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; Mélissa Nothias-Esposito; Katherine N. Maloney; Biswapriya B. Misra; Alexey V. Melnik; Aleksandr Smirnov; Xiuxia Du; Kenneth L. Jones; Kathleen Dorrestein; Morgan Panitchpakdi; Madeleine Ernst; Justin J.J. van der Hooft; Mabel Gonzalez; Chiara Carazzone; Adolfo Amézquita; Chris Callewaert; James T. Morton; Robert A. Quinn; Amina Bouslimani; Andrea Albarracín 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; Raphaël 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; Kirill Veselkov

doi:10.1038/s41587-020-0700-3

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, Mélissa Nothias-Esposito, Katherine N. Maloney, Biswapriya B. Misra, Alexey V. Melnik, Aleksandr Smirnov, Xiuxia Du, Kenneth L. Jones, Kathleen Dorrestein, Morgan Panitchpakdi, Madeleine Ernst, Justin J.J. van der Hooft, Mabel GonzalezChiara Carazzone, Adolfo Amézquita, Chris Callewaert, James T. Morton, Robert A. Quinn, Amina Bouslimani, Andrea Albarracín 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, Raphaël 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, Kirill Veselkov

Research output: Contribution to journal › Article › peer-review

45 Scopus citations

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.

Original language	English
Pages (from-to)	169-173
Number of pages	5
Journal	Nature Biotechnology
Volume	39
Issue number	2
DOIs	https://doi.org/10.1038/s41587-020-0700-3
State	Published - 1 Feb 2021

ASJC Scopus subject areas

Biotechnology
Bioengineering
Biomedical Engineering
Applied Microbiology and Biotechnology
Molecular Medicine

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10.1038/s41587-020-0700-3

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Aksenov, A. A., Laponogov, I., Zhang, Z., Doran, S. L. F., Belluomo, I., Veselkov, D., Bittremieux, W., Nothias, L. F., Nothias-Esposito, M., Maloney, K. N., Misra, B. B., Melnik, A. V., Smirnov, A., Du, X., Jones, K. L., Dorrestein, K., Panitchpakdi, M., Ernst, M., van der Hooft, J. J. J., ... 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

@article{4d28987a1be64419a49950a3b938b0c8,

title = "Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data",

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.",

author = "Aksenov, {Alexander A.} and Ivan Laponogov and Zheng Zhang and Doran, {Sophie L.F.} and Ilaria Belluomo and Dennis Veselkov and Wout Bittremieux and Nothias, {Louis Felix} and M{\'e}lissa Nothias-Esposito and Maloney, {Katherine N.} and Misra, {Biswapriya B.} and Melnik, {Alexey V.} and Aleksandr Smirnov and Xiuxia Du and Jones, {Kenneth L.} and Kathleen Dorrestein and Morgan Panitchpakdi and Madeleine Ernst and {van der Hooft}, {Justin J.J.} and Mabel Gonzalez and Chiara Carazzone and Adolfo Am{\'e}zquita and Chris Callewaert and Morton, {James T.} and Quinn, {Robert A.} and Amina Bouslimani and Orio, {Andrea Albarrac{\'i}n} and Daniel Petras and Smania, {Andrea M.} and Couvillion, {Sneha P.} and Burnet, {Meagan C.} and Nicora, {Carrie D.} and Erika Zink and Metz, {Thomas O.} and Viatcheslav Artaev and Elizabeth Humston-Fulmer and Rachel Gregor and Meijler, {Michael M.} and Itzhak Mizrahi and Stav Eyal and Brooke Anderson and Rachel Dutton and Rapha{\"e}l Lugan and Boulch, {Pauline Le} and Yann Guitton and Stephanie Prevost and Audrey Poirier and Gaud Dervilly and {Le Bizec}, Bruno and Aaron Fait and Persi, {Noga Sikron} and Chao Song and Kelem Gashu and Roxana Coras and Monica Guma and Julia Manasson and Scher, {Jose U.} and Barupal, {Dinesh Kumar} and Saleh Alseekh and Fernie, {Alisdair R.} and Reza Mirnezami and Vasilis Vasiliou and Robin Schmid and Borisov, {Roman S.} and Kulikova, {Larisa N.} and Rob Knight and Mingxun Wang and Hanna, {George B.} and Dorrestein, {Pieter C.} and Kirill Veselkov",

