cancer-genomics-analysis

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)
• Analyzing DNA damage response pathway disruption in tumors
• Calculating tumor mutational burden for immunotherapy biomarker assessment

Related Skills: For cancer mutation databases use cosmic-database. For variant clinical significance use clinvar-database. For gene annotations use ensembl-database. For pathway enrichment use kegg-database or reactome-database.

Installation

uv pip install pyvcf3 cyvcf2 pysam scikit-learn networkx gseapy pandas numpy matplotlib

For command-line tools (optional):

# GATK, SnpEff, CNVkit are installed separately
# conda install -c bioconda gatk4 snpeff cnvkit

Quick Start

import cyvcf2
import pandas as pd

# Parse somatic VCF
vcf = cyvcf2.VCF('somatic_mutations.vcf.gz')
variants = []
for v in vcf:
    if v.FILTER is None or v.FILTER == 'PASS':
        variants.append({
            'chrom': v.CHROM, 'pos': v.POS,
            'ref': v.REF, 'alt': ','.join(v.ALT),
            'qual': v.QUAL,
            'depth': v.INFO.get('DP'),
            'af': v.INFO.get('AF')
        })

df = pd.DataFrame(variants)
print(f"PASS variants: {len(df)}")
print(df.head())