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "32", "col_id": "thousandgenomes_african.esn_af", "description": "ESN", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "33", "col_id": "thousandgenomes_african.asw_af", "description": "ASW", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "34", "col_id": "thousandgenomes_african.acb_af", "description": "ACB", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "35", "col_id": "thousandgenomes_east_asian.chb_af", "description": "CHB", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "36", "col_id": "thousandgenomes_east_asian.jpt_af", "description": "JPT", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "37", "col_id": "thousandgenomes_east_asian.chs_af", "description": "CHS", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "38", "col_id": "thousandgenomes_east_asian.cdx_af", "description": "CDX", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "39", "col_id": "thousandgenomes_east_asian.khv_af", "description": "KHV", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "40", "col_id": "thousandgenomes_european.ceu_af", "description": "CEU", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "41", "col_id": "thousandgenomes_european.tsi_af", "description": "TSI", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "42", "col_id": "thousandgenomes_european.fin_af", "description": "FIN", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "43", "col_id": "thousandgenomes_european.gbr_af", "description": "GBR", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "44", "col_id": "thousandgenomes_european.ibs_af", "description": "IBS", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "45", "col_id": "thousandgenomes_south_asian.gih_af", "description": "GIH", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "46", "col_id": "thousandgenomes_south_asian.pjl_af", "description": "PJL", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "47", "col_id": "thousandgenomes_south_asian.beb_af", "description": "BEB", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "48", "col_id": "thousandgenomes_south_asian.stu_af", "description": "STU", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "49", "col_id": "thousandgenomes_south_asian.itu_af", "description": "ITU", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "50", "col_id": "abraom.allele_freq", "description": "AF", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "51", "col_id": "aloft.transcript", "description": "Transcript", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "52", "col_id": "aloft.affect", "description": "Transcripts", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "53", "col_id": "aloft.tolerant", "description": "Tolerant", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "54", "col_id": "aloft.recessive", "description": "Recessive", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "55", "col_id": "aloft.dominant", "description": "Dominant", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "56", "col_id": "aloft.pred", "description": "Classification", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "57", "col_id": "aloft.conf", "description": "Confidence", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "58", "col_id": "aloft.all", "description": "All", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "list-o-dicts" }, { "col_num": "59", "col_id": "arrvars.lqt", "description": "LQT", - "parse": "true|false", + "parse": "false", "parse_type": 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"false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "385", "col_id": "linsight.value", "description": "Value", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "386", "col_id": "lrt.lrt_score", "description": "Score", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "387", "col_id": "lrt.lrt_converted_rankscore", "description": "Rank", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "388", "col_id": "lrt.lrt_pred", "description": "Prediction", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "389", "col_id": "lrt.lrt_omega", "description": "Omega", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "390", "col_id": "litvar.rsid", "description": "rsID", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "391", "col_id": "loftool.loftool_score", "description": "LoF", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "392", "col_id": "mitomap.disease", "description": "Disease", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "393", "col_id": "mitomap.score", "description": "MitoTip", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "394", "col_id": "mitomap.quartile", "description": "Quartile", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "395", "col_id": "mitomap.status", "description": "Status", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "396", "col_id": "mitomap.pubmed", "description": "PubMed", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "397", "col_id": "mavedb.score", "description": "Score", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "398", "col_id": "mavedb.vis", "description": "MaveVis", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "399", "col_id": "mavedb.accession", "description": "Score", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "400", "col_id": "metalr.score", "description": "Score", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "401", "col_id": "metalr.rankscore", "description": "Rank", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "402", "col_id": "metalr.pred", "description": "Prediction", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "403", "col_id": "metasvm.score", "description": "Score", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "404", "col_id": "metasvm.rankscore", "description": "Rank", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "405", "col_id": "metasvm.pred", "description": "Prediction", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "406", "col_id": "mupit.link", "description": "Link", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "407", "col_id": "mupit.hugo", "description": "Hugo", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "408", "col_id": "mutpred1.transcript", "description": "Transcripts", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "409", "col_id": "mutpred1.external_protein_id", "description": "Uniprot", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "410", "col_id": "mutpred1.amino_acid_substitution", "description": "Variant", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "411", "col_id": "mutpred1.mutpred_general_score", "description": "Score", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "412", "col_id": "mutpred1.mutpred_rankscore", "description": "Rank", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "413", "col_id": "mutpred1.mutpred_top5_mechanisms", "description": "All", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "list-o-dicts" }, { "col_num": "414", "col_id": "mutpred_indel.score", "description": "Score", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "415", "col_id": "mutpred_indel.rankscore", "description": "Rank", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "416", "col_id": "mutpred_indel.property", "description": "Property", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { @@ -2929,1407 +2929,1407 @@ "col_num": "418", "col_id": "mutation_assessor.score", "description": "Score", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "419", "col_id": "mutation_assessor.rankscore", "description": "Rank", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "420", "col_id": "mutation_assessor.impact", "description": "Functional", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "421", "col_id": "mutation_assessor.all", "description": "All", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "list-o-dicts" }, { "col_num": "422", "col_id": "mutationtaster.transcript", "description": "Transcript", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "423", "col_id": "mutationtaster.score", "description": "Score", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "424", "col_id": "mutationtaster.rankscore", "description": "Rank", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "425", "col_id": "mutationtaster.prediction", "description": "Prediction", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "426", "col_id": "mutationtaster.model", "description": "Model", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "427", "col_id": "mutationtaster.all", "description": "All", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "list-o-dicts" }, { "col_num": "428", "col_id": "mutpanning.No_Cancer_Types", "description": "No.", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "429", "col_id": "mutpanning.Max_Frequency", "description": "Mutation", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "430", "col_id": "mutpanning.Best_Q_Value", "description": "Best", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "431", "col_id": "mutpanning.Supporting_Literature", "description": "Supporting", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "432", "col_id": "mutpanning.TCGA_Marker_Papers", "description": "TCGA", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "433", "col_id": "mutpanning.dNdS_Study", "description": "dNdS", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "434", "col_id": "mutpanning.Tumorportal", "description": "Tumor", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "435", "col_id": "mutpanning.Bailey_Database", "description": "Bailey", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "436", "col_id": "mutpanning.Cancer_Type", "description": "Cancer", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "437", "col_id": "ncbigene.ncbi_desc", "description": "Description", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "false", + "parse_type": "none" }, { "col_num": "438", "col_id": "ncbigene.entrez", "description": "Entrez", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "439", "col_id": "ndex_chd.numhit", "description": "Number", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "440", "col_id": "ndex_chd.networkid", "description": "Network", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "441", "col_id": "ndex_chd.networkname", "description": "Network", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "442", "col_id": "ndex_chd.numhit", "description": "Number", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "443", "col_id": "ndex_chd.networkid", "description": "Network", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "444", "col_id": "ndex_chd.networkname", "description": "Network", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "445", "col_id": "ndex.numhit", "description": "Number", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "446", "col_id": "ndex.networkid", "description": "Network", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "447", "col_id": "ndex.networkname", "description": "Network", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "448", "col_id": "ndex_signor.numhit", "description": "Number", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "449", "col_id": "ndex_signor.networkid", "description": "Network", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "450", "col_id": "ndex_signor.networkname", "description": "Network", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "451", "col_id": "ndex_signor.numhit", "description": "Number", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "452", "col_id": "ndex_signor.networkid", "description": "Network", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "453", "col_id": "ndex_signor.networkname", "description": "Network", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "454", "col_id": "omim.omim_id", "description": "Entry", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "455", "col_id": "oncokb.oncogenic", "description": "Oncogenic", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "456", "col_id": "oncokb.knownEffect", "description": "Known", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "457", "col_id": "oncokb.hotspot", "description": "Hotspot", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "458", "col_id": "oncokb.highestSensitiveLevel", "description": "Highest", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "459", "col_id": "oncokb.highestResistanceLevel", "description": "Highest", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "460", "col_id": "oncokb.highestDiagnosticImplicationLevel", "description": "Highest", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "461", "col_id": "oncokb.highestPrognosticImplicationLevel", "description": "Highest", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "462", "col_id": "oncokb.pmids", "description": "PubMed", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "463", "col_id": "oncokb.geneSummary", "description": "Gene", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "464", "col_id": "oncokb.variantSummary", "description": "Variant", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "465", "col_id": "oncokb.tumorSummary", "description": "Tumor", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "466", "col_id": "oncokb.all", "description": "All", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "467", "col_id": "original_input.chrom", "description": "Chrom", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "468", "col_id": "original_input.pos", "description": "Pos", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "469", "col_id": "original_input.ref_base", "description": "Reference", