HMDB 4.0: the human metabolome database for 2018
Open accessAbstract
The Human Metabolome Database or HMDB (www.hmdb.ca) is a web-enabled metabolomic database containing comprehensive information about human metabolites along with their biological roles, physiological concentrations, disease associations, chemical reactions, metabolic pathways, and reference spectra. First described in 2007, the HMDB is now considered the standard metabolomic resource for human metabolic studies. Over the past decade the HMDB has continued to grow and evolve in response to emerging needs for metabolomics researchers and continuing changes in web standards. This year's update, HMDB 4.0, represents the most significant upgrade to the database in its history. For instance, the number of fully annotated metabolites has increased by nearly threefold, the number of experimental spectra has grown by almost fourfold and the number of illustrated metabolic pathways has grown by a factor of almost 60. Significant improvements have also been made to the HMDB's chemical taxonomy, chemical ontology, spectral viewing, and spectral/text searching tools. A great deal of brand new data has also been added to HMDB 4.0. This includes large quantities of predicted MS/MS and GC-MS reference spectral data as well as predicted (physiologically feasible) metabolite structures to facilitate novel metabolite identification. Additional information on metabolite-SNP interactions and the influence of drugs on metabolite levels (pharmacometabolomics) has also been added. Many other important improvements in the content, the interface, and the performance of the HMDB website have been made and these should greatly enhance its ease of use and its potential applications in nutrition, biochemistry, clinical chemistry, clinical genetics, medicine, and metabolomics science.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Short-chain fatty acids are detectable in low amounts in the human brain under physiological conditions.
“SCFAs are, in fact, detectable in low amounts in the human brain under physiological conditions (102).”
From The gut-brain axis: how microbiota and host inflammasome influence brain physiology and pathology (2020) · cites this paper as [102]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
KEGG: new perspectives on genomes, pathways, diseases and drugs
Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y., & Morishima, K. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1092 · Cited by 8,865 · see citing works
UniProt: the Universal Protein knowledgebase
Apweiler, R., Bairoch, A., Wu, C. H., Barker, W. C., Boeckmann, B., Ferro, S., Gasteiger, E., Huang, H.-Z., Lopez, R., Magrane, M., et al. (2004) · Nucleic Acids Res. · doi:10.1093/nar/gkh131 · Cited by 8,795 · see citing works
PubChem Substance and Compound databases
Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv951 · Cited by 5,436 · 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
UniProt: the universal protein knowledgebase
The UniProt Consortium (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1099 · Cited by 4,436 · see citing works
Caspi, R., Billington, R., Ferrer, L., Foerster, H., Fulcher, C. A., Keseler, I. M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1164 · Cited by 3,395 · see citing works
HMDB: the Human Metabolome Database
Wishart, D. S., Tzur, D., Knox, C., Eisner, R., Guo, A. C., Young, N., Cheng, D., Jewell, K., Arndt, D., Sawhney, S., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkl923 · Cited by 3,219 · see citing works
Self-citationHMDB 3.0—The Human Metabolome Database in 2013
Wishart, D. S., Jewison, T., Guo, A. C., Wilson, M., Knox, C., Liu, Y., Djoumbou-Feunang, Y., Mandal, R., Aziat, F., Dong, E., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1065 · Cited by 2,845 · see citing works
