Speaker:Prof. Tao Jiang, Department of Computer Science and Engineering,
University of California – Riverside
Venue:B924, Zhixin Building
Date:Monday, March 26, 2012.
Time:15:00-16:30 pm
Sponsor:School of Mathematics
Abstract:
Due to alternative splicing, a gene may be transcribed into several different mRNA transcripts (called isoforms) in eukaryotic species. How to detect isoforms on a genomic scale and measure their abundance levels in a cell is a central problem in transcriptomics and has broad applications in biology and medicine. Traditional experimental methods for this purpose are time consuming and cost ineffective. Although deep sequencing technologies such as RNA-Seq provide a possible effective method to address this problem, the inference of isoforms from tens of millions of short sequence reads produced by RNA-Seq has remained computationally challenging. In this talk, I will first briefly survey the state-of-the-art methods for inferring isoforms from RNA-Seq short reads including Cufflinks, Scripture and IsoInfer, and then describe the algorithmic framework behind IsoInfer in more detail. The design of IsoInfer exhibits an interesting combination of combinatorial optimization techniques (e.g., convex quadratic programming) and statistical concepts (e.g., maximum likelihood estimation and p-values). Finally, I will introduce our recent improvement of IsoInfer, called IsoLasso. The new method incorporates the well-known LASSO regression method into the quadratic program of IsoInfer and is likely to deliver isoform solutions with both good accuracy and sparsity. Our extensive experiments on both simulated and real RNA-Seq data demonstrate that this addition could help IsoLasso to filter out lowly expressed isoforms (which are often noisy) and achieve higher sensitivity and precision simultaneously than the existing transcriptome assembly tools.
Biography of Speaker:
Tao Jiang received B.S. in Computer Science and Technology from the University of Science and Technology of China, Hefei, in July 1984 and Ph.D. in Computer Science from University of Minnesota in Nov. 1988. He was a faculty member at McMaster University, Hamilton, Ontario, Canada during Jan. 1989 – July 2001 and is now Professor of Computer Science and Engineering at University of California – Riverside (UCR). He is also a member of the UCR Institute for Integrative Genome Biology, a member of the Center for Plant Cell Biology, a principal scientist at Shanghai Center for Bioinformation Technology, and Chair Visiting Professor at Tsinghua University. Tao Jiang’s recent research interest includes combinatorial algorithms, computational molecular biology, bioinformatics, and computational aspects of information retrieval. He is a fellow of the Association for Computing Machinery (ACM) and of the American Association for the Advancement of Science (AAAS), and held a Presidential Chair Professor position at UCR during 2007-2010. He has published over 220 papers in computer science and bioinformatics journals and conferences, and won several best paper awards.
More information about his work can be found athttp://www.cs.ucr.edu/~jiang
For further information, please visit: http://www.maths.sdu.edu.cn/