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.
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---
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
```bash
dmr_results.txt # DMR results output by the metilene
dge_result.csv # DEG results output by DESeq2
```
### Outputs
```bash
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
```bash
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).