Germline and somatic short-variant calling pipeline for Illumina short-read data. Use when the user mentions BWA, BWA-MEM2, GATK, HaplotypeCaller, Mutect2, VCF, GVCF, VQSR, variant calling, germline, somatic, SNV, or indel calling from FASTQ/BAM.
Germline and somatic short-variant calling pipeline for Illumina short-read data. Use when the user mentions BWA, BWA-MEM2, GATK, HaplotypeCaller, Mutect2, VCF, GVCF, VQSR, variant calling, germline, somatic, SNV, or indel calling from FASTQ/BAM.
usage
Invoke when running germline or somatic variant calling with BWA-MEM2, GATK4 HaplotypeCaller, Mutect2, or VQSR.
Adds decision logic for VQSR vs hard filters, WES vs WGS parameter differences, and GATK4-specific gotchas that LLMs frequently get wrong (filter thresholds, GVCF requirements, reference consistency).
Usage
Activate when choosing between VQSR and hard filters for a given cohort size
Activate when setting up WES vs WGS pipeline parameters
Activate when running Mutect2 tumor/normal or tumor-only somatic calling
Core Concepts
Decision Logic
Germline calling strategy:
├── Single sample, no future joint calling → HaplotypeCaller direct VCF
└── Cohort (≥2 samples) → HaplotypeCaller -ERC GVCF → GenomicsDBImport → GenotypeGVCFs
Filtering strategy:
├── ≥30 WGS samples OR ≥30 WES exomes → VQSR
└── <30 samples OR single sample → Hard filters
Somatic calling:
├── Tumor + matched normal → Mutect2 with both BAMs
└── Tumor-only → Mutect2 + PoN (MANDATORY) + gnomAD germline resource
WES vs WGS parameter differences:
Step
WGS
WES
BQSR / HaplotypeCaller
No -L
-L capture.bed -ip 100
Known sites for BQSR
Genome-wide
Genome-wide (do NOT subset)
Filtering
VQSR (≥30 samples)
Hard filters unless ≥30 exomes
Joint genotyping intervals
Per-chromosome
-L capture.bed
Critical Parameters
Hard filter thresholds (GATK Best Practices):
Filter
SNP threshold
Indel threshold
QD
< 2.0
< 2.0
FS
> 60.0
> 200.0
MQ
< 40.0
—
MQRankSum
< -12.5
—
ReadPosRankSum
< -8.0
< -20.0
SOR
> 3.0
> 10.0
VQSR truth sensitivity levels:
SNPs: 99.7
Indels: 99.0
Indel --max-gaussians 4 (fewer training variants than SNPs)
GetPileupSummaries + CalculateContamination before FilterMutectCalls
Common Mistakes
Wrong: Aligning without proper @RG headers (missing ID, SM, PL, LB)
Right: Always specify at alignment: -R '@RG\tID:x\tSM:x\tPL:ILLUMINA\tLB:x'Why: GATK refuses to run or silently merges samples when SM tags are wrong
Wrong: Running HaplotypeCaller without -ERC GVCF for cohort analysis
Right: Always produce GVCFs when joint genotyping will be performed
Why: Regular VCFs cannot be joint-genotyped; must re-call from BAM
Wrong: Reusing SNP hard-filter thresholds for indels (e.g., FS > 60 for indels)
Right: Use FS > 200 for indels, FS > 60 for SNPs
Why: Indels tolerate higher strand bias; SNP thresholds over-filter real indels
Wrong: Applying VQSR to <30 samples
Right: Use hard filters for small cohorts
Why: VQSR needs many variants to train its Gaussian mixture model
Wrong: Subsetting --known-sites VCFs to WES capture BED for BQSR
Right: Use genome-wide known-sites; only restrict analysis intervals via -LWhy: BQSR needs genome-wide known sites to model base quality errors
Wrong: Skipping samtools index between GATK steps
Right: Index after every BAM-producing step
Why: GATK requires BAM indices; missing them causes immediate failure
Wrong: Mixing reference files from different genome builds
Right: Ensure ref.dict, ref.fa.fai, BWA index, and all VCFs match the same build
Why: Mismatched dictionaries cause silent failures or cryptic errors
Wrong: Running Mutect2 tumor-only without a panel of normals
Right: Always provide --panel-of-normals for tumor-only calling
Why: Without PoN, recurrent sequencing artifacts are called as somatic mutations
Response Format
Lead with the command or code the user needs — explain after
