BAYESIAN INFERENCES OF CIS-REGULATORY MODULES AND SIGNAL TRANSDUCTION PATHWAYS

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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. This project has two complementary goals: 1) the identification of the cis-regulatory modules of gonad specific promoter regulatory elements; and 2) improvements of statistical technology for the identification of such modules. To these ends, we will apply Bayesian statistical inference methods to identify genes whose level of expression are altered as a direct result of changes in the levels of TAF4b and we will enhance our current algorithms, based on Gibbs sampling, with a model of the phylogenetic relationship among genes from related species. In addition, we will update the algorithm with an enhanced model of the spatial distribution among sites; an improved method for detection of short cis-binding patterns; a higher order model for the background portions of sequence data; and a method for the clustering of motif models to improve convergence.