Introduction

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.

Publications

  1. HMM-DM: identifying differentially methylated regions using a hidden Markov model.
    Cite this
    Yu X, Sun S, 2016-03-01 - Statistical applications in genetics and molecular biology

Credits

  1. Xiaoqing Yu
    Developer

  2. Shuying Sun
    Investigator

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Summary
AccessionBT000128
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Submitted ByShuying Sun