differential-methylation
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.
Best use case
differential-methylation is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using differential-methylation 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/differential-methylation/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How differential-methylation Compares
| Feature / Agent | differential-methylation | 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 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.
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
# WGBS Differential Methylation with metilene
## Overview
- Refer to the **Inputs & Outputs** section to check available inputs and design the output structure.
- **Always prompt user** for which columns in the BED files are methylation fraction/percent. Never decide by yourself.
- Convert heterogeneous inputs to a **per‑sample 4‑column Metilene table** (chrom, start, end, methylation_fraction). Sort the BED files after conversion.
- Generate the merged bed file as the input of metilene.
- **Run metilene**: call DMRs and DMCs with tunable parameters
- **Visualize**: quick plots (Δmethylation vs –log10(q), length histograms).
---
## Inputs & Outputs
### Inputs
```bash
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.
### Outputs
```bash
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
```
---
## Decision Tree
### Step 1: Standardize BED file
- extract information from input BED files into **per‑sample 4‑column Metilene table** and sort
```bash
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
```
### Step 2: Build the merged methylation matrix (fractions per sample)
Call:
- `mcp__methyl-tools__generate_metilene_input`
with:
- `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 input
- `group1_name`: Identifier of group 1
- `group2_name`: Identifier of group 2
This tool will:
- Generate a input file for metilene
### Step 3: Run metilene (DMR mode)
Call:
- `mcp__methyl-tools__run_metilene`
with:
- `merged_bed_path`: file path for metilene input
- `group_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 analysis
- `threads`: Always use 1 threads to avoid error
- `output_results_path`: Output path for the DMR results
### Step 4: Run metilene (DMC mode)
Call:
- `mcp__methyl-tools__run_metilene`
with:
- `merged_bed_path`: file path for metilene input
- `group_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 analysis
- `output_results_path`: Output path for the DMC results
### Step 5: Filter significant DMRs and export BED
Call:
- `mcp__methyl-tools__filter_dmrs`
with:
- `metilene_results_path`: DMR results from Step 3
- `significant_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.
### Step 6: Filter significant DMCs and export BED
Call:
- `mcp__methyl-tools__filter_dmrs`
with:
- `metilene_results_path`: DMC results from Step 4
- `significant_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.
### Step 6: Visualization (quick, optional)
**Volcano-like plot (Δmethylation vs –log10(q))**
1. Call:
- `mcp__methyl-tools__plot_dmr_volcano`
with:
- `metilene_results_path`: DMR results from Step 3
- `output_pdf_path`
- `q_threshold`, `delta_threshold` as agreed.
- Optional tuning of `point_size`, `alpha` as needed.
**DMR length histogram**
Call:
- `mcp__methyl-tools__plot_dmr_length_hist`
with:
- `significant_bed_path`: Path for the signimicant DMRs (BED format from Step 5)
- `output_pdf_path`
---
## Troubleshooting
- **Chromosome naming mismatches**: standardize to a single scheme (`chr1` vs `1`) across all samples.Related Skills
differential-tad-analysis
This skill performs differential topologically associating domain (TAD) analysis using HiCExplorer's hicDifferentialTAD tool. It compares Hi-C contact matrices between two conditions based on existing TAD definitions to identify significantly altered chromatin domains.
differential-review
Perform security-focused review of code diffs and pull requests, identifying newly introduced vulnerabilities, security regressions, and unsafe patterns in changed code.
differential-region-analysis
The differential-region-analysis pipeline identifies genomic regions exhibiting significant differences in signal intensity between experimental conditions using a count-based framework and DESeq2. It supports detection of both differentially accessible regions (DARs) from open-chromatin assays (e.g., ATAC-seq, DNase-seq) and differential transcription factor (TF) binding regions from TF-centric assays (e.g., ChIP-seq, CUT&RUN, CUT&Tag). The pipeline can start from aligned BAM files or a precomputed count matrix and is suitable whenever genomic signal can be summarized as read counts per region.
correlation-methylation-epiFeatures
This skill provides a complete pipeline for integrating CpG methylation data with chromatin features such as ATAC-seq signal, H3K27ac, H3K4me3, or other histone marks/TF signals.
TF-differential-binding
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.
global-methylation-profile
This skill performs genome-wide DNA methylation profiling. It supports single-sample and multi-sample workflows to compute methylation density distributions, genomic feature distribution of the methylation profile, and sample-level clustering/PCA. Use it when you want to systematically characterize global methylation patterns from WGBS or similar per-CpG methylation call files.
methylation-variability-analysis
This skill provides a complete and streamlined workflow for performing methylation variability and epigenetic heterogeneity analysis from whole-genome bisulfite sequencing (WGBS) data. It is designed for researchers who want to quantify CpG-level variability across biological samples or conditions, identify highly variable CpGs (HVCs), and explore epigenetic heterogeneity.
bio-methylation-calling
Extract methylation calls from Bismark BAM files using bismark_methylation_extractor. Generates per-cytosine reports for CpG, CHG, and CHH contexts. Use when extracting methylation levels from aligned bisulfite sequencing data for downstream analysis.
bgo
Automated Blender build-go workflow. Automatically builds, removes old version, installs, enables, and launches Blender with your extension/add-on. Use when you want to quickly test changes, execute complete build-to-launch cycle, or run custom packaging scripts with automatic Blender launch.
github-search
Search GitHub for repos, code, and usage examples using gh CLI. Capabilities: repo discovery, code search, finding library usage patterns, issue/PR search. Actions: search, find, discover repos/code/examples. Keywords: gh, github, search repos, search code, find examples, how to use library, stars, language filter. Use when: finding repositories, searching code patterns, discovering how libraries are used, exploring open source.
github-repo-skill
Guide for creating new GitHub repos and best practice for existing GitHub repos, applicable to both code and non-code projects
github-repo-analysis
Analyze GitHub repositories to extract insights about commit frequency, outstanding contributors, release timeline, and project health metrics. Use when users request repository analysis, commit history investigation, contributor identification, release tracking, or development activity assessment for any GitHub project.