Bio Copy Number Cnvkit Analysis

Version Compatibility

Reference examples tested with: CNVkit 0.9.10+, samtools 1.19+, bedtools 2.31+, Python 3.10+, R 4.3+ with DNAcopy 1.76+.

Before using code patterns, verify installed versions match. If versions differ:

• CLI: cnvkit.py version then cnvkit.py batch --help to confirm flags
• Python: pip show cnvkit then python -c "import cnvlib; help(cnvlib.read)"
• R: packageVersion('DNAcopy') (CBS backend)

If a command throws an unrecognized-argument or AttributeError, introspect the installed version and adapt the example rather than retrying. CNVkit segmentation methods (hmm, hmm-tumor, hmm-germline) depend on pomegranate; CBS depends on Bioconductor DNAcopy.

CNVkit Copy Number Analysis

"Detect copy number variants from my exome / panel data" -> Run a read-depth pipeline: normalize on-target and off-target coverage against a reference, segment the log2-ratio profile, and call gains/losses. CNVkit is a depth-only caller — it estimates relative copy number and cannot, on its own, resolve tumor purity, ploidy, or allele-specific state. Choosing CNVkit is a decision that the experiment does not require allelic resolution.

• CLI: cnvkit.py batch tumor.bam --normal normal.bam --targets panel.bed --fasta ref.fa
• Python API: cnvlib.read('sample.cnr') for downstream filtering

Where CNVkit Sits — Caller Taxonomy

CNVkit's niche: a fast, single-sample depth caller for hybrid-capture panels and exomes where antitarget reads recover genome-wide resolution. Its limit: it answers "is this region gained or lost relative to baseline" — not "how many absolute copies, on which haplotype, in what fraction of cells." For tumor integer CN, purity, LOH, or whole-genome doubling, escalate to allele-specific-copy-number.

Decision Tree by Scenario

Core Pipeline — Tumor-Normal Pair

The batch command wraps target/antitarget generation, coverage, reference building, fix, and segment:

cnvkit.py batch tumor.bam \
    --normal normal.bam \
    --targets panel.bed \
    --annotate refFlat.txt \
    --fasta reference.fa \
    --access access-excludes.bed \
    --output-reference reference.cnn \
    --output-dir results/ \
    --drop-low-coverage \
    --diagram --scatter

--access restricts antitarget bins to mappable, non-gap genome (generate once with cnvkit.py access reference.fa -o access.bed). --drop-low-coverage is effectively mandatory for tumor, FFPE, or any sample with coverage dropout.

Panel of Normals — The Reference Determines Call Quality

A reference built from pooled normals is the single largest quality lever. Process is: build the reference from normals once, then run every tumor against it.

# Build pooled reference from process-matched normals (same capture kit, same lab)
cnvkit.py batch --normal normal*.bam \
    --targets panel.bed --annotate refFlat.txt --fasta reference.fa \
    --access access.bed \
    --output-reference pooled_reference.cnn

# Run each tumor against the pre-built reference
cnvkit.py batch tumor*.bam --reference pooled_reference.cnn \
    --output-dir results/ --drop-low-coverage --scatter --diagram

Step-by-Step Pipeline (Fine-Grained Control)

cnvkit.py target panel.bed --annotate refFlat.txt --split -o targets.bed
cnvkit.py antitarget panel.bed --access access.bed -o antitargets.bed
cnvkit.py coverage tumor.bam targets.bed -o tumor.targetcoverage.cnn
cnvkit.py coverage tumor.bam antitargets.bed -o tumor.antitargetcoverage.cnn
cnvkit.py reference normal*.{target,antitarget}coverage.cnn --fasta reference.fa -o reference.cnn
cnvkit.py fix tumor.targetcoverage.cnn tumor.antitargetcoverage.cnn reference.cnn -o tumor.cnr
cnvkit.py segment tumor.cnr -o tumor.cns --drop-low-coverage
cnvkit.py call tumor.cns -o tumor.call.cns

