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Phase II Reactions: Methylation Reactions

Phase II Reactions: Methylation Reactions

Methylation is a phase II biotransformation process involving the attachment of a methyl group to a substrate. Enzymes known as methyltransferases orchestrate this reaction.
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Updated: May 2, 2026

Targeted DNA Methylation Analysis by Next-generation Sequencing

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

MAGI: Methylation analysis using genome information.

Douglas D Baumann¹, R W Doerge²

¹Department of Mathematics; University of Wisconsin, La Crosse; La Crosse, WI USA.

|March 5, 2014

Summary

This summary is machine-generated.

Annotation-informed analysis of next-generation sequencing DNA methylation data improves performance. Methylation Analysis using Genome Information (MAGI) enhances statistical power and interpretation for various study designs, even with low sequencing depth.

Keywords:

annotation informed differential methylation epigenetics epigenomics statistical bioinformatics testing methylation

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Targeted DNA Methylation Analysis by Next-generation Sequencing

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Published on: October 21, 2011

Area of Science:

Genomics
Epigenetics
Bioinformatics

Background:

DNA methylation analysis is crucial for understanding gene regulation.
Current methods for analyzing next-generation sequencing DNA methylation data have limitations, particularly with low sequencing depth and in interpreting significance.

Purpose of the Study:

To introduce and evaluate Methylation Analysis using Genome Information (MAGI), an annotation-informed approach for DNA methylation data analysis.
To demonstrate improved performance over existing testing procedures.

Main Methods:

Incorporation of genomic annotation information into the analysis pipeline.
Application of the MAGI method to both unreplicated and replicated next-generation sequencing DNA methylation datasets.
Comparison of MAGI with current standard testing procedures.

Main Results:

MAGI demonstrates effective analysis for studies with low sequencing depth.
Annotation-informed tests show increased statistical power compared to current tests.
MAGI provides a significance-based interpretation of differential methylation.

Conclusions:

Integrating annotation information significantly enhances DNA methylation data analysis.
MAGI offers a powerful and versatile tool for epigenomic studies, improving statistical rigor and interpretability.