tools-only Super-Enhancers - Usage Guide
Overview
Identify super-enhancers from H3K27ac ChIP-seq data using ROSE or HOMER. Super-enhancers are large clusters of enhancers that control cell identity genes and are often altered in cancer.
Prerequisites
# ROSE (standard method)
git clone https://github.com/stjude/ROSE.git
# Requires: samtools, R, bedtools
# HOMER (alternative)
conda install -c bioconda homer
Quick Start
Tell your AI agent what you want to do:
- "Identify super-enhancers from my H3K27ac ChIP-seq data"
- "Run ROSE to find super-enhancers and generate a hockey stick plot"
- "Compare super-enhancers between two conditions"
Example Prompts
Basic Super-Enhancer Calling
"Run ROSE on my H3K27ac peaks to identify super-enhancers"
Using HOMER
"Use HOMER findPeaks with -style super to identify super-enhancers"
Gene Assignment
"Find which genes are associated with each super-enhancer"
Differential Analysis
"Compare super-enhancers between tumor and normal samples"
What the Agent Will Do
- Verify H3K27ac peak file and BAM file inputs
- Run ROSE or HOMER to stitch enhancers and rank by signal
- Generate hockey stick plot showing inflection point
- Output super-enhancer BED file and statistics table
- Optionally assign super-enhancers to nearest genes
ROSE Workflow
python ROSE_main.py \
-g hg38 \
-i H3K27ac_peaks.bed \
-r H3K27ac.bam \
-o output_directory \
-s 12500 \
-t 2500
Input Requirements
- Peak file (BED or GFF): H3K27ac peaks from MACS3
- BAM file: Aligned H3K27ac ChIP-seq reads
- Control BAM (optional): Input control
Output Files
*_AllEnhancers.table.txt: All stitched enhancers with signal*_SuperEnhancers.table.txt: Super-enhancers only*_Enhancers_withSuper.bed: BED file with SE annotations
HOMER Alternative
findPeaks H3K27ac_tagdir/ -style super \
-o auto \
-superSlope 1000 \
-L 0 \
-fdr 0.001
Tips
- Use narrow peaks (not broad) as input
- Increase stitching distance for tissue samples
- Exclude TSS regions to avoid promoter signals
- Validate with independent marks (Med1, BRD4)
- Hockey stick plot inflection point separates typical enhancers from super-enhancers
Downstream Analysis
Assign to Genes
bedtools closest \
-a SuperEnhancers.bed \
-b genes.bed \
> SE_genes.txt
Differential Super-Enhancers
import pandas as pd
se_a = pd.read_csv('conditionA_SuperEnhancers.txt', sep='\t')
se_b = pd.read_csv('conditionB_SuperEnhancers.txt', sep='\t')