Dr. Tao Jiang
Department of Computer Science and Engineering
University of California, Riverside
and Tsinghua University, Beijing
http://www1.cs.ucr.edu/~jiang
Time: Friday, Nov 7th, 2014, 11am
Location: EB 3105
Abstract: As a fundamental tool for discovering genes involved in a
disease or biological process, differential gene expression analysis
plays an important role in genomics research. High throughput
sequencing technologies such as RNA-Seq are increasingly being used for
differential gene expression analysis which was dominated by the
microarray technology in the past decade. However, inferring
differential gene expression based on the observed difference of
RNA-Seq read counts has unique challenges that were not present in
microarray-based analysis. The differential expression estimation may
be biased against low read count values such that the differential
expression of genes with high read counts is more easily detected. The
estimation bias may further propagate in downstream analyses at the
systems biology level if it is not corrected. In this work, we propose
a new efficient algorithm for detecting differentially expressed genes
based on a markov random field (MRF) model, called MRFSeq, that uses
additional coexpression data to enhance the prediction power. Our main
technical contribution is a careful construction of the clique
potential functions in the MRF so its maximum a posteriori (MAP)
estimation can be reduced to the well-known maximum flow problem and
thus solved in polynomial time. Our extensive experiments on simulated
and real RNA-Seq datasets demonstrate that MRFSeq is more accurate and
less biased against genes with low read counts than the existing
methods based on RNA-Seq data alone. For example, on the well-studied
MAQC dataset, MRFSeq improved the sensitivity from 11.6% to 38.8% for
genes with low read counts. Bio: 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 ofComputer 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 260 papers in computer science and bioinformatics journals and conferences, and won several best paper awards. More information about his work can be found at http://www1.cs.ucr.edu/~jiang Host: Dr. Yanni Sun |