| name | chipseq-super-enhancers |
| description | 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. |
| tool_type | mixed |
| primary_tool | ROSE |
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+.
ROSE is unmaintained Python 2; ROSE2 is the Python 3 port with the same algorithm. 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 (ROSE / ROSE2):
python ROSE_main.py -g HG38 -i peaks.gff -r h3k27ac.bam -c input.bam -s 12500 -t 2500
- 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. Functional CRISPR-tiling at SE loci (Hnisz 2017; Dukler 2017) shows only 1-3 constituent elements per SE are essential; 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 | Python 2 only; unmaintained; ROSE_main.py crashes on Python 3 |
| ROSE2 (Lin 2016; linlabbcm) | Same algorithm, Python 3 port | Maintained; identical output to ROSE | None vs ROSE |
| 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.
git clone https://github.com/linlabbcm/rose2.git
pip install ./rose2
awk 'BEGIN{OFS="\t"} {print $1,"peaks","enhancer",$2,$3,".",$6,".","ID="NR}' \
peaks.narrowPeak > peaks.gff
bedtools intersect -a peaks.narrowPeak -b promoters_2kb.bed -v > enhancer_peaks.bed
rose2 -g HG38 -i peaks.gff \
-r h3k27ac.bam -c input.bam \
-o rose_output/ \
-s 12500 \
-t 2500
ROSE2 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_se <- rtracklayer::import('union_SE.bed')
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)
git clone https://github.com/linlabbcm/CRC2.git
python CRC2/crc.py -e SE_table.txt -g genes.gtf -b h3k27ac.bam
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://api.wenglab.org/screen_v13/screen_human_ccres_simple.bed.gz
gunzip screen_human_ccres_simple.bed.gz
awk -F'\t' '$NF == "dELS"' screen_human_ccres_simple.bed > dels.bed
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 ROSE_main.py on a modern system (Python 3 only).
Mechanism: ROSE is Python 2 code; print statements without parens, dict.iteritems(), etc.
Symptom: SyntaxError on first import.
Fix: Use ROSE2 (linlabbcm Python 3 port); 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 |
| SE shifts location between replicates | Marginal calls below inflection; hockey-stick inflection noisy | Use top N SE by rank for robustness; or require SE in ≥ 2/3 replicates |
| LILY and ROSE2 disagree on SE count | LILY's input subtraction differs | Trust ROSE2 unless input quality is poor (low FRiP) |
Common Errors
| Error / symptom | Cause | Solution |
|---|
SyntaxError: invalid syntax in ROSE | Python 2 codebase | Use ROSE2 (linlabbcm) Python 3 port |
| GFF format error | Wrong column ordering | Use awk template: chr<TAB>peaks<TAB>enhancer<TAB>start<TAB>end<TAB>.<TAB>strand<TAB>.<TAB>ID=N |
| ROSE2 reports 0 SE | Hockey-stick inflection failed; too few enhancers | Inspect _Plot_points.png; ≥ 5000 peaks input recommended |
| Genome flag error in ROSE2 | Genome not pre-configured | Genome flag must be one of HG18, HG19, HG38, MM8, MM9, MM10 |
| All SE at promoters | TSS exclusion too narrow OR data dominated by promoter signal | Verify -t 2500; check input is H3K27ac at enhancers not full chromatin |
| Cross-condition SE gain/loss not reproducible | No spike-in normalization | Spike-in (ChIP-Rx) or quantitative differential on union SE |
References
- Whyte WA et al 2013 Cell 153:307 (super-enhancers, ROSE)
- Lovén J et al 2013 Cell 153:320 (SE characterization, BET sensitivity)
- Hnisz D et al 2013 Cell 155:934 (SE in cell identity)
- Pott S & Lieb JD 2015 Nat Genet 47:8 (SE as continuum critique)
- Lin CY et al 2016 Nature 530:57-62 (MYC enhancers; ROSE2-era analyses)
- Saint-André V et al 2016 Genome Res 26:385 (core regulatory circuitry)
- Boeva V et al 2017 Cell Rep 21:1357 (LILY; neuroblastoma SE)
- Hnisz D et al 2017 Cell 169:13 (CRISPR-tiling SE function)
- Dukler N et al 2017 Genome Res 27:1869 (SE constituent function)
- Sengupta S & George RE 2017 Trends Cancer 3:269 (SE function review)
Related Skills
- chip-seq/peak-calling - Generate H3K27ac / MED1 / BRD4 peaks for SE input
- chip-seq/chipseq-qc - Filter hyper-ChIPable peaks before SE calling
- chip-seq/spike-in-normalization - Mandatory for cross-condition SE comparison
- chip-seq/differential-binding - Quantitative differential testing on union SE set
- chip-seq/peak-annotation - Annotate SE-associated genes; cross-reference dELS
- chip-seq/cut-and-run-tag - SE calling on CUT&RUN/CUT&Tag H3K27ac (different spike-in)
- atac-seq/enhancer-gene-linking - ENCODE-rE2G for SE-target gene assignment
- data-visualization/genome-tracks - SE region visualization