Self-citationMassBank: 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
METLIN—a metabolite mass spectral database
Smith, C. A., Maille, G. O., Want, E. J., Qin, C., Trauger, S. A., Brandon, T. R., Custodio, D. E., Abagyan, R., & Siuzdak, G. (2005) · Therapeutic Drug Monitoring · doi:10.1097/01.ftd.0000179845.53213.39 · Cited by 2,226 · see citing works
DrugBank 4.0: shedding new light on drug metabolism
Law, V., Knox, C., Djoumbou-Feunang, Y., Jewison, T., Guo, A. C., Liu, Y., Maciejewski, A., Arndt, D., Wilson, M., Neveu, V., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1068 · Cited by 1,995 · see citing works
Self-citationHMDB: a knowledgebase for the human metabolome
Wishart, D. S., Knox, C., Guo, A. C., Eisner, R., Young, N., Gautam, B., Hau, D., Psychogios, N., Dong, E., Bouatra, S., et al. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn810 · Cited by 1,874 · see citing works
Self-citationExpansion of the Gene Ontology knowledgebase and resources
The Gene Ontology Consortium (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1108 · Cited by 1,850 · 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
Self-citationUlrich, E. L., Akutsu, H., Doreleijers, J. F., Harano, Y., Ioannidis, Y., Lin, J. C., Livny, M., Mading, S., Maziuk, D., Miller, Z., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm957 · Cited by 1,472 · see citing works
Bouatra, S., Aziat, F., Mandal, R., Guo, A. C., Wilson, M. R., Knox, C., Bjorndahl, T. C., Krishnamurthy, R., Saleem, F., Liu, P., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073076 · Cited by 1,457 · see citing works
Self-citationLMSD: LIPID MAPS structure database
Sud, M., Fahy, E., Cotter, D., Brown, A., Dennis, E. A., Glass, C. K., Merrill, A. H., Murphy, R. C., Raetz, C. R. H., Russell, D. W., et al. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl838 · Cited by 1,388 · see citing works
The Reactome pathway Knowledgebase
Fabregat, A., Sidiropoulos, K., Garapati, P., Gillespie, M., Hausmann, K., Haw, R., Jassal, B., Jupe, S., Korninger, F., McKay, S., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1351 · Cited by 1,201 · see citing works
ChEBI in 2016: Improved services and an expanding collection of metabolites
Hastings, J., Owen, G., Dekker, A., Ennis, M., Kale, N., Muthukrishnan, V., Turner, S., Swainston, N., Mendes, P., & Steinbeck, C. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1031 · Cited by 1,083 · see citing works
Sud, M., Fahy, E., Cotter, D., Azam, K., Vadivelu, I., Burant, C., Edison, A. S., Fiehn, O., Higashi, R. M., Nair, K. S., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1042 · Cited by 1,008 · see citing works
Metabolomics: applications to food science and nutrition research
Wishart, D. S. (2008) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2008.03.003 · Cited by 672 · see citing works
Self-citationBenson, D. A., Cavanaugh, M. L., Clark, K., Karsch‐Mizrachi, I., Lipman, D. J., Ostell, J., & Sayers, E. W. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1070 · Cited by 629 · 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
SMPDB 2.0: Big Improvements to the Small Molecule Pathway Database
Jewison, T., Su, Y., Disfani, F. M., Liang, Y., Knox, C., Maciejewski, A., Poelzer, J., Huynh, J., Zhou, Y. L., Arndt, D., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1067 · Cited by 476 · see citing works
Self-citationAllen, F., Pon, A., Wilson, M., Greiner, R., & Wishart, D. S. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku436 · Cited by 462 · see citing works
Self-citationSMPDB: The Small Molecule Pathway Database
