# Bio Chipseq Super Enhancers

Identifies super-enhancers from H3K27ac ChIP-seq data using ROSE and related tools.

- Repository: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills (3090 stars)
- Category: Engineering
- License: see repository
- Last updated: 2026-07-21
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-super-enhancers -g -a claude-code -y`
Codex: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-super-enhancers -g -a codex -y`
Cursor: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-super-enhancers -g -a cursor -y`
Gemini CLI: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-super-enhancers -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## Version Compatibility

Reference examples tested with: GenomicRanges 1.54+, bedtools 2.31+, ggplot2 3.5+, samtools 1.19+

Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.

# Super-Enhancer Calling

**"Identify super-enhancers from H3K27ac ChIP-seq"** → Stitch nearby enhancer peaks and 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.

## Background

Super-enhancers are:
- Large clusters of enhancer regions
- Marked by H3K27ac, Med1, BRD4
- Control cell identity genes
- Often altered in disease/cancer

## ROSE (Rank Ordering of Super-Enhancers)

### Installation

```bash
git clone https://github.com/stjude/ROSE.git
cd ROSE
# Requires samtools, R, bedtools
```

### 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
# Basic usage
python ROSE_main.py \
    -g HG38 \
    -i peaks.gff \
    -r h3k27ac.bam \
    -o output_dir \
    -s 12500 \
    -t 2500

# With control/input
python ROSE_main.py \
    -g HG38 \
    -i peaks.gff \
    -r h3k27ac.bam \
    -c input.bam \
    -o output_dir
```

The rest: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/blob/HEAD/skills/bio-chipseq-super-enhancers/SKILL.md