Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data.
Author(s)
Alexander A. Aksenov; Ivan Laponogov; Zheng Zhang; Sophie L. F. Doran; Ilaria Belluomo; Dennis Veselkov; Wout Bittremieux; Louis Felix Nothias; Mélissa Nothias-Esposito; Katherine N. Maloney; Biswapriya B. Misra; Alexey V. Melnik; Aleksandr Smirnov; Xiuxia Du; Kenneth L. Jones II; Kathleen Dorrestein; Morgan Panitchpakdi; Madeleine Ernst; Justin J. J. van der Hooft ; Mabel Gonzalez; Chiara Carazzone; Adolfo Amézquita; Chris Callewaert; James T. Morton; Robert A. Quinn; Amina Bouslimani; Andrea Albarracín Orio; Daniel Petras; Andrea M. Smania; Sneha P. Couvillion; Meagan C. Burnet; Carrie D. Nicora; Erika Zink; Thomas O. Metz; Viatcheslav Artaev; Elizabeth Humston-Fulmer; Rachel Gregor; Michael M. Meijler; Itzhak Mizrahi; Stav Eyal; Brooke Anderson; Rachel Dutton; Raphaël Lugan; Pauline Le Boulch; Yann Guitton; Stephanie Prevost; Audrey Poirier; Gaud Dervilly ; Bruno Le Bizec; Aaron Fait; Noga Sikron Persi; Chao Song; Kelem Gashu; Roxana Coras; Monica Guma; Julia Manasson; Jose U. Scher; Dinesh Kumar Barupal; Saleh Alseekh; Alisdair R. Fernie; Reza Mirnezami; Vasilis Vasiliou; Robin Schmid; Roman S. Borisov; Larisa N. Kulikova; Rob Knight; Mingzun Wang; George B. Hanna; Pietr C. Dorrestein; Kirill Veselkov
Annotation
The current project engineered a machine-learning approach, MSHub, to enable auto-deconvolution of gas chromatography–mass spectrometry (GC–MS) data.
Abstract
It 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. (publisher abstract modified)
