STATISTICAL APPROACHES TO INTEGRATION OF MASS SPECTRAL AND GENOMIC DATA OF YEAS

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Gene activities in eukaryotic cells are concertedly regulated by transcription factors and chromatin structure. Extensive studies have been done to understand how various transcription factors are recruited to activate or repress gene expression in response to different environmental conditions. However, researches in probing the regulatory role of chromatin are still very limited. The basic repeating unit of chromatin is the nucleosome, an octamer containing two copies of each of four core histones. Combinations of covalent modifications of these histones guide the chromatin-DNA interaction and in turn affects gene transcription, replication, and recombination. Nonetheless, the precise mechanism remains elusive. Recently, high resolution mass spectrometry and genome-wide microarray for detection and localization of histone modifications have become available, offering researchers an unprecedented opportunity to delineate the regulatory role of histone modifications. The proposed work will combine high-resolution mass spectral and genomic data for Saccharomyces cerevisiae to build computational models to estimate the effect of the histone modifications on TF binding and gene expression in yeast. Taken together, the proposed work will provide a unique perspective to test the histone code hypothesis through effectively integrating information from sequence, gene expression, histone modification, and nucleosome data. We will: 1. Develop link-free model selection methods for the multivariate response model to identify target genes of histone modifications. 2. Develop computational and statistical methods to identify the DNA sequence features of histone modifications through integrating the expression values of genes, with the combination of the DNA sequence, nuclesome occupancy as well as genome-wide histone modification data. 3. Develop computational and statistical methods to predict histone modifications and their interactions.