HMDB 4.0: the human metabolome database for 2018

David S. Wishart(University of Alberta), Yannick Djoumbou-Feunang, Ana Marcu, An Chi Guo, Kevin Y. H. Liang, Rosa Vázquez‐Fresno, Tanvir Sajed(University of Alberta), Daniel L. Johnson, Carin Li, Naama Karu, Zinat Sayeeda(University of Alberta), Elvis Lo, Nazanin Assempour, Mark Berjanskii, Sandeep K. Singhal, David Arndt, Yonjie Liang, Hasan Badran, Jason R. Grant, Arnau Serra-Cayuela, Yifeng Liu(University of Alberta), Rupa Mandal, Vanessa Neveu(Centre International de Recherche sur le Cancer), Allison Pon, Craig Knox, Michael Wilson, Claudine Manach(Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement), Augustin Scalbert(Centre International de Recherche sur le Cancer)
Nucleic Acids Research
October 23, 2017
Cited by 3,532Open Access
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Abstract

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.


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