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.

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).