About

The Human Metabolome Database (HMDB) is a freely available electronic database containing detailed information about small molecule metabolites found in the human body. It is intended to be used for applications in metabolomics, clinical chemistry, biomarker discovery and general education. The database is designed to contain or link three kinds of data: 1) chemical data, 2) clinical data, and 3) molecular biology/biochemistry data. The database currently contains nearly 2500 metabolite entries including both water-soluble and lipid soluble metabolites as well as metabolites that would be regarded as either abundant (> 1 uM) or relatively rare (< 1 nM). Additionally, approximately 5500 protein (and DNA) sequences are linked to these metabolite entries. Each MetaboCard entry contains more than 90 data fields with half of the information being devoted to chemical/clinical data and the other half devoted to enzymatic or biochemical data. Many data fields are hyperlinked to other databases (KEGG, PubChem, MetaCyc, ChEBI, PDB, Swiss-Prot, and GenBank) and a variety of structure and pathway viewing applets. The HMDB database supports extensive text, sequence, chemical structure and relational query searches. Two additional databases, DrugBank and FooDB are also part of the HMDB. DrugBank contains equivalent information on 1500 drugs while FooDB contains equivalent information on 3500 food components and food additives.

Openness

Freely available, but requires permission for commercial re-use or re-distribution:

HMDB is offered to the public as a freely available resource. Use and re-distribution of the data, in whole or in part, for commercial purposes requires explicit permission of the authors and explicit acknowledgment of the source material (HMDB) and the original publication (see below). We ask that users who download significant portions of the database cite the HMDB paper in any resulting publications.

A database which contains structures and annotations of biologically relevant metabolites from public repositories such as LIPID MAPS, ChEBI, HMDB, PubChem, and KEGG. Users can search for molecular structure based on substructure, text, or mass.

A manually curated database of small molecule metabolites found in or produced by Saccharomyces cerevisiae (also known as Baker's yeast and Brewer's yeast). This database covers metabolites described in textbooks, scientific journals, metabolic reconstructions and other electronic databases. YMDB contains metabolites arising from normal S. cerevisiae metabolism under defined laboratory conditions as well as metabolites generated by S. cerevisiae when used in baking and in the production of wines, beers and spirits. YMDB currently contains 2027 small molecules with 857 associated enzymes and 138 associated transporters. Each small molecule has 48 data fields describing the metabolite, its chemical properties and links to spectral and chemical databases. Each enzyme/transporter is linked to its associated metabolites and has 30 data fields describing both the gene and corresponding protein. Users may search through the YMDB using a variety of database-specific tools. The simple text query supports general text queries of the textual component of the database. By selecting either metabolites or proteins in the search for field it is possible to restrict the search and the returned results to only those data associated with metabolites or with proteins. Clicking on the Browse button generates a tabular synopsis of YMDB's content. This browser view allows users to casually scroll through the database or re-sort its contents. Clicking on a given MetaboCard button brings up the full data content for the corresponding metabolite. A complete explanation of all the YMDB fields and sources is available. Under the Search link users will find a number of search options listed in a pull-down menu. The Chem Query option allows users to draw (using MarvinSketch applet or a ChemSketch applet) or to type (SMILES string) a chemical compound and to search the YMDB for chemicals similar or identical to the query compound. The Advanced Search option supports a more sophisticated text search of the text portion of YMDB. The Sequence Search button allows users to conduct BLASTP (protein) sequence searches of all sequences contained in YMDB. Both single and multiple sequence (i.e. whole proteome) BLAST queries are supported. YMDB also supports a Data Extractor option that allows specific data fields or combinations of data fields to be searched and/or extracted. Spectral searches of YMDB's reference compound NMR and MS spectral data are also supported through its MS, MS/MS, GC/MS and NMR Spectra Search links. Users may download YMDB's complete textual data, chemical structures and sequence data by clicking on the Download button.

Database of known biochemical compounds collected from existing biochemical databases, as well as computationally generated human phase I and phase II metabolites of known compounds.

Database containing detailed information about small molecules produced by human microbiome. Provides metabolite data including structure, names, descriptions, chemical taxonomy, chemical ontology, physico-chemical data, spectra and contains detailed information about microbes that produce these chemicals, enzymatic reactions responsible for their production, bioactivity of chemicals and anatomical location of these chemicals and microbes. Many data fields in the database are hyperlinked to other databases including FooDB, HMDB, KEGG, PubChem, MetaCyc, ChEBI, UniProt, and GenBank. Database is FAIR compliant.The data in MiMeDB are released under the Creative Commons (CC) 4.0 License.

