Variant Annotation

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BioTender-max Updated Sep 17, 2026

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name: bio-variant-annotation description: Comprehensive variant annotation using bcftools annotate/csq, VEP, SnpEff, and ANNOVAR. Add database annotations, predict functional consequences, and assess clinical significance. Use when annotating variants with functional and clinical information. tool_type: mixed primary_tool: VEP measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command

Variant Annotation

Tool Comparison

Tool	Best For	Speed	Output
bcftools csq	Simple consequence prediction	Fast	VCF
VEP	Comprehensive with plugins	Moderate	VCF/TXT
SnpEff	Fast batch annotation	Fast	VCF
ANNOVAR	Flexible databases	Moderate	TXT

bcftools annotate

Add Annotations from Database

bcftools annotate -a dbsnp.vcf.gz -c ID input.vcf.gz -Oz -o annotated.vcf.gz

Annotation Columns (`-c`)

Option	Description
`ID`	Copy ID column
`INFO`	Copy all INFO fields
`INFO/TAG`	Copy specific INFO field
`+INFO/TAG`	Add to existing values

Add rsIDs from dbSNP

bcftools annotate -a dbsnp.vcf.gz -c ID input.vcf.gz -Oz -o with_rsids.vcf.gz

Add Multiple Annotations

bcftools annotate -a database.vcf.gz -c ID,INFO/AF,INFO/CAF input.vcf.gz -Oz -o annotated.vcf.gz

Add from BED/TAB Files

# BED with 4th column as annotation
bcftools annotate -a regions.bed.gz -c CHROM,FROM,TO,INFO/REGION \
    -h <(echo '##INFO=<ID=REGION,Number=1,Type=String,Description="Region name">') \
    input.vcf.gz -Oz -o annotated.vcf.gz

# Tab file: CHROM POS VALUE
bcftools annotate -a annotations.tab.gz -c CHROM,POS,INFO/SCORE \
    -h <(echo '##INFO=<ID=SCORE,Number=1,Type=Float,Description="Custom score">') \
    input.vcf.gz -Oz -o annotated.vcf.gz

Remove Annotations

bcftools annotate -x INFO/DP,INFO/MQ input.vcf.gz -Oz -o clean.vcf.gz
bcftools annotate -x INFO input.vcf.gz -Oz -o minimal.vcf.gz  # Remove all INFO

Set ID from Fields

bcftools annotate --set-id '%CHROM\_%POS\_%REF\_%ALT' input.vcf.gz -Oz -o with_ids.vcf.gz

bcftools csq

Simple consequence prediction using GFF annotation.

bcftools csq -f reference.fa -g genes.gff3.gz input.vcf.gz -Oz -o consequences.vcf.gz

Consequence Types

Consequence	Description
`synonymous`	No amino acid change
`missense`	Amino acid change
`stop_gained`	Introduces stop codon
`frameshift`	Changes reading frame
`splice_donor/acceptor`	Affects splicing

Ensembl VEP

Installation

conda install -c bioconda ensembl-vep
vep_install -a cf -s homo_sapiens -y GRCh38 --CONVERT

Basic Annotation

vep -i input.vcf -o output.vcf --vcf --cache --offline

Comprehensive Annotation

vep -i input.vcf -o output.vcf \
    --vcf \
    --cache --offline \
    --species homo_sapiens \
    --assembly GRCh38 \
    --everything \
    --fork 4

--everything Enables

--sift b - SIFT predictions
--polyphen b - PolyPhen predictions
--hgvs - HGVS nomenclature
--symbol - Gene symbols
--canonical - Canonical transcript
--af - 1000 Genomes frequencies
--af_gnomade/g - gnomAD frequencies
--pubmed - PubMed IDs

Filter by Impact

vep -i input.vcf -o output.vcf --vcf \
    --cache --offline \
    --pick \
    --filter "IMPACT in HIGH,MODERATE"

Plugins

# CADD scores
vep -i input.vcf -o output.vcf --vcf \
    --cache --offline \
    --plugin CADD,whole_genome_SNVs.tsv.gz

# dbNSFP (multiple predictors)
vep -i input.vcf -o output.vcf --vcf \
    --cache --offline \
    --plugin dbNSFP,dbNSFP4.3a.gz,ALL

# Multiple plugins
vep -i input.vcf -o output.vcf --vcf \
    --cache --offline \
    --plugin CADD,cadd.tsv.gz \
    --plugin dbNSFP,dbnsfp.gz,SIFT_score,Polyphen2_HDIV_score \
    --plugin SpliceAI,spliceai.vcf.gz

