Detect DNA copy number gains and losses in exome or targeted sequencing data with CNVkit. Process tumor-normal pairs or tumor-only samples, adjust calls for tumor purity and ploidy, and produce plots and exported result files.
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Detect copy number variants from targeted/exome sequencing using CNVkit. Supports tumor-normal pairs, tumor-only, and germline CNV calling. Use when detecting CNVs from WES or targeted panel sequencing data.
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Reference examples tested with: GATK 4.5+, bedtools 2.31+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signatures<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Detect copy number variants from my exome data" → Run a read-depth-based pipeline that normalizes on/off-target coverage against a reference, segments the log2 ratio profile, and calls gains/losses.
cnvkit.py batch tumor.bam --normal normal.bamGoal: Run the complete CNVkit pipeline on a tumor-normal pair to detect copy number variants.
Approach: Execute the batch command which wraps target/antitarget generation, coverage calculation, reference building, and segmentation into one step.
# Complete pipeline for tumor-normal pair
cnvkit.py batch tumor.bam \
--normal normal.bam \
--targets targets.bed \
--fasta reference.fa \
--output-reference my_reference.cnn \
--output-dir results/
Goal: Create a robust reference from pooled normal samples, then run tumor samples against it.
Approach: Build a panel-of-normals reference first, then batch-process tumors using the pre-built reference.
# Step 1: Build reference from multiple normals (recommended)
cnvkit.py batch \
--normal normal1.bam normal2.bam normal3.bam \
--targets targets.bed \
--fasta reference.fa \
--output-reference pooled_reference.cnn
# Step 2: Run on tumor samples using pre-built reference
cnvkit.py batch tumor1.bam tumor2.bam \
--reference pooled_reference.cnn \
--output-dir results/
Goal: Call CNVs when no matched normal sample is available.
Approach: Generate a flat reference from target regions and the reference genome, assuming diploid baseline.
# When no matched normal is available
cnvkit.py batch tumor.bam \
--targets targets.bed \
--fasta reference.fa \
--output-reference flat_reference.cnn \
--output-dir results/
Goal: Detect CNVs from whole genome sequencing data without a targets file.
Approach: Run CNVkit batch with --method wgs to use genome-wide binning instead of target/antitarget regions.
# For whole genome sequencing (no targets file)
cnvkit.py batch tumor.bam \
--normal normal.bam \
--fasta reference.fa \
--method wgs \
--output-dir results/
Goal: Run CNVkit from bedGraph coverage files instead of BAM files for privacy-sensitive data sharing.
Approach: Pre-compute coverage from BAM, then feed compressed bedGraph to the coverage step.
# Generate bedGraph: bedtools genomecov -ibam sample.bam -bg | bgzip > sample.bed.gz && tabix -p bed sample.bed.gz
cnvkit.py coverage sample.bed.gz targets.target.bed -o sample.targetcoverage.cnn
Goal: Execute CNVkit as individual steps for fine-grained control over each stage.
Approach: Run target/antitarget generation, coverage, reference building, fix, segment, and call sequentially.
# 1. Generate target and antitarget regions
cnvkit.py target targets.bed --annotate refFlat.txt -o targets.target.bed
cnvkit.py antitarget targets.bed -o targets.antitarget.bed
# 2. Calculate coverage
cnvkit.py coverage tumor.bam targets.target.bed -o tumor.targetcoverage.cnn
cnvkit.py coverage tumor.bam targets.antitarget.bed -o tumor.antitargetcoverage.cnn
cnvkit.py coverage normal.bam targets.target.bed -o normal.targetcoverage.cnn
cnvkit.py coverage normal.bam targets.antitarget.bed -o normal.antitargetcoverage.cnn
# 3. Build reference
cnvkit.py reference normal.targetcoverage.cnn normal.antitargetcoverage.cnn \
--fasta reference.fa -o reference.cnn
# 4. Fix and call
cnvkit.py fix tumor.targetcoverage.cnn tumor.antitargetcoverage.cnn reference.cnn -o tumor.cnr
cnvkit.py segment tumor.cnr -o tumor.cns
cnvkit.py call tumor.cns -o tumor.call.cns
Goal: Choose the optimal segmentation algorithm for the sample type.
Approach: Select from CBS, HMM, or HMM variants tuned for tumor heterogeneity or germline tightness.
