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Publications

Journal Article Epublish 115 citations

PubMed: 32244411 DOI: 10.3390/metabo10040135

"notame": Workflow for Non-Targeted LC-MS Metabolic Profiling.

Metabolites 10(4)

Authors: Anton Klåvus, Marietta Kokla, Stefania Noerman, Ville M Koistinen, Marjo Tuomainen, Iman Zarei, Topi Meuronen, Merja R Häkkinen, Soile Rummukainen, Ambrin Farizah Babu, Taisa Sallinen, Olli Kärkkäinen, Jussi Paananen, David Broadhurst, Carl Brunius, Kati Hanhineva

Affiliations

  • University of Eastern Finland, Department of Clinical Nutrition and Public Health, 70210 Kuopio, Finland.
  • University of Eastern Finland, School of Pharmacy, 70210 Kuopio, Finland.
  • University of Eastern Finland, Institute of Biomedicine, 70210 Kuopio, Finland.
  • Centre for Integrative Metabolomics & Computational Biology, School of Science, Edith Cowan University, Joondalup 6027, Australia.
  • Chalmers University of Technology, Department of Biology and Biological Engineering, 41296 Gothenburg, Sweden.
  • Chalmers University of Technology, Chalmers Mass Spectrometry Infrastructure, 41296 Gothenburg, Sweden.
  • University of Turku, Department of Biochemistry, Food Chemistry and Food Development unit, 20014 Turun Yliopisto, Finland.

Abstract

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.

MeSH

Supervised Machine Learning, Workflow, Metabolomics, Unsupervised Machine Learning, Liquid Chromatography-Mass Spectrometry

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