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Feature-based molecular networking in the GNPS analysis environment

Nature Methods volume 17, pages 905–908 (2020)Cite this article

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Abstract

Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry.

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Fig. 1: Methods for the generation of molecular networks from non-targeted MS data with the GNPS web platform.
Fig. 2: Comparisons of classical MN and FBMN.

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Data availability

The LC–MS2 data for the E. dendroides dataset, along with the MZmine project and parameters used, can be accessed on the MassIVE submission (MSV000080502; Creative Commons CC0 1.0 Universal license). The classical MN and FBMN jobs can be accessed via the GNPS website at https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=189e8bf16af145758b0a900f1c44ff4a and https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=672d0a5372384cff8c47297c2048d789, respectively.

LC–MS2 data for the AGP were downloaded from MassIVE (MSV000080186; Creative Commons CC0 1.0 Universal license) and processed with MZmine (v2.37). The MZmine project along with parameters and export files were deposited (MSV000084095; Creative Commons CC0 1.0 Universal license). The classical MN and FBMN jobs can be accessed at https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=3c27e43d908c4044bace405cc394cd25 and https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=0a8432b5891a48d7ad8459ba4a89969f, respectively.

The LC–MS2 data for the EDTA case are available on the MassIVE submission (MSV00008263; Creative Commons CC0 1.0 Universal license). The classical MN job can be accessed at https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=fbac1a5061ba4ad683a284ef55d45df6. The OpenMS and FBMN jobs are available at https://proteomics2.ucsd.edu/ProteoSAFe/status.jsp?task=83a0a417a49b4b76b61e9a8191a6ea2d at https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=8f40420c11694cf9ab06fdf7a5a4c53b, respectively.

The MS acquisition method, data and parameters used for the processing of the serum analysis with the timsTOF mass spectrometer were deposited (MSV000084402). Classical MN and FBMN jobs can be accessed at https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=f2adc2cf33c646548798d0e285197a96 and https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=0d89db67b0974939a91cb7d5bfe87072, respectively.

Code availability

The FBMN workflow is available as a web interface on the GNPS web platform (https://gnps-quickstart.ucsd.edu/featurebasednetworking/). The workflow code is open source and available on GitHub (https://github.com/CCMS-UCSD/GNPS_Workflows/tree/master/feature-based-molecular-networking/). It is released under the license of The Regents of the University of California San Diego and free for non-profit research (https://github.com/CCMS-UCSD/GNPS_Workflows/blob/master/LICENSE/). The workflow was written in Python (v3.7) and deployed with the ProteoSAFE workflow manager used by GNPS (https://proteomics.ucsd.edu/Software/ProteoSAFe/). We also provide documentation, support, example files and additional information on the GNPS documentation website (https://ccms-ucsd.github.io/GNPSDocumentation/featurebasedmolecularnetworking/). The source code of the GNPSExport module in MZmine is available at https://github.com/mzmine/mzmine2/ under the GNU General Public License. The source code of the GNPSExport tool in OpenMS is available at https://github.com/Bioinformatic-squad-DorresteinLab/OpenMS/under the BSD license. The source code for the GNPSExport custom function for XCMS is available at https://github.com/jorainer/xcms-gnps-tools/ under the GNU General Public License.

References

  1. Watrous, J. et al. Mass spectral molecular networking of living microbial colonies. Proc. Natl Acad. Sci. USA 109, E1743–E1752 (2012).

    PubMed  CAS  Google Scholar 

  2. Quinn, R. A. et al. Molecular networking as a drug discovery, drug metabolism and precision medicine strategy. Trends Pharmacol. Sci. 38, 143–154 (2017).

    PubMed  CAS  Google Scholar 

  3. Traxler, M. F. & Kolter, R. A massively spectacular view of the chemical lives of microbes. Proc. Natl Acad. Sci. USA 109, 10128–10129 (2012).

    PubMed  CAS  Google Scholar 

  4. Wang, M. et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat. Biotechnol. 34, 828–837 (2016).

    PubMed  PubMed Central  CAS  Google Scholar 

  5. Frank, A. M. et al. Clustering millions of tandem mass spectra. J. Proteome Res. 7, 113–122 (2008).

    PubMed  CAS  Google Scholar 

  6. Hoffmann, N. et al. mzTab-M: a data standard for sharing quantitative results in mass spectrometry metabolomics. Anal. Chem. 91, 3302–3310 (2019).

