Higher order methylation features for clustering and prediction in epigenomic studies
Higher order methylation features for clustering and prediction in epigenomic studies
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DOI：
10.1093/bioinformatics/btw432
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发表时间：
2016-09-01
期刊：
影响因子：
5.8
通讯作者：
Sanguinetti, Guido
中科院分区：
文献类型：
--
作者：
Kapourani, Chantriolnt-Andreas;Sanguinetti, Guido
动机：DNA甲基化是一种深入研究的表观遗传标记，但其功能作用尚不完全清楚。尝试定量地将平均DNA甲基化与基因表达联系起来，在众所周知的CpG岛上的甲基化开关之外的相关性很差。结果：在这里，我们使用概率机器学习来提取与定义区域的甲基化特征相关联的更高阶特征。这些特征精确地量化了甲基化图谱的形状的概念，捕捉了DNA甲基化跨基因组区域的空间相关性。利用这些跨越启动子近端区域的高阶特征，我们能够构建一个强大的基因表达机器学习预测因子，显著提高平均DNA甲基化水平的预测能力。此外，我们可以使用更高阶的特征来聚集启动子-近端区域，表明在不同的细胞系中，启动子上发生了五种主要的甲基化模式，我们提供的证据表明，超出CpG岛的甲基化可能与基因表达的调控有关。我们的结果支持了先前关于空间相关性在甲基化模式中的功能作用的报道，并为下游分析提供了一种量化这些特征的手段。
Motivation: DNA methylation is an intensely studied epigenetic mark, yet its functional role is incompletely understood. Attempts to quantitatively associate average DNA methylation to gene expression yield poor correlations outside of the well-understood methylation-switch at CpG islands.Results: Here, we use probabilistic machine learning to extract higher order features associated with the methylation profile across a defined region. These features quantitate precisely notions of shape of a methylation profile, capturing spatial correlations in DNA methylation across genomic regions. Using these higher order features across promoter-proximal regions, we are able to construct a powerful machine learning predictor of gene expression, significantly improving upon the predictive power of average DNA methylation levels. Furthermore, we can use higher order features to cluster promoter-proximal regions, showing that five major patterns of methylation occur at promoters across different cell lines, and we provide evidence that methylation beyond CpG islands may be related to regulation of gene expression. Our results support previous reports of a functional role of spatial correlations in methylation patterns, and provide a mean to quantitate such features for downstream analyses.