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HMDB: the Human Metabolome Database
Open accessAbstract
The Human Metabolome Database (HMDB) is currently the most complete and comprehensive curated collection of human metabolite and human metabolism data in the world. It contains records for more than 2180 endogenous metabolites with information gathered from thousands of books, journal articles and electronic databases. In addition to its comprehensive literature-derived data, the HMDB also contains an extensive collection of experimental metabolite concentration data compiled from hundreds of mass spectra (MS) and Nuclear Magnetic resonance (NMR) metabolomic analyses performed on urine, blood and cerebrospinal fluid samples. This is further supplemented with thousands of NMR and MS spectra collected on purified, reference metabolites. Each metabolite entry in the HMDB contains an average of 90 separate data fields including a comprehensive compound description, names and synonyms, structural information, physico-chemical data, reference NMR and MS spectra, biofluid concentrations, disease associations, pathway information, enzyme data, gene sequence data, SNP and mutation data as well as extensive links to images, references and other public databases. Extensive searching, relational querying and data browsing tools are also provided. The HMDB is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. The HMDB is available at: www.hmdb.ca.
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Cites
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302
Cited by
Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool
Chen, E. Y., Tan, C. M., Kou, Y., Duan, Q., Wang, Z., Meirelles, G. V., Clark, N. R., & Ma’ayan, A. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-128 · Cited by 7,814
Turnbaugh, P. J., Ley, R. E., Hamady, M., Fraser-Liggett, C. M., Knight, R., & Gordon, J. I. (2007) · Nature · doi:10.1038/nature06244 · Cited by 6,091
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
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
Self-citationMetabolomics: beyond biomarkers and towards mechanisms
Johnson, C. H., Ivanisevic, J., & Siuzdak, G. (2016) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2016.25 · Cited by 2,829
Towards a knowledge-based Human Protein Atlas
Uhlen, M., Oksvold, P., Fagerberg, L., Lundberg, E., Jonasson, K., Forsberg, M., Zwahlen, M., Kampf, C., Wester, K., Hober, S., et al. (2010) · Nature Biotechnology · doi:10.1038/nbt1210-1248 · Cited by 2,440
Gut microbiota-derived metabolites as key actors in inflammatory bowel disease
Lavelle, A., & Sokol, H. (2020) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-019-0258-z · Cited by 2,015
Gut microbiome structure and metabolic activity in inflammatory bowel disease
Franzosa, E. A., Sirota-Madi, A., Avila-Pacheco, J., Fornelos, N., Haiser, H. J., Reinker, S., Vatanen, T., Hall, A. B., Mallick, H., McIver, L. J., et al. (2018) · Nature Microbiology · doi:10.1038/s41564-018-0306-4 · Cited by 1,995
De Filippis, F., Pellegrini, N., Vannini, L., Jeffery, I. B., La Storia, A., Laghi, L., Serrazanetti, D. I., Di Cagno, R., Ferrocino, I., Lazzi, C., et al. (2016) · Gut · doi:10.1136/gutjnl-2015-309957 · Cited by 1,638
Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum
Gieger, C., Geistlinger, L., Altmaier, E., Hrabé de Angelis, M., Kronenberg, F., Meitinger, T., Mewes, H.-W., Wichmann, H.-E., Weinberger, K. M., Adamski, J., et al. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000282 · Cited by 737
