Cnv Annotation

What the source does

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Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha c8c1fc3a2b13 · 19 Oct 2026Report an issue or request a re-scan

• PublisherGPTomics GitHub organizationOwner of the repository this skill is read from
• Source folderGPTomics/bioSkills/copy-number/cnv-annotationThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc8c1fc3a2b13A new audit runs when the source changes
• Repository statusArchivedLast push 65 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Annotate copy number variant segments with overlapping genes, dosage-sensitivity scores, cancer driver databases, population frequencies, and clinical-variant content. Covers bedtools/pybedtools interval intersection,…

---
name: bio-copy-number-cnv-annotation
description: Annotate copy number variant segments with overlapping genes, dosage-sensitivity scores, cancer driver databases, population frequencies, and clinical-variant content. Covers bedtools/pybedtools interval intersection, AnnotSV comprehensive annotation and ranking, ClinGen haploinsufficiency/triplosensitivity scoring, gnomAD-SV/DGV frequency filtering, COSMIC Cancer Gene Census, and ClinVar overlap. Use when interpreting which genes a CNV affects, distinguishing the driver gene of a focal event from passengers, filtering against population CNVs, separating whole-gene from partial-gene overlap, or preparing CNVs for clinical classification.
tool_type: mixed
primary_tool: bedtools
---

## Version Compatibility

Reference examples tested with: bedtools 2.31+, AnnotSV 3.4+, Python 3.10+ with pybedtools 0.9+, pandas 2.2+, pysam 0.22+; R 4.3+ with clusterProfiler 4.10+.

Before using code patterns, verify installed versions match. If versions differ:
- CLI: `bedtools --version`, `AnnotSV --version`
- Python: `pip show pybedtools pandas pysam`
- R: `packageVersion('clusterProfiler')`

If code throws an error, introspect the installed package and adapt the example. AnnotSV output column names change between major versions — verify against the installed version.

# CNV Annotation

**"Annotate my CNV calls with the genes they affect"** -> Overlap CNV segments with gene models, dosage-sensitivity maps, and clinical databases. The hard part is not the intersection — it is deciding *which* genes matter. A focal amplification overlapping 30 genes usually has one driver (the peak gene); a deletion's consequence depends on whether each gene is dosage-sensitive and whether the whole gene or only part is removed.

- CLI: `bedtools intersect -a cnvs.bed -b genes.bed -wa -wb`; `AnnotSV` for full annotation
- Python: `pybedtools` for interval logic; `pysam` for VCF database queries

## Annotation Strategy — Pick the Database for the Question

| Question | Resource | What it answers |
|----------|----------|-----------------|
| Which genes does this CNV span? | RefSeq/GENCODE gene BED | Raw overlap (not yet consequence) |
| Is loss of this gene damaging? | ClinGen haploinsufficiency (HI) score | Dosage sensitivity to deletion |
| Is gain of this gene damaging? | ClinGen triplosensitivity (TS) score | Dosage sensitivity to duplication |
| Is this CNV common in the population? | gnomAD-SV, DGV, 1000G CNV | Benign-frequency filtering |
| Is this a known recurrent disorder locus? | ClinGen dosage regions, DECIPHER | Genomic-disorder context |
| Is this a cancer driver? | COSMIC Cancer Gene Census, OncoKB | Oncogene vs tumor-suppressor role |
| Is there pathogenic small-variant content? | ClinVar | Coincident SNV/indel pathogenicity |
| One-shot comprehensive annotation + ranking | AnnotSV | Aggregates most of the above |

For constitutional CNV *classification* (assigning pathogenic/VUS/benign), the annotated output feeds the ACMG/ClinGen points framework — see germline-cnv-interpretation. For cohort-level recurrence and driver-peak identification, see recurrent-cnv.

## The Core Distinction: Overlap Is Not Consequence

A CNV overlapping a gene does not necessarily change that gene's dosage in a way that matters. Three refinements separate annotation from interpretation:

1. **Whole-gene vs partial overlap.** A deletion spanning an entire gene removes one copy (clean haploinsufficiency test). A deletion removing only the last two exons creates a truncated allele — a different, often more damaging, consequence. Always record the fraction of each gene covered and whether coding exons or only introns/UTRs are hit.
2. **Dosage sensitivity.** Most genes tolerate single-copy loss. ClinGen HI/TS scores (3 = sufficient evidence for dosage sensitivity, 0 = no evidence, 30 = gene associated with an autosomal-recessive phenotype, 40 = dosage sensitivity unlikely) indicate which genes' loss/gain is actually consequential.
3. **Driver vs passenger in focal events.** A focal amplification carries many genes; the driver is the one under selection, typically at the recurrence peak across a cohort (GISTIC) and a known oncogene. Annotating all 30 genes as "amplified" overstates.

## Gene Overlap with bedtools

**Goal:** Find genes overlapping each CNV segment, recording overlap extent.

**Approach:** Convert segments to BED, intersect with a gene model, keep both feature sets (`-wo` reports the overlap length) so partial vs whole-gene overlap is recoverable.

```bash
# Segments to BED (CNVkit .cns example; columns chrom/start/end/log2)
awk 'NR>1 {print $1"\t"$2"\t"$3"\t"$5}' sample.cns > sample.cnv.bed

# Intersect; -wo appends the number of overlapping bases
bedtools intersect -a sample.cnv.bed -b gencode.genes.bed -wo > cnv_gene_overlap.txt
```

## Comprehensive Annotation with AnnotSV

**Goal:** Annotate CNVs against genes, dosage maps, population frequency, and clinical databases in one pass, with a built-in pathogenicity ranking.

**Approach:** Export CNVs to VCF or BED and run AnnotSV; it returns a "full" line per gene plus a "split" summary, with an ACMG-aligned rank (1-5).

```bash
AnnotSV \
    -SVinputFile sample.cnv.vcf \
    -genomeBuild GRCh38 \
    -annotationMode both \
    -outputFile sample_annotated.tsv

# Output includes: overlapped genes, ClinGen HI/TS, gnomAD-SV/DGV frequency, OMIM,
# ClinVar, DECIPHER, and an ACMG-class rank per SV.
```

AnnotSV's rank is a useful triage signal, not a final classification — confirm against the ClinGen points framework for clinical reporting.

## Dosage-Sensitivity and Driver Annotation

**Goal:** Tag each affected gene with its dosage sensitivity and, for tumors, its driver role, so passengers can be separated from drivers.

**Approach:** Join the gene-overlap table to the ClinGen dosage map (HI/TS scores) and to the COSMIC Cancer Gene Census; flag CNVs whose direction matches a known mechanism (oncogene amplified, tumor sup

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

pip install pybedtools pandas pysam

git clone https://github.com/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source c8c1fc3a2b13

Questions about Cnv Annotation

• github.com/GPTomics/bioSkills/tree/main/copy-number/cnv-annotati — the audited source at commit c8c1fc3a2b13 and every finding aboveObserved · 19 Oct 2026

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