题目：MSIQ: Joint Modeling of Multiple RNA-seq Samples for Accurate Isoform Quantification
报告人：Jingyi Jessica Li
时间： 2016年12月21日上午10:30-11:30am
地点： 403会议室
邀请人： 黄德双教授
报告人简介：Dr. Jingyi Jessica Li is an Assistant Professor in the Department of Statistics and Department of Human Genetics at University of California, Los Angeles. She is also a faculty member in the Interdepartmental Ph.D. Program in Bioinformatics and a member in the Jonsson Comprehensive Cancer Center (JCCC) Gene Regulation Research Program Area. Prior to joining UCLA, she obtained her Ph.D. degree from the Interdepartmental Group in Biostatistics at University of California, Berkeley, where she worked with Profs Peter J. Bickel and Haiyan Huang. She received her B.S. (summa cum laude) from Department of Biological Sciences and Technology at Tsinghua University, China in 2007.
内容提要：Next-generation RNA sequencing (RNA-seq) technology has been widely used to assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq data offer insight into gene expression levels and transcriptome structure, enabling us to better understand the regulation of gene expression and fundamental biological processes. Accurate isoform quantification from RNA-seq data is a challenging task due to the information loss in sequencing experiments. Recent accumulation of multiple RNA-seq data sets from the same biological condition provides new opportunities to improve the isoform quantification accuracy. However, existing statistical or computational methods for multiple RNA-seq samples either pool the samples into one sample or assign equal weights to the samples in estimating isoform abundance. These methods ignore the possible heterogeneity in the quality of different samples, and could have biased and unrobust estimates.
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