CNVkit Analysis Usage Guide

CNVkit Analysis Usage Guide

Overview

CNVkit is the standard tool for detecting copy number variants from targeted sequencing (exome, gene panels). It uses both on-target and off-target reads to infer copy number across the genome.

Prerequisites

# Requires Python >= 3.10, NumPy 2.x, Pandas 3.x compatible
conda install -c bioconda cnvkit
# or
pip install cnvkit
# For HMM segmentation: pomegranate >= 1.0 (installed automatically)

Dependencies: R with DNAcopy package (for CBS segmentation).

Quick Start

Tell your AI agent what you want to do:

• "Run CNVkit on my tumor-normal exome pair to call copy number variants"
• "Build a panel of normals from my control samples for CNV calling"
• "Export my CNVkit segments to VCF format for downstream analysis"
• "Check quality metrics for my CNVkit run and identify noisy samples"
• "Generate bedGraph coverage files for sharing without exposing raw sequences"

Input Requirements

1. BAM files: Aligned, sorted, indexed
2. Target BED: Capture regions (exome targets)
3. Reference FASTA: Same as used for alignment
4. Gene annotations (optional): refFlat.txt for gene names

Workflow Selection

Complete Tumor-Normal Pipeline

# Variables
TUMOR=tumor.bam
NORMAL=normal.bam
TARGETS=exome_targets.bed
FASTA=reference.fa
REFFLAT=refFlat.txt
OUTDIR=cnvkit_results

mkdir -p $OUTDIR

# Run batch command
cnvkit.py batch $TUMOR \
    --normal $NORMAL \
    --targets $TARGETS \
    --annotate $REFFLAT \
    --fasta $FASTA \
    --output-reference $OUTDIR/reference.cnn \
    --output-dir $OUTDIR

# Call with purity adjustment (if known)
cnvkit.py call $OUTDIR/tumor.cns \
    --purity 0.7 \
    -o $OUTDIR/tumor.call.cns

# Generate plots
cnvkit.py scatter $OUTDIR/tumor.cnr -s $OUTDIR/tumor.cns -o $OUTDIR/tumor_scatter.png
cnvkit.py diagram $OUTDIR/tumor.cnr -s $OUTDIR/tumor.cns -o $OUTDIR/tumor_diagram.pdf

Building a Panel of Normals

For best results, use 5-10+ matched normal samples:

# Step 1: Generate reference from normals
cnvkit.py batch \
    --normal normal1.bam normal2.bam normal3.bam normal4.bam normal5.bam \
    --targets targets.bed \
    --annotate refFlat.txt \
    --fasta reference.fa \
    --output-reference panel_of_normals.cnn

# Step 2: Process tumors with PON
for tumor in tumor*.bam; do
    sample=$(basename $tumor .bam)
    cnvkit.py batch $tumor \
        --reference panel_of_normals.cnn \
        --output-dir results/$sample
done

Privacy-Preserving Workflow (bedGraph)

CNVkit accepts pre-computed bedGraph files in place of BAMs, enabling coverage sharing without exposing raw sequences:

# Step 1: Generate bedGraph from BAM
bedtools genomecov -ibam tumor.bam -bg | bgzip > tumor.bed.gz
tabix -p bed tumor.bed.gz

# Step 2: Use bedGraph as input to CNVkit coverage
cnvkit.py coverage tumor.bed.gz targets.target.bed -o tumor.targetcoverage.cnn
cnvkit.py coverage tumor.bed.gz antitargets.bed -o tumor.antitargetcoverage.cnn

# Step 3: Continue with fix/segment/call as normal

Interpreting Results

CNR file (copy ratios)

• Each row is a bin (target or off-target region)
• log2 column: log2 ratio vs reference (0 = diploid)
• weight: confidence weight

CNS file (segments)

• Merged adjacent bins with similar log2
• cn: absolute copy number (after calling)
• probes: number of bins in segment

Thresholds for calling:

Quality Control

# Check metrics for all samples
cnvkit.py metrics *.cnr -s *.cns

# Good quality indicators:
# - Median absolute deviation (MAD) < 0.5
# - Biweight midvariance < 0.5
# - Few bins with extreme values

# Check inferred sex
cnvkit.py sex *.cnr *.cnn

Detailed Metrics

Both segmetrics and genemetrics share most statistics flags: --ci, --pi, --iqr, --alpha, --bootstrap, --mean, --median, --stdev, --sem, --mad.

