dna-methylation

Analyze DNA methylation data from Illumina arrays and bisulfite sequencing.

29|3|Updated Jun 11, 2026
One-click install
npx skills add https://github.com/inflexa-ai/inflexa --skill dna-methylation
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: dna-methylation
Source: https://github.com/inflexa-ai/inflexa/tree/main/skills/dna-methylation
Command: npx skills add https://github.com/inflexa-ai/inflexa --skill dna-methylation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires minfi, ChAMP, DMRcate, missMethyl, EpiDISH, methylclock, and includes references (resource) components.

What problem does it solve?

This Skill addresses the complexity of processing and analyzing DNA methylation data, providing a structured approach to handle array-based and sequencing-based methylation studies while avoiding common statistical pitfalls.

Core Features & Use Cases

  • Pipeline Orchestration: Provides standardized workflows for IDAT processing, normalization, and differential methylation analysis.
  • Advanced Analytics: Includes modules for epigenetic clock calculation, cell type deconvolution, and epigenome-wide association studies (EWAS).
  • Use Case: A researcher can use this Skill to perform differential methylation analysis on EPIC array data, adjusting for cell type composition and batch effects to identify biologically relevant DMRs.

Quick Start

Use the dna-methylation skill to perform differential methylation analysis on your normalized beta matrix and generate a report of significant DMPs and DMRs.

Frequently Asked Questions about dna-methylation

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

FAQPage Schema
How do I identify differentially methylated regions from EPIC array data?

Differential methylation analysis identifies DMRs from EPIC array data by processing IDAT files, normalizing beta values, and applying rigorous statistical modeling using M-values with covariate adjustment.

Can I adjust for cell type composition when running an epigenome-wide association study?

Epigenome-wide association studies (EWAS) can adjust for cell type composition by using deconvolution modules to estimate proportions, ensuring differentially methylated positions reflect biologically relevant signals rather than cellular heterogeneity.

How do I calculate epigenetic age from DNA methylation data?

Epigenetic age prediction calculates biological age from DNA methylation data by applying specialized clock algorithms to normalized methylation matrices, providing reproducible age estimates for biomarker research.

Does this pipeline support bisulfite sequencing data or only Illumina arrays?

The DNA methylation pipeline supports both Illumina methylation arrays and bisulfite sequencing data, providing standardized workflows for IDAT processing, normalization, and differential methylation analysis across both platforms.

What is the best way to handle batch effects in DNA methylation analysis?

Handling batch effects in DNA methylation analysis requires rigorous statistical modeling using M-values and covariate adjustment within the normalization pipeline, ensuring reproducible biological results free from technical artifacts.