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Feature-based molecular networking in the GNPS analysis environment

2020; Nature Portfolio; Volume: 17; Issue: 9 Linguagem: Inglês

10.1038/s41592-020-0933-6

1548-7105

Louis‐Félix Nothias, Daniel Petras, Robin Schmid, Kai Dührkop, Johannes Rainer, Abinesh Sarvepalli, Ivan Protsyuk, Madeleine Ernst, Hiroshi Tsugawa, Markus Fleischauer, Fabian Aicheler, Alexander A. Aksenov, Oliver Alka, Pierre‐Marie Allard, Aiko Barsch, Xavier Cachet, Andrés Mauricio Caraballo‐Rodríguez, Ricardo Silva, Tam Dang, Neha Garg, Julia M. Gauglitz, Alexey Gurevich, Giorgis Isaac, Alan K. Jarmusch, Zdeněk Kameník, Kyo Bin Kang, Nikolas Kessler, Irina Koester, Ansgar Korf, Audrey Le Gouëllec, Marcus Ludwig, Christian Martin, Laura‐Isobel McCall, Jonathan McSayles, Sven Meyer, Hosein Mohimani, Mustafa Morsy, Oriane Moyne, Steffen Neumann, Heiko Neuweger, Ngoc Hung Nguyen, Mélissa Nothias-Esposito, Julien Paolini, Vanessa V. Phelan, Tomáš Pluskal, Robert A. Quinn, Simon Rogers, Bindesh Shrestha, Anupriya Tripathi, Justin J. J. van der Hooft, Fernando Vargas, Kelly C. Weldon, Michael Witting, Heejung Yang, Zheng Zhang, Florian Zubeil, Oliver Kohlbacher, Sebastian Böcker, Theodore Alexandrov, Nuno Bandeira, Mingxun Wang, Pieter C. Dorrestein,

Bioinformatics and Genomic Networks

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. Feature-based molecular networking allows the generation of molecular networks for mass spectrometry data that can recognize isomers, incorporate relative quantification and integrate ion mobility data.