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, 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. and Veselkov, Kirill (2021) Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data. Nature Biotechnology, 39 (2). ISSN 1087-0156

PDF - Otro Disponible bajo Licencia CC Atribución-NoComercial-SinDerivadas 4.0 Internacional. Descargar (69kB)

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.

Tipo de documento:	Artículo
DOI:	https://doi.org/10.1038/s41587-020-0700-3
Palabras clave:	Aprendizaje automático. Diseño. Cromatografía. Espectrometría.
Temas:	Q Ciencia > Q Ciencia (General) S Agricultura > S Agricultura (General)
Unidad académica:	Universidad Católica de Córdoba > Facultad de Ciencias Agropecuarias
Google Académico:	Ver citaciones
URI:	http://pa.bibdigital.ucc.edu.ar/id/eprint/3478

Editar ítem