The Comparative Toxicogenomics Database (CTD)

Department of Bioinformatics, Mount Desert Island Biological Laboratory, Salsbury Cove, Maine 04672, USA.
Environmental Health Perspectives (Impact Factor: 7.98). 06/2003; 111(6):793-5. DOI: 10.1289/txg.6028
Source: PubMed


The Mount Desert Island Biological Laboratory in Salsbury Cove, Maine, USA, is developing the Comparative Toxicogenomics Database (CTD), a community-supported genomic resource devoted to genes and proteins of human toxicologic significance. CTD will be the first publicly available database to a) provide annotated associations among genes, proteins, references, and toxic agents, with a focus on annotating data from aquatic and mammalian organisms; b) include nucleotide and protein sequences from diverse species; c) offer a range of analysis tools for customized comparative studies; and d) provide information to investigators on available molecular reagents. This combination of features will facilitate cross-species comparisons of toxicologically significant genes and proteins. These comparisons will promote understanding of molecular evolution, the significance of conserved sequences, the genetic basis of variable sensitivity to environmental agents, and the complex interactions between the environment and human health. CTD is currently under development, and the planned scope and functions of the database are described herein. The intent of this report is to invite community participation in the development of CTD to ensure that it will be a valuable resource for environmental health, molecular biology, and toxicology research.

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Available from: John N Forrest, Sep 27, 2014
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    • "Another database that provides information about autoimmune disorders is the Autoimmune Disease Database, which gives descriptions of autoimmune disorders and links these diseases to candidate genes, which is, again, a database that useful only for researchers [7]. The Comparative Toxicogenomic Database (CTD) is a rich resource for researchers to access information about the etiology of environmental diseases and explore chemical-gene and protein interactions [8]. Such attempts have contributed enormously to efforts related to the prevention, diagnosis and treatment of diseases and have resulted in the development of new approaches to alleviate the consequences of life-threatening illnesses. "
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    ABSTRACT: The scope of the Human Disease Insight (HDI) database is not limited to researchers or physicians as it also provides basic information to non-professionals and creates disease awareness, thereby reducing the chances of patient suffering due to ignorance. HDI is a knowledge-based resource providing information on human diseases to both scientists and the general public. Here, our mission is to provide a comprehensive human disease database containing most of the available useful information, with extensive cross-referencing. HDI is a knowledge management system that acts as a central hub to access information about human diseases and associated drugs and genes. In addition, HDI contains well-classified bioinformatics tools with helpful descriptions. These integrated bioinformatics tools enable researchers to annotate disease-specific genes and perform protein analysis, search for biomarkers and identify potential vaccine candidates. Eventually, these tools will facilitate the analysis of disease-associated data. The HDI provides two types of search capabilities and includes provisions for downloading, uploading and searching disease/gene/drug-related information. The logistical design of the HDI allows for regular updating. The database is designed to work best with Mozilla Firefox and Google Chrome and is freely accessible at
    Full-text · Article · Nov 2015 · Journal of Infection and Public Health
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    • "A large amount of GeneWeaver data comes from major bioinformatics resources including NCBI, ENSEMBL and various model organism databases, including MGD (9), Rat Genome Database [RGD (27)], HUGO Gene Nomenclature Committee [HGNC (28)], Saccharomyces Genome Database [SGD (29)], FlyBase (30), WormBase (31) and the Zebrafish Model Organism Database [ZFIN (32)]. Some of these data are converted to gene sets, including GO and MP annotations, Comparative Toxicogenomics Database (33) associations and QTL positional candidates from RGD and MGI. These data sources are updated every 6 months. "
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    ABSTRACT: High-throughput genome technologies have produced a wealth of data on the association of genes and gene products to biological functions. Investigators have discovered value in combining their experimental results with published genome-wide association studies, quantitative trait locus, microarray, RNA-sequencing and mutant phenotyping studies to identify gene-function associations across diverse experiments, species, conditions, behaviors or biological processes. These experimental results are typically derived from disparate data repositories, publication supplements or reconstructions from primary data stores. This leaves bench biologists with the complex and unscalable task of integrating data by identifying and gathering relevant studies, reanalyzing primary data, unifying gene identifiers and applying ad hoc computational analysis to the integrated set. The freely available GeneWeaver ( powered by the Ontological Discovery Environment is a curated repository of genomic experimental results with an accompanying tool set for dynamic integration of these data sets, enabling users to interactively address questions about sets of biological functions and their relations to sets of genes. Thus, large numbers of independently published genomic results can be organized into new conceptual frameworks driven by the underlying, inferred biological relationships rather than a pre-existing semantic framework. An empirical 'ontology' is discovered from the aggregate of experimental knowledge around user-defined areas of biological inquiry.
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    • "Despite this observation, the mechanism of action and the potential influences of most chemicals on many diseases are not known [19,20,31,32]. To gain a better understanding about the impact environmental chemicals have on human health, the Comparative Toxicogenomics Database (CTD) [33,34] has been developed by Mount Desert Island Biological Laboratory. It serves as a unique centralised and freely available resource to explore the interactions amongst chemicals, genes or proteins and diseases in diverse species. "
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    ABSTRACT: Due to recent advances in data storage and sharing for further data processing in predictive toxicology, there is an increasing need for flexible data representations, secure and consistent data curation and automated data quality checking. Toxicity prediction involves multidisciplinary data. There are hundreds of collections of chemical, biological and toxicological data that are widely dispersed, mostly in the open literature, professional research bodies and commercial companies. In order to better manage and make full use of such large amount of toxicity data, there is a trend to develop functionalities aiming towards data governance in predictive toxicology to formalise a set of processes to guarantee high data quality and better data management. In this paper, data quality mainly refers in a data storage sense (e.g. accuracy, completeness and integrity) and not in a toxicological sense (e.g. the quality of experimental results). This paper reviews seven widely used predictive toxicology data sources and applications, with a particular focus on their data governance aspects, including: data accuracy, data completeness, data integrity, metadata and its management, data availability and data authorisation. This review reveals the current problems (e.g. lack of systematic and standard measures of data quality) and desirable needs (e.g. better management and further use of captured metadata and the development of flexible multi-level user access authorisation schemas) of predictive toxicology data sources development. The analytical results will help to address a significant gap in toxicology data quality assessment and lead to the development of novel frameworks for predictive toxicology data and model governance. While the discussed public data sources are well developed, there nevertheless remain some gaps in the development of a data governance framework to support predictive toxicology. In this paper, data governance is identified as the new challenge in predictive toxicology, and a good use of it may provide a promising framework for developing high quality and easy accessible toxicity data repositories. This paper also identifies important research directions that require further investigation in this area.
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