The ECMDB is an expertly curated database containing extensive metabolomic data and metabolic pathway diagrams about Escherichia coli (strain K12, MG1655). This database includes significant quantities of “original” data compiled by members of the Wishart laboratory as well as additional material derived from hundreds of textbooks, scientific journals, metabolic reconstructions and other electronic databases. Each metabolite is linked to more than 100 data fields describing the compound, its ontology, physical properties, reactions, pathways, references, external links and associated proteins or enzymes.

The Yeast Metabolome Database (YMDB) is a manually curated database of small molecule metabolites found in or produced by Saccharomyces cerevisiae (also known as Baker’s yeast and Brewer’s yeast).

A database which supports high-throughput NMR and MS approaches to the identification and quantification of metabolites present in biological samples. MMCD serves as a hub for information on small molecules of biological interest gathered from electronic databases and the scientific literature. Each metabolite entry in the MMCD is supported by information in separate data fields, which provide the chemical formula, names and synonyms, structure, physical and chemical properties, NMR and MS data on pure compounds under defined conditions where available, NMR chemical shifts determined by empirical and/or theoretical approaches, calculated isotopomer masses, information on the presence of the metabolite in different biological species, and links to images, references, and other public databases. The MMCD search engine supports versatile data mining and allows users to make individual or bulk queries on the basis of experimental NMR and/or MS data plus other criteria.

The METLIN (Metabolite and Tandem Mass Spectrometry) Database is a repository of metabolite information as well as tandem mass spectrometry data, providing public access to its comprehensive MS and MS/MS metabolite data. An annotated list of known metabolites and their mass, chemical formula, and structure are available, with each metabolite linked to external resources for further reference and inquiry.

Bacterial infection of crops is a major concern for farmers. Depending on the crop, yield losses can be significant. Management of bacterial disease requires applying antibiotic and copper chemical sprays, breeding for disease resistance, and inducing systematic acquired resistance, a type of whole plant immunity. Research using the model plant Arabidopsis has provided insights into plant-microbe interactions. The knowledge gained from researching Arabidopsis can be applied to crops. However, incomplete databases have limited the capabilities of identifying new signaling metabolites that set the basal defense state of Arabidopsis so that these plants can reduce bacterial infections. Analytical and natural products chemistry techniques in combination with omics-based methods are necessary for characterizing a larger suite of bioactive metabolites.

BridgeDb ID mapping database for metabolites, using HMDB 3.6 (26 August 2017), ChEBI 156, and Wikidata (8 October 2017) as data sources. No significant changes but just updated data.If you use this data in your research, please cite that data set, and the BridgeDb, ChEBI, and HMDB articles.

MMMDB, Mouse Multiple tissue Metabolome DataBase, is a freely available metabolomic database containing a collection of metabolites measured from multiple tissues from single mice. The datases are collected using a single instrument and not integrated from literatures, which is useful for capturing the holistic overview of large metabolomic pathway. Currently data from cerabra, cerebella, thymus, spleen, lung, liver, kidney, heart, pancreas, testis, and plasma are provided. Non-targeted analyses were performed by capillary electropherograms time-of-flight mass spectrometry (CE-TOFMS) and, therefore, both identified metabolites and unknown (without matched standard) peaks were uploaded to this database. Not only quantified concentration but also processed raw data such as electropherogram, mass spectrometry, and annotation (such as isotope and fragment) are provided.

BridgeDb ID mapping database for metabolites, using HMDB 3.6, ChEBI 146, and Wikidata (26 December 2016) as data sources. See the attached QC for more details on the changes.

