Genomic Domain and Network Characterization

多条证据表明， 基因组。小鼠基因组似乎被组织成多个区域--连续的，可能重叠的， 包含功能上相关的元素组的染色体片段(包括但不限于 基因和调控元件)，以及不连续的结构域和孤立的元件在网络中相互作用。 该项目将开发使用多种不同数据集进行分析所需的生物信息学工具 这些关系。 目标2a。我们将开发方法和软件来识别和表征领域，从分析开始 基于动态规划的更复杂的基于隐马尔可夫模型的方法。我们会 包括用于组合多个数据源以形成复合域结构方法。 目标2b。我们将使用图形表示法生成不同数据所隐含的网络，其中 节点是染色体位置(或间隔)，而边表示相互作用(直接或间接 这些节点。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 将开发用于比较替代网络的方法和软件，特别侧重于 确定具有统计意义的常见子网。 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 由这笔赠款生成的数据(例如丘吉尔项目的基因表达数据、佩特科夫项目的重组数据以及Paigen在项目中的整体整合数据)，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网络界面。

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.