T3DB: the toxic exposome database
By David S. Wishart, David Arndt, Allison Pon, Tanvir Sajed, Anchi Guo, Yannick Djoumbou, Craig Knox, Michael Wilson, Yongjie Liang, Jason R. Grant and 3 more. Nucleic Acids Res., 2014
295 citations recorded by the source corpus.
The exposome is defined as the totality of all human environmental exposures from conception to death. It is often regarded as the complement to the genome, with the interaction between the exposome and the genome ultimately determining one's phenotype. The ‘toxic exposome’ is the complete collection of chronically or acutely toxic compounds to which humans can be exposed. Considerable interest in defining the toxic exposome has been spurred on by the realization that most human injuries, deaths and diseases are directly or indirectly caused by toxic substances found in the air, water, food, home or workplace. The Toxin-Toxin-Target Database (T3DB - www.t3db.ca) is a resource that was specifically designed to capture information about the toxic exposome. Originally released in 2010, the first version of T3DB contained data on nearly 2900 common toxic substances along with detailed information on their chemical properties, descriptions, targets, toxic effects, toxicity thresholds, sequences (for both targets and toxins), mechanisms and references. To more closely align itself with the needs of epidemiologists, toxicologists and exposome scientists, the latest release of T3DB has been substantially upgraded to include many more compounds (>3600), targets (>2000) and gene expression datasets (>15 000 genes). It now includes extensive data on ‘normal’ toxic compound concentrations in human biofluids as well as detailed chemical taxonomies, informative chemical ontologies and a large number of referential NMR, MS/MS and GC-MS spectra. This manuscript describes the most recent update to the T3DB, which was previously featured in the 2010 NAR Database Issue.
Subfields: Food Science, General Agricultural and Biological Sciences
View Paper: https://www.semanticscholar.org/paper/59573d5dffc8af61d82236cb7517e2e0bc7f2384
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Citations in the corpus
- ClassyFire: automated chemical classification with a comprehensive, computable taxonomy (2016, methodology): “A test set was built by randomly selecting 800 unique structures from DrugBank, the LIPID MAPS Lipidomics Gateway, HMDB [32], and T3DB [44].”
- The BioGRID interaction database: 2017 update (2016, methodology): “We surveyed the content of the major specialized chemical interaction databases, including DrugBank (37), HMdb (61), T3DB (62), BindingDB (63), CTD (64), Therapeutic Target DB (65), ChemBank (66), PharmGKB (67), DGIdb (68), Pub-Chem (69) and ChEMBL (70) to determine the shared fields housed in each…”