Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra
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Abstract
Metabolomics using nontargeted tandem mass spectrometry can detect thousands of molecules in a biological sample. However, structural molecule annotation is limited to structures present in libraries or databases, restricting analysis and interpretation of experimental data. Here we describe CANOPUS (class assignment and ontology prediction using mass spectrometry), a computational tool for systematic compound class annotation. CANOPUS uses a deep neural network to predict 2,497 compound classes from fragmentation spectra, including all biologically relevant classes. CANOPUS explicitly targets compounds for which neither spectral nor structural reference data are available and predicts classes lacking tandem mass spectrometry training data. In evaluation using reference data, CANOPUS reached very high prediction performance (average accuracy of 99.7% in cross-validation) and outperformed four baseline methods. We demonstrate the broad utility of CANOPUS by investigating the effect of microbial colonization in the mouse digestive system, through analysis of the chemodiversity of different Euphorbia plants and regarding the discovery of a marine natural product, revealing biological insights at the compound class level.
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CANOPUS utilizes predicted fingerprints to predict the natural product class of the unknown natural product.
“CANOPUS166 utilizes these predicted fingerprints, which are esoteric to chemists, to predict the natural product class167 of the unknown natural product.”
From Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products (2024) · cites this paper as [166]
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Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888 · see citing works
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430 · see citing works
(2014) · Cited by 33,419 · see citing works
On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other
Mann, H. B., & Whitney, D. R. (1947) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177730491 · Cited by 14,320 · see citing works
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2019) · Apollo - University of Cambridge Repository · doi:10.17863/cam.64134 · Cited by 10,460 · see citing works
Extended-Connectivity Fingerprints
Rogers, D. O. W., & Hahn, M. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100050t · Cited by 7,597 · see citing works
Comparison of the predicted and observed secondary structure of T4 phage lysozyme
Matthews, B. W. (1975) · Biochimica et Biophysica Acta (BBA) - Protein Structure · doi:10.1016/0005-2795(75)90109-9 · Cited by 5,399 · see citing works
Wang, M., Carver, J. J., Phelan, V. V., Sanchez, L. M., Garg, N., Peng, Y., Nguyen, D. D., Watrous, J., Kapono, C. A., Luzzatto-Knaan, T., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3597 · Cited by 4,629 · see citing works
Self-citationPluskal, T., Castillo, S., Villar‐Briones, A., & Orešič, M. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-395 · Cited by 3,852 · see citing works
HMDB 4.0: the human metabolome database for 2018
Wishart, D. S., Feunang, Y. D., Marcu, A., Guo, A. C., Liang, K., Vázquez-Fresno, R., Sajed, T., Johnson, D., Li, C., Karu, N., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1089 · Cited by 3,438 · see citing works
MassBank: a public repository for sharing mass spectral data for life sciences
Horai, H., Arita, M., Kanaya, S., Nihei, Y., Ikeda, T., Suwa, K., Ojima, Y., Tanaka, K., Tanaka, S., Aoshima, K., et al. (2010) · Journal of Mass Spectrometry · doi:10.1002/jms.1777 · Cited by 2,529 · see citing works
SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information
Dührkop, K., Fleischauer, M., Ludwig, M., Aksenov, A. A., Melnik, A. V., Meusel, M., Dorrestein, P. C., Rousu, J., & Böcker, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0344-8 · Cited by 2,088 · see citing works
