An optimized algorithm for detecting and annotating regional differential methylation.
An optimized algorithm for detecting and annotating regional differential methylation.
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DOI：
10.1186/1471-2105-14-s5-s10
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发表时间：
2013
影响因子：
3
通讯作者：
Mason CE
中科院分区：
文献类型：
--
作者：
Li S;Garrett-Bakelman FE;Akalin A;Zumbo P;Levine R;To BL;Lewis ID;Brown AL;D'Andrea RJ;Melnick A;Mason CE
DNA甲基化分析揭示了基因组中重要的差异甲基化区域（DMRs），这些区域在发育过程中发生改变或受到疾病的干扰。迄今为止，很少有程序存在的区域分析富集或全基因组亚硫酸氢盐转化测序数据，尽管这类数据越来越普遍。在这里，我们描述了一种开源的、优化的方法，用于从高通量序列数据中确定基于经验的dmr (eDMR)，该方法适用于富集的全基因组甲基化分析数据集，以及其他全球富集的表观遗传修饰数据。我们的双峰分布模型和用于优化区域甲基化分析的加权代价函数提供了包含显著表观遗传修饰的区域的准确边界。我们的算法将CpGs的空间分布考虑到富集分析中，允许优化差异甲基化经验区域的定义。结合区域p值组合的相关调整和DMR注释，我们提供了一种可应用于各种数据集的快速DMR分析方法。我们的方法对DMRs的方向性及其全基因组分布进行了分类，并通过对两种急性髓性白血病（AML）肿瘤亚型的正确分层观察到其具有临床相关性。我们的加权优化算法eDMR用于调用DMR，扩展了已建立的DMR R管道（methylKit），并提供了表观基因组学所需的资源。我们的方法能够在高通量甲基化测序实验中精确和可扩展地找到DMRs。eDMR可从http://code.google.com/p/edmr/下载。
DNA methylation profiling reveals important differentially methylated regions (DMRs) of the genome that are altered during development or that are perturbed by disease. To date, few programs exist for regional analysis of enriched or whole-genome bisulfate conversion sequencing data, even though such data are increasingly common. Here, we describe an open-source, optimized method for determining empirically based DMRs (eDMR) from high-throughput sequence data that is applicable to enriched whole-genome methylation profiling datasets, as well as other globally enriched epigenetic modification data. Here we show that our bimodal distribution model and weighted cost function for optimized regional methylation analysis provides accurate boundaries of regions harboring significant epigenetic modifications. Our algorithm takes the spatial distribution of CpGs into account for the enrichment assay, allowing for optimization of the definition of empirical regions for differential methylation. Combined with the dependent adjustment for regional p-value combination and DMR annotation, we provide a method that may be applied to a variety of datasets for rapid DMR analysis. Our method classifies both the directionality of DMRs and their genome-wide distribution, and we have observed that shows clinical relevance through correct stratification of two Acute Myeloid Leukemia (AML) tumor sub-types. Our weighted optimization algorithm eDMR for calling DMRs extends an established DMR R pipeline (methylKit) and provides a needed resource in epigenomics. Our method enables an accurate and scalable way of finding DMRs in high-throughput methylation sequencing experiments. eDMR is available for download at http://code.google.com/p/edmr/.