Differential Gene Expression Pattern Analysis Using Exon-Level RNA-Seq Data

• Activity Number: 571
• Type: Contributed
• Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Zhaonan Sun*+ and Yu Zhu
• Companies: Purdue University and Purdue University
• Keywords: RNA-Seq ; Differential Expression ; Alternative Splicing

Abstract

Gene differential expression analysis is an important task in transcriptome study, because it can cast light on the mechanism of gene regulation and also help for disease gene detection. In conventional differential expression analysis, the expression level of each gene is summarized by a single value, and a variety of statistical methods have been proposed to test if this value changes over certain biological or environmental conditions. Due to the prevalence of alternative splicing, using a single index to characterize the expression of a gene is an oversimplification that ignores the complexity of gene expression. With the help of RNA-Seq, it is possible to capture all the transcriptional activities of a gene. In this talk, we propose an exon level model to represent the expression pattern of a gene and develop methods for identifying differentially expressed genes and novel alternative splicing. The proposed model and methods were applied to analyze real RNA-Seq data. The results will be reported in this talk.