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "470", "col_id": "original_input.alt_base", "description": "Alternate", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "471", "col_id": "prec.prec", "description": "P(rec)", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "472", "col_id": "prec.stat", "description": "Known", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "473", "col_id": "provean.transcript", "description": "Transcript", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "474", "col_id": "provean.uniprot", "description": "UniProt", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "475", "col_id": "provean.score", "description": "Score", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "476", "col_id": "provean.rankscore", "description": "Rank", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "477", "col_id": "provean.prediction", "description": "Prediction", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "478", "col_id": "provean.all", "description": "All", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "list-o-dicts" }, { "col_num": "479", "col_id": "pangalodb.cell_type", "description": "Cell", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "480", "col_id": "pangalodb.ui", "description": "Ubiquitousness", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "481", "col_id": "pangalodb.desc", "description": "Description", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "482", "col_id": "pangalodb.germlayer", "description": "Germ", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "483", "col_id": "pangalodb.organ", "description": "Organ", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "484", "col_id": "pangalodb.sensitivity", 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}, { "col_num": "589", "col_id": "gnomad.af_nfe", "description": "Non-Fin", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "590", "col_id": "gnomad.af_oth", "description": "Other", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "591", "col_id": "gnomad.af_sas", "description": "South", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "592", "col_id": "gnomad_gene.transcript", "description": "Transcript", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "593", "col_id": "gnomad_gene.oe_lof", "description": "Obv/Exp", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "594", "col_id": "gnomad_gene.oe_mis", "description": "Obv/Exp", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "595", "col_id": "gnomad_gene.oe_syn", "description": "Obv/Exp", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "596", "col_id": "gnomad_gene.lof_z", "description": "LoF", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "597", "col_id": "gnomad_gene.mis_z", "description": "Mis", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "598", "col_id": "gnomad_gene.syn_z", "description": "Syn", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "599", "col_id": "gnomad_gene.pLI", "description": "pLI", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "600", "col_id": "gnomad_gene.pRec", "description": "pRec", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "601", "col_id": "gnomad_gene.pNull", "description": "pNull", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "602", "col_id": "gnomad_gene.all", "description": "All", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "list-o-dicts" }, { "col_num": "603", "col_id": "gnomad3.af", "description": "Global", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" }, { "col_num": "604", "col_id": "gnomad3.af_afr", "description": "African", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "605", "col_id": "gnomad3.af_asj", "description": "Ashkenazi", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "606", "col_id": "gnomad3.af_eas", "description": "East", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "607", "col_id": "gnomad3.af_fin", "description": "Finnish", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "608", "col_id": "gnomad3.af_lat", "description": "Latino", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "609", "col_id": "gnomad3.af_nfe", "description": "Non-Fin", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "610", "col_id": "gnomad3.af_oth", "description": "Other", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "611", "col_id": "gnomad3.af_sas", "description": "South", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "612", "col_id": "mirbase.transcript", "description": "Transcript", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "613", "col_id": "mirbase.id", "description": "Accession", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "614", "col_id": "mirbase.name", "description": "Name", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "615", "col_id": "mirbase.derives_from", "description": "Derives", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "616", "col_id": "ncrna.ncrnaclass", "description": "Class", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "617", "col_id": "ncrna.ncrnaname", "description": "Name", - "parse": "true|false", + "parse": "false", "parse_type": "list-o-dicts,list,none" }, { "col_num": "618", "col_id": "phi.phi", "description": "P(HI)", - "parse": "true|false", - "parse_type": "list-o-dicts,list,none" + "parse": "true", + "parse_type": "none" } ] From 9c9a8901de5d0f9fc85249e7f15d59990da8b077 Mon Sep 17 00:00:00 2001 From: Brandon Wilk Date: Tue, 25 Apr 2023 11:04:20 -0500 Subject: [PATCH 08/13] first pass rework to center parsing around all_mappings column --- opencravat_2.3.0_config.json | 7 +- opencravat_test.test_config.json | 104 +++++++++++++++++- .../parse_annotated_vars.py | 68 ++++++------ 3 files changed, 143 insertions(+), 36 deletions(-) diff --git a/opencravat_2.3.0_config.json b/opencravat_2.3.0_config.json index ee32106..202ef4c 100644 --- a/opencravat_2.3.0_config.json +++ b/opencravat_2.3.0_config.json @@ -87,8 +87,7 @@ "col_num": "12", "col_id": "all_mappings", "description": "All", - "parse": "true", - "parse_type": "list" + "parse": false }, { "col_num": "13", @@ -487,7 +486,7 @@ "col_id": "biogrid.acts", "description": "Interactors", "parse": "true", - "parse_type": "list" + "parse_type": "none" }, { "col_num": "70", @@ -2433,7 +2432,7 @@ "col_id": "gtex.gtex_tissue", "description": "Tissue", "parse": "true", - "parse_type": "list" + "parse_type": "none" }, { "col_num": "348", diff --git a/opencravat_test.test_config.json b/opencravat_test.test_config.json index 9991029..da5d36b 100644 --- a/opencravat_test.test_config.json +++ b/opencravat_test.test_config.json @@ -1 +1,103 @@ -[{"col_num": "0", "col_id": "uid", "description": "UID", "parse": true, "parse_type": "list-o-dicts,list,none"}, {"col_num": "1", "col_id": "chrom", "description": "Chrom", "parse": true, "parse_type": "list-o-dicts,list,none"}, {"col_num": "2", "col_id": "pos", "description": "Position", "parse": true, "parse_type": "list-o-dicts,list,none"}, {"col_num": "3", "col_id": "ref_base", "description": "Ref", "parse": true, "parse_type": "list-o-dicts,list,none"}, {"col_num": "4", "col_id": "alt_base", "description": "Alt", "parse": true, "parse_type": "list-o-dicts,list,none"}, {"col_num": "5", "col_id": "note", "description": "Note", "parse": true, "parse_type": "list-o-dicts,list,none"}, {"col_num": "6", "col_id": "coding", "description": "Coding", "parse": true, "parse_type": "list-o-dicts,list,none"}, {"col_num": "7", "col_id": "hugo", "description": "Gene", "parse": true, "parse_type": "list-o-dicts,list,none"}, {"col_num": "8", "col_id": "transcript", "description": "Transcript", "parse": true, "parse_type": "list-o-dicts,list,none"}, {"col_num": "9", "col_id": "so", "description": "Sequence", "parse": true, "parse_type": "list-o-dicts,list,none"},{"col_num": "12", "col_id": "all_mappings", "description": "All", "parse": true, "parse_type": "list-o-dicts,list,none"},{"col_num": "85", "col_id": "chasmplus.all", "description": "All", "parse": true, "parse_type": "list-o-dicts,list,none"},{"col_num": "69", "col_id": "biogrid.acts", "description": "Interactors", "parse": true, "parse_type": "list-o-dicts,list,none"},{"col_num": "270", "col_id": "clinvar.sig_conf", "description": "Significance", "parse": true, "parse_type": "list-o-dicts,list,none"},{"col_num": "266", "col_id": "clinvar.disease_refs", "description": "Disease", "parse": true, "parse_type": "list-o-dicts,list,none"},{"col_num": "337", "col_id": "funseq2.all", "description": "All", "parse": true, "parse_type": "list-o-dicts,list,none"},{"col_num": "381", "col_id": "intact.intact", "description": "Raw", "parse": true, "parse_type": "list-o-dicts,list,none"}] \ No newline at end of file +[ + { "col_num": "0", "col_id": "uid", "description": "UID", "parse": true, "parse_type": "list-o-dicts,list,none" }, + { + "col_num": "1", + "col_id": "chrom", + "description": "Chrom", + "parse": true, + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "2", + "col_id": "pos", + "description": "Position", + "parse": true, + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "3", + "col_id": "ref_base", + "description": "Ref", + "parse": true, + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "4", + "col_id": "alt_base", + "description": "Alt", + "parse": true, + "parse_type": "list-o-dicts,list,none" + }, + { "col_num": "5", "col_id": "note", "description": "Note", "parse": true, "parse_type": "list-o-dicts,list,none" }, + { + "col_num": "6", + "col_id": "coding", + "description": "Coding", + "parse": true, + "parse_type": "list-o-dicts,list,none" + }, + { "col_num": "7", "col_id": "hugo", "description": "Gene", "parse": true, "parse_type": "list-o-dicts,list,none" }, + { + "col_num": "8", + "col_id": "transcript", + "description": "Transcript", + "parse": true, + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "9", + "col_id": "so", + "description": "Sequence", + "parse": true, + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "12", + "col_id": "all_mappings", + "description": "All", + "parse": true, + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "85", + "col_id": "chasmplus.all", + "description": "All", + "parse": true, + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "69", + "col_id": "biogrid.acts", + "description": "Interactors", + "parse": true, + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "270", + "col_id": "clinvar.sig_conf", + "description": "Significance", + "parse": true, + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "266", + "col_id": "clinvar.disease_refs", + "description": "Disease", + "parse": true, + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "337", + "col_id": "funseq2.all", + "description": "All", + "parse": true, + "parse_type": "list-o-dicts,list,none" + }, + { + "col_num": "381", + "col_id": "intact.intact", + "description": "Raw", + "parse": true, + "parse_type": "list-o-dicts,list,none" + } +] diff --git a/src/annotation_parsing/parse_annotated_vars.py b/src/annotation_parsing/parse_annotated_vars.py index 25d2fec..39101e9 100644 --- a/src/annotation_parsing/parse_annotated_vars.py +++ b/src/annotation_parsing/parse_annotated_vars.py @@ -4,26 +4,7 @@ import json import csv - -# Example fields for parsing and normalizing: -# 'all_mappings': 'ENST00000253952.9:THOC6:Q86W42:missense_variant:p.Val234Leu:c.700G>C; ENST00000326266.13:THOC6:Q86W42:missense_variant:p.Val234Leu:c.700G>C; ENST00000389347.4:BICDL2:A1A5D9:2kb_downstream_variant::c.*936C>G; ENST00000572449.6:BICDL2:A1A5D9:2kb_downstream_variant::c.*936C>G; ENST00000573514.5:BICDL2:A1A5D9:2kb_downstream_variant::c.*936C>G; ENST00000574549.5:THOC6:Q86W42:missense_variant:p.Val210Leu:c.628G>C; ENST00000575576.5:THOC6:Q86W42:missense_variant:p.Val210Leu:c.628G>C; ENST00000642419.1:BICDL2::2kb_downstream_variant::c.*936C>G' -# 'chasmplus.all': '[["ENST00000574549.5", 0.064, 0.314], ["ENST00000575576.5", 0.064, 0.314], ["NM_001142350.1", 0.055, 0.358], ["NM_024339.3", 0.047, 0.405]]' -# 'biogrid.acts': 'EFTUD2;PPP2R1A;RRP9;SNRNP200;THOC1;THOC7;TPR;TRIM55;U2AF1;U2AF2;UTRN;VDAC2;ZC3H15;ZCCHC8;ZNF326' -# 'clinvar.sig_conf': 'Pathogenic(1)|Likely pathogenic(2)|Uncertain significance(3)' -# 'clinvar.disease_refs': 'MONDO:MONDO:0013362,MedGen:C3150939,OMIM:613680,Orphanet:ORPHA363444|MeSH:D030342,MedGen:C0950123|MedGen:CN517202' -# 'funseq2.all': '[["", "", "", "", "", "", "4"]]' -# 'intact.intact': 'GABARAPL2[20562859]|NUDC[25036637]|JUN[25609649]|THOC1[19165146;26496610]|THOC2[19165146;26496610]|DDX41[25920683]|THOC5[26496610]|ESR2[21182203]|GABARAP[20562859]|THOC7[26496610]|PLEKHA7[28877994]|BCLAF1[26496610]|MAP1LC3A[20562859]|ID1[26496610]|ABI1[26496610]|NCBP3[26496610;26382858]|' - -# TODO create config for field mappings and parsing logic needed for various field types from examples above - - -# list of dictionaries (that looks like a list of lists, can have empty values), this will require mapping configuration - - -# list - - -# lists that don't need parsing +ALL_MAPPINGS_COLUMN_ID = "all_mappings" def create_data_config(annot_csv, outfile = None): @@ -44,8 +25,23 @@ def create_data_config(annot_csv, outfile = None): "col_id": info[1], "description": info[2], "parse": True, - "parse_type": "list-o-dicts,list,none", - "separator": ";" + "parse_type": { + "none":{}, + "list_index": { + "separator": ";" + }, + "list":{ + "trx_index_col": "fathmm.ens_tid", + "separator": ";" + }, + "list-o-dicts":{ + "dict_index": { + 0: "column_name", + 1: "column_name1" + }, + "trx_mapping_col_index": 0 + } + } }) if cntr > 2000: break @@ -82,19 +78,29 @@ def parse_annotations(annot_csv, data_config_file, outfile): # the column "all_mappings" is the key split-by column to separate results on a per variant + transcript with open(outfile, "w", newline="") as paserdcsv: - csvwriter = csv.DictWriter(paserdcsv, fieldnames=[colconf["col_id"] for colconf in data_config]) + parse_fieldnames = [colconf["col_id"] for colconf in data_config] + hardcoded_fieldnames = ["trx", "gene", "consequence", "protein_hgvs", "cdna_hgvs"] + csvwriter = csv.DictWriter(paserdcsv, fieldnames=hardcoded_fieldnames + parse_fieldnames) csvwriter.writeheader() with open(annot_csv, "r", newline="") as csvfile: reader = csv.DictReader(filter(lambda row: row[0]!