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cancer-genomics-analysis

Computational cancer genomics workflows. Somatic mutation detection and annotation, structural variation characterization, copy number analysis, tumor purity/ploidy estimation, NMF metagene extraction, and DNA damage response network analysis. For cancer mutation databases use cosmic-database; for variant clinical significance use clinvar-database.

backend/cli/skills/biology/cancer-genomics-analysis/SKILL.md

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# Cancer Genomics Analysis: Computational Workflows

## Overview

Cancer Genomics Analysis provides computational pipelines for processing and interpreting cancer genomics data. This skill covers somatic mutation detection and annotation (GATK Mutect2 integration), structural variation characterization, copy number analysis (CNVkit workflows), tumor purity and ploidy estimation, NMF-based metagene extraction from expression data, DNA damage response network analysis, and tumor mutational burden calculation. All workflows produce quantitative outputs suitable for clinical interpretation and publication.

## When to Use This Skill

- Processing somatic variant calls from tumor-normal paired sequencing
- Annotating VCF files with gene names, functional impact, and clinical significance
- Detecting and classifying structural variants (deletions, duplications, inversions, translocations)
- Running copy number analysis pipelines (coverage, segmentation, calling)
- Estimating tumor purity and ploidy from sequencing data
- Extracting gene expression signatures via NMF (metagene programs)