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MassIVE.quant: a community resource of quantitative mass spectrometry–based proteomics datasets

Nature Methods volume 17, pages 981–984 (2020)Cite this article

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Abstract

MassIVE.quant is a repository infrastructure and data resource for reproducible quantitative mass spectrometry–based proteomics, which is compatible with all mass spectrometry data acquisition types and computational analysis tools. A branch structure enables MassIVE.quant to systematically store raw experimental data, metadata of the experimental design, scripts of the quantitative analysis workflow, intermediate input and output files, as well as alternative reanalyses of the same dataset.

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Fig. 1: Outline of MassIVE.quant repository structure and reanalysis of three DDA-based experiments.
Fig. 2: Reanalyses of DIA:Selevsek2015, profiling changes in proteome abundance of Saccharomyces cerevisiae over six time points: T0 (0 min), T1 (15 min), T2 (30 min), T3 (60 min), T4 (90 min) and T5 (120 min), n = 3 biologically independent samples per each time point, in response to osmotic stress (RMSV000000251).

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Data availability

All the datasets that support this study are publicly available in MassIVE.quant (https://massive.ucsd.edu/ProteoSAFe/static/massive-quant.jsp) with MassIVE and ProteomeXchange identifiers. Additionally, identifiers for all the datasets are listed in Supplementary Tables 2, 3 and 5.

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Acknowledgements

This work was supported in part by NSF CAREER award no. DBI-1054826, grant no. NSF DBI-1759736 and the Chan-Zuckerberg foundation to O.V., grant no. NIH-NLM 1R01LM013115 to N.B. and O.V., NSF award no. ABI 1759980, NIH award nos. P41GM103484 and R24GM127667 to N.B. and the Personalized Health and Related Technologies (grant no. PHRT 0-21411-18) strategic focus area of ETH to B.W. The CRG/UPF Proteomics Unit is part of the Spanish Infrastructure for Omics Technologies (ICTS OmicsTech) and it is a member of the ProteoRed PRB3 consortium that is supported by grant no. PT17/0019 of the PE I+D+i 2013–2016 from the Instituto de Salud Carlos III (ISCIII) and ERDF. We acknowledge support from the Spanish Ministry of Science, Innovation and Universities, ‘Centro de Excelencia Severo Ochoa 2013–2017’, SEV-2012–0208 and Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya (grant no. 2017SGR595). This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 823839 (EPIC-XS). Y.P.-R. acknowledges the Wellcome Trust (grant no. 208391/Z/17/Z). We thank the MacCoss laboratory (Department of Genome Sciences, University of Washington) for the Skyline analyses and contributing the processed data, the Slavov laboratory (College of Engineering, Northeastern University) for providing the data and the Guo laboratory (School of Life Sciences, Westlake University, China) for providing the data.

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Authors and Affiliations

  1. Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA

    Meena Choi, Ting Huang, Tsung-Heng Tsai & Olga Vitek

  2. Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA

    Jeremy Carver, Benjamin Pullman & Nuno Bandeira

  3. Proteomics Unit, Center for Genomics Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain

    Cristina Chiva & Eduard Sabidó

  4. Proteomics Unit, Universitat Pompeu Fabra, Barcelona, Spain

    Cristina Chiva & Eduard Sabidó

  5. Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Hoffmann-La Roche Ltd, Basel, Switzerland

    Manuel Tzouros & Tom Dunkley

  6. Biognosys, Zurich, Switzerland

    Oliver M. Bernhardt, Jan Muntel & Lukas Reiter

  7. Department of Molecular and Cellular Pharmacology, University of California, San Francisco, San Francisco, CA, USA

    Ruth Hüttenhain & Erik Verschueren

  8. Department of Pathology, University of Michigan, Ann Arbor, MI, USA

    Guo Ci Teo & Alexey I. Nesvizhskii

  9. Proteomics Services, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK

    Yasset Perez-Riverol

  10. Department of Health Sciences and Technology, Institute of Translational Medicine, ETH, Zurich, Switzerland

    Maik Müller, Sandra Goetze, Maria Pavlou & Bernd Wollscheid

  11. Swiss Institute of Bioinformatics, Lausanne, Switzerland

    Sandra Goetze & Bernd Wollscheid

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  1. Meena Choi
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Contributions

M.C., J.C., N.B. and O.V. designed the research. M.C. and T.H. collected datasets and performed statistical analysis. J.C. and B.P. implemented MassIVE.quant. T.-H.T. performed statistical analysis. C.C., E.S. and M.T. experimented with new controlled mixtures. C.C., M.T., R.H., G.C.T., Y.P-R., J.M., M.M., S.G., M.P., E.V., B.W., O.M.B., A.I.N., L.R., T.D. and E.S. analyzed data up to quantification. M.C., N.B. and O.V. wrote the manuscript, with input from all authors.

Corresponding authors

Correspondence to Nuno Bandeira or Olga Vitek.

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Competing interests

O.M.B., J.M. and L.R. are employees of Biognosys AG. Spectronaut is a trademark of Biognosys AG. M.T. and T.D. are employees of Hoffmann-La Roche Ltd. All other authors declare no competing interests.

Additional information

Peer review information Arunima Singh was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary information

Supplementary Information

Supplementary Note 1, Figs. 1–4, Tables 1–5 and references.

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Choi, M., Carver, J., Chiva, C. et al. MassIVE.quant: a community resource of quantitative mass spectrometry–based proteomics datasets. Nat Methods 17, 981–984 (2020). https://doi.org/10.1038/s41592-020-0955-0

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