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bio-copy-number-recurrent-cnv

Identify recurrent and driver copy number alterations across a tumor cohort with GISTIC2 (G-score, Ziggurat deconstruction, focal vs broad/arm-level analysis, q-values from permutation) and quantify copy-number signatures with the Steele 2022 COSMIC framework and the Drews 2022 CINSignatures framework. Covers driver-gene localization from recurrence peaks, distinguishing focal drivers from arm-level passengers, and the caller-sensitivity caveats of copy-number signatures. Use when finding recurrently amplified or deleted regions in a cohort, localizing driver genes, separating focal from broad

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeships scriptsNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill recurrent-cnv --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 3
SKILL.md size: 13 KB
Bundled scripts: yes
Path: skills/bioskills/recurrent-cnv/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

## Version Compatibility Reference examples tested with: GISTIC 2.0.23, R 4.3+ with CINSignatureQuantification 1.2+; Python 3.10+ with SigProfilerAssignment 0.1+ (optional, COSMIC CN signatures). Before using code patterns, verify installed versions match. If versions differ: - CLI: `gistic2 --help` (GISTIC 2.0 is a MATLAB-compiled binary; needs the MCR runtime) - R: `packageVersion('CINSignatureQuantification')` - Python: `pip show SigProfilerAssignment` GISTIC 2.0 has had no substantive release since ~2017; it is effectively frozen. It runs as a compiled binary against the MATLAB Compiler Runtime — there is no R or Python package. Verify the reference (`-refgene`) `.mat` file matches the genome build. # Recurrent and Driver Copy Number Alteration **"Which copy number changes recur across my cohort, and which gene is the driver"** -> A CNV in one tumor is an observation; a CNV recurring across many tumors beyond chance is evidence of selection. GISTIC2 separates recurrent driver events from passengers by modeling a background rate and scoring each locus by how often, and how strongly, it is altered. Copy-number signatures decompose the genome-wide pattern of alterations into the m

What's inside
Steps it walks through
  1. Version Compatibility
  2. How GISTIC2 Works — and Its Limits
  3. Decision Tree
  4. Running GISTIC2
  5. Copy-Number Signatures
  6. Failure Modes
  7. Comparing q-values across cohorts of different size
  8. Oversegmented input produces spurious peaks
  9. Mis-centered seg file inverts everything
  10. Treating a wide GISTIC peak as a single-gene call
  11. Copy-number signatures from relative copy number
  12. Reconciliation
  13. Quantitative Thresholds
  14. Common Errors
Ships with 2 files
  • examples/run_gistic2.sh
  • usage-guide.md
Commands it runs
Segment file: 6 columns -- sample, chrom, start, end, num_markers, seg.mean (log2).
It MUST be diploid-centered. Pool per-sample segments (e.g. cnvkit.py export seg).
gistic2 \
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About this skill
What does the bio-copy-number-recurrent-cnv skill do?

Identify recurrent and driver copy number alterations across a tumor cohort with GISTIC2 (G-score, Ziggurat deconstruction, focal vs broad/arm-level analysis, q-values from permutation) and quantify copy-number signatures with the Steele 2022 COSMIC framework and the Drews 2022 CINSignatures framework. Covers driver-gene localization from recurrence peaks, distinguishing focal drivers from arm-level passengers, and the caller-sensitivity caveats of copy-number signatures. Use when finding recurrently amplified or deleted regions in a cohort, localizing driver genes, separating focal from broad

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill recurrent-cnv --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.

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