Fröhlich, E. E., Farzi, A., Mayerhofer, R., Reichmann, F., Jačan, A., Wagner, B., Zinser, E., Bordag, N., Magnes, C., Fröhlich, E., et al. (2016) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2016.02.020 · Cited by 720
Krishnan, S., Ding, Y., Saedi, N., Choi, M., Sridharan, G. V., Sherr, D. H., Yarmush, M. L., Alaniz, R. C., Jayaraman, A., & Lee, K. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.109 · Cited by 679
Network-based prediction of human tissue-specific metabolism
Shlomi, T., Cabili, M. N., Herrgård, M. J., Palsson, B. Ø., & Ruppin, E. (2008) · Nature Biotechnology · doi:10.1038/nbt.1487 · Cited by 637
Herrgård, M. J., Swainston, N., Dobson, P., Dunn, W. B., Arga, K. Y., Arvas, M., Blüthgen, N., Borger, S., Costenoble, R., Heinemann, M., et al. (2008) · Nature Biotechnology · doi:10.1038/nbt1492 · Cited by 588
Scalbert, A., Brennan, L., Fiehn, O., Hankemeier, T., Kristal, B. S., van Ommen, B., Pujos-Guillot, E., Verheij, E., Wishart, D., & Wopereis, S. (2009) · Metabolomics · doi:10.1007/s11306-009-0168-0 · Cited by 565
Self-citationThe Edinburgh human metabolic network reconstruction and its functional analysis
Ma, H., Sorokin, A., Mazein, A., Selkov, A., Selkov, E., Demin, O., & Goryanin, I. (2007) · Molecular Systems Biology · doi:10.1038/msb4100177 · Cited by 435
Metabolic signatures of exercise in human plasma
Lewis, G. D., Farrell, L., Wood, M. J., Martinovic, M., Arany, Z., Rowe, G. C., Souza, A., Cheng, S., McCabe, E. L., Yang, E., et al. (2010) · Science Translational Medicine · doi:10.1126/scitranslmed.3001006 · Cited by 432
Jarada, T. N., Rokne, J. G., & Alhajj, R. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00450-7 · Cited by 384
Livestock metabolomics and the livestock metabolome: A systematic review
Goldansaz, S. A., Guo, A. C., Sajed, T., Steele, M. A., Plastow, G. S., & Wishart, D. S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177675 · Cited by 379
Self-citationImpact of Dietary Resistant Starch on the Human Gut Microbiome, Metaproteome, and Metabolome
Maier, T. V., Lucio, M., Lee, L. H., VerBerkmoes, N. C., Brislawn, C. J., Bernhardt, J., Lamendella, R., McDermott, J. E., Bergeron, N., Heinzmann, S. S., et al. (2017) · mBio · doi:10.1128/mbio.01343-17 · Cited by 316
“KEGG (28) (Kyoto Encyclopedia of Genes and Genomes), HMDB (45) (Human Metabolome Database), and”
Vulevic, J., Juric, A., Walton, G. E., Claus, S. P., Tzortzis, G., Toward, R. E., & Gibson, G. R. (2015) · British Journal of Nutrition · doi:10.1017/s0007114515001889 · Cited by 317
Iron traffics in circulation bound to a siderocalin (Ngal)–catechol complex
Bao, G., Clifton, M., Hoette, T. M., Mori, K., Deng, S.-X., Qiu, A., Viltard, M., Williams, D., Paragas, N., Leete, T., et al. (2010) · Nature Chemical Biology · doi:10.1038/nchembio.402 · Cited by 307
Deep learning based multimodal biomedical data fusion: an overview and comparative review
Duan, J., Xiong, J., Li, Y., & Ding, W. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102536 · Cited by 284
Elgogary, A., Xu, Q., Poore, B., Alt, J., Zimmermann, S. C., Zhao, L., Fu, J., Chen, B., Xia, S., Liu, Y., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1611406113 · Cited by 234
Metabolomics View on Gut Microbiome Modulation by Polyphenol-Rich Foods
Moco, S., Martin, F.-P. J., & Rezzi, S. (2012) · Journal of Proteome Research · doi:10.1021/pr300581s · Cited by 234
MyCompoundID: using an evidence-based metabolome library for metabolite identification
