cnvkit-copy-number
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 wgsmode - 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 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
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