note = "Funding Information: The conversion of the data from different repositories was supported by grant R03 CA211211 on reuse of metabolomics data, to build enabling chemical analysis tools for the ocean symbiosis program, and the development of a user-friendly interface for GC–MS analysis was supported by the Gordon and Betty Moore Foundation through grant GBMF7622. The University of California, San Diego Center for Microbiome Innovation supported the campus-wide seed grant awards for data collection that enabled the development of some of this infrastructure. P.C.D. was supported by the National Science Foundation (grant no. IOS-1656475) and the National Institutes of Health (NIH) (grant nos. U19 AG063744 01, P41 GM103484, R03 CA211211 and R01 GM107550). K.V. and I.L. are very grateful for the support of the Vodafone Foundation as part of the DRUGS/DreamLab project. The MSHub platform development was supported by NIH/NIAAA grant (R21 AA028432) on integrated machine learning for mass spectrometry data in liver disease, Intelligify Limited and Vodafone Foundation{\textquoteright}s DRUGS/CORONA-AI projects on network machine learning for drug repositioning and discovery of hyperfoods with antiviral/anticancer molecules. M.E. was supported by the University of Corsica. L.F.N. was supported by the NIH (R01 GM107550) and the European Union{\textquoteright}s Horizon 2020 Research and Innovation Programme (MSCA-GF, 704786). A.B. was supported by the National Institute of Justice Award (2015-DN-BX-K047). Additional support for data acquisition and data storage was provided by the Center for Computational Mass Spectrometry (P41 GM103484). The collection of data from the HomeChem Project was supported by the Sloan Foundation. G.B.H., S.L.F.D., I.L., K.V. and I.B. are grateful for the support of the OG cancer breath analysis study by the National Institute for Health Research London Invitro Diagnostic Co-operative and the NIHR Imperial Biomedical Research Centre, the Rosetrees and Stonegate Trusts and the Imperial College Charity. D.V. acknowledges support from ERC-Consolidator grant 724228 (LEMAN). I.B. acknowledges the contribution of Q. Wen and M. Colavita in the production of the training video. C. Callewaert was supported by the Research Foundation Flanders, with support from the industrial research fund of Ghent University. W.B. was supported by the Research Foundation Flanders. A.A.O. acknowledges the support of the Fulbright Commission and Consejo Nacional de Investigaciones Cient{\'i}ficas y T{\'e}cnicas (CONICET-Argentina). The work of R.L. and P.L.B. on the data set 30 was supported by Metaboscope, part of the {\textquoteleft}Platform 3A{\textquoteright}, funded by the European Regional Development Fund, the French Ministry of Research, Higher Education and Innovation, the Provence-Alpes-C{\^o}te d{\textquoteright}Azur region, the Departmental Council of Vaucluse and the Urban Community of Avignon. S.A. and A.R.F. acknowledge the PlantaSYST project by the European Union{\textquoteright}s Horizon 2020 Research and Innovation Programme (SGA-CSA nos. 664621 and 739582 under FPA no. 664620). V.V. acknowledges support from the National Institute on Alcohol Abuse and Alcoholism award R24AA022057. M. Guma and R.C. acknowledge the support of the Krupp Endowed Fund grant. A portion of mass spectra in the public reference library was produced within the framework of the State Task for the Topchiev Institute of Petrochemical Synthesis RAS and with the support of the RUDN University Program 5-100. R.S.B. acknowledges support of the State Task for the Topchiev Institute of Petrochemical Synthesis RAS. L.N.K. acknowledges support of the RUDN University Program 5-100. I.M. acknowledges support of the Israel Science Foundation (project no. 1947/19) and European Research Council under the European Union{\textquoteright}s Horizon 2020 Research and Innovation Programme (project no. 640384). J.S. has been supported by NIH/NIAMS R03AR072182, the Colton Center for Autoimmunity, the Rheumatology Research Foundation, the Riley Family Foundation and the Snyder Family Foundation. J. Manasson acknowledges support from the 2017 Group for Research and Assessment of Psoriasis and Psoriatic Arthritis Pilot Research Grant and NIH/NIAMS T32AR069515. R.G. is grateful to the Azrieli Foundation for the award of an Azrieli Fellowship. J.J.J.v.d.H. acknowledges support from an ASDI eScience grant (ASDI.2017.030) from the Netherlands eScience Center-NLeSC. B.A. was supported by the National Science Foundation through the Graduate Research Fellowship Program. GC–MS analyses for collection of the MSV000083743 data set were supported by the Pacific Northwest National Laboratory, Laboratory-Directed Research and Development Program, and were contributed by the Microbiomes in Transition Initiative; data were collected in the Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by the Department of Energy (DOE) Office of Biological and Environmental Research and located at the Pacific Northwest National Laboratory (PNNL). PNNL is operated by the Battelle Memorial Institute for the DOE under contract DEAC05-76RLO1830. M. Guma and R.C. acknowledge the support of the Krupp Endowed Fund grant. R.C. was also funded by T32AR064194-07. The authors are grateful to R. da Silva for his contribution to developing the first prototype of the EI data network and his continuous assistance with further development and testing of the infrastructure. The authors are also grateful to M. Vance and D. Farmer, who organized the sampling for HomeChem Indoor Chemistry Project (https://indoorchem.org/projects/homechem/) that allowed the collection of samples for the MSV000083598 data set. B. Ross has assisted with collecting data for the MSV000084348 data set. GC–MS analyses for collection of the MSV000084211 and MSV000084212 data sets were supported by N757 Doctorados Nacionales and project EXT-2016-69-1713 from the Departamento Administrativo de Ciencia, Tecnolog{\'i}a e Innovaci{\'o}n (COLCIENCIAS), the seed project INV-2019-67-1747 and the FAPA project of Chiara Carazzone from the Faculty of Science at Universidad de los Andes and the grant FP80740-064-2016 of COLCIENCIAS. The authors are grateful to L. M. Garz{\'o}n, P. Palacios, M. Gonzalez and J. Hernandez for their contributions to collecting the samples and to J. Oswaldo Turizo for designing and manufacturing the amphibian electrical stimulator. A.S. and X.D. acknowledge support from National Cancer Institute award U01CA235507. The authors are grateful to S. Neuman for feedback regarding the XCMS deconvolution tool. Publisher Copyright: {\textcopyright} 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.",

