Title（问题）：Bayesian model-based methods for analyzing RNA-Seq data

Speaker（报告人）：Associate Professor Qin Zhaohui, from Emory University

Time（时间）：2011年1月5日（周三）下昼2:00-3:00

Place（所在）：北京大学理科一号楼1303课堂

Abstract（摘要）：RNA sequencing (RNA-seq) is a powerful new technology for mapping and quantifying transcriptomes using ultra high-throughput next generation sequencing technologies. Using deep sequencing, gene expression levels can be quantified thus providing a digital measure of the presence and prevalence of all transcripts including novel ones. Although extremely promising, the massive amounts of data that are generated by RNA-seq, substantial biases, and uncertainty in short read alignment pose daunting challenges for data analysis. In particular, large base-specific variations and between-base correlations make naive approaches, such as averaging to normalizing RNA-seq data and quantifying gene expressions, ineffective. We propose to develop Poisson mixed effects models to characterize RNA-seq data. These models will accommodate the biases, variations, and correlations present in RNA-seq data so as to accurately estimate gene expression levels and to facilitate gene expression comparison and novel transcript structure or activities discovery.