armored-cart-design-agent
Design armored CAR-T cells with cytokine payloads and resistance mechanisms.
Maintainer FreedomIntelligence · Last updated April 1, 2026
Identifies super-enhancers from H3K27ac ChIP-seq data using ROSE and related tools. Use when studying cell identity genes, cancer-associated regulatory elements, or master transcription factor binding regions that cluster into large enhancer domains.
Original source
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-chipseq-super-enhancers
Skill Snapshot
Source Doc
git clone https://github.com/stjude/ROSE.git
cd ROSE
## Input Requirements
1. **BAM file** - H3K27ac ChIP-seq aligned reads
2. **Peak file** - Called peaks (BED or GFF)
3. **Genome annotation** - TSS annotations
## Run ROSE
**Goal:** Identify super-enhancers by stitching nearby enhancer peaks and ranking by H3K27ac signal.
**Approach:** Run ROSE_main.py with a GFF peak file, ChIP-seq BAM, and optional input control to stitch enhancers within 12.5 kb, rank by signal, and identify the inflection point separating super-enhancers from typical enhancers.
```bash
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