Imported from openai/plugins (plugins/ngs-analysis/skills/ngs-dna-somatic-variants/SKILL.md). Install upstream with npx skills add openai/plugins --skill ngs-dna-somatic-variants. Copyright stays with the author.

Somatic DNA Variants

Use this skill for tumor-normal or tumor-only somatic SNV/indel calling from FASTQ, BAM, or CRAM. If the request is inherited germline calling or family analysis, use ngs-dna-germline-variants.

Essential Inputs

Confirm:

• tumor-normal, tumor-only, relapse-baseline, or multi-tumor design
• WGS, WES, or panel assay and target BED when applicable
• input type and whether reads are already aligned
• tumor/normal pairing table and sample identifiers
• reference build, known-sites, germline resource, and annotation cache
• panel-of-normals availability and matched-normal availability
• tumor purity, contamination expectations, and minimum allele fraction goals
• desired outputs: raw calls, filtered calls, VEP/SnpEff annotation, MAF, CNV/SV handoff

Route

Prefer nf-core/sarek for an end-to-end public workflow when its supported callers fit the request. Use direct GATK Mutect2 or bcftools/samtools utilities for focused validation or prepared BAMs.

Preflight command:

python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline dna_somatic_variants --emit-install-plan

For compact local checks from prepared tumor/normal BAM/CRAM files, use the dedicated Mutect2 runner:

python plugins/ngs-analysis/scripts/run_dna_somatic_variants.py \
  --sample-sheet somatic_pairs.tsv \
  --reference-fasta reference.fa \
  --germline-resource af-only-gnomad.vcf.gz \
  --panel-of-normals pon.vcf.gz \
  --execute

This produces a tumor-normal/tumor-only pairing table, Mutect2 command plan, contamination/filtering artifacts, somatic QC summary, qc/somatic_pair_review.{tsv,json}, visualization index, and filtered VCF outputs when the local GATK resources are available. For nf-core execution, use plugins/ngs-analysis/scripts/run_nfcore_pipeline.py --pipeline sarek.

The direct runner also emits resources/resource_plan.json, resource_manifest.tsv, resource_env.sh, and resource_readiness.md. The resource check is advisory by default so custom or reduced references can still be planned; add --genome-build, --bundle-root <bundle>=<path>, and --require-resource-plan when missing registered reference bundles should block readiness.

Decision Points

• Verify tumor-normal pair metadata before execution. A swapped or missing normal changes the biological meaning of the calls.
• For tumor-only analysis, explicitly state the false-positive risk and require a germline resource plus careful filtering.
• Use panel-of-normals when available and reference-matched; do not reuse a PON across incompatible capture kits or genome builds.
• Track contamination, orientation bias, strand artifacts, mapping quality, coverage, tumor purity, and allele-fraction filters.
• Keep germline filtering separate from somatic interpretation; avoid presenting tumor-only calls as confirmed somatic without supporting evidence.

Outputs

Produce:

• validated pairing/sample sheet
• caller/filter settings and reference/resource manifest
• QC summary: tumor/normal depth, contamination, duplication, insert size, on-target rate for panels/WES
• per-pair review table covering matched-normal state, PON/germline-resource availability, contamination-table status, filtered VCF status, and parsed variant counts
• VCF/MAF/annotation paths and a filtered-vs-raw call count summary
• caveats for tumor-only calls, low-purity tumors, low-depth regions, or missing matched normals

Clinical actionability and treatment recommendations are out of scope unless the user supplies a validated clinical interpretation workflow.