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Format_english_specification v20151001
- Example
CHR | POS | ID | REF | ALT | VarType | Allele |
---|---|---|---|---|---|---|
1 | 987189 | rs540580770 | C | T | snv | T |
- Specifications
Column | Specification |
---|---|
CHR | Chromosome |
POS | Position |
ID | Identifiers derived from dbSNP or COSMIC database, . represents that there is no record for this position |
REF | Reference base(s) |
ALT | Comma separated list of alternate non-reference alleles called on at least one of the samples |
VarType | Variant type |
Allele | One of the bases list in ALT
|
- Common practice
- Perform filter on
VarType
by using built-in filter of Excel to gain variants of specific variant type; - Perform filter on
CHR
by using built-in filter of Excel to gain variant that located on specific chromosomes; - Perform filter on
POS
by using built-in filter of Excel to gain variants on specific position; - Perform filter on
ID
by using built-in filter of Excel to gain variants with specific identifiers;
Note
- The pipeline only consider positions of corresponding variants but not the bases listed in
ALT
when trying to present the identifiers inID
. If the record is an InDel, then a identifier will be presented as long as its position located within the InDel regions;- Genotype in the candidate list follows the VCF format. The allele values are 0 for the reference allele (what is in
REF
), 1 for the allele listed in ALT, 2 for the second allele list inALT
and so on. If a call cannot be made for a sample at a given locus, '.' is specified for each missing allele;- There will be several records for a certain position if more than one alternate is found (comma separated list of alternate non-reference alleles in
ALT
).
- Example
# of databases (AF>0.005) | KG | ESP | PVFD | IN-HOUSE-1 | IN-HOUSE-2 | IN-HOUSE-3 | CG | HapMap | Wellderly | ExAC |
---|---|---|---|---|---|---|---|---|---|---|
0 | 0.0006 | 0.0002 | 0.0005 | 0.0005 | -1 | -1 | -1 | -1 | -1 | 0.00074549 |
- Specifications
Column | Specification |
---|---|
# of databases (AF>0.005) | Number of databases in which the allele (present ahead) frequency is >0.005. 10 databases (both public and in-house databases) are used in this pipeline. If no frequency is available, '-1' will be presented in the corresponding column. |
KG | Public database, ~2500 WES/WGS |
ESP | Public database, ~6500 WES |
PVFD | BGI in-house database, ~1000 WES/WGS |
IN-HOUSE-1 | BGI in-house database, ~1000 WES |
IN-HOUSE-2 | BGI in-house database, ~1000 WES |
IN-HOUSE-3 | BGI in-house database, ~200 WGS |
CG | Public database, 51 WGS |
HapMap | Public database, 270 WGS |
Wellderly | Public database, 454 WGS |
ExAC | Public database, ~60,000 WES |
- Recommended manipulations
- Quick filter:Filter out records with >N
# of databases(AF>0.005)
. N could be 0,1,2,3. - Advanced filter:Use different thresholds for different databases. The most frequently used threshold is 0.005. Researchers are encouraged to modify the threshold based on the prevalence of the specific disease.
- Example
SureSelect_Human_All_Exon_V1 | SureSelect_Human_All_Exon_V2 | SureSelect_Human_All_Exon_50Mb | SureSelect_Human_All_Exon_V4 | SureSelect_Human_All_Exon_V4+UTRs | SeqCapEZ_Exome_v2.0 | SeqCapEZ_Exome_v3.0 |
---|---|---|---|---|---|---|
19 | 702 | 274 | 21 | 6 | 643 | 48 |
- Specifications
Column | Capture Platforms | Capture chips | # of samples |
---|---|---|---|
SureSelect_Human_All_Exon_V1 | Agilent | 38Mbp | 71 |
SureSelect_Human_All_Exon_V2 | Agilent | 44Mbp | 1103 |
SureSelect_Human_All_Exon_50Mb | Agilent | 50Mbp | 390 |
SureSelect_Human_All_Exon_V4 | Agilent | 51Mbp | 27 |
SureSelect_Human_All_Exon_V4+UTRs | Agilent | 71Mbp | 7 |
SeqCapEZ_Exome_v2.0 | NimbleGen | 44.1Mbp | 1026 |
SeqCapEZ_Exome_v3.0 | NimbleGen | 64Mbp | 82 |
- Recommended manipulations
- Choose different thresholds for various capture chips. Threshold of 20 is a regular choice.
Note
- In-house capture chips databases are used to get rid of system error. Suppose that a variant is of low frequency in population, but frequently detected by NGS, then it is more likely to be a system error rather than a variant;
- In-house capture chips databases are used only in non-profit projects.
