Reproducible molecular networking of untargeted mass spectrometry data using GNPS

Allegra T. Aron, Emily C. Gentry, Kerry L. McPhail, Louis Félix Nothias, Mélissa Nothias-Esposito, Amina Bouslimani, Daniel Petras, Julia M. Gauglitz, Nicole Sikora, Fernando Vargas, Justin J.J. van der Hooft, Madeleine Ernst, Kyo Bin Kang, Christine M. Aceves, Andrés Mauricio Caraballo-Rodríguez, Irina Koester, Kelly C. Weldon, Samuel Bertrand, Catherine Roullier, Kunyang SunRichard M. Tehan, Cristopher A. Boya P, Martin H. Christian, Marcelino Gutiérrez, Aldo Moreno Ulloa, Javier Andres Tejeda Mora, Randy Mojica-Flores, Johant Lakey-Beitia, Victor Vásquez-Chaves, Yilue Zhang, Angela I. Calderón, Nicole Tayler, Robert A. Keyzers, Fidele Tugizimana, Nombuso Ndlovu, Alexander A. Aksenov, Alan K. Jarmusch, Robin Schmid, Andrew W. Truman, Nuno Bandeira, Mingxun Wang*, Pieter C. Dorrestein

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

Global Natural Product Social Molecular Networking (GNPS) is an interactive online small molecule–focused tandem mass spectrometry (MS2) data curation and analysis infrastructure. It is intended to provide as much chemical insight as possible into an untargeted MS2 dataset and to connect this chemical insight to the user’s underlying biological questions. This can be performed within one liquid chromatography (LC)-MS2 experiment or at the repository scale. GNPS-MassIVE is a public data repository for untargeted MS2 data with sample information (metadata) and annotated MS2 spectra. These publicly accessible data can be annotated and updated with the GNPS infrastructure keeping a continuous record of all changes. This knowledge is disseminated across all public data; it is a living dataset. Molecular networking—one of the main analysis tools used within the GNPS platform—creates a structured data table that reflects the molecular diversity captured in tandem mass spectrometry experiments by computing the relationships of the MS2 spectra as spectral similarity. This protocol provides step-by-step instructions for creating reproducible, high-quality molecular networks. For training purposes, the reader is led through a 90- to 120-min procedure that starts by recalling an example public dataset and its sample information and proceeds to creating and interpreting a molecular network. Each data analysis job can be shared or cloned to disseminate the knowledge gained, thus propagating information that can lead to the discovery of molecules, metabolic pathways, and ecosystem/community interactions.

Original languageEnglish
Pages (from-to)1954–1991
JournalNature protocols
Volume15
Early online date13 May 2020
DOIs
Publication statusPublished - Jun 2020

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    Aron, A. T., Gentry, E. C., McPhail, K. L., Nothias, L. F., Nothias-Esposito, M., Bouslimani, A., Petras, D., Gauglitz, J. M., Sikora, N., Vargas, F., van der Hooft, J. J. J., Ernst, M., Kang, K. B., Aceves, C. M., Caraballo-Rodríguez, A. M., Koester, I., Weldon, K. C., Bertrand, S., Roullier, C., ... Dorrestein, P. C. (2020). Reproducible molecular networking of untargeted mass spectrometry data using GNPS. Nature protocols, 15, 1954–1991. https://doi.org/10.1038/s41596-020-0317-5