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Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data.

Aksenov, Alexander A; Laponogov, Ivan; Zhang, Zheng; Doran, Sophie L F; Belluomo, Ilaria; Veselkov, Dennis; Bittremieux, Wout; Nothias, Louis Felix; Nothias-Esposito, Mélissa; Maloney, Katherine N; Misra, Biswapriya B; Melnik, Alexey V; Smirnov, Aleksandr; Du, Xiuxia; Jones, Kenneth L; Dorrestein, Kathleen; Panitchpakdi, Morgan; Ernst, Madeleine; van der Hooft, Justin J J; Gonzalez, Mabel; Carazzone, Chiara; Amézquita, Adolfo; Callewaert, Chris; Morton, James T; Quinn, Robert A; Bouslimani, Amina; Orio, Andrea Albarracín; Petras, Daniel; Smania, Andrea M; Couvillion, Sneha P; Burnet, Meagan C; Nicora, Carrie D; Zink, Erika; Metz, Thomas O; Artaev, Viatcheslav; Humston-Fulmer, Elizabeth; Gregor, Rachel; Meijler, Michael M; Mizrahi, Itzhak; Eyal, Stav; Anderson, Brooke; Dutton, Rachel; Lugan, Raphaël; Boulch, Pauline Le; Guitton, Yann; Prevost, Stephanie; Poirier, Audrey; Dervilly, Gaud; Le Bizec, Bruno; Fait, Aaron.

Nat Biotechnol ; 39(2): 169-173, 2021 02.

Artículo en Inglés | MEDLINE | ID: mdl-33169034

RESUMEN

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.

Asunto(s)

Algoritmos , Cromatografía de Gases y Espectrometría de Masas , Metabolómica , Animales , Anuros , Humanos

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