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Open AccessProtocol

“Notame”: Workflow for Non-Targeted LC–MS Metabolic Profiling

1
Department of Clinical Nutrition and Public Health, University of Eastern Finland, 70210 Kuopio, Finland
2
School of Pharmacy, University of Eastern Finland, 70210 Kuopio, Finland
3
Institute of Biomedicine, University of Eastern Finland, 70210 Kuopio, Finland
4
Centre for Integrative Metabolomics & Computational Biology, School of Science, Edith Cowan University, Joondalup, WA 6027, Australia
5
Department of Biology and Biological Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden
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Chalmers Mass Spectrometry Infrastructure, Chalmers University of Technology, 41296 Gothenburg, Sweden
7
Department of Biochemistry, Food Chemistry and Food Development unit, University of Turku, 20014 Turun yliopisto, Finland
*
Authors to whom correspondence should be addressed.
These authors have contributed equally to this work.
Metabolites 2020, 10(4), 135; https://doi.org/10.3390/metabo10040135
Received: 2 March 2020 / Revised: 25 March 2020 / Accepted: 28 March 2020 / Published: 31 March 2020
Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography–mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting. View Full-Text
Keywords: metabolomics; LC–MS; mass spectrometry; metabolic profiling; computational statistical; unsupervised learning; supervised learning; pathway analysis metabolomics; LC–MS; mass spectrometry; metabolic profiling; computational statistical; unsupervised learning; supervised learning; pathway analysis
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MDPI and ACS Style

Klåvus, A.; Kokla, M.; Noerman, S.; Koistinen, V.M.; Tuomainen, M.; Zarei, I.; Meuronen, T.; Häkkinen, M.R.; Rummukainen, S.; Farizah Babu, A.; Sallinen, T.; Kärkkäinen, O.; Paananen, J.; Broadhurst, D.; Brunius, C.; Hanhineva, K. “Notame”: Workflow for Non-Targeted LC–MS Metabolic Profiling. Metabolites 2020, 10, 135. https://doi.org/10.3390/metabo10040135

AMA Style

Klåvus A, Kokla M, Noerman S, Koistinen VM, Tuomainen M, Zarei I, Meuronen T, Häkkinen MR, Rummukainen S, Farizah Babu A, Sallinen T, Kärkkäinen O, Paananen J, Broadhurst D, Brunius C, Hanhineva K. “Notame”: Workflow for Non-Targeted LC–MS Metabolic Profiling. Metabolites. 2020; 10(4):135. https://doi.org/10.3390/metabo10040135

Chicago/Turabian Style

Klåvus, Anton; Kokla, Marietta; Noerman, Stefania; Koistinen, Ville M.; Tuomainen, Marjo; Zarei, Iman; Meuronen, Topi; Häkkinen, Merja R.; Rummukainen, Soile; Farizah Babu, Ambrin; Sallinen, Taisa; Kärkkäinen, Olli; Paananen, Jussi; Broadhurst, David; Brunius, Carl; Hanhineva, Kati. 2020. "“Notame”: Workflow for Non-Targeted LC–MS Metabolic Profiling" Metabolites 10, no. 4: 135. https://doi.org/10.3390/metabo10040135

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