Extracting reliable information from microarray data

从微阵列数据中提取可靠信息

• 批准号: 7230160
• 负责人: Zoltan Szallasi
• 金额: $ 17.66万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-15 至 2009-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7230160
• 关键词: Algorithms; Base Sequence; Biochemical; Bioconductor; Biomedical Research; Clinical; Communities; Computational algorithm; Condition; Data; Data Files; Data Set; Data Sources; Databases; Deposition; Disease; Ensure; Evaluation; Gene Expression; Gene Expression Profiling; Genes; Institution; Label; Life; Measurement; Methods; Microarray Analysis; Molecular Profiling; Noise; Numbers; Online Systems; Quantitative Reverse Transcriptase PCR; RNA; Relative (related person); Research; Sequence Homology; Signal Transduction; Standards of Weights and Measures; Technology; Testing; Validation; base; computerized tools; disease classification; improved; novel; novel therapeutics; platform-independent; prevent; therapeutic target; tool

DESCRIPTION (provided by applicant): Due to a variety of experimental and computational issues, gene expression microarray data sets are far from optimally exploited. Limited sensitivity, wide spread and spurious cross hybridization and varying labeling efficiency are some of the well-known problems preventing the extraction of robust platform independent results from gene expression data. A careful combination of statistical and biochemical methods will be needed to produce reliable gene expression estimates. However, the current ingenious computational algorithms to analyze microarray data cannot be appropriately evaluated due to the lack of unbiased, realistic data sets, complete with raw data files and a large number of independent confirmations. We will establish exactly such a data base depositing our own data and actively collecting submissions from collaborators. We will demonstrate the utility of the proposed data base by determining the relative merit of several widely used microarray. normalization algorithms. We will also develop probe sequence based methods to reduce cross-hybridization noise in microarray measurements in order to enable the robust analysis of disease associated global gene expression profiles across different technologies and different data sources. The subset of reliable probes will be empirically verified then they will be distributed through Bioconductor. Microarray analysis is a powerful tool for improving disease classification, identifying novel therapeutic targets etc. The proposed data base along with the identification of reliable microarray probes will increase the reliability and potential utility of this promising technology.

描述（由申请人提供）：由于各种实验和计算问题，基因表达微阵列数据集远未得到最佳利用。有限的灵敏度、广泛传播和虚假交叉杂交以及不同的标记效率是阻碍从基因表达数据中提取稳健的平台独立结果的一些众所周知的问题。需要仔细结合统计和生化方法来产生可靠的基因表达估计。然而，由于缺乏公正、真实的数据集、完整的原始数据文件和大量独立的确认，目前用于分析微阵列数据的巧妙计算算法无法得到适当的评估。我们将建立这样一个数据库，存放我们自己的数据并积极收集合作者的意见。我们将通过确定几种广泛使用的微阵列的相对优点来证明所提出的数据库的实用性。归一化算法。我们还将开发基于探针序列的方法，以减少微阵列测量中的交叉杂交噪声，以便能够跨不同技术和不同数据源对疾病相关的全局基因表达谱进行稳健分析。可靠探针的子集将经过经验验证，然后通过 Bioconductor 进行分发。微阵列分析是改进疾病分类、识别新的治疗靶点等的有力工具。所提出的数据库以及可靠的微阵列探针的识别将增加这种有前途的技术的可靠性和潜在效用。