Identifies super-enhancers from H3K27ac, MED1, or BRD4 ChIP-seq using ROSE, ROSE2, LILY, HOMER -style super, and ENCODE dELS cross-referencing. Handles peak stitching parameters, ranking choices, hockey-stick inflection, marker choice (H3K27ac vs MED1/BRD4), and cross-condition comparison with spike-in normalization. Constructs core regulatory circuitry (Saint-Andre 2016) from SE-encoded TFs. Use when identifying cell-identity / cancer-associated regulatory domains, comparing super-enhancers between conditions, identifying master transcription factor networks, or predicting BET-inhibitor responsiveness.
Identifies super-enhancers from H3K27ac, MED1, or BRD4 ChIP-seq using ROSE, ROSE2, LILY, HOMER -style super, and ENCODE dELS cross-referencing. Handles peak stitching parameters, ranking choices, hockey-stick inflection, marker choice (H3K27ac vs MED1/BRD4), and cross-condition comparison with spike-in normalization. Constructs core regulatory circuitry (Saint-Andre 2016) from SE-encoded TFs. Use when identifying cell-identity / cancer-associated regulatory domains, comparing super-enhancers between conditions, identifying master transcription factor networks, or predicting BET-inhibitor responsiveness.
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 (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
Marker
Captures
When to prefer
H3K27ac
Active regulatory elements broadly
Most widely available; standard for SE definition since Whyte 2013
MED1
Mediator complex accumulation (the defining biology)
Direct readout of SE; less common antibody; lower signal-to-noise
BRD4
BET cofactor accumulation
Most predictive of BET-inhibitor responsiveness; clinical relevance
H3K27ac + MED1 intersection
High-confidence SE
Gold standard if both available
dELS from ENCODE cCREs
Cell-type-agnostic distal enhancer registry
Cross-reference; not SE-specific by itself
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
Tool
Method
Strength
Fails when
ROSE (Whyte 2013)
Stitch within 12.5 kb, exclude ±2.5 kb of TSS, rank by signal, hockey-stick inflection
Original; widely cited; canonical reference
Original Young-lab code is Python 2; run the stjude/ROSE Py3 fork instead
ROSE2 (linlabbcm/rose2)
Same algorithm, Python 3 port
Maintained; pip-installable rose2 console command
Released genomeDict has no HG38 (HG18/HG19/MM8/MM9/MM10/RN4/RN6 only) -> use stjude/ROSE for hg38
LILY (Boeva 2017)
ROSE-like with input-control background subtraction
Works on lower-quality H3K27ac data; subtracts input
Adds complexity; less validated; specific to neuroblastoma/glioma in original paper
HOMER -style super
Native ROSE-like in HOMER framework; stitching without TSS exclusion
Integrated with HOMER workflow
Different stitching defaults; not directly comparable to ROSE counts
Custom hockey-stick (R)
Generic rank-by-signal + tangent inflection
Flexible; works on any signal definition
Reinvents algorithm; verify against ROSE on known dataset
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
Scenario
Recommended pipeline
Standard SE discovery, H3K27ac available
ROSE2 with default -s 12500 -t 2500; input control for subtraction
Predict BET-inhibitor response
BRD4 ChIP -> ROSE2 (or H3K27ac SE intersected with BRD4 peaks)
Compare SE between conditions (drug treatment)
ROSE2 per condition + spike-in normalization (HDACi/BETi/EZH2i need ChIP-Rx)
Build core regulatory circuitry
ROSE2 + Saint-Andre 2016 algorithm: identify TFs encoded by SE that bind own SE + cross-bind other SE-encoded TFs
Low-quality H3K27ac (low FRiP)
LILY with input subtraction
Compare with ENCODE dELS atlas
ROSE2 + intersect with ENCODE cCRE dELS BED
Differential SE between conditions
ROSE2 per condition + signal-quantitative differential (DiffBind on SE regions)
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:
Spike-in normalize signal between conditions (see chip-seq/spike-in-normalization)
Build a union SE set from both conditions
Quantify signal at union SE regions per condition (DiffBind on the union)
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:
List all TFs encoded by SE-associated genes
For each such TF, check if its motif appears in its own SE (auto-regulation)
Build a graph where TFs encoded by SE-A bind to motifs in SE-B
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)
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).
ROSE / ROSE2 -- TSS exclusion can remove promoter-associated enhancers
Trigger: Default -t 2500 (exclude peaks within 2.5 kb of TSS) on promoter-proximal enhancers (e.g., pELS class).
Mechanism: TSS-proximal enhancers are filtered out; SE definition becomes distal-only.
Symptom: Lower SE counts than expected for cell types with promoter-enhancer architecture (e.g., human ES cells).
Fix: Reduce TSS exclusion to -t 500 or -t 0 if including promoter-proximal regulatory regions; document the decision.
H3K27ac SE vs BRD4 SE -- BET-inhibitor mismatch
Trigger: Calling SE on H3K27ac and claiming BET-inhibitor responsiveness.
Mechanism: H3K27ac marks active enhancers broadly; not all H3K27ac-positive SE have BRD4 accumulation.
Symptom: Predicted BET-sensitive genes don't respond to BET inhibitors in cell-based assays.
Fix: For BET-inhibitor claims, use BRD4 ChIP for SE calling, or intersect H3K27ac SE with BRD4 peaks.
Cross-condition SE counting -- Wrong normalization
Trigger: Comparing SE counts in HDACi-treated vs DMSO without spike-in normalization.
Mechanism: SE calling thresholds depend on absolute signal; HDACi globally increases H3K27ac, raising every region's signal and shifting the hockey-stick inflection.
Symptom: Reports "1000 SE in HDACi vs 500 in DMSO" when biology is just global H3K27ac increase.
Fix: Spike-in normalize BAMs (ChIP-Rx with Drosophila chromatin), call SE on scaled signal; OR quantify signal at a union peak set rather than calling SE per condition.
LILY -- Input subtraction artifacts
Trigger: Running LILY without high-quality matched input control.
Mechanism: LILY subtracts background based on input signal; mismatched input introduces artifactual negative signal.
Fix: Use LILY only when input quality is good (same library prep, same depth, same fragmentation); otherwise use ROSE2 with standard input handling.
Hockey-stick inflection -- Sensitive to peak count
Trigger: Calling SE on a small peak set (< 5000 enhancers).
Mechanism: Hockey-stick inflection depends on having a long "tail" of typical enhancers; few peaks distort the inflection.
Symptom: SE count is unreasonably high (50%+ of all peaks called SE) or unreasonably low (< 50 SE).
Fix: Require ≥ 5000 enhancer peaks input to ROSE2; if fewer, use absolute signal cutoff (e.g., top 5% by signal density) rather than hockey-stick.
Reconciliation: When SE Calls Disagree
Pattern
Likely cause
Action
ROSE2 vs HOMER -style super differ
Different stitching distance / TSS handling
Use ROSE2 standard for cross-paper comparison; HOMER for HOMER-integrated workflows
H3K27ac SE ≠ MED1 SE at same locus
H3K27ac is broad; MED1 marks subset of active SE
MED1 SE is the more functional definition; H3K27ac includes inactive-but-acetylated regions
SE called in DMSO but not in BETi (or vice versa)
Global signal shift confounds threshold
Spike-in normalize; compare quantitatively at union SE set