='#', csvfile)) for row in reader: - for column in data_config: - # TODO rewrite parsing and configs to focus on "all_mappings" column - if column["parse_type"] == "list-o-dicts": - parse_list_of_dicts(row[column["col_id"]]) - elif column["parse_type"] == "list": - row[column["col_id"]].split(column["separator"]) - else: - row[column["col_id"]] + for variant_trx in row[ALL_MAPPINGS_COLUMN_ID].split(";"): + vtrx_cols = variant_trx.split(":") + trx = vtrx_cols[0].split(".")[0] + gene = vtrx_cols[1] + vtrx_consequence = vtrx_cols[3] + protein_hgvs = vtrx_cols[4] + cdna_hgvs = vtrx_cols[5] + for column in parse_fieldnames: + if "list-o-dicts" in column["parse_type"]: + parse_list_of_dicts(row[column["col_id"]]) + elif "list" in column["parse_type"]: + row[column["col_id"]].split(column["separator"]) + elif "list_index" in column["parse_type"]: + continue + else: + row[column["col_id"]] def is_valid_output_file(p, arg): From 82d38c29e90681feb7609382f5267c4d508b2a34 Mon Sep 17 00:00:00 2001 From: Brandon Wilk Date: Tue, 25 Apr 2023 12:12:24 -0500 Subject: [PATCH 09/13] updating parsing to handle janky list for fathmm --- .../parse_annotated_vars.py | 80 ++++++++++++------- 1 file changed, 50 insertions(+), 30 deletions(-) diff --git a/src/annotation_parsing/parse_annotated_vars.py b/src/annotation_parsing/parse_annotated_vars.py index 39101e9..ef24354 100644 --- a/src/annotation_parsing/parse_annotated_vars.py +++ b/src/annotation_parsing/parse_annotated_vars.py @@ -27,11 +27,9 @@ def create_data_config(annot_csv, outfile = None): "parse": True, "parse_type": { "none":{}, - "list_index": { - "separator": ";" - }, "list":{ "trx_index_col": "fathmm.ens_tid", + "column_list": ["fathmm.fathmm_score", "fathmm.fathmm_pred"], "separator": ";" }, "list-o-dicts":{ @@ -52,21 +50,33 @@ def create_data_config(annot_csv, outfile = None): json.dump(columns,ofp) -def parse_list_of_dicts(data_value): - list_of_dicts = list() - if data_value.startswith("[["): - # parse list of dicts that uses json formatting - for sublist in json.loads(data_value): - sublist_dict = dict() - list_of_dicts.append(sublist_dict) - for index, value in enumerate(sublist): - sublist_dict[index] = value - else: - for sublist in data_value.split(";"): - sublist_dict = dict() - list_of_dicts.append(sublist_dict) - for index, value in enumerate(sublist.trim().split(":")): - sublist_dict[index] = value +def parse_list_of_dicts(data_value, column_config): + dict_of_dicts = dict() + for sublist in json.loads(data_value): + sublist_dict = dict() + trx_id = sublist[column_config["trx_mapping_col_index"]].split(".")[0] + dict_of_dicts[trx_id] = sublist_dict + for index, value in enumerate(sublist): + # look up column name by index value, assign column name as key in return dict + sublist_dict[column_config["dict_index"][index]] = value + + return dict_of_dicts + + +def parse_multicolumn_list_of_dicts(index_column, multi_column_config, data_cols_dict): + # data_cols_n_configs => list of tuples where tuple[0] is column value, tuple[1] is column config + index_mapping = dict() + dict_of_dicts = dict() + for index, index_value in enumerate(index_column.split(multi_column_config["separator"])): + index_mapping[index] = index_value.split(".")[0] + dict_of_dicts[index_mapping[index]] = dict() + + for column, value in data_cols_dict.items(): + sublist = value.split(multi_column_config["separator"]) + for index, data_value in enumerate(sublist): + dict_of_dicts[index_mapping[index]][column] = data_value + + return dict_of_dicts def parse_annotations(annot_csv, data_config_file, outfile): @@ -78,29 +88,39 @@ def parse_annotations(annot_csv, data_config_file, outfile): # the column "all_mappings" is the key split-by column to separate results on a per variant + transcript with open(outfile, "w", newline="") as paserdcsv: - parse_fieldnames = [colconf["col_id"] for colconf in data_config] hardcoded_fieldnames = ["trx", "gene", "consequence", "protein_hgvs", "cdna_hgvs"] + # TODO add list of printed field names accounting for list and list-o-dicts csvwriter = csv.DictWriter(paserdcsv, fieldnames=hardcoded_fieldnames + parse_fieldnames) csvwriter.writeheader() with open(annot_csv, "r", newline="") as csvfile: reader = csv.DictReader(filter(lambda row: row[0]!='#', csvfile)) for row in reader: + # parse list of dict columns first since this only needs to be done once per row and cached + cached_dicts_o_dicts = dict() + for column in [colconf for colconf in data_config if "list-o-dicts" in colconf["parse_type"]]: + cached_dicts_o_dicts[column["col_id"]] = parse_list_of_dicts(row[column["col_id"]], column) + + # probably do parse_multicolumn_list_of_dicts in the cache of dict here as well + for variant_trx in row[ALL_MAPPINGS_COLUMN_ID].split(";"): vtrx_cols = variant_trx.split(":") trx = vtrx_cols[0].split(".")[0] - gene = vtrx_cols[1] - vtrx_consequence = vtrx_cols[3] - protein_hgvs = vtrx_cols[4] - cdna_hgvs = vtrx_cols[5] - for column in parse_fieldnames: - if "list-o-dicts" in column["parse_type"]: - parse_list_of_dicts(row[column["col_id"]]) + annot_variant = dict() + annot_variant["trx"] = trx + annot_variant["gene"] = vtrx_cols[1] + annot_variant["consequence"] = vtrx_cols[3] + annot_variant["protein_hgvs"] = vtrx_cols[4] + annot_variant["cdna_hgvs"] = vtrx_cols[5] + for column in data_config: + if "list-o-dicts" in column["parse_type"] \ + and trx in cached_dicts_o_dicts[column["col_id"]]: + annot_variant.update(cached_dicts_o_dicts[column["col_id"]][trx]) elif "list" in column["parse_type"]: - row[column["col_id"]].split(column["separator"]) - elif "list_index" in column["parse_type"]: - continue - else: + # TODO do some special processing for multicolumns like we did list of dicts + elif "none" in column["parse_type"]: row[column["col_id"]] + else: + continue def is_valid_output_file(p, arg): From 4fd32fa2f31ca090a67418070124086ff7d2b1b2 Mon Sep 17 00:00:00 2001 From: Brandon Wilk Date: Wed, 26 Apr 2023 11:12:13 -0500 Subject: [PATCH 10/13] completed first pass parsing --- .../parse_annotated_vars.py | 146 +++++++++++------- 1 file changed, 94 insertions(+), 52 deletions(-) diff --git a/src/annotation_parsing/parse_annotated_vars.py