Please use this identifier to cite or link to this item: https://doi.org/10.3390/metabo9070145
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dc.titleMetabolomics analytics workflow for epidemiological research: Perspectives from the consortium of metabolomics studies (COMETS)
dc.contributor.authorPlaydon, M.C.
dc.contributor.authorJoshi, A.D.
dc.contributor.authorTabung, F.K.
dc.contributor.authorCheng, S.
dc.contributor.authorHenglin, M.
dc.contributor.authorKim, A.
dc.contributor.authorLin, T.
dc.contributor.authorvan Roekel, E.H.
dc.contributor.authorHuang, J.
dc.contributor.authorKrumsiek, J.
dc.contributor.authorWang, Y.
dc.contributor.authorMathé, E.
dc.contributor.authorTemprosa, M.
dc.contributor.authorMoore, S.
dc.contributor.authorChawes, B.
dc.contributor.authorEliassen, A.H.
dc.contributor.authorGsur, A.
dc.contributor.authorGunter, M.J.
dc.contributor.authorHarada, S.
dc.contributor.authorLangenberg, C.
dc.contributor.authorOresic, M.
dc.contributor.authorPerng, W.
dc.contributor.authorSeow, W.J.
dc.contributor.authorZeleznik, O.A.
dc.date.accessioned2021-12-29T04:34:43Z
dc.date.available2021-12-29T04:34:43Z
dc.date.issued2019
dc.identifier.citationPlaydon, M.C., Joshi, A.D., Tabung, F.K., Cheng, S., Henglin, M., Kim, A., Lin, T., van Roekel, E.H., Huang, J., Krumsiek, J., Wang, Y., Mathé, E., Temprosa, M., Moore, S., Chawes, B., Eliassen, A.H., Gsur, A., Gunter, M.J., Harada, S., Langenberg, C., Oresic, M., Perng, W., Seow, W.J., Zeleznik, O.A. (2019). Metabolomics analytics workflow for epidemiological research: Perspectives from the consortium of metabolomics studies (COMETS). Metabolites 9 (7) : 145. ScholarBank@NUS Repository. https://doi.org/10.3390/metabo9070145
dc.identifier.issn22181989
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/212294
dc.description.abstractThe application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting of study findings. To inform the development of such guidelines, we conducted a survey of 47 cohort representatives from the Consortium of Metabolomics Studies (COMETS) to gain insights into the current strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide. The results indicated a variety of applied analytical strategies, from biospecimen and data pre-processing and quality control to statistical analysis and reporting of study findings. These strategies included methods commonly used within the metabolomics community and applied in epidemiological research, as well as novel approaches to pre-processing pipelines and data analysis. To help with these discrepancies, we propose use of open-source initiatives such as the online web-based tool COMETS Analytics, which includes helpful tools to guide analytical workflow and the standardized reporting of findings from metabolomics analyses within epidemiological studies. Ultimately, this will improve the quality of statistical analyses, research findings, and study reproducibility. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.
dc.publisherMDPI AG
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceScopus OA2019
dc.subjectAnalytical methods
dc.subjectData analysis
dc.subjectEpidemiology
dc.subjectMetabolomics
dc.subjectPre-processing
dc.subjectReporting
dc.subjectStatistical analysis
dc.typeArticle
dc.contributor.departmentSAW SWEE HOCK SCHOOL OF PUBLIC HEALTH
dc.description.doi10.3390/metabo9070145
dc.description.sourcetitleMetabolites
dc.description.volume9
dc.description.issue7
dc.description.page145
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