Development of Software for Comparative/Quantitative Clinical Proteomics

描述（由申请人提供）：目前对蛋白质组学软件的需求很大，可以快速准确地进行数据分析。近年来蛋白质组学仪器和通量的进步加上生物数据库的持续扩展，使得研究人员能够在相对较短的时间内产生非常大量的数据。由于信息学的发展没有跟上分析技术的进步，数据分析现在是基于蛋白质组学的发现的主要瓶颈。因此，有一个显着的软件，帮助研究人员在分析他们的复杂的蛋白质组学数据在高通量的方式需要。本申请中提出的研究是设计一种易于使用的跨平台软件包，该软件包将允许用户1）整合来自多个数据库搜索引擎的结果2）提供大型蛋白质组数据集的统计验证，3）促进蛋白质组之间的轻松比较，4）定量生物样品之间的蛋白质表达，和5）用来自公共数据库的生物学数据注释蛋白质鉴定。这项建议所述的综合软件包一旦商品化，将大大提高蛋白质组学研究的速度和准确性。建议的研究将提供一个软件包，大大帮助研究人员利用他们的蛋白质组学数据。由于蛋白质组学领域正在不断发展，相信这里开发的软件将成为各种研究人员的宝贵资源。将该软件提供给科学界将增加蛋白质组学研究的吞吐量并加快发现过程。

DESCRIPTION (provided by applicant): There is currently a significant demand for proteomics software which enables rapid and accurate data analysis. The advances over recent years in proteomics instrumentation and throughput coupled with the continued expansion of biological databases has resulted is researchers being able to produce very large amounts of data in relatively short time periods. Since informatics developments have not kept pace with the advances in analytical technologies, data analysis now represents a major bottleneck in proteomics-based discoveries. Thus, there is a significant need for software which assists researchers in analyzing their complex proteomics data in a high-throughput manner. The research proposed in this application is to design an a easy to use, cross- platform software package that will allow users to 1) integrate results from multiple database search engines 2) provide statistical validation of large proteome datasets, 3) facilitate easy comparisons between proteomes, 4) quantitate protein expression between biological samples, and 5) annotate protein identifications with biological data from public databases. The integrated software package described in this proposal, once commercialized, will greatly enhance the speed and accuracy of proteomics research.The proposed studies will provide a software package which will greatly assist researchers in making use of their proteomics data. Since the proteomics field is continually growing, it is believed that the software developed here will be a valuable resource to a diverse set of researchers. Making this software available to the scientific community will increase the throughput of proteomics research and speed the discovery process.