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ngs-dna-somatic-variants

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.

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openai/plugins
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3 de junho de 2026 às 15:08
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SKILL.md
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ngs-dna-somatic-variants
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.
# 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: ```bash 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: ```bash 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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