OLAV: Towards high-throughput tandem mass spectrometry data identification
OLAV: Towards high-throughput tandem mass spectrometry data identification
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DOI：
10.1002/pmic.200300485
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发表时间：
2003-08-01
期刊：
影响因子：
3.4
通讯作者：
Magnin, J
中科院分区：
文献类型：
--
作者：
Colinge, J;Masselot, A;Magnin, J
质谱与数据库搜索相结合已成为蛋白质组学项目中鉴定蛋白质的首选方法。蛋白质被一种或几种酶消化以获得肽，并通过质谱分析。我们引入了一系列新的评分方案，名为 OLAV，旨在从串联质谱中识别数据库中的肽。 OLAV 评分方案基于信号检测理论，比以前的方案更广泛地利用质谱信息。我们还引入了结构匹配的新概念，它使用模式检测方法来更好地区分真值和误报。我们展示了 OLAV 评分方案相对于广泛使用的识别程序 MASCOT 的优越性。我们相信这项工作引入了一种设计评分方案的新方法，特别适合高通量项目，例如 GeneProt 大规模人血浆项目，在该项目中手动检查所有标识是不切实际的。
Mass spectrometry combined with database searching has become the preferred method for identifying proteins in proteomics projects. Proteins are digested by one or several enzymes to obtain peptides, which are analyzed by mass spectrometry. We introduce a new family of scoring schemes, named OLAV, aimed at identifying peptides in a database from their tandem mass spectra. OLAV scoring schemes are based on signal detection theory, and exploit mass spectrometry information more extensively than previously existing schemes. We also introduce a new concept of structural matching that uses pattern detection methods to better separate true from false positives. We show the superiority of OLAV scoring schemes compared to MASCOT, a widely used identification program. We believe that this work introduces a new way of designing scoring schemes that are especially adapted to high-throughput projects such as GeneProt large-scale human plasma project, where it is impractical to check all identifications manually.