bio-copy-number-cnvkit-analysis

---
name: bio-copy-number-cnvkit-analysis
description: 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.
tool_type: cli
primary_tool: cnvkit
---

## Version Compatibility

Reference examples tested with: GATK 4.5+, bedtools 2.31+

Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.

# CNVkit CNV Analysis

**"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.
- CLI: `cnvkit.py batch tumor.bam --normal normal.bam`

## Basic Workflow

**Goal:** 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.

```bash
# 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/
```

## Build Reference from Normal Samples

**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.

```bash
# 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/
```

## Flat Reference (No Matched Normal)

**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.

```bash
# 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/
```

## WGS Mode

**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.

```bash
# For whole genome sequencing (no targets file)
cnvkit.py batch tumor.bam \
    --normal normal.bam \
    --fasta reference.fa \
    --method wgs \
    --output-dir results/
```

## bedGraph Input (Privacy-Preserving)

**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.

```bash
# 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
```

## Step-by-Step Pipeline

**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.

```bash
# 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
```

## Segmentation Options

**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.

```bash
# 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
```

## CNV Calling with Ploidy/Purity

**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.

```bash
# 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
```

## Export Results

**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.

```bash
# 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
```

## Visualization

**Goal:** Generate CNV profile plots for visual inspection and publication.

**Approach:** Use CNVkit built-in commands for scatter, diagram, and heatmap views.

```bash
# 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
```

## Key Output Files

| Extension | Description |
|-----------|-------------|
| .cnn | Reference or coverage file |
| .cnr | Copy ratios (log2) per bin |
| .cns | Segmented copy ratios |
| .call.cns | Called copy number states |

## Python API

**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.

```python
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)
```

## Quality Control

**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.

```bash
# 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
```

## Key Parameters

| 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 |

## Related Skills

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