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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yu Xiaoqing & Sun Shuying, 2016. "HMM-DM: identifying differentially methylated regions using a hidden Markov model," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(1), pages 69-81, March.
    • Handle: RePEc:bpj:sagmbi:v:15:y:2016:i:1:p:69-81:n:6
    DOI: 10.1515/sagmb-2015-0077
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    References listed on IDEAS

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