Two key differences: (1) t-test flag naming -- segmetrics uses --t-test (hyphenated), genemetrics uses --ttest (no hyphen); (2) --smooth-bootstrap exists only in segmetrics (smoothed CI).

# Per-segment confidence intervals and prediction intervals
cnvkit.py segmetrics sample.cnr -s sample.cns --ci --pi --bootstrap 100 -o sample.segmetrics.cns

# Gene-level CNV report with confidence intervals
# 0.2: CNVkit default gain/loss log2 threshold (2^0.2 ~ 15% copy number change)
cnvkit.py genemetrics sample.cnr -s sample.cns --threshold 0.2 --ci --bootstrap 100 -o sample.genemetrics.tsv

Common Issues

Noisy data

# Increase smoothing during segmentation
cnvkit.py segment sample.cnr --smooth-cbs -o sample.cns

# Or use HMM which handles tumor heterogeneity better than CBS
cnvkit.py segment sample.cnr --method hmm-tumor -o sample.cns

Low coverage samples

# Lower minimum coverage thresholds
cnvkit.py batch tumor.bam --normal normal.bam \
    --targets targets.bed --fasta reference.fa \
    --target-avg-size 200

Wrong ploidy/purity

# Estimate from data
cnvkit.py call sample.cns --center median -o sample.call.cns

# Or specify if known
cnvkit.py call sample.cns --purity 0.7 --ploidy 2 -o sample.call.cns

Integration with Other Tools

Export for downstream analysis

# For GISTIC2 (identifying recurrent CNVs)
cnvkit.py export seg *.cns -o cohort.seg

# For integration with SVs
cnvkit.py export vcf sample.cns -o sample.cnv.vcf

Python analysis

import cnvlib
import pandas as pd

# Load and filter
cns = cnvlib.read('sample.cns')
amplified = cns[(cns['log2'] > 0.5) & (cns['cn'] >= 4)]
deleted = cns[(cns['log2'] < -0.5) & (cns['cn'] <= 1)]

# Genes in amplified regions
print(amplified[['chromosome', 'start', 'end', 'gene', 'log2', 'cn']])

Example Prompts

"Run CNVkit on my tumor-normal exome pair and call copy number variants"

"Build a panel of normals from my 10 control samples for CNV calling"

"Export my CNVkit segments to VCF format for integration with SNV calls"

"Check the quality metrics for my CNVkit run and identify noisy samples"

"Generate per-segment confidence intervals and identify genes with significant copy number changes"

What the Agent Will Do

1. Prepare target and antitarget regions from capture BED
2. Calculate coverage from BAM or bedGraph input
3. Build or apply a reference from normal samples
4. Segment and call copy number states
5. Generate plots and export results
6. Run quality metrics and gene-level reports

Tips

• Panel of normals - Use 5-10+ normal samples for best results
• HMM segmentation - hmm-tumor handles noisy tumor data better than CBS
• MAD threshold - Check MAD < 0.5 in metrics output as a quality indicator
• Purity/ploidy - Use --purity and --ploidy when values are known for more accurate calls
• bedGraph input - Enables sharing coverage data without exposing raw sequences

Related Skills

• alignment-files/bam-statistics - QC of input BAMs
• copy-number/cnv-visualization - Advanced CNV plotting
• copy-number/cnv-annotation - Gene-level annotation of CNV calls
• copy-number/gatk-cnv - Alternative CNV caller
• long-read-sequencing/structural-variants - Complementary SV calling