VEP Output Fields

Field	Description
Consequence	SO term (e.g., missense_variant)
IMPACT	HIGH, MODERATE, LOW, MODIFIER
SYMBOL	Gene symbol
HGVSc/HGVSp	HGVS coding/protein change
SIFT/PolyPhen	Pathogenicity predictions

SnpEff

Installation

conda install -c bioconda snpeff
snpEff download GRCh38.105

Basic Annotation

snpEff ann GRCh38.105 input.vcf > output.vcf

With Statistics

snpEff ann -v -stats stats.html -csvStats stats.csv GRCh38.105 input.vcf > output.vcf

Filter by Impact

snpEff ann GRCh38.105 input.vcf | \
    SnpSift filter "(ANN[*].IMPACT = 'HIGH')" > high_impact.vcf

SnpEff Impact Categories

Impact	Examples
HIGH	Stop gained, frameshift, splice donor/acceptor
MODERATE	Missense, inframe indel
LOW	Synonymous, splice region
MODIFIER	Intron, intergenic, UTR

SnpSift Database Annotations

# dbSNP
SnpSift annotate dbsnp.vcf.gz input.vcf > annotated.vcf

# ClinVar
SnpSift annotate clinvar.vcf.gz input.vcf > annotated.vcf

# dbNSFP
SnpSift dbnsfp -db dbNSFP4.3a.txt.gz input.vcf > annotated.vcf

# Chain multiple
snpEff ann GRCh38.105 input.vcf | \
    SnpSift annotate dbsnp.vcf.gz | \
    SnpSift annotate clinvar.vcf.gz > fully_annotated.vcf

SnpSift Filtering

SnpSift filter "(QUAL >= 30) & (DP >= 10)" input.vcf > filtered.vcf
SnpSift filter "(exists CLNSIG) & (CLNSIG has 'Pathogenic')" input.vcf > pathogenic.vcf

ANNOVAR

Installation

# Download from https://annovar.openbioinformatics.org/ (registration required)
annotate_variation.pl -buildver hg38 -downdb -webfrom annovar refGene humandb/
annotate_variation.pl -buildver hg38 -downdb -webfrom annovar gnomad30_genome humandb/

Table Annotation

table_annovar.pl input.vcf humandb/ \
    -buildver hg38 \
    -out annotated \
    -remove \
    -protocol refGene,gnomad30_genome,clinvar_20230416,dbnsfp42a \
    -operation g,f,f,f \
    -nastring . \
    -vcfinput

Python: Parse Annotated VCF

Parse VEP CSQ

from cyvcf2 import VCF

def parse_vep_csq(csq_string, csq_header):
    fields = csq_header.split('|')
    values = csq_string.split('|')
    return dict(zip(fields, values))

vcf = VCF('vep_output.vcf')
csq_header = None
for h in vcf.header_iter():
    if h['HeaderType'] == 'INFO' and h['ID'] == 'CSQ':
        csq_header = h['Description'].split('Format: ')[1].rstrip('"')
        break

for variant in vcf:
    csq = variant.INFO.get('CSQ')
    if csq:
        for transcript in csq.split(','):
            parsed = parse_vep_csq(transcript, csq_header)
            if parsed.get('IMPACT') in ('HIGH', 'MODERATE'):
                print(f"{variant.CHROM}:{variant.POS} {parsed['SYMBOL']} {parsed['Consequence']}")

Parse SnpEff ANN

from cyvcf2 import VCF

def parse_snpeff_ann(ann_string):
    fields = ['Allele', 'Annotation', 'Impact', 'Gene_Name', 'Gene_ID',
              'Feature_Type', 'Feature_ID', 'Transcript_BioType', 'Rank',
              'HGVS_c', 'HGVS_p', 'cDNA_pos', 'CDS_pos', 'Protein_pos', 'Distance']
    values = ann_string.split('|')
    return dict(zip(fields, values[:len(fields)]))

for variant in VCF('snpeff_output.vcf'):
    ann = variant.INFO.get('ANN')
    if ann:
        for transcript in ann.split(','):
            parsed = parse_snpeff_ann(transcript)
            if parsed['Impact'] == 'HIGH':
                print(f"{variant.CHROM}:{variant.POS} {parsed['Gene_Name']} {parsed['Annotation']}")

Complete Annotation Pipeline

#!/bin/bash
set -euo pipefail

INPUT=$1
REFERENCE=$2
VEP_CACHE=$3
OUTPUT_PREFIX=$4

# Normalize variants
bcftools norm -f $REFERENCE -m-any $INPUT -Oz -o ${OUTPUT_PREFIX}_norm.vcf.gz
bcftools index ${OUTPUT_PREFIX}_norm.vcf.gz

