Collaborative Research:Graphical models for characterizing global RNA methylation

描述（由申请人提供）：RNA甲基化开始作为一种通用的表观遗传标记出现，可能在基因调控中发挥关键作用。然而，旨在识别和表征转录组范围RNA甲基化（甲基转录组）的技术仍处于早期阶段。这在很大程度上是因为与DNA甲基化不同，RNA甲基化必须考虑转录本丰度、基因表达水平的变化、mRNA降解以及最重要的由转录本异构体引起的位置偏差。此外，在两种不同的细胞环境（例如正常与应激）或不同的疾病状态（例如良性与癌症）中RNA甲基化的差异对表征甲基转录组提出了另一个计算挑战。该提案的总体目标是首次开发计算图形模型，以实现1）准确和可重现的全局mRNA甲基化检测，以及2）正常和疾病状态下的上下文特异性差异RNA甲基化。为了实现这些目标，我们提出了三个具体目标：在目标1中，我们将开发用于检测mRNA甲基化的图形模型，该模型解释了生物学变异和读取偏差。我们还将开发用于检测剪接特异性甲基化位点的图形模型。在目标2中，我们将开发用于检测上下文特异性差异甲基化的图形模型。在目标3中，我们将表征和实验验证正常和疾病状态下的转录组范围内的细胞类型特异性m5C和m6A甲基化。这些目标的成功完成不仅将创建一系列综合工具，能够识别全局和上下文特异性mRNA甲基化，而且还将揭示mRNA甲基化在调节基因表达，剪接，RNA编辑和RNA稳定性中的作用。该项目利用我们在表观遗传学，计算建模，高性能计算，生物信息学和高通量测序方面的专业知识，为RNA甲基化的新兴领域增加了一个新的维度，并极大地促进了计算建模和学习的进步。

DESCRIPTION (provided by applicant): RNA methylation is beginning to emerge as an universal epigenetic mark that may play a critical role in gene regulation. However, technologies aimed at identifying and characterizing transcriptome-wide RNA methylation (methyltranscriptome) are still at their early stages. This is largely because unlike DNA methylation, RNA methylation has to take into consideration transcript abundance, variations in gene expression levels, mRNA degradation, and most importantly positional bias caused by transcript isoforms. Furthermore, differences in RNA methylation in two different cellular contexts (e.g. normal vs stress) or different disease states (e.g. benign vs. cancer) pose yet another computational challenge for characterizing methyltranscriptome. The overall goal of this proposal is to develop, for the first time, computational graphical models to enable 1) accurate and reproducible detection of global mRNA methylations, and 2) context-specific differential RNA methylations in normal and disease states. To achieve these goals, we propose three specific aims: in Aim 1, we will develop graphical models for detecting mRNA methylation that accounts for biological variations and read biases. We will also develop graphical model for detecting splicing-specific methylation sites. In Aim 2, we will develop graphical models for detecting context-specific differential methylation. In Aim 3, we will characterize and experimentally validate the transcriptome-wide, cell type-specific m5C and m6A methylation in normal and disease states. Successful completion of these aims will not only create a collection of comprehensive tools that enable the identification of global and context-specific mRNA methylations, but will also shed lights on the role of mRNA methylation in regulating gene expression, splicing, RNA editing, and RNA stability. This project leverages our expertise in epigenetics, computational modeling, high performance computing, bioinformatics and high throughput sequencing to add a new dimension to the emerging field of RNA methylaton and greatly contribute to the advances of computational modeling and learning.