b/src/annotation_parsing/parse_annotated_vars.py index ef24354..ad95314 100644 --- a/src/annotation_parsing/parse_annotated_vars.py +++ b/src/annotation_parsing/parse_annotated_vars.py @@ -7,47 +7,48 @@ ALL_MAPPINGS_COLUMN_ID = "all_mappings" -def create_data_config(annot_csv, outfile = None): - #Column description. Column 0 uid=UID - #Column description. Column 1 chrom=Chrom - #Column description. Column 2 pos=Position - #Column description. Column 3 ref_base=Ref Base - #Column description. Column 4 alt_base=Alt Base +def create_data_config(annot_csv, outfile=None): + # Column description. Column 0 uid=UID + # Column description. Column 1 chrom=Chrom + # Column description. Column 2 pos=Position + # Column description. Column 3 ref_base=Ref Base + # Column description. Column 4 alt_base=Alt Base columns = list() with open(annot_csv) as csvfp: cntr = 0 for line in csvfp: if line.startswith("#Column description. Column"): - line = line.replace("#Column description. Column ","").strip() - info = line.replace("="," ").split(" ") - columns.append({ - "col_num": info[0], - "col_id": info[1], - "description": info[2], - "parse": True, - "parse_type": { - "none":{}, - "list":{ - "trx_index_col": "fathmm.ens_tid", - "column_list": ["fathmm.fathmm_score", "fathmm.fathmm_pred"], - "separator": ";" - }, - "list-o-dicts":{ - "dict_index": { - 0: "column_name", - 1: "column_name1" + line = line.replace("#Column description. Column ", "").strip() + info = line.replace("=", " ").split(" ") + columns.append( + { + "col_num": info[0], + "col_id": info[1], + "description": info[2], + "parse_type": { + "none": "none", + "list": { + "trx_index_col": "fathmm.ens_tid", + "column_list": [ + "fathmm.fathmm_score", + "fathmm.fathmm_pred", + ], + "separator": ";", + }, + "list-o-dicts": { + "dict_index": {0: "column_name", 1: "column_name1"}, + "trx_mapping_col_index": 0, }, - "trx_mapping_col_index": 0 - } + }, } - }) + ) if cntr > 2000: break else: cntr += 1 with open(outfile, "wt") as ofp: - json.dump(columns,ofp) + json.dump(columns, ofp) def parse_list_of_dicts(data_value, column_config): @@ -67,7 +68,9 @@ def parse_multicolumn_list_of_dicts(index_column, multi_column_config, data_cols # data_cols_n_configs => list of tuples where tuple[0] is column value, tuple[1] is column config index_mapping = dict() dict_of_dicts = dict() - for index, index_value in enumerate(index_column.split(multi_column_config["separator"])): + for index, index_value in enumerate( + index_column.split(multi_column_config["separator"]) + ): index_mapping[index] = index_value.split(".")[0] dict_of_dicts[index_mapping[index]] = dict() @@ -84,23 +87,58 @@ def parse_annotations(annot_csv, data_config_file, outfile): data_config = list() with open(data_config_file, "rt") as dcfp: # parse and filter for column configs that needing parsing - data_config = [filter(lambda colconf: colconf["parse"], json.load(dcfp))] + data_config = json.load(dcfp) # the column "all_mappings" is the key split-by column to separate results on a per variant + transcript with open(outfile, "w", newline="") as paserdcsv: - hardcoded_fieldnames = ["trx", "gene", "consequence", "protein_hgvs", "cdna_hgvs"] - # TODO add list of printed field names accounting for list and list-o-dicts - csvwriter = csv.DictWriter(paserdcsv, fieldnames=hardcoded_fieldnames + parse_fieldnames) + hardcoded_fieldnames = [ + "trx", + "gene", + "consequence", + "protein_hgvs", + "cdna_hgvs", + ] + parsed_fieldnames = list() + for colconf in data_config: + if "list" in colconf["parse_type"]: + parsed_fieldnames += colconf["parse_type"]["list"]["column_list"] + elif "list-o-dicts" in colconf["parse_type"]: + parsed_fieldnames += colconf["parse_type"]["list-o-dicts"][ + "dict_index" + ].values() + else: + parsed_fieldnames.append(colconf["col_id"]) + + csvwriter = csv.DictWriter(paserdcsv, fieldnames=hardcoded_fieldnames) csvwriter.writeheader() + with open(annot_csv, "r", newline="") as csvfile: - reader = csv.DictReader(filter(lambda row: row[0]!='#', csvfile)) + reader = csv.DictReader(filter(lambda row: row[0] != "#", csvfile)) for row in reader: # parse list of dict columns first since this only needs to be done once per row and cached cached_dicts_o_dicts = dict() - for column in [colconf for colconf in data_config if "list-o-dicts" in colconf["parse_type"]]: - cached_dicts_o_dicts[column["col_id"]] = parse_list_of_dicts(row[column["col_id"]], column) - - # probably do parse_multicolumn_list_of_dicts in the cache of dict here as well + for column in [ + filter( + lambda colconf: "list-o-dicts" in colconf["parse_type"], + data_config, + ) + ]: + cached_dicts_o_dicts[column["col_id"]] = parse_list_of_dicts( + row[column["col_id"]], column + ) + + # parse list which is a list of dicts spread across multiple columns + for column in [ + filter(lambda colconf: "list" in colconf["parse_type"], data_config) + ]: + col_data_dict = { + subcolumn: row[subcolumn] for subcolumn in column["column_list"] + } + cached_dicts_o_dicts[ + column["col_id"] + ] = parse_multicolumn_list_of_dicts( + row[column["col_id"]], column, col_data_dict + ) for variant_trx in row[ALL_MAPPINGS_COLUMN_ID].split(";"): vtrx_cols = variant_trx.split(":") @@ -112,16 +150,18 @@ def parse_annotations(annot_csv, data_config_file, outfile): annot_variant["protein_hgvs"] = vtrx_cols[4] annot_variant["cdna_hgvs"] = vtrx_cols[5] for column in data_config: - if "list-o-dicts" in column["parse_type"] \ - and trx in cached_dicts_o_dicts[column["col_id"]]: - annot_variant.update(cached_dicts_o_dicts[column["col_id"]][trx]) - elif "list" in column["parse_type"]: - # TODO do some special processing for multicolumns like we did list of dicts - elif "none" in column["parse_type"]: - row[column["col_id"]] + if "none" in column["parse_type"]: + annot_variant[column["col_id"]] = row[column["col_id"]] + elif trx in cached_dicts_o_dicts[column["col_id"]]: + annot_variant.update( + cached_dicts_o_dicts[column["col_id"]][trx] + ) else: continue + # print parsed variant + transcript annotations to csv file output + csvwriter.writerow(annot_variant) + def is_valid_output_file(p, arg): if os.access(Path(os.path.expandvars(arg)).parent, os.W_OK): @@ -145,7 +185,7 @@ def is_valid_file(p, arg): PARSER = argparse.ArgumentParser( description="Simple parser for creating data model, data parsing config, and data parsing of annotations from OpenCravat", - formatter_class=argparse.ArgumentDefaultsHelpFormatter + formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) PARSER.add_argument( @@ -154,7 +194,7 @@ def is_valid_file(p, arg): help="File path to the CSV file of annotated variants from OpenCravat", required=True, type=lambda x: is_valid_file(PARSER, x), - metavar="\b" + metavar="\b", ) PARSER.add_argument( @@ -163,7 +203,7 @@ def is_valid_file(p, arg): help="Determine