mRNA甲基化检测概率图模型

项目名称： mRNA甲基化检测概率图模型

项目编号： No.61473232

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 自动化技术、计算机技术

项目作者： 张绍武

作者单位： 西北工业大学

项目金额： 83万元

中文摘要： MeRIP-seq技术能够在全转录组范围内描述RNA甲基化，从其高通量数据中挖掘全部RNA甲基化模式，有助于揭示mRNA甲基化在调控基因表达、剪切等方面所发挥的潜在功能，有效指导癌症的干预治疗。然而，MeRIP-seq数据分析计算方法面临许多计算挑战，现有DNA甲基化数据分析方法不能直接用来分析RNA甲基化数据，迫切需要发展有效的计算方法。本项目将对MeRIP-seq数据分析中所面临的一些重大计算问题进行研究，整合多层隐马模型、Dirichlet 过程混合模型、负二项回归、层次贝叶斯模型、稀疏回归等复杂模型理论方法，构建系列mRNA甲基化检测概率图模型，基于吉布斯采样等复杂贝叶斯理论估计模型参数，发展有效的推理和预测算法，在基因及异构体层次上实现： mRNA甲基化位点及甲基化状态的精确预测； mRNA差异甲基化状态的精确检测。开发mRNA甲基化可视化分析平台及软件工具，方便生物学家使用。

中文关键词： RNA甲基化；概率图模型；甲基化位点检测；差异甲基化检测；MeRIP-seq数据

英文摘要： RNA methylation is emerging to be a pervasive epigenetic mark that plays a critical role in gene regulation. The recently developed technology of methylated RNA immunoprecipitation sequencing (MeRIP-seq) allows transcriptome-wide profiling of RNA methylation. Mining the patterns of global mRNA methylation from these MeRIP-seq data can help reveal the potential functional roles of these mRNA methylations in regulating gene expression, splicing, RNA editing and RNA stability and provide leads for more effective therapeutic intervention for cancer. However, MeRIP-seq is still at its early stage and many computational issues still need to be resolved to fully unleash its power. The computational methods of DNA methylation detecting are not suitable for identifying RNA methylations.This project addresses important computational challenges in MeRIP-seq data analysis and our goal is to develop, for the first time, computational graphical models for to enable 1) accurate detection of global mRNA methylations and 2) accurate identification of differential methylation both at the gene and its isoform levels. The main thrust of this project is to leverage our combined expertise in computation modeling, bioinformatics, and high throughput sequencing analysis to develop new graphical models for MeRIP-seq to enable accurate detection of global mRNA methylation and differential methylation. This proposal is highly innovative because such models are virtually unavailable. We will fully capitalize the power of graphical models to address many important and challenging issues arising from the unique requirement of peak calling and differential methylation analysis on transcript in MeRIP-seq. The constructed new models will integrate several sophisticated models including multi-layer hidden Markov model, Negative Binomial regression, Dirichlet process of mixture models, hierarchical Bayesian model and sparse regression et al.The Gibbs sampling and other complex Bayes theory and methods will be employed to estimate the model parameters, and efficient inference and prediction algorithms will also be developed. They will contribute to the advances of computational modeling and learning. Particular efforts are also planned to develop the software and user-friendly tools to facilitate the mRNA methylation research by biologists and computational scientists.

英文关键词： RNA methylation;graphical models;peak calling;differential methylation detecting;MeRIP-seq data