# VEP annotation
vep -i ${OUTPUT_PREFIX}_norm.vcf.gz \
    -o ${OUTPUT_PREFIX}_vep.vcf \
    --vcf --cache --offline --dir_cache $VEP_CACHE \
    --assembly GRCh38 --everything --pick --fork 4

bgzip ${OUTPUT_PREFIX}_vep.vcf
bcftools index ${OUTPUT_PREFIX}_vep.vcf.gz

# Filter high/moderate impact
bcftools view -i 'INFO/CSQ~"HIGH" || INFO/CSQ~"MODERATE"' \
    ${OUTPUT_PREFIX}_vep.vcf.gz -Oz -o ${OUTPUT_PREFIX}_filtered.vcf.gz

Pathogenicity Predictors

Predictor	Deleterious	Benign
SIFT	< 0.05	>= 0.05
PolyPhen-2 (HDIV)	> 0.957 (probably), > 0.453 (possibly)	<= 0.453
CADD	> 20 (top 1%), > 30 (top 0.1%)	< 10
REVEL	> 0.5	< 0.5

Clinical Significance (ClinVar)

Code	Meaning
Pathogenic	Disease-causing
Likely_pathogenic	Probably disease-causing
Uncertain_significance	VUS
Likely_benign	Probably not disease-causing
Benign	Not disease-causing

Quick Reference

Task	Command
Add rsIDs	`bcftools annotate -a dbsnp.vcf.gz -c ID in.vcf.gz`
VEP annotation	`vep -i in.vcf -o out.vcf --vcf --cache --everything`
SnpEff annotation	`snpEff ann GRCh38.105 in.vcf > out.vcf`
Consequences only	`bcftools csq -f ref.fa -g genes.gff in.vcf.gz`

Related Skills

variant-calling/variant-normalization - Normalize before annotating
variant-calling/filtering-best-practices - Filter by annotations
variant-calling/vcf-basics - Query annotated fields
database-access/entrez-fetch - Download annotation databases

