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      A multi-center study benchmarks software tools for label-free proteome quantification

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          Abstract

          The consistent and accurate quantification of proteins by mass spectrometry (MS)-based proteomics depends on the performance of instruments, acquisition methods and data analysis software. In collaboration with the software developers, we evaluated OpenSWATH, SWATH2.0, Skyline, Spectronaut and DIA-Umpire, five of the most widely used software methods for processing data from SWATH-MS (sequential window acquisition of all theoretical fragment ion spectra), a method that uses data-independent acquisition (DIA) for label-free protein quantification. We analyzed high-complexity test datasets from hybrid proteome samples of defined quantitative composition acquired on two different MS instruments using different SWATH isolation windows setups. For consistent evaluation we developed LFQbench, an R-package to calculate metrics of precision and accuracy in label-free quantitative MS, and report the identification performance, robustness and specificity of each software tool. Our reference datasets enabled developers to improve their software tools. After optimization, all tools provided highly convergent identification and reliable quantification performance, underscoring their robustness for label-free quantitative proteomics.

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          Most cited references54

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          Universal sample preparation method for proteome analysis.

          We describe a method, filter-aided sample preparation (FASP), which combines the advantages of in-gel and in-solution digestion for mass spectrometry-based proteomics. We completely solubilized the proteome in sodium dodecyl sulfate, which we then exchanged by urea on a standard filtration device. Peptides eluted after digestion on the filter were pure, allowing single-run analyses of organelles and an unprecedented depth of proteome coverage.
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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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              Skyline: an open source document editor for creating and analyzing targeted proteomics experiments.

              Skyline is a Windows client application for targeted proteomics method creation and quantitative data analysis. It is open source and freely available for academic and commercial use. The Skyline user interface simplifies the development of mass spectrometer methods and the analysis of data from targeted proteomics experiments performed using selected reaction monitoring (SRM). Skyline supports using and creating MS/MS spectral libraries from a wide variety of sources to choose SRM filters and verify results based on previously observed ion trap data. Skyline exports transition lists to and imports the native output files from Agilent, Applied Biosystems, Thermo Fisher Scientific and Waters triple quadrupole instruments, seamlessly connecting mass spectrometer output back to the experimental design document. The fast and compact Skyline file format is easily shared, even for experiments requiring many sample injections. A rich array of graphs displays results and provides powerful tools for inspecting data integrity as data are acquired, helping instrument operators to identify problems early. The Skyline dynamic report designer exports tabular data from the Skyline document model for in-depth analysis with common statistical tools. Single-click, self-updating web installation is available at http://proteome.gs.washington.edu/software/skyline. This web site also provides access to instructional videos, a support board, an issues list and a link to the source code project.
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                Author and article information

                Journal
                9604648
                20305
                Nat Biotechnol
                Nat. Biotechnol.
                Nature biotechnology
                1087-0156
                1546-1696
                3 October 2016
                3 October 2016
                November 2016
                03 April 2017
                : 34
                : 11
                : 1130-1136
                Affiliations
                [1 ]Institute for Immunology, University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany
                [2 ]Department of Biology, Institute of Molecular Systems Biology, Eidgenoessische Technische Hochschule (IMSB-ETH) Zurich, Zurich, Switzerland
                [3 ]Biognosys AG, Schlieren, Switzerland
                [4 ]Department of Genome Sciences, University of Washington, Seattle, Washington, USA
                [5 ]AB Sciex, Concord, Ontario, Canada
                [6 ]Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
                [7 ]PhD Program in Systems Biology, University of Zurich and Eidgenoessische Technische Hochschule (ETH) Zurich, Zurich, Switzerland
                [8 ]European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
                [9 ]Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
                [10 ]Faculty of Science, University of Zurich, Zurich, Switzerland
                Author notes
                Corresponding authors: Stefan Tenzer ( tenzer@ 123456uni-mainz.de ) or Pedro Navarro ( pnavarro@ 123456uni-mainz.de )
                Article
                EMS69887
                10.1038/nbt.3685
                5120688
                27701404
                0895af8e-dae7-4982-82d1-59f58be47c7d

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                Biotechnology

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