A Markov random field-based Bayesian model to identify genes with differential methylation

Xiao Wang, Jinghua Gu, Jianhua Xuan, Robert Clarke, Leena Hilakivi-Clarke

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The rapid development of biotechnology makes it possible to explore genome-wide DNA methylation mapping which has been demonstrated to be related to diseases including cancer. However, it also posts substantial challenges in identifying biologically meaningful methylation pattern changes. Several algorithms have been proposed to detect differential methylation events, such as differentially methylated CpG sites and differentially methylated regions. However, the intrinsic dependency of the CpG sites in a neighboring area has not yet been fully considered. In this paper, we propose a novel method for the identification of differentially methylated genes in a Markov random field-based Bayesian framework. Specifically, we use Markov random field to model the dependency of the neighboring CpG sites, and then estimate the differential methylation score of the CpG sites in a Bayesian framework through a sampling scheme. Finally, the differential methylation statuses of the genes are determined by the estimated scores of the involved CpG sites. In addition, significance test is conducted to assess the significance of the identified differentially methylated genes. Experimental results on both synthetic data and real data demonstrate the effectiveness of the proposed method in identifying genes with differential methylation patterns under different conditions.

Original languageEnglish (US)
Title of host publication2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2014
PublisherIEEE Computer Society
ISBN (Print)9781479945368
DOIs
StatePublished - 2014
Externally publishedYes
Event2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2014 - Honolulu, HI, United States
Duration: May 21 2014May 24 2014

Publication series

Name2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2014

Conference

Conference2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2014
CountryUnited States
CityHonolulu, HI
Period5/21/145/24/14

Keywords

  • Bayesian framework
  • dependency structure
  • differential methylation events
  • Gibbs sampling
  • Markov random field
  • significance test

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