bio-chipseq-super-enhancers
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 respo
npx skills add BioTender-max/awesome-bio-agent-skills --skill super-enhancers --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## 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 si
- Version Compatibility
- Marker Choice: H3K27ac vs MED1 vs BRD4
- Algorithmic Taxonomy
- Decision Tree: SE Calling Workflow
- ROSE / ROSE2 Workflow
- Cross-Condition SE Comparison
- Core Regulatory Circuitry (Saint-André 2016)
- ENCODE dELS Cross-Reference
- Per-Tool Failure Modes
- ROSE -- Python 2 dependency
- ROSE / ROSE2 -- Stitching distance default not appropriate for all biology
- ROSE / ROSE2 -- TSS exclusion can remove promoter-associated enhancers
- H3K27ac SE vs BRD4 SE -- BET-inhibitor mismatch
- Cross-condition SE counting -- Wrong normalization
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 ROSE2 with input controlWhat does the bio-chipseq-super-enhancers skill do?
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 respo
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill super-enhancers --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.