Li, L., Li, R., Zhou, J., Zuniga, A., Stanislaus, A. E., Wu, Y., Huan, T., Zheng, J., Shi, Y., Wishart, D. S., et al. (2013) · Analytical Chemistry · doi:10.1021/ac400099b · Cited by 229
Self-citationLussey-Lepoutre, C., Hollinshead, K. E. R., Ludwig, C., Menara, M., Morin, A., Castro-Vega, L.-J., Parker, S. J., Janin, M., Martinelli, C., Ottolenghi, C., et al. (2015) · Nature Communications · doi:10.1038/ncomms9784 · Cited by 227
Pereira, S., Kildegaard, H. F., & Andersen, M. R. (2018) · Biotechnology Journal · doi:10.1002/biot.201700499 · Cited by 214
Metabolomics of Human Brain Aging and Age-Related Neurodegenerative Diseases
Jové, M., Portero-Otín, M., Naudí, A., Ferrer, I., & Pamplona, R. (2014) · Journal of Neuropathology & Experimental Neurology · doi:10.1097/nen.0000000000000091 · Cited by 195
Formulating genome‐scale kinetic models in the post‐genome era
Jamshidi, N., & Palsson, B. Ø. (2008) · Molecular Systems Biology · doi:10.1038/msb.2008.8 · Cited by 180
A landscape of metabolic variation across tumor types
Reznik, E., Luna, A., Aksoy, B. A., Liu, E. M., La, K., Ostrovnaya, I., Creighton, C. J., Hakimi, A. A., & Sander, C. (2018) · Cell Systems · doi:10.1016/j.cels.2017.12.014 · Cited by 172
Clinical and biochemical features of different molecular etiologies of familial chylomicronemia
Hegele, R. A., Berberich, A. J., Ban, M. R., Wang, J., Digenio, A., Alexander, V. J., D'Erasmo, L., Arca, M., Jones, A., Bruckert, E., et al. (2018) · Journal of Clinical Lipidology · doi:10.1016/j.jacl.2018.03.093 · Cited by 165
Web-based resources for mass-spectrometry-based metabolomics: a user’s guide
Tohge, T., & Fernie, A. R. (2009) · Phytochemistry · doi:10.1016/j.phytochem.2009.02.004 · Cited by 152
Discovery of phosphatidylcholines and sphingomyelins as biomarkers for ovarian endometriosis
Vouk, K., Hevir, N., Ribič-Pucelj, M., Haarpaintner, G., Scherb, H., Osredkar, J., Möller, G., Prehn, C., Rižner, T. L., & Adamski, J. (2012) · Human Reproduction · doi:10.1093/humrep/des152 · Cited by 143
Gralka, E., Luchinat, C., Tenori, L., Ernst, B., Thurnheer, M., & Schultes, B. (2015) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.115.110536 · Cited by 128
Tottey, W., Feria-Gervasio, D., Gaci, N., Laillet, B., Pujos, E., Martin, J.-F., Sebedio, J.-L., Sion, B., Jarrige, J.-F., Alric, M., et al. (2017) · Journal of Neurogastroenterology and Motility · doi:10.5056/jnm16042 · Cited by 125
Edmands, W. M. B., Beckonert, O. P., Stella, C., Campbell, A., Lake, B. G., Lindon, J. C., Holmes, E., & Gooderham, N. J. (2011) · Journal of Proteome Research · doi:10.1021/pr200326k · Cited by 119
Pasikanti, K. K., Esuvaranathan, K., Hong, Y., Ho, P. C., Mahendran, R., Raman Nee Mani, L., Chiong, E., & Chan, E. C. Y. (2013) · Journal of Proteome Research · doi:10.1021/pr4000448 · Cited by 113
Kleensang, A., Vantangoli, M. M., Odwin-DaCosta, S., Andersen, M. E., Boekelheide, K., Bouhifd, M., Fornace, A. J., Li, H.-H., Livi, C. B., Madnick, S., et al. (2016) · Scientific Reports · doi:10.1038/srep28994 · Cited by 114
Current and future perspectives of functional metabolomics in disease studies–A review
Yan, M., & Xu, G. (2018) · Analytica Chimica Acta · doi:10.1016/j.aca.2018.04.006 · Cited by 105
Harmonization of quality metrics and power calculation in multi-omic studies
Tarazona, S., Balzano-Nogueira, L., Gómez-Cabrero, D., Schmidt, A., Imhof, A., Hankemeier, T., Tegnér, J., Westerhuis, J. A., & Conesa, A. (2020) · Nature Communications · doi:10.1038/s41467-020-16937-8 · Cited by 105