- Example
# of tools (predicted harmful or conserved) | SIFT | PolyPhen2_HDIV | PolyPhen2_HVAR | LRT | MutationTaster | MutationAssessor | FATHMM | GERP_plus | PhyloP | SiPhy | Gerp | PhastCons | GWAWA |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6 | 0.03 | 0.979 | 0.92 | 0.000074 | 0 | 4.06 | 2.16 | 3.74 | 0.783 | 11.0086 | 3.74 | 0.997 | 0.42 |
- Specifications
Column | Specification |
---|---|
# of tools (predicted harmful or conserved) | The number of hazard prediction tools that have predicted the variant as a deleterious mutation |
SIFT | Deleterious(<0.05) |
PolyPhen2_HDIV | Probably damaging (>=0.957), possibly damaging (0.453<=pp2_hdiv<=0.956); benign (<=0.452) |
PolyPhen2_HVAR | Probably damaging (>=0.909),possibly damaging (0.447<=pp2_hdiv<=0.909); benign (<=0.446) |
LRT | - |
MutationTaster | - |
MutationAssessor | Deleterious(>1.938) |
FATHMM | Deleterious(<-1.5) |
GERP++ | Deleterious(>3) |
PhyloP | Deleterious(>2.5) |
SiPhy | - |
Gerp | |
PhastCons | Deleterious(>0.6) |
GWAWA | - |
- Recommended manipulations
- Quick filter:Filter out records with <N
# of tools (predicted harmful or conserved)
. Since hazard prediction tools have high false positive and high false negative, these information are for reference only. Therefore, N could be 0; - Advanced filter: Use different thresholds for various tools.
- Example
OMIM | GeneTag | GO_BP | GO_MF | GO_CC | KEGG_Pathway | Proteins_Expression_profiles_of_Normal_Tissue |
---|---|---|---|---|---|---|
Neutrophilia, hereditary, 162830 (3) | novel | GO:0006952,defense response|GO:0007155,cell adhesion|GO:0007165,signal transduction | GO:0004872,receptor activity|GO:0004896,cytokine receptor activity | GO:0005576,extracellular region|GO:0005886,plasma membrane|GO:0005887,integral to plasma membrane | hsa04060,Cytokine-cytokine receptor interaction|hsa04630,Jak-STAT signaling pathway|hsa04640,Hematopoietic cell lineage|hsa05200,Pathways in cancer | "ENSG00000119535" "skin 2" "epidermal cells" "Not detected" "APE" "Supportive" |
- Specifications
Column | Specification |
---|---|
OMIM | Annotations derived from OMIM database for the mutated gene |
Genetag | Whether the mutated gene has ever been reported for the corresponding disease before. If the mutation is in a causal gene for the studying disease, it will present "known" for that variant in this column; otherwise, "novel" will be presented. This utility depends on the information (HGNC symbol of known causative genes) provided to the pipeline |
GO BP | Gene ontology annotation in terms of biological process |
GO MF | Gene ontology annotation in terms of molecular function |
GO CC | Gene ontology annotation in terms of cellular component |
KEGG | |
The Human Protein Atlas |
- Recommended manipulations
- Use the built-in filter of Excel to view variants in known causative genes by tag 'known' in
Genetag
. - If you are interested in genes with information in OMIM, perform filter on
OMIM
. - If you are interested in genes expressed in certain tissue, perform filter on
The Human Protein Atlas
. - If you are interested in genes with GO or KEGG annotation, perform filter on the corresponding columns.
Due to alternative splicing, there may be multiple transcripts for a single gene. The same variant within different transcripts might have different consequence. For each variant the pipeline offers annotations for two transcripts that contain the corresponding variant. One is the transcript whose function was most affected by the mutation and the other is the canonical transcript of the gene defined by Ensembl.
