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Reproducible Molecular Networking Of Untargeted Mass Spectrometry Data Using GNPS.

submitted on 07.08.2019, 15:27 and posted on 08.08.2019, 14:42 by Allegra T. Aron, Emily Gentry, Kerry L. McPhail, Louis Felix 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 Sun, Richard M. Tehan, Cristopher A. Boya, Christian Martin H., Marcelino Gutiérrez, Aldo Moreno Ulloa, Javier Andres Tejeda Mora, Randy Mojica-Flores, Johant Lakey-Beitia, Victor Vásquez-Chaves, 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 Dorrestein
Herein, we present a protocol for the use of Global Natural Products Social (GNPS) Molecular Networking, an interactive online chemistry-focused mass spectrometry data curation and analysis infrastructure. The goal of GNPS is to provide as much chemical insight for an untargeted tandem mass spectrometry data set as possible and to connect this chemical insight to the underlying biological questions a user wishers to address. This can be performed within one experiment or at the repository scale. GNPS not only serves as a public data repository for untargeted tandem mass spectrometry data with the sample information (metadata), it also captures community knowledge that is disseminated via living data across all public data. One or the main analysis tools used by the GNPS community is molecular networking. Molecular networking creates a structured data table that reflects the chemical space from tandem mass spectrometry experiments via computing the relationships of the tandem mass spectra through spectral similarity. This protocol provides step-by-step instructions for creating reproducible high-quality molecular networks. For training purposes, the reader is led through the protocol from recalling a public data set and its sample information 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.




PE 2600/1

R03 CA211211

P41 GM103484

NIH S10RR029121


Email Address of Submitting Author


University of California, San Diego


United States of America

ORCID For Submitting Author


Declaration of Conflict of Interest

no conflict of interest


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