Bioinformatics Seminar – Using Finite Poisson Mixture Models for RNA-Seq Data Analysis and Transcript Expression Level Quantification

Time – Tuesday, January 31, 2012
04:30 PM

Place – Department of Statistics, Purdue University – in PHYS 223

Speaker – Yu (Michael) Zhu

RNA-Seq has emerged as a powerful technique for transcriptome study. As much as the improved sensitivity and coverage, RNA-Seq also brings challenges for data analysis. The massive amount of sequence read data, excessive variability, uncertainties, and bias and noises stemming from multiple sources make the analysis of RAN-Seq data difficult. Despite much progress, RNA-Seq data analysis still has room for improvement, especially on the quantification of transcript/gene expression levels…(read more)

Reference – Ming Hu. Yu Zhu, Jeremy M.G. Taylor, Jun S. Liu, Zhaohui S. Qui. (2011) Using Poisson mixed-effects model to quantify transcript-level gene expression in RNA-Seq. Bioinformatics 28, 1, 63-68. [abstract]