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ngs-dna-somatic-variants

Plan or run DNA analysis to find small genetic changes in tumors, using tumor samples alone or paired with normal tissue. Check sample pairing, contamination, data quality, filtering, and annotations that add information to detected variants.

OriginalShow the original description

Run or plan tumor-normal, tumor-only, WGS, WES, or cancer-panel somatic variant workflows with pairing, contamination, panel-of-normals, purity, QC, and annotation checks.

See how to install

Good for

  • Planning analysis of paired tumor and normal samples
  • Reviewing filtering and limitations for tumor-only analysis
  • Checking quality in whole-genome, exome, or cancer-panel data
  • Organizing detected variants, annotations, and review tables

Files included (2)

  • SKILL.md3.7 KB
  • agents/openai.yaml302 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

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.

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