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CTD - Comparative Toxicogenomics Database

NAR Molecular Biology Database Collection entry number 1188
Davis, A.P.1, Grondin, C.1, Lennon-Hopkins, K.1, Saraceni-Richards, C.1, Sciaky, D.1, King, B.2, Wiegers, T.1, and Mattingly, C.1
1Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617

2Department of Bioinformatics, The Mount Desert Island Biological Laboratory, Salisbury Cove, ME 04672

Database Description

Exposure to environmental chemicals may influence human health; however, the molecular mechanisms of action between chemicals and gene products are not well understood. Towards that end, the Comparative Toxicogenomics Database (CTD; provides information about interactions between environmental chemicals and gene products, and their relationships to diseases. Biocurators manually curate chemical-gene, chemical-disease, and gene-disease relationships from the scientific literature (1). This core data is then internally integrated to generate inferred chemical-gene-disease networks. Additionally, the core data is integrated with external data sets (such as Gene Ontology and pathway annotations) to predict many novel associations between different data types. A unique and powerful feature of CTD is the inferred relationships generated by data integration: if chemical A interacts with gene B, and independently gene B is directly associated with disease C, then chemical A has an inferred relationship to disease C (inferred via gene B). Thus, via integration, CTD helps turn knowledge into discoveries by identifying novel connections between chemicals/drugs, genes/proteins, diseases, taxa, phenotypes, Gene Ontology (GO) annotations, pathways, and interaction modules that might not otherwise be apparent. These inferences are powerful data sets for generating testable hypotheses about molecular mechanisms and the pre-disease state. CTD evaluates these inferred relationships and assigns statistical scores and p-values, which allow users to sort and rank the inferred relationships to help prioritize hypothesis testing. As of September 2014, CTD contains over 24 million toxicogenomic relationships ( All CTD data files are freely available from individual web pages or from our Downloads tab ( in multiple formats (CSV, TSV, XML, Excel, and OBO). New users of CTD should consult our Help ( and FAQ ( guides.

Recent Developments

CTD provides users with several analysis and visualization tools (, including:
1. Batch Query: downloads custom data associated with a set of chemicals, diseases, genes, Gene Ontology terms, pathways, or references (2).
2. Venn: suite of online tools (VennViewer, MyGeneVenn, and MyVenn) that compare associated data sets for chemicals, genes, diseases, GO, pathways, and references (2).
3. Set Analyzer: calculates enriched diseases, GO annotations, pathways, and gene-gene interactions for sets of genes or chemicals (3).
4. Comps: finds comparable chemicals, genes, and diseases based upon shared toxicogenomic profiles (4, 5).
5. PathwayView: generates toxicogenomic modules of gene-gene interactions into customizable, Cytoscape-based, interactive diagrams.
6. MEDIC and MEDIC-slim ( a novel, merged disease vocabulary that allows diseases to be grouped for meta-analysis, visualization, and better data management (6).
7. Chemical-phenotype interactions: over 38,000 manually curated interactions connecting 2,850 chemicals, 738 genes, 121 phenotypes, and 544 anatomical terms for 59 taxa, allowing users to explore the pre-disease state of chemically influenced diseases (7).
Together, this wealth of expanded chemical-gene-phenotype-disease data, combined with novel ways to analyze and view content, continues to help users generate testable hypotheses about the molecular mechanisms of environmental diseases.


CTD is supported by the National Institute of Environmental Health Sciences (NIEHS) grants Comparative Toxicogenomics Database [grant number R01-ES014065] and Generation of a centralized and integrated resource for exposure data [grant number R01-ES019604].


1. Davis, A.P., Wiegers, T.C., Rosenstein, M.C., Murphy, C.G., and Mattingly, C.J. (2011) The curation paradigm and application tool used for manual curation of the scientific literature at the Comparative Toxicogenomics Database. Database, Sep 20;2011:bar034.
2. Davis, A.P., King, B.L., Mockus, S., Murphy, C.G., Saraceni-Richards, C., Rosenstein, M. Wiegers, T., and Mattingly, C.J. (2011) The Comparative Toxicogenomics Database: update 2011. Nucleic Acids Res., 39, D1067-1072.
3. Davis, A.P., Murphy, C.G., Johnson R., Lay J.M., Saraceni-Richards, C., King, B.L., Rosenstein, M.C., Wiegers, T.C., and Mattingly, C.J. (2013) The Comparative Toxicogenomics Database: update 2013. Nucleic Acids Res., 41, D1104-1114.
4. Davis, A.P., Murphy, C.G., Saraceni-Richards, C.A., Rosenstein, M.C., Wiegers, T.C., Hampton, T.H., and Mattingly, C.J. (2009) GeneComps and ChemComps: a new CTD metric to identify genes and chemicals with shared toxicogenomic profiles. Bioinformation, 4, 173-174.
5. Davis, A.P., Rosenstein, M.C., Wiegers, T.C., and Mattingly, C.J. (2011) DiseaseComps: a metric that discovers similar diseases based upon common toxicogenomics profiles at CTD. Bioinformation, 7, 154-156.
6. Davis, A.P., Wiegers, T.C., Rosenstein, M.C. and Mattingly, C.J. (2012) MEDIC: a practical disease vocabulary used at the Comparative Toxicogenomics Database. Database, Mar 20;2012:bar065.
7. Davis A.P., Wiegers T.C., Roberts P.M., King B.L., Lay J.M., Lennon-Hopkins K., Sciaky D., Johnson R., Keating H., Greene N., Hernandez R., McConnell K.J., Enayetallah A.E., Mattingly C.J. (2013) A CTD-Pfizer collaboration: manual curation of 88,000 scientific articles text mined for drug-disease and drug-phenotype interactions. Database, Nov 28:bat080.

Go to the abstract in the NAR 2009 Database Issue.
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