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

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Title: Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data
Authors: Aksenov, AA
Laponogov, I
Zhang, Z
Doran, SLF
Belluomo, I
Veselkov, D
Bittremieux, W
Nothias, LF
Nothias-Esposito, M
Maloney, KN
Misra, BB
Melnik, AV
Smirnov, A
Du, X
Jones, KL
Dorrestein, K
Panitchpakdi, M
Ernst, M
Van der Hooft, JJJ
Gonzalez, M
Carazzone, C
Amézquita, A
Callewaert, C
Morton, JT
Quinn, RA
Bouslimani, A
Orio, AA
Petras, D
Smania, AM
Couvillion, SP
Burnet, MC
Nicora, CD
Zink, E
Metz, TO
Artaev, V
Humston-Fulmer, E
Gregor, R
Meijler, MM
Mizrahi, I
Eyal, S
Anderson, B
Dutton, R
Lugan, R
Boulch, PL
Guitton, Y
Prevost, S
Poirier, A
Dervilly, G
Le Bizec, B
Fait, A
Persi, NS
Song, C
Gashu, K
Coras, R
Guma, M
Manasson, J
Scher, JU
Barupal, DK
Alseekh, S
Fernie, AR
Mirnezami, R
Vasiliou, V
Schmid, R
Borisov, RS
Kulikova, LN
Knight, R
Wang, M
Hanna, GB
Dorrestein, PC
Veselkov, K
Item Type: Journal Article
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.
Issue Date: 9-Nov-2020
Date of Acceptance: 9-Sep-2020
URI: http://hdl.handle.net/10044/1/84598
DOI: 10.1038/s41587-020-0700-3
ISSN: 1087-0156
Publisher: Nature Research
Start Page: 169
End Page: 173
Journal / Book Title: Nature Biotechnology
Volume: 39
Copyright Statement: © 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.
Sponsor/Funder: The Vodafone Foundation
National Institutes of Health
The Vodafone Foundation
Funder's Grant Number: N/A
GR109132(CON-80002316)
P87048
Keywords: Science & Technology
Life Sciences & Biomedicine
Biotechnology & Applied Microbiology
METABOLOMICS
REPOSITORY
STANDARDS
Publication Status: Published
Conference Place: United States
Online Publication Date: 2020-11-09
Appears in Collections:Department of Surgery and Cancer
Faculty of Medicine