Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements.
Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements.
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DOI：
10.1186/s13059-015-0581-9
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发表时间：
2015-01-24
期刊：
影响因子：
12.3
通讯作者：
Engelhardt BE
中科院分区：
文献类型：
--
作者：
Zhang W;Spector TD;Deloukas P;Bell JT;Engelhardt BE
最近个体特异性全基因组DNA甲基化谱的测定使得表观基因组关联研究能够鉴定与表型相关的特定CpG位点。CpG位点特异性甲基化水平的计算预测对于实现全基因组分析至关重要，但目前的方法解决了基因座内的平均甲基化，并且通常限于特定的基因组区域。我们描述了全基因组DNA甲基化模式，并表明CpG位点之间的相关性迅速衰减，仅基于相邻位点进行预测具有挑战性。我们构建了一个随机森林分类器，使用包括相邻CpG位点甲基化水平和基因组距离、与编码区的共定位、CpG岛（CGI）和ENCODE项目的调控元件在内的特征来预测CpG位点分辨率下的甲基化水平。我们的方法在单CpG位点精度下实现了92%的全基因组甲基化水平预测准确率。当仅限于CGI内的CpG位点时，准确度增加至98%，并且在平台和细胞类型异质性之间具有稳健性。我们的分类器优于其他类型的分类器，并确定有助于预测准确性的功能：相邻的CpG位点甲基化，CGI，共定位的DNA酶I超敏位点，转录因子结合位点，组蛋白修饰被认为是最能预测甲基化水平。我们对DNA甲基化模式的观察使我们开发了一种分类器，以高精度预测CpG位点分辨率下的DNA甲基化水平。此外，我们的方法确定了与DNA甲基化相互作用的基因组特征，表明了DNA甲基化修饰和调控的机制，并将不同的表观遗传过程联系起来。本文的在线版本（doi：10.1186/s13059-015-0581-9）包含补充材料，可供授权用户使用。
Recent assays for individual-specific genome-wide DNA methylation profiles have enabled epigenome-wide association studies to identify specific CpG sites associated with a phenotype. Computational prediction of CpG site-specific methylation levels is critical to enable genome-wide analyses, but current approaches tackle average methylation within a locus and are often limited to specific genomic regions. We characterize genome-wide DNA methylation patterns, and show that correlation among CpG sites decays rapidly, making predictions solely based on neighboring sites challenging. We built a random forest classifier to predict methylation levels at CpG site resolution using features including neighboring CpG site methylation levels and genomic distance, co-localization with coding regions, CpG islands (CGIs), and regulatory elements from the ENCODE project. Our approach achieves 92% prediction accuracy of genome-wide methylation levels at single-CpG-site precision. The accuracy increases to 98% when restricted to CpG sites within CGIs and is robust across platform and cell-type heterogeneity. Our classifier outperforms other types of classifiers and identifies features that contribute to prediction accuracy: neighboring CpG site methylation, CGIs, co-localized DNase I hypersensitive sites, transcription factor binding sites, and histone modifications were found to be most predictive of methylation levels. Our observations of DNA methylation patterns led us to develop a classifier to predict DNA methylation levels at CpG site resolution with high accuracy. Furthermore, our method identified genomic features that interact with DNA methylation, suggesting mechanisms involved in DNA methylation modification and regulation, and linking diverse epigenetic processes. The online version of this article (doi:10.1186/s13059-015-0581-9) contains supplementary material, which is available to authorized users.
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DOI：
10.1016/j.bbrc.2008.07.077
发表时间：
2008-09-26
影响因子：
3.1
作者：
Fan, Shicai;Zhang, Michael Q.;Zhang, Xuegong
通讯作者：
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影响因子：
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影响因子：
3.5
作者：
Bernat, John A.;Crawford, Gregory E.;Kondrashov, Alexey S.
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