what should be done: create a new data config file or parse the annotations from the OpenCravat CSV file", required=True, choices=EXECUTIONS, - metavar="\b" + metavar="\b", ) OPTIONAL_ARGS = PARSER.add_argument_group("Override Args") @@ -172,7 +212,7 @@ def is_valid_file(p, arg): "--output", help="Output from parsing", type=lambda x: is_valid_output_file(PARSER, x), - metavar="\b" + metavar="\b", ) PARSER.add_argument( @@ -180,7 +220,7 @@ def is_valid_file(p, arg): "--version", help="Verison of OpenCravat used to generate the config file (only required during config parsing)", type=str, - metavar="\b" + metavar="\b", ) PARSER.add_argument( @@ -188,13 +228,15 @@ def is_valid_file(p, arg): "--config", help="File path to the data config JSON file that determines how to parse annotated variants from OpenCravat", type=lambda x: is_valid_file(PARSER, x), - metavar="\b" + metavar="\b", ) ARGS = PARSER.parse_args() if ARGS.exec == "config" and not ARGS.version: - print("Version of OpenCravat must be specified when creating a config from their data for tracking purposes") + print( + "Version of OpenCravat must be specified when creating a config from their data for tracking purposes" + ) raise SystemExit(1) if ARGS.exec == "config": From 96c1474a531f79d48d60eca4db5e611c55eb57ea Mon Sep 17 00:00:00 2001 From: Brandon Wilk Date: Wed, 26 Apr 2023 11:34:59 -0500 Subject: [PATCH 11/13] updated test config --- opencravat_test.test_config.json | 153 ++++++++++-------- .../parse_annotated_vars.py | 6 +- 2 files changed, 86 insertions(+), 73 deletions(-) diff --git a/opencravat_test.test_config.json b/opencravat_test.test_config.json index da5d36b..ac3b4c7 100644 --- a/opencravat_test.test_config.json +++ b/opencravat_test.test_config.json @@ -1,103 +1,118 @@ [ - { "col_num": "0", "col_id": "uid", "description": "UID", "parse": true, "parse_type": "list-o-dicts,list,none" }, { - "col_num": "1", "col_id": "chrom", - "description": "Chrom", - "parse": true, - "parse_type": "list-o-dicts,list,none" + "parse_type": { + "none": "none" + } }, { - "col_num": "2", "col_id": "pos", - "description": "Position", - "parse": true, - "parse_type": "list-o-dicts,list,none" + "parse_type": { + "none": "none" + } }, { - "col_num": "3", "col_id": "ref_base", - "description": "Ref", - "parse": true, - "parse_type": "list-o-dicts,list,none" + "parse_type": { + "none": "none" + } }, { - "col_num": "4", "col_id": "alt_base", - "description": "Alt", - "parse": true, - "parse_type": "list-o-dicts,list,none" + "parse_type": { + "none": "none" + } }, - { "col_num": "5", "col_id": "note", "description": "Note", "parse": true, "parse_type": "list-o-dicts,list,none" }, { - "col_num": "6", - "col_id": "coding", - "description": "Coding", - "parse": true, - "parse_type": "list-o-dicts,list,none" + "col_id": "thousandgenomes.af", + "parse_type": { + "none": "none" + } }, - { "col_num": "7", "col_id": "hugo", "description": "Gene", "parse": true, "parse_type": "list-o-dicts,list,none" }, { - "col_num": "8", - "col_id": "transcript", - "description": "Transcript", - "parse": true, - "parse_type": "list-o-dicts,list,none" + "col_id": "fathmm.ens_tid", + "parse_type": { + "list": { + "trx_index_col": "fathmm.ens_tid", + "column_list": ["fathmm.fathmm_score"], + "separator": ";" + } + } }, { - "col_num": "9", - "col_id": "so", - "description": "Sequence", - "parse": true, - "parse_type": "list-o-dicts,list,none" + "col_id": "fathmm_mkl.fathmm_mkl_coding_score", + "parse_type": { + "none": "none" + } }, { - "col_num": "12", - "col_id": "all_mappings", - "description": "All", - "parse": true, - "parse_type": "list-o-dicts,list,none" + "col_id": "gerp.gerp_nr", + "parse_type": { + "none": "none" + } }, { - "col_num": "85", - "col_id": "chasmplus.all", - "description": "All", - "parse": true, - "parse_type": "list-o-dicts,list,none" + "col_id": "gerp.gerp_rs", + "parse_type": { + "none": "none" + } }, { - "col_num": "69", - "col_id": "biogrid.acts", - "description": "Interactors", - "parse": true, - "parse_type": "list-o-dicts,list,none" + "col_id": "gerp.gerp_rs_rank", + "parse_type": { + "none": "none" + } }, { - "col_num": "270", - "col_id": "clinvar.sig_conf", - "description": "Significance", - "parse": true, - "parse_type": "list-o-dicts,list,none" + "col_id": "mutation_assessor.all", + "parse_type": { + "list-o-dicts": { + "dict_index": { + "1": "mutation_assessor.impact", + "2": "mutation_assessor.score", + "3": "mutation_assessor.rankscore" + }, + "trx_mapping_col_index": 0 + } + } }, { - "col_num": "266", - "col_id": "clinvar.disease_refs", - "description": "Disease", - "parse": true, - "parse_type": "list-o-dicts,list,none" + "col_id": "mutationtaster.all", + "parse_type": { + "list-o-dicts": { + "dict_index": { + "1": "mutationtaster.score", + "2": "mutationtaster.rankscore", + "3": "mutationtaster.prediction", + "4": "mutationtaster.model" + }, + "trx_mapping_col_index": 0 + } + } }, { - "col_num": "337", - "col_id": "funseq2.all", - "description": "All", - "parse": true, - "parse_type": "list-o-dicts,list,none" + "col_id": "gnomad_gene.all", + "parse_type": { + "list-o-dicts": { + "dict_index": { + "1": "gnomad_gene.oe_lof", + "2": "gnomad_gene.oe_mis", + "3": "gnomad_gene.oe_syn", + "4": "gnomad_gene.lof_z", + "5": "gnomad_gene.mis_z", + "6": "gnomad_gene.syn_z", + "7": "gnomad_gene.pLI", + "8": "gnomad_gene.pRec", + "9": "gnomad_gene.pNull" + }, + "trx_mapping_col_index": 0 + } + } }, { - "col_num": "381", - "col_id": "intact.intact", - "description": "Raw", - "parse": true, - "parse_type": "list-o-dicts,list,none" + "col_id": "gnomad3.af", + "parse_type": { + "none": "none" + } } ] diff --git a/src/annotation_parsing/parse_annotated_vars.py b/src/annotation_parsing/parse_annotated_vars.py index ad95314..743d5ec 100644 --- a/src/annotation_parsing/parse_annotated_vars.py +++ b/src/annotation_parsing/parse_annotated_vars.py @@ -22,9 +22,7 @@ def create_data_config(annot_csv, outfile=None): info = line.replace("=", " ").split(" ") columns.append( { - "col_num": info[0], "col_id": info[1], - "description": info[2], "parse_type": { "none": "none", "list": { @@ -59,7 +57,7 @@ def parse_list_of_dicts(data_value, column_config): dict_of_dicts[trx_id] = sublist_dict for index, value in enumerate(sublist): # look up column name by index value, assign column name as key in return dict - sublist_dict[column_config["dict_index"][index]] = value + sublist_dict[column_config["dict_index"][str(index)]] = value return dict_of_dicts @@ -111,7 +109,7 @@ def parse_annotations(annot_csv, data_config_file, outfile): csvwriter = csv.DictWriter(paserdcsv, fieldnames=hardcoded_fieldnames) csvwriter.writeheader() - + with open(annot_csv, "r", newline="") as csvfile: reader = csv.DictReader(filter(lambda row: row[0] != "#", csvfile)) for