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      Comparative Analyses of Data Independent Acquisition Mass Spectrometric Approaches: DIA, WiSIM‐DIA, and Untargeted DIA

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          Abstract

          Data‐independent acquisition (DIA) is an emerging technology for quantitative proteomics. Current DIA focusses on the identification and quantitation of fragment ions that are generated from multiple peptides contained in the same selection window of several to tens of  m/ z. An alternative approach is WiSIM‐DIA, which combines conventional DIA with wide‐SIM (wide selected‐ion monitoring) windows to partition the precursor  m/ z space to produce high‐quality precursor ion chromatograms. However, WiSIM‐DIA has been underexplored; it remains unclear if it is a viable alternative to DIA. We demonstrate that WiSIM‐DIA quantified more than 24 000 unique peptides over five orders of magnitude in a single 2 h analysis of a neuronal synapse‐enriched fraction, compared to 31 000 in DIA. There is a strong correlation between abundance values of peptides quantified in both the DIA and WiSIM‐DIA datasets. Interestingly, the S/N ratio of these peptides is not correlated. We further show that peptide identification directly from DIA spectra identified >2000 proteins, which included unique peptides not found in spectral libraries generated by DDA.

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          OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data.

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            DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics.

            As a result of recent improvements in mass spectrometry (MS), there is increased interest in data-independent acquisition (DIA) strategies in which all peptides are systematically fragmented using wide mass-isolation windows ('multiplex fragmentation'). DIA-Umpire (http://diaumpire.sourceforge.net/), a comprehensive computational workflow and open-source software for DIA data, detects precursor and fragment chromatographic features and assembles them into pseudo-tandem MS spectra. These spectra can be identified with conventional database-searching and protein-inference tools, allowing sensitive, untargeted analysis of DIA data without the need for a spectral library. Quantification is done with both precursor- and fragment-ion intensities. Furthermore, DIA-Umpire enables targeted extraction of quantitative information based on peptides initially identified in only a subset of the samples, resulting in more consistent quantification across multiple samples. We demonstrated the performance of the method with control samples of varying complexity and publicly available glycoproteomics and affinity purification-MS data.
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              Proteomic parsimony through bipartite graph analysis improves accuracy and transparency.

              Assembling peptides identified from LC-MS/MS spectra into a list of proteins is a critical step in analyzing shotgun proteomics data. As one peptide sequence can be mapped to multiple proteins in a database, naïve protein assembly can substantially overstate the number of proteins found in samples. We model the peptide-protein relationships in a bipartite graph and use efficient graph algorithms to identify protein clusters with shared peptides and to derive the minimal list of proteins. We test the effects of this parsimony analysis approach using MS/MS data sets generated from a defined human protein mixture, a yeast whole cell extract, and a human serum proteome after MARS column depletion. The results demonstrate that the bipartite parsimony technique not only simplifies protein lists but also improves the accuracy of protein identification. We use bipartite graphs for the visualization of the protein assembly results to render the parsimony analysis process transparent to users. Our approach also groups functionally related proteins together and improves the comprehensibility of the results. We have implemented the tool in the IDPicker package. The source code and binaries for this protein assembly pipeline are available under Mozilla Public License at the following URL: http://www.mc.vanderbilt.edu/msrc/bioinformatics/.
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                Author and article information

                Contributors
                frank.koopmans@vu.nl
                Journal
                Proteomics
                Proteomics
                10.1002/(ISSN)1615-9861
                PMIC
                Proteomics
                John Wiley and Sons Inc. (Hoboken )
                1615-9853
                1615-9861
                15 January 2018
                January 2018
                : 18
                : 1 ( doiID: 10.1002/pmic.v18.1 )
                : 1700304
                Affiliations
                [ 1 ] Department of Molecular and Cellular Neurobiology CNCR Amsterdam Neuroscience Vrije Universiteit Amsterdam The Netherlands
                [ 2 ] Thermo Fisher Scientific Hemel Hempstead UK
                Author notes
                [*] [* ] Correspondence: Frank Koopmans, Department of Molecular and Cellular Neurobiology, Vrije Universiteit, De Boelelaan 1085, 1081HV Amsterdam, The Netherlands

                E‐mail: frank.koopmans@ 123456vu.nl

                Author information
                http://orcid.org/0000-0002-4973-5732
                Article
                PMIC12770
                10.1002/pmic.201700304
                5817406
                29134766
                8258466e-7648-433d-8b9b-6f07fe58ff4c
                © 2017 The Authors. Proteomics Published by WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim

                This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 August 2017
                : 04 November 2017
                Page count
                Figures: 3, Tables: 0, Pages: 6, Words: 5066
                Funding
                Funded by: Netherlands Organisation for Scientific Research
                Categories
                Accelerated Article
                Technology
                Accelerated Article
                Custom metadata
                2.0
                pmic12770
                January 2018
                Converter:WILEY_ML3GV2_TO_NLMPMC version:version=5.3.2.2 mode:remove_FC converted:19.02.2018

                Molecular biology
                data‐independent analysis,quantitative proteomics,spectral library
                Molecular biology
                data‐independent analysis, quantitative proteomics, spectral library

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