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      DIA-NN: Neural networks and interference correction enable deep proteome coverage in high throughput

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          Abstract

          We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics experiments. DIA-NN improves the identification and quantification performance in conventional DIA proteomic applications, and is particularly beneficial for high-throughput applications, as it is fast and enables deep and confident proteome coverage when employed in combination with fast chromatographic methods.

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          Most cited references16

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          Is Open Access

          Data‐independent acquisition‐based SWATH ‐ MS for quantitative proteomics: a tutorial

          Abstract Many research questions in fields such as personalized medicine, drug screens or systems biology depend on obtaining consistent and quantitatively accurate proteomics data from many samples. SWATH‐MS is a specific variant of data‐independent acquisition (DIA) methods and is emerging as a technology that combines deep proteome coverage capabilities with quantitative consistency and accuracy. In a SWATH‐MS measurement, all ionized peptides of a given sample that fall within a specified mass range are fragmented in a systematic and unbiased fashion using rather large precursor isolation windows. To analyse SWATH‐MS data, a strategy based on peptide‐centric scoring has been established, which typically requires prior knowledge about the chromatographic and mass spectrometric behaviour of peptides of interest in the form of spectral libraries and peptide query parameters. This tutorial provides guidelines on how to set up and plan a SWATH‐MS experiment, how to perform the mass spectrometric measurement and how to analyse SWATH‐MS data using peptide‐centric scoring. Furthermore, concepts on how to improve SWATH‐MS data acquisition, potential trade‐offs of parameter settings and alternative data analysis strategies are discussed.
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            OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data.

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              Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra.

              To take advantage of the potential quantitative benefits offered by tandem mass spectrometry, we have modified the method in which tandem mass spectrum data are acquired in 'shotgun' proteomic analyses. The proposed method is not data dependent and is based on the sequential isolation and fragmentation of precursor windows (of 10 m/z) within the ion trap until a desired mass range has been covered. We compared the quantitative figures of merit for this method to those for existing strategies by performing an analysis of the soluble fraction of whole-cell lysates from yeast metabolically labeled in vivo with (15)N. To automate this analysis, we modified software (RelEx) previously written in the Yates lab to generate chromatograms directly from tandem mass spectra. These chromatograms showed improvements in signal-to-noise ratio of approximately three- to fivefold over corresponding chromatograms generated from mass spectrometry scans. In addition, to demonstrate the utility of the data-independent acquisition strategy coupled with chromatogram reconstruction from tandem mass spectra, we measured protein expression levels in two developmental stages of Caenorhabditis elegans.
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                Author and article information

                Journal
                101215604
                Nat Methods
                Nat. Methods
                Nature methods
                1548-7091
                1548-7105
                11 October 2019
                25 November 2019
                January 2020
                25 May 2020
                : 17
                : 1
                : 41-44
                Affiliations
                [1 ]Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
                [2 ]The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, United Kingdom
                [3 ]Department of Biochemistry, Charité Universitätsmedizin Berlin, Berlin, Germany
                Author notes
                [* ]To whom correspondence should be addressed: markus.ralser@ 123456crick.ac.uk
                Article
                EMS84588
                10.1038/s41592-019-0638-x
                6949130
                31768060
                b77af23d-8d5f-49d4-b52b-0c78ab5c04ea

                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

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                Life sciences
                Life sciences

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