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

IF 0.9 4区 数学 Q3 Mathematics
Xiaoqing Yu, Shuying Sun
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引用次数: 30

Abstract

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.
HMM-DM:使用隐马尔可夫模型识别差异甲基化区域
DNA甲基化是一种参与生物发育和细胞分化的表观遗传修饰。鉴定差异甲基化有助于研究与疾病相关的基因组区域。亚硫酸酯测序(BS)技术使单cg分辨率的差异甲基化研究成为可能。然而,仍然缺乏有效的统计方法来识别BS数据中的差异甲基化(DM)区域。我们开发了一种名为HMM-DM的新方法,利用BS数据检测两种生物状态之间的DM区域。该方法首先利用隐马尔可夫模型(HMM)识别DM CG位点,考虑CG位点之间的空间相关性和样本间的差异,然后将识别出的位点归纳为区域。我们通过仿真研究证明,与BSmooth相比,我们的方法具有优越的性能。我们还使用真实的乳腺癌数据集说明了HMM-DM的应用。
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来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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