name: bio-chipseq-chromatin-state-segmentation description: 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-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types. origin: openai4s category: bioskills/chip-seq metadata: tool_type: cli primary_tool: ChromHMM third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a license: MIT
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>.
"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.
BinarizeBam -> LearnModel -> OverlapEnrichment / NeighborhoodEnrichmenttrain -> posterior -> annotateChromatin state segmentation requires a panel of histone marks; minimum 4-5 marks (e.g., H3K4me3, H3K27ac, H3K4me1, H3K36me3, H3K27me3) for meaningful states. With fewer marks, simpler peak-based annotation (chipseq/peak-annotation) is more appropriate.
| Tool | Method | Strength | Fails when |
|---|---|---|---|
| ChromHMM (Ernst & Kellis 2012; v1.27 current) | Multivariate HMM on binarized 200 bp bins | Canonical; widely used; integrated with Roadmap Epigenomics 15-state model; mature toolchain | Binarization throws away signal quantitation; default 200 bp bins may be too coarse for sharp boundaries |
| Segway (Hoffman 2012) | Dynamic Bayesian Network on continuous signal | Higher resolution; uses signal magnitudes not binarized | More complex setup; slower; less standardized output |
| EpiSegMix (Schmitz, Aggarwal, Laufer, Walter, Salhab, Rahmann 2024 Bioinformatics 40:btae178) | HMM with flexible read-count distributions + duration modeling | Modern; handles both narrow and broad mark distributions in one model | Newer; smaller user base |
| EpiLogos (Meuleman lab) | Multi-biosample visualization tool | Built on top of ChromHMM/Segway segmentations; compare ChromHMM states across 100s of biosamples | Visualization tool, not a segmentation method itself |
| IDEAS (Zhang 2016) | Cell-type-aware joint inference | Across-cell-type segmentation respecting cell-type identity | Slower; complex parameter tuning |
| EpiCSeg (Mammana 2015) | Negative binomial mixture | Read-count-based; doesn't need binarization | Less standardized output |
| GenoSTAN | HMM with various emission distributions | Flexible | Less actively developed |
| Roadmap 25-state model (Kundaje 2015) | ChromHMM 25-state precomputed model | Reference for cross-cell-type interpretation | Requires the Roadmap imputed 12-mark panel |
| Full-stack ChromHMM (Vu Ernst 2022) | 100-state segmentation across 1032 datasets / 127 reference epigenomes | Comprehensive cross-tissue annotation | Computationally intensive to retrain |
ChromHMM is the de facto standard. The workflow has 4 stages:
# Build cellMarkFileTable: cell_type<TAB>mark<TAB>file<TAB>(optional control)
cat > cellMarkFileTable.txt << EOF
GM12878 H3K4me3 gm12878_h3k4me3.bam gm12878_input.bam
GM12878 H3K27me3 gm12878_h3k27me3.bam gm12878_input.bam
GM12878 H3K27ac gm12878_h3k27ac.bam gm12878_input.bam
GM12878 H3K4me1 gm12878_h3k4me1.bam gm12878_input.bam
GM12878 H3K36me3 gm12878_h3k36me3.bam gm12878_input.bam
EOF
# Binarize BAMs into 200 bp bins; emission = whether mark exceeds Poisson threshold
java -mx16G -jar ChromHMM.jar BinarizeBam \
-b 200 \
chromsizes_hg38.txt \
bam_dir/ \
cellMarkFileTable.txt \
binarized_output/
Output: per-chromosome _binary.txt files, one row per 200 bp bin, columns = marks, values 0/1.
