Bio Chipseq Super Enhancers

Version Compatibility

Reference examples tested with: ROSE (stjude/ROSE, 2018+), ROSE2 (linlabbcm/rose2, 2021+), LILY (BoevaLab/LILY, 2020+), HOMER 4.11+, samtools 1.19+, bedtools 2.31+, GenomicRanges 1.54+.

The original Young-lab ROSE is Python 2; ROSE2 (linlabbcm/rose2) and the stjude/ROSE fork are the Python-3 implementations with the same algorithm. For hg38 data use stjude/ROSE (python ROSE_main.py, whose genomeDict includes HG38); rose2's released genomeDict covers only HG18/HG19/MM8/MM9/MM10/RN4/RN6, so rose2 -g HG38 fails. LILY (Boeva 2017) is a refactored implementation with input-control background subtraction for low-quality H3K27ac data.

Super-Enhancer Calling

"Identify super-enhancers driving cell identity / cancer biology" -> Stitch nearby active enhancer peaks (H3K27ac, MED1, or BRD4) within a stitching window, exclude proximal-promoter signal, rank by total signal, find the hockey-stick inflection point where signal sharply increases, and classify all stitched regions above the inflection as super-enhancers.

• CLI (stjude/ROSE, Python 3): python ROSE_main.py -g HG38 -i peaks.gff -r h3k27ac.bam -c input.bam -s 12500 -t 2500 -o rose_out/
• CLI (HOMER): findPeaks tag_dir/ -style super -i input_tag_dir/
• CLI (LILY): variant with input-control background subtraction
• R (custom hockey-stick): rank enhancers by signal, find tangent-line inflection

The SE concept (Whyte 2013) is a thresholding heuristic on a continuous signal distribution (Pott & Lieb 2015 Nat Genet), not a categorical biological category. Genetic dissection of super-enhancers (Hay 2016; Moorthy 2017) shows constituent elements contribute unequally and many are individually dispensable/redundant; the "SE" label is a useful operational definition for BET-inhibitor responsiveness and cell-identity gene regulation, not an absolute biological property.

Marker Choice: H3K27ac vs MED1 vs BRD4

Operational rule: H3K27ac for discovery; MED1 or BRD4 ChIP for functional / therapeutic claims. SE called on H3K27ac alone may not respond to BET inhibitors; SE called on BRD4 will.

Algorithmic Taxonomy

Most papers use ROSE/ROSE2 with default stitching (12.5 kb) and TSS exclusion (2.5 kb). This is the de facto standard for cross-paper comparison. HOMER's -style super produces different counts and is not directly comparable.

Decision Tree: SE Calling Workflow

ROSE / ROSE2 Workflow

Goal: Identify super-enhancers by stitching nearby active enhancer peaks within a stitching distance and ranking by total signal.

Approach: Convert peaks to GFF, exclude promoter-proximal peaks via -t (TSS exclusion window), stitch enhancers within -s (default 12.5 kb), rank by total H3K27ac (or MED1/BRD4) signal, find the hockey-stick inflection point, classify regions above as super-enhancers.

# Install ROSE2 (Python 3 port; unmaintained ROSE Py2 not recommended)
git clone https://github.com/linlabbcm/rose2.git
pip install ./rose2

# Convert peaks BED to GFF (ROSE requires GFF input)
awk 'BEGIN{OFS="\t"} {print $1,"peaks","enhancer",$2,$3,".",$6,".","ID="NR}' \
    peaks.narrowPeak > peaks.gff

# Filter promoter peaks before SE calling (within 2.5 kb of TSS)
# ROSE handles this via -t flag; preferable to pre-filter for clarity
bedtools intersect -a peaks.narrowPeak -b promoters_2kb.bed -v > enhancer_peaks.bed

# Run stjude/ROSE with input control (Python-3 fork; genomeDict includes HG38)
python ROSE_main.py -g HG38 -i peaks.gff \
    -r h3k27ac.bam -c input.bam \
    -o rose_output/ \
    -s 12500 \
    -t 2500

ROSE outputs:

• *_AllEnhancers.table.txt — all stitched enhancer regions ranked by signal
• *_SuperEnhancers.table.txt — SE only (above hockey-stick inflection)
• *_Enhancers_withSuper.bed — BED with SE / TE classification
• *_Plot_points.png — hockey-stick plot

Cross-Condition SE Comparison

This is the analysis most often done wrong. SE calling thresholds depend on absolute signal, so any global shift (HDACi, BETi, EZH2i) confounds direct SE-count comparison.

