MetaboShiny: interactive analysis and metabolite annotation of mass spectrometry-based metabolomics data
Wolthuis, Joanna C; Magnusdottir, Stefania; Pras-Raves, Mia; Moshiri, Maryam; Jans, Judith J M; Burgering, Boudewijn; van Mil, Saskia; de Ridder, Jeroen
(2020) Metabolomics, volume 16, issue 9, pp.
(Article)
Abstract
Direct infusion untargeted mass spectrometry-based metabolomics allows for rapid insight into a sample's metabolic activity. However, analysis is often complicated by the large array of detected m/z values and the difficulty to prioritize important m/z and simultaneously annotate their putative identities. To address this challenge, we developed MetaboShiny, a novel
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R/RShiny-based metabolomics package featuring data analysis, database- and formula-prediction-based annotation and visualization. To demonstrate this, we reproduce and further explore a MetaboLights metabolomics bioinformatics study on lung cancer patient urine samples. MetaboShiny enables rapid and rigorous analysis and interpretation of direct infusion untargeted mass spectrometry-based metabolomics data.
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Keywords: Annotation, Direct infusion, Machine learning, Mass spectrometry, Metabolomics, R, Statistics, Biochemistry, Clinical Biochemistry, Endocrinology, Diabetes and Metabolism, Research Support, Non-U.S. Gov't, Journal Article
ISSN: 1573-3882
Publisher: Springer New York
Note: Funding Information: This research is supported by the Dutch Technology Foundation STW, which is the Applied Science Division of NWO, and Technology Programme of the Ministry of Economic Affairs. This research is also supported by DSM Nutritional Products. JdR is supported by a Vidi Fellowship (639.072.715) from the Dutch Organization for Scientific Research (Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO). Acknowledgements Publisher Copyright: © 2020, The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
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