Integration and Visualization of Diverse Biological Data

多种生物数据的整合与可视化

• 批准号: 7404447
• 负责人: OLGA G TROYANSKAYA
• 金额: $ 24.3万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-04-01 至 2010-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7404447
• 关键词: Address; Algorithms; Binding; Bioinformatics; Biological; Biological Models; Biological Process; Biology; Collaborations; Communities; Compatible; Computer software; Consultations; Data; Data Analyses; Data Set; Data Sources; Databases; Depth; Development; Effectiveness; Evaluation; Expert Systems; Explosion; Gene Expression; Generations; Genes; Genome; Genomics; Goals; Grouping; Growth; Human; Human Genome; Imagery; Individual; Information Systems; Institutes; Investigation; Knock-out; Knowledge; Laboratories; Learning; Literature; Machine Learning; Magic; Methods; Molecular Biology; Monitor; Nature; Online Systems; Organism; Phenotype; Pliability; Probability; Process; Protein-Protein Interaction Map; Proteomics; Regulation; Research; Research Personnel; Resolution; Saccharomyces; Saccharomyces cerevisiae; Scientist; Side; Source; Specificity; Staining method; Stains; Structure; System; Systems Biology; Technology; Test Result; Testing; Two-Hybrid System Techniques; Universities; Visualization software; Work; Yeasts; base; comparative; computer based statistical methods; concept; data integration; design; functional genomics; gene function; genome database; high throughput analysis; improved; model organisms databases; novel; parallel computing; programs; protein function; prototype; research study; software development; tandem mass spectrometry; tool; ultra high resolution; web interface

DESCRIPTION (provided by applicant): Currently a gap exists between the explosion of high-throughput data generation in molecular biology and the relatively slower growth of reliable functional information extracted from the data. This gap is largely due to the lack of specificity necessary for accurate gene function prediction in the currently available large-scale experimental technologies for rapid protein function assessment. Bioinformatics methods that integrate diverse data sources in their analysis achieve higher accuracy and thus alleviate this lack of specificity, but there's a paucity of generalizable, efficient, and accurate methods for data integration. In addition, no efficient methods exist to effectively display diverse genomic data, even though visualization has been very valuable for analysis of data from large scale technologies such as gene expression microarrays. The long-term goal of this proposal is to develop an accurate and generalizable bioinformatics framework for integrated analysis and visualization of heterogeneous biological data. We propose to address the data integration problem with a Bayesian network approach and effective visualization methods. We have shown the efficacy of this method in a proof-of-principle system that increased the accuracy of gene function prediction for Saccharomyces cerevisiae compared to individual data sources. Building on our previous work, we present a two-part plan to improve and expand our system and to develop novel visualization methods for genomic data based on the scalable display technology. First, we will investigate the computational and theoretical issues behind accurate integration, analysis and effective visualization of heterogeneous high-throughput data. Then, leveraging our existing system and algorithmic improvements developed in the first part of the project, we will design and implement a full-scale data integration and function prediction system for Saccharomyces cerevisiae that will be incorporated with the Saccharomyces Genome Database (SGD), a model organism database for yeast. The proposed system would provide highly accurate automatic function prediction that can accelerate genomic functional annotation through targeted experimental testing. Furthermore, our system will perform general integration and will offer researchers a unified view of the diverse high-throughput data through effective integration and visualization tools, thereby facilitating hypothesis generation and data analysis. Our scalable visualization methods will enable teams of researchers to examine biological data interactively and thus support the highly collaborative nature of genomic research. In addition to contributing to S. cerevisiae genomics, the technology for efficient and accurate heterogeneous data integration and visualization developed as a result of this proposal will form a basis for systems that address the same set of issues for other organisms, including the human.

描述（申请人提供）：目前，分子生物学中高通量数据生成的爆炸与从数据中提取的可靠功能信息的增长相对较慢之间存在差距。这一差距很大程度上是由于缺乏在当前可用的大规模实验技术中用于快速蛋白质功能评估的精确基因功能预测所必需的特异性。将各种数据源整合到其分析中的生物信息学方法实现了更高的准确性，从而减轻了这种缺乏特异性，但是缺乏可推广，高效和准确的数据集成方法。此外，即使可视化对于分析来自基因表达微阵列等大型技术的数据非常有价值，也没有有效显示各种基因组数据的有效方法。该提案的长期目标是开发一个准确且可推广的生物信息学框架，以综合分析和可视化异质生物学数据。 我们建议通过贝叶斯网络方法和有效的可视化方法解决数据集成问题。我们已经在原理系统中显示了该方法的功效，该系统与单个数据源相比提高了酿酒酵母基因功能预测的准确性。在我们以前的工作的基础上，我们提出了一个分为两部分的计划，以改进和扩展系统，并根据可扩展显示技术开发基因组数据的新颖可视化方法。首先，我们将研究准确整合，分析和有效可视化异质高通量数据背后的计算和理论问题。然后，利用项目第一部分中的现有系统和算法改进，我们将设计和实施酿酒酵母的全尺度数据集成和功能预测系统，该系统将与Saccharomyces Genome数据库数据库（SGD）（SGD）合并，该数据库的模型有机体数据库的模型有机体数据库。 所提出的系统将提供高度准确的自动功能预测，可以通过靶向实验测试加速基因组功能注释。此外，我们的系统将执行一般集成，并通过有效的集成和可视化工具为研究人员提供对各种高通量数据的统一观点，从而促进假设的产生和数据分析。我们可扩展的可视化方法将使研究人员团队能够交互性地检查生物学数据，从而支持基因组研究的高度协作性质。除了促进酿酒酵母基因组学的贡献外，由于该提案而开发的有效，准确的异质数据集成和可视化技术还将为解决包括人类在内的其他生物体（包括人类）的相同问题的系统构成基础。