CNVkit Copy Number Analysis

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")'