easyanno is a simple tool to do gene-based annotation (based on refGene) with genomic position.
I make token efforts to omit some laborious steps (i.e., prepare annovar input, annotate, and then map back to your files) in the use of ANNOVAR.
With easyanno, you can use a (tab separated) file (e.g., gwas summary) as input and obtain an output with annotations.
LinuxPython3(3.9.6) withpandas(1.4.3),numpy(1.20.3),argparse,os,sys,time,subprocess
Versions I used are in bracket (no necessary to be the same).ANNOVAR
Follow the instruction, obtain the download link (like http://xxx/annovar.latest.tar.gz).
Then:
Download ANNOVAR and hg38 refGene:
wget -c http://xxx/annovar.latest.tar.gz
tar zxvf annovar.latest.tar.gz
annovar/annotate_variation.pl -buildver hg38 -downdb -webfrom annovar refGene annovar/humandb/
# (Optional) dbNSFP is a database developed for functional prediction.
# Download it if you need to annotate this information (they are LARGE):
annovar/annotate_variation.pl -buildver hg19 -downdb -webfrom annovar dbnsfp42c annovar/humandb/
annovar/annotate_variation.pl -buildver hg38 -downdb -webfrom annovar dbnsfp42c annovar/humandb/
Clone this repository via the commands:
git clone https://github.com/zhanghaoyang0/easyanno.git
cd easyanno
If annovar/ is not in your easyanno folder, add a soft link:
ln -s path_of_your_annovar ./
# check if it link correctly
ls annovar/ # you will see the scripts and data
Once the above has completed, you can try to annotate by specifying:
--build hg19 or hg38, default is hg19
--only_find_gene only find Func.refGene and Gene.refGene, T or F, default is T
--anno_dbnsfp annotate dbnsfp, T or F, default is F
--chr_col field name of CHR, default is CHR
--pos_col field name of POS, default is POS
--ref_col field name of REF, default is A2
--alt_col field name of ALT, default is A1
--file_in tab[\t] separated input file, gzip (file_in end with '.gz') input can also be recongized
--file_out output file
Two examples (one for hg19 and one for hg38):
python ./code/easyanno.py \
--build hg19 \
--only_find_gene F \
--anno_dbnsfp F \
--chr_col CHR --pos_col POS --ref_col A2 --alt_col A1 \
--file_in ./example/df_hg19.txt \
--file_out ./example/df_hg19_annoed.txt
python ./code/easyanno.py \
--build hg38 \
--only_find_gene T \
--anno_dbnsfp F \
--chr_col chrom --pos_col pos --ref_col ref --alt_col alt \
--file_in ./example/df_hg38.txt \
--file_out ./example/df_hg38_annoed.txt
gzip (file_in end with '.gz') input can be recongized:
python ./code/easyanno.py \
--build hg19 \
--only_find_gene T \
--anno_dbnsfp F \
--chr_col CHR --pos_col POS --ref_col A2 --alt_col A1 \
--file_in ./example/df_hg19.txt.gz \
--file_out ./example/df_hg19_annoed.txt
The input file is like:
CHR POS A1 A2 FRQ BETA SE P
2 48543917 A G 0.4673 0.0045 0.0088 0.6101
5 87461867 A G 0.7151 0.0166 0.0096 0.08397
14 98165673 T C 0.1222 -0.0325 0.014 0.02035
12 104289454 T C 0.534 0.0085 0.0088 0.3322
11 26254654 T C 0.0765 0.0338 0.0167 0.04256
4 163471758 T C 0.612 0.0119 0.0094 0.2057
If easyanno is running, you will see:
setting:
build: hg19
chr_col: CHR
pos_col: POS
ref_col: A2
alt_col: A1
file_in: ./example/df_hg19.txt
file_out: ./example/df_hg19_annoed.txt
only_find_gene: F
anno_dbnsfp: T
loading input...
using annovar to annotate...
...
deleting temporary files...
