title: HMM-DM: identifying differentially methylated regions using a hidden Markov model.
abstract: DNA methylation is an epigenetic modification involved in organism development and cellular differentiation. Identifying differential methylations can help to study genomic regions associated with diseases. Differential methylation studies on single-CG resolution have become possible with the bisulfite sequencing (BS) technology. However, there is still a lack of efficient statistical methods for identifying differentially methylated (DM) regions in BS data. We have developed a new approach named HMM-DM to detect DM regions between two biological conditions using BS data. This new approach first uses a hidden Markov model (HMM) to identify DM CG sites accounting for spatial correlation across CG sites and variation across samples, and then summarizes identified sites into regions. We demonstrate through a simulation study that our approach has a superior performance compared to BSmooth. We also illustrate the application of HMM-DM using a real breast cancer dataset.
authors.forename: Xiaoqing
authors.initials: X
authors.lastname: Yu
authors.forename: Shuying
authors.initials: S
authors.lastname: Sun
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics; D003198:Computer Simulation; D019175:DNA Methylation; D057890:Epigenomics / Q000379:methods*; D005260:Female; D006801:Humans; D008390:Markov Chains; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity