integrative-DMR-DEG

This skill performs correlation analysis between differential methylation and differential gene expression, identifying genes with coordinated epigenetic regulation. It provides preprocessing and integration workflows, using promoter-level methylation–expression relationships.

$ Instalar

git clone https://github.com/BIsnake2001/ChromSkills /tmp/ChromSkills && cp -r /tmp/ChromSkills/23.integrative-DMR-DEG ~/.claude/skills/ChromSkills

// tip: Run this command in your terminal to install the skill


name: integrative-DMR-DEG description: This skill performs correlation analysis between differential methylation and differential gene expression, identifying genes with coordinated epigenetic regulation. It provides preprocessing and integration workflows, using promoter-level methylation–expression relationships.


Integrative Methylation–Expression Correlation Analysis

Overview

This skill integrates differential methylation and differential expression datasets to reveal coordinated epigenetic regulation patterns.

  • Refer to Inputs & Outputs to verify necessary files.
  • Always prompt user for genome assembly used.
  • Prepare the DMR regions into 6-column standard format BED file received by HOMER.
  • Annotate the differential methylation regions to the gene promoter.
  • Preprocess differential methylation and expression tables into a standard format.
  • Integrate methylation and expression data by promoter proximity.
  • Calculate correlation between methylation change and expression fold change.
  • Classify patterns such as hypermethylation–downregulation or hypomethylation–upregulation.

Inputs & Outputs

Inputs

dmr_results.txt # DMR results output by the metilene
dge_result.csv # DEG results output by DESeq2

Outputs

corr_DMR_DEG/
  stats/
    integrated_results.tsv
    pattern_counts.tsv
    summary_stats.tsv
    correlation_plot.pdf
  temp/
    homer_dmr.bed
    ... # Other temp files

Decision Tree

Step 1: Prepare the DMR regions into 6-column standard format BED file received by HOMER

awk -F'\t' 'BEGIN {OFS="\t"} {print $1, $2, $3, "peak_"NR, "*", "+"}' dmr_results.txt > homer_dmr.bed

Step 2: Annotate the differential methylation regions to the gene promoter.

Call:

  • mcp__homer-tools__homer_simple_annotate_peaks

with:

  • peaks_path: 6-column standard format BED file from Step 1.
  • genome: Provide by user.
  • output_path: Output path of the annotated file

Step 3: Preprocess differential methylation and expression tables into a standard format

Call:

  • mcp__methyl-tools__preprocess_differential_table

(1) with:

  • input_path: dmr_results.txt
  • output_path
  • data_type: methyl
  • source: metilene

(2) with:

  • input_path: dge_result.csv
  • output_path
  • data_type: expr
  • source: deseq2

Step 4: Integrate methylation and expression data by promoter proximity

Call:

  • mcp__methyl-tools__integrate_methylation_expression

with:

methyl_path: Path to standardized methylation TSV with columns: chr,start,end,pvalue,meth_diff (from Step 3) methyl_annot_path: Path to methylation annotation TSV from HOMER (from Step 2). expr_path: Path to standardized expression TSV with columns: gene,pvalue,log2FoldChange (from Step 3). output_prefix: Prefix for all output files (e.g. 'corr_DMR_DEG/stats/integrative'). methyl_diff: Absolute methylation difference threshold (fraction points). expr_fc: Fold-change threshold for expression (absolute, e.g. 1.5 for 1.5x).