🔎 Variant Calling

Germline and somatic small variant calling (SNVs, Indels). Supports GATK HaplotypeCaller, Mutect2, DeepVariant, and FreeBayes.

Core Capabilities

1. GATK HaplotypeCaller: Gold standard for germline variant calling with GVCF cohort workflow
2. GATK Mutect2: Somatic variant calling for tumor-normal pairs
3. DeepVariant: Deep learning-based variant caller (CNN)
4. FreeBayes: Bayesian haplotype-based caller
5. VQSR / Hard Filtering: Machine learning and rule-based variant quality filtering

CLI Reference

python omicsclaw.py run genomics-variant-calling --demo
python omicsclaw.py run genomics-variant-calling --input <data.bam> --output <dir>

Algorithm / Methodology

GATK Single-Sample Calling

# Basic HaplotypeCaller
gatk HaplotypeCaller \
    -R reference.fa \
    -I sample.bam \
    -O sample.vcf.gz

# With standard annotations
gatk HaplotypeCaller \
    -R reference.fa \
    -I sample.bam \
    -O sample.vcf.gz \
    -A Coverage -A QualByDepth -A FisherStrand -A StrandOddsRatio \
    -A MappingQualityRankSumTest -A ReadPosRankSumTest

# Target intervals (exome/panel)
gatk HaplotypeCaller \
    -R reference.fa \
    -I sample.bam \
    -L targets.interval_list \
    -O sample.vcf.gz

GVCF Workflow (Recommended for Cohorts)

# Step 1: Generate GVCFs per sample
gatk HaplotypeCaller \
    -R reference.fa \
    -I sample.bam \
    -O sample.g.vcf.gz \
    -ERC GVCF

# Step 2: Combine GVCFs (GenomicsDBImport)
gatk GenomicsDBImport \
    --genomicsdb-workspace-path genomicsdb \
    --sample-name-map sample_map.txt \
    -L intervals.interval_list

# Step 3: Joint Genotyping
gatk GenotypeGVCFs \
    -R reference.fa \
    -V gendb://genomicsdb \
    -O cohort.vcf.gz

VQSR (Variant Quality Score Recalibration)

Machine learning-based filtering using known variant sites. Requires many variants (WGS preferred).

# Build SNP model
gatk VariantRecalibrator \
    -R reference.fa -V cohort.vcf.gz \
    --resource:hapmap,known=false,training=true,truth=true,prior=15.0 hapmap.vcf.gz \
    --resource:omni,known=false,training=true,truth=false,prior=12.0 omni.vcf.gz \
    --resource:1000G,known=false,training=true,truth=false,prior=10.0 1000G.vcf.gz \
    --resource:dbsnp,known=true,training=false,truth=false,prior=2.0 dbsnp.vcf.gz \
    -an QD -an MQ -an MQRankSum -an ReadPosRankSum -an FS -an SOR \
    -mode SNP -O snp.recal --tranches-file snp.tranches

# Apply SNP filter
gatk ApplyVQSR \
    -R reference.fa -V cohort.vcf.gz -O cohort.snp_recal.vcf.gz \
    --recal-file snp.recal --tranches-file snp.tranches \
    --truth-sensitivity-filter-level 99.5 -mode SNP

Hard Filtering (When VQSR Not Suitable)

For small datasets, exomes, or single samples where VQSR fails.

# Filter SNPs
gatk VariantFiltration -R reference.fa -V snps.vcf.gz -O snps.filtered.vcf.gz \
    --filter-expression "QD < 2.0" --filter-name "QD2" \
    --filter-expression "FS > 60.0" --filter-name "FS60" \
    --filter-expression "MQ < 40.0" --filter-name "MQ40" \
    --filter-expression "MQRankSum < -12.5" --filter-name "MQRankSum-12.5" \
    --filter-expression "ReadPosRankSum < -8.0" --filter-name "ReadPosRankSum-8" \
    --filter-expression "SOR > 3.0" --filter-name "SOR3"

