bio-copy-number-cnvkit-analysis
Detect somatic and germline copy number variants from targeted, exome, and whole-genome sequencing with CNVkit, a read-depth caller that combines on-target and off-target (antitarget) coverage. Covers panel-of-normals construction, flat-reference tumor-only calling, hybrid/amplicon/WGS modes, CBS vs HMM segmentation selection, purity-aware integer calling, and reconciliation against GATK and allele-specific callers. Use when calling CNVs from hybrid-capture panels or exomes, deciding whether CNVkit (depth-only) is the right tool versus an allele-specific caller, building a panel of normals, di
npx skills add BioTender-max/awesome-bio-agent-skills --skill cnvkit-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## 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 batc
- Version Compatibility
- Where CNVkit Sits — Caller Taxonomy
- Decision Tree by Scenario
- Core Pipeline — Tumor-Normal Pair
- Panel of Normals — The Reference Determines Call Quality
- Step-by-Step Pipeline (Fine-Grained Control)
- Segmentation Method Selection
- Purity-Aware Integer Calling
- Failure Modes
- Flat reference (tumor-only) — systematic false focal calls
- Antitarget bins on amplicon panels — pure noise
- Low tumor purity — the death zone below ~40%
- Hyper-aneuploid / whole-genome-doubled genome — baseline miscalled
- FFPE / low-input dropout read as homozygous deletions
cnvkit.py batch tumor.bam \
Build pooled reference from process-matched normals (same capture kit, same lab)
cnvkit.py batch --normal normal*.bam \
Run each tumor against the pre-built reference
cnvkit.py batch tumor*.bam --reference pooled_reference.cnn \
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.cnnWhat does the bio-copy-number-cnvkit-analysis skill do?
Detect somatic and germline copy number variants from targeted, exome, and whole-genome sequencing with CNVkit, a read-depth caller that combines on-target and off-target (antitarget) coverage. Covers panel-of-normals construction, flat-reference tumor-only calling, hybrid/amplicon/WGS modes, CBS vs HMM segmentation selection, purity-aware integer calling, and reconciliation against GATK and allele-specific callers. Use when calling CNVs from hybrid-capture panels or exomes, deciding whether CNVkit (depth-only) is the right tool versus an allele-specific caller, building a panel of normals, di
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill cnvkit-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.