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

Alexander Aksenov (1, 2) , Ivan Laponogov (3) , Zheng Zhang (1) , Sophie Doran (3) , Ilaria Belluomo (3) , Dennis Veselkov (3) , Wout Bittremieux (1, 2, 4) , Louis Felix Nothias (1, 2) , Mélissa Nothias-Esposito (1, 2) , Katherine Maloney (1) , Biswapriya Misra (5) , Alexey Melnik (1) , Aleksandr Smirnov (6) , Xiuxia Du (6) , Kenneth Jones (1) , Kathleen Dorrestein (1, 2) , Morgan Panitchpakdi (1) , Madeleine Ernst (1, 7) , Justin van Der Hooft (1, 8) , Mabel Gonzalez (9) , Chiara Carazzone (9) , Adolfo Amézquita (9) , Chris Callewaert (10, 11) , James Morton (11, 12) , Robert Quinn (13) , Amina Bouslimani (1, 2) , Andrea Albarracín Orio (14) , Daniel Petras (1, 2) , Andrea Smania (15) , Sneha Couvillion (16) , Meagan Burnet (16) , Carrie Nicora (16) , Erika Zink (16) , Thomas Metz (16) , Viatcheslav Artaev (17) , Elizabeth Humston-Fulmer (17) , Rachel Gregor (18) , Michael Meijler (18) , Itzhak Mizrahi (18) , Stav Eyal (18) , Brooke Anderson (11) , Rachel Dutton (11) , Raphaël Lugan (19) , Pauline Le Boulch (19) , Yann Guitton (20) , Stéphanie Prévost (20) , Audrey Poirier (20) , Gaud Dervilly (20) , Bruno Le Bizec (20) , Aaron Fait (18) , Noga Sikron Persi (18) , Chao Song (18) , Kelem Gashu (18) , Roxana Coras (11) , Monica Guma (11) , Julia Manasson (21) , Jose Scher (21) , Dinesh Kumar Barupal (22) , Saleh Alseekh (23, 24) , Alisdair Fernie (23, 24) , Reza Mirnezami (25) , Vasilis Vasiliou (26) , Robin Schmid (27) , Roman Borisov (28) , Larisa Kulikova (29) , Rob Knight (11) , Mingxun Wang (1, 2) , George Hanna (3) , Pieter Dorrestein (11) , Kirill Veselkov (3)

1 Skaggs School of Pharmacy and Pharmaceutical Sciences [San Diego]
2 Collaborative Mass Spectrometry Innovation Center
3 Imperial College London
4 UA - University of Antwerp
5 Wake Forest School of Medicine [Winston-Salem]
6 UNC - University of North Carolina [Charlotte]
7 Statens Serum Institut [Copenhagen]
8 WUR - Wageningen University and Research [Wageningen]
9 UNIANDES - Universidad de los Andes [Bogota]
10 CMET - Center for Microbial Ecology and Technology
11 UC San Diego - University of California [San Diego]
12 Flatiron Institute
13 Michigan State University [East Lansing]
14 Universidad Católica de Córdoba
15 Universidad Nacional de Córdoba [Argentina]
16 PNNL - Pacific Northwest National Laboratory
17 LECO Corporation
18 BGU - Ben-Gurion University of the Negev
19 UMR QualiSud - Démarche intégrée pour l'obtention d'aliments de qualité
20 LABERCA - Laboratoire d'étude des Résidus et Contaminants dans les Aliments
21 NYU - New York University School of Medicine
22 MSSM - Icahn School of Medicine at Mount Sinai [New York]
23 CPSBB - Center of Plant Systems Biology and Biotechnology [Plovdiv]
24 MPI-MP - Max Planck Institute of Molecular Plant Physiology
25 NHS Foundation Trust [London]
26 YSPH - Yale School of Public Health
27 WWU - Westfälische Wilhelms-Universität Münster = University of Münster
28 TIPS - A.V. Topchiev Institute of Petrochemical Synthesis
29 RUDN - Peoples Friendship University of Russia [RUDN University]

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.

Mots clés: Algorithms; Gas Chromatography-Mass Spectrometry; Metabolomics; Anura; Animals; Humans

Domaines: Sciences du Vivant [q-bio]; Chimie analytique; Machine Learning [stat.ML]

Alexander Aksenov, Ivan Laponogov, Zheng Zhang, Sophie Doran, Ilaria Belluomo, et al.. Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data. Nature Biotechnology, 2021, 39 (2), pp.169-173. ⟨10.1038/s41587-020-0700-3⟩. ⟨hal-03173187⟩