Dataset Information

Quality assurance of metabolomics.

ABSTRACT: Metabolomics promises a holistic phenotypic characterization of biological responses to toxicants. This technology is based on advanced chemical analytical tools with reasonable throughput, including mass-spectroscopy and NMR. Quality assurance, however - from experimental design, sample preparation, metabolite identification, to bioinformatics data-mining - is urgently needed to assure both quality of metabolomics data and reproducibility of biological models. In contrast to microarray-based transcriptomics, where consensus on quality assurance and reporting standards has been fostered over the last two decades, quality assurance of metabolomics is only now emerging. Regulatory use in safety sciences, and even proper scientific use of these technologies, demand quality assurance. In an effort to promote this discussion, an expert workshop discussed the quality assurance needs of metabolomics. The goals for this workshop were 1) to consider the challenges associated with metabolomics as an emerging science, with an emphasis on its application in toxicology and 2) to identify the key issues to be addressed in order to establish and implement quality assurance procedures in metabolomics-based toxicology. Consensus has still to be achieved regarding best practices to make sure sound, useful, and relevant information is derived from these new tools.

SUBMITTER: Bouhifd M

PROVIDER: S-EPMC5578451 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Quality assurance of metabolomics.

Bouhifd Mounir M Beger Richard R Flynn Thomas T Guo Lining L Harris Georgina G Hogberg Helena H Kaddurah-Daouk Rima R Kamp Hennicke H Kleensang Andre A Maertens Alexandra A Odwin-DaCosta Shelly S Pamies David D Robertson Donald D Smirnova Lena L Sun Jinchun J Zhao Liang L Hartung Thomas T

ALTEX 20150101 4

Metabolomics promises a holistic phenotypic characterization of biological responses to toxicants. This technology is based on advanced chemical analytical tools with reasonable throughput, including mass-spectroscopy and NMR. Quality assurance, however - from experimental design, sample preparation, metabolite identification, to bioinformatics data-mining - is urgently needed to assure both quality of metabolomics data and reproducibility of biological models. In contrast to microarray-based tr ...[more]

PMID: 26536290

Similar Datasets

Project description:IntroductionThe metabolomics quality assurance and quality control consortium (mQACC) evolved from the recognized need for a community-wide consensus on improving and systematizing quality assurance (QA) and quality control (QC) practices for untargeted metabolomics.ObjectivesIn this work, we sought to identify and share the common and divergent QA and QC practices amongst mQACC members and collaborators who use liquid chromatography-mass spectrometry (LC-MS) in untargeted metabolomics.MethodsAll authors voluntarily participated in this collaborative research project by providing the details of and insights into the QA and QC practices used in their laboratories. This sharing was enabled via a six-page questionnaire composed of over 120 questions and comment fields which was developed as part of this work and has proved the basis for ongoing mQACC outreach.ResultsFor QA, many laboratories reported documenting maintenance, calibration and tuning (82%); having established data storage and archival processes (71%); depositing data in public repositories (55%); having standard operating procedures (SOPs) in place for all laboratory processes (68%) and training staff on laboratory processes (55%). For QC, universal practices included using system suitability procedures (100%) and using a robust system of identification (Metabolomics Standards Initiative level 1 identification standards) for at least some of the detected compounds. Most laboratories used QC samples (>86%); used internal standards (91%); used a designated analytical acquisition template with randomized experimental samples (91%); and manually reviewed peak integration following data acquisition (86%). A minority of laboratories included technical replicates of experimental samples in their workflows (36%).ConclusionsAlthough the 23 contributors were researchers with diverse and international backgrounds from academia, industry and government, they are not necessarily representative of the worldwide pool of practitioners due to the recruitment method for participants and its voluntary nature. However, both questionnaire and the findings presented here have already informed and led other data gathering efforts by mQACC at conferences and other outreach activities and will continue to evolve in order to guide discussions for recommendations of best practices within the community and to establish internationally agreed upon reporting standards. We very much welcome further feedback from readers of this article.

Project description:Objectives:The aim of the study was to explore what components of the General Medical Council's (GMC) Quality Assurance Framework work, for whom, in what circumstances and how?Setting:UK undergraduate and postgraduate medical education and training.Participants:We conducted interviews with a stratified sample of 36 individuals. This included those who had direct experiences, as well as those with external insights, representing local, national and international organisations within and outside medicine.Intervention:The GMC quality assure education to protect patient and public safety utilising complex intervention components including meeting standards, institutional visits and monitoring performance. However, the context in which these are implemented matters. We undertook an innovative realist evaluation to test an initial programme theory. Data were analysed using framework analysis.Results:Across components of the intervention, we identified key mechanisms, including transparent reporting to promote quality improvement; dialogical feedback; partnership working facilitating interactions between regulators and providers, and role clarity in conducting proportionate interventions appropriate to risk. The GMC's framework was commended for being comprehensive and enabling a broad understanding of an organisation's performance. Unintended consequences included confusion over roles and boundaries in different contexts which often undermined effectiveness.Conclusions:This realist evaluation substantiates the literature and reveals deeper understandings about quality assuring medical education. While standardised approaches are implemented, interventions need to be contextually proportionate. Routine communication is beneficial to verify data, share concerns and check risk; however, ongoing partnership working can foster assurance. The study provides a modified programme theory to explicate how education providers and regulators can work more effectively together to uphold education quality, and ultimately protect public safety. The findings have influenced the GMC's approach to quality assurance which impacts on all medical students and doctors in training.

Dataset Information

Quality assurance of metabolomics.

Publications

Quality assurance of metabolomics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets