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

bio-copy-number-cnvkit-analysis

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

Detect copy number variants from targeted/exome sequencing using CNVkit. Supports tumor-normal pairs, tumor-only, and germline CNV calling. Use when detecting CNVs from WES or targeted panel sequencing data.

OpenClawNanoClaw分析处理复现实验bio-copy-number-cnvkit-analysis🧬 bioinformatics (gptomics bio-* suite)bioinformatics — clinical databases & variant analysisdetect

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-copy-number-cnvkit-analysis

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：cnvkit.py batch tumor.bam --normal normal.bam。
检测 copy number variants ，面向 my exome data" → Run read-depth-based pipeline that normalizes on/off-target coverage against reference，segments log2 ratio profile，、 calls gains/losses. CLI：cnvkit.py batch tumor.bam --normal normal.bam。
cnvkit.py batch tumor.bam \ --normal normal.bam \ --targets targets.bed \ --fasta reference.fa \ --output-reference my_reference.cnn \ --output-dir results/。

原始文档

SKILL.md 摘录

Basic Workflow

Goal: Run the complete CNVkit pipeline on a tumor-normal pair to detect copy number variants.

Approach: Execute the batch command which wraps target/antitarget generation, coverage calculation, reference building, and segmentation into one step.


## Build Reference from Normal Samples

**Goal:** Create a robust reference from pooled normal samples, then run tumor samples against it.

**Approach:** Build a panel-of-normals reference first, then batch-process tumors using the pre-built reference.

```bash

## Step 1: Build reference from multiple normals (recommended)

cnvkit.py batch \
    --normal normal1.bam normal2.bam normal3.bam \
    --targets targets.bed \
    --fasta reference.fa \
    --output-reference pooled_reference.cnn