Reproducible molecular networking of untargeted mass spectrometry data using GNPS

Reproducible molecular networking of untargeted mass spectrometry data using GNPS

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.

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Bibliographic Details
Main Authors: Aron, Allegra T., Gentry, Emily C., McPhail, Kerry L., Nothias, Louis Félix, Nothias-Esposito, Mélissa, Bouslimani, Amina, Petras, Daniel, Gauglitz, Julia M., Sikora, Nicole, Vargas, Fernando, van der Hooft, Justin J.J., Ernst, Madeleine, Kang, Kyo Bin, Aceves, Christine M., Caraballo-Rodríguez, Andrés Mauricio, Koester, Irina, Weldon, Kelly C., Bertrand, Samuel, Roullier, Catherine, Sun, Kunyang, Tehan, Richard M., Boya P, Cristopher A., Christian, Martin H., Gutiérrez, Marcelino, Ulloa, Aldo Moreno, Tejeda Mora, Javier Andres, Mojica-Flores, Randy, Lakey-Beitia, Johant, Vásquez-Chaves, Victor, Zhang, Yilue, Calderón, Angela I., Tayler, Nicole, Keyzers, Robert A., Tugizimana, Fidele, Ndlovu, Nombuso, Aksenov, Alexander A., Jarmusch, Alan K., Schmid, Robin, Truman, Andrew W., Bandeira, Nuno, Wang, Mingxun, Dorrestein, Pieter C.
Format: Article/Letter to editor biblioteca
Language:English
Subjects:Life Science,
Online Access:https://research.wur.nl/en/publications/reproducible-molecular-networking-of-untargeted-mass-spectrometry
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Description
Summary: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.

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