Computational framework for analysis of microarray gene expression data

微阵列基因表达数据分析的计算框架

• 批准号: 7537700
• 负责人: Dariusz Wroblewski
• 金额: $ 15万
• 依托单位: BIOFORMATIX, INC.
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-01-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7537700
• 关键词: Address; Benchmarking; Biological; Biological Markers; Biological Neural Networks; Cells; Clinical; DNA Microarray Chip; Data; Data Analyses; Data Set; Databases; Detection; Development; Diagnostic; Discrimination; Disease; Effectiveness; Elements; Feasibility Studies; Foundations; Gene Expression; Gene Expression Microarray Analysis; Gene Expression Regulation; Genes; Genomics; Gray unit of radiation dose; Institutes; Methods; Microarray Analysis; Modeling; Molecular Profiling; Mus; Noise; Normal Statistical Distribution; Normal tissue morphology; Organ; Organism; Pattern; Performance; Pharmacogenomics; Phase; Polymerase Chain Reaction; Principal Component Analysis; Rattus; Research; Sampling; Statistical Methods; Stress; Structure; Students; Technology; Testing; Tissue Differentiation; Tissue-Specific Gene Expression; Tissues; Toxicogenomics; Transcript; Validation; base; computer framework; cost; improved; novel; novel strategies; outcome forecast; public health relevance; research study; response; tool

DESCRIPTION (provided by applicant): Identification of transcripts that are differentially regulated in response to studied experimental conditions is one of critical steps in analysis of DNA microarray data. Currently employed statistical approaches become particularly ineffective for experiments with small number of biological replicates, which are prevalent in the differential expression studies. We propose to develop and validate a novel numerical framework for identification of differentially expressed transcripts, with emphasis on analysis of experiments with small number of replicates and genes with moderate levels of expression. The proposed approach is based on a novel, non-parametric method for assessment of noise distributions in microarray data, which are derived directly from the analyzed data set. Three distinct, univariate and multivariate methods for identification of differentially expressed genes will be implemented and their results will be compared to the results of leading advanced statistical methods. In the Phase I feasibility study we will analyze differential gene expression between at least nine normal tissues with varying levels of similarity, in rat and mouse. Publicly available data from SymAtlas database (Genomics Institute of the Novartis Research Foundation), obtained with Affymetrix microarrays, will be employed. The utility of newly developed numerical methods will be established through biological and/or experimental validation of identified genomic biomarkers using functional analysis (if functional annotation is available) and/or quantitative polymerase chain reaction analysis. PUBLIC HEALTH RELEVANCE: DNA microarray technology enables simultaneous profiling of thousands of transcripts expressed in particular organism, cells or tissues. Its current applications include gene profiling, gene regulation studies, disease biomarker discovery, toxicogenomics, pharmacogenomics, and clinical diagnostics and prognosis. Despite recent impressive technological advances, major bottlenecks to the realization of the full potential of the microarray technology exist and include incomplete functional gene annotation and the lack of effective computational data analysis tools. The analysis methods developed in this project will improve the ability to reliably identify differentially expressed genes in experiments with small number of biological replicates, which will improve the overall effectiveness of this technology and reduce the cost of microarray gene expression studies.

描述（由申请人提供）：根据研究实验条件差异调节的转录本是分析DNA微阵列数据的关键步骤之一。目前使用的统计方法对于具有少量生物学重复的实验尤为无效，这些实验在差异表达研究中很普遍。我们建议开发和验证一个新的数值框架，以识别差异表达的转录本，重点是分析具有少量重复和基因表达水平的实验。所提出的方法基于一种新型的非参数方法，用于评估微阵列数据中的噪声分布，该方法直接从分析的数据集中得出。将实施三种不同的单变量和多变量方法，用于鉴定差异表达的基因，并将其结果与领先的先进统计方法的结果进行比较。在I阶段的可行性研究中，我们将分析至少九个正常组织之间具有不同水平的大鼠和小鼠的差异基因表达。使用Affymetrix微阵列获得的Symatlas数据库（Novartis Research Foundation的基因组学研究所）的公开数据。新开发的数值方法的实用性将通过使用功能分析（如果有功能注释可用）和/或定量聚合酶链反应分析来确定的基因组生物标志物来确定基因组生物标志物。公共卫生相关性：DNA微阵列技术可以同时分析成千上万在有机体，细胞或组织中表达的笔录。它当前的应用包括基因分析，基因调节研究，疾病生物标志物发现，毒理基因组学，药物基因组学以及临床诊断和预后。尽管最近令人印象深刻的技术进步，但仍然存在微阵列技术的全部潜力的主要瓶颈，并包括功能基因注释不完整以及缺乏有效的计算数据分析工具。该项目中开发的分析方法将提高在具有少量生物学重复的实验中可靠鉴定差异表达基因的能力，这将提高该技术的整体有效性并降低微阵列基因表达研究的成本。