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学术钻研会

商务统计与经济计量系讲座一

2011-01-04

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.

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