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

descriptionPublicationkeyboard_double_arrow_right Article 09 Nov 2020 Argentina, Argentina, Belgium, United States, United Kingdom, France, France, Netherlands, France, Russian Federation Publisher:Springer Science and Business Media LLCJournal:Nature Biotechnology, volume 39, pages 169-173 (issn: 1087-0156, eissn: 1546-1696,

Authors: Alexander A. Aksenov; Vasilis Vasiliou; Adolfo Amézquita; Alisdair R. Fernie; Reza Mirnezami; Dennis Veselkov; Madeleine Ernst; +67 Authors

doi: 10.1038/s41587-020-0700-3

pmid: 33169034

pmc: PMC7971188

handle: 21.11116/0000-0007-F5C8-3 , 10067/1735230151162165141 , 11336/141080 , 10044/1/84598

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

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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.

Countries

Argentina, Argentina, Belgium, United States, United Kingdom, France, France, Netherlands, France, Russian Federation

Related Organizations

Michigan State University
United States
UC San Diego Health System
United States
Imperial College London
United Kingdom
Max Planck Institute of Neurobiology
Germany
Russian Academy of Sciences
Russian Federation

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Keywords

[SDV]Life Sciences [q-bio], Fragmentation patterns, S Agricultura (General), Mass spectrometry data, Nonnegative matrix factorization, Machine learning approaches, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], https://purl.org/becyt/ford/1.4, animal, Factorization, MESH: Metabolomics, Natural products, Gas chromatography, Reproducibilities, mass fragmentography, metabolomics, [SDV] Life Sciences [q-bio], GC-MS, MACHINE LEARNING, Anura, Engineering sciences. Technology, Life Sciences & Biomedicine, Algorithms, REPOSITORY, STANDARDS, 570, [CHIM.ANAL] Chemical Sciences/Analytical chemistry, MESH: Algorithms, Turing machines, Work-flows, METABOLOMICS, NATURAL PRODUCTS, Gas Chromatography-Mass Spectrometry, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, Life Science, Animals, Humans, Metabolomics, human, https://purl.org/becyt/ford/1, Biology, Q Ciencia (General), algorithm, Science & Technology, Mass spectrometry, Drug products, 540, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], MESH: Gas Chromatography-Mass Spectrometry, Biotechnology & Applied Microbiology

5 Research products, page 1 of 1

GNPS_GC_fig software on GitHub
IsRelatedTo
mshub_process software on Bitbucket
IsRelatedTo
pyMolNetEnhancer software on GitHub
IsRelatedTo
GNPS_Workflows software on GitHub
IsRelatedTo
GNPS_GC software on GitHub
IsRelatedTo

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	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	110
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	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 1%