基于非参数贝叶斯推断的RNA甲基化谱分解及关键致病酶基因的预测

项目名称： 基于非参数贝叶斯推断的RNA甲基化谱分解及关键致病酶基因的预测

项目编号： No.61501466

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 无线电电子学、电信技术

项目作者： 张林

作者单位： 中国矿业大学

项目金额： 19万元

中文摘要： 高通量MeRIP-Seq测序技术的出现，掀起了RNA m6A甲基化相关领域的研究热潮，同时提出了全新的计算问题。近年来MeRIP-Seq测序数据的研究仍停留在数据的基本处理，严重缺乏深入挖掘各种调控关系的生物信息学方法。.作为目前已明确的RNA甲基化调控因子，RNA甲基化酶对RNA甲基化谱的影响异常复杂，酶的异常与疾病等生命现象的产生密切相关。因此，本项目针对MeRIP-Seq测序数据中存在大量低表达基因、生物性重复样本组内可变性及调控RNA甲基化酶个数尚不明确等关键问题，基于Dirichlet过程建立贝塔二项式无限混合非参数贝叶斯模型实现RNA甲基化谱的分解，在此基础上，研究RNA甲基化酶基因与修饰位点间的调控关系，设计预测RNA甲基化酶基因特异性、关键致病酶基因的生物信息学方法。.该项目有望加快RNA甲基化功能的理解和认识，并将为RNA甲基化应用于人类疾病的研究提供有力的理论依据。

中文关键词： RNA甲基化谱；非参数贝叶斯方法；聚类分析；二代测序；生物信息处理

英文摘要： The development of high-throughput MeRIP-Seq technology sets off a new upsurge of N6-methyladenosine (m6A) investigation and also poses computational challenges. Until recent years, most MeRIP-Seq data analysis still focuses on basic data processing, and the algorithms which are designed to mine regulation relationship from MeRIP-Seq data have not been sufficiently studied..RNA methylases, which have been identified as m6A regulators, exert a complex influence on RNA methylome, imposes important relationship with many biological processes, even some disease. Therefore, on the condition such as the presence of large amount of lowly expressed genes, within-group variation from biological replicates and the uncertain number of unknown RNA methylases, the project plans to construct a Dirichlet process based beta binomial infinite mixture model in nonparametric Bayesian way to unravel the RNA methylome, then investigate the relationship between RNA methylases and their regulated methylation sites, design bioinformatics algorithms for prediction of RNA methylases specificity and key enzyme genes that directly lead to dysregulation..Hopefully, this project will accelerate the understanding of RNA methylation, and will also provide strong theoretical basis for the study of applying RNA methylation in the diagnosis and treatment in human disease.

英文关键词： RNA methylome;non-parametric Bayesian approach;clustering analysis;next generation sequencing;biological information processing