Super Enhancers

What the source does

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• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
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Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha c8c1fc3a2b13 · 19 Oct 2026Report an issue or request a re-scan

• PublisherGPTomics GitHub organizationOwner of the repository this skill is read from
• Source folderGPTomics/bioSkills/chip-seq/super-enhancersThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc8c1fc3a2b13A new audit runs when the source changes
• Repository statusArchivedLast push 65 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its 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…

---
name: bio-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+.

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

| 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 

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/linlabbcm/rose2.git
pip install ./rose2
pip install git+https://github.com/linlabcode/CRC.git
git clone https://github.com/stjude/ROSE.git

git clone https://github.com/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source c8c1fc3a2b13

Questions about Super Enhancers

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