Open AccessImproving Proteomics Data Reproducibility with a Dual-Search Strategy
Author(s) -
Carolina Fernández-Costa,
Salvador MartínezBartolomé,
Daniel B. McClatchy,
John R. Yates
Publication year - 2019
Publication title -
analytical chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.117
H-Index - 332
eISSN - 1520-6882
pISSN - 0003-2700
DOI - 10.1021/acs.analchem.9b04955
Subject(s) - proteomics , identification (biology) , computer science , mass spectrometry , chemistry , reproducibility , data quality , data mining , chromatography , metric (unit) , biochemistry , botany , operations management , gene , economics , biology
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.