Frolkis, A., Knox, C., Lim, E. L., Jewison, T., Law, V., Hau, D., Liu, P., Gautam, B., Ly, S., Guo, A. C., et al. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp1002 · Cited by 376 · see citing works
Self-citationThe University of Minnesota Biocatalysis/Biodegradation Database: improving public access
Gao, J., Ellis, L. B. M., & Wackett, L. P. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp771 · Cited by 347 · see citing works
Dame, Z. T., Aziat, F., Mandal, R., Krishnamurthy, R., Bouatra, S., Borzouie, S., Guo, A. C., Sajed, T., Deng, L., Lin, H., et al. (2015) · Metabolomics · doi:10.1007/s11306-015-0840-5 · Cited by 275 · see citing works
Self-citationCurrent Progress in computational metabolomics
Wishart, D. S. (2007) · Briefings in Bioinformatics · doi:10.1093/bib/bbm030 · Cited by 234 · see citing works
Self-citationJeffryes, J. G., Colastani, R. L., Elbadawi-Sidhu, M., Kind, T., Niehaus, T. D., Broadbelt, L. J., Hanson, A. D., Fiehn, O., Tyo, K. E. J., & Henry, C. S. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0087-1 · Cited by 210 · see citing works
MetaboLights: An Open‐Access Database Repository for Metabolomics Data
Kale, N., Haug, K., Conesa, P., Jayseelan, K., Moreno, P. A., Rocca‐Serra, P., Nainala, V. C., Spicer, R., Williams, M., Li, X., et al. (2016) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi1413s53 · Cited by 187 · see citing works
mzML: A single, unifying data format for mass spectrometer output
Deutsch, E. W. (2008) · PROTEOMICS · doi:10.1002/pmic.200890049 · Cited by 170 · see citing works
Metabolomic Signatures for Drug Response Phenotypes: Pharmacometabolomics Enables Precision Medicine
Kaddurah-Daouk, R. F., & Weinshilboum, R. M. (2015) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.134 · Cited by 162 · see citing works
Dias, D. A., Jones, O. A. H., Beale, D. J., Boughton, B. A., Benheim, D., Kouremenos, K. A., Wolfender, J., & Wishart, D. S. (2016) · Metabolites · doi:10.3390/metabo6040046 · Cited by 160 · see citing works
Self-citationAllen, F., Pon, A., Greiner, R., & Wishart, D. S. (2016) · Analytical Chemistry · doi:10.1021/acs.analchem.6b01622 · Cited by 159 · see citing works
Self-citationLiu, Y., Liang, Y., & Wishart, D. S. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv383 · Cited by 148 · see citing works
Self-citationNeveu, V., Moussy, A., Rouaix, H., Wedekind, R., Pon, A., Knox, C., Wishart, D. S., & Scalbert, A. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw980 · Cited by 148 · see citing works
Self-citationGenetics of human metabolism: an update
Kastenmüller, G., Raffler, J., Gieger, C., & Suhre, K. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv263 · Cited by 128 · see citing works
SPLASH, a hashed identifier for mass spectra
Wohlgemuth, G., Mehta, S. S., Mejia, R. F., Neumann, S., Pedrosa, D., Pluskal, T., Schymanski, E., Willighagen, E., Wilson, M., Wishart, D. S., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3689 · Cited by 70 · see citing works
Self-citationLei, Z., Li, J., Qiu, F., Zhang, H., Huhman, D. V., Zhou, Z., & Sumner, L. W. (2015) · Analytical Chemistry · doi:10.1021/acs.analchem.5b01559 · Cited by 55 · see citing works
Proteomics and the Human Metabolome Project
Wishart, D. S. (2007) · Expert Review of Proteomics · doi:10.1586/14789450.4.3.333 · Cited by 45 · see citing works
Self-citationPon, A., Jewison, T., Su, Y., Liang, Y., Knox, C., Maciejewski, A., Wilson, M., & Wishart, D. S. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv399 · Cited by 33 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Identification of bioactive metabolites using activity metabolomics
Rinschen, M. M., Ivanisevic, J., Giera, M., & Siuzdak, G. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0108-4 · Cited by 1,008 · see citing works
BIOPEP-UWM Database of Bioactive Peptides: Current Opportunities
