Integrative Analysis of Cross-Platform Microarray Data

跨平台微阵列数据的综合分析

• 批准号: 7545505
• 负责人: XIANGHONG Jasmine ZHOU
• 金额: $ 19.07万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-01-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7545505
• 关键词: Address; Aging; Algorithms; Binding Sites; Biochemical; Biological; Caenorhabditis elegans; Cell physiology; Classification; Collection; Communities; Computer software; DNA Binding; Data; Data Set; Databases; Disease; Fungal Genome; Gene Expression; Gene Expression Profiling; Gene Expression Regulation; Genes; Genetic; Genetic Structures; Genetic Transcription; Genome; Goals; Graph; Human; Individual; Internet; Jasminum; Knowledge; Laboratories; Mammals; Methodology; Methods; Microarray Analysis; Mus; Organism; Pathway interactions; Pattern; Phenotype; Plants; Positioning Attribute; Proteins; Protocols documentation; Rattus; Recurrence; Research Personnel; Saccharomyces cerevisiae; Solutions; Statistical Methods; Technology; Translating; Variant; Yeasts; base; design; fly; gene function; gene interaction; graphical user interface; improved; novel; novel strategies; programs; repository; research study; software development

Microarray gene expression profiling is performed in many laboratories, resulting in the rapid data accumulation in public repositories. However, due to the existence of different technology platforms and the lack of standard experimental protocols, systematic variation among data sets often exceeds the capability of statistical normalization. Currently, there is an urgent need for methodology to integrate cross-platform microarray data. This proposal addresses this need. We aim at developing novel computational and statistical methods to integrate cross-platform microarray data. Specifically, we will (1) detect recurrent expression patterns across many microarray datasets; (2) perform functional and transcriptional annotation for multiple genomes; (3) predict transcription regulators for higher eukaryotic genes without prior information on protein-DNA binding sites; and (4) identify genetic networks that are signatures of diseases. Using our approach, we are in a position to extract an order of magnitude more information for any genome for which massive microarray data is available. We will perform "context-specific" functional and transcriptional annotation for the genomes of yeast (S. cerevisiae), worm (C. elegans), fly (D. melanogaster), plant (A. thaliana), mouse (M. musculus), rat (R. norvegicus) and human (H. sapiens). That is, we will conditionally annotate the functions/regulations of genes, depending on which set of other genes they are interacting with and under which sets of conditions such interactions occur. When releasing our prediction results, we will attach to each annotation the necessary context information. Finally, we will develop a software package ARRAYMINEfor biologists to perform integrative analysis of cross-platform microarray data. Our algorithms and software will significantly facilitate the re-use of the vast amount of existing microarray data, reduce the necessity to generate new data, and improve our understanding of cellular functions and networks under a variety of perturbations.

微阵列基因表达分析是在许多实验室中进行的，导致数据快速数据 在公共存储库中积累。但是，由于存在不同的技术平台和 缺乏标准实验协议，数据集之间的系统变化通常超过功能 统计归一化。目前，迫切需要方法来整合跨平台 微阵列数据。该提案解决了这一需求。我们旨在开发新颖的计算和 集成跨平台微阵列数据的统计方法。具体来说，我们将（1）检测复发 许多微阵列数据集的表达模式； （2）执行功能和转录注释 用于多个基因组； （3）预测没有先验的较高真核基因的转录调节剂 有关蛋白-DNA结合位点的信息； （4）确定遗传网络是疾病的签名。 使用我们的方法，我们可以提取任何基因组的数量级更多信息 为此提供大量的微阵列数据。我们将执行“特定于上下文”的功能，并且 酵母基因组（S. cerevisiae），蠕虫（秀丽隐杆线虫）的转录注释（D. melanogaster），植物（A. thaliana），小鼠（M. musculus），大鼠（R. norvegicus）和人类（H. Sapiens）。那 是，我们将有条件地注释基因的功能/法规，具体取决于其他基因集 他们正在与发生这种相互作用的条件集相互作用。释放我们的 预测结果，我们将附加到每个注释必要的上下文信息。最后，我们会的 开发一个软件包ArrayMinefor生物学家以执行跨平台的综合分析 微阵列数据。我们的算法和软件将大大促进大量的重复使用 现有的微阵列数据，减少生成新数据的必要性，并提高我们对 在各种扰动下的蜂窝函数和网络。