Jones Andrew R, Siepen Jennifer A, Hubbard Simon J, Paton Norman W
Department of Preclinical Veterinary Science, Faculty of Veterinary Science, University of Liverpool, Liverpool, UK.
Proteomics. 2009 Mar;9(5):1220-9. doi: 10.1002/pmic.200800473.
LC-MS experiments can generate large quantities of data, for which a variety of database search engines are available to make peptide and protein identifications. Decoy databases are becoming widely used to place statistical confidence in result sets, allowing the false discovery rate (FDR) to be estimated. Different search engines produce different identification sets so employing more than one search engine could result in an increased number of peptides (and proteins) being identified, if an appropriate mechanism for combining data can be defined. We have developed a search engine independent score, based on FDR, which allows peptide identifications from different search engines to be combined, called the FDR Score. The results demonstrate that the observed FDR is significantly different when analysing the set of identifications made by all three search engines, by each pair of search engines or by a single search engine. Our algorithm assigns identifications to groups according to the set of search engines that have made the identification, and re-assigns the score (combined FDR Score). The combined FDR Score can differentiate between correct and incorrect peptide identifications with high accuracy, allowing on average 35% more peptide identifications to be made at a fixed FDR than using a single search engine.
液相色谱-质谱联用（LC-MS）实验能够产生大量数据，针对这些数据有多种数据库搜索引擎可用于进行肽段和蛋白质鉴定。反向数据库正被广泛用于对结果集进行统计学置信度评估，从而能够估计错误发现率（FDR）。不同的搜索引擎会产生不同的鉴定集，因此如果能够定义一种合适的数据合并机制，使用多个搜索引擎可能会使鉴定出的肽段（和蛋白质）数量增加。我们基于错误发现率开发了一种独立于搜索引擎的评分方法，它能够将来自不同搜索引擎的肽段鉴定结果进行合并，称为错误发现率评分（FDR评分）。结果表明，在分析由所有三个搜索引擎、每对搜索引擎或单个搜索引擎做出的鉴定集时，观察到的错误发现率存在显著差异。我们的算法根据做出鉴定的搜索引擎集将鉴定结果分配到不同组，并重新分配评分（合并后的错误发现率评分）。合并后的错误发现率评分能够以高精度区分正确和错误的肽段鉴定结果，与使用单个搜索引擎相比，在固定的错误发现率下平均能够多鉴定出35%的肽段。
