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

ABSTRACT: 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.

SUBMITTER: Aksenov AA 

PROVIDER: S-EPMC7971188 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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

Aksenov Alexander A AA   Laponogov Ivan I   Zhang Zheng Z   Doran Sophie L F SLF   Belluomo Ilaria I   Veselkov Dennis D   Bittremieux Wout W   Nothias Louis Felix LF   Nothias-Esposito Mélissa M   Maloney Katherine N KN   Misra Biswapriya B BB   Melnik Alexey V AV   Smirnov Aleksandr A   Du Xiuxia X   Jones Kenneth L KL   Dorrestein Kathleen K   Panitchpakdi Morgan M   Ernst Madeleine M   van der Hooft Justin J J JJJ   Gonzalez Mabel M   Carazzone Chiara C   Amézquita Adolfo A   Callewaert Chris C   Morton James T JT   Quinn Robert A RA   Bouslimani Amina A   Orio Andrea Albarracín AA   Petras Daniel D   Smania Andrea M AM   Couvillion Sneha P SP   Burnet Meagan C MC   Nicora Carrie D CD   Zink Erika E   Metz Thomas O TO   Artaev Viatcheslav V   Humston-Fulmer Elizabeth E   Gregor Rachel R   Meijler Michael M MM   Mizrahi Itzhak I   Eyal Stav S   Anderson Brooke B   Dutton Rachel R   Lugan Raphaël R   Boulch Pauline Le PL   Guitton Yann Y   Prevost Stephanie S   Poirier Audrey A   Dervilly Gaud G   Le Bizec Bruno B   Fait Aaron A   Persi Noga Sikron NS   Song Chao C   Gashu Kelem K   Coras Roxana R   Guma Monica M   Manasson Julia J   Scher Jose U JU   Barupal Dinesh Kumar DK   Alseekh Saleh S   Fernie Alisdair R AR   Mirnezami Reza R   Vasiliou Vasilis V   Schmid Robin R   Borisov Roman S RS   Kulikova Larisa N LN   Knight Rob R   Wang Mingxun M   Hanna George B GB   Dorrestein Pieter C PC   Veselkov Kirill K  

Nature biotechnology 20201109 2

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 quantifi  ...[more]

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