Individual skill

tooluniverse-structural-variant-analysis

Structural variant (SV) clinical interpretation: deletions, duplications, inversions, translocations, complex rearrangements. Applies ACMG-adapted criteria with ClinGen HI/TS dosage scores, gnomAD frequencies, and ClinVar evidence. Produces 5-tier classification with explicit per-criterion evidence. Use for clinical genomics SV review, dosage-sensitivity assessment, breakpoint analysis, and CNV pathogenicity calls. Gene-dosage-driven reasoning.

skills/tooluniverse-structural-variant-analysis/SKILL.md

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Repository stars (not skill ratings): 1,777
Repository last updated: Nov 7, 2026
License: Apache-2.0

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ANALYSIS_PROCEDURES.mddoc · 11,341 bytes
CLASSIFICATION_GUIDE.mddoc · 8,818 bytes
EXAMPLES.mddoc · 45,028 bytes
REPORT_TEMPLATE.mddoc · 4,912 bytes
SKILL.mdcore · 11,543 bytes

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## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

# Structural Variant Analysis Workflow

Systematic analysis of structural variants (deletions, duplications, inversions, translocations, complex rearrangements) for clinical genomics interpretation using ACMG-adapted criteria.

**LOOK UP DON'T GUESS** - Always retrieve ClinGen HI/TS scores, gnomAD frequencies, and ClinVar evidence from tools. Do not infer dosage sensitivity from gene function alone.

**KEY PRINCIPLES**:
1. **Report-first approach** - Create SV_analysis_report.md FIRST, then populate progressively
2. **ACMG-style classification** - Pathogenic/Likely Pathogenic/VUS/Likely Benign/Benign with explicit evidence
3. **Evidence grading** - Grade all findings by confidence level (High/Moderate/Limited)
4. **Dosage sensitivity critical** - Gene dosage effects drive SV pathogenicity