# Train HMM with N states; common choices: 15, 18, 25
# 15 states: Ernst & Kellis 2011 model; canonical
# 18 states: extends with additional regulatory states
# 25 states: Roadmap Epigenomics extended model
java -mx16G -jar ChromHMM.jar LearnModel \
-p 8 \
binarized_output/ \
model_15state/ \
15 \
hg38
# Output: model_15state.txt (emission + transition matrices),
# emissions_15.png (visualization), transitions_15.png,
# per-chromosome _segments.bed (state assignments)
# AND automatically runs OverlapEnrichment + NeighborhoodEnrichment
| Roadmap 15-state assignments (canonical) |
|---|
| 1_TssA — Active TSS (high H3K4me3, H3K27ac) |
| 2_TssAFlnk — Flanking TSS (H3K4me3, H3K27ac, lower) |
| 3_TxFlnk — Transcript flanking |
| 4_Tx — Strong transcription (H3K36me3, H3K79me2 if available) |
| 5_TxWk — Weak transcription |
| 6_EnhG — Enhancer in gene body (H3K4me1, H3K27ac) |
| 7_Enh — Generic enhancer (H3K4me1, H3K27ac) |
| 8_ZNF/Rpts — Zinc-finger / repeats |
| 9_Het — Heterochromatin (H3K9me3) |
| 10_TssBiv — Bivalent TSS (H3K4me3 + H3K27me3) |
| 11_BivFlnk — Bivalent flanking |
| 12_EnhBiv — Bivalent enhancer (H3K4me1 + H3K27me3) |
| 13_ReprPC — Polycomb-repressed (H3K27me3) |
| 14_ReprPCWk — Weak Polycomb |
| 15_Quies — Quiescent (no signal) |
# OverlapEnrichment: enrichment of each state for external feature sets.
# Options (e.g. -labels) MUST precede the three positional args.
java -mx16G -jar ChromHMM.jar OverlapEnrichment \
-labels \
model_15state/GM12878_15_segments.bed \
/path/to/anchor_files/ \
enrichment_output/GM12878
# NeighborhoodEnrichment: enrichment relative to anchor positions (e.g., TSS)
java -mx16G -jar ChromHMM.jar NeighborhoodEnrichment \
-labels \
model_15state/GM12878_15_segments.bed \
/path/to/tss_anchors.txt \
enrichment_output/GM12878_TSS
Anchor files: BED files of features (CGIs, repeats, conserved elements, etc.) for OverlapEnrichment; position files for NeighborhoodEnrichment.
| States | Use case | Mark panel size |
|---|---|---|
| 8-10 | Initial exploration; small mark panel (3-4 marks) | 3-5 marks |
| 15 | Roadmap Epigenomics canonical | 5 core (H3K4me3, H3K4me1, H3K36me3, H3K27me3, H3K9me3) |
| 18 | Roadmap extended (adds H3K27ac -> fine enhancer subtypes) | 6 marks (core 5 + H3K27ac) |
| 25 | Roadmap Epigenomics extended; cross-cell-type compatibility | 12 imputed marks |
| 50+ | Full-stack model (Vu Ernst 2022) | Many marks across many cell types |
Practical workflow: Train at N=15, 18, 25; compare emission matrices; choose the smallest N where biology is interpretable. Higher N risks over-segmentation (state splitting random variation).
# Segway reads only the Genomedata format, so first pack the signal tracks
# (one track per bigWig) plus the genome sequence into an archive.
genomedata-load \
-s hg38.fa \
-t h3k4me3=h3k4me3.bw \
-t h3k27ac=h3k27ac.bw \
-t h3k4me1=h3k4me1.bw \
-t h3k36me3=h3k36me3.bw \
-t h3k27me3=h3k27me3.bw \
signal.genomedata
# Train Segway model; GENOMEDATA and TRAINDIR are positional
segway train \
--num-labels=25 \
--num-instances=3 \
--resolution=100 \
signal.genomedata traindir/
# Posterior probabilities + hard-call annotation (GENOMEDATA TRAINDIR OUTDIR, positional)
segway posterior signal.genomedata traindir/ posteriordir/
segway annotate signal.genomedata traindir/ identifydir/
# Output: identifydir/segway.bed.gz (state assignments)
Segway uses continuous signal (from the genomedata archive) vs ChromHMM's binarized bins. Trade-off: more information per region (continuous) but more complex training.
EpiLogos doesn't perform segmentation; it visualizes existing ChromHMM/Segway segmentations across many biosamples (epilogos.org).
# Use precomputed ChromHMM segmentations across multiple cell types
# Web interface: https://epilogos.altius.org/
# Local: github.com/meuleman/epilogos
Useful for: cross-cell-type comparison; identifying tissue-specific regulatory states; cohort-level chromatin landscape summaries.
