Artículo
Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS
Aksenov, Alexander; 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.; 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; Quinn, Robert; Bouslimani, Amina; Albarracín Orio, Andrea Georgina
; Petras, Daniel; Smania, Andrea
; Couvillion, Sneha P.; Burnet, Meagan C.; Nicora, Carrie D.; Zink, Erika; Metz, Thomas O.; Artaev, Viatcheslav; Humston Fulmer, Elizabeth; Gregor, Rachel; Meijler, Michael M.; MizrahiI, tzhak; Eyal, Stav; Anderson, Brooke; Dutton, Rachel; Lugan, Raphaël; Le Boulch, Pauline; Guitton, Yann; Prevost, Stephanie; Poirier, Audrey; Dervilly, Gaud; Le Bizec, Bruno; Fait, Aaron; Sikron Persi, Noga; Song, Chao; Gashu, Kelem; Coras, Roxana; Vasiliou, Vasilis; Schmid, Robin; Borisov, Roman S.; Kulikova, Larisa N.; Knight, Rob; Wang, Mingxun; Hanna, George B; Dorrestein, Pieter; Veselkov, Kirill
Fecha de publicación:
14/01/2020
Editorial:
Cold Spring Harbor Laboratory Press
Revista:
Nature Biotechnology
ISSN:
1087-0156
e-ISSN:
1943-0264
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Gas chromatography-mass spectrometry (GC-MS) represents an analytical technique with significant practical societal impact. Spectral deconvolution is an essential step for interpreting GC-MS data. No public GC-MS repositories that also enable repository-scale analysis exist, in part because deconvolution requires significant user input. We therefore engineered a scalable machine learning workflow for the Global Natural Product Social Molecular Networking (GNPS) analysis platform to enable the mass spectrometry community to store, process, share, annotate, compare, and perform molecular networking of GC-MS data. The workflow performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization, using a Fast Fourier Transform-based strategy to overcome scalability limitations. We introduce a "balance score" that quantifies the reproducibility of fragmentation patterns across all samples. We demonstrate the utility of the platform with breathomics analysis applied to the early detection of oesophago-gastric cancer, and by creating the first molecular spatial map of the human volatilome.
Palabras clave:
METABOLOMICS
,
GC-MS
,
MOLECULAR NETWORKING
,
NATURAL PRODUCTS
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Colecciones
Articulos(CIQUIBIC)
Articulos de CENTRO DE INVEST.EN QCA.BIOL.DE CORDOBA (P)
Articulos de CENTRO DE INVEST.EN QCA.BIOL.DE CORDOBA (P)
Articulos(IRNASUS)
Articulos de INSTITUTO DE INVESTIGACIONES EN RECURSOS NATURALES Y SUSTENTABILIDAD JOSE SANCHEZ LABRADOR S.J.
Articulos de INSTITUTO DE INVESTIGACIONES EN RECURSOS NATURALES Y SUSTENTABILIDAD JOSE SANCHEZ LABRADOR S.J.
Citación
Aksenov, Alexander; Laponogov, Ivan; Zhang, Zheng; Doran, Sophie L. F.; Belluomo, Ilaria; et al.; Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS; Cold Spring Harbor Laboratory Press; Nature Biotechnology; 39; 14-1-2020; 1-25
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