Mass spectrometry-based proteomics is an invaluable tool for addressing important biological questions. Data-dependent acquisition methods effectuate stochastic acquisition of data in complex mixtures, which results in missing identifications across replicates. We developed a search approach that improves the reproducibility of data acquired from any mass spectrometer. In our approach, a spectral library is built from the identification results from a database search, and then, the library is used to research the same data files to obtain the final result. We showed that higher identification and quantification reproducibility is achieved with the dual-search approach than with a typical database search. Four datasets with different complexity were compared: (1) data from a cell lysate study performed in our lab, (2) data from an interactome study performed in our lab, (3) a publicly available extracellular vesicles dataset, and (4) a publicly available phosphoproteomics dataset. Our results show that the dual-search approach can be widely and easily used to improve data quality in proteomics data.

中文翻译：

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通过双重搜索策略提高蛋白质组学数据的可重复性。

基于质谱的蛋白质组学是解决重要生物学问题的宝贵工具。依赖数据的采集方法可以实现复杂混合物中数据的随机采集，从而导致重复数据之间的标识丢失。我们开发了一种搜索方法，可以提高从任何质谱仪获取的数据的可重复性。在我们的方法中，根据数据库搜索的识别结果构建光谱库，然后使用该库研究相同的数据文件以获得最终结果。我们显示，与典型的数据库搜索相比，双重搜索方法可实现更高的鉴定和定量重现性。比较了四个具有不同复杂性的数据集：（1）来自我们实验室进行的细胞裂解液研究的数据，（2）来自我们实验室进行的相互作用组研究的数据，（3）可公开获得的细胞外囊泡数据集，以及（4）可公开获得的磷酸化蛋白质组学数据集。我们的结果表明，双重搜索方法可以广泛，轻松地用于改善蛋白质组学数据的数据质量。