- Example
CLIN_SIG | IMPACT | Consequence | HGNC(SYMBOL) | Feature | BIOTYPE | HGVSc | HGVSp | EXON | INTRON | DOMAINS | SWISSPROT | TREMBL | UNIPARC | SIFT | PolyPhen |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
. | MED:11 | missense_variant | EPHA2 | ENST00000358432 | protein_coding | ENST00000358432.5:c.71C>T | ENSP00000351209.5:p.Ala24Val | '1/17' | '.' | Low_complexity_(Seg):Seg&Cleavage_site_(Signalp):Sigp&PIRSF_domain:PIRSF000666 | EPHA2_HUMAN | Q96HF4_HUMAN&Q8IZL0_HUMAN | UPI00000731AB | tolerated(0.56) | possibly_damaging(0.75) |
- Specifications
Column | Specification |
---|---|
CLIN_SIG | Clinical significance of variant from dbSNP; It could be one of the following value [unknown, untested, non-pathogenic, probable-non-pathogenic, probable, pathogenic, pathogenic, drug-response, histocompatibility, other] |
IMPACT | Impact of variant, ranging from 1 to 34, with "1" meaning the most serious impact to the function |
Consequence | Consequence type caused by the corresponding variation, detailed information please refer to VEP consequences |
HGNC(SYMBOL) | Gene symbol from HGNC |
Feature | Ensembl stable ID of feature |
BIOTYPE | Biotype of transcript or regulatory feature |
HGVSc | The HGVS coding sequence name |
HGVSp | The HGVS protein sequence name |
Exon | The exon number involved in the mutation (out of total number) |
Intron | The intron number involved in the mutation (out of total number) |
DOMAINS | The source and identifer of any overlapping protein domains |
SWISSPROT | UniProtKB/Swiss-Prot identifier of protein product |
TREMBL | UniProtKB/TrEMBL identifier of protein product |
UNIPARC | UniParc identifier of protein product |
SIFT | Prediction and/or score from hazard prediction tools of SIFT |
PolyPhen | Prediction and/or score from hazard prediction tools of PolyPhen |
- Recommended manipulations
- Filter based on information of the transcript whose function was mostly affected.
- Filter based on values in
IMPACT
. - Use prediction from SIFT and PolyPhen as references.
- Filter based on information of the canonical transcript.
- Use information such as HGVS, Exon, Intron, and DOMAINS as references.
Filter |
---|
ACC,TR,PASS |
ACC,TR,VQSRTrancheSNP99.00to99.90 |
- Specifications
Field | Specification | Values |
---|---|---|
Concordance | Two variants calling strategies are applied in parallel. The first is the common practice of using GATK HaplotypeCaller, and the second one is to call variants on cohorts of samples using the HaplotypeCaller in GVCF mode. There are discrepancies between the results from these two calling strategies. Since being unable to tell which is correct by now, tags in the first filed indicate whether the two results are concordant. "ACC" means results from the two calling strategies are concordant, while "DIFF" means the opposite | ACC, DIFF |
Target region or not | whether the varaint is inside ("TR") or outside ("FLANK") the target region of the capture chip used for this project | TR, FLANK |
GATK filters | whether the corresponding variant has passed the GATK Variant Quality Score Recalibration(VQSR). Only the tag "PASS" means the variant is reliable according to the recalibration processure. However, there are false positive and false negative. | PASS, LowQual, VQSRTrancheSNP99.00to99.90, VQSRTrancheINDEL99.00to99.90, VQSRTrancheSNP99.90to100.00, VQSRTrancheINDEL99.90to100.00 |
- Note
- Target regions refer to those that are covered exactly by the designed probes. Generally speaking, such regions should have been well-sequenced. Flanking regions refers to +/-200bp around each target regions. Though flanking regions might not sequenced as well as the target regions, it is recommended to make best use of it.
- Different variant calling tools might produce different genotypes for the same position. The pipeline have integrated four calling tools (GATK, SOAPsnp, SAMtools, and Platypus). It is not uncommon these tools produce discordant genotypes for same positions. The pipeline will perform modification when there is discrepancy. However, not all modification are correct. Sanger sequencing has the final say in such situations.
- The pipeline performs GATK VQSR on variants resulted from GATK. Briefly, VQSR use Gaussian model to score variant quality for filtering purposes. Variants with "PASS" are treated as reliable.
- Example
AD | AR | XL |
---|---|---|
AD:YY:2:3:0 | AR:YN:0:3:0 | XL:YN:0:0:0 |
- Specifications
Field | Specification | Value |
---|---|---|
Mode of inheritance | AD/AR/XL/Compound Heterozygous | |
Genotype | Y/N | |
Cosegregate | Y/N | |
Number of cases that fit cosegregation | [0, #cases] | |
Number of controls that fit cosegregation | [0, #controls] | |
Number of individuals with unknown phenotype that fit cosegregation | [0, #individuals with unknown phenotype] |
- Recommended manipulations
- Since mutations causing rare diseases tend to affect gene functions directly, we usually suppose that causal mutations cosegregate with affected individuals within a famlily. Figure below show how to filter based on cosegregation when the studying disease is AR inheritance. There are 2 affected samples (cases) and 3 unaffected samples (controls). "AR:YY:2:3:0" means that 2 cases and 3 controls have been successfully be genotyped at the corresponding position, and all of their genotypes follow the cosegregation. Records with such tags should be kept for further investigation. "AR:NY:2:2:0" indicates 1 control has not been genotyped. However, samples that have been successfully genotyped follow the cosegregation. Variants like these kind should also be kept to avoid false negative. Sanger sequencing can be a remedy when NGS and bioinformatic analysis fail to genotype samples.
