@article{fa4e4c5b2f874e7b9ec6048888778427,
title = "Metabolomics analytics workflow for epidemiological research: Perspectives from the consortium of metabolomics studies (COMETS)",
abstract = "The 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.",
keywords = "Analytical methods, Data analysis, Epidemiology, Metabolomics, Pre-processing, Reporting, Statistical analysis",
author = "Playdon, {Mary C.} and Joshi, {Amit D.} and Tabung, {Fred K.} and Susan Cheng and Mir Henglin and Andy Kim and Tengda Lin and {van Roekel}, {Eline H.} and Jiaqi Huang and Jan Krumsiek and Ying Wang and Ewy Math{\'e} and Marinella Temprosa and Steven Moore and Bo Chawes and Eliassen, {A. Heather} and Andrea Gsur and Gunter, {Marc J.} and Sei Harada and Claudia Langenberg and Matej Oresic and Wei Perng and Seow, {Wei Jie} and Zeleznik, {Oana A.}",
note = "Funding Information: Metabolomics resource sponsored by the Common Fund of the National Institutes of Health. Program for statistical, functional and integrative analysis of metabolomics data. A toolbox for metabolomic data analysis, interpretation, and integrative exploration. A modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. Funding Information: Funding: This research received no external funding. Mary C. Playdon was supported by the National Cancer Institute grant number 5R00CA218694-03 and Huntsman Cancer Institute Cancer Center Support Grant number P30CA040214. E.H. van Roekel was financially supported by Wereld Kanker Onderzoek Fonds (WKOF), as part of the World Cancer Research Fund International grant programme (grant number 2016/1620); A.D.J. was supported by NIDDK grant number K01-DK110267. Fred K. Tabung was supported by National Cancer Institute grant number R00CA207736. Oana A. Zeleznik was supported by the National Cancer Institute grant numbers P01CA087969 and R01CA050385. Publisher Copyright: {\textcopyright} 2019 by the authors. Licensee MDPI, Basel, Switzerland.",
year = "2019",
month = jul,
doi = "10.3390/metabo9070145",
language = "English",
volume = "9",
journal = "Metabolites",
issn = "2218-1989",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "7",
}
