Integrative Analysis of Cross-Platform Microarray Data

跨平台微阵列数据的综合分析

• 批准号: 7166819
• 负责人: XIANGHONG Jasmine ZHOU
• 金额: $ 19.73万
• 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/7166819
• 关键词: Address; Aging; Algorithms; Binding Sites; Biochemical Genetics; Biological; Caenorhabditis elegans; Cell physiology; Classification; Collection; Communities; Computer software; Condition; 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; Numbers; Organism; Pathway interactions; Pattern; Phenotype; Plants; Positioning Attribute; Proteins; Protocols documentation; Rattus; Recurrence; Research Personnel; Saccharomyces cerevisiae; Solutions; Standards of Weights and Measures; 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

DESCRIPTION (provided by applicant): 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 ARRAYMINE for 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.

描述（由申请人提供）：在许多实验室中进行微阵列基因表达分析，从而导致公共存储库的快速数据积累。但是，由于存在不同的技术平台以及缺乏标准实验协议，数据集之间的系统变化通常超过统计归一化的能力。当前，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。我们旨在开发新的计算和统计方法来整合跨平台微阵列数据。具体而言，我们将（1）检测许多微阵列数据集中的复发表达模式； （2）对多个基因组执行功能和转录注释； （3）预测较高真核基因的转录调节剂，而没有有关蛋白质-DNA结合位点的先前信息； （4）确定遗传网络是疾病的签名。使用我们的方法，我们可以为任何可用的大量微阵列数据的基因组提取更多的数量级信息。我们将对酵母（S. cerevisiae），蠕虫（C. elegrans），Fly（D。melanogaster），植物（A. thaliana），小鼠（M. Musculus），大鼠（R. Norvegicus）和人类（H. Sapiens）进行酵母基因组（S. scerevisiae），蠕虫（C. elegrans）的基因组的功能和转录注释。也就是说，我们将有条件地注释基因的功能/法规，具体取决于它们与哪一组其他基因相互作用以及在哪些情况下发生这种相互作用的情况。在发布我们的预测结果时，我们将附加到每个注释必要的上下文信息。最后，我们将开发一个软件包Arraymine供生物学家进行跨平台微阵列数据的综合分析。我们的算法和软件将显着促进重复使用大量现有微阵列数据，减少生成新数据的必要性，并提高我们对各种扰动下的蜂窝功能和网络的理解。