BAYESIAN INFERENCES OF CIS-REGULATORY MODULES AND SIGNAL TRANSDUCTION PATHWAYS

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目及 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 该项目有两个互补的目标：1）确定性腺特异性启动子调控元件的顺式调控模块;和2）改进用于识别此类模块的统计技术。 为了这些目的，我们将应用贝叶斯统计推断方法，以确定基因的表达水平改变的TAF 4 b的水平的变化的直接结果，我们将提高我们目前的算法，吉布斯抽样的基础上，从相关物种的基因之间的系统发育关系的模型。 此外，我们将更新的算法与增强模型的网站之间的空间分布;一个改进的方法，用于检测短顺式结合模式;一个更高阶的模型的背景部分的序列数据;和一个方法的基序模型的聚类，以提高收敛性。

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.