A Next Generation Biological Database Management System for Search Intensive Discovery in Genomics and Proteomics

数据库服务是基因组学中的一个爆炸性问题，并且已经是蛋白质组学中的一个关键瓶颈。为了解释蛋白质组学实验，其中使用质谱仪对数百或数千种蛋白质进行测量，研究人员组装一个大型计算机农场来处理单个质谱仪产生的数据并不罕见。该提案资助的研究将产生一个可扩展的数据库管理系统，专门用于支持对生物数据类型的复杂查询，包括对蛋白质组学至关重要的序列和质谱数据。一个中心主题是（1）一个焦点树为基础的存储结构，使相似性搜索必要的识别蛋白质通过数据库检索快得多，（2）扩展结构化查询语言（SQL）的发展，使更好地利用蛋白质识别算法。这将导致更快的生物信息学算法，使复杂的生物信息学数据库的快速开发成为可能，就像SQL使大型业务系统的有效程序的开发成为可能一样。

Database services are an exploding concern in genomics, and are already a critical bottleneck in proteomics. In order to interpret proteomics experiments, in which measurements on hundreds or thousands of proteins are made using mass spectrometers, it is not unusual for researchers to assemble a large farm of computers to process the data produced by a single mass-spectrometer. The research funded by this proposal will result in an extendable database management system designed specifically to support complex inqueries on biological data types, including the sequence and mass spectra data central to proteomics. A central theme is (1) an focus tree-based storage structures to make the similarity searches necessary to identify proteins through database retrieval much faster and (2) the development of extensions to Structured Query Language (SQL) to enable better use of protein identification algorithms. This should lead to much faster bioinformatics algorithms, enabling rapid development of complex bioinformatics databases just as SQL has enabled development of effective programs for very large business systems.