Quantitative Proteomics Using All-Against-All Biological Data Analysis

使用全面的生物数据分析进行定量蛋白质组学

• 批准号: 0640923
• 负责人: Daniel Miranker
• 金额: --
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-04-15 至 2011-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0640923&HistoricalAwards=false
• 关键词: Quantitative; Proteomics; Using; Against; Biological

The University of Texas at Austin is awarded a grant to develop new algorithms and validate a software system for large-scale protein identification and measurement of protein expression level by mass spectra look-up. These algorithms will be integrated into MoBIoS, a metric-distance database management system dedicated to the management of biological data. Mass-spectroscopy analysis of the proteins of a cell lysate often yields 30,000 spectra. Current analysis methods iterate through the spectra. Metric-distance join algorithms on sets of experimental mass spectra promise order-of magnitude performance improvements. Mass spectral data will be expanded and integrated into the capabilities in the Open Proteomic Database (OPD) thereby building a reference library of experimental peptide mass spectra. These spectra can be used to supplement or supplant the use of the approximate, computationally predicted peptide mass spectra currently used for spectral look-ups in proteomics, thereby further improving the ability to identify peptide spectra in proteomics experiments. The ultimate goal of this project is to integrate OPD and MoBIoS to create a MoBIoS-based spectral matching application and enable spectral queries of OPD. This will allow mapping of all spectra in an experiment against all experimental spectra in OPD (i.e., reference-library based analysis of proteomics data). More generally, this will allow discovery of recurring experimental spectra, leading to better rules for computationally generating peptide fragmentation spectra, as well as to identification of putative post-translational modifications and mutations independently of predicted reference spectra. The team will participate in the Computer Science department's First Bytes program, a co-ed summer camp that encourages high school students to major in computer science. The integration of Computational Biology material into this program is part of an explicit effort to broaden the appeal of Computer Science by providing a face to computers that contrasts to computer games. Both platform and application software will be made available for open academic use. The protein quantitation analysis application achieves its result without the use of isotope labeling. Thus, laboratories that cannot afford these proprietary reagents will be able to conduct quantitation experiments.

德克萨斯大学奥斯汀分校获得了一笔赠款，用于开发新的算法并验证一个软件系统，用于大规模蛋白质鉴定和通过质谱查找测量蛋白质表达水平。 这些算法将被集成到MoBIoS中，这是一个专门用于生物数据管理的度量距离数据库管理系统。细胞裂解物的蛋白质的质谱分析通常产生30，000个谱。目前的分析方法主要是通过光谱进行分析。 实验质谱集上的度量距离连接算法保证了数量级的性能改进。 质谱数据将被扩展并整合到开放蛋白质组数据库（OPD）中，从而建立实验肽质谱的参考库。这些光谱可用于补充或取代目前用于蛋白质组学中光谱查找的近似的、计算预测的肽质谱的使用，从而进一步提高在蛋白质组学实验中鉴定肽光谱的能力。 该项目的最终目标是将OPD和MoBIoS集成，以创建基于MoBIoS的光谱匹配应用程序，并实现OPD的光谱查询。这将允许将实验中的所有光谱映射到OPD中的所有实验光谱（即，基于参考文库的蛋白质组学数据分析）。更一般地，这将允许发现重复的实验光谱，从而导致用于计算生成肽片段化光谱的更好的规则，以及独立于预测的参考光谱鉴定推定的翻译后修饰和突变。 该团队将参加计算机科学系的第一个夏令营项目，这是一个男女同校的夏令营，鼓励高中生主修计算机科学。将计算生物学材料整合到这个程序中是通过提供与计算机游戏形成对比的计算机面孔来扩大计算机科学吸引力的明确努力的一部分。 平台和应用软件都将提供给开放的学术使用。蛋白质定量分析应用程序在不使用同位素标记的情况下实现其结果。因此，无法负担这些专有试剂的实验室将能够进行定量实验。