A Bayesian approach to joint modeling of DNA-protein binding data, gene expression data and DNA sequence data

Yang Xie
Division of Biostatistics
University of Minnesota
*Candidate for the Assistant or Associate Professor position

Friday, February 24th
10:00am
Moos 2-690
Minneapolis Campus

Abstract:
Accurate identification of the genes whose transcription is controlled by a specific regulator is a crucial step towards understanding gene regulation on a genome-wide scale and deciphering the principles of regulatory networks. Exploration of the structure and function of such networks is regarded as a fundamental problem for the coming decades. The genome-wide maps of transcriptional regulators (DNA-protein binding data), DNA sequence data and expression data obtained using whole-genome DNA microarrays represent complementary means to deciphering global and local transcriptional regulatory circuits. Combining these different types of data can not only improve the statistical power, but also provide a more comprehensive picture of gene regulation. In my work, I propose a joint model to combine DNA-protein binding data, gene expression data and DNA sequence data. I specify hierarchical Bayes models and use Markov chain Monte Carlo simulation to draw the inferences. Both the simulation studies and analysis of experimental data show that the proposed joint modeling method can significantly improve the specificity and sensitivity of identification of target genes as compared to conventional approaches relying on a single data source.

A social tea will be held at 9:30A.M. in A434 Mayo. All are Welcome.
For more details contact 612-624-4655 or see http://www.biostat.umn.edu/seminar_academic.html