The Importance of the Biological Impact of Exposure to the Concept of the Exposome
Dennis, K. K., Auerbach, S. S., Balshaw, D. M., Cui, Y., Fallin, M. D., Smith, M. T., Spira, A., Sumner, S., & Miller, G. W. (2016) · Environmental Health Perspectives · doi:10.1289/ehp140 · Cited by 103
GAM: a web-service for integrated transcriptional and metabolic network analysis
Sergushichev, A. A., Loboda, A. A., Jha, A. K., Vincent, E. E., Driggers, E. M., Jones, R. G., Pearce, E. J., & Artyomov, M. N. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw266 · Cited by 94
A role for cytosolic fumarate hydratase in urea cycle metabolism and renal neoplasia
Adam, J., Yang, M., Bauerschmidt, C., Kitagawa, M., O’Flaherty, L., Maheswaran, P., Özkan, G., Sahgal, N., Baban, D., Kato, K., et al. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.04.006 · Cited by 97
Network Medicine in Pathobiology
Lee, L. Y.-H., & Loscalzo, J. (2019) · The American Journal of Pathology · doi:10.1016/j.ajpath.2019.03.009 · Cited by 95
Gut microbiota modulate the metabolism of brown adipose tissue in mice
Mestdagh, R., Dumas, M.-E., Rezzi, S., Kochhar, S., Holmes, E., Claus, S. P., & Nicholson, J. K. (2012) · Journal of Proteome Research · doi:10.1021/pr200938v · Cited by 97
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
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
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
Tsugawa, H., Ikeda, K., Tanaka, W., Senoo, Y., Arita, M., & Arita, M. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0205-3 · Cited by 82
Rasmussen, L. G., Winning, H., Savorani, F., Toft, H., Larsen, T. M., Dragsted, L. O., Astrup, A., & Engelsen, S. B. (2012) · Nutrients · doi:10.3390/nu4020112 · Cited by 81
Nikolic, S. B., Sharman, J. E., Adams, M. J., & Edwards, L. M. (2014) · Journal of Hypertension · doi:10.1097/hjh.0000000000000168 · Cited by 79
Towards nutrition with precision: unlocking biomarkers as dietary assessment tools
Cuparencu, C., Bulmuş-Tüccar, T., Stanstrup, J., La Barbera, G., Roager, H. M., & Dragsted, L. O. (2024) · Nature Metabolism · doi:10.1038/s42255-024-01067-y · Cited by 77
(2017) · PubMed Central · Cited by 76
Metabolic Profiling Identifies Phospholipids as Potential Serum Biomarkers for Schizophrenia
Wang, D., Cheng, S. L., Fei, Q., Gu, H., Raftery, D., Cao, B., Sun, X., Yan, J., Zhang, C., & Wang, J. (2019) · Psychiatry Research · doi:10.1016/j.psychres.2018.12.008 · Cited by 73
Adam, J., Brandmaier, S., Leonhardt, J., Scheerer, M. F., Mohney, R. P., Xu, T., Bi, J., Rotter, M., Troll, M., Chi, S., et al. (2016) · Diabetes · doi:10.2337/db16-0512 · Cited by 68
Narath, S. H., Mautner, S. I., Svehlikova, E., Schultes, B., Pieber, T. R., Sinner, F. M., Gander, E., Libiseller, G., Schimek, M. G., Sourij, H., et al. (2016) · PLOS ONE · doi:10.1371/journal.pone.0161425 · Cited by 65
Garali, I., Adanyeguh, I. M., Ichou, F., Perlbarg, V., Seyer, A., Colsch, B., Moszer, I., Guillemot, V., Durr, A., Mochel, F., et al. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx060 · Cited by 68
Mukherjee, K., Edgett, B. A., Burrows, H. W., Castro, C., Griffin, J. L., Schwertani, A. G., Gurd, B. J., & Funk, C. D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0092031 · Cited by 65
Gürdeniz, G., Kristensen, M., Skov, T., & Dragsted, L. O. (2012) · Metabolites · doi:10.3390/metabo2010077 · Cited by 61
Automatic 3D Nonlinear Registration of Mass Spectrometry Imaging and Magnetic Resonance Imaging Data
