The availability of complete genome sequences is ushering in a new era in the analysis of gene regulation. The full genome sequence of the yeast Saccharomyces cerevisiae is ideal for such a study. Many yeast biochemical and genetic pathways are well understood, and its genome contains a manageable number (6000) of genes which are arranged in a compact fashion. We have extracted the sequences of all potential yeast promoters in order to study genes whose transcription is co-regulated during the yeast cell cycle. We search the literature for co-expressed genes, identify sequence motifs which are shared among the promoters of these genes using a statistical method called Gibbs sampling and by using word frequency methods, and then search for this sequence motif in other yeast promoters using a other statistical methods. Our first analysis was on the replication dependent histone genes which are transcribed during S phase. We have identified a sequence motif shared among the nine histone promoters, and have pinpointed other genes which contain copies of this motif. As it has been shown previously, this motif is partly responsible for the cell cycle dependent expression of histones, we predicted that these other genes are co-regulated with the histones. We have extended this analysis to other cell cycle regulated genes in a project where data from a gene microarray experiment was used to identify genes which were co-expressed during the yeast cell cycle. Our predictions about cell cycle regulation will help to better characterize known genes, and lead to suggestions about the functions of yet-unstudied open reading frames. Furthermore, such work may result in a better understanding of the complex regulatory networks that orchestrate correct quantitative and temporal patterns of gene expression. We are also analyzing a large set of human promoters using several new techniques and statistics developed in this collaboration. A significant novelty of these developments is the use of promoters which are flush at the 3'end to the start of transcription allowing a set positional marker. In addition, data from ChIP-CHIP and ChIP-seq experiments are being included for analysis in order to further assess the role of chromatin organization and protein binding during transcription. We have also addressed the role that histone modifications play in chromatin organization in yeast. We have shown that there are strong positional preferences for sequence-specific chromatin modifying protein-binding motifs in potential regulatory regions. We have used DNA-binding motifs recognition algorithms and gene ontology enrichment tools to make these discoveries. We continue to interrogate many datasets to establish common properties of chromatin during a variety of active states and are in the process of analyzing more complex genomic data from mouse knockout experiments.
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