# Default CBS (Circular Binary Segmentation)
cnvkit.py segment sample.cnr -o sample.cns
# Use HMM for better performance
cnvkit.py segment sample.cnr --method hmm -o sample.cns
# HMM for tumor samples (broader state transitions for heterogeneity)
cnvkit.py segment sample.cnr --method hmm-tumor -o sample.cns
# HMM for germline (tighter priors around diploid)
cnvkit.py segment sample.cnr --method hmm-germline -o sample.cns
# Adjust smoothing
cnvkit.py segment sample.cnr --smooth-cbs -o sample.cns
Goal: Convert segmented log2 ratios into integer copy number states accounting for tumor composition.
Approach: Supply tumor purity and ploidy estimates (and optionally B-allele frequencies from a VCF) to the call step.
# Specify tumor purity and ploidy
cnvkit.py call sample.cns \
--purity 0.7 \
--ploidy 2 \
-o sample.call.cns
# With B-allele frequencies (from VCF)
cnvkit.py call sample.cns \
--vcf sample.vcf \
--purity 0.7 \
-o sample.call.cns
Goal: Convert CNVkit output to standard formats for downstream tools or databases.
Approach: Export called segments to BED, VCF, SEG (for GISTIC2), or Nexus format.
# Export to BED format
cnvkit.py export bed sample.call.cns -o sample.cnv.bed
# Export to VCF
cnvkit.py export vcf sample.call.cns -o sample.cnv.vcf
# Export segments for GISTIC2
cnvkit.py export seg *.cns -o samples.seg
# Markers file for GISTIC2 (pairs with 'export seg' above: segments + markers)
cnvkit.py export gistic *.cnr -o samples.markers
# Export for Nexus
cnvkit.py export nexus-basic sample.cnr -o sample.nexus.txt
Goal: Generate CNV profile plots for visual inspection and publication.
Approach: Use CNVkit built-in commands for scatter, diagram, and heatmap views.
# Scatter plot with segments
cnvkit.py scatter sample.cnr -s sample.cns -o sample_scatter.png
# Single chromosome
cnvkit.py scatter sample.cnr -s sample.cns -c chr17 -o sample_chr17.png
# Diagram (ideogram style)
cnvkit.py diagram sample.cnr -s sample.cns -o sample_diagram.pdf
# Heatmap across samples
cnvkit.py heatmap *.cns -o heatmap.pdf
| Extension | Description |
|---|---|
| .cnn | Reference or coverage file |
| .cnr | Copy ratios (log2) per bin |
| .cns | Segmented copy ratios |
| .call.cns | Called copy number states |
Goal: Programmatically load and filter CNVkit results for custom downstream analysis.
Approach: Use cnvlib to read .cnr/.cns files as DataFrames, filter by chromosome or log2 ratio, and export.
import cnvlib
# Load data
cnr = cnvlib.read('sample.cnr')
cns = cnvlib.read('sample.cns')
# Filter by chromosome
chr17 = cnr[cnr.chromosome == 'chr17']
# log2 > 0.5 (~3+ copies): moderate amplification filter
amps = cns[cns['log2'] > 0.5]
# log2 < -0.5 (~1 copy): moderate deletion filter
dels = cns[cns['log2'] < -0.5]
# Export
cnr.to_csv('sample.cnr.tsv', sep='\t', index=False)
Goal: Assess CNVkit run quality, check for sex mismatches, and compute per-segment confidence intervals.
Approach: Run metrics, sex, segmetrics, and genemetrics commands on output files.
# Check reference quality
cnvkit.py metrics *.cnr -s *.cns
# Check for gender mismatches
cnvkit.py sex *.cnr *.cnn
# Median absolute deviation (lower is better)
# Biweight midvariance (sample heterogeneity)
# Per-segment confidence intervals
cnvkit.py segmetrics sample.cnr -s sample.cns --ci --pi -o sample.segmetrics.cns
# Gene-level CNV detection with confidence intervals
# 10 bootstrap iterations (default 100); reduce for speed, increase for publication CIs
cnvkit.py genemetrics sample.cnr -s sample.cns --threshold 0.2 --ci --bootstrap 10 -o sample.genemetrics.tsv
| Parameter | Default | Description |
|---|---|---|
| --method | hybrid | hybrid, wgs, amplicon |
| --segment-method | cbs | cbs, hmm, hmm-tumor, hmm-germline, haar, flasso, none |
| --drop-low-coverage | off | Drop low-coverage bins |
| --purity | 1.0 | Tumor purity (0-1) |
| --ploidy | 2 | Sample ploidy |
| --center | none | Log2 centering for call: mean, median, mode, biweight |
| --thresholds | -1.1,-0.25,0.2,0.7 | CN state thresholds |
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