Modeling Methylation Patterns with Long Read Sequencing Data

Modeling Methylation Patterns with Long Read Sequencing Data
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使用长读测序数据对甲基化模式进行建模

DOI:
10.1109/tcbb.2017.2721943
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发表时间:
2018
期刊:
IEEE/ACM Transactions on Computational Biology and Bioinformatics
影响因子:
--
通讯作者:
M. Lacey
M. Lacey
中科院分区:
--
文献类型:
--
作者:
K. N. Meyer;M. Lacey

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CpG二核苷酸处胞嘧啶甲基化的变化通常在基因组区域中观察到,并且分析通常集中于估计在给定区域中观察到的甲基化位点的比例,并在样品之间比较这些水平以确定与感兴趣的病症的关联。虽然位点被默认为独立的,但当在单个分子水平上观察时,甲基化模式表现出局部空间依赖性的强有力证据。我们以前开发了一个相邻位点模型来解释在卵巢癌的两个串联重复区域中观察到的相关性和聚类行为。我们现在介绍扩展的模型,占之间的距离,以及从头甲基化和去甲基化率的不对称相关性的影响。我们将我们的模型应用于全基因组亚硫酸氢盐测序实验的已发表数据,使用长读段,估计跨越21至67个位点的CpG密集区域的选择模型参数。我们的方法检测了局部空间相关性作为位点间距离的函数的证据,并证明了在表观遗传学研究中使用长读段测序数据的附加价值。
Variation in cytosine methylation at CpG dinucleotides is often observed in genomic regions, and analysis typically focuses on estimating the proportion of methylated sites observed in a given region and comparing these levels across samples to determine association with conditions of interest. While sites are tacitly treated as independent, when observed at the level of individual molecules methylation patterns exhibit strong evidence of local spatial dependence. We previously developed a neighboring sites model to account for correlation and clustering behavior observed in two tandem repeat regions in a collection of ovarian carcinomas. We now introduce extensions of the model that account for the effect of distance between sites as well as asymmetric correlation in de novo methylation and demethylation rates. We apply our models to published data from a whole genome bisulfite sequencing experiment using long reads, estimating model parameters for a selection of CpG-dense regions spanning between 21 and 67 sites. Our methods detect evidence of local spatial correlation as a function of site-to-site distance and demonstrate the added value of employing long read sequencing data in epigenetic research.
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