Genomic Domain and Network Characterization

多条证据表明， 基因组看来老鼠的基因组被组织成多个结构域--连续的，可能重叠的， 含有功能相关的元件组的染色体片段（包括但不限于 基因和调控元件），以及非连续结构域和分离元件在网络中相互作用。 本项目将开发生物信息学工具，用于分析多种多样的数据集。 这些关系。 目标2a。我们将开发方法和软件来识别和表征域，从分析 从基于动态规划的方法到基于隐马尔可夫模型的更复杂的方法。我们将 包括用于组合多个数据源以形成复合域结构的方法。 目标2b。我们将使用图形表示来生成不同数据所隐含的网络， 节点是染色体位置（或间隔），边缘意味着染色体之间的相互作用（直接或间接） 节点。与域一样，我们将生成特定于数据的网络和复合网络以供分析。我们 将开发方法和软件来比较替代网络，特别侧重于方法， 确定统计上显著共同变量。 目标2c。我们将向本提案中的其他项目提供我们的计算工具，用于数据分析 由该授权生成的（例如，基因表达数据来自丘吉尔的项目，重组数据来自Petkov的项目，以及Paigen的项目中的整体整合），以及从外部来源获得的数据（例如，GO注释或KEGG数据库）。我们还将与计算核心合作，为我们产生的数据和分析定义高效和全面的数据库和Web界面。

Multiple lines of evidence point to a distinctly non-random organization of functional elements in the genome. It appears that the mouse genome is organized into domains-contiguous, possibly overlapping, segments of chromosomes containing functionally related groups of elements (including, but not limited to genes and regulatory elements) and that non-contiguous domains and isolated elements interact in networks. This project will develop the bioinformatics tools required for using multiple, diverse data sets in analyzing these relationships. Aim 2a. we will develop methods and software to identify and characterize domains, moving from analyses based on dynamic programming to more sophisticated methods based on Hidden Markov Models. We will include methods for combining multiple data sources to form composite domain structures. Aim 2b. we will generate the networks implied by disparate data, using a graph representation in which nodes are chromosome positions (or intervals) and edges imply an interaction (direct or indirect) between the nodes. As with domains, we will generate both data-specific and composite networks for analysis. We will develop methods and software to compare alternative networks, focusing specifically on methods to identify statistically significant common subnets. Aim 2c. we will provide our computational tools to the other projects in this proposal for the analysis of data generated by this grant (e.g., gene expression data from Project by Churchill, and recombination data from Project by Petkov, as well as overall integration in Project by Paigen), as well as data obtained from external sources (e.g., GO annotations or the KEGG database). We will work also with the Computational Core to define efficient and comprehensive databases and web interfaces for the data and analysis we produce.