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A complete and flexible workflow for metaproteomics data analysis based on MetaProteomeAnalyzer and Prophane

Nature Protocols volume 15, pages 3212–3239 (2020)Cite this article

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Abstract

Metaproteomics, the study of the collective protein composition of multi-organism systems, provides deep insights into the biodiversity of microbial communities and the complex functional interplay between microbes and their hosts or environment. Thus, metaproteomics has become an indispensable tool in various fields such as microbiology and related medical applications. The computational challenges in the analysis of corresponding datasets differ from those of pure-culture proteomics, e.g., due to the higher complexity of the samples and the larger reference databases demanding specific computing pipelines. Corresponding data analyses usually consist of numerous manual steps that must be closely synchronized. With MetaProteomeAnalyzer and Prophane, we have established two open-source software solutions specifically developed and optimized for metaproteomics. Among other features, peptide-spectrum matching is improved by combining different search engines and, compared to similar tools, metaproteome annotation benefits from the most comprehensive set of available databases (such as NCBI, UniProt, EggNOG, PFAM, and CAZy). The workflow described in this protocol combines both tools and leads the user through the entire data analysis process, including protein database creation, database search, protein grouping and annotation, and results visualization. To the best of our knowledge, this protocol presents the most comprehensive, detailed and flexible guide to metaproteomics data analysis to date. While beginners are provided with robust, easy-to-use, state-of-the-art data analysis in a reasonable time (a few hours, depending on, among other factors, the protein database size and the number of identified peptides and inferred proteins), advanced users benefit from the flexibility and adaptability of the workflow.

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Fig. 1: The stages of the workflow.
Fig. 2: Protein grouping methods provided by MPA.
Fig. 3: Prophane workflow for metaproteome annotation.
Fig. 4: Project view of MPA.
Fig. 5: Input spectra view of MPA.
Fig. 6: The user interface of the Prophane web service.
Fig. 7: Sunburst and Sankey diagrams for complex data visualization.

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Data availability

The mass spectrometric datasets analyzed during the current study are available in the PRIDE repository, https://www.ebi.ac.uk/pride/archive/projects/PXD010550. Protein databases, converted mass spectrometry files and example analyses results are available in the Zenodo repository, https://doi.org/10.5281/zenodo.3727600.

Code availability

The code of the tools used in the current study is available in the Zenodo repository (Prophane: https://doi.org/10.5281/zenodo.3727758; MPA: https://doi.org/10.5281/zenodo.3735146) and GitLab (Prophane: https://gitlab.com/s.fuchs/prophane).

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Acknowledgements

We are grateful to D. Zühlke and L. Gierse for suggesting the importance of Prophane and reporting bugs, F. Hartkopf and X. Wang for carefully evaluating this protocol, and D. Micheel and R. Zoun for helping with the Prophane web service. This project has been supported by the Deutsche Forschungsgemeinschaft (DFG; grant numbers RE3474/5-1 and RE3474/2-2), the de.NBI network (MetaProtServ de-NBI-039), and the BMBF-funded de.NBI Cloud within the German Network for Bioinformatics Infrastructure (de.NBI; 031A537B, 031A533A, 031A538A, 031A533B, 031A535A, 031A537C, 031A534A and 031A532B).

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Authors and Affiliations

  1. Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany

    Henning Schiebenhoefer, Bernhard Y. Renard, Kathrin Trappe & Thilo Muth

  2. Hasso Plattner Institute, Faculty for Digital Engineering, University of Potsdam, Potsdam, Germany

    Henning Schiebenhoefer & Bernhard Y. Renard

  3. Bioprocess Engineering, Otto von Guericke University, Magdeburg, Germany

    Kay Schallert & Dirk Benndorf

  4. ID Computational & Data Science Support, Eidgenössische Technische Hochschule, Zurich, Switzerland

    Emanuel Schmid

  5. Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany

    Dirk Benndorf

  6. Center for Functional Genomics of Microbes (CFGM), Institute of Microbiology, University of Greifswald, Greifswald, Germany

    Katharina Riedel

  7. Section S.3 eScience, Federal Institute for Materials Research and Testing (BAM), Berlin, Germany

    Thilo Muth

  8. Department of Infectious Diseases, Robert Koch Institute, Wernigerode, Germany

    Stephan Fuchs

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Contributions

Workflow design: Steps 1−13, H.S., B.R., K.S., E.S., T.M., K.R. and S.F.; Steps 14−43, K.S., T.M. and D.B.; Steps 44−53, H.S., B.R., K.S., E.S., T.M., K.R. and S.F.; Step 54, H.S., K.S., T.M. and S.F.; training data: K.S. and D.B.; protocol testing: H.S., K.S. and S.F.; manuscript writing: all authors.

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Correspondence to Stephan Fuchs.

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Key references using this protocol

Lassek, C. et al. Mol. Cell. Proteomics 14, 989–1008 (2015): https://www.mcponline.org/content/early/2015/02/11/mcp.M114.043463

Muth, T. et al. J. Proteome Res. 6, 14:1557–1565 (2015): https://pubs.acs.org/doi/10.1021/pr501246w

Grube, M. et al. ISME J. 9, 412–424 (2015): https://www.nature.com/articles/ismej2014138

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Schiebenhoefer, H., Schallert, K., Renard, B.Y. et al. A complete and flexible workflow for metaproteomics data analysis based on MetaProteomeAnalyzer and Prophane. Nat Protoc 15, 3212–3239 (2020). https://doi.org/10.1038/s41596-020-0368-7

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