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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.
The mechanism of methylation unfolds in two stages. The first stage sees a methyltransferase enzyme facilitating the transfer of a methyl group from S-adenosylmethionine (SAM) to the substrate, forming S-adenosylhomocysteine (SAH). The second stage involves further metabolism of SAH into homocysteine, which can be recycled...

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Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Published on: February 24, 2015

methylClass: an R package to construct DNA methylation-based classification models.

¹Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA.

Briefings in Bioinformatics

|January 11, 2024

Summary

This summary is machine-generated.

We developed methylClass, an R package for DNA methylation-based cancer classification. Its ensemble-based support vector machine (eSVM) model offers superior accuracy and efficiency compared to existing methods.

Keywords:

ensemble feature selection methylation multi-omics pan-cancer support vector machine

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

DNA methylation profiling is crucial for accurate cancer diagnosis.
A comprehensive R package for methylation-based classification is currently lacking.
Existing methods like random forest and traditional support vector machines have limitations in accuracy and speed.

Purpose of the Study:

To develop an R package, methylClass, for advanced methylation-based cancer classification.
To introduce the ensemble-based support vector machine (eSVM) for improved classification accuracy and efficiency.
To provide novel feature selection methods and support for multi-omics studies.

Main Methods:

Development of the methylClass R package.
Implementation of the ensemble-based support vector machine (eSVM) model.
Inclusion of novel feature selection algorithms and multi-omics data integration functions.

Main Results:

The methylClass package, particularly the eSVM model, demonstrated significantly higher accuracy in methylation data classification compared to random forest.
eSVM effectively addressed the time-consuming nature of traditional support vector machines.
The package showed accurate performance across four diverse datasets, highlighting its utility in both methylation and multi-omics analyses.

Conclusions:

The methylClass R package provides a powerful and efficient tool for DNA methylation-based cancer classification.
The eSVM model integrated within methylClass offers superior performance over existing methods.
methylClass is a valuable resource for researchers conducting methylation and multi-omics studies in cancer.