Statistical Approaches to Integration of Mass Spectral and Genomic Data of Yeast Histone Modifications

University Of Illinois At Urbana-Champaign, Urbana IL

Investigators

Abstract

New statistical and analytical methods will be developed to study regulatory role of histone modifications in Saccharomyces cerevisiae. Gene activities in eukaryotic cells are concertedly regulated by transcription factors and chromatin structure. The basic repeating unit of chromatin is the nucleosome, an octamer containing two copies each of four core histone proteins. While nucleosome occupancy in promoter regions typically occludes transcription factor binding, thereby repressing global gene expression, the role of histone modification is more complex. Histone tails can be modified in various ways, including acetylation, methylation, phosphorylation, and ubiquitination. Even the regulatory role of histone acetylation, the best characterized modification to date, is still not fully understood. Mass spectral and genome-wide microarray data from Saccharomyces cerevisiae have offered new opportunities for investigators to evaluate the regulatory effects of histone modifications. The investigators will develop statistical methods for identifying target genes of histone modifications and associated DNA sequence features of histone modifications. The investigators will also develop computational and statistical methods for predicting histone modifications and their interactions. Experimental data are noisy and high dimensional, which renders many tradition statistical methods ineffective. How to build prediction models with only a small set of informative variables adds another layer of complexity. New statistical methods will be developed to surmount the challenges. The proposed methods lead to a statistical framework for integrating multiple types of proteomic and genomic data. A complete framework for such integration has not been developed and tested in the statistics and computational biology literature. The proposed method can produce innovative methodologies for analyzing very large amounts of heterogeneous data, suggest new lines of quantitative investigations in systems biology, and offer opportunities for students to participant in inter-disciplinary research.