权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Integrative Analysis of Cross-Platform Microarray Data

跨平台微阵列数据的综合分析

基本信息

批准号：
8053558
负责人：
XIANGHONG Jasmine ZHOU
金额：
$ 6.55万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-05-01 至 2010-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8053558
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

项目摘要

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.

微阵列基因表达谱分析在许多实验室进行，导致快速数据