The full-stack model trained on 1032 datasets / 127 reference epigenomes:
# Use precomputed model from Ernst lab
# github.com/ernstlab/full_stack_ChromHMM_annotations
# Annotate new sample by applying model to binarized data
java -mx16G -jar ChromHMM.jar MakeSegmentation \
full_stack_model_100states.txt \
binarized_sample/ \
full_stack_output/
Useful for: applying a comprehensive cross-tissue annotation to a new sample; comparing to canonical Roadmap states.
Trigger: Studying TF binding boundaries or sharp enhancer transitions at 200 bp resolution.
Mechanism: ChromHMM default 200 bp bins; biology may shift within a bin.
Fix: Reduce to -b 100 or -b 50 (smaller bin); increases memory and compute time but improves boundary resolution. Re-train model at finer resolution.
Trigger: Distinguishing low- from high-signal regions of the same state.
Mechanism: ChromHMM binarizes each 200 bp bin to 0/1 per mark; state assignment uses combinatorial pattern, not magnitude.
Fix: Use Segway (continuous signal) or EpiSegMix (flexible distributions) for magnitude-aware segmentation.
Trigger: Training with N=50 states on a 4-mark panel; or N=10 on a 7-mark panel.
Mechanism: Excess states fragment biology; insufficient states force unrelated regions into the same state.
Symptom: Emission matrix shows redundant states (multiple states with same emission profile) at high N; or biologically distinct regions lumped together at low N.
Fix: Train at N=15, 18, 25; inspect emission matrix similarity; choose the smallest N where states are interpretable as distinct biology.
Trigger: Applying Roadmap 25-state model to a sample with different mark panel.
Mechanism: Model was trained on specific marks; emission probabilities are mark-specific. Applying to different mark panel produces nonsensical state assignments.
Fix: Either train a new model on the available mark panel; OR ensure the exact same marks (and ordering) as used in the model.
Trigger: Using IgG controls in BinarizeBam for histone marks.
Mechanism: ChromHMM's binarization compares mark signal to control; histone mark biology assumes input (sonicated chromatin) as background, not IgG.
Fix: Use sonicated input as control for histone mark ChIP. IgG is not appropriate for ChromHMM binarization of histone marks.
Trigger: Training separate models per cell type and trying to compare state assignments.
Mechanism: State 5 in cell type A may not correspond to state 5 in cell type B if trained independently.
Fix: Train one model on concatenated data from all cell types (joint segmentation); or apply a single precomputed model (Roadmap 15-state, full-stack) to all samples for consistent state labels.
| Pattern | Likely cause | Action |
|---|---|---|
| ChromHMM and Segway segments differ | Different bin sizes / binarization vs continuous | Both can be valid; inspect emission matrices; pick the tool matching the resolution needs |
| State assignment varies wildly between replicates | Insufficient marks; over-binned | Increase mark panel; reduce state count |
| Active TSS state overlaps polycomb state at promoters | Bivalent biology (Bernstein 2006) | Expected for ESC-like cells; not an error; consider bivalent-specific state in N=18 model |
| Roadmap 25-state model annotates unknown cell type | Cross-cell-type generalization | Use cautiously; verify against tissue-specific tracks |
| Heterochromatin (H3K9me3) state has too many bins | H3K9me3 covers large fraction of genome | Expected; heterochromatin is genome-wide |
| Error / symptom | Cause | Solution |
|---|---|---|
java.lang.OutOfMemoryError |
Insufficient JVM heap | java -mx32G -jar ChromHMM.jar ... |
BinarizeBam very slow |
Large BAMs without index | samtools index all BAMs first |
| All states have similar emissions | Mark panel too small | Need at least 5 marks for canonical 15-state model |
| Segments file empty for some chromosomes | Chromosome not in chromsizes file | Add or use -chrom flag to restrict |
| State labels don't match Roadmap | Trained model independently | Use Roadmap precomputed model OR map states by emission similarity |
| ChromHMM "no signal in marks" | All bins binarized to 0 | Check signal quality; verify control normalization |