biostudies-literatureTu CAmerican Heart AssociationCenter for Protein Therapeutics, University at BuffaloNICHD NIH HHSNIDDK NIH HHSNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteNHLBI NIH HHSNational Institute of Child Health and Human Development4662-73https://www.ebi.ac.uk/biostudies/studies/S-EPMC4859434biostudies-literatureUnknown14(11)The two key steps for analyzing proteomic data generated by high-resolution MS are database searching and postprocessing. While the two steps are interrelated, studies on their combinatory effects and the optimization of these procedures have not been adequately conducted. Here, we investigated the performance of three popular search engines (SEQUEST, Mascot, and MS Amanda) in conjunction with five filtering approaches, including respective score-based filtering, a group-based approach, local false discovery rate (LFDR), PeptideProphet, and Percolator. A total of eight data sets from various proteomes (e.g., E. coli, yeast, and human) produced by various instruments with high-accuracy survey scan (MS1) and high- or low-accuracy fragment ion scan (MS2) (LTQ-Orbitrap, Orbitrap-Velos, OrbitraJournal of proteome researchOptimization of Search Engines and Postprocessing Approaches to Maximize Peptide and Protein Identification for High-Resolution Mass Data.PMC4859434R01 DK081750R01DK08175012SDG9450036U54HD071594HL103411R01 HL103411U54 HD071594Yi ZTu CLi JShyr YQu JSheng QWang XMa DShen XfalseOptimization of Search Engines and Postprocessing Approaches to Maximize Peptide and Protein Identification for High-Resolution Mass Data.The two key steps for analyzing proteomic data generated by high-resolution MS are database searching and postprocessing. While the two steps are interrelated, studies on their combinatory effects and the optimization of these procedures have not been adequately conducted. Here, we investigated the performance of three popular search engines (SEQUEST, Mascot, and MS Amanda) in conjunction with five filtering approaches, including respective score-based filtering, a group-based approach, local false discovery rate (LFDR), PeptideProphet, and Percolator. A total of eight data sets from various proteomes (e.g., E. coli, yeast, and human) produced by various instruments with high-accuracy survey scan (MS1) and high- or low-accuracy fragment ion scan (MS2) (LTQ-Orbitrap, Orbitrap-Velos, Orbitra2015-01-01T00:00:00Z2015 Nov2025-04-04T02:42:57.404Z2019-03-27T02:13:13ZS-EPMC48594342639008010.1021/acs.jproteome.5b00536