Minkiewicz, P., Iwaniak, A., & Darewicz, M. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20235978 · Cited by 958 · see citing works
Using machine learning approaches for multi-omics data analysis: A review
Reel, P. S., Reel, S., Pearson, E., Trucco, E., & Jefferson, E. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107739 · Cited by 903 · see citing works
Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra
Dührkop, K., Nothias, L.-F., Fleischauer, M., Reher, R., Ludwig, M., Hoffmann, M. A., Petras, D., Gerwick, W. H., Rousu, J., Dorrestein, P. C., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-0740-8 · Cited by 860 · see citing works
The gut-brain axis: how microbiota and host inflammasome influence brain physiology and pathology
Rutsch, A., Kantsjö, J. B., & Ronchi, F. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.604179 · Cited by 821 · see citing works
Systems biology and multi-omics integration: viewpoints from the metabolomics research community
Pinu, F. R., Beale, D. J., Paten, A. M., Kouremenos, K., Swarup, S., Schirra, H. J., & Wishart, D. (2019) · Metabolites · doi:10.3390/metabo9040076 · Cited by 723 · see citing works
Self-citationComprehensive quantification of fuel use by the failing and nonfailing human heart
Murashige, D., Jang, C., Neinast, M., Edwards, J. J., Cowan, A., Hyman, M. C., Rabinowitz, J. D., Frankel, D. S., & Arany, Z. (2020) · Science · doi:10.1126/science.abc8861 · Cited by 580 · see citing works
Microbiota-derived metabolites as drivers of gut–brain communication
Ahmed, H., Leyrolle, Q., Koistinen, V., Kärkkäinen, O., Layé, S., Delzenne, N., & Hanhineva, K. (2022) · Gut Microbes · doi:10.1080/19490976.2022.2102878 · Cited by 462 · see citing works
High-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
Dan, Z., Mao, X., Liu, Q., Guo, M., Zhuang, Y., Liu, Z., Chen, K., Chen, J., Xu, R., Tang, J., et al. (2020) · Gut Microbes · doi:10.1080/19490976.2020.1747329 · Cited by 350 · see citing works
Chen, L., Zhernakova, D. V., Kurilshikov, A., Andreu-Sánchez, S., Wang, D., Augustijn, H. E., Vich Vila, A., Study, L. C., Weersma, R. K., Medema, M. H., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-02014-8 · Cited by 327 · see citing works
Mass spectrometry searches using MASST
Wang, M., Jarmusch, A. K., Vargas, F., Aksenov, A. A., Gauglitz, J. M., Weldon, K., Petras, D., da Silva, R., Quinn, R., Melnik, A. V., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0375-9 · Cited by 281 · 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
Determinants of accelerated metabolomic and epigenetic aging in a UK cohort
Robinson, O., Chadeau Hyam, M., Karaman, I., Climaco Pinto, R., Ala-Korpela, M., Handakas, E., Fiorito, G., Gao, H., Heard, A., Jarvelin, M., et al. (2020) · Aging Cell · doi:10.1111/acel.13149 · Cited by 205 · see citing works
Moretti, S., Tran, V. D. T., Mehl, F., Ibberson, M., & Pagni, M. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa992 · Cited by 177 · see citing works
Systems protobiology: origin of life in lipid catalytic networks
Lancet, D., Zidovetzki, R., & Markovitch, O. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2018.0159 · Cited by 182 · see citing works
Applications of metabolomics to study cancer metabolism
Kaushik, A. K., & DeBerardinis, R. J. (2018) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2018.04.009 · Cited by 179 · see citing works
Gault, J., Liko, I., Landreh, M., Shutin, D., Bolla, J. R., Jefferies, D., Agasid, M., Yen, H.-Y., Ladds, M. J. G. W., Lane, D. P., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0821-0 · Cited by 171 · see citing works
Zhou, Y., Hou, Y., Shen, J., Mehra, R., Kallianpur, A., Culver, D. A., Gack, M. U., Farha, S., Zein, J., Comhair, S., et al. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000970 · Cited by 180 · see citing works