- Example
Detail_INFO_Format | detail-case-1 | detail-case-2 | detail-control-3 | detail-control-4 | detail-control-5 | case-1 | case-2 | control-3 | control-4 | control-5 |
---|---|---|---|---|---|---|---|---|---|---|
BGI_GD:DNM:SL_GT:ROH:FL_GT:AD:GQ | 'd(M-P)|d(P-M):DNM-FP:0/1:ROH-lt5M:0/1:2,6:45' | 'd(M-P)|d(P-M):DNM-FP:0/1:ROH-lt5M:0/1:7,3:86' | 'gd-unknown:DNM-unknown:0/0:ROH-lt5M:0/0:11,0:21' | 'gd-unknown:DNM-unknown:0/0:ROH-lt5M:0/0:6,0:.' | 'g(B-N):DNM-unknown:0/0:ROH-Unkown:0/0:7,0:21' | '0/1' | '0/1' | '0/0' | '0/0' | '0/0' |
- Specification
Column / Field | Specification | Value |
---|---|---|
Detail_INFO_Format | This column specifies the tag types and order (colon-separated). This is followed by one column per sample, with the colon-separated data in this column. Seven keywords are presented. | BGI_GD:DNM:SL_GT:ROH:FL_GT:AD:GQ |
BGI_GD | Tag that given to each sample based on the genotypes of his/her parents at the corresponding position. It describes from who each allele comes from. | g(B-N), d(M-P)|d(P-M), g(B-N), d(M-P), d(P-M), d(N-B)fp, d(M-P)|d(N-B)fp, g(B-N)|d(P-M), g(B-N)|d(P-M), g(B-N)|d(M-P), d(P-M)|d(N-B)fp, g(B-N)|d(M-P), gd-unknown, g(B-N), g(B-N) |
DNM | Random forest classifier to distinguish true de novo SNVs | DNM-TP, DNM-FP |
SL_GT | As mentioned before, there two calling strategies in this pipeline. One is based on sample level, the other is based on family level. "SL_GT" is Genotype result from GATK at sample level | |
ROH | Runs of homozygosity | ROH-unknown, ROH-lt5M, ROH-gt5M |
FL_GT | Genotype result from GATK at family level | |
AD | Allelic read depths for the reference and alternate alleles | |
GQ | Genotype quality score derived from GATK at family level | 0~99 |
- Recommended manipulations
ROH:When the studying pedigree is consanguineous union, homozygosity mapping analysis will be of great help. ROH tag in each sample detailed information column indicates such information. As showed below, for AR inheritance disease of consanguineous families, first filter out records that do not follow the AR inheritance pattern, then keep those with ROH-gt5M. ROH regions with greater size are more likely the identical haplotypes which the parents themselves derived from the common ancestor.
- Recommended manipulations
DNM: BGI_GD and DNM tags can both be used to find de novo mutations. Figure below show first keep records that cosegregate with corresponding inheritance (usually the AD inheritance), then use the DNM tag to locate de novo SNVs that are considered as true positive by random forest classifier.
BGI_GD
- Format:
"a(b-c)" or "gd-unknown"
- Specification:
This tag is given to each sample based on the genotype of his/her parents at the corresponding position. It can be used to describe from whom each allele inherits. a, indicates whether the allele is germline or a de novo mutation. Values for it could be [g, d], which represents germline and de novo, respectly.
(b-c), '-' inside brackets separates two characters that describe the source of the allele. Values for 'b' and 'c' could be [M, P, B, N], which represents maternal, paternal, both, none.
gd_unknown, meaning that the pipeline can not tell the source of each allele for that sample. This happens when parents genotypes are unknown.
- Example:
g(B-N), means that both allele of the sample are germline;
d(M-P), means that allele from paternal side is a de novo mutation.
- Application: 1. Filter out records with d(b-c) in any of the case detailed information column when germline genotypes are primarily interest; 2. Filter out records with g(b-c) in any of the case detailed information column when de novo mutations are primarily interest. If the variants are de novo SNVs, then tag DNM should be used to tell which are more reliable. When you are interested in de novo InDels, first kept records with "indel" presented in
VarType
and then focus on variants with d(b-c) tag to do the interpretation.
Feedback please contact rdscreening development group