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

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.

# 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