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Variant Pathogenicity Predictor

通过 Python 脚本接收 chr:pos:ref:alt 格式变异或 VCF 文件，结合 REVEL、CADD、PolyPhen-2、SIFT、MutationTaster 等评分，输出致病性分类、ACMG 指南解读、单项评分明细与置信度评估，可写出 JSON 报告。自述无需额外 Python 包、无外部 API 调用；材料未包含评分数据库、示例数据和测试用例。

ec-cyber258
ec-cyber258
热度 81.96
最近更新 2026-05-11 17:31:20
分类：数据依赖：代码执行 / 读写文件应用场景：研究 / 数据处理 / 报告撰写

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add variant-pathogenicity-predictor
Metadata
name
variant-pathogenicity-predictor
description
Integrate REVEL, CADD, PolyPhen scores to predict variant pathogenicity
version
1.0.0
category
Bioinfo
tags
[]
author
AIPOCH
license
MIT
status
Draft
risk_level
Medium
skill_type
Tool/Script
owner
AIPOCH
reviewer
last_updated
2026-02-06

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