The Comparative Toxicogenomics Database's 10th year anniversary: update 2015

Abstract

Ten years ago, the Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) was developed out of a need to formalize, harmonize and centralize the information on numerous genes and proteins responding to environmental toxic agents across diverse species. CTD's initial approach was to facilitate comparisons of nucleotide and protein sequences of toxicologically significant genes by curating these sequences and electronically annotating them with chemical terms from their associated references. Since then, however, CTD has vastly expanded its scope to robustly represent a triad of chemical-gene, chemical-disease and gene-disease interactions that are manually curated from the scientific literature by professional biocurators using controlled vocabularies, ontologies and structured notation. Today, CTD includes 24 million toxicogenomic connections relating chemicals/drugs, genes/proteins, diseases, taxa, phenotypes, Gene Ontology annotations, pathways and interaction modules. In this 10th year anniversary update, we outline the evolution of CTD, including our increased data content, new 'Pathway View' visualization tool, enhanced curation practices, pilot chemical-phenotype results and impending exposure data set. The prototype database originally described in our first report has transformed into a sophisticated resource used actively today to help scientists develop and test hypotheses about the etiologies of environmentally influenced diseases.

Figure 1.

A brief history of CTD. The timeline shows the diversity of CTD's development over the last decade with respect to curation processes (yellow boxes), curated content (blue boxes), imported annotations (red boxes), data inferences (orange boxes) and analytical tools (green boxes). All features have been described in detail in previous CTD publications (http://ctdbase.org/about/publications/#ctdpubs). Abbreviations: C (chemical), G (gene), D (disease), ixns (interactions manually curated from the literature), GO (Gene Ontology), assoc (associations).

Figure 2.

CTD growth. Four graphs show the cumulative growth of CTD over the last 10 years. (A) The number of manually curated direct interactions that compose the core triad of chemical–gene, chemical–disease and gene–disease statements (y axis in thousands, K). (B) The number of manually curated articles from whence the direct interactions in graph A were extracted (y axis in thousands, K). (C) The number of inferred relationships derived by integrating the direct interactions in graph A with each other as well as with external GO and pathway data sets (y axis in millions, M). (D) The number of external papers citing their use of CTD. For graphs A, B and C, the content increase in 2011 is our curation derived from the Pfizer project (20). In all four graphs, data for year 2014 are not complete.

Figure 3.

Building potential toxicogenomic network modules from CTD curated content. On the chemical page for chlorpyrifos, the ‘Disease’ data tab (orange) lists the inferred genes that can putatively connect this pesticide to numerous diseases. Here, 63 genes are part of an inference network between chlorpyrifos and prostate cancer, based upon CTD curated content. By clicking on the network icon (red arrow), users launch CTD's new ‘Pathway View’ feature that displays and builds a toxicogenomic interaction module for the inference genes based upon protein and gene interaction data from BioGRID (red dotted inset). The Cytoscape-based map can be easily navigated and customized by the user for a variety of display parameters, as well as be exported to the user's desktop. Edges (black lines) connecting nodes (here, the genes ATM and PARP1) can be clicked to view the BioGRID data tab (black arrow and box) that details the interaction (e.g. the type of assay used, source organism, target organism and PMID reference). The size and color of a gene node is scaled based on the total number of its BioGRID interactions, allowing users to easily detect any highly connected ‘hub’ genes in the network.