    PubMed  PubMed Central  CAS  Google Scholar 

  7. Nothias, L.-F. et al. Bioactivity-based molecular networking for the discovery of drug leads in natural product bioassay-guided fractionation. J. Nat. Prod. 81, 758–767 (2018).

    PubMed  CAS  Google Scholar 

  8. Cohen, L. J. et al. Functional metagenomic discovery of bacterial effectors in the human microbiome and isolation of commendamide, a GPCR G2A/132 agonist. Proc. Natl Acad. Sci. USA. 112, E4825–E4834 (2015).

    PubMed  CAS  Google Scholar 

  9. McDonald, D. et al. American Gut: an open platform for citizen-science microbiome research. mSystems 3, e0031–18 (2018).

    Google Scholar 

  10. Röst, H. L. et al. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat. Methods 13, 741–748 (2016).

    PubMed  Google Scholar 

  11. Pluskal, T., Castillo, S., Villar-Briones, A. & Oresic, M. MZmine 2: modular framework for processing, visualizing and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11, 395 (2010).

    PubMed  PubMed Central  Google Scholar 

  12. Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).

    PubMed  PubMed Central  CAS  Google Scholar 

  13. Xia, J., Sinelnikov, I. V., Han, B. & Wishart, D. S. MetaboAnalyst 3.0—making metabolomics more meaningful. Nucleic Acids Res. 43, W251–W257 (2015).

    PubMed  PubMed Central  CAS  Google Scholar 

  14. Protsyuk, I., Melnik, A. V., Nothias, L. F. & Rappez, L. 3D molecular cartography using LC–MS facilitated by Optimus and’ili software. Nat. Protoc. 13, 134–154 (2018).

    PubMed  CAS  Google Scholar 

  15. Dührkop, K. et al. SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Nat. Methods 16, 299–302 (2019).

    PubMed  Google Scholar 

  16. Mohimani, H. et al. Dereplication of peptidic natural products through database search of mass spectra. Nat. Chem. Biol. 13, 30–37 (2017).

    PubMed  CAS  Google Scholar 

  17. van der Hooft, J. J. J., Wandy, J., Barrett, M. P., Burgess, K. E. V. & Rogers, S. Topic modeling for untargeted substructure exploration in metabolomics. Proc. Natl Acad. Sci. USA. 113, 13738–13743 (2016).

    PubMed  Google Scholar 

  18. Tripathi, A. et al. Chemically-informed analyses of metabolomics mass spectrometry data with qemistree. Preprint at bioRxiv 2020.05.04.077636 (2020) https://doi.org/10.1101/2020.05.04.077636.

  19. Tsugawa, H. et al. A lipidome atlas in MS-DIAL 4. Nat. Biotechnol. https://doi.org/10.1038/s41587-020-0531-2 (2020).

  20. Wang, M. et al. Mass spectrometry searches using MASST. Nat. Biotechnol. 38, 23–26 (2020).

    PubMed  PubMed Central  CAS  Google Scholar 

  21. Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012).

    PubMed  PubMed Central  CAS  Google Scholar 

  22. Winnikoff, J. R., Glukhov, E., Watrous, J., Dorrestein, P. C. & Gerwick, W. H. Quantitative molecular networking to profile marine cyanobacterial metabolomes. J. Antibiot. 67, 105–112 (2014).

    PubMed  CAS  Google Scholar 

  23. Olivon, F., Grelier, G., Roussi, F., Litaudon, M. & Touboul, D. MZmine 2 data-preprocessing to enhance molecular networking reliability. Anal. Chem. 89, 7836–7840 (2017).

    PubMed  CAS  Google Scholar 

  24. Ono, K., Demchak, B. & Ideker, T. Cytoscape tools for the web age: D3.js and Cytoscape.js exporters. F1000Res. 3, 143 (2014).

    PubMed  PubMed Central  Google Scholar 

  25. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    PubMed  PubMed Central  CAS  Google Scholar 

  26. Tsugawa, H. et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat. Methods 12, 523–526 (2015).

    PubMed  PubMed Central  CAS  Google Scholar 

  27. Tautenhahn, R., Böttcher, C. & Neumann, S. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics 9, 504 (2008).

    PubMed  PubMed Central  Google Scholar 

  28. Libiseller, G. et al. IPO: a tool for automated optimization of XCMS parameters. BMC Bioinformatics 16, 118 (2015).

    PubMed  PubMed Central  Google Scholar 

  29. McLean, C. & Kujawinski, E. B. AutoTuner: high fidelity and robust parameter selection for metabolomics data processing. Anal. Chem. 92, 5724–5732 (2020).