Metabolites are referenced in spectral, structural and pathway databases with a diverse array of schemas, including various internal database identifiers and large tables of common name synonyms. Cross-linking metabolite identifiers is a required step for meta-analysis of metabolomic results across studies but made difficult due to the lack of a consensus identifier system. We have implemented metLinkR, an R package that leverages RefMet and RaMP-DB to automate and simplify cross-linking metabolite identifiers across studies and generating common names. MetLinkR accepts as input metabolite common names and identifiers from five different databases (HMDB, KEGG, ChEBI, LIPIDMAPS and PubChem) to exhaustively search for possible overlap in supplied metabolites from input data sets. In an example of 13 metabolomic data sets totaling 10,400 metabolites, metLinkR identified and provided common names for 1377 metabolites in common between at least 2 data sets in less than 18 min and produced standardized names for 74.4% of the input metabolites. In another example comprising five data sets with 3512 metabolites, metLinkR identified 715 metabolites in common between at least two data sets in under 12 min and produced standardized names for 82.3% of the input metabolites. Outputs of MetLInR include output tables and metrics allowing users to readily double check the mappings and to get an overview of chemical classes represented. Overall, MetLinkR provides a streamlined solution for a common task in metabolomic epidemiology and other fields that meta-analyze metabolomic data. The R package, vignette and source code are freely downloadable at https://github.com/ncats/metLinkR.

BridgeDb ID mapping database for metabolites, using HMDB 4.0 (Release of April 2020), ChEBI 186 (Release of April 2020), and Wikidata (10 June 2020) as data sources.

We report an analytical tool to facilitate metabolite identification based on an MS/MS spectral match of an unknown to a library of predicted MS/MS spectra of possible human metabolites. To construct the spectral library, the known endogenous human metabolites in the Human Metabolome Database (HMDB) (8,021 metabolites) and their predicted metabolic products via one metabolic reaction in the Evidence-based Metabolome Library (EML) (375,809 predicted metabolites) were subjected to in silico fragmentation to produce the predicted MS/MS spectra. This spectral library is hosted at the public MCID Web site (www.MyCompoundID.org), and a spectral search program, MCID MS/MS, has been developed to allow a user to search one or a batch of experimental MS/MS spectra against the library spectra for possible match(s). Using MS/MS spectra generated from standard metabolites and a human urine sample, we demonstrate that this tool is very useful for putative metabolite identification. It allows a user to narrow down many possible structures initially found by using an accurate mass search of an unknown metabolite to only one or a few candidates, thereby saving time and effort in selecting or synthesizing metabolite standard(s) for eventual positive metabolite identification.

Curated collection of human metabolite and human metabolism data which contains records for endogenous metabolites, with each metabolite entry containing detailed chemical, physical, biochemical, concentration, and disease information. This is further supplemented with thousands of NMR and MS spectra collected on purified reference metabolites.

The rapid development of metabolomics has significantly advanced health and disease related research. However, metabolite identification remains a major analytical challenge for untargeted metabolomics. While the use of collision cross-section (CCS) values obtained in ion mobility-mass spectrometry (IM-MS) effectively increases identification confidence of metabolites, it is restricted by the limited number of available CCS values for metabolites. Here, we demonstrated the use of a machine-learning algorithm called support vector regression (SVR) to develop a prediction method that utilized 14 common molecular descriptors to predict CCS values for metabolites. In this work, we first experimentally measured CCS values (ΩN2) of ∼400 metabolites in nitrogen buffer gas and used these values as training data to optimize the prediction method. The high prediction precision of this method was externally validated using an independent set of metabolites with a median relative error (MRE) of ∼3%, better than conventional theoretical calculation. Using the SVR based prediction method, a large-scale predicted CCS database was generated for 35 203 metabolites in the Human Metabolome Database (HMDB). For each metabolite, five different ion adducts in positive and negative modes were predicted, accounting for 176 015 CCS values in total. Finally, improved metabolite identification accuracy was demonstrated using real biological samples. Conclusively, our results proved that the SVR based prediction method can accurately predict nitrogen CCS values (ΩN2) of metabolites from molecular descriptors and effectively improve identification accuracy and efficiency in untargeted metabolomics. The predicted CCS database, namely, MetCCS, is freely available on the Internet.

A public repository of metabolite information as well as tandem mass spectrometry data is provided to facilitate metabolomics experiments. It contains structures and represents a data management system designed to assist in a broad array of metabolite research and metabolite identification. An annotated list of known metabolites and their mass, chemical formula, and structure are available. Each metabolite is linked to outside resources for further reference and inquiry. MS/MS data is also available on many of the metabolites.

Database of metabolite structures and annotations. The sources are from multiple existing metabolic and chemical databases such as HMDB, PubChem, CHEBI, BioCyc, and KEGG.