Abdelmoula, W. M., Regan, M. S., Lopez, B. G. C., Randall, E. C., Lawler, S., Mladek, A. C., Nowicki, M. O., Marin, B. M., Agar, J. N., Swanson, K. R., et al. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.9b00854 · Cited by 62
Tiziani, S., Kang, Y., Choi, J. S., Roberts, W., & Paternostro, G. (2011) · Nature Communications · doi:10.1038/ncomms1562 · Cited by 60
Human metabolic network reconstruction and its impact on drug discovery and development
Ma, H., & Goryanin, I. (2008) · Drug Discovery Today · doi:10.1016/j.drudis.2008.02.002 · Cited by 59
Organotypic 3D decellularized matrix tumor spheroids for high-throughput drug screening
Ferreira, L. P., Gaspar, V. M., Mendes, L., Duarte, I. F., & Mano, J. F. (2021) · Biomaterials · doi:10.1016/j.biomaterials.2021.120983 · Cited by 59
Kell, D. B. (2007) · IUBMB Life · doi:10.1080/15216540701694252 · Cited by 52
Continuous in vivo Metabolism by NMR
Judge, M. T., Wu, Y., Tayyari, F., Hattori, A., Glushka, J., Ito, T., Arnold, J., & Edison, A. S. (2019) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2019.00026 · Cited by 53
Harshman, S. W., Pitsch, R. L., Smith, Z. K., O’Connor, M. L., Geier, B. A., Qualley, A. V., Schaeublin, N. M., Fischer, M. V., Eckerle, J. J., Strang, A. J., et al. (2018) · PLOS ONE · doi:10.1371/journal.pone.0203133 · Cited by 51
Urinary metabolic phenotyping for Alzheimer’s disease
Kurbatova, N., Garg, M., Whiley, L., Chekmeneva, E., Jiménez, B., Gómez-Romero, M., Pearce, J., Kimhofer, T., D’Hondt, E., Soininen, H., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-78031-9 · Cited by 44
Self-citationImaging 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
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
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
Maurer, D., Koziel, J., Engelken, T., Cooper, V., & Funk, J. (2018) · Separations · doi:10.3390/separations5010018 · Cited by 27
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
Bio-engineered and native red blood cells from cord blood exhibit the same metabolomic profile
Darghouth, D., Giarratana, M.-C., Oliveira, L., Jolly, S., Marie, T., Boudah, S., Mario, N., Junot, C., Douay, L., & Romeo, P.-H. (2016) · Haematologica · doi:10.3324/haematol.2015.141465 · Cited by 10
Kennedy, L., Sandhu, J. K., Harper, M.-E., & Cuperlovic-Culf, M. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06051-1 · Cited by 4
Li, X., He, Z., Liu, A., Meng, F., Zhang, X., Li, N., Liu, H., Lu, Y., Wu, Z., Fan, H., et al. (2026) · Communications Biology · doi:10.1038/s42003-026-09523-z · Cited by 1
Metadata
- Authors
- Wishart, D. S., Tzur, D., Knox, C., Eisner, R., Guo, A. C., Young, N., Cheng, D., Jewell, K., Arndt, D., Sawhney, S., Fung, C., Nikolai, L., Lewis, M., Coutouly, M.-A., Forsythe, I., Tang, P., Shrivastava, S., Jeroncic, K., Stothard, P., Amegbey, G., Block, D., Hau, D. D., Wagner, J., Miniaci, J., Clements, M., Gebremedhin, M., Guo, N., Zhang, Y., Duggan, G. E., MacInnis, G. D., Weljie, A. M., Dowlatabadi, R., Bamforth, F., Clive, D., Greiner, R., Li, L., Marrie, T., Sykes, B. D., Vogel, H. J., & Querengesser, L.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-01-03
- Volume
- 35
- Issue
- Database
- Pages
- D521-D526
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2165681080
- Cited by
- 3,231
- References
- 1
- FWCI
- 44.54
- Type
- journal article
- Language
- en
- Open access
- gold
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