Collaborative Research:Graphical models for characterizing global RNA methylation

合作研究：表征全局 RNA 甲基化的图形模型

基本信息

批准号：
8916526
负责人：
Yufei Huang
金额：
$ 35.8万
依托单位：
UNIVERSITY OF TEXAS SAN ANTONIO
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-01 至 2017-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8916526
关键词：
Accounting Address Advanced Development Algorithms Benign Bioinformatics Biological Biological Markers Biological Process Breast Breast Cancer Cell Breast Cancer cell line Breast Epithelial Cells Cells ChIP-seq Collection Computer Simulation DNA Methylation Detection Dimensions Disease Epigenetic Process Exons Gene Expression Gene Expression Profile Gene Expression Regulation Genes Genome Genomics Goals Health High Performance Computing High-Throughput Nucleotide Sequencing Histones Immunoprecipitation Learning Malignant Neoplasms Marriage Messenger RNA Methylation Modeling Modification Play Protein Isoforms Protocols documentation RNA RNA Editing RNA Splicing RNA Stability RNA methylation Reading Research Role Sampling Site Staging Stress Technology Therapeutic Intervention Time Transcript Variant base cell type exome human disease mRNA Transcript Degradation meetings success theories tool transcriptome sequencing

项目摘要

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.

描述（由申请人提供）：RNA甲基化开始成为一种普遍的表观遗传标记，可能在基因调节中起关键作用。然而，旨在识别和表征整个转录组RNA甲基化（甲基转录组）的技术仍处于早期阶段。这主要是因为与DNA甲基化不同，RNA甲基化必须考虑转录物的丰度，基因表达水平的变化，mRNA降解以及最重要的是由转录本同工型引起的位置偏置。此外，在两个不同的细胞环境（例如正常与压力）或不同疾病状态（例如良性与癌症）中RNA甲基化的差异提出了表征甲基转录组的另一个计算挑战。该提案的总体目标是首次开发计算图形模型以启用1）对全球mRNA甲基化的准确检测，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，我们提出了三个特定的目标：在AIM 1中，我们将开发图形模型来检测造成生物学变化和读取偏见的mRNA甲基化。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AIM 2中，我们将开发用于检测上下文特异性差甲基化的图形模型。在AIM 3中，我们将在正常和疾病状态中表征并实验验证整个转录组，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，从而能够鉴定全球和上下文特定的mRNA甲基化，而且还将阐明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。该项目利用了我们在表观遗传学，计算建模，高性能计算，生物信息学和高吞吐量测序方面的专业知识，从而为RNA Methylaton的新兴领域增加了新的维度，并极大地促进了计算建模和学习的进步。