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Pré-Publication, Document De Travail Année : 2021

Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS

Alexander Aksenov (1, 2) , Ivan Laponogov (3) , Zheng Zhang (1) , Sophie Lf Doran (3) , Ilaria Belluomo (3) , Dennis Veselkov , Wout Bittremieux (1, 2, 4) , Louis Felix Nothias (1, 2) , Mélissa Nothias-Esposito (1, 2) , Katherine Maloney (1) , Biswapriya Misra (5) , Alexey Melnik (1) , Kenneth Jones (1) , Kathleen Dorrestein (1, 2) , Morgan Panitchpakdi (1) , Madeleine Ernst (1, 6) , Justin J.J. van Der Hooft (1, 7) , Mabel Gonzalez (8) , Chiara Carazzone (8) , Adolfo Amézquita (8) , Chris Callewaert (9, 10) , James Morton (10) , Robert Quinn (11) , Amina Bouslimani (1, 2) , Andrea Albarracín Orio (12) , Daniel Petras (1, 2) , Andrea Smania (13) , Sneha Couvillion (14) , Meagan Burnet (14) , Carrie Nicora (14) , Erika Zink (14) , Thomas Metz (14) , Viatcheslav Artaev (15) , Elizabeth Humston-Fulmer (15) , Rachel Gregor (16) , Michael Meijler (16) , Itzhak Mizrahi (16) , Stav Eyal (16) , Brooke Anderson (17) , Rachel Dutton (17) , Raphaël Lugan (18) , Pauline Le Boulch (18) , Yann Guitton (19) , Stéphanie Prévost (19) , Audrey Poirier (19) , Gaud Dervilly (19) , Bruno Le Bizec (19) , Aaron Fait (16) , Noga Sikron Persi (16) , Chao Song (16) , Kelem Gashu (16) , Roxana Coras (4) , Monica Guma (4) , Julia Manasson (20) , Jose Scher (20) , Dinesh Barupal (21) , Saleh Alseekh (22, 23) , Alisdair Fernie (22, 23) , Reza Mirnezami (24) , Vasilis Vasiliou (25) , Robin Schmid (26) , Roman Borisov (27) , Larisa Kulikova (28) , Rob Knight (4) , Mingxun Wang (1, 2) , George Hanna (3) , Pieter Dorrestein (1, 2, 4) , Kirill Veselkov (3)
Dennis Veselkov
  • Fonction : Auteur
Rob Knight

Résumé

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.

Dates et versions

hal-03173091 , version 1 (18-03-2021)

Identifiants

Citer

Alexander Aksenov, Ivan Laponogov, Zheng Zhang, Sophie Lf Doran, Ilaria Belluomo, et al.. Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS. 2021. ⟨hal-03173091⟩
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