year = "2021",

month = feb,

day = "1",

doi = "10.1038/s41587-020-0700-3",

language = "English",

volume = "39",

pages = "169--173",

journal = "Nature Biotechnology",

issn = "1087-0156",

publisher = "Nature Publishing Group",

number = "2",

}

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 2021, 'Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data', Nature Biotechnology, vol. 39, no. 2, pp. 169-173. https://doi.org/10.1038/s41587-020-0700-3

TY - JOUR

T1 - Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data

AU - Aksenov, Alexander A.

AU - Laponogov, Ivan

AU - Zhang, Zheng

AU - Doran, Sophie L.F.

AU - Belluomo, Ilaria

AU - Veselkov, Dennis

AU - Bittremieux, Wout

AU - Nothias, Louis Felix

AU - Nothias-Esposito, Mélissa

AU - Maloney, Katherine N.

AU - Misra, Biswapriya B.

AU - Melnik, Alexey V.

AU - Smirnov, Aleksandr

AU - Du, Xiuxia

AU - Jones, Kenneth L.

AU - Dorrestein, Kathleen

AU - Panitchpakdi, Morgan

AU - Ernst, Madeleine

AU - van der Hooft, Justin J.J.

AU - Gonzalez, Mabel

AU - Carazzone, Chiara

AU - Amézquita, Adolfo

AU - Callewaert, Chris

AU - Morton, James T.

