--- name: bio-workflows-somatic-variant-pipeline description: End-to-end somatic variant calling from tumor-normal paired samples using Mutect2 or Strelka2. Covers preprocessing, variant calling, filtering, and annotation for cancer genomics. Use when calling somatic mutations from tumor-normal pairs. tool_type: cli primary_tool: GATK Mutect2 --- ## Version Compatibility Reference examples tested with: CNVkit 0.9+, Ensembl VEP 111+, GATK 4.5+, SnpEff 5.2+, bcftools 1.19+, picard 3.1+ Before using code patterns, verify installed versions match. If versions differ: - CLI: ` --version` then ` --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. # Somatic Variant Pipeline **"Call somatic mutations from my tumor-normal pair"** -> Orchestrate alignment, Mutect2 somatic calling, contamination filtering, variant annotation (Funcotator/VEP), TMB calculation, and mutational signature analysis. Complete workflow for calling somatic mutations from tumor-normal paired samples. ## Pipeline Overview ``` Tumor BAM + Normal BAM │ ├── Preprocessing (if needed) │ └── MarkDuplicates, BQSR │ ├── Variant Calling │ ├── Mutect2 (GATK) - SNVs + indels │ └── Strelka2 - SNVs + indels (faster) │ ├── Filtering │ ├── FilterMutectCalls │ ├── Contamination estimation │ └── Orientation bias filtering │ ├── Annotation │ ├── Funcotator / VEP │ └── Cancer-specific databases │ └── Output: Filtered somatic VCF ``` ## Mutect2 Workflow (GATK) ### Step 1: Panel of Normals (Optional but Recommended) ```bash # Create PON from multiple normal samples for normal in normal1.bam normal2.bam normal3.bam; do sample=$(basename $normal .bam) gatk Mutect2 \ -R reference.fa \ -I $normal \ --max-mnp-distance 0 \ -O ${sample}.vcf.gz done # Combine into PON gatk GenomicsDBImport \ -R reference.fa \ --genomicsdb-workspace-path pon_db \ -V normal1.vcf.gz \ -V normal2.vcf.gz \ -V normal3.vcf.gz \ -L intervals.bed gatk CreateSomaticPanelOfNormals \ -R reference.fa \ -V gendb://pon_db \ -O pon.vcf.gz ``` ### Step 2: Call Somatic Variants ```bash gatk Mutect2 \ -R reference.fa \ -I tumor.bam \ -I normal.bam \ -normal normal_sample_name \ --germline-resource af-only-gnomad.vcf.gz \ --panel-of-normals pon.vcf.gz \ --f1r2-tar-gz f1r2.tar.gz \ -O unfiltered.vcf.gz ``` ### Step 3: Learn Orientation Bias ```bash gatk LearnReadOrientationModel \ -I f1r2.tar.gz \ -O read-orientation-model.tar.gz ``` ### Step 4: Calculate Contamination ```bash gatk GetPileupSummaries \ -I tumor.bam \ -V small_exac_common.vcf.gz \ -L small_exac_common.vcf.gz \ -O tumor_pileups.table gatk GetPileupSummaries \ -I normal.bam \ -V small_exac_common.vcf.gz \ -L small_exac_common.vcf.gz \ -O normal_pileups.table gatk CalculateContamination \ -I tumor_pileups.table \ -matched normal_pileups.table \ -O contamination.table \ --tumor-segmentation segments.table ``` ### Step 5: Filter Variants ```bash gatk FilterMutectCalls \ -R reference.fa \ -V unfiltered.vcf.gz \ --contamination-table contamination.table \ --tumor-segmentation segments.table \ --ob-priors read-orientation-model.tar.gz \ -O filtered.vcf.gz # Extract PASS variants bcftools view -f PASS filtered.vcf.gz -Oz -o somatic_final.vcf.gz ``` ## Strelka2 Workflow (Faster Alternative) ```bash # Configure configureStrelkaSomaticWorkflow.py \ --normalBam normal.bam \ --tumorBam tumor.bam \ --referenceFasta reference.fa \ --runDir strelka_run # Execute strelka_run/runWorkflow.py -m local -j 16 # Output files # strelka_run/results/variants/somatic.snvs.vcf.gz # strelka_run/results/variants/somatic.indels.vcf.gz # Merge SNVs and indels bcftools concat \ strelka_run/results/variants/somatic.snvs.vcf.gz \ strelka_run/results/variants/somatic.indels.vcf.gz \ -a -Oz -o strelka_somatic.vcf.gz ``` ## Annotation ### Funcotator (GATK) ```bash gatk Funcotator \ -R reference.fa \ -V somatic_final.vcf.gz \ -O annotated.vcf.gz \ --output-file-format VCF \ --data-sources-path funcotator_dataSources.v1.7 \ --ref-version hg38 ``` ### VEP with Cancer Databases ```bash vep -i somatic_final.vcf.gz -o annotated.vcf \ --vcf --cache --offline \ --assembly GRCh38 \ --everything \ --plugin CADD,cadd_scores.tsv.gz \ --custom cosmic.vcf.gz,COSMIC,vcf,exact,0,CNT \ --fork 4 ``` ## Complete Pipeline Script ```bash #!