The Comparative Toxicogenomics Database: update 2013

By Allan Peter Davis, Cynthia G. Murphy, Robin J. Johnson, Jean M. Lay, Kelley Lennon-Hopkins, Cynthia A. Saraceni-Richards, Daniela Sciaky, B. King, Michael C. Rosenstein, Thomas C. Wiegers and 1 more. Nucleic Acids Res., 2012

514 citations recorded by the source corpus.

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) provides information about interactions between environmental chemicals and gene products and their relationships to diseases. Chemical–gene, chemical–disease and gene–disease interactions manually curated from the literature are integrated to generate expanded networks and predict many novel associations between different data types. CTD now contains over 15 million toxicogenomic relationships. To navigate this sea of data, we added several new features, including DiseaseComps (which finds comparable diseases that share toxicogenomic profiles), statistical scoring for inferred gene–disease and pathway–chemical relationships, filtering options for several tools to refine user analysis and our new Gene Set Enricher (which provides biological annotations that are enriched for gene sets). To improve data visualization, we added a Cytoscape Web view to our ChemComps feature, included color-coded interactions and created a ‘slim list’ for our MEDIC disease vocabulary (allowing diseases to be grouped for meta-analysis, visualization and better data management). CTD continues to promote interoperability with external databases by providing content and cross-links to their sites. Together, this wealth of expanded chemical–gene–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.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0758a501039f9e2dfb7607507f9734155c52c7fc

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Citations in the corpus

STITCH 4: integration of protein–chemical interactions with user data (2013, background): “The second channel is made up of manually curated drug–target databases: DrugBank (12), GLIDA (13), Matador (14), TTD (15) and CTD (16); and pathway databases: KEGG (17), NCI/Nature Pathway Interaction Database (18), Reactome (19) and BioCyc (20) .”
Understanding multicellular function and disease with human tissue-specific networks (2015, unknown): “Recent advances now allow for high-throughput discovery of genes expressed in specific lineages in solid tissues 9,64 .”
DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes (2015, methodology): “The repositories of GDAs use different disease vocabularies: MIM terms for OMIM diseases (used by UniProt, CTD and MGD), MeSH terms (used by CTD, LHGDN and RGD).”