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.

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 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.

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.

The Human Metabolome Database (HMDB) is a database containing detailed information about small molecule metabolites found in the human body.It contains or links 1) chemical 2) clinical and 3) molecular biology/biochemistry data.

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.

Ranks of reference metabolites obtained for CoLaus pseudospectra, with UMDB as the spectral reference database, and with: peak- (P) or multiplet-mode (M), χ2- (X) or Z-scoring (Z), and without (C) or with decorrelation (D). Neighborhood parameter is δ = 0.03 in peak-mode, 0.01 in multiplet-mode. Shrinkage parameter is λ = 0.5 for decorrelation, 1 without. Reference metabolites are obtained from testable associations collected from targeted mGWAS [8, 18, 19]. Squares (□) indicate ranks not in the top 10% of UMDB listed metabolites, that is ranks greater than 18. Individual metabomatching figures including the eight highest ranked metabolite candidates for each pseudospectrum can be found in S1 Fig. Due to the differences in the peak and multiplet descriptions, the association of the HPD SNP with α-hydroxyisobutyrate is testable only in peak mode (S1F Fig), the association with 3-hydroxyisovalerate only in multiplet mode (S1G Fig).

BridgeDb ID mapping database for metabolites, using HMDB 4.0 (Release of 29-09-2018), ChEBI 169 (Release of 01 Nov. 2018), and Wikidata (26 Nov. 2018) as data sources.Several updated mappings, with additional test to eliminate double mappings in WikiPathways (from Wikidata to any other type of included database).This work was funded by ELIXIR, the research infrastructure for life-science data.

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.

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).

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 154, and Wikidata (26 August 2017) as data sources. Two significant changes: Mappings to the EPA CompTox Dashboard have been added (about 36 thousand) and it is using a newer HMDB 3.6 version with many more compounds. If you experience problems, please report on the project page. See the attached QC for more details on the changes.If you use this data in your research, please cite that data set, and the BridgeDb, ChEBI, and HMDB articles.

In 2014, EFSA has commissioned the compilation of a database specific for the pesticide residues including active substances and their metabolites, which comprises different genotoxicity endpoints, i.e. point mutations, structural and numerical chromosome aberrations, and DNA damage.

Data collection on individual genotoxicity studies has been retrieved from regulatory toxicological reports (Draft or Renewal Assessment Reports, i.e. DARs or RARs, respectively) as provided by the Rapporteur Member State (RMS) during the pesticide peer review process at European Level. The final EFSA conclusion on the overall genotoxic potential of active substance or metabolites taking into account all available information is not included in the database.

The database contains identity and genotoxicity information on more than 290 active substances and some of their metabolites.

The database represents a practical tool to complement in-silico tools i.e. QSAR (Quantitative structure–activity relationship models), grouping and read across for prediction of the genotoxicity hazard of the pesticides residues, and it supposes to enlarge the chemical domains for their application.

Format: xls; contact: data.collection@efsa.europa.eu, pesticides.ppr@efsa.europa.eu

DISCLAIMER Without prejudice to the legal notice applicable to EFSA's website available here, the following legal notice applies to the Pesticide genotoxicity database and any documents, data or information contained therein. Users are advised to read this legal notice carefully before accessing, using or reading any document, data or information made available in this context, or making any other use of the Database. The Pesticide genotoxicity database is a compilation of chemical and genotoxicity information on active substances and some of their metabolites. The database contains the results of individual studies as initially assessed by the Rapporteur member state (RMS) and reported in the respective Draft assessment reports (DARs) or Renewal Assessment Reports (RARs). The final EFSA Conclusions on the respective active substances are available to the public on the EFSA Journal. The database includes the data that was available at the moment of compilation of the database (December 2016) and will be updated on a regular basis by including or deleting of information as a result of renewal procedure of active substances (Regulation (EU) No 1107/2009). EFSA makes no representations or warranties about the accuracy or suitability of any document, information, data provided in the Database. In case of discrepancy between the data provided in the original scientific output (DARs/RARs) and that in this database, preference shall be given to the former. This database does not disclose any commercially sensitive or otherwise confidential information. Unless otherwise stated, the owners of the data compiled in this database are the applicants under Regulation (EU) No 1107/2009, and by acceiding the Database you acknowledge that agreement for reuse of these data should be sought from them. The information provided in the Database and related materials are not intended to constitute advice of any kind or the rendering of consulting, or other professional services of any kind. Acceding the Database does not establish any contractual relationship with EFSA. Users are advised to consult with an attorney, food consultant or other professional to determine what may be best for your individual needs. By acceding the Database, you also acknowledge that the documents, data or information made available by EFSA may contain inaccuracies or errors. The content of the information provided is for your information and use only. It may be subject to change at any time and without prior notice by EFSA.

This data is the raw offline data of untargeted metabolomics in this study, including four groups of samples: MPXV group, HIV-MPXV group, HIV group, and healthy group. MPXV infection may affect the metabolic levels of patients. Through non targeted metabolomics techniques, it is known that mpox caused metabolic disorders in the body, especially downregulation of steroid hormone synthesis pathways. This study used urine samples, mpox samples were collected from hospitalized patients, HIV samples were collected from outpatient clinics, and healthy samples were recruited from volunteers. All samples were collected within 2 months. Immediately freeze the collected samples in liquid nitrogen and store them in a -80 ° C freezer for later use. After all samples are collected, they will be inactivated in a biosafety level 3 laboratory and then packaged and stored for later use. By analyzing this raw data, identified metabolites and differential metabolites can be obtained.

The analysis of bronchoalveolar lavage fluid (BALF) using mass spectrometry-based metabolomics can provide insight into lung diseases, such as asthma. However, the important step of compound identification is hindered by the lack of a small molecule database that is specific for BALF. Here we describe prototypic, small molecule databases derived from human BALF samples (n=117). Human BALF was extracted into lipid and aqueous fractions and analyzed using liquid chromatography mass spectrometry. Following filtering to reduce contaminants and artifacts, the resulting BALF databases (BALF-DBs) contain 11,737 lipid and 658 aqueous compounds. Over 10% of these were found in 100% of samples. Testing the BALF-DBs using nested test sets produced a 99% match rate for lipids and 47% match rate for aqueous molecules. Searching an independent dataset resulted in 45% matching to the lipid BALF-DB compared to < 25% when general databases are searched. Overall, the BALF-DBs can reduce false positives and improve confidence in compound identification compared to when general databases are used.

Chimeric MS/MS spectra contain fragments from multiple precursor ions and therefore hinder compound identification in metabolomics. Historically, deconvolution of these chimeric spectra has been challenging and relied upon specific experimental methods that introduce variation in the ratios of precursor ions between multiple MS/MS scans. DecoID introduces a complementary, method-independent approach where database spectra are computationally mixed to match an experimentally acquired spectrum using LASSO regression. When applied to human plasma, DecoID increased the number of identified metabolites by over 30%.

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.

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 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.

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.