/bin/bash set -euo pipefail TUMOR_BAM=$1 NORMAL_BAM=$2 NORMAL_NAME=$3 REFERENCE=$4 OUTPUT_PREFIX=$5 GNOMAD=$6 PON=$7 THREADS=16 echo "=== Step 1: Mutect2 calling ===" gatk Mutect2 \ -R $REFERENCE \ -I $TUMOR_BAM \ -I $NORMAL_BAM \ -normal $NORMAL_NAME \ --germline-resource $GNOMAD \ --panel-of-normals $PON \ --f1r2-tar-gz ${OUTPUT_PREFIX}_f1r2.tar.gz \ --native-pair-hmm-threads $THREADS \ -O ${OUTPUT_PREFIX}_unfiltered.vcf.gz echo "=== Step 2: Learn orientation bias ===" gatk LearnReadOrientationModel \ -I ${OUTPUT_PREFIX}_f1r2.tar.gz \ -O ${OUTPUT_PREFIX}_orientation.tar.gz echo "=== Step 3: Pileup summaries ===" gatk GetPileupSummaries \ -I $TUMOR_BAM \ -V $GNOMAD \ -L $GNOMAD \ -O ${OUTPUT_PREFIX}_tumor_pileups.table gatk GetPileupSummaries \ -I $NORMAL_BAM \ -V $GNOMAD \ -L $GNOMAD \ -O ${OUTPUT_PREFIX}_normal_pileups.table echo "=== Step 4: Calculate contamination ===" gatk CalculateContamination \ -I ${OUTPUT_PREFIX}_tumor_pileups.table \ -matched ${OUTPUT_PREFIX}_normal_pileups.table \ -O ${OUTPUT_PREFIX}_contamination.table \ --tumor-segmentation ${OUTPUT_PREFIX}_segments.table echo "=== Step 5: Filter variants ===" gatk FilterMutectCalls \ -R $REFERENCE \ -V ${OUTPUT_PREFIX}_unfiltered.vcf.gz \ --contamination-table ${OUTPUT_PREFIX}_contamination.table \ --tumor-segmentation ${OUTPUT_PREFIX}_segments.table \ --ob-priors ${OUTPUT_PREFIX}_orientation.tar.gz \ -O ${OUTPUT_PREFIX}_filtered.vcf.gz echo "=== Step 6: Extract PASS variants ===" bcftools view -f PASS ${OUTPUT_PREFIX}_filtered.vcf.gz \ -Oz -o ${OUTPUT_PREFIX}_somatic.vcf.gz bcftools index -t ${OUTPUT_PREFIX}_somatic.vcf.gz echo "=== Step 7: Statistics ===" bcftools stats ${OUTPUT_PREFIX}_somatic.vcf.gz > ${OUTPUT_PREFIX}_stats.txt echo "=== Pipeline complete ===" echo "Somatic variants: ${OUTPUT_PREFIX}_somatic.vcf.gz" echo "Stats: ${OUTPUT_PREFIX}_stats.txt" ``` ## Tumor-Only Mode When matched normal is unavailable (e.g., archival FFPE, cell lines): ```bash gatk Mutect2 \ -R reference.fa \ -I tumor.bam \ --germline-resource af-only-gnomad.vcf.gz \ --panel-of-normals pon.vcf.gz \ -O tumor_only.vcf.gz ``` Higher false positive rate without matched normal -- many germline variants will pass filters. The PoN and gnomAD germline resource become critical for artifact and germline removal respectively. ## Consensus Calling (Improved Accuracy) Running multiple callers and requiring agreement improves both precision and recall: ```bash # Run Mutect2, Strelka2, and MuSE independently, then intersect # Majority voting (2/3 agreement) achieves F1 ~0.927 for SNVs bcftools isec -n+2 -p consensus_dir \ mutect2_pass.vcf.gz strelka2_pass.vcf.gz muse_pass.vcf.gz # For indels: Mutect2 + Strelka2 + VarScan2 with 2/3 agreement ``` Strict intersection (all agree) sacrifices too much recall; union includes too many false positives. Majority voting provides the best balance. ## Emerging: DeepSomatic DeepSomatic extends DeepVariant's CNN approach to somatic calling with platform-specific models (Illumina, PacBio HiFi, ONT). Published Nature Biotechnology 2025, it achieves higher F1 than existing callers across all platforms and supports tumor-only and FFPE modes. ## Key Resources | Resource | Purpose | |----------|---------| | gnomAD AF-only | Germline filtering | | Panel of Normals | Technical artifact removal | | COSMIC | Known cancer mutations | | Funcotator data sources | Functional annotation | ## Quality Metrics ```bash # Variant counts by filter status bcftools query -f '%FILTER\n' filtered.vcf.gz | sort | uniq -c # Ti/Tv ratio (expect ~2-3 for somatic) bcftools stats filtered.vcf.gz | grep TSTV # Variant allele frequency distribution bcftools query -f '%AF\n' somatic_final.vcf.gz | \ awk '{print int($1*100)/100}' | sort -n | uniq -c ``` ## Related Skills - variant-calling/gatk-variant-calling - Germline variant calling - variant-calling/filtering-best-practices - Filtering strategies - variant-calling/variant-annotation - VEP/SnpEff annotation - variant-calling/structural-variant-calling - Somatic SV detection (Manta tumor-normal mode) - copy-number/cnvkit-analysis - Somatic CNV calling