Integrative Analysis of Cross-Platform Microarray Data

跨平台微阵列数据的综合分析

• 批准号: 7753856
• 负责人: XIANGHONG Jasmine ZHOU
• 金额: $ 18.87万
• 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 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7753856
• 关键词: Address; Aging; Algorithms; Binding Sites; Biochemical; Biological; Caenorhabditis elegans; Cell physiology; 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.

基因表达谱微阵列在许多实验室进行，导致快速的数据 在公共存储库中积累。然而，由于不同技术平台的存在， 由于缺乏标准的实验方案，数据集之间的系统性差异往往超出能力 统计标准化。目前，迫切需要一种跨平台集成的方法论 微阵列数据。本建议满足了这一需要。我们的目标是开发新的计算和 整合跨平台微阵列数据的统计方法。具体而言，我们将（1）检测复发性 跨许多微阵列数据集的表达模式;（2）执行功能和转录注释 （3）预测高等真核生物基因的转录调控因子，而无需事先 关于蛋白质-DNA结合位点的信息;以及（4）识别作为疾病特征的遗传网络。 使用我们的方法，我们能够为任何基因组提取更多的信息 有大量的微阵列数据可供使用。我们将执行“特定于上下文”的功能， 酵母基因组的转录注释（S.酿酒酵母）、蠕虫（C. elegans）、蝇（D. melanogaster）、植物（A. thaliana）、小鼠（M. musculus）、大鼠（R. norvegicus）和人（H. sapiens）。的 我们将有条件地注释基因的功能/调控，这取决于哪组其他基因 它们与一组条件相互作用，并且在该组条件下发生这种相互作用。在释放我们的 预测结果时，我们将为每个注释附加必要的上下文信息。最后我们将 开发了一个软件包ARRAYMINE，供生物学家进行跨平台的综合分析 微阵列数据。我们的算法和软件将极大地促进大量 现有的微阵列数据，减少产生新数据的必要性，并提高我们对 在各种扰动下的细胞功能和网络。