row in reader: From 26a3dfd45a2b2e670869c58e1cbf0b107179524e Mon Sep 17 00:00:00 2001 From: Brandon Wilk Date: Wed, 26 Apr 2023 12:28:34 -0500 Subject: [PATCH 12/13] fixed several data dependent issues with parsing and updated field parsing logic --- .../parse_annotated_vars.py | 49 +++++++++++++------ 1 file changed, 35 insertions(+), 14 deletions(-) diff --git a/src/annotation_parsing/parse_annotated_vars.py b/src/annotation_parsing/parse_annotated_vars.py index 743d5ec..cfa9fae 100644 --- a/src/annotation_parsing/parse_annotated_vars.py +++ b/src/annotation_parsing/parse_annotated_vars.py @@ -3,6 +3,12 @@ import os import json import csv +import ctypes as ct + +# dealing with large fields in a CSV requires more memory allowed per field +# see https://stackoverflow.com/questions/15063936/csv-error-field-larger-than-field-limit-131072 for discussion +# and this solution +csv.field_size_limit(int(ct.c_ulong(-1).value // 2)) ALL_MAPPINGS_COLUMN_ID = "all_mappings" @@ -51,12 +57,18 @@ def create_data_config(annot_csv, outfile=None): def parse_list_of_dicts(data_value, column_config): dict_of_dicts = dict() + if data_value == "": + return dict_of_dicts + for sublist in json.loads(data_value): sublist_dict = dict() trx_id = sublist[column_config["trx_mapping_col_index"]].split(".")[0] dict_of_dicts[trx_id] = sublist_dict for index, value in enumerate(sublist): # look up column name by index value, assign column name as key in return dict + if str(index) not in column_config["dict_index"]: + continue + sublist_dict[column_config["dict_index"][str(index)]] = value return dict_of_dicts @@ -66,6 +78,10 @@ def parse_multicolumn_list_of_dicts(index_column, multi_column_config, data_cols # data_cols_n_configs => list of tuples where tuple[0] is column value, tuple[1] is column config index_mapping = dict() dict_of_dicts = dict() + + if index_column == "": + return dict_of_dicts + for index, index_value in enumerate( index_column.split(multi_column_config["separator"]) ): @@ -107,7 +123,9 @@ def parse_annotations(annot_csv, data_config_file, outfile): else: parsed_fieldnames.append(colconf["col_id"]) - csvwriter = csv.DictWriter(paserdcsv, fieldnames=hardcoded_fieldnames) + csvwriter = csv.DictWriter( + paserdcsv, fieldnames=hardcoded_fieldnames + parsed_fieldnames + ) csvwriter.writeheader() with open(annot_csv, "r", newline="") as csvfile: @@ -115,32 +133,35 @@ def parse_annotations(annot_csv, data_config_file, outfile): for row in reader: # parse list of dict columns first since this only needs to be done once per row and cached cached_dicts_o_dicts = dict() - for column in [ - filter( - lambda colconf: "list-o-dicts" in colconf["parse_type"], - data_config, - ) - ]: + + for column in filter( + lambda colconf: "list-o-dicts" in colconf["parse_type"], data_config + ): cached_dicts_o_dicts[column["col_id"]] = parse_list_of_dicts( - row[column["col_id"]], column + row[column["col_id"]], column["parse_type"]["list-o-dicts"] ) # parse list which is a list of dicts spread across multiple columns - for column in [ - filter(lambda colconf: "list" in colconf["parse_type"], data_config) - ]: + for column in filter( + lambda colconf: "list" in colconf["parse_type"], data_config + ): col_data_dict = { - subcolumn: row[subcolumn] for subcolumn in column["column_list"] + subcolumn: row[subcolumn] + for subcolumn in column["parse_type"]["list"]["column_list"] } cached_dicts_o_dicts[ column["col_id"] ] = parse_multicolumn_list_of_dicts( - row[column["col_id"]], column, col_data_dict + row[column["col_id"]], + column["parse_type"]["list"], + col_data_dict, ) for variant_trx in row[ALL_MAPPINGS_COLUMN_ID].split(";"): vtrx_cols = variant_trx.split(":") trx = vtrx_cols[0].split(".")[0] + if len(vtrx_cols) < 6: + print("hello") annot_variant = dict() annot_variant["trx"] = trx annot_variant["gene"] = vtrx_cols[1] @@ -240,5 +261,5 @@ def is_valid_file(p, arg): if ARGS.exec == "config": create_data_config(ARGS.input_csv, f"opencravat_{ARGS.version}_config.json") else: - outfile = ARGS.outfile if ARGS.outfile else f"{Path(ARGS.input_csv).stem}.csv" + outfile = ARGS.output if ARGS.output else f"{Path(ARGS.input_csv).stem}.csv" parse_annotations(ARGS.input_csv, ARGS.config, outfile) From 1e14ffb02f2c7e2e2b4a5d2616acd3ee3127aec3 Mon Sep 17 00:00:00 2001 From: Brandon Wilk Date: Wed, 26 Apr 2023 13:53:01 -0500 Subject: [PATCH 13/13] working test parsing :tada: --- .../parse_annotated_vars.py | 54 +++++++++++++------ 1 file changed, 37 insertions(+), 17 deletions(-) diff --git a/src/annotation_parsing/parse_annotated_vars.py b/src/annotation_parsing/parse_annotated_vars.py index cfa9fae..e4925ba 100644 --- a/src/annotation_parsing/parse_annotated_vars.py +++ b/src/annotation_parsing/parse_annotated_vars.py @@ -159,24 +159,44 @@ def parse_annotations(annot_csv, data_config_file, outfile): for variant_trx in row[ALL_MAPPINGS_COLUMN_ID].split(";"): vtrx_cols = variant_trx.split(":") - trx = vtrx_cols[0].split(".")[0] - if len(vtrx_cols) < 6: - print("hello") + trx = vtrx_cols[0].split(".")[0].strip() annot_variant = dict() - annot_variant["trx"] = trx - annot_variant["gene"] = vtrx_cols[1] - annot_variant["consequence"] = vtrx_cols[3] - annot_variant["protein_hgvs"] = vtrx_cols[4] - annot_variant["cdna_hgvs"] = vtrx_cols[5] - for column in data_config: - if "none" in column["parse_type"]: - annot_variant[column["col_id"]] = row[column["col_id"]] - elif trx in cached_dicts_o_dicts[column["col_id"]]: - annot_variant.update( - cached_dicts_o_dicts[column["col_id"]][trx] - ) - else: - continue + if len(vtrx_cols) < 6: + # parse intergenic variant + annot_variant["trx"] = "" + annot_variant["gene"] = "" + annot_variant["consequence"] = "" + annot_variant["protein_hgvs"] = "" + annot_variant["cdna_hgvs"] = "" + for column in data_config: + if "none" in column["parse_type"]: + annot_variant[column["col_id"]] = row[column["col_id"]] + elif "list-o-dicts" in column["parse_type"]: + for subcol in column["parse_type"]["list-o-dicts"][ + "dict_index" + ].values(): + annot_variant[subcol] = row[subcol] + else: + for subcol in column["parse_type"]["list"][ + "column_list" + ]: + annot_variant[subcol] = row[subcol] + else: + # parse variant with transcript info + annot_variant["trx"] = trx + annot_variant["gene"] = vtrx_cols[1] + annot_variant["consequence"] = vtrx_cols[3] + annot_variant["protein_hgvs"] = vtrx_cols[4] + annot_variant["cdna_hgvs"] = vtrx_cols[5] + for column in data_config: + if "none" in column["parse_type"]: + annot_variant[column["col_id"]] = row[column["col_id"]] + elif trx in cached_dicts_o_dicts[column["col_id"]]: + annot_variant.update( + cached_dicts_o_dicts[column["col_id"]][trx] + ) + else: + continue # print parsed variant + transcript annotations to csv file output csvwriter.writerow(annot_variant)