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

Alexander A. Aksenov, Ivan Laponogov, Zheng Zhang, Sophie L. F. Doran, Ilaria Belluomo, Dennis Veselkov, Wout Bittremieux, Louis Felix Nothias, Melissa Nothias-Esposito, Katherine N. Maloney, et al.
(2021) NATURE BIOTECHNOLOGY. 39(2). p.169-173
Author
Organization
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. A machine learning workflow enables auto-deconvolution of gas chromatography-mass spectrometry data.
Keywords
METABOLOMICS, REPOSITORY, STANDARDS

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MLA
Aksenov, Alexander A., et al. “Auto-Deconvolution and Molecular Networking of Gas Chromatography-Mass Spectrometry Data.” NATURE BIOTECHNOLOGY, vol. 39, no. 2, 2021, pp. 169–73, doi:10.1038/s41587-020-0700-3.
APA
Aksenov, A. A., Laponogov, I., Zhang, Z., Doran, S. L. F., Belluomo, I., Veselkov, D., … Veselkov, K. (2021). Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data. NATURE BIOTECHNOLOGY, 39(2), 169–173. https://doi.org/10.1038/s41587-020-0700-3
Chicago author-date
Aksenov, Alexander A., Ivan Laponogov, Zheng Zhang, Sophie L. F. Doran, Ilaria Belluomo, Dennis Veselkov, Wout Bittremieux, et al. 2021. “Auto-Deconvolution and Molecular Networking of Gas Chromatography-Mass Spectrometry Data.” NATURE BIOTECHNOLOGY 39 (2): 169–73. https://doi.org/10.1038/s41587-020-0700-3.
Chicago author-date (all authors)
Aksenov, Alexander A., Ivan Laponogov, Zheng Zhang, Sophie L. F. Doran, Ilaria Belluomo, Dennis Veselkov, Wout Bittremieux, Louis Felix Nothias, Melissa Nothias-Esposito, Katherine N. Maloney, Biswapriya B. Misra, Alexey V. Melnik, Aleksandr Smirnov, Xiuxia Du, II Jones, Kenneth L., Kathleen Dorrestein, Morgan Panitchpakdi, Madeleine Ernst, Justin J. J. van der Hooft, Mabel Gonzalez, Chiara Carazzone, Adolfo Amezquita, Chris Callewaert, James T. Morton, Robert A. Quinn, Amina Bouslimani, Andrea Albarracin Orio, Daniel Petras, Andrea M. Smania, Sneha P. Couvillion, Meagan C. Burnet, Carrie D. Nicora, Erika Zink, Thomas O. Metz, Viatcheslav Artaev, Elizabeth Humston-Fulmer, Rachel Gregor, Michael M. Meijler, Itzhak Mizrahi, Stav Eyal, Brooke Anderson, Rachel Dutton, Raphael Lugan, Pauline Le Boulch, Yann Guitton, Stephanie Prevost, Audrey Poirier, Gaud Dervilly, Bruno Le Bizec, Aaron Fait, Noga Sikron Persi, Chao Song, Kelem Gashu, Roxana Coras, Monica Guma, Julia Manasson, Jose U. Scher, Dinesh Kumar Barupal, Saleh Alseekh, Alisdair R. Fernie, Reza Mirnezami, Vasilis Vasiliou, Robin Schmid, Roman S. Borisov, Larisa N. Kulikova, Rob Knight, Mingxun Wang, George B. Hanna, Pieter C. Dorrestein, and Kirill Veselkov. 2021. “Auto-Deconvolution and Molecular Networking of Gas Chromatography-Mass Spectrometry Data.” NATURE BIOTECHNOLOGY 39 (2): 169–173. doi:10.1038/s41587-020-0700-3.
Vancouver
1.
Aksenov AA, Laponogov I, Zhang Z, Doran SLF, Belluomo I, Veselkov D, et al. Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data. NATURE BIOTECHNOLOGY. 2021;39(2):169–73.
IEEE
[1]
A. A. Aksenov et al., “Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data,” NATURE BIOTECHNOLOGY, vol. 39, no. 2, pp. 169–173, 2021.
@article{8684984,
  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. 
A machine learning workflow enables auto-deconvolution of gas chromatography-mass spectrometry data.}},
  author       = {{Aksenov, Alexander A. and Laponogov, Ivan and Zhang, Zheng and Doran, Sophie L. F. and Belluomo, Ilaria and Veselkov, Dennis and Bittremieux, Wout and Nothias, Louis Felix and Nothias-Esposito, Melissa and Maloney, Katherine N. and Misra, Biswapriya B. and Melnik, Alexey V. and Smirnov, Aleksandr and Du, Xiuxia and Jones, Kenneth L., II and Dorrestein, Kathleen and Panitchpakdi, Morgan and Ernst, Madeleine and van der Hooft, Justin J. J. and Gonzalez, Mabel and Carazzone, Chiara and Amezquita, Adolfo and Callewaert, Chris and Morton, James T. and Quinn, Robert A. and Bouslimani, Amina and Orio, Andrea Albarracin and Petras, Daniel and Smania, Andrea M. and Couvillion, Sneha P. and Burnet, Meagan C. and Nicora, Carrie D. and Zink, Erika and Metz, Thomas O. and Artaev, Viatcheslav and Humston-Fulmer, Elizabeth and Gregor, Rachel and Meijler, Michael M. and Mizrahi, Itzhak and Eyal, Stav and Anderson, Brooke and Dutton, Rachel and Lugan, Raphael and Le Boulch, Pauline and Guitton, Yann and Prevost, Stephanie and Poirier, Audrey and Dervilly, Gaud and Le Bizec, Bruno and Fait, Aaron and Persi, Noga Sikron and Song, Chao and Gashu, Kelem and Coras, Roxana and Guma, Monica and Manasson, Julia and Scher, Jose U. and Barupal, Dinesh Kumar and Alseekh, Saleh and Fernie, Alisdair R. and Mirnezami, Reza and Vasiliou, Vasilis and Schmid, Robin and Borisov, Roman S. and Kulikova, Larisa N. and Knight, Rob and Wang, Mingxun and Hanna, George B. and Dorrestein, Pieter C. and Veselkov, Kirill}},
  issn         = {{1087-0156}},
  journal      = {{NATURE BIOTECHNOLOGY}},
  keywords     = {{METABOLOMICS,REPOSITORY,STANDARDS}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{169--173}},
  title        = {{Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data}},
  url          = {{http://doi.org/10.1038/s41587-020-0700-3}},
  volume       = {{39}},
  year         = {{2021}},
}
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