    PubMed  PubMed Central  CAS  Google Scholar 

  30. Lawson, T. N. et al. msPurity: automated evaluation of precursor ion purity for mass spectrometry-based fragmentation in metabolomics. Anal. Chem. 89, 2432–2439 (2017).

    PubMed  CAS  Google Scholar 

  31. Junker, J. et al. TOPPAS: a graphical workflow editor for the analysis of high-throughput proteomics data. J. Proteome Res. 11, 3914–3920 (2012).

    PubMed  CAS  Google Scholar 

  32. Kuhl, C., Tautenhahn, R., Böttcher, C., Larson, T. R. & Neumann, S. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography–mass spectrometry datasets. Anal. Chem. 84, 283–289 (2012).

    PubMed  CAS  Google Scholar 

  33. da Silva, R. R. et al. Propagating annotations of molecular networks using in silico fragmentation. PLoS Comput. Biol. 14, e1006089 (2018).

    PubMed  PubMed Central  Google Scholar 

  34. Ernst, M. et al. MolNetEnhancer: enhanced molecular networks by integrating metabolome mining and annotation tools. Metabolites 9, 144 (2019).

    PubMed Central  CAS  Google Scholar 

  35. Beauxis, Y. & Genta-Jouve, G. Metwork: a web server for natural products anticipation. Bioinformatics 35, 1795–1796 (2019).

    PubMed  CAS  Google Scholar 

  36. Allen, F., Greiner, R. & Wishart, D. Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification. Metabolomics 11, 98–110 (2015).

    CAS  Google Scholar 

  37. Ruttkies, C., Schymanski, E. L., Wolf, S., Hollender, J. & Neumann, S. MetFrag relaunched: incorporating strategies beyond in silico fragmentation. J. Cheminform. 8, 1–16 (2016).

    Google Scholar 

  38. Ludwig, M. et al. ZODIAC: database-independent molecular formula annotation using Gibbs sampling reveals unknown small molecules. Preprint at bioRxiv https://doi.org/10.1101/842740 (2019).

  39. Dührkop, K. et al. Classes for the masses: systematic classification of unknowns using fragmentation spectra. Preprint at bioRxiv https://doi.org/10.1101/2020.04.17.046672 (2020).

  40. Dührkop, K., Shen, H., Meusel, M., Rousu, J. & Böcker, S. Searching molecular structure databases with tandem mass spectra using CSI:FingerID. Proc. Natl Acad. Sci. USA 112, 12580–12585 (2015).

    PubMed  Google Scholar 

  41. Gurevich, A. et al. Increased diversity of peptidic natural products revealed by modification-tolerant database search of mass spectra. Nat. Microbiol. 3, 319–327 (2018).

    PubMed  PubMed Central  CAS  Google Scholar 

  42. Gerlich, M. & Neumann, S. MetFusion: integration of compound identification strategies. J. Mass Spectrom. 48, 291–298 (2013).

    PubMed  CAS  Google Scholar 

  43. Wandy, J. et al. Ms2lda.org: web-based topic modelling for substructure discovery in mass spectrometry. Bioinformatics 34, 317–318 (2017).

    PubMed Central  Google Scholar 

  44. Feunang, Y. D. et al. ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J. Cheminform. 8, 61 (2016).

    Google Scholar 

  45. Cohen, L. J. et al. Commensal bacteria make GPCR ligands that mimic human signalling molecules. Nature 549, 48–53 (2017).

    PubMed  PubMed Central  CAS  Google Scholar 

  46. Simón-Manso, Y. et al. Metabolite profiling of a NIST standard reference material for human plasma (SRM 1950): GC–MS, LC–MS, NMR and clinical laboratory analyses, libraries and web-based resources. Anal. Chem. 85, 11725–11731 (2013).

    PubMed  Google Scholar 

  47. Meier, F. et al. Online parallel accumulation-serial fragmentation (PASEF) with a novel trapped ion mobility mass spectrometer. Mol. Cell. Proteomics 17, 2534–2545 (2018).

