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: 4.48). 10/2007; 99(1):26-34. DOI: 10.1093/toxsci/kfm090
Source: PubMed

ABSTRACT 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.


Available from: Stephen Edwards, Apr 17, 2015
  • 01/1970: pages 323-359;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Integration, re-use and meta-analysis of high content study data, typical of DNA microarray studies, can increase its scientific utility. Access to study data and design parameters would enhance the mining of data integrated across studies. However, without standards for which data to include in exchange, and common exchange formats, publication of high content data is time-consuming and often prohibitive. The MGED Society ( was formed in response to the widespread publication of microarray data, and the recognition of the utility of data re-use for meta-analysis. The NIEHS has developed the Chemical Effects in Biological Systems (CEBS) database, which can manage and integrate study data and design from biological and biomedical studies. As community standards are developed for study data and metadata it will become increasingly straightforward to publish high content data in CEBS, where they will be available for meta-analysis. Different exchange formats for study data are being developed: Standard for Exchange of Nonclinical Data (SEND;; Tox-ML ( and Simple Investigation Formatted Text (SIFT) from the NIEHS. Data integration can be done at the level of conclusions about responsive genes and phenotypes, and this workflow is supported by CEBS. CEBS also integrates raw and preprocessed data within a given platform. The utility and a method for integrating data within and across DNA microarray studies is shown in an example analysis using DrugMatrix data deposited in CEBS by Iconix Pharmaceuticals.
    Toxicology and Applied Pharmacology 11/2008; 233(1-233):54-62. DOI:10.1016/j.taap.2008.06.015 · 3.63 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Environmental health risk assessors are challenged to understand and incorporate new data streams as the field of toxicology continues to adopt new molecular and systems biology technologies. Systematic screening reviews can help risk assessors and assessment teams determine which studies to consider for inclusion in a human health assessment. A tool for systematic reviews should be standardized and transparent in order to consistently determine which studies meet minimum quality criteria prior to performing in-depth analyses of the data. The Systematic Omics Analysis Review (SOAR) tool is focused on assisting risk assessment support teams in performing systematic reviews of transcriptomic studies. SOAR is a spreadsheet tool of 35 objective questions developed by domain experts, focused on transcriptomic microarray studies, and including four main topics: test system, test substance, experimental design, and microarray data. The tool will be used as a guide to identify studies that meet basic published quality criteria, such as those defined by the Minimum Information About a Microarray Experiment standard and the Toxicological Data Reliability Assessment Tool. Seven scientists were recruited to test the tool by using it to independently rate 15 published manuscripts that study chemical exposures with microarrays. Using their feedback, questions were weighted based on importance of the information and a suitability cutoff was set for each of the four topic sections. The final validation resulted in 100% agreement between the users on four separate manuscripts, showing that the SOAR tool may be used to facilitate the standardized and transparent screening of microarray literature for environmental human health risk assessment.
    PLoS ONE 12/2014; 9(12):e110379. DOI:10.1371/journal.pone.0110379 · 3.53 Impact Factor