A MACHINE LEARNING APPROACH FOR FINE-SCALE GENOME WIDE DNA METHYLATION ANALYSIS

描述（由申请人提供）：通过DNA甲基化变异对基因表达的表观遗传学调控在包括细胞分化、人类疾病和癌症在内的一系列生物过程中起着关键作用。确定全基因组甲基化模式精细结构的新方法导致了该领域研究的爆炸式增长。基因启动子附近的甲基化与基因沉默相关，而未甲基化的启动子具有潜在的活性。目前的计算方法基于转录起始位点周围几百个碱基对窗口的CpG甲基化状态的粗略计算，将基因分为甲基化和沉默或未甲基化和潜在活性。启动子甲基化的详细空间模式可能是基因表达的重要决定因素，但目前的计算方法忽略了这一有价值的信息。最近的研究发现，CpG岛启动子附近的区域被称为“CpG岛海岸”，其甲基化状态与转录相关。然而，CpG岛海岸的定义很松散，这一概念很难在实践中应用。虽然其他甲基化特征也可能与基因表达相关，但尚未建立识别和研究它们的一般框架。新的计算工具用于识别和详细的甲基化模式与基因表达的相关性，对于提高我们对基因调控的表观遗传学的理解至关重要。我们建议开发软件工具来检测与表达相关的基因启动子的新甲基化特征。这将在一个正式的框架内完成，以便这些特征可以用于确定各种研究设计中的差异甲基化基因。利用短范围内甲基化高度相关的事实，我们将在TSS周围10 kb窗口内插入单个CpG位点的甲基化数据，以产生独立于初级序列特征的每个启动子的甲基化特征。然后，我们将使用拓扑中的度量，即离散Frechet距离，来计算甲基化特征之间的相似性，并将该度量应用于相似类型的聚类特征。然后，我们将确定与表达相关的甲基化特征簇。包含沉默基因和表达基因的基因簇将被检查，看看是否可以使用其他局部初级序列特征来区分基于表达的基因。这种方法将用于检测病例对照研究和两两比较中的甲基化变化，例如需要进行时间过程分析或检测不同的分化状态。这里开发的一般框架可以在未来扩展，以检查增强子和基因体的甲基化特征，以及组蛋白标记或其他携带详细空间信息的基因组信号。

DESCRIPTION (provided by applicant): Epigenetic regulation of gene expression through variation in DNA methylation plays a critical role in a range of biological processes including cellular differentiation, human disease and cancer. New methods for determining the fine structure of methylation patterns genome-wide have led to an explosion of research in this field. Methylation near the gene promoter is correlated with gene silencing, while unmethylated promoters are potentially active. Current computational methods classify genes as either methylated and silenced or unmethylated and potentially active based on a coarse calculation of CpG methylation state across a window of a few hundred base pairs around the transcription start site. The detailed spatial pattern of methylation across the promoter may be an important determinant of gene expression, but current computational methods ignore this valuable information. Recent work has found regions proximal to CpG island promoters, dubbed "CpG island shores", whose methylation state correlates with transcription. CpG island shores are loosely defined, however, and this concept is difficult to apply in practice. While other methylation signatures are also likely to correlate with gene expression, a general framework to identify and study them has not been established. New computational tools for identification and correlation of detailed methylation patterns with gene expression are critical for advancing our understanding of the epigenetics of gene regulation. We propose to develop software tools to detect new methylation signatures at gene promoters that correlate with expression. This will be done within a formal framework so that these signatures can be used to determine differentially methylated genes in a variety of study designs. Taking advantage of the fact that methylation over short ranges are highly correlated, we will interpolate methylation data at individual CpG sites in a 10 kb window around the TSS to yield a methylation signature at each promoter that is independent of primary sequence features. We will then use a metric from topology, the discrete Frechet distance, to calculate the similarity between methylation signatures, and apply this metric to cluster signatures of similar type. We will then determine clusters with methylation signatures that correlate with expression. Clusters that contain both silenced and expressed genes will be examined to see if other local primary sequence features can be used to discriminate the genes based on expression. This approach will be used to detect methylation changes in case-control studies and in pairwise comparisons, such as needed for timecourse analysis or for the detection of different states of differentiation. The general framework developed here can be expanded in the future to examine methylation signatures at enhancers and gene bodies, as well as to histone marks or other genomic signals that carry detailed spatial information.