BIMMER: A Bi-layer hidden Markov model for differential methylation analysis

Zijing Mao, Tim H.M. Huang, Yidong Chen, Yufei Huang

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

Abstract

Methyl-CpG binding domain-based capture followed by sequencing (MBDCap-seq) is a cost-effective method for genome-wide methylation analyses especially in CpG-rich regions. In this study, we developed BIMMER, a BI-layer hidden Markov model for differential Methylation Regions (DMRs) identification BIMMER using MBDCap-seq samples derived from two different phenotypes. BIMMER models and generates a posterior probability for a 100bp bin to be a methylation site in either normal or disease samples by its first hidden layer, and then integrate these posterior probabilities in the second hidden layer to obtain the posterior probability of bin-specific differential methylation between the normal and disease samples. Based on these posterior probabilities, the decisions on the methylation and differential statuses for each bin can be calculated. Simulated results showed 94.3% area under precision-recall curve for BIMMER (BIMMER is programmed in Java and available by request).

Original languageEnglish (US)
Title of host publicationProceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
Pages99-102
Number of pages4
DOIs
StatePublished - Dec 1 2013
Event2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013 - Shanghai, China
Duration: Dec 18 2013Dec 21 2013

Publication series

NameProceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013

Other

Other2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
CountryChina
CityShanghai
Period12/18/1312/21/13

Keywords

  • DNA methylation
  • Hidden Markov Model (HMM)
  • MBDCap-seq
  • differential methylation

ASJC Scopus subject areas

  • Biomedical Engineering

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