Identifying drug targets in tissues and whole blood with thermal-shift profiling
Perrin, J., Werner, T., Kurzawa, N., Rutkowska, A., Childs, D. D., Kalxdorf, M., Poeckel, D., Stonehouse, E., Strohmer, K., Heller, B., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0388-4 · Cited by 178 · see citing works
Perez De Souza, L., Alseekh, S., Brotman, Y., & Fernie, A. R. (2020) · Expert Review of Proteomics · doi:10.1080/14789450.2020.1766975 · Cited by 145 · see citing works
Spichak, S., Bastiaanssen, T. F. S., Berding, K., Vlckova, K., Clarke, G., Dinan, T. G., & Cryan, J. F. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.02.044 · Cited by 127 · see citing works
Finger sweat analysis enables short interval metabolic biomonitoring in humans
Brunmair, J., Gotsmy, M., Niederstaetter, L., Neuditschko, B., Bileck, A., Slany, A., Feuerstein, M. L., Langbauer, C., Janker, L., Zanghellini, J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26245-4 · Cited by 96 · see citing works
Bioactivity descriptors for uncharacterized chemical compounds
Bertoni, M., Duran-Frigola, M., Badia-i-Mompel, P., Pauls, E., Orozco-Ruiz, M., Guitart-Pla, O., Alcalde, V., Diaz, V. M., Berenguer-Llergo, A., Brun-Heath, I., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24150-4 · Cited by 92 · see citing works
Metabolomics of Childhood Exposure to Perfluoroalkyl Substances: A Cross-Sectional Study
Kingsley, S. L., Walker, D. I., Calafat, A. M., Chen, A., Papandonatos, G. D., Xu, Y., Jones, D. P., Lanphear, B. P., Pennell, K. D., & Braun, J. M. (2019) · Metabolomics · doi:10.1007/s11306-019-1560-z · Cited by 90 · see citing works
Rapid growth and metabolism of uropathogenic Escherichia coli in relation to urine composition
Reitzer, L., & Zimmern, P. (2019) · Clinical Microbiology Reviews · doi:10.1128/cmr.00101-19 · Cited by 85 · see citing works
Structural basis of BRCC36 function in DNA repair and immune regulation
Rabl, J., Bunker, R. D., Schenk, A. D., Cavadini, S., Gill, M. E., Abdulrahman, W., Andrés-Pons, A., Luijsterburg, M. S., Ibrahim, A. F. M., Branigan, E., et al. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.06.002 · Cited by 84 · see citing works
From QCEIMS to QCxMS: A Tool to Routinely Calculate CID Mass Spectra Using Molecular Dynamics
Koopman, J., & Grimme, S. (2021) · Journal of the American Society for Mass Spectrometry · doi:10.1021/jasms.1c00098 · Cited by 80 · see citing works
Water and Life: The Medium is the Message
Frenkel-Pinter, M., Rajaei, V., Glass, J. B., Hud, N. V., & Williams, L. D. (2021) · Journal of Molecular Evolution · doi:10.1007/s00239-020-09978-6 · Cited by 71 · see citing works
Combining CRISPRi and metabolomics for functional annotation of compound libraries
Anglada-Girotto, M., Handschin, G., Ortmayr, K., Campos, A. I., Gillet, L., Manfredi, P., Mulholland, C. V., Berney, M., Jenal, U., Picotti, P., et al. (2022) · Nature Chemical Biology · doi:10.1038/s41589-022-00970-3 · Cited by 60 · see citing works
Biomedical data, computational methods and tools for evaluating disease–disease associations
Xiang, J., Zhang, J., Zhao, Y., Wu, F.-X., & Li, M. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac006 · Cited by 49 · see citing works
Cheng, Y., Schlosser, P., Hertel, J., Sekula, P., Oefner, P. J., Spiekerkoetter, U., Mielke, J., Freitag, D. F., Schmidts, M., GCKD Investigators, et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20877-8 · Cited by 37 · see citing works
Imaging of glucose metabolism by 13C-MRI distinguishes pancreatic cancer subtypes in mice
Kishimoto, S., Brender, J. R., Crooks, D. R., Matsumoto, S., Seki, T., Oshima, N., Merkle, H., Lin, P., Reed, G., Chen, A. P., et al. (2019) · eLife · doi:10.7554/elife.46312 · Cited by 41 · see citing works
