Seminars/Colloquia/Invited Talks

Seminars

Erin Conlon

Integrating DNA Motif Discovery and Genome-Wide Expression Analysis

PLACE:	Clark 314
EVENT:	CIS Seminar Series
DATE:	November 15, 2005
TIME:	1:00 - 2:00

Abstract

I will present a novel statistical and bioinformatic method, Motif Regressor, that identifies short motifs in regulatory DNA sequence using the combination of microarray and DNA sequence data. I will first present a Bayesian hierarchical model to determine overexpressed genes in replicated cDNA microarray experiments. Motif Regressor uses these overexpressed genes to find candidate motifs in the respective regulatory regions. Candidate motifs are statistically tested for association between motif occurrences and the global gene expression pattern in order to identify significant regulatory motifs. An additive linear model determines motifs acting concurrently to control gene expression. Model organisms yeast and bacteria are used to illustrate these methods.

Brief biography

I am an assistant professor in the Department of Mathematics and Statistics at the University of Massachusetts. Prior to my current position, I was a postdoctoral fellow at the University of Washington and Harvard University, studying statistical genetics and bioinformatics. My doctoral degree is in Biostatistics from the University of Minnesota.
My research interests include microarray and DNA sequence analysis, Bayesian models for the analysis of genomic data, and comparative genomics. My current work focuses on the model organisms yeast and bacteria.