Self-citationFeature-based molecular networking in the GNPS analysis environment
Nothias-Scaglia, L.-F., Petras, D., Schmid, R., Dührkop, K., Rainer, J., Sarvepalli, A., Protsyuk, I., Ernst, M., Tsugawa, H., Fleischauer, M., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0933-6 · Cited by 1,658 · see citing works
Self-citationThe KEGG databases at GenomeNet
Kanehisa, M., Goto, S., Kawashima, S., & Nakaya, A. (2002) · Nucleic Acids Res. · doi:10.1093/nar/30.1.42 · Cited by 1,547 · see citing works
ClassyFire: automated chemical classification with a comprehensive, computable taxonomy
Djoumbou-Feunang, Y., Eisner, R., Knox, C., Chepelev, L. L., Hastings, J., Owen, G., Fahy, E., Steinbeck, C., Subramanian, S., Bolton, E., et al. (2016) · Journal of Cheminformatics · doi:10.1186/s13321-016-0174-y · Cited by 1,543 · see citing works
Searching molecular structure databases with tandem mass spectra using CSI:FingerID
Dührkop, K., Shen, H., Meusel, M., Rousu, J., & Böcker, S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1509788112 · Cited by 1,190 · see citing works
Self-citationMetFrag relaunched: incorporating strategies beyond in silico fragmentation
Ruttkies, C., Schymanski, E., Wolf, S., Hollender, J., & Neumann, S. (2016) · Journal of Cheminformatics · doi:10.1186/s13321-016-0115-9 · Cited by 1,063 · see citing works
METLIN: A Technology Platform for Identifying Knowns and Unknowns
Guijas, C., Montenegro-Burke, J. R., Domingo-Almenara, X., Palermo, A., Warth, B., Hermann, G., Koellensperger, G., Huan, T., Uritboonthai, W., Aisporna, A., et al. (2018) · Analytical Chemistry · doi:10.1021/acs.analchem.7b04424 · Cited by 931 · see citing works
A Computer Program for Classifying Plants
Rogers, D. J., & Tanimoto, T. T. (1960) · Science · doi:10.1126/science.132.3434.1115 · Cited by 874 · see citing works
Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics
Blaženović, I., Kind, T., Ji, J., & Fiehn, O. (2018) · Metabolites · doi:10.3390/metabo8020031 · Cited by 761 · see citing works
In silico fragmentation for computer assisted identification of metabolite mass spectra
Wolf, S., Schmidt, S., Müller-Hannemann, M., & Neumann, S. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-148 · Cited by 698 · see citing works
Tsugawa, H., Kind, T., Nakabayashi, R., Yukihira, D., Tanaka, W., Čajka, T., Saito, K., Fiehn, O., & Arita, M. (2016) · Analytical Chemistry · doi:10.1021/acs.analchem.6b00770 · Cited by 689 · see citing works
OpenMS: a flexible open-source software platform for mass spectrometry data analysis
Röst, H. L., Sachsenberg, T. T., Aiche, S., Bielow, C., Weisser, H., Aicheler, F., Andreotti, S., Ehrlich, H., Gutenbrunner, P., Kenar, E., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3959 · Cited by 680 · see citing works
The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013
Hastings, J., de Matos, P., Dekker, A., Ennis, M., Harsha, B., Kale, N., Muthukrishnan, V., Owen, G. I., Turner, S., Williams, M., et al. (2012) · Nucleic Acids Res. · doi:10.1093/nar/gks1146 · Cited by 613 · see citing works
Global chemical effects of the microbiome include new bile-acid conjugations
Quinn, R. A., Melnik, A. V., Vrbanac, A., Fu, T., Patras, K. A., Christy, M. P., Bodai, Z., Belda-Ferre, P., Tripathi, A., Chung, L. K., et al. (2020) · Nature · doi:10.1038/s41586-020-2047-9 · Cited by 579 · see citing works
Self-citationVinaixa, M., Schymanski, E., Neumann, S., Navarro, M., Salek, R. M., & Yanes, Ó. (2015) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2015.09.005 · Cited by 566 · see citing works
ROGERS, F. B. (1965) · Nature · doi:10.1038/205236a0 · Cited by 565 · see citing works
Illuminating the dark matter in metabolomics
Silva, R., Dorrestein, P. C., & Quinn, R. A. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1516878112 · Cited by 552 · see citing works