Multiomics Implicate Gut Microbiota in Altered Lipid and Energy Metabolism in Parkinson’s Disease
Pereira, P. A. B., Trivedi, D. K., Silverman, J., Duru, I. C., Paulin, L., Auvinen, P., & Scheperjans, F. (2022) · npj Parkinson s Disease · doi:10.1038/s41531-022-00300-3 · Cited by 40 · see citing works
Preoperative Metabolic Signatures of Prostate Cancer Recurrence Following Radical Prostatectomy
Clendinen, C. S., Gaul, D. A., Monge, M. E., Arnold, R. S., Edison, A. S., Petros, J. A., & Fernández, F. M. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00926 · Cited by 39 · see citing works
Wang, H., Wang, Y., Li, X., Deng, X., Kong, Y., Wang, W., & Zhou, Y. (2022) · Cardiovascular Diabetology · doi:10.1186/s12933-022-01716-0 · Cited by 34 · see citing works
Daly, G., Ghini, V., Adessi, A., Fondi, M., Buchan, A., & Viti, C. (2022) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuac020 · Cited by 30 · see citing works
Matzko, R., & Konur, S. (2024) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-024-00455-4 · Cited by 28 · see citing works
Metabolomic Biomarkers in Polycystic Ovary Syndrome: A Review of the Evidence
Alesi, S., Ghelani, D., & Mousa, A. (2021) · Seminars in Reproductive Medicine · doi:10.1055/s-0041-1729841 · Cited by 16 · see citing works
Fasciotti, M., Souza, G. H. M. F., Astarita, G., Costa, I. C. R., Monteiro, T. V. C., Teixeira, C. M. L. L., Eberlin, M. N., & Sarpal, A. S. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.9b02172 · Cited by 15 · see citing works
Mangoba, M. A. A., & Alvindia, D. D. G. (2019) · Journal of Biologically Active Products from Nature · doi:10.1080/22311866.2019.1702898 · Cited by 8 · see citing works
Metabolomic alterations in mammary glands from pubertal mice fed a high-fat diet
Yan, L., Rust, B. M., Sundaram, S., & Bukowski, M. R. (2023) · Nutrition and Metabolic Insights · doi:10.1177/11786388221148858 · Cited by 6 · see citing works
- Authors
- Wishart, D. S., Feunang, Y. D., Marcu, A., Guo, A. C., Liang, K., Vázquez-Fresno, R., Sajed, T., Johnson, D., Li, C., Karu, N., Sayeeda, Z., Lo, E., Assempour, N., Berjanskii, M., Singhal, S., Arndt, D., Liang, Y., Badran, H., Grant, J., Serra-Cayuela, A., Liu, Y., Mandal, R., Neveu, V., Pon, A., Knox, C., Wilson, M., Manach, C., & Scalbert, A.
- Affiliations
- University of Alberta (CA)
- Centre international de recherche sur le cancer (FR)
- Université Clermont Auvergne (FR)
- Corresponding author
- Wishart, David S
- ORCID
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-11-11
- Volume
- 46
- Issue
- D1
- Pages
- D608-D617
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC5753273
- OpenAlex
- W2767683865
- Cited by
- 3,438
- References
- 42
- Citations by year
- 2026: 156 · 2025: 227 · 2024: 279 · 2023: 355 · 2022: 583 · 2021: 675 · 2020: 585 · 2019: 424 · 2018: 149 · 2017: 2 · 2014: 2
- FWCI
- 129.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by-nc)
- Funders
- World Health Organization
- Genome Canada
- Alberta Innovates - Health Solutions
- Alberta Innovates
- Genome Alberta
- Canadian Institutes of Health Research
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Clinical Biochemistry › Metabolism and Genetic Disorders
- Keywords
- HMDB, human metabolites, metabolomics databases, spectral data, MS/MS spectra, metabolic pathways, chemotaxonomy, pharmacometabolomics, metabolite identification, disease association
- MeSH terms
- Humans, Gas Chromatography-Mass Spectrometry, User-Computer Interface, Databases, Factual, Nuclear Magnetic Resonance, Biomolecular, Tandem Mass Spectrometry, Metabolic Networks and Pathways, Metabolomics, Metabolome, Databases, Chemical
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review