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Searchgui proteomics1/4/2023 ![]() ![]() Vaudel M, Barsnes H, Berven FS, Sickmann A, Martens L, SearchGUI: An open-source graphical user interface for simultaneous OMSSA and X!Tandem searches, Proteomics 11 :996–999, 2011. Shteynberg D, Nesvizhskii AI, Moritz RL, Deutsch EW, Combining results of multiple search engines in proteomics, Mol Cell Proteomics 12 :2383–2393, 2013. Kapp EA, Schütz F, Connely LM, Chakel JA, Meza JE, Miller CA, Fenyo D, Eng JK, Adkins JN, Omenn GS, Simpson RJ, An evaluation, comparison, and accurate benchmarking of several publicly available MS/MS search algorithms: Sensitivity and specificity analysis, Proteomics 5 :3475–3490, 2005. Sadygov RG, Cociorva D, Yates JR, Large-scale database searching using tandem mass spectra: Looking up the answer in the back of the book, Nat Methods 1 :195–202, 2004. Brosch M, Swamy S, Hubbard T, Choudhary J, Comparison of Mascot and X!Tandem performance for low and high accuracy mass spectrometry and the development of an adjusted Mascot threshold, Mol Cell Proteomics 7 :962–970, 2008. Verheggen K, Martens L, Berven FS, Barsnes H, Vaudel M, Database search engines: Paradigms, challenges and solutions, Adv Exp Med Biol 919 :147–156, 2016. Savage N, Proteomics: High-protein research, Nature 527 :S6–S7, 2015. #SEARCHGUI PROTEOMICS MANUAL#These settings resulted in rise of the efficiency of our customized pipeline unobtainable by manual scouting: the analysis of 192 files searched against human proteome (42153 entries) downloaded from UniProt took 11 h. We added homemade java-script to automatize pipeline from file picking to report generation. We selected combination of X!Tandem, MS-GF + and OMMSA as the most time-efficient and productive combination of search. We also compared the results of our search combination with Mascot results using protein kit UPS2, containing 48 human proteins. We compared the contribution of each of the eight search engines (X!Tandem, MS-GF +, MS Amanda, MyriMatch, Comet, Tide, Andromeda, and OMSSA) integrated in an open-source graphical user interface SearchGUI ( ) into total result of proteoforms identification and optimized set of engines working simultaneously. In our work, the necessity of single-thread analysis of bulky data emerged during interpretation of HepG2 proteome profiling results for proteoforms searching. Nowadays, the routine manual search does not satisfy the “speed” of modern science any longer. Proteomic challenges, stirred up by the advent of high-throughput technologies, produce large amount of MS data. ![]()
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