Segmentation Method Selection

CNVkit's segment step is where the bias-variance trade-off is set. The default CBS is not always correct — see copy-ratio-segmentation for the full algorithm comparison.

cnvkit.py segment tumor.cnr -m cbs -o tumor.cns          # default; precise on focal events
cnvkit.py segment tumor.cnr -m hmm-tumor -o tumor.cns    # heterogeneous tumor, broad states
cnvkit.py segment tumor.cnr -m hmm-germline -o tumor.cns # germline, priors near diploid
cnvkit.py segment tumor.cnr -m haar -o tumor.cns         # fast, low-depth WGS

Rule of thumb: CBS for panels/exomes with adequate depth (precise on small segments); hmm-tumor for impure or heterogeneous tumors where CBS over-fragments; haar for shallow WGS where CBS recall degrades.

Purity-Aware Integer Calling

call converts segmented log2 ratios to copy-number states. The clonal method rescales by tumor purity before rounding to integers — without it, an impure tumor's true CN=4 amplification rounds to CN=3 or CN=2.

# Threshold method (default): fixed log2 cutpoints, no purity correction
cnvkit.py call tumor.cns -o tumor.call.cns

# Clonal method: rescale by purity, then round to integer CN
cnvkit.py call tumor.cns -m clonal --purity 0.65 --ploidy 2 -o tumor.call.cns

# Overlay B-allele frequency from a SNV VCF (for LOH visualization, NOT joint ASCN)
cnvkit.py call tumor.cns -m clonal --purity 0.65 --vcf tumor.vcf.gz -o tumor.call.cns

CNVkit can read BAF from a VCF and report a baf column, but it segments log2 and BAF separately and does not jointly fit purity from them. For a true joint allele-specific model (ASPCF, FACETS joint segmentation), use allele-specific-copy-number.

Failure Modes

Flat reference (tumor-only) — systematic false focal calls

Trigger: No --normal and no pooled PoN; CNVkit builds a flat reference (uniform log2 0) from the FASTA.

Mechanism: A flat reference corrects only GC and (optionally) RepeatMasker content via the FASTA. It cannot correct capture efficiency, which varies 10-100x across probes and is the dominant bias in hybrid capture. Per-probe capture bias is then misread as copy number.

Symptom: Recurrent "CNVs" at the same loci across unrelated tumor-only samples; spiky .cnr profiles; high MAD; calls concentrated at probe boundaries.

Fix: Never rely on a flat reference for clinical or focal calls. Build a pooled PoN from >= 5 process-matched normals. If truly no normal exists, treat tumor-only CNVkit output as hypothesis-generating only and escalate to PureCN (allele-specific-copy-number), which models a normal database explicitly.

Antitarget bins on amplicon panels — pure noise

Trigger: Running default hybrid mode on an amplicon (multiplex-PCR) panel.

Mechanism: Amplicon panels produce essentially no off-target reads. Antitarget bins then contain a handful of stray reads, giving wildly variable log2 that the segmenter chases.

Symptom: Huge antitarget bin spread; nonsensical genome-wide segments between the targeted genes.

Fix: Use --method amplicon, which drops antitargets entirely and calls only from on-target bins. Accept that resolution is limited to the targeted genes.

Low tumor purity — the death zone below ~40%

Trigger: Tumor cellularity below ~40% (common in breast, lung adenocarcinoma, melanoma, low-cellularity biopsies).

Mechanism: Each somatic CN change is diluted by 2-copy normal DNA. A true single-copy loss at 30% purity produces log2 ~ -0.23 — inside the diploid threshold band.

Symptom: Genome looks near-flat; few or no calls; known driver amplifications (e.g. ERBB2, MYC) missed.

Fix: CNVkit cannot rescue this. Confirm purity with an allele-specific caller (BAF gives an orthogonal purity estimate). Below ~20% purity, no depth-based caller is reliable — report as indeterminate.