methylation-variability-analysis

Identify CpG-level methylation variability across WGBS samples using R workflows.

12|3|Updated Nov 4, 2025

Search Tags:#heterogeneity #methylation #WGBS #CpG #variability #epigenetics

Ready to run

npx skills add https://github.com/BIsnake2001/ChromSkills --skill methylation-variability-analysis

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: methylation-variability-analysis
Source: https://github.com/BIsnake2001/ChromSkills/tree/main/25.methylation-variability
Command: npx skills add https://github.com/BIsnake2001/ChromSkills --skill methylation-variability-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

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.

Core Features & Use Cases

Build a multi-sample CpG methylation matrix from WGBS coverage data.
Compute between-sample variability metrics (variance, MAD, CV) across CpGs.
Identify highly variable CpGs (HVCs) and explore epigenetic heterogeneity between groups.
Extend to region-level analyses and differential variability contrasts across conditions.

Quick Start

Run the methylation variability workflow on your WGBS methylation BED files to identify highly variable CpGs across samples.

Frequently Asked Questions about methylation-variability-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I quantify CpG methylation variability from WGBS data across multiple samples?▼

You can quantify CpG methylation variability by building a multi-sample matrix from WGBS data and computing statistics like variance, MAD, and CV. This identifies highly variable CpGs to reveal epigenetic heterogeneity across biological samples.

What is epigenetic heterogeneity analysis and when do I need it for methylation data?▼

Epigenetic heterogeneity analysis identifies highly variable CpGs (HVCs) across multiple samples to reveal biological diversity. You need it when comparing WGBS methylation differences between groups or conditions to understand variability beyond average methylation levels.

Do I need R and methylKit workflows to analyze methylation variability?▼

Yes, analyzing methylation variability requires R and methylKit-like workflows. You must also provide your own genome assembly and standard methylation BED columns to properly construct the multi-sample CpG matrix and compute the variability statistics.

Can I detect differential variability between conditions using WGBS BED files?▼

Yes, you can detect differential variability between conditions using WGBS BED files. The workflow computes between-sample variability metrics and extends to region-level analyses, allowing you to contrast differential variability across different biological groups.

What's the best way to identify highly variable CpGs from whole-genome bisulfite sequencing data?▼

The best way to identify highly variable CpGs is by computing variance, MAD, and CV metrics across a multi-sample WGBS matrix. This approach detects top-variable CpGs and generates visualizations to compare epigenetic heterogeneity between groups.

Why does my CpG methylation variability analysis require user-provided genome assembly?▼

Your CpG methylation variability analysis requires a user-provided genome assembly because the workflow needs exact genomic coordinates to properly construct the multi-sample matrix. This ensures accurate alignment of methylation BED columns across all samples.