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Feature-based molecular networking in the GNPS analysis environment


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.

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 and, 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 and, 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 The OpenMS and FBMN jobs are available at at, 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 and, respectively.

Code availability

The FBMN workflow is available as a web interface on the GNPS web platform ( The workflow code is open source and available on GitHub ( It is released under the license of The Regents of the University of California San Diego and free for non-profit research ( The workflow was written in Python (v3.7) and deployed with the ProteoSAFE workflow manager used by GNPS ( We also provide documentation, support, example files and additional information on the GNPS documentation website ( The source code of the GNPSExport module in MZmine is available at under the GNU General Public License. The source code of the GNPSExport tool in OpenMS is available at the BSD license. The source code for the GNPSExport custom function for XCMS is available at under the GNU General Public License.


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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.

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Authors and Affiliations



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.

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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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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 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).

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