Title: The New Data Quality Task Group (DQTG): ensuring high quality data today and in the future
Authors: BEARDEN Daniel W.BEGER Richard D.BROADHURST DavidDUNN WarwickEDISON ArthurGUILLOU ClaudeTRENGOVE RobertVIANT MarkWILSON Ian
Citation: METABOLOMICS vol. 10 no. 4 p. 539-540
Publisher: SPRINGER
Publication Year: 2014
JRC N°: JRC92439
ISSN: 1573-3882
URI: http://link.springer.com/article/10.1007/s11306-014-0679-1/fulltext.html
http://publications.jrc.ec.europa.eu/repository/handle/JRC92439
DOI: 10.1007/s11306-014-0679-1
Type: Articles in periodicals and books
Abstract: A new Task Group has been formed within the international Metabolomics Society to provide a focal point for discussions related to experimental data quality within metabolomics experiments. The current group, which was formed in May 2014, consists of co-chairs Dan Bearden (NIST, USA) and Richard Beger (FDA, USA), and an international panel of scientists listed above as co-authors. The voluntary service of these members to this Task Group is predicated on a one-year commitment with the possibility of renewal based upon mutual agreement. The goal of the DQTG is to promote robust quality assurance (QA) and quality control (QC) in the metabolomics community through increased awareness via communication, outreach and education and through the promotion of best practices. If the Task Group is successful in increasing the use of QC protocols in metabolomics experiments, this will lead to more meaningful data quality metrics and subsequent improvements in data quality. Improvements in data quality will facilitate data exchange, improve inter-laboratory repeatability, enhance the usefulness of publications and improve submissions to metabolomics data repositories. Improved data quality will leverage the potential provided by metabolomics research, findings and approaches, not only in scientific areas, but also for policy and decision makers in regulatory contexts. The strategy of the DQTG is to develop both short term and longer term initiatives. These include: the development of a clear and concise vocabulary and definitions for QA/QC practitioners; educational programs promoting the need for QC in metabolomics; delineation of the various types of QC measurements possible in metabolomics studies; and where and when each type is most suitable for demonstrating data quality. Suggested QC measurements include: blanks, technical replicates, reference materials, pooled samples, spiked samples, synthetic samples, instrument-specific quality measurements, and development of community consensus acceptance criteria for good practice. The DQTG will solicit input from the metabolomics community to obtain a comprehensive view of QA/QC requirements, practices, and approaches in the international community. The DQTG will ask metabolomics scientists to provide feedback through the Metabolomics Society web portal and various public venues. The goal of these efforts is to move toward more consistent QA/QC practices, and potentially, organization of validation exercises for analytical QC techniques with multiple international metabolomics groups. This effort will benefit from the resources, coordination role and global communication channels available through the international Metabolomics Society. The DQTG intend that the information and products generated by the DQTG will be beneficial in persuading researchers, metabolomics services companies, instrument manufacturers and others to provide support for further international analytical QC standardization. Given the increasing role of metabolomics in clinical, pharmaceutical, environmental and general biological research, it is important to start preparing for long-term quality control solutions that can be applied across large numbers of laboratories and measurement platforms that will be applicable for data acquired across many years. Furthermore, it is important to prepare for quality control protocols that are fit-for-purpose for regulatory practice, e.g. in regulatory toxicology. It is equally important that researchers in fields such as systems biology and personalized medicine are able to integrate, confidently and efficiently, metabolomics data with other omics data in large studies. The members of this Task Group are all committed to the importance of data quality and measurement robustness and welcomes your input as the DQTG moves toward a comprehensive QA/QC framework.
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