AU - Quinn, Robert A.

AU - Bouslimani, Amina

AU - Orio, Andrea Albarracín

AU - Petras, Daniel

AU - Smania, Andrea M.

AU - Couvillion, Sneha P.

AU - Burnet, Meagan C.

AU - Nicora, Carrie D.

AU - Zink, Erika

AU - Metz, Thomas O.

AU - Artaev, Viatcheslav

AU - Humston-Fulmer, Elizabeth

AU - Gregor, Rachel

AU - Meijler, Michael M.

AU - Mizrahi, Itzhak

AU - Eyal, Stav

AU - Anderson, Brooke

AU - Dutton, Rachel

AU - Lugan, Raphaël

AU - Boulch, Pauline Le

AU - Guitton, Yann

AU - Prevost, Stephanie

AU - Poirier, Audrey

AU - Dervilly, Gaud

AU - Le Bizec, Bruno

AU - Fait, Aaron

AU - Persi, Noga Sikron

AU - Song, Chao

AU - Gashu, Kelem

AU - Coras, Roxana

AU - Guma, Monica

AU - Manasson, Julia

AU - Scher, Jose U.

AU - Barupal, Dinesh Kumar

AU - Alseekh, Saleh

AU - Fernie, Alisdair R.

AU - Mirnezami, Reza

AU - Vasiliou, Vasilis

AU - Schmid, Robin

AU - Borisov, Roman S.

AU - Kulikova, Larisa N.

AU - Knight, Rob

AU - Wang, Mingxun

AU - Hanna, George B.

AU - Dorrestein, Pieter C.

