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

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...

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Detalles Bibliográficos
Autores principales: 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, Persi, Noga Sikron, Song, Chao, Gashu, Kelem, Coras, Roxana, Guma, Monica, Manasson, Julia, Scher, Jose U., Barupal, Dinesh Kumar, Alseekh, Saleh, Fernie, Alisdair R., Mirnezami, Reza, Vasiliou, Vasilis, Schmid, Robin, Borisov, Roman S., Kulikova, Larisa N., Knight, Rob, Wang, Mingxun, Hanna, George B., Dorrestein, Pieter C., Veselkov, Kirill
Formato: Artículo
Lenguaje:Español
Publicado: Nature Research 2021
Materias:
Q Ciencia (General)
S Agricultura (General)
Acceso en línea:http://pa.bibdigital.ucc.edu.ar/3478/1/A_Aksenov_Laponogov_Zhang.pdf
Aporte de:
Producción Académica Universidad Católica de Córdoba (UCCor) de Universidad Católica de Córdoba
Descripción
Sumario: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.

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