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      Statistical control of peptide and protein error rates in large-scale targeted DIA analyses

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          Abstract

          Liquid chromatography coupled to tandem mass spectrometry is the main method for high-throughput identification and quantification of peptides and inferred proteins. Within this field, data-independent acquisition (DIA) combined with peptide-centric scoring, exemplified by SWATH-MS, emerged as a scalable method to achieve deep and consistent proteome coverage across large-scale datasets. Here we discuss the adaptation of statistical concepts developed for discovery proteomics based on spectrum-centric scoring to large-scale DIA experiments analyzed with peptide-centric scoring strategies and provide guidance on their application. We show that optimal tradeoffs between sensitivity and specificity require careful considerations of the relationship between proteins in the samples and proteins represented in the spectral library. We propose the application of a global analyte constraint to prevent accumulation of false positives across large-scale datasets. Furthermore, to increase the quality and reproducibility of published proteomic results, well-established confidence criteria should be reported for detected peptide queries, peptides and inferred proteins.

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          Most cited references43

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Is Open Access

            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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              Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.

              We present a statistical model to estimate the accuracy of peptide assignments to tandem mass (MS/MS) spectra made by database search applications such as SEQUEST. Employing the expectation maximization algorithm, the analysis learns to distinguish correct from incorrect database search results, computing probabilities that peptide assignments to spectra are correct based upon database search scores and the number of tryptic termini of peptides. Using SEQUEST search results for spectra generated from a sample of known protein components, we demonstrate that the computed probabilities are accurate and have high power to discriminate between correctly and incorrectly assigned peptides. This analysis makes it possible to filter large volumes of MS/MS database search results with predictable false identification error rates and can serve as a common standard by which the results of different research groups are compared.
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                Author and article information

                Journal
                101215604
                32338
                Nat Methods
                Nat. Methods
                Nature methods
                1548-7091
                1548-7105
                1 August 2017
                21 August 2017
                September 2017
                21 February 2018
                : 14
                : 9
                : 921-927
                Affiliations
                [1 ]Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, CH-8093 Zurich, Switzerland
                [2 ]PhD Program in Systems Biology, University of Zurich and ETH Zurich, CH-8093 Zurich, Switzerland
                [3 ]ID Scientific IT Services, ETH Zurich, CH-8092 Zurich, Switzerland
                [4 ]PhD program in Molecular and Translational Biomedicine, Competence Center Personalized Medicine (CC-PM), ETH Zurich and University of Zurich, CH-8044 Zurich, Switzerland
                [5 ]SCIEX, 1201 Radio Road, Redwood City, CA 94065, USA
                [6 ]Department of Genome Sciences, University of Washington, Seattle, WA 98195–5065, USA
                [7 ]Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
                [8 ]Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
                [9 ]Biognosys, Wagistrasse 25, CH-8952 Schlieren, Switzerland
                [10 ]SCIEX, Concord, Ontario L4K 4V8, Canada
                [11 ]Faculty of Science, University of Zurich, CH-8057 Zurich, Switzerland
                Author notes
                []Corresponding authors: Correspondence to aebersold@ 123456imsb.biol.ethz.ch or collins@ 123456imsb.biol.ethz.ch
                [§]

                Authors ordered alphabetically

                Article
                EMS73581
                10.1038/nmeth.4398
                5581544
                28825704
                0cd049f0-96f7-44d4-9766-ca8d0ff53a87

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                Life sciences

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