    PubMed  PubMed Central  CAS  Google Scholar 

  48. Kind, T. et al. LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat. Methods 10, 755–758 (2013).

    PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

We gratefully acknowledge financial support from the U.S. National Institutes of Health (NIH) for the Center for Computational Mass Spectrometry grant (P41 GM103484), the reuse of metabolomics data (R03 CA211211) and the tools for rapid and accurate structure elucidation of natural products (R01 GM107550 and U19 AG063744 01) to P.C.D.; the NIH grants R24GM127667 and 1R01LM013115 and a National Science Foundation (NSF) award (ABI 1759980) to N.B.; the European Union’s Horizon 2020 grants 704786 (MSCA-GF to L.-F.N.), 634402 and 777222 (T.A. and I.P.) and a European Research Council Consolidator grant METACELL (T.A.). L.-F.N. was supported by the Center for Microbiome Innovation from the University of California San Diego (support program award). D.P. was supported by the German Research Foundation (DFG; grant no. PE 2600/1). S.N. acknowledges funding from Bundesministerium für Bildung und Forschung (FKZ 031L0107) and the European Commission (EC654241). R.S. acknowledges funding by the German Chemical Industry Fund (FCI) fellowship. H.T. was supported by KAKENHI (18H02432 and 18K19155). A.M.C.-R. was supported by an NSF grant (IOS-1656481) to P.C.D. O.A. acknowledges funding from the Bundesministerium für Ernährung und Landwirtschaft (FKZ 2816501214), the Bundesministerium für Wirtschaft und Energie (FKZ AiF18475N), the Bundesministerium für Bildung und Forschung (FKZ 031A430C) and the European Commission (823839), which also supported F.A. and O.K. S.B. acknowledges funding from Deutsche Forschungsgemeinschaft (BO 1910/20). M.L. was supported by the Deutsche Forschungsgemeinschaft (BO 1910/20-1). J.J.J.v.d.H. was supported by an Accelerating Scientific Discoveries Grant funded by the Netherlands eScience Center (NLeSC; no. ASDI.2017.030). S.N. acknowledges funding from BMBF (grant no. 031L0107) and the European Commission (PhenoMeNal grant EC654241). F.V. was funded by the Department of Navy, Office of Naval Research Multidisciplinary University Research Initiative (MURI) award (N00014-15-1-2809). V.V.P. acknowledges support from the ALSAM Foundation (Therapeutic Innovation Award and L.S. Skaggs Professorship) and the NIH (R35 GM128690). T.P. is a Simons Foundation Fellow of the Helen Hay Whitney Foundation. Z.K. was supported by the project International Mobility of Researchers (CZ.02.2.69/0.0/0.0/16_027/0007990). A.K.J. was supported by the American Society for Mass Spectrometry (Postdoctoral Career Development Award). K.B.K. was supported by a grant from the National Research Foundation (NRF) of Korea (MSIT; NRF-2019R1F1A1058068). H.Y. was supported by the Basic Science Research Program through the NRF grant (NRF- 2018R1C1B6002574). A.L.G. was supported by Vaincre la mucoviscidose and Association Grégory Lemarchal. The work of H.M. was supported by a research fellowship from the Alfred P. Sloan Foundation and an NIH New Innovator Award (DP2GM137413). The authors thank N. Hoffman for maintaining the mzTab-M format. Finally, we acknowledge the continuous feedback from the GNPS community and the contribution of all researchers and associated institutions who are committed to depositing their MS data in public repositories.

Author information

Author notes

  1. These authors contributed equally: Louis-Félix Nothias, Daniel Petras, Robin Schmid.

Authors and Affiliations

  1. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA

    Louis-Félix Nothias, Daniel Petras, Abinesh Sarvepalli, Madeleine Ernst, Alexander A. Aksenov, Andres Mauricio Caraballo-Rodriguez, Julia M. Gauglitz, Alan K. Jarmusch, Kyo Bin Kang, Irina Koester, Ngoc Hung Nguyen, Melissa Nothias-Esposito, Anupriya Tripathi, Justin J. J. van der Hooft, Fernando Vargas, Kelly C. Weldon, Zheng Zhang, Theodore Alexandrov, Nuno Bandeira, Mingxun Wang & Pieter C. Dorrestein

