ChromHMM Chromatin State Inference

Overview

This skill enables comprehensive chromatin state analysis using chromHMM for histone modification ChIP-seq data. ChromHMM uses a multivariate Hidden Markov Model to segment the genome into discrete chromatin states based on combinatorial patterns of histone modifications.

Main steps include:

• •Refer to Inputs & Outputs to verify necessary files.
• •Always prompt user if required files are missing.
• •Always prompt user for genome assembly used.
• •Always prompt user for the bin size for generating binarized files.
• •Always prompt user for the bin size for the number of states the ChromHMM target.
• •Run chromHMM workflow: Binarization → Learning.

When to use this skill

Use this skill when you need to infer chromatin states from histone modification ChIP-seq data using chromHMM.

Inputs & Outputs

Inputs

(1) Option 1: BED files of aligned reads

<mark1>.bed
<mark2>.bed
... # Other marks

(1) Option 2: BAM files of aligned reads

<mark1>.bam
<mark2>.bam
... # Other marks

Outputs

chromhmm_output/
  binarized/
    *.txt 
  model/
    *.txt
    ... # other files output by the ChromHMM

Decision Tree

Step 0: Initialize Project

Call:

• •mcp__project-init-tools__project_init

with:

• •sample: all
• •task: chromhmm

Step 1: Prepare the cellmarkfile (skip this step if signal files are provided)

• •

  Prepare a .txt file (without header) containing following three columns:

  • •sample name
  • •marker name
  • •name of the BED/BAM file
  • •control file of the sample (only provided if the input/control file is available)
• •

  example of the cellmark.txt file

cell1    mark1    cell1_mark2.bam    cell1_control.bam
cell1   mark2    cell1_mark2.bam    cell1/control.bam

Step 2: Data Binarization

• •

  For BAM inputs:
  Call:

  • •mcp__chromhmm-tools__binarize_bam with:
  • •path_chrom_sized: Provide by user or detect from the working directory
  • •input_dir: Directory containing BAM files
  • •cellmarkfile: Cell mark file defining histone modifications
  • •output_dir: (e.g. binarized/)
  • •bin_size: Provided by user
• •

  For BED inputs:
  Call mcp__chromhmm-tools__binarize_bed instead.

• •

  For Signal inputs:
  Call: mcp__chromhmm-tools__binarize_signal with:

  • •input_dir: Directory of signals
  • •output_dir: (e.g. binarized/)

Step 3: Model Learning

Call

• •mcp__chromhmm-tools__learn_model

with:

• •binarized_dir: Directory binarized file located in
• •num_states: Provide by user (e.g. 15)
• •output_model_dir: (e.g. model_15_states/)
• •genome: Provide by user (e.g. hg38)
• •threads: Provide by user (e.g. 16)

Parameter Optimization

Number of States

• •8 states: Basic chromatin states
• •15 states: Standard comprehensive states
• •25 states: High-resolution states
• •Optimization: Use Bayesian Information Criterion (BIC)

Bin Size

• •200bp: Standard resolution
• •100bp: High resolution (requires more memory)
• •500bp: Low resolution (faster computation)

State Interpretation

Common Chromatin States

1. •Active Promoter: H3K4me3, H3K27ac
2. •Weak Promoter: H3K4me3
3. •Poised Promoter: H3K4me3, H3K27me3
4. •Strong Enhancer: H3K27ac, H3K4me1
5. •Weak Enhancer: H3K4me1
6. •Insulator: CTCF
7. •Transcribed: H3K36me3
8. •Repressed: H3K27me3
9. •Heterochromatin: Low signal across marks

Troubleshooting

• •Memory errors: Reduce bin size or number of states
• •Convergence problems: Increase iterations or adjust learning rate
• •Uninterpretable states: Check input data quality and mark combinations
• •Missing chromosomes: Verify chromosome naming consistency