bisnake2001/chromatin-state-inference
15.chromatin-state-inference/SKILL.md
This skill should be used when users need to infer chromatin states from histone modification ChIP-seq data using chromHMM. It provides workflows for chromatin state segmentation, model training, state annotation.
$ install --global
skillsauthnpx skillsauth add bisnake2001/chromskills chromatin-state-inferenceInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- chromatin-state-inference
- description:
- This skill should be used when users need to infer chromatin states from histone modification ChIP-seq data using chromHMM. It provides workflows for chromatin state segmentation, model training, state annotation.
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: alltask: 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_bamwith:path_chrom_sized: Provide by user or detect from the working directoryinput_dir: Directory containing BAM filescellmarkfile: Cell mark file defining histone modificationsoutput_dir: (e.g.binarized/)bin_size: Provided by user
-
For BED inputs:
Callmcp__chromhmm-tools__binarize_bedinstead. -
For Signal inputs:
Call:mcp__chromhmm-tools__binarize_signalwith:input_dir: Directory of signalsoutput_dir: (e.g.binarized/)
Step 3: Model Learning
Call
mcp__chromhmm-tools__learn_model
with:
binarized_dir: Directory binarized file located innum_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
- Active Promoter: H3K4me3, H3K27ac
- Weak Promoter: H3K4me3
- Poised Promoter: H3K4me3, H3K27me3
- Strong Enhancer: H3K27ac, H3K4me1
- Weak Enhancer: H3K4me1
- Insulator: CTCF
- Transcribed: H3K36me3
- Repressed: H3K27me3
- 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
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