Self-citationWillighagen, E., Mayfield, J. W., Alvarsson, J., Berg, A., Carlsson, L., Jeliazkova, N., Kühn, S., Pluskal, T., Rojas‐Chertó, M., Spjuth, O., et al. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0220-4 · Cited by 530 · see citing works
Euphorbia Diterpenes: Isolation, Structure, Biological Activity, and Synthesis (2008–2012)
Vasas, A., & Hohmann, J. (2014) · Chemical Reviews · doi:10.1021/cr400541j · Cited by 511 · see citing works
Identification of small molecules using accurate mass MS/MS search
Kind, T., Tsugawa, H., Čajka, T., Ma, Y., Lai, Z., Mehta, S. S., Wohlgemuth, G., Barupal, D. K., Showalter, M. R., Arita, M., et al. (2017) · Mass Spectrometry Reviews · doi:10.1002/mas.21535 · Cited by 481 · see citing works
Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification
Allen, F., Greiner, R., & Wishart, D. S. (2014) · Metabolomics · doi:10.1007/s11306-014-0676-4 · Cited by 430 · see citing works
Allard, P., Péresse, T., Bisson, J. I., Gindro, K., Marcourt, L., Pham, V. C., Roussi, F., Litaudon, M., & Wolfender, J. (2016) · Analytical Chemistry · doi:10.1021/acs.analchem.5b04804 · Cited by 427 · see citing works
MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools
Ernst, M., Kang, K. B., Caraballo‐Rodríguez, A. M., Nothias-Scaglia, L.-F., Wandy, J., Chen, C., Wang, M., Rogers, S., Medema, M. H., Dorrestein, P. C., et al. (2019) · Metabolites · doi:10.3390/metabo9070144 · Cited by 396 · see citing works
Self-citationChemical substructures that enrich for biological activity
Klekota, J., & Roth, F. P. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn479 · Cited by 392 · see citing works
Molecular Networking As a Drug Discovery, Drug Metabolism, and Precision Medicine Strategy
Quinn, R. A., Nothias-Scaglia, L.-F., Vining, O. B., Meehan, M. J., Esquenazi, E., & Dorrestein, P. C. (2016) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2016.10.011 · Cited by 378 · see citing works
Self-citationTopic modeling for untargeted substructure exploration in metabolomics
van der Hooft, J. J. J., Wandy, J., Barrett, M. P., Burgess, K. E., & Rogers, S. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1608041113 · Cited by 368 · see citing works
Propagating annotations of molecular networks using in silico fragmentation
Silva, R., Wang, M., Nothias-Scaglia, L.-F., van der Hooft, J. J. J., Caraballo‐Rodríguez, A. M., Fox, E., Balunas, M. J., Klassen, J. L., Lopes, N. P., & Dorrestein, P. C. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006089 · Cited by 361 · see citing works
Self-citationIon identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment
Schmid, R., Petras, D., Nothias-Scaglia, L.-F., Wang, M., Aron, A. T., Jagels, A., Tsugawa, H., Rainer, J., Garcia‐Aloy, M., Dührkop, K., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23953-9 · Cited by 356 · see citing works
Self-citationBarupal, D. K., & Fiehn, O. (2017) · Scientific Reports · doi:10.1038/s41598-017-15231-w · Cited by 339 · see citing works
Portoghese, P. S., Sultana, M., Nagase, H., et al. (1988) · Journal of Medicinal Chemistry · doi:10.1021/jm00397a001 · Cited by 302 · see citing works
A cheminformatics approach to characterize metabolomes in stable-isotope-labeled organisms
Tsugawa, H., Nakabayashi, R., Mori, T., Yamada, Y., Takahashi, M., Rai, A., Sugiyama, R., Yamamoto, H., Nakaya, T., Yamazaki, M., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0358-2 · Cited by 299 · see citing works
Benton, H. P., Wong, D. M., Trauger, S. A., & Siuzdak, G. E. (2008) · Analytical Chemistry · doi:10.1021/ac800795f · Cited by 266 · see citing works
Database-independent molecular formula annotation using Gibbs sampling through ZODIAC
Ludwig, M., Nothias-Scaglia, L.-F., Dührkop, K., Koester, I., Fleischauer, M., Hoffmann, M., Petras, D., Vargas, F., Morsy, M. R., Aluwihare, L. I., et al. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00234-6 · Cited by 234 · see citing works
Self-citationGlobal chemical analysis of biology by mass spectrometry
