Integrative Analysis of Cross-Platform Microarray Data

描述(申请人提供)：微阵列基因表达谱在许多实验室进行，导致公共储存库中的数据快速积累。然而，由于不同技术平台的存在以及缺乏标准的实验方案，数据集之间的系统差异往往超过了统计归一化的能力。目前，迫切需要一种方法学来整合跨平台的微阵列数据。这项建议满足了这一需求。我们的目标是开发新的计算和统计方法来整合跨平台的微阵列数据。具体地说，我们将(1)检测许多微阵列数据集上的重复表达模式；(2)对多个基因组进行功能和转录注释；(3)在没有蛋白质-DNA结合位点先验信息的情况下预测高等真核基因的转录调节因子；以及(4)识别作为疾病特征的遗传网络。使用我们的方法，我们能够为任何有大量微阵列数据的基因组提取更多数量级的信息。我们将对酵母(S.cerevisiae)、蠕虫(C.elegans)、苍蝇(D.Blackogaster)、植物(A.thaliana)、小鼠(M.Musculus)、大鼠(R.norveicus)和人类(H.seniens)的基因组进行“特定于上下文”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/调节，这取决于它们与哪组其他基因相互作用，以及这种相互作用发生在哪组条件下。在发布预测结果时，我们将在每个注释中附加必要的上下文信息。最后，我们将开发一个供生物学家进行跨平台微阵列数据综合分析的软件包ARRAYMINE。我们的算法和软件将极大地促进现有大量微阵列数据的再利用，减少生成新数据的必要性，并提高我们对细胞功能和网络在各种扰动下的理解。

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.