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.

About the Speaker（报告人简介）：Undergraduate student in the Department of Probability and Statistics at Peking University from 1990-1994. Currently Associate Professor at Department of Biostatistics and Bioinformatics at Emory University.