21.differential-methylation/SKILL.md
This skill performs differential DNA methylation analysis (DMRs and DMCs) between experimental conditions using WGBS methylation tracks (BED/BedGraph). It standardizes input files into per-sample four-column Metilene tables, constructs a merged methylation matrix, runs Metilene for DMR detection, filters the results, and generates quick visualizations.
npx skillsauth add bisnake2001/chromskills differential-methylationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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sample1.bed # raw methylation BED files, standardize it according to the following steps
sample2.bed
Assumptions: All samples share the same reference genome build and chromosome naming scheme.
DMR_DMC_detection/
stats/
dmr_results.txt # raw metilene output.
dmc_results.txt
significant_dmrs.txt # filtered significant DMRs (TSV).
significant_dmrs.bed # BED for genome browser.
significant_dmcs.txt
significant_dmcs.bed
dmr_summary.txt # counts and length statistics.
plots/
volcano.pdf
length_hist.pdf
temp/
sample1.sorted.bed
... # other sorted BED files
merged_input.bed
for sample in samples;do
awk -F'\t' 'BEGIN {OFS="\t"} {print $1, $2, $3, $<n>/100}' sample.bed | sort -V -k1,1 -k2,2n # n is provide by user, devided by 100 if is percentage
done
Call:
mcp__methyl-tools__generate_metilene_inputwith:
group1_files: Comma-separated group 1 bedGraph/BED files (from Step 1, must be sorted)group1_files: Comma-separated group 2 bedGraph/BED files (from Step 1, must be sorted)output_path: Output file path for generated metilene inputgroup1_name: Identifier of group 1group2_name: Identifier of group 2This tool will:
Call:
mcp__methyl-tools__run_metilenewith:
merged_bed_path: file path for metilene inputgroup_a_name: name of group A (e.g. "case")group_b_name: name of group B (e.g. "control")mode: Mode for metilene CLI (e.g. 1: de-novo, 2: pre-defined regions, 3: DMCs), assign 1 for DMR analysisthreads: Always use 1 threads to avoid erroroutput_results_path: Output path for the DMR resultsCall:
mcp__methyl-tools__run_metilenewith:
merged_bed_path: file path for metilene inputgroup_a_name: name of group A (e.g. "case")group_b_name: name of group B (e.g. "control")mode: Mode for metilene CLI (e.g. 1: de-novo, 2: pre-defined regions, 3: DMCs), assign 3 for DMR analysisoutput_results_path: Output path for the DMC resultsCall:
mcp__methyl-tools__filter_dmrs
with:metilene_results_path: DMR results from Step 3significant_tsv_path: Output path for the DMR results (e.g. significant_dmrs.tsv)significant_bed_path: Output path for the DMR results (e.g. significant_dmrs.bed)q_threshold, delta_threshold as agreed.Call:
mcp__methyl-tools__filter_dmrs
with:metilene_results_path: DMC results from Step 4significant_tsv_path: Output path for the DMC results (e.g. significant_dmcs.tsv)significant_bed_path: Output path for the DMC results (e.g. significant_dmcs.bed)q_threshold, delta_threshold as agreed.Volcano-like plot (Δmethylation vs –log10(q))
mcp__methyl-tools__plot_dmr_volcano
with:metilene_results_path: DMR results from Step 3output_pdf_pathq_threshold, delta_threshold as agreed.point_size, alpha as needed.DMR length histogram Call:
mcp__methyl-tools__plot_dmr_length_histwith:
significant_bed_path: Path for the signimicant DMRs (BED format from Step 5)output_pdf_pathchr1 vs 1) across all samples.development
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.
development
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.
data-ai
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.
devops
The TF-differential-binding pipeline performs differential transcription factor (TF) binding analysis from ChIP-seq datasets (TF peaks) using the DiffBind package in R. It identifies genomic regions where TF binding intensity significantly differs between experimental conditions (e.g., treatment vs. control, mutant vs. wild-type). Use the TF-differential-binding pipeline when you need to analyze the different function of the same TF across two or more biological conditions, cell types, or treatments using ChIP-seq data or TF binding peaks. This pipeline is ideal for studying regulatory mechanisms that underlie transcriptional differences or epigenetic responses to perturbations.