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Updated: Apr 13, 2026

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

Estimation of the methylation pattern distribution from deep sequencing data.

Peijie Lin¹, Sylvain Forêt², Susan R Wilson^3,4

¹Mathematical Sciences Institute, Australian National University, Canberra, ACT 2601, Australia. paul.lin@unsw.edu.au.

BMC Bioinformatics

|May 7, 2015

Summary

This summary is machine-generated.

This study introduces a statistical model to accurately quantify DNA methylation patterns, accounting for sequencing errors. The MPFE R package provides tools to analyze the true distribution of these complex methylation patterns in biological samples.

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DNA Methylation: Bisulphite Modification and Analysis

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Published on: May 20, 2018

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Last Updated: Apr 13, 2026

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

DNA Methylation: Bisulphite Modification and Analysis

DNA Methylation: Bisulphite Modification and Analysis

Published on: October 21, 2011

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Published on: May 20, 2018

Area of Science:

Genomics
Epigenetics
Bioinformatics

Background:

Bisulphite sequencing detects cytosine methylation, revealing complex DNA methylation patterns beyond average levels.
Understanding methylation pattern diversity is crucial for analyzing DNA methylation landscapes.
Accurate quantification is challenged by sequencing errors and incomplete bisulphite conversion.

Purpose of the Study:

To develop a statistical model for accurately quantifying DNA methylation pattern distributions.
To account for technical limitations like sequencing errors and spurious signals.

Main Methods:

Developed a statistical model to estimate the true distribution of DNA methylation patterns.
Incorporated parameters for sequencing errors and spurious reads.

Main Results:

The model accurately estimates the underlying distribution of DNA methylation patterns.
Successfully accounts for noise introduced by sequencing and conversion processes.

Conclusions:

The developed statistical approach is implemented in the R Bioconductor package MPFE.
MPFE facilitates the analysis of DNA methylation pattern diversity.
Source code and documentation are publicly available for the MPFE package.