/cnvkit-copy-number

name: "cnvkit-copy-number"
description: "Detect somatic CNVs from WES/WGS/targeted BAMs (CNVkit v0.9.x). Bin coverage in target/antitarget regions, normalize vs reference, segment with CBS/HMM, call amps/dels, scatter/diagram plots, purity/ploidy, VCF/SEG export. CLI plus Python API (cnvlib). Use GATK CNV for deep WGS with population controls; use CNVkit for targeted/exome where antitarget bins matter."
license: "Apache-2.0"

CNVkit Copy Number Analysis

Overview

CNVkit detects somatic copy number variants (CNVs) from whole-exome sequencing (WES), whole-genome sequencing (WGS), or targeted panel BAM files. It calculates read depth in both on-target (capture) bins and off-target (antitarget) bins, corrects for GC bias and library depth, segments the log2 copy ratio profile with circular binary segmentation (CBS) or a hidden Markov model (HMM), and calls amplifications and deletions. CNVkit provides both a CLI (`cnvkit.py`) and a Python API (`cnvlib`) for integration into analysis pipelines, and produces scatter plots, chromosome diagrams, heatmaps, and export files in VCF, BED, and SEG formats.

When to Use

• Calling somatic copy number variants from tumor-normal paired exome (WES) or targeted panel sequencing
• Detecting copy number alterations in tumor-only samples using a pooled normal reference
• Running CNV analysis on whole-genome sequencing (WGS) data with the `--method wgs` mode
• Estimating tumor purity and ploidy for samples where purity is unknown, to interpret copy ratio calls
• Generating SEG format copy number files for GISTIC2, cBioPortal, or IGV visualization
• Identifying focal amplifications (e.g., ERBB2, MYC) or homozygous deletions (e.g., CDKN2A, RB1)
• Use **omics-plotting** SKILL for generic coverage/log2-ratio figures from exported tables; genome-wide CNV views use `cnvkit.py scatter/diagram`
• Use **GATK CNV** (`gatk DenoiseReadCounts` / `gatk ModelSegments`) instead for deep WGS cohorts with large matched panel-of-normals (PoN); CNVkit is better suited for targeted/exome data
• Use **Control-FREEC** instead when you need allele-frequency-based B-allele fraction modeling alongside CNV calling

Prerequisites

• **Software**: CNVkit v0.9.x, Python 3.8+, R (for CBS segmentation), samtools
• **Python packages**: `cnvlib` (installed as part of CNVkit), `matplotlib`, `pandas`
• **Input files**: sorted, indexed BAM files (tumor ± matched normal); BED file of capture targets; reference genome FASTA; access to R with DNAcopy package for CBS
• **Data requirements**: minimum ~50× mean target coverage for WES; WGS works at 20-30×

> **Check before installing**: The tool may already be available in the current environment (e.g., inside a `pixi` / `conda` env). Run `command -v cnvkit.py` first and skip the install commands below if it returns a path. When running inside a pixi project, invoke the tool via `pixi run cnvkit.py` rather than bare `cnvkit.py`.

# Install CNVkit via conda (recommended — handles R/DNAcopy dependency)
conda install -c bioconda cnvkit

# Or via pip (requires R + DNAcopy already installed)
pip install cnvkit

# Verify
cnvkit.py version
# cnvkit 0.9.10

# Install R DNAcopy (for CBS segmentation)
Rscript -e 'if (!requireNamespace("BiocManager")) install.packages("BiocManager"); BiocManager::install("DNAcopy")'

# Index BAM files if not already indexed
samtools index tumor.bam
samtools index normal.bam

Quick Start

# One-command paired tumor/normal CNV analysis (WES)
cnvkit.py batch tumor.bam \
    --normal normal.bam \
    --targets targets.bed \
    --fasta GRCh38.fa \
    --output-dir cnvkit_results/ \
    --diagram --scatter \
    --method hybrid

# Output files in cnvkit_results/:
#   tumor.targetcoverage.cnn   — target bin coverage
#   tumor.antitargetcoverage.cnn — antitarget coverage
#   tumor.cnr                  — copy number ratios
#   tumor.cns                  — segmented copy numbers
#   tumor-scatter.png          — genome-wide scatter plot
#   tumor-diagram.pdf          — chromosome diagram
echo "CNV analysis complete"

Workflow

Step 1: Create Copy Number Reference

Build a reference from one or more normal BAM files. This corrects for systematic biases (GC content, mappability) and sets the neutral baseline.

# Option A: Paired normal reference (single matched normal)
cnvkit.py reference normal.targetcoverage.cnn normal.antitargetcoverage.cnn \
    --fasta GRCh38.fa \
    -o reference_normal.cnn

# Option B: Flat reference (no normal; uses GC/mappability correction only)
# Use when no matched normal is available
cnvkit.py reference \
    --targets targets.bed \
    --fasta GRCh38.fa \
    --output flat_reference.cnn

# Option C: Pooled normal reference from multiple normals (most robust)
cnvkit.py batch \
    normal1.bam normal2.bam normal3.bam \
    --normal \
    --targets targets.bed \
    --fasta GRCh38.fa \
    --output-reference pooled_reference.cnn \
    --output-dir normals_cov/

echo "Reference created: pooled_reference.cnn"

Step 2: Calculate Coverage in Target and Antitarget Bins

Bin the target BED file and compute per-bin read depth for tumor and normal samples.

# First, create accessible bins from the target BED
cnvkit.py target targets.bed \
    --annotate refFlat.txt \
    --split \
    -o targets.split.bed

cnvkit.py antitarget targets.bed \
    --access data/access-5k-mappable.hg38.bed \
    -o antitargets.bed

# Calculate coverage for tumor sample
cnvkit.py coverage tumor.bam targets.split.bed \
    -o tumor.targetcoverage.cnn

cnvkit.py coverage tumor.bam antitargets.bed \
    -o tumor.antitargetcoverage.cnn

echo "Coverage files:"
echo "  tumor.targetcoverage.cnn"
echo "  tumor.antitargetcoverage.cnn"

# Python API equivalent: compute coverage with cnvlib
import cnvlib

# Load and inspect coverage files
target_cov = cnvlib.read("tumor.targetcoverage.cnn")
antitarget_cov = cnvlib.read("tumor.antitargetcoverage.cnn")

print(f"Tar