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.
Best use case
integrative-DMR-DEG is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using integrative-DMR-DEG should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/23-integrative-dmr-deg/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How integrative-DMR-DEG Compares
| Feature / Agent | integrative-DMR-DEG | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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).Related Skills
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