Gene Regulation E.coli Database Intergrated Environment

描述（由申请人提供）：本申请的目的是 围绕数据库实现集成计算环境， E.杆菌这个数据库RegulonDB包含 从有关调控元件和操纵子的文献中收集的信息 组织，它们在基因组中的位置，以及实验证据支持 超过1000个Medline原始文献参考。该项目将 将数据库转化为分析转录组的有用工具， 蛋白质组实验目标1是收集有关生长条件及其 相关的信号代谢物，并扩大图形功能的 系统目标2在于实现数据库并将其与工具耦合， 基因组调控分析，如序列检索，模式发现和 模式搜索，以及句法识别器，以检测多个潜在的 上游区域内的调节元件。目标3的核心是 将使用来自转录组实验的一组基因作为输入， 上游有关其操纵子组织的图形或表格信息 调控位点、基因的功能类别和受影响的调控因子。所有 这些工具将整合一个灵活的导航路径， query是另一个query的输入。目标4是扩大和应用 设计了贝叶斯聚类方法来处理异构类型的 基因调控和代谢的信息。e.大肠杆菌就是这个模型 然而，这种方法和工具原则上可以应用于 研究其他微生物，从而找到更有效的方法， 利用现有的大量知识， 更好地了解它们的生物学， 影响人类健康。

DESCRIPTION (provided by applicant): The purpose of this application is to implement an integrated computational environment around a database on transcriptional regulation in E. coli. This database, RegulonDB, contains information gathered from the literature on regulatory elements and operon organization, their location in the genome, and experimental evidence supported by more than 1000 Medline original literature references. The project would transform the database into a useful tool for analysis of transcriptome and proteome experiments. Aim 1 is to gather data on growth conditions and their associated signal metabolites, and to expand the graphic capabilities of the system. Aim 2 consists in implementing and coupling the database with tools for genomic regulatory analyses, such as sequence retrieval, pattern discovery and pattern search, as well as a syntactic recognizer to detect multiple potential regulatory elements within an upstream region. Aim 3 centers on programs which would use as input a set of genes from a transcriptome experiment, and generate graphical or tabular information about their operon organization, upstream regulatory sites, functional classes of genes, and regulators affected. All these tools would integrate a flexible navigation path where the output of one query is the input for another one. Aim 4 consists in expanding and applying a Bayesian clustering method designed to deal with the heterogeneous type of information of gene regulation and metabolism. E. coli is here the model system, however, this approach and tools can in principle be applied to the study of other microbial organisms, resulting in more efficient ways to make use of currently available massive amounts of knowledge for the purpose of a better understanding of their biology, and potentially of their mechanisms affecting human health.