SkillHub

variant-pathogenicity-predictor

v0.1.0

Integrate REVEL, CADD, PolyPhen scores to predict variant pathogenicity

Sourced from ClawHub, Authored by EC-cyber258

Installation

Please help me install the skill `variant-pathogenicity-predictor` from SkillHub official store. npx skills add EC-cyber258/variant-pathogenicity-predictor

Variant Pathogenicity Predictor

Integrate REVEL, CADD, PolyPhen and other scores to predict variant pathogenicity.

Usage

python scripts/main.py --variant "chr17:43094692:G:A" --gene "BRCA1"
python scripts/main.py --vcf variants.vcf --output report.json

Parameters

  • --variant: Variant in format chr:pos:ref:alt
  • --vcf: VCF file with variants
  • --gene: Gene symbol
  • --scores: Prediction scores to use (REVEL,CADD,PolyPhen)

Integrated Scores

  • REVEL (Rare Exome Variant Ensemble Learner)
  • CADD (Combined Annotation Dependent Depletion)
  • PolyPhen-2 (Polymorphism Phenotyping)
  • SIFT (Sorting Intolerant From Tolerant)
  • MutationTaster

Output

  • Pathogenicity classification
  • ACMG guideline interpretation
  • Individual score breakdown
  • Confidence assessment

Risk Assessment

Risk Indicator Assessment Level
Code Execution Python/R scripts executed locally Medium
Network Access No external API calls Low
File System Access Read input files, write output files Medium
Instruction Tampering Standard prompt guidelines Low
Data Exposure Output files saved to workspace Low

Security Checklist

  • [ ] No hardcoded credentials or API keys
  • [ ] No unauthorized file system access (../)
  • [ ] Output does not expose sensitive information
  • [ ] Prompt injection protections in place
  • [ ] Input file paths validated (no ../ traversal)
  • [ ] Output directory restricted to workspace
  • [ ] Script execution in sandboxed environment
  • [ ] Error messages sanitized (no stack traces exposed)
  • [ ] Dependencies audited

Prerequisites

No additional Python packages required.

Evaluation Criteria

Success Metrics

  • [ ] Successfully executes main functionality
  • [ ] Output meets quality standards
  • [ ] Handles edge cases gracefully
  • [ ] Performance is acceptable

Test Cases

  1. Basic Functionality: Standard input → Expected output
  2. Edge Case: Invalid input → Graceful error handling
  3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

  • Current Stage: Draft
  • Next Review Date: 2026-03-06
  • Known Issues: None
  • Planned Improvements:
  • Performance optimization
  • Additional feature support