Integration and Visualization of Diverse Biological Data

描述（由申请人提供）：目前，在分子生物学中高通量数据生成的爆炸性增长与从数据中提取的可靠功能信息的相对较慢的增长之间存在差距。这一差距主要是由于缺乏准确的基因功能预测所需的特异性，在目前可用的大规模实验技术快速蛋白质功能评估。在分析中整合不同数据源的生物信息学方法实现了更高的准确性，从而缓解了这种缺乏特异性的情况，但缺乏可推广的，有效的和准确的数据整合方法。此外，不存在有效的方法来有效地显示不同的基因组数据，即使可视化对于分析来自大规模技术如基因表达微阵列的数据非常有价值。该提案的长期目标是开发一个准确和可推广的生物信息学框架，用于异构生物数据的综合分析和可视化。 我们建议解决数据集成问题的贝叶斯网络方法和有效的可视化方法。我们已经证明了这种方法在原理验证系统中的有效性，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。在我们以前的工作的基础上，我们提出了一个两部分的计划，以改善和扩展我们的系统，并开发新的可视化方法的基因组数据的基础上可扩展的显示技术。首先，我们将研究异构高通量数据的准确集成、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算法改进开发的第一部分的项目，我们将设计和实现一个全面的数据集成和功能预测系统的酿酒酵母，将与酵母基因组数据库（SGD），酵母的模式生物数据库。 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，我们的系统将执行一般的集成，并通过有效的集成和可视化工具为研究人员提供不同的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。除了对S。酿酒酵母基因组学，有效和准确的异构数据集成和可视化技术开发的结果，这一建议将形成一个基础的系统，解决同样的一套问题，为其他生物体，包括人类。

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.