Claude-skill-registry 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.

install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/23-integrative-dmr-deg" ~/.claude/skills/majiayu000-claude-skill-registry-integrative-dmr-deg && rm -rf "$T"
manifest: skills/data/23-integrative-dmr-deg/SKILL.md
source content

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).