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      Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry

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          Abstract

          Quantitative proteomics employing mass spectrometry is an indispensable tool in life science research. Targeted proteomics has emerged as a powerful approach for reproducible quantification but is limited in the number of proteins quantified. SWATH-mass spectrometry consists of data-independent acquisition and a targeted data analysis strategy that aims to maintain the favorable quantitative characteristics (accuracy, sensitivity, and selectivity) of targeted proteomics at large scale. While previous SWATH-mass spectrometry studies have shown high intra-lab reproducibility, this has not been evaluated between labs. In this multi-laboratory evaluation study including 11 sites worldwide, we demonstrate that using SWATH-mass spectrometry data acquisition we can consistently detect and reproducibly quantify >4000 proteins from HEK293 cells. Using synthetic peptide dilution series, we show that the sensitivity, dynamic range and reproducibility established with SWATH-mass spectrometry are uniformly achieved. This study demonstrates that the acquisition of reproducible quantitative proteomics data by multiple labs is achievable, and broadly serves to increase confidence in SWATH-mass spectrometry data acquisition as a reproducible method for large-scale protein quantification.

          Abstract

          SWATH-mass spectrometry consists of a data-independent acquisition and a targeted data analysis strategy that aims to maintain the favorable quantitative characteristics on the scale of thousands of proteins. Here, using data generated by eleven groups worldwide, the authors show that SWATH-MS is capable of generating highly reproducible data across different laboratories.

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          Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ *

          Protein quantification without isotopic labels has been a long-standing interest in the proteomics field. However, accurate and robust proteome-wide quantification with label-free approaches remains a challenge. We developed a new intensity determination and normalization procedure called MaxLFQ that is fully compatible with any peptide or protein separation prior to LC-MS analysis. Protein abundance profiles are assembled using the maximum possible information from MS signals, given that the presence of quantifiable peptides varies from sample to sample. For a benchmark dataset with two proteomes mixed at known ratios, we accurately detected the mixing ratio over the entire protein expression range, with greater precision for abundant proteins. The significance of individual label-free quantifications was obtained via a t test approach. For a second benchmark dataset, we accurately quantify fold changes over several orders of magnitude, a task that is challenging with label-based methods. MaxLFQ is a generic label-free quantification technology that is readily applicable to many biological questions; it is compatible with standard statistical analysis workflows, and it has been validated in many and diverse biological projects. Our algorithms can handle very large experiments of 500+ samples in a manageable computing time. It is implemented in the freely available MaxQuant computational proteomics platform and works completely seamlessly at the click of a button.
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            Statistical significance for genomewide studies.

            With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genomewide data set are tested against some null hypothesis, where a number of features are expected to be significant. Here we propose an approach to measuring statistical significance in these genomewide studies based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q value is associated with each tested feature. The q value is similar to the well known p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.
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              The Proteomics Identifications (PRIDE) database and associated tools: status in 2013

              The PRoteomics IDEntifications (PRIDE, http://www.ebi.ac.uk/pride) database at the European Bioinformatics Institute is one of the most prominent data repositories of mass spectrometry (MS)-based proteomics data. Here, we summarize recent developments in the PRIDE database and related tools. First, we provide up-to-date statistics in data content, splitting the figures by groups of organisms and species, including peptide and protein identifications, and post-translational modifications. We then describe the tools that are part of the PRIDE submission pipeline, especially the recently developed PRIDE Converter 2 (new submission tool) and PRIDE Inspector (visualization and analysis tool). We also give an update about the integration of PRIDE with other MS proteomics resources in the context of the ProteomeXchange consortium. Finally, we briefly review the quality control efforts that are ongoing at present and outline our future plans.
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                Author and article information

                Contributors
                aebersold@imsb.biol.ethz.ch
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                21 August 2017
                21 August 2017
                2017
                : 8
                : 291
                Affiliations
                [1 ]ISNI 0000 0001 2156 2780, GRID grid.5801.c, Department of Biology, , Institute of Molecular Systems Biology, ; ETH Zurich, 8093 Zurich Switzerland
                [2 ]SCIEX, 1201 Radio Road, Redwood City, CA 94065 USA
                [3 ]ISNI 0000 0000 8687 5377, GRID grid.272799.0, , Buck Institute for Research on Aging, ; 8001 Redwood Boulevard, Novato, CA 94945 USA
                [4 ]ISNI 0000 0004 1937 0650, GRID grid.7400.3, PhD. Program in Systems Biology, , University of Zurich and ETH Zurich, ; Zurich, 8057 Switzerland
                [5 ]ISNI 0000 0004 0463 2320, GRID grid.64212.33, , Institute for Systems Biology, ; 401 Terry Avenue North, Seattle, WA 98109 USA
                [6 ]ISNI 0000 0001 2171 9311, GRID grid.21107.35, Department of Pathology, Clinical Chemistry Division, , Johns Hopkins University School of Medicine, ; Baltimore, MD 21231 USA
                [7 ]ISNI 0000 0001 2297 6811, GRID grid.266102.1, Department of Pharmaceutical Chemistry, , University of California, ; San Francisco, CA 94143 USA
                [8 ]ISNI 0000 0004 0473 9881, GRID grid.416166.2, , Lunenfeld-Tanenbaum Research Institute, Sinai Health System, ; Toronto, M5G 1X5 Ontario Canada
                [9 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Department of Molecular Genetics, , University of Toronto, ; Toronto, M5S 1A8 Ontario Canada
                [10 ]ISNI 0000 0001 2355 7002, GRID grid.4367.6, Departments of Medicine and Anesthesiology, , Washington University School of Medicine, 660 South Euclid Avenue, ; St. Louis, MO 63110 USA
                [11 ]ISNI 0000 0001 0660 6749, GRID grid.274841.c, Department of Pharmaceutical Microbiology, , Faculty of Life Sciences, Kumamoto University, 5-1 Oe-honmachi, ; Chuo-ku, Kumamoto 862-0973 Japan
                [12 ]ISNI 0000 0001 2034 1839, GRID grid.21155.32, , Proteomics Division, BGI-Shenzhen, ; Shenzhen, 518083 China
                [13 ]ISNI 0000 0001 2158 5405, GRID grid.1004.5, Department of Chemistry and Biomolecular Sciences, Australian Proteome Analysis Facility (APAF), , Macquarie University, ; Sydney, 2109 Australia
                [14 ]Functional Genomics Center Zurich, ETH Zurich/University of Zurich, Winterthurerstr. 190, 8057 Zurich, Switzerland
                [15 ]ISNI 0000 0004 1937 0650, GRID grid.7400.3, Faculty of Science, , University of Zurich, ; Zurich, Switzerland
                Author information
                http://orcid.org/0000-0003-0827-3495
                http://orcid.org/0000-0002-2626-3912
                http://orcid.org/0000-0002-1655-6789
                http://orcid.org/0000-0002-3216-9447
                http://orcid.org/0000-0003-1679-5453
                Article
                249
                10.1038/s41467-017-00249-5
                5566333
                28827567
                99c1e987-5adc-4beb-828e-a153c13de5e4
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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                : 17 March 2017
                : 12 June 2017
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