# Filter Indels
gatk VariantFiltration -R reference.fa -V indels.vcf.gz -O indels.filtered.vcf.gz \
    --filter-expression "QD < 2.0" --filter-name "QD2" \
    --filter-expression "FS > 200.0" --filter-name "FS200" \
    --filter-expression "ReadPosRankSum < -20.0" --filter-name "ReadPosRankSum-20" \
    --filter-expression "SOR > 10.0" --filter-name "SOR10"

BQSR (Base Quality Score Recalibration)

# Step 1: BaseRecalibrator
gatk BaseRecalibrator -R reference.fa -I sample.bam \
    --known-sites dbsnp.vcf.gz --known-sites known_indels.vcf.gz \
    -O recal_data.table

# Step 2: ApplyBQSR
gatk ApplyBQSR -R reference.fa -I sample.bam \
    --bqsr-recal-file recal_data.table -O sample.recal.bam

Complete Single-Sample Pipeline

#!/bin/bash
SAMPLE=$1; REF=reference.fa
DBSNP=dbsnp.vcf.gz; KNOWN_INDELS=known_indels.vcf.gz

# BQSR
gatk BaseRecalibrator -R $REF -I ${SAMPLE}.bam \
    --known-sites $DBSNP --known-sites $KNOWN_INDELS -O ${SAMPLE}.recal.table
gatk ApplyBQSR -R $REF -I ${SAMPLE}.bam \
    --bqsr-recal-file ${SAMPLE}.recal.table -O ${SAMPLE}.recal.bam

# Call variants
gatk HaplotypeCaller -R $REF -I ${SAMPLE}.recal.bam -O ${SAMPLE}.g.vcf.gz -ERC GVCF

# Genotype
gatk GenotypeGVCFs -R $REF -V ${SAMPLE}.g.vcf.gz -O ${SAMPLE}.vcf.gz

# Hard filter
gatk VariantFiltration -R $REF -V ${SAMPLE}.vcf.gz -O ${SAMPLE}.filtered.vcf.gz \
    --filter-expression "QD < 2.0" --filter-name "LowQD" \
    --filter-expression "FS > 60.0" --filter-name "HighFS" \
    --filter-expression "MQ < 40.0" --filter-name "LowMQ"

Key Annotations Reference

Resource Files

Parameters

--method

gatk

--mode

germline

--reference

--intervals

Why This Exists

• Without it: Raw alignments contain spurious mismatch artifacts from sequencing chemistry or mapping errors
• With it: Machine learning and Bayesian models distinguish true biology from technical noise
• Why OmicsClaw: Standardizes the notoriously complex GATK/DeepVariant execution graphs

Workflow

1. Calculate: Accumulate pileups and local haplotype graphs.
2. Execute: Calculate likelihood of variant states per locus.
3. Assess: Perform VQSR machine learning or hard filtering.
4. Generate: Output structural VCF catalogs.
5. Report: Tabulate key transition/transversion metrics.

Example Queries

• "Call germline traits from this bam using GATK"
• "Use Mutect2 for somatic variants on tumor-normal pairs"

Output Structure

output_directory/
├── report.md
├── result.json
├── variants.vcf.gz
├── figures/
│   └── vqsr_tranches.png
├── tables/
│   └── variant_quality_summary.csv
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    └── checksums.sha256

Safety

• Local-first: Strict offline processing without external upload.
• Disclaimer: Requires OmicsClaw reporting structures and disclaimers.
• Audit trail: Hyperparameters and operational flow states are logged fully.

Integration with Orchestrator

Trigger conditions:

• Automatically invoked dynamically based on tool metadata and user intent matching.

Chaining partners:

• align

  — Upstream BAM mapping

• annotation

  — Downstream consequence indexing

Version Compatibility

Reference examples tested with: GATK 4.5+, bcftools 1.19+

Dependencies

Required: GATK 4.x, samtools, bcftools Optional: DeepVariant, FreeBayes

Citations

• GATK — McKenna et al., Genome Research 2010
• DeepVariant — Poplin et al., Nature Biotechnology 2018
• FreeBayes — Garrison & Marth, arXiv 2012

Related Skills

• genomics-qc

  — QC before variant calling

• align

  — Read alignment upstream

• vcf-ops

  — Post-calling VCF manipulation

• variant-annotate

  — Variant annotation downstream