AU - Veselkov, Kirill

N1 - Funding Information: The conversion of the data from different repositories was supported by grant R03 CA211211 on reuse of metabolomics data, to build enabling chemical analysis tools for the ocean symbiosis program, and the development of a user-friendly interface for GC–MS analysis was supported by the Gordon and Betty Moore Foundation through grant GBMF7622. The University of California, San Diego Center for Microbiome Innovation supported the campus-wide seed grant awards for data collection that enabled the development of some of this infrastructure. P.C.D. was supported by the National Science Foundation (grant no. IOS-1656475) and the National Institutes of Health (NIH) (grant nos. U19 AG063744 01, P41 GM103484, R03 CA211211 and R01 GM107550). K.V. and I.L. are very grateful for the support of the Vodafone Foundation as part of the DRUGS/DreamLab project. The MSHub platform development was supported by NIH/NIAAA grant (R21 AA028432) on integrated machine learning for mass spectrometry data in liver disease, Intelligify Limited and Vodafone Foundation’s DRUGS/CORONA-AI projects on network machine learning for drug repositioning and discovery of hyperfoods with antiviral/anticancer molecules. M.E. was supported by the University of Corsica. L.F.N. was supported by the NIH (R01 GM107550) and the European Union’s Horizon 2020 Research and Innovation Programme (MSCA-GF, 704786). A.B. was supported by the National Institute of Justice Award (2015-DN-BX-K047). Additional support for data acquisition and data storage was provided by the Center for Computational Mass Spectrometry (P41 GM103484). The collection of data from the HomeChem Project was supported by the Sloan Foundation. G.B.H., S.L.F.D., I.L., K.V. and I.B. are grateful for the support of the OG cancer breath analysis study by the National Institute for Health Research London Invitro Diagnostic Co-operative and the NIHR Imperial Biomedical Research Centre, the Rosetrees and Stonegate Trusts and the Imperial College Charity. D.V. acknowledges support from ERC-Consolidator grant 724228 (LEMAN). I.B. acknowledges the contribution of Q. Wen and M. Colavita in the production of the training video. C. Callewaert was supported by the Research Foundation Flanders, with support from the industrial research fund of Ghent University. W.B. was supported by the Research Foundation Flanders. A.A.O. acknowledges the support of the Fulbright Commission and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET-Argentina). The work of R.L. and P.L.B. on the data set 30 was supported by Metaboscope, part of the ‘Platform 3A’, funded by the European Regional Development Fund, the French Ministry of Research, Higher Education and Innovation, the Provence-Alpes-Côte d’Azur region, the Departmental Council of Vaucluse and the Urban Community of Avignon. S.A. and A.R.F. acknowledge the PlantaSYST project by the European Union’s Horizon 2020 Research and Innovation Programme (SGA-CSA nos. 664621 and 739582 under FPA no. 664620). V.V. acknowledges support from the National Institute on Alcohol Abuse and Alcoholism award R24AA022057. M. Guma and R.C. acknowledge the support of the Krupp Endowed Fund grant. A portion of mass spectra in the public reference library was produced within the framework of the State Task for the Topchiev Institute of Petrochemical Synthesis RAS and with the support of the RUDN University Program 5-100. R.S.B. acknowledges support of the State Task for the Topchiev Institute of Petrochemical Synthesis RAS. L.N.K. acknowledges support of the RUDN University Program 5-100. I.M. acknowledges support of the Israel Science Foundation (project no. 1947/19) and European Research Council under the European Union’s Horizon 2020 Research and Innovation Programme (project no. 640384). J.S. has been supported by NIH/NIAMS R03AR072182, the Colton Center for Autoimmunity, the Rheumatology Research Foundation, the Riley Family Foundation and the Snyder Family Foundation. J. Manasson acknowledges support from the 2017 Group for Research and Assessment of Psoriasis and Psoriatic Arthritis Pilot Research Grant and NIH/NIAMS T32AR069515. R.G. is grateful to the Azrieli Foundation for the award of an Azrieli Fellowship. J.J.J.v.d.H. acknowledges support from an ASDI eScience grant (ASDI.2017.030) from the Netherlands eScience Center-NLeSC. B.A. was supported by the National Science Foundation through the Graduate Research Fellowship Program. GC–MS analyses for collection of the MSV000083743 data set were supported by the Pacific Northwest National Laboratory, Laboratory-Directed Research and Development Program, and were contributed by the Microbiomes in Transition Initiative; data were collected in the Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by the Department of Energy (DOE) Office of Biological and Environmental Research and located at the Pacific Northwest National Laboratory (PNNL). PNNL is operated by the Battelle Memorial Institute for the DOE under contract DEAC05-76RLO1830. M. Guma and R.C. acknowledge the support of the Krupp Endowed Fund grant. R.C. was also funded by T32AR064194-07. The authors are grateful to R. da Silva for his contribution to developing the first prototype of the EI data network and his continuous assistance with further development and testing of the infrastructure. The authors are also grateful to M. Vance and D. Farmer, who organized the sampling for HomeChem Indoor Chemistry Project (https://indoorchem.org/projects/homechem/) that allowed the collection of samples for the MSV000083598 data set. B. Ross has assisted with collecting data for the MSV000084348 data set. GC–MS analyses for collection of the MSV000084211 and MSV000084212 data sets were supported by N757 Doctorados Nacionales and project EXT-2016-69-1713 from the Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS), the seed project INV-2019-67-1747 and the FAPA project of Chiara Carazzone from the Faculty of Science at Universidad de los Andes and the grant FP80740-064-2016 of COLCIENCIAS. The authors are grateful to L. M. Garzón, P. Palacios, M. Gonzalez and J. Hernandez for their contributions to collecting the samples and to J. Oswaldo Turizo for designing and manufacturing the amphibian electrical stimulator. A.S. and X.D. acknowledge support from National Cancer Institute award U01CA235507. The authors are grateful to S. Neuman for feedback regarding the XCMS deconvolution tool. Publisher Copyright: © 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.

PY - 2021/2/1

Y1 - 2021/2/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85095678581&partnerID=8YFLogxK

U2 - 10.1038/s41587-020-0700-3

DO - 10.1038/s41587-020-0700-3

M3 - Article

C2 - 33169034

AN - SCOPUS:85095678581

SN - 1087-0156

VL - 39

SP - 169

EP - 173

JO - Nature Biotechnology

JF - Nature Biotechnology

IS - 2

ER -

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

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