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

bio-ctdna-mutation-detection

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

Detects somatic mutations in circulating tumor DNA using variant callers optimized for low allele fractions with UMI-based error suppression. Reliably detects mutations at VAF above 0.5 percent using consensus-based approaches. Use when identifying tumor mutations from plasma DNA or tracking specific variants.

OpenClawNanoClaw分析处理复现实验bio-ctdna-mutation-detection🧬 bioinformatics (gptomics bio-* suite)bioinformatics — clinical databases & variant analysisdetects

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-ctdna-mutation-detection

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

CLI：vardict-java ，用于 low-VAF variant calling ，面向 cfDNA。
检测 mutations in my cfDNA sample" → Identify somatic variants at low allele fractions (0.1-1%) ，面向 cell-free DNA ，使用 error-suppressed consensus calling 、 specialized callers. CLI：vardict-java ，用于 low-VAF variant calling ，面向 cfDNA。
检测 somatic mutations in cfDNA at low variant allele fractions。

原始文档

SKILL.md 摘录

Input Requirements

Requirement	Specification
Data type	Targeted panel or WES (NOT sWGS)
Depth	>= 1000x for low VAF detection
UMIs	Highly recommended for < 1% VAF
Input	Preprocessed BAM (UMI consensus if available)

VAF Detection Limits

VAF Range	Reliability	Notes
> 1%	Reliable	Standard callers work
0.5-1%	Good with UMIs	Requires error suppression
0.1-0.5%	Challenging	Needs deep UMI consensus
< 0.1%	Unreliable	Near noise floor

Tracking Known Mutations

Goal: Quantify the variant allele fraction of specific known mutations across serial liquid biopsy samples for minimal residual disease monitoring.

Approach: For each target mutation, pileup reads at the variant position, count reference and alternative alleles, and compute VAF with depth statistics.