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

bio-chipseq-super-enhancers

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

Identifies super-enhancers from H3K27ac ChIP-seq data using ROSE and related tools. Use when studying cell identity genes, cancer-associated regulatory elements, or master transcription factor binding regions that cluster into large enhancer domains.

OpenClawNanoClaw分析处理复现实验bio-chipseq-super-enhancers🧬 bioinformatics (gptomics bio-* suite)bioinformatics — epigenomics & chromatinidentifies

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-chipseq-super-enhancers

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：ROSE_main.py -g hg38 -i peaks.gff -r chip.bam -c input.bam。
Identify super-enhancers ，面向 H3K27ac ChIP-seq" → Stitch nearby enhancer peaks 、 rank by signal to find large regulatory domains controlling cell identity genes. CLI：ROSE_main.py -g hg38 -i peaks.gff -r chip.bam -c input.bam。
Identify super-enhancers (SEs) - large clusters of enhancers that control cell identity genes。
Large clusters of enhancer regions。
Marked by H3K27ac，Med1，BRD4。

原始文档

SKILL.md 摘录

Installation

git clone https://github.com/stjude/ROSE.git
cd ROSE

## Input Requirements

1. **BAM file** - H3K27ac ChIP-seq aligned reads
2. **Peak file** - Called peaks (BED or GFF)
3. **Genome annotation** - TSS annotations

## Run ROSE

**Goal:** Identify super-enhancers by stitching nearby enhancer peaks and ranking by H3K27ac signal.

**Approach:** Run ROSE_main.py with a GFF peak file, ChIP-seq BAM, and optional input control to stitch enhancers within 12.5 kb, rank by signal, and identify the inflection point separating super-enhancers from typical enhancers.

```bash

适用场景

适合在studying cell identity genes，cancer-associated regulatory elements，或 master transcription factor binding regions that cluster into large enhancer domains时使用。