Integration and Visualization of Diverse Biological Data

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相对缓慢增长之间存在差距。这一差距很大程度上是由于目前可用的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。生物信息学方法在其分析中集成了不同的数据源，从而实现了更高的准确性，从而缓解了这种特异性的缺乏，但缺乏可推广的，高效的，准确的数据集成方法。此外，尽管可视化对于基因表达微阵列等大规模技术的数据分析非常有价值，但目前还没有有效的方法来有效地显示不同的基因组数据。本提案的长期目标是为异构生物数据的集成分析和可视化开发一个准确和可推广的生物信息学框架。

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.