  2. Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA

    Louis-Félix Nothias, Daniel Petras, Abinesh Sarvepalli, Madeleine Ernst, Alexander A. Aksenov, Andres Mauricio Caraballo-Rodriguez, Ricardo R. Da Silva, Tam Dang, Julia M. Gauglitz, Alan K. Jarmusch, Kyo Bin Kang, Irina Koester, Ngoc Hung Nguyen, Melissa Nothias-Esposito, Justin J. J. van der Hooft, Fernando Vargas, Kelly C. Weldon, Zheng Zhang, Theodore Alexandrov, Nuno Bandeira, Mingxun Wang & Pieter C. Dorrestein

  3. Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA

    Daniel Petras & Irina Koester

  4. Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany

    Robin Schmid & Ansgar Korf

  5. Chair for Bioinformatics, Friedrich-Schiller University, Jena, Germany

    Kai Dührkop, Markus Fleischauer, Marcus Ludwig & Sebastian Böcker

  6. Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy

    Johannes Rainer

  7. Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany

    Ivan Protsyuk & Theodore Alexandrov

  8. Section for Clinical Mass Spectrometry, Department of Congenital Disorders, Danish Center for Neonatal Screening, Statens Serum Institut, Copenhagen, Denmark

    Madeleine Ernst

  9. RIKEN Center for Sustainable Resource Science, Yokohama, Japan

    Hiroshi Tsugawa

  10. RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Hiroshi Tsugawa

  11. Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, Germany

    Fabian Aicheler, Oliver Alka & Oliver Kohlbacher

  12. Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany

    Fabian Aicheler, Oliver Alka & Oliver Kohlbacher

  13. Department of Phytochemistry and Bioactive Natural Products, University of Geneva, Geneva, Switzerland

    Pierre-Marie Allard

  14. Bruker Daltonics, Bremen, Germany

    Aiko Barsch, Nikolas Kessler, Sven W. Meyer, Heiko Neuweger & Florian Zubeil

  15. Equipe PNAS, UMR 8038 CiTCoM CNRS, Faculté de Pharmacie de Paris, Université Paris Descartes, Paris, France

    Xavier Cachet

  16. Department of Physics and Chemistry, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil

    Ricardo R. Da Silva

  17. Institute of Chemistry, Technische Universität Berlin, Berlin, Germany

    Tam Dang

  18. School of Chemistry and Biochemistry, Center for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, GA, USA

    Neha Garg

  19. Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia

    Alexey Gurevich

  20. Waters Corporation, Milford, MA, USA

    Giorgis Isaac & Bindesh Shrestha

  21. Institute of Microbiology of the Czech Academy of Sciences, Prague, Czech Republic

    Zdeněk Kameník

  22. College of Pharmacy, Sookmyung Women’s University, Seoul, Republic of Korea

    Kyo Bin Kang

  23. Univ. Grenoble Alpes, CNRS, Grenoble INP, CHU Grenoble Alpes, TIMC-IMAG, Grenoble, France

    Audrey Le Gouellec & Oriane Moyne

  24. Centro de Biodiversidad y Descubrimiento de Drogas, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Panama, Republic of Panama

    Christian Martin H.

  25. Department of Chemistry and Biochemistry, Department of Microbiology and Plant Biology and Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma, Norman, OK, USA

    Laura-Isobel McCall

  26. Nonlinear Dynamics, Milford, MA, USA

    Jonathan McSayles

  27. Computational Biology Department, School of Computer Sciences, Carnegie Mellon University, Pittsburgh, PA, USA

    Hosein Mohimani

  28. Department of Biological and Environmental Sciences, University of West Alabama, Livingston, AL, USA

    Mustafa Morsy

  29. Department of Pediatrics, University of California San Diego, La Jolla, CA, USA

    Oriane Moyne, Anupriya Tripathi & Pieter C. Dorrestein

  30. Bioinformatics and Scientific Data, Leibniz Institute of Plant Biochemistry, Halle, Germany

    Steffen Neumann

  31. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany

    Steffen Neumann

  32. Laboratoire de Chimie des Produits Naturels, UMR CNRS SPE, Université de Corse Pascal Paoli, Corte, France

    Julien Paolini

  33. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado, Denver, Aurora, CO, USA

