Integration and Visualization of Diverse Biological Data

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性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。这一差距在很大程度上是由于目前可用于快速评估蛋白质功能的大规模实验技术中缺乏准确预测基因功能所必需的特异性。在分析中集成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问题，但缺乏可推广的、高效的和准确的数据集成方法。此外，没有有效的方法来有效地显示不同的基因组数据，尽管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。这项提议的长期目标是开发一个准确和可推广的生物信息学框架，用于对异质生物数据进行综合分析和可视化。 我们提出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。我们已经在原理证明系统中展示了该方法的有效性，该系统与单独的数据源相比提高了酿酒酵母基因功能预测的准确性。在我们以前工作的基础上，我们提出了一个分两部分的计划，以改进和扩展我们的系统，并基于可伸缩显示技术开发新的基因组数据可视化方法。首先，我们将研究异质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，利用我们在项目第一部分中开发的现有系统和算法改进，我们将设计和实现一个全面的酿酒酵母数据集成和功能预测系统，该系统将与酵母菌基因组数据库(SGD)相结合，SGD是酵母的模式生物数据库。 该系统将提供高精度的自动功能预测，可以通过有针对性的实验测试来加速基因组功能注释。此外，我们的系统将进行全面的整合，并将通过有效的整合和可视化工具为研究人员提供各种高通量数据的统一视图，从而促进假设生成和数据分析。我们的可扩展可视化方法将使研究团队能够交互地检查生物数据，从而支持基因组研究的高度协作性质。除了对酿酒酵母基因组学做出贡献外，根据这一提议开发的高效和准确的异质数据集成和可视化技术将成为解决包括人类在内的其他生物体的同一组问题的系统的基础。

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.