BIMMER: A novel algorithm for detecting differential DNA methylation regions from MBDCap-seq data

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

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

DNA methylation is a common epigenetic marker that regulates gene expression. A robust and cost-effective way for measuring whole genome methylation is Methyl-CpG binding domain-based capture followed by sequencing (MBDCap-seq). In this study, we proposed BIMMER, a Hidden Markov Model (HMM) for differential Methylation Regions (DMRs) identification, where HMMs were proposed to model the methylation status in normal and cancer samples in the first layer and another HMM was introduced to model the relationship between differential methylation and methylation statuses in normal and cancer samples. To carry out the prediction for BIMMER, an Expectation-Maximization algorithm was derived. BIMMER was validated on the simulated data and applied to real MBDCap-seq data of normal and cancer samples. BIMMER revealed that 8.83% of the breast cancer genome are differentially methylated and the majority are hypo-methylated in breast cancer.

Original languageEnglish (US)
Article numberS6
JournalBMC bioinformatics
Volume15
Issue number12
DOIs
StatePublished - Nov 6 2014

Keywords

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

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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