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      SWATH2stats: An R/Bioconductor Package to Process and Convert Quantitative SWATH-MS Proteomics Data for Downstream Analysis Tools

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      1 , * , 1 , 2 , 1 , 3

      PLoS ONE

      Public Library of Science

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          Abstract

          SWATH-MS is an acquisition and analysis technique of targeted proteomics that enables measuring several thousand proteins with high reproducibility and accuracy across many samples. OpenSWATH is popular open-source software for peptide identification and quantification from SWATH-MS data. For downstream statistical and quantitative analysis there exist different tools such as MSstats, mapDIA and aLFQ. However, the transfer of data from OpenSWATH to the downstream statistical tools is currently technically challenging. Here we introduce the R/Bioconductor package SWATH2stats, which allows convenient processing of the data into a format directly readable by the downstream analysis tools. In addition, SWATH2stats allows annotation, analyzing the variation and the reproducibility of the measurements, FDR estimation, and advanced filtering before submitting the processed data to downstream tools. These functionalities are important to quickly analyze the quality of the SWATH-MS data. Hence, SWATH2stats is a new open-source tool that summarizes several practical functionalities for analyzing, processing, and converting SWATH-MS data and thus facilitates the efficient analysis of large-scale SWATH/DIA datasets.

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          Most cited references 9

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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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              mProphet: automated data processing and statistical validation for large-scale SRM experiments.

              Selected reaction monitoring (SRM) is a targeted mass spectrometric method that is increasingly used in proteomics for the detection and quantification of sets of preselected proteins at high sensitivity, reproducibility and accuracy. Currently, data from SRM measurements are mostly evaluated subjectively by manual inspection on the basis of ad hoc criteria, precluding the consistent analysis of different data sets and an objective assessment of their error rates. Here we present mProphet, a fully automated system that computes accurate error rates for the identification of targeted peptides in SRM data sets and maximizes specificity and sensitivity by combining relevant features in the data into a statistical model.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                7 April 2016
                2016
                : 11
                : 4
                Affiliations
                [1 ]Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093, Zurich, Switzerland
                [2 ]PhD program in Molecular and Translational Biomedicine, Competence Center Personalized Medicine UZH/ETH & Life Science Zurich Graduate School, ETH Zurich and University of Zurich, 8044, Zurich, Switzerland
                [3 ]Faculty of Science, University of Zurich, 8057, Zurich, Switzerland
                UGent / VIB, BELGIUM
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: PB MH RA. Performed the experiments: PB MH. Analyzed the data: PB MH. Wrote the paper: PB MH RA. Conceived and implemented the FDR estimation and FDR-filtering functions: MH.

                Article
                PONE-D-16-05523
                10.1371/journal.pone.0153160
                4824525
                27054327
                © 2016 Blattmann et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 1, Tables: 1, Pages: 7
                Product
                Funding
                This work was supported by the Swiss SystemsX.ch initiative, evaluated by the Swiss National Science Foundation, to PB. MH was supported by grants from the European research council [233226-PROTEOMICS v3.0] and the Institut Mérieux to RA. The RA group is supported by the Swiss National Science Foundation [3100A0-130530], Advanced ERC grant [#670821], ETH Zurich and SystemsX.ch. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Biochemistry
                Proteomics
                Peptide Mapping
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Data
                Biology and Life Sciences
                Biochemistry
                Proteins
                Post-Translational Modification
                Signal Peptides
                Biology and Life Sciences
                Biochemistry
                Peptides
                Research and Analysis Methods
                Database and Informatics Methods
                Biological Databases
                Proteomic Databases
                Biology and Life Sciences
                Biochemistry
                Proteomics
                Proteomic Databases
                Engineering and Technology
                Signal Processing
                Signal Filtering
                Computer and Information Sciences
                Information Technology
                Data Processing
                Research and Analysis Methods
                Research Design
                Experimental Design
                Custom metadata
                The SWATH2stats package is deposited on Bioconductor ( http://bioconductor.org/packages/SWATH2stats/). The example script was run with SWATH2stats v1.1.14 currently found on http://bioconductor.org/packages/devel/bioc/html/SWATH2stats.html. The SWATH-MS data to run the example script is deposited on Peptideatlas ( http://www.peptideatlas.org/PASS/PASS00289).

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