bio-variant-annotation

FreedomIntelligence/OpenClaw-Medical-SkillsBiology Medicine and BioinformaticsAudit pendingSnapshot ab3cbfa0b964

Summary

This source did not publish a separate summary. Review SKILL.md before using the skill.

SKILL.md

Version Compatibility

Reference examples tested with: bcftools 1.19+

Before using code patterns, verify installed versions match. If versions differ:

Python: pip show <package> then help(module.function) to check signatures
CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

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

Goal: Add or remove INFO/ID annotations from external databases using bcftools.

Approach: Match variants by position and allele against annotation VCF/BED/TAB files, copying specified columns.

"Add rsIDs to my VCF from dbSNP" → Match variant positions against a database and copy identifiers or annotation fields into the VCF.

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

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

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

# 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

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

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

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

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

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

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

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

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

# 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

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

conda install -c bioconda snpeff
snpEff download GRCh38.105

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

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

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

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

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

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

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']}")

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']}")

#!/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

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

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

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`

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

bio-variant-annotation

Summary

Version Compatibility

Variant Annotation

Tool Comparison

bcftools annotate

Add Annotations from Database

Annotation Columns (-c)

Add rsIDs from dbSNP

Add Multiple Annotations

Add from BED/TAB Files

Remove Annotations

Set ID from Fields

bcftools csq

Consequence Types

Ensembl VEP

Installation

Basic Annotation

Comprehensive Annotation

--everything Enables

Filter by Impact

Plugins

VEP Output Fields

SnpEff

Installation

Basic Annotation

With Statistics

Filter by Impact

SnpEff Impact Categories

SnpSift Database Annotations

SnpSift Filtering

ANNOVAR

Installation

Table Annotation

Python: Parse Annotated VCF

Parse VEP CSQ

Parse SnpEff ANN

Complete Annotation Pipeline

Pathogenicity Predictors

Clinical Significance (ClinVar)

Quick Reference

Related Skills

Related skills

Annotation Columns (`-c`)