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（由申请人提供）：目前，分子生物学中高通量数据生成的爆炸式增长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目前用于快速蛋白质功能评估的大规模实验技术缺乏准确预测基因功能所需的特异性。在分析中整合不同数据源的生物信息学方法可以实现更高的准确性，从而缓解这种特异性的缺乏，但缺乏通用、高效和准确的数据整合方法。此外，尽管可视化对于分析基因表达微阵列等大规模技术的数据非常有价值，但尚不存在有效显示不同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，用于异构生物数据的集成分析和可视化。 我们建议使用贝叶斯网络方法和有效的可视化方法来解决数据集成问题。我们已经在原理验证系统中展示了该方法的有效性，与单个数据源相比，该系统提高了酿酒酵母基因功能预测的准确性。在我们之前的工作的基础上，我们提出了一个由两部分组成的计划，以改进和扩展我们的系统，并开发基于可扩展显示技术的基因组数据新颖的可视化方法。首先，我们将研究异构高通量数据的精确集成、分析和有效可视化背后的计算和理论问题。然后，利用我们现有的系统和项目第一部分开发的算法改进，我们将设计和实施一个全面的酿酒酵母数据集成和功能预测系统，该系统将与酿酒酵母基因组数据库（SGD）相结合，酵母模型生物数据库。 所提出的系统将提供高度准确的自动功能预测，可以通过有针对性的实验测试加速基因组功能注释。此外，我们的系统将进行总体集成，并通过有效的集成和可视化工具为研究人员提供多样化高通量数据的统一视图，从而促进假设生成和数据分析。我们可扩展的可视化方法将使研究团队能够交互式地检查生物数据，从而支持基因组研究的高度协作性质。除了为酿酒酵母基因组学做出贡献之外，由于该提案而开发的高效、准确的异构数据集成和可视化技术还将成为解决包括人类在内的其他生物体的相同问题的系统的基础。