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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.
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Based on SOC occupation classification
| 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. |
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:
Use this skill when you need to infer chromatin states from histone modification ChIP-seq data using chromHMM.
(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
chromhmm_output/
binarized/
*.txt
model/
*.txt
... # other files output by the ChromHMM
Call:
mcp__project-init-tools__project_initwith:
sample: alltask: chromhmmcellmarkfile (skip this step if signal files are provided)Prepare a .txt file (without header) containing following three columns:
example of the cellmark.txt file
cell1 mark1 cell1_mark2.bam cell1_control.bam
cell1 mark2 cell1_mark2.bam cell1/control.bam
For BAM inputs:
Call:
mcp__chromhmm-tools__binarize_bam
with: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 userFor BED inputs:
Call mcp__chromhmm-tools__binarize_bed instead.
For Signal inputs:
Call: mcp__chromhmm-tools__binarize_signal
with:
input_dir: Directory of signalsoutput_dir: (e.g. binarized/)Call
mcp__chromhmm-tools__learn_modelwith:
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)Align bisulfite sequencing DNA methylation reads using Bismark only, with explicit validation of reference preparation, library layout detection, output organization, logging, and alignment QC. Use it for WGBS, RRBS, or other bisulfite-converted DNA methylation sequencing data when raw FASTQ files must be aligned before methylation extraction and downstream analysis.
Align ChIP-seq or ATAC-seq FASTQ files to a reference genome using Bowtie2, with strict input validation, library layout detection, output organization and logging. Use it when raw sequencing reads must be converted into sorted/indexed BAM files before downstream QC, peak calling, or footprinting.
This skill is used to perform genomic feature annotation and visualization for any file containing genomic region information using Homer (Hypergeometric Optimization of Motif EnRichment). It annotates regions such as promoters, exons, introns, intergenic regions, and TSS proximity, and generates visual summaries of feature distributions. ChIPseeker mode is also supported according to requirements.
Perform GO and KEGG functional enrichment using HOMER from genomic regions (BED/narrowPeak/broadPeak) or gene lists, and produce R-based barplot/dotplot visualizations. Use this skill when you want to perform GO and KEGG functional enrichment using HOMER from genomic regions or just want to link genomic region to genes.
This skill should be used when users need to perform known motif enrichment analysis on ChIP-seq, ATAC-seq, or other genomic peak files using HOMER (Hypergeometric Optimization of Motif EnRichment). It identifies enrichment of known transcription factor binding motifs from established databases in genomic regions.
Perform peak calling for ChIP-seq or ATAC-seq data using MACS3, with intelligent parameter detection from user feedback. Use it when you want to call peaks for ChIP-seq data or ATAC-seq data.