Toward a checklist for exchange and interpretation of data from a toxicology study

NIEHS, LMIT ITSS Contract, Research Triangle Park, North Carolina 27709-2233, USA.
Toxicological Sciences (Impact Factor: 3.85). 10/2007; 99(1):26-34. DOI: 10.1093/toxsci/kfm090
Source: PubMed


Data from toxicology and toxicogenomics studies are valuable, and can be combined for meta-analysis using public data repositories such as Chemical Effects in Biological Systems Knowledgebase, ArrayExpress, and Gene Expression Omnibus. In order to fully utilize the data for secondary analysis, it is necessary to have a description of the study and good annotation of the accompanying data. This study annotation permits sophisticated cross-study comparison and analysis, and allows data from comparable subjects to be identified and fully understood. The Minimal Information About a Microarray Experiment Standard was proposed to permit deposition and sharing of microarray data. We propose the first step toward an analogous standard for a toxicogenomics/toxicology study, by describing a checklist of information that best practices would suggest be included with the study data. When the information in this checklist is deposited together with the study data, the checklist information helps the public explore the study data in context of time, or identify data from similarly treated subjects, and also explore/identify potential sources of experimental variability. The proposed checklist summarizes useful information to include when sharing study data for publication, deposition into a database, or electronic exchange with collaborators. It is not a description of how to carry out an experiment, but a definition of how to describe an experiment. It is anticipated that once a toxicology checklist is accepted and put into use, then toxicology databases can be configured to require and output these fields, making it straightforward to annotate data for interpretation by others.

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    • "Chemical Effects in Biological Systems (CEBS) is the first public repository which captures toxicogenomics data developed by the National Center for Toxicogenomics (NCT) within the National Institute of Environmental Health Science (NIEHS) [23,29]. A distinguishing feature of CEBS is that it contains very detailed animal-level study information including treatment protocols, study design, study time-line, metadata for microarray and proteomics data, histopathology and even raw genomic microarray results [23]. "
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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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    • "A number of efforts are underway to create such a standard. Recently the result of a consensus about the minimal information to include was reported (32). In addition, a format for data exchange is being developed by the Standard for Exchange of Non-clinical Data (SEND) Consortium ( and an ontology for describing a biomedical investigation, which would include a toxicology study, is under development by the OBI (Ontology for Biomedical Investigations) Working Group ( "
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