Aksenov, A. A., Silva, R., Knight, R. D., Lopes, N. P., & Dorrestein, P. C. (2017) · Nature Reviews Chemistry · doi:10.1038/s41570-017-0054 · Cited by 225 · see citing works
Self-citationHorn, J. W., van Ee, B. W., Morawetz, J. J., Riina, R., Steinmann, V. W., Berry, P. E., & Wurdack, K. J. (2012) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2011.12.022 · Cited by 215 · see citing works
Böcker, S., & Dührkop, K. (2016) · Journal of Cheminformatics · doi:10.1186/s13321-016-0116-8 · Cited by 208 · see citing works
Self-citationMetabolite identification and molecular fingerprint prediction through machine learning
Heinonen, M., Shen, H., Zamboni, N., & Rousu, J. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts437 · Cited by 186 · see citing works
Self-citationStructure Annotation of All Mass Spectra in Untargeted Metabolomics
Blaženović, I., Kind, T., Michael, R., Ji, J., Vaniya, A., Wancewicz, B., Roberts, B. S., Torbašinović, H., Lee, T., Mehta, S. S., et al. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.8b04698 · Cited by 186 · see citing works
Critical Assessment of Small Molecule Identification 2016: automated methods
Schymanski, E., Ruttkies, C., Krauss, M. K. V., Brouard, C., Kind, T., Dührkop, K., Allen, F. R., Vaniya, A., Verdegem, D., Böcker, S., et al. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0207-1 · Cited by 177 · see citing works
Self-citationPubChem chemical structure standardization
Hähnke, V. D., Kim, S., & Bolton, E. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0293-8 · Cited by 163 · see citing works
Evolutionary bursts in Euphorbia (Euphorbiaceae) are linked with photosynthetic pathway
Horn, J. W., Xi, Z., Riina, R., Peirson, J. A., Yang, Y. J., Dorsey, B. L., Berry, P. E., Davis, C. “., & Wurdack, K. J. (2014) · Evolution · doi:10.1111/evo.12534 · Cited by 154 · see citing works
Riina, R., Peirson, J. A., Geltman, D. V., et al. (2013) · Taxon · doi:10.12705/622.3 · Cited by 152 · see citing works
Feature-based Molecular Networking in the GNPS Analysis EnvironmentPreprint
Nothias-Scaglia, L.-F., Petras, D., Schmid, R., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/812404 · Cited by 144 · see citing works
Self-citationAutomatic Chemical Structure Annotation of an LC–MSn Based Metabolic Profile from Green Tea
Ridder, L., van der Hooft, J. J. J., Verhoeven, S., de Vos, R. C. H., Bino, R. J., & Vervoort, J. J. M. (2013) · Analytical Chemistry · doi:10.1021/ac400861a · Cited by 130 · see citing works
Phylogenetics, morphological evolution, and classification of Euphorbia subgenus Euphorbia
Dorsey, B. L., Haevermans, T., Aubriot, X., et al. (2013) · Taxon · doi:10.12705/622.1 · Cited by 128 · see citing works
MSnet: A Neural Network which Classifies Mass Spectra
Curry, B., & Rumelhart, D. E. (1990) · Tetrahedron Computer Methodology · doi:10.1016/0898-5529(90)90053-b · Cited by 127 · see citing works
Identifying the Unknowns by Aligning Fragmentation Trees
Rasche, F., Scheubert, K., Hufsky, F., Zichner, T., Kai, M., Svatoš, A., & Böcker, S. (2012) · Analytical Chemistry · doi:10.1021/ac300304u · Cited by 117 · see citing works
Self-citationSources of variation in foliar secondary chemistry in a tropical forest tree community
Sedio, B. E., Echeverri, J. C. R., P., C. A. B., et al. (2016) · Ecology · doi:10.1002/ecy.1689 · Cited by 116 · see citing works
METLIN: A Tandem Mass Spectral Library of Standards
Montenegro-Burke, J. R., Guijas, C., & Siuzdak, G. E. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0239-3_9 · Cited by 115 · see citing works
Production of equol from daidzein by gram-positive rod-shaped bacterium isolated from rat intestine
Minamida, K., Tanaka, M., Abe, A., Sone, T., Tomita, F., Hara, H., & Asano, K. (2006) · Journal of Bioscience and Bioengineering · doi:10.1263/jbb.102.247 · Cited by 114 · see citing works
Moorthy, A. S., Wallace, W. E., Kearsley, A. J., Tchekhovskoi, D. V., & Stein, S. E. (2017) · Analytical Chemistry · doi:10.1021/acs.analchem.7b03320 · Cited by 108 · see citing works