---name: variant-annotation-2description: ---name: bio-variant-annotationdescription: Comprehensive variant annotation using bcftools annotate/csq, VEP, SnpEff, and ANNOVAR. Add database annotations, predict functional consequences, and assess clinical significance. Use when annotating variants with functional and clinical information.tool_type: mixedprimary_tool: VEPmeasurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.allowed-tools: - read_file - run_shell_command---# Variant Annotation## Tool Comparison| Tool | Best For | Speed | Output ||------|----------|-------|--------|| bcftools csq | Simple consequence prediction | Fast | VCF || VEP | Comprehensive with plugins | Moderate | VCF/TXT || SnpEff | Fast batch annotation | Fast | VCF || ANNOVAR | Flexible databases | Moderate | TXT |## bcftools annotate### Add Annotations from Database```bashbcftools annotate -a dbsnp.vcf.gz -c ID input.vcf.gz -Oz -o annotated.vcf.gz```### Annotation Columns (`-c`)| Option | Description ||--------|-------------|| `ID` | Copy ID column || `INFO` | Copy all INFO fields || `INFO/TAG` | Copy specific INFO field || `+INFO/TAG` | Add to existing values |### Add rsIDs from dbSNP```bashbcftools annotate -a dbsnp.vcf.gz -c ID input.vcf.gz -Oz -o with_rsids.vcf.gz```### Add Multiple Annotations```bashbcftools annotate -a database.vcf.gz -c ID,INFO/AF,INFO/CAF input.vcf.gz -Oz -o annotated.vcf.gz```### Add from BED/TAB Files```bash# BED with 4th column as annotationbcftools annotate -a regions.bed.gz -c CHROM,FROM,TO,INFO/REGION \ -h <(echo '##INFO=<ID=REGION,Number=1,Type=String,Description="Region name">') \ input.vcf.gz -Oz -o annotated.vcf.gz# Tab file: CHROM POS VALUEbcftools annotate -a annotations.tab.gz -c CHROM,POS,INFO/SCORE \ -h <(echo '##INFO=<ID=SCORE,Number=1,Type=Float,Description="Custom score">') \ input.vcf.gz -Oz -o annotated.vcf.gz```### Remove Annotations```bashbcftools annotate -x INFO/DP,INFO/MQ input.vcf.gz -Oz -o clean.vcf.gzbcftools annotate -x INFO input.vcf.gz -Oz -o minimal.vcf.gz # Remove all INFO```### Set ID from Fields```bashbcftools annotate --set-id '%CHROM\_%POS\_%REF\_%ALT' input.vcf.gz -Oz -o with_ids.vcf.gz```## bcftools csqSimple consequence prediction using GFF annotation.```bashbcftools csq -f reference.fa -g genes.gff3.gz input.vcf.gz -Oz -o consequences.vcf.gz```### Consequence Types| Consequence | Description ||-------------|-------------|| `synonymous` | No amino acid change || `missense` | Amino acid change || `stop_gained` | Introduces stop codon || `frameshift` | Changes reading frame || `splice_donor/acceptor` | Affects splicing |## Ensembl VEP### Installation```bashconda install -c bioconda ensembl-vepvep_install -a cf -s homo_sapiens -y GRCh38 --CONVERT```### Basic Annotation```bashvep -i input.vcf -o output.vcf --vcf --cache --offline```### Comprehensive Annotation```bashvep -i input.vcf -o output.vcf \ --vcf \ --cache --offline \ --species homo_sapiens \ --assembly GRCh38 \ --everything \ --fork 4```### --everything Enables- `--sift b` - SIFT predictions- `--polyphen b` - PolyPhen predictions- `--hgvs` - HGVS nomenclature- `--symbol` - Gene symbols- `--canonical` - Canonical transcript- `--af` - 1000 Genomes frequencies- `--af_gnomade/g` - gnomAD frequencies- `--pubmed` - PubMed IDs### Filter by Impact```bashvep -i input.vcf -o output.vcf --vcf \ --cache --offline \ --pick \ --filter "IMPACT in HIGH,MODERATE"```### Plugins```bash# CADD scoresvep -i input.vcf -o output.vcf --vcf \ --cache --offline \ --plugin CADD,whole_genome_SNVs.tsv.gz# dbNSFP (multiple predictors)vep -i input.vcf -o output.vcf --vcf \ --cache --offline \ --plugin dbNSFP,dbNSFP4.3a.gz,ALL# Multiple pluginsvep -i input.vcf -o output.vcf --vcf \ --cache --offline \ --plugin CADD,cadd.tsv.gz \ --plugin dbNSFP,dbnsfp.gz,SIFT_score,Polyphen2_HDIV_score \ --plugin SpliceAI,spliceai.vcf.gz```### VEP Output Fields| Field | Description ||-------|-------------|| Consequence | SO term (e.g., missense_variant) || IMPACT | HIGH, MODERATE, LOW, MODIFIER || SYMBOL | Gene symbol || HGVSc/HGVSp | HGVS coding/protein change || SIFT/PolyPhen | Pathogenicity predictions |## SnpEff### Installation```bashconda install -c bioconda snpeffsnpEff download GRCh38.105```### Basic Annotation```bashsnpEff ann GRCh38.105 input.vcf > output.vcf```### With Statistics```bashsnpEff ann -v -stats stats.html -csvStats stats.csv GRCh38.105 input.vcf > output.vcf```### Filter by Impact```bashsnpEff ann GRCh38.105 input.vcf | \ SnpSift filter "(ANN[*].IMPACT = 'HIGH')" > high_impact.vcf```### SnpEff Impact Categories| Impact | Examples ||--------|----------|| HIGH | Stop gained, frameshift, splice donor/acceptor || MODERATE | Missense, inframe indel || LOW | Synonymous, splice region || MODIFIER | Intron, intergenic, UTR |### SnpSift Database Annotations```bash# dbSNPSnpSift annotate dbsnp.vcf.gz input.vcf > annotated.vcf# ClinVarSnpSift annotate clinvar.vcf.gz input.vcf > annotated.vcf# dbNSFPSnpSift dbnsfp -db dbNSFP4.3a.txt.gz input.vcf > annotated.vcf# Chain multiplesnpEff ann GRCh38.105 input.vcf | \ SnpSift annotate dbsnp.vcf.gz | \ SnpSift annotate clinvar.vcf.gz > fully_annotated.vcf```### SnpSift Filtering```bashSnpSift filter "(QUAL >= 30) & (DP >= 10)" input.vcf > filtered.vcfSnpSift filter "(exists CLNSIG) & (CLNSIG has 'Pathogenic')" input.vcf > pathogenic.vcf```## ANNOVAR### Installation```bash# Download from https://annovar.openbioinformatics.org/ (registration required)annotate_variation.pl -buildver hg38 -downdb -webfrom annovar refGene humandb/annotate_variation.pl -buildver hg38 -downdb -webfrom annovar gnomad30_genome humandb/```### Table Annotation```bashtable_annovar.pl input.vcf humandb/ \ -buildver hg38 \ -out annotated \ -remove \ -protocol refGene,gnomad30_genome,clinvar_20230416,dbnsfp42a \ -operation g,f,f,f \ -nastring . \ -vcfinput```## Python: Parse Annotated VCF### Parse VEP CSQ```pythonfrom cyvcf2 import VCFdef parse_vep_csq(csq_string, csq_header): fields = csq_header.split('|') values = csq_string.split('|') return dict(zip(fields, values))vcf = VCF('vep_output.vcf')csq_header = Nonefor h in vcf.header_iter(): if h['HeaderType'] == 'INFO' and h['ID'] == 'CSQ': csq_header = h['Description'].split('Format: ')[1].rstrip('"') breakfor variant in vcf: csq = variant.INFO.get('CSQ') if csq: for transcript in csq.split(','): parsed = parse_vep_csq(transcript, csq_header) if parsed.get('IMPACT') in ('HIGH', 'MODERATE'): print(f"{variant.CHROM}:{variant.POS} {parsed['SYMBOL']} {parsed['Consequence']}")```### Parse SnpEff ANN```pythonfrom cyvcf2 import VCFdef parse_snpeff_ann(ann_string): fields = ['Allele', 'Annotation', 'Impact', 'Gene_Name', 'Gene_ID', 'Feature_Type', 'Feature_ID', 'Transcript_BioType', 'Rank', 'HGVS_c', 'HGVS_p', 'cDNA_pos', 'CDS_pos', 'Protein_pos', 'Distance'] values = ann_string.split('|') return dict(zip(fields, values[:len(fields)]))for variant in VCF('snpeff_output.vcf'): ann = variant.INFO.get('ANN') if ann: for transcript in ann.split(','): parsed = parse_snpeff_ann(transcript) if parsed['Impact'] == 'HIGH': print(f"{variant.CHROM}:{variant.POS} {parsed['Gene_Name']} {parsed['Annotation']}")```## Complete Annotation Pipeline```bash#!/bin/bashset -euo pipefailINPUT=$1REFERENCE=$2VEP_CACHE=$3OUTPUT_PREFIX=$4# Normalize variantsbcftools norm -f $REFERENCE -m-any $INPUT -Oz -o ${OUTPUT_PREFIX}_norm.vcf.gzbcftools index ${OUTPUT_PREFIX}_norm.vcf.gz# VEP annotationvep -i ${OUTPUT_PREFIX}_norm.vcf.gz \ -o ${OUTPUT_PREFIX}_vep.vcf \ --vcf --cache --offline --dir_cache $VEP_CACHE \ --assembly GRCh38 --everything --pick --fork 4bgzip ${OUTPUT_PREFIX}_vep.vcfbcftools index ${OUTPUT_PREFIX}_vep.vcf.gz# Filter high/moderate impactbcftools view -i 'INFO/CSQ~"HIGH" || INFO/CSQ~"MODERATE"' \ ${OUTPUT_PREFIX}_vep.vcf.gz -Oz -o ${OUTPUT_PREFIX}_filtered.vcf.gz```## Pathogenicity Predictors| Predictor | Deleterious | Benign ||-----------|-------------|--------|| SIFT | < 0.05 | >= 0.05 || PolyPhen-2 (HDIV) | > 0.957 (probably), > 0.453 (possibly) | <= 0.453 || CADD | > 20 (top 1%), > 30 (top 0.1%) | < 10 || REVEL | > 0.5 | < 0.5 |## Clinical Significance (ClinVar)| Code | Meaning ||------|---------|| Pathogenic | Disease-causing || Likely_pathogenic | Probably disease-causing || Uncertain_significance | VUS || Likely_benign | Probably not disease-causing || Benign | Not disease-causing |## Quick Reference| Task | Command ||------|---------|| Add rsIDs | `bcftools annotate -a dbsnp.vcf.gz -c ID in.vcf.gz` || VEP annotation | `vep -i in.vcf -o out.vcf --vcf --cache --everything` || SnpEff annotation | `snpEff ann GRCh38.105 in.vcf > out.vcf` || Consequences only | `bcftools csq -f ref.fa -g genes.gff in.vcf.gz` |## Related Skills- variant-calling/variant-normalization - Normalize before annotating- variant-calling/filtering-best-practices - Filter by annotations- variant-calling/vcf-basics - Query annotated fields- database-access/entrez-fetch - Download annotation databases

BioTender-max/awesome-bio-agent-skills/tree/main/skills/openclaw/variant-annotation commit b3dbf35a3d

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npx skillmds@latest add biotender-max/variant-annotation-2