HMM-DM: identifying differentially methylated regions using a hidden Markov model

Yu Xiaoqing () and Sun Shuying
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Yu Xiaoqing: Department of Biostatistics, Yale University, New Haven, CT 06511, USA
Sun Shuying: Department of Mathematics, Texas State University, San Marcos, TX 78666, USA

Statistical Applications in Genetics and Molecular Biology, 2016, vol. 15, issue 1, 69-81

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.

Keywords: bisulfite sequencing; cancer; differential methylation; hidden Markov model (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1515/sagmb-2015-0077

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