Molecular phylogenetics and classification of Euphorbia subgenus Chamaesyce (Euphorbiaceae)
Yang, Y. J., Riina, R., Morawetz, J. J., et al. (2012) · Taxon · doi:10.1002/tax.614005 · Cited by 106 · see citing works
Advances in computational metabolomics and databases deepen the understanding of metabolisms
Tsugawa, H. (2018) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2018.01.008 · Cited by 97 · see citing works
Fast metabolite identification with Input Output Kernel Regression
Brouard, C., Shen, H., Dührkop, K., et al. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw246 · Cited by 87 · see citing works
Self-citationPeirson, J. A., Bruyns, P. V., Riina, R., et al. (2013) · Taxon · doi:10.12705/626.12 · Cited by 76 · see citing works
Discovering Regulated Metabolite Families in Untargeted Metabolomics Studies
Treutler, H., Tsugawa, H., Porzel, A., Gorzolka, K., Tissier, A. F., Neumann, S., & Balcke, G. U. (2016) · Analytical Chemistry · doi:10.1021/acs.analchem.6b01569 · Cited by 71 · see citing works
Frainay, C., Schymanski, E., Neumann, S., Merlet, B., Salek, R. M., Jourdan, F., & Yanes, Ó. (2018) · Metabolites · doi:10.3390/metabo8030051 · Cited by 69 · see citing works
Hybrid Search: A Method for Identifying Metabolites Absent from Tandem Mass Spectrometry Libraries
Cooper, B. T., Yan, X., Simón-Manso, Y., Tchekhovskoi, D. V., Mirokhin, Y. A., & Stein, S. E. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.9b03415 · Cited by 69 · see citing works
Classification of mass spectra
Werther, W., Lohninger, H., Stancl, F., et al. (1994) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(94)85018-6 · Cited by 54 · see citing works
Chemical Diversity and Classification of Secondary Metabolites in Nine Bryophyte Species
Peters, K., Treutler, H., Döll, S., Kindt, A. S. D., Hankemeier, T., & Neumann, S. (2019) · Metabolites · doi:10.3390/metabo9100222 · Cited by 52 · see citing works
Computer‐aided interpretation of mass spectra
Venkataraghavan, R., McLafferty, F. W., & Lear, G. E. V. (1969) · Organic Mass Spectrometry · doi:10.1002/oms.1210020102 · Cited by 48 · see citing works
Assessing Specialized Metabolite Diversity in the Cosmopolitan Plant Genus Euphorbia L.
Ernst, M., Nothias-Scaglia, L.-F., van der Hooft, J. J. J., et al. (2019) · Frontiers in Plant Science · doi:10.3389/fpls.2019.00846 · Cited by 47 · see citing works
CANPA: Computer-Assisted Natural Products Anticipation
Ramos, A., Pavesi, C., Litaudon, M., et al. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.9b02216 · Cited by 40 · see citing works
Automated compound classification using a chemical ontology
Bobach, C., Böhme, T., Laube, U., et al. (2012) · Journal of Cheminformatics · doi:10.1186/1758-2946-4-40 · Cited by 34 · see citing works
Ion Identity Molecular Networking in the GNPS EnvironmentPreprint
Schmid, R., Petras, D., Nothias-Scaglia, L.-F., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.11.088948 · Cited by 25 · see citing works
Self-citationLowry, S. R., ISENHOUR, T. L., Justice, J. B., et al. (1977) · Analytical Chemistry · doi:10.1021/ac50020a022 · Cited by 24 · see citing works
Yang, M., Lu, Z., Yu, K., et al. (2012) · International Journal of Mass Spectrometry · doi:10.1016/j.ijms.2012.07.006 · Cited by 12 · see citing works
ARISTO: ontological classification of small molecules by electron ionization-mass spectrometry
Askenazi, M., & Michal Linial, B. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr403 · Cited by 10 · see citing works
CANPA: Computer-Assisted Natural Products AnticipationPreprint
Ramos, A., Pavesi, C., Litaudon, M., et al. (2019) · ChemRxiv · doi:10.26434/chemrxiv.8015039 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- Shinbo, Y. et al. in Plant Metabolomics, Vol. 57. (eds. K. Saito, R.A. Dixon & L. Willmitzer) 165-181 (#SV#, 2006).
- Abadi, M.n. et al. in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16) 265-283 (2016).
- Platt, J.C. in Advances in large margin classifiers (MIT Press, Cambridge, Massachusetts; 2000).
