Computational Analysis of Nucleosome Positioning Signals in Eukaryotic Genomes

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The University of Massachusetts Medical School and the University of Washington have received a grant to develop predictive models of the packing of DNA within eukaryotic cells. This research will provide important insights into important cellular control mechanisms for gene expression and gene suppression. As a first goal the research team will use existing genomic data sets to identify sequences that associated with higher or lower nucleosome density. The second goal is to design, train and validate sequence-based discriminative and generative models of nucleosome positioning. The third goal is to apply the trained models to data from a variety of organisms and human cell lines, with the goal of understanding how the nucleosome positioning signal encoded in the DNA interacts with other nucleosome positioning factors and which aspects of the nucleosome positioning signal are most strongly conserved, and which vary in divergent species. The project will also lead to predictive models of nucleosome formation. Such models can be used to design DNA sequences that form stable nucleosomes in vivo. These sequences can be used to manipulate chromatin structure remodeling and transcriptional regulation. In this project, we will train three PhD students at two participating institutions. The project will provide content for courses at the undergraduate and graduate levels, as well as for two summer research programs for underrepresented minority high school and undergraduate students at both institutions. The algorithms, software, and data sets resulting from this project will be made freely available to the research community via the web. Additional information may be found at http://zlab.umassmed.edu and http://noble.gs.washington.edu/