Agent skill · Data & Analytics

bio-chipseq-chromatin-state-segmentation

Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeships scriptsNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill chromatin-state-segmentation --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 3
SKILL.md size: 15 KB
Bundled scripts: yes
Path: skills/bioskills/chromatin-state-segmentation/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

## Version Compatibility Reference examples tested with: ChromHMM 1.27+, Segway 3.0+, EpiSegMix 1.0+, EpiLogos (Meuleman lab), IDEAS 1.20+, samtools 1.19+, bedtools 2.31+. ChromHMM requires Java 8+; runs as `java -mx<MEMORY> -jar ChromHMM.jar <command>`. # Chromatin State Segmentation **"Integrate multiple histone modification ChIP-seq tracks into chromatin states"** -> Learn a small set of recurring combinatorial patterns of histone marks (active promoter, active enhancer, poised enhancer, polycomb-repressed, heterochromatic, transcribed, etc.) and segment the genome by which state each region belongs to. Output: per-state genomic intervals, state-by-mark emission matrix, and state-state transition matrix. - CLI (canonical): ChromHMM `BinarizeBam` -> `LearnModel` -> `OverlapEnrichment` / `NeighborhoodEnrichment` - CLI (continuous signal): Segway `train` -> `posterior` -> `identify` - CLI (flexible distributions): EpiSegMix (2024) - Visualization across biosamples: EpiLogos (Meuleman lab) - Cell-type-aware joint: IDEAS Chromatin state segmentation requires a panel of histone marks; minimum 4-5 marks (e.g., H3K4me3, H3K27ac, H3K4me1, H3K36me3, H3K27me3) for meaningful states. With f

What's inside
Steps it walks through
  1. Version Compatibility
  2. Tool Taxonomy
  3. ChromHMM Workflow
  4. Step 1: Binarize ChIP-seq signal
  5. Step 2: Learn model
  6. Step 3: Interpret states from emission matrix
  7. Step 4: Functional enrichment of states
  8. Choosing State Count
  9. Segway Workflow
  10. EpiLogos Visualization
  11. Full-Stack ChromHMM (Vu Ernst 2022)
  12. Per-Tool Failure Modes
  13. ChromHMM -- Bin size 200 bp too coarse for sharp boundaries
  14. ChromHMM -- Binarization throws away signal quantitation
Ships with 2 files
  • examples/chromhmm_pipeline.sh
  • usage-guide.md
Commands it runs
Build cellMarkFileTable: cell_type<TAB>mark<TAB>file<TAB>(optional control)
cat > cellMarkFileTable.txt << EOF
Binarize BAMs into 200 bp bins; emission = whether mark exceeds Poisson threshold
java -mx16G -jar ChromHMM.jar BinarizeBam \
chromsizes_hg38.txt \
bam_dir/ \
cellMarkFileTable.txt \
binarized_output/
Train HMM with N states; common choices: 15, 18, 25
java -mx16G -jar ChromHMM.jar LearnModel \
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About this skill
What does the bio-chipseq-chromatin-state-segmentation skill do?

Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill chromatin-state-segmentation --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.

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