    Vanessa V. Phelan

  34. Whitehead Institute for Biomedical Research, Cambridge, MA, USA

    Tomáš Pluskal

  35. Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA

    Robert A. Quinn

  36. School of Computing Science, University of Glasgow, Glasgow, UK

    Simon Rogers

  37. Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA

    Anupriya Tripathi

  38. Bioinformatics Group, Wageningen University, Wageningen, the Netherlands

    Justin J. J. van der Hooft

  39. Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA

    Kelly C. Weldon & Pieter C. Dorrestein

  40. Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, München, Germany

    Michael Witting

  41. College of Pharmacy, Kangwon National University, Chuncheon-si, Republic of Korea

    Heejung Yang

  42. Institute for Translational Bioinformatics, University Hospital Tübingen, Tübingen, Germany

    Oliver Kohlbacher

  43. Biomolecular Interactions, Max Planck Institute for Developmental Biology, Tübingen, Germany

    Oliver Kohlbacher

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

    Nuno Bandeira & Mingxun Wang

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  1. Louis-Félix Nothias
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Contributions

L.-F.N., D.P., M.W. and P.C.D. conceived the method and supervised its implementation and wrote the manuscript. I.P., L.-F.N., M.E. and T.A. created the FBMN prototype in Optimus. M.W., L.-F.N., D.P. and Z.Z. created the FBMN workflow on GNPS. R.S., L.-F.N, M.W., D.P., A.K., M.F., Z.Z., A.S. and T.P. developed the GNPSExport module in MZmine. K.D., A.K., M.L. and S.B. developed the spectral clustering algorithm and SIRIUS export in MZmine. A.S. and L.-F.N. created the GNPSExport tool in OpenMS, with guidance from F.A., O.A. and O.K. J.R. and M.W. created the XCMS export tool. H.T., M.W. and L.-F.N. enabled the integration with MS-DIAL. L.-F.N., A.B., H.N., F.Z. and T.D. enabled the integration with MetaboScape. M.W., G.I., B.S., S.W.M. and J.M. enabled the integration with Progenesis QI. F.V. performed the MS for the plasma and NIST1950SRM samples. A.A.A. performed the MS for the AGP samples. A.K.J., L.-F.N. and A.T. analyzed the results of the plasma samples. J.R. and L.-F.N. performed the XCMS processing of the forensic dataset. L.-F.N. and M.W. created the FBMN documentation. The serum sample analysis in PASEF mode and the data processing with MetaboScape were performed by F.Z., and the subsequent FBMN analysis was performed by L.-F.N. D.P., L.-F.N. and R.d.S. created the MZmine documentation. K.B.K. and H.Y. created the MS-DIAL documentation. F.V., J.M.G., K.W. and A.K.J. prepared the MS-DIAL video tutorial. M.W., R.S. and D.P. prepared the MZmine video tutorials. M.E., R.d.S., J.R., O.M. and S.N. created the XCMS documentation. L.-F.N. and A.S. created the OpenMS documentation. L.-F.N., N.H.N. and T.D. created the MetaboScape documentation. A.M.C.-R. and L.-I.M. documented the FBMN interface workflow. M.N.-E., I.K. and C.M. created the Cytoscape documentation. H.M., A.G., M.W. and L.-F.N. made the integration with DEREPLICATOR. M.W., J.J.J.v.d.H., M.E. and S.R. made the integration with MS2LDA. R.d.S made the integration with NAP. M.M., N.B., X.C., V.V.P., J.P., N.G., R.A.Q., A.A.A., Z.K. and S.N. tested and provided suggestions on how to improve the methods. J.J.J.v.d.H., T.A., A.K.J., T.P., V.V.P., A.L.G., L.-I.M., P.-M.A., S.B. and S.N. improved the manuscript. All authors contributed to the final manuscript.

Corresponding authors

Correspondence to Mingxun Wang or Pieter C. Dorrestein.

Ethics declarations

Competing interests

P.C.D. is a scientific advisor for Sirenas, Galileio and Cybele and scientific advisor and founder of Ometa labs and Enveda. M.W. is a founder of Ometa Labs. T.P. is a consultant for Ginkgo Bioworks. A.A.A. is a consultant for Ometa Labs. T.A. is on the Scientific Advisory Board of SCiLS, a Bruker company. K.D., M.L., M.F. and S.B. are founders of Bright Giant. A.B., S.W.M., H.N. and F.Z. are employees of Bruker Daltonics. G.I., J.M. and B.S. are employees of Waters.

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Supplementary Figs. 1–19, Supplementary Notes 1–4 and Supplementary Tables 1 and 2.

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Nothias, LF., Petras, D., Schmid, R. et al. Feature-based molecular networking in the GNPS analysis environment. Nat Methods 17, 905–908 (2020). https://doi.org/10.1038/s41592-020-0933-6

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