Context Modeling for Detecting Cis-Regulatory Signals

RAND Statistics Seminar Series

Context Modeling for Detecting Cis-Regulatory Signals

Presented by Qing Zhou—University of California, Los Angeles

Wednesday, January 19th, 2011
10:30 a.m. – 12:00 p.m. PT / 1:30pm – 3:00pm ET
Conference Room 4312
RAND Corporation, Santa Monica, CA

Please contact Denise Miller if you would like to attend this seminar.


Spatially one of the challenging problems in computational molecular biology and bioinformatics is to decode gene regulatory circuits. We developed two new methods for computational identification of transcription factor binding motifs, i.e., the sequence binding patterns of transcription factors (TFs). The first method, the Contrast Motif Finder (CMF), utilizes the contrast between two sets of sequences to find motifs that separate the sequence sets. Applying CMF to a collection of genome-wide ChIP-seq/chip data in mice, we achieved higher accuracy in motif finding compared to a few popular methods and discovered different motifs that may be recognized by the same TF dependent on its co-regulators. The second method builds a generative model to consider the background heterogeneity in nucleotide composition and evolutionary conservation across multiple species. Simulation studies and empirical evidence from biological data sets reveal the dramatic effect of background modeling on motif finding, and demonstrate that the proposed approach is able to achieve substantial improvements over commonly used background models.

Speaker Bio

Dr. Zhou is an Assistant professor of statistics at UCLA. He received his PhD in Statistics from Harvard University in 2006. One of his research interest is computational biology, in which he aims to develop computational and statistical methods for efficient analysis of large-scale high-throughput genomic data. Another research interest is the development of innovative Monte Carlo methods to characterize statistical and topological structures of probability distributions, with applications in Bayesian inference and statistical physics. Dr. Zhou's personal website can be viewed at:

Attending a Seminar

Other Locations/Times:
Washington, D.C. Conf. Rm. 4132: 1:30 p.m. ET
Pittsburgh Conf. Rm. 6202: 1:30 p.m. ET

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