Wrong approach: Call ROSE2 on condition A and condition B separately, intersect SE BEDs, report "gained/lost SE."

Right approach:

1. Spike-in normalize signal between conditions (see chip-seq/spike-in-normalization)
2. Build a union SE set from both conditions
3. Quantify signal at union SE regions per condition (DiffBind on the union)
4. Apply differential testing with appropriate normalization (background-bin TMM or spike-in)

library(DiffBind)
# Union of SE BED files from condition A and B
union_se <- rtracklayer::import('union_SE.bed')
# Run DiffBind quantification on this region set with spike-in normalization

For BET-inhibitor experiments: the biology IS that all SE decrease globally; spike-in is mandatory.

Core Regulatory Circuitry (Saint-André 2016)

The CRC algorithm identifies master TF networks from SE annotations:

1. List all TFs encoded by SE-associated genes
2. For each such TF, check if its motif appears in its own SE (auto-regulation)
3. Build a graph where TFs encoded by SE-A bind to motifs in SE-B
4. Identify highly-interconnected sub-networks (CRC)

# Install CRC pipeline (console command is `crc`; -g is the genome BUILD, not a GTF)
pip install git+https://github.com/linlabcode/CRC.git

# Requires: SE enhancer table, subpeak BED, chromosome-FASTA dir
crc -e SE_table.txt -g HG38 -s subpeaks.bed -c chroms/ -o crc_out/ -n SAMPLE

CRC outputs the connected components of the regulatory network. Master TFs typically appear in the largest component with high out-degree.

ENCODE dELS Cross-Reference

ENCODE distal Enhancer-Like Signatures (dELS) are the cell-type-agnostic regulatory atlas (see chip-seq/peak-annotation). Cross-referencing SE against dELS:

• Validates SE constituents are at canonical regulatory elements
• Identifies SE constituents NOT in the dELS registry (potentially cell-type-specific)
• Provides chromatin-state context (DNase + H3K27ac signatures)

wget https://downloads.wenglab.org/Registry-V4/GRCh38-cCREs.bed
awk -F'\t' '$NF == "dELS"' GRCh38-cCREs.bed > dels.bed

# Fraction of SE constituents overlapping dELS
bedtools intersect -a SuperEnhancers.bed -b dels.bed -u | wc -l
bedtools intersect -a SuperEnhancers.bed -b dels.bed -wa -wb > se_with_dels.tsv

Per-Tool Failure Modes

ROSE -- Python 2 dependency

Trigger: Running the original Young-lab ROSE_main.py (Python 2 code) on a modern system.

Mechanism: The original ROSE is Python 2 code; print statements without parens, dict.iteritems(), etc.

Symptom: SyntaxError on first import.

Fix: Use the stjude/ROSE fork (python ROSE_main.py, Python 3, genomeDict includes HG38) or ROSE2 (rose2, Python 3, but its released genomeDict has no HG38 -- HG18/HG19/MM8/MM9/MM10/RN4/RN6 only); identical algorithm and output format.

ROSE / ROSE2 -- Stitching distance default not appropriate for all biology

Trigger: Using default -s 12500 (12.5 kb) on small genomes or compact gene structures.

Mechanism: Default was set on human/mouse vertebrate genomes; Drosophila / yeast / plants have different regulatory architecture.

Fix: For non-vertebrate genomes, reduce stitching distance proportionally (e.g., -s 2500 for Drosophila, -s 500 for yeast).