数据与复现临床医学与医药FreedomIntelligence/OpenClaw-Medical-Skills数据与复现
BI

bio-clinical-databases-variant-prioritization

维护者 FreedomIntelligence · 最近更新 2026年4月1日

Filter and prioritize variants by pathogenicity, population frequency, and clinical evidence for rare disease analysis. Use when identifying candidate disease-causing variants from exome or genome sequencing.

OpenClawNanoClaw分析处理复现实验bio-clinical-databases-variant-prioritization🧬 bioinformatics (gptomics bio-* suite)bioinformatics — clinical databases & variant analysisfilter

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-clinical-databases-variant-prioritization

维护者
FreedomIntelligence
许可
MIT
最近更新
2026年4月1日

技能摘要

来自 SKILL.md 的关键信息

2 min

核心说明

  • Python:pandas ,用于 multi-criteria filtering ,支持 ACMG/AMP 分类 logic。
  • Prioritize candidate disease variants ,面向 my exome data" → Filter 、 rank variants by pathogenicity scores,population frequency,inheritance pattern,、 clinical evidence to identify candidate disease-causing mutations. Python:pandas ,用于 multi-criteria filtering ,支持 ACMG/AMP 分类 logic。
  • Goal:Filter variants to retain rare,potentially pathogenic candidates ,用于 rare disease analysis。
  • Approach:Apply gnomAD population frequency 、 ClinVar significance filters,retaining pathogenic,VUS,、 unannotated variants。

原始文档

SKILL.md 摘录

ACMG-Style Filtering

Goal: Score variants using ACMG-style evidence criteria for pathogenicity assessment.

Approach: Evaluate PM2 (population rarity) and PVS1 (loss-of-function) evidence, then compute a weighted priority score.

Multi-Database Prioritization

Goal: Prioritize variants using aggregated evidence from ClinVar, gnomAD, CADD, and REVEL in a single query.

Approach: Fetch annotations via myvariant.info, then compute a composite priority score weighting clinical, population, and computational evidence.

Inheritance-Based Filtering

Goal: Filter variants by expected inheritance pattern (autosomal dominant, recessive, or X-linked).

Approach: Select heterozygous ultra-rare variants for AD, or homozygous plus compound heterozygous candidates for AR.

适用场景

  • 适合在identifying candidate disease-causing variants ,面向 exome 或 genome sequencing时使用。

不适用场景

  • Do not rely on this catalog entry alone ,用于 installation 或 maintenance details。

上游相关技能

  • clinvar-lookup - ClinVar pathogenicity queries
  • gnomad-frequencies - Population frequency filtering
  • variant-calling/clinical-interpretation - ACMG classification
  • variant-calling/filtering-best-practices - Quality filtering

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