Statistical Approaches to the Analysis of Genome-wide Measurements: Significance of Periodic Gene Expression and Correlations of Gene Content in Completely Sequenced Genomes

The goal of this project is to advance interdisciplinary research between mathematical sciences and modern biological sciences. The specific aims of this project are to: (1) expand the PI's skills and knowledge in molecular biology and bioinformatics, (2) pursue projects at the intersection of molecular biology and statistics, and (3) develop new statistical modeling techniques or improve the existing statistical methods for analyzing genetic data sets. Research activities will include: developing statistical methods to identify statistically significant periodically expressed genes in the cell cycle expression data sets with controllable false discovery rate (FDR); establishing an optimal clustering strategy that produces biologically meaningful clusters for gene expression data sets; and investigating the canonical correlation between gene content vectors so that optimal relations between genes or protein vectors can be revealed. The Bioinformatics Center at the Stowers Institute for Medical Research in Kansas City, Missouri will host the PI for this project. Outcomes of the project may have profound impact in promoting teaching, training, and learning at the intersection of statistics, molecular biology, and bioinformatics. Bringing together knowledge and expertise across disciplines will generate new knowledge and understanding in statistical bioinformatics. Moreover, such newly acquired knowledge will enrich the educational experiences and enhance career options for students at the PI's home institution. Thus, this project will leverage cross-discipline research collaboration and advanced scientific educational opportunity to advance scientific knowledge. This IGMS project is jointly supported by the MPS Office of Multidisciplinary Activities (OMA) and the Division of Mathematical Sciences (DMS).