Cited by
Sorted by citations
NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products
Kim, H. W., Wang, M., Leber, C. A., Nothias, L.-F., Reher, R., Kang, K. B., van der Hooft, J. J. J., Dorrestein, P. C., Gerwick, W. H., & Cottrell, G. W. (2021) · Journal of Natural Products · doi:10.1021/acs.jnatprod.1c00399 · Cited by 551 · see citing works
Self-citationHigh-confidence structural annotation of metabolites absent from spectral libraries
Hoffmann, M. A., Nothias, L.-F., Ludwig, M., Fleischauer, M., Gentry, E. C., Witting, M., Dorrestein, P. C., Dührkop, K., & Böcker, S. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01045-9 · Cited by 388 · see citing works
Self-citationNew software tools, databases, and resources in metabolomics: updates from 2020
Misra, B. B. (2021) · Metabolomics · doi:10.1007/s11306-021-01796-1 · Cited by 227 · see citing works
MSNovelist: de novo structure generation from mass spectra
Stravs, M. A., Dührkop, K., Böcker, S., & Zamboni, N. (2022) · Nature Methods · doi:10.1038/s41592-022-01486-3 · Cited by 226 · see citing works
Self-citationRole of diet and its effects on the gut microbiome in the pathophysiology of mental disorders
Horn, J., Mayer, D. E., Chen, S., & Mayer, E. A. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-01922-0 · Cited by 193 · see citing works
Annotating metabolite mass spectra with domain-inspired chemical formula transformers
Goldman, S., Wohlwend, J., Stražar, M., Haroush, G., Xavier, R. J., & Coley, C. W. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00708-3 · Cited by 87 · see citing works
Vitale, G. A., Geibel, C., Minda, V., Wang, M., Aron, A. T., & Petras, D. (2024) · Natural Product Reports · doi:10.1039/d3np00050h · Cited by 66 · see citing works
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Reher, R., Aron, A. T., Fajtová, P., Stincone, P., Wagner, B., Pérez-Lorente, A. I., Liu, C., Shalom, I. Y. B., Bittremieux, W., Wang, M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32016-6 · Cited by 53 · see citing works
Self-citationAyoub, N., Badr, N., Al-Ghamdi, S. S., Alsanosi, S., Alzahrani, A. R., Abdel-Naim, A. B., Nematallah, K. A., & Swilam, N. (2021) · Nutrients · doi:10.3390/nu14010028 · Cited by 19 · see citing works
Voronov, G., Lightheart, R., Frandsen, A., Bargh, B., Haynes, S. E., Spencer, E., Schoenhardt, K. E., Davidson, C., Schaum, A., Macherla, V. R., et al. (2022) · doi:10.1101/2022.10.09.511482 · Cited by 18 · see citing works
Self-citationNative Metabolomics Unveils Suomilide Analogs with Potent Trypsin Inhibitory Activity
Naimi, A., Ulbricht, C., Trinh, T. L., Kehraus, S., Dreckmann, T. M., Linne, U., Hardes, K., Reher, R., & Baunach, M. (2026) · Journal of Natural Products · doi:10.1021/acs.jnatprod.5c01380 · Cited by 1 · see citing works
Self-citation
- Authors
- Dührkop, K., Nothias, L.-F., Fleischauer, M., Reher, R., Ludwig, M., Hoffmann, M. A., Petras, D., Gerwick, W. H., Rousu, J., Dorrestein, P. C., & Böcker, S.
- Affiliations
- Friedrich Schiller University Jena (DE)
- Scripps Institution of Oceanography (US)
- Max Planck Institute for Chemical Ecology (DE)
- Helsinki Institute for Information Technology (FI)
- University of California San Diego (US)
- Corresponding author
- Böcker, Sebastian
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2020-11-23
- Volume
- 39
- Issue
- 4
- Pages
- 462-471
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W3108604517
- Cited by
- 860
- References
- 82
- Citations by year
- 2026: 167 · 2025: 204 · 2024: 185 · 2023: 134 · 2022: 90 · 2021: 76 · 2020: 4
- FWCI
- 24.66
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Biotechnology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (BO 1910/20, PE 2600/1)
- U.S. Department of Health & Human Services | National Institutes of Health (R01 GM107550, R01GN107550, R01GM107550)
- EC | Horizon 2020 Framework Programme (MSCA-GF, 704786)
- Gordon and Betty Moore Foundation (GBMF7622)
- Academy of Finland (310107/MACOME)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- Canopus, unknown metabolites, deep neural network, untargeted metabolomics, tandem mass spectrometry, chemical ontology, microbial colonization, mouse gut microbiome, marine natural products, metabolite classification
- MeSH terms
- Gastrointestinal Microbiome, Animals, Biological Products, Chromatography, Liquid, Neural Networks, Computer, Computational Biology, Euphorbia, Mice, Tandem Mass Spectrometry, Metabolomics, Aquatic Organisms
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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