2000 snp are annotated (i.e., found Gene.refGene) successfully
done!
spend 00 min 05 sec
If you set only_find_gene as T, The output file is like:
CHR POS A1 A2 FRQ BETA SE P Func.refGene Gene.refGene
2 48543917 A G 0.4673 0.0045 0.0088 0.6101 intronic FOXN2
5 87461867 A G 0.7151 0.0166 0.0096 0.08397 intergenic LINC02488;TMEM161B
14 98165673 T C 0.1222 -0.0325 0.014 0.02035 intergenic LINC02291;LINC02312
12 104289454 T C 0.534 0.0085 0.0088 0.3322 ncRNA_intronic TTC41P
11 26254654 T C 0.0765 0.0338 0.0167 0.04256 intronic ANO3
4 163471758 T C 0.612 0.0119 0.0094 0.2057 intergenic FSTL5;MIR4454
If you set only_find_gene as F, you get more information in refgene database:
CHR POS A1 A2 FRQ BETA SE P Func.refGene Gene.refGene GeneDetail.refGene ExonicFunc.refGene AAChange.refGene
2 48543917 A G 0.4673 0.0045 0.0088 0.6101 intronic FOXN2 . . .
5 87461867 A G 0.7151 0.0166 0.0096 0.08397 intergenic LINC02488;TMEM161B dist=486641;dist=22266 . .
14 98165673 T C 0.1222 -0.0325 0.014 0.02035 intergenic LINC02291;LINC02312 dist=12678;dist=49928 . .
12 104289454 T C 0.534 0.0085 0.0088 0.3322 ncRNA_intronic TTC41P . . .
11 26254654 T C 0.0765 0.0338 0.0167 0.04256 intronic ANO3 . . .
4 163471758 T C 0.612 0.0119 0.0094 0.2057 intergenic FSTL5;MIR4454 dist=386572;dist=542968 . .
If you set anno_dbnsfp as T, you get much more information in refgene and dbnsfp database:
CHR POS A1 A2 FRQ BETA SE P Chr Start End Ref Alt Func.refGene Gene.refGene GeneDetail.refGene ExonicFunc.refGene AAChange.refGene SIFT_score SIFT_converted_rankscore SIFT_pred SIFT4G_score SIFT4G_converted_rankscore SIFT4G_pred LRT_score LRT_converted_rankscore LRT_pred MutationTaster_score MutationTaster_converted_rankscore MutationTaster_pred MutationAssessor_score MutationAssessor_rankscore MutationAssessor_pred FATHMM_score FATHMM_converted_rankscore FATHMM_pred PROVEAN_score PROVEAN_converted_rankscore PROVEAN_pred MetaSVM_score MetaSVM_rankscoreMetaSVM_pred MetaLR_score MetaLR_rankscore MetaLR_pred MetaRNN_score MetaRNN_rankscore MetaRNN_pred M-CAP_score M-CAP_rankscore M-CAP_pred 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 Aloft_pred Aloft_Confidence 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-PC-raw_coding Eigen-PC-raw_coding_rankscore integrated_fitCons_score integrated_fitCons_rankscore integrated_confidence_valueGERP++_NR GERP++_RS GERP++_RS_rankscore phyloP100way_vertebrate phyloP100way_vertebrate_rankscore phyloP30way_mammalian phyloP30way_mammalian_rankscore phastCons100way_vertebrate phastCons100way_vertebrate_rankscore phastCons30way_mammalian phastCons30way_mammalian_rankscore SiPhy_29way_logOdds SiPhy_29way_logOdds_rankscore Interpro_domain GTEx_V8_gene GTEx_V8_tissue
2 48543917 A G 0.4673 0.0045 0.0088 0.6101 2 48543917 48543917 G A intronic FOXN2 . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .
5 87461867 A G 0.7151 0.0166 0.0096 0.08397 5 87461867 87461867 G A intergenic LINC02488;TMEM161B dist=486641;dist=22266 . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . .
easyanno is based on ANNOVAR, please cite ANNOVAR paper if you use it.
Add an issue or send email to zhanghaoyang0@hotmail.com