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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

DM-BLD: differential methylation detection using a hierarchical Bayesian model exploiting local dependency.

Xiao Wang¹, Jinghua Gu¹, Leena Hilakivi-Clarke²

¹Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA 22203, USA.

Bioinformatics (Oxford, England)

|September 13, 2016

Summary

This summary is machine-generated.

We developed DM-BLD, a novel Bayesian method for detecting differential DNA methylation. This approach accurately identifies cancer-related methylated genes, even with high sample variability.

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Last Updated: Mar 15, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Published on: May 20, 2018

DNA Methylation: Bisulphite Modification and Analysis

DNA Methylation: Bisulphite Modification and Analysis

Published on: October 21, 2011

Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

High-throughput DNA methylation profiling aids cancer research.
Detecting differential methylation is challenging due to high tumor sample variability.

Purpose of the Study:

To develop a novel probabilistic approach for accurate differential methylation detection.
To identify differentially methylated genes associated with cancer recurrence.

Main Methods:

Developed DM-BLD (Differential Methylation detection using a hierarchical Bayesian model exploiting Local Dependency).
Employed a joint model capturing local loci dependency (Leroux conditional autoregressive structure) and sample methylation change dependency (discrete Markov random field).
Utilized a hierarchical Bayesian model with differential states as hidden variables.

Main Results:

DM-BLD outperforms existing methods, especially with moderate methylation changes and high sample variability.
Applied DM-BLD to breast cancer data, identifying key methylated genes linked to recurrence.
Identified polycomb target genes and genes involved in transcription factor activity as significant.

Conclusions:

DM-BLD offers a robust method for identifying differentially methylated genes in cancer research.
The approach effectively handles high variability in tumor samples.
Identified novel candidate genes for breast cancer recurrence, providing insights for targeted therapies.