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A proteomics sample metadata representation for multiomics integration and big data analysis

Author

Listed:
  • Chengxin Dai

    (Chongqing University of Posts and Telecommunications)

  • Anja Füllgrabe

    (European Bioinformatics Institute, Wellcome Genome Campus)

  • Julianus Pfeuffer

    (Freie Universität Berlin
    Visualization and Data analysis, Zuse Institute Berlin)

  • Elizaveta M. Solovyeva

    (Moscow Institute of Physics and Technology
    N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences)

  • Jingwen Deng

    (Chongqing University of Posts and Telecommunications)

  • Pablo Moreno

    (European Bioinformatics Institute, Wellcome Genome Campus)

  • Selvakumar Kamatchinathan

    (European Bioinformatics Institute, Wellcome Genome Campus)

  • Deepti Jaiswal Kundu

    (European Bioinformatics Institute, Wellcome Genome Campus)

  • Nancy George

    (European Bioinformatics Institute, Wellcome Genome Campus)

  • Silvie Fexova

    (European Bioinformatics Institute, Wellcome Genome Campus)

  • Björn Grüning

    (Albert-Ludwigs-University Freiburg)

  • Melanie Christine Föll

    (Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg
    Northeastern University)

  • Johannes Griss

    (Medical University of Vienna)

  • Marc Vaudel

    (University of Bergen)

  • Enrique Audain

    (Universitätsklinikum Schleswig-Holstein Kiel)

  • Marie Locard-Paulet

    (University of Copenhagen)

  • Michael Turewicz

    (Ruhr University Bochum, Medical Faculty, Medizinisches Proteom-Center
    Ruhr University Bochum, Center for Protein Diagnostics (PRODI), Medical Proteome Analysis)

  • Martin Eisenacher

    (Ruhr University Bochum, Medical Faculty, Medizinisches Proteom-Center
    Ruhr University Bochum, Center for Protein Diagnostics (PRODI), Medical Proteome Analysis)

  • Julian Uszkoreit

    (Ruhr University Bochum, Medical Faculty, Medizinisches Proteom-Center
    Ruhr University Bochum, Center for Protein Diagnostics (PRODI), Medical Proteome Analysis)

  • Tim Bossche

    (VIB – UGent Center for Medical Biotechnology, VIB
    Ghent University)

  • Veit Schwämmle

    (University of Southern Denmark, Campusvej 55)

  • Henry Webel

    (University of Copenhagen)

  • Stefan Schulze

    (University of Pennsylvania, Department of Biology)

  • David Bouyssié

    (University of Toulouse, CNRS, UPS)

  • Savita Jayaram

    (nference Labs)

  • Vinay Kumar Duggineni

    (nference Labs)

  • Patroklos Samaras

    (Technical University of Munich)

  • Mathias Wilhelm

    (Technical University of Munich)

  • Meena Choi

    (Proteomics and Lipidomics, Genentech)

  • Mingxun Wang

    (University of California San Diego)

  • Oliver Kohlbacher

    (University of Tübingen
    University of Tübingen
    University Hospital Tübingen)

  • Alvis Brazma

    (European Bioinformatics Institute, Wellcome Genome Campus)

  • Irene Papatheodorou

    (European Bioinformatics Institute, Wellcome Genome Campus)

  • Nuno Bandeira

    (University of California San Diego
    University of California)

  • Eric W. Deutsch

    (Institute for Systems Biology, 401 Terry Ave N)

  • Juan Antonio Vizcaíno

    (European Bioinformatics Institute, Wellcome Genome Campus)

  • Mingze Bai

    (Chongqing University of Posts and Telecommunications
    Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics)

  • Timo Sachsenberg

    (University of Tübingen)

  • Lev I. Levitsky

    (N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences)

  • Yasset Perez-Riverol

    (European Bioinformatics Institute, Wellcome Genome Campus)

Abstract

The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets.

Suggested Citation

  • Chengxin Dai & Anja Füllgrabe & Julianus Pfeuffer & Elizaveta M. Solovyeva & Jingwen Deng & Pablo Moreno & Selvakumar Kamatchinathan & Deepti Jaiswal Kundu & Nancy George & Silvie Fexova & Björn Grüni, 2021. "A proteomics sample metadata representation for multiomics integration and big data analysis," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26111-3
    DOI: 10.1038/s41467-021-26111-3
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    References listed on IDEAS

    as
    1. Philipp Mertins & D. R. Mani & Kelly V. Ruggles & Michael A. Gillette & Karl R. Clauser & Pei Wang & Xianlong Wang & Jana W. Qiao & Song Cao & Francesca Petralia & Emily Kawaler & Filip Mundt & Karste, 2016. "Proteogenomics connects somatic mutations to signalling in breast cancer," Nature, Nature, vol. 534(7605), pages 55-62, June.
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    Cited by:

    1. Tine Claeys & Tim Van Den Bossche & Yasset Perez-Riverol & Kris Gevaert & Juan Antonio Vizcaíno & Lennart Martens, 2023. "lesSDRF is more: maximizing the value of proteomics data through streamlined metadata annotation," Nature Communications, Nature, vol. 14(1), pages 1-4, December.

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