“Notame”: Workflow for non-targeted LC-MS metabolic profiling
Journal article, 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.
Pathway analysis
Computational statistical
LC-MS
Unsupervised learning
Metabolic profiling
Supervised learning
Metabolomics
Mass spectrometry
Author
Anton Klåvus
University of Eastern Finland
Marietta Kokla
University of Eastern Finland
Stefania Noerman
University of Eastern Finland
Ville Mikael Koistinen
University of Eastern Finland
Marjo Tuomainen
University of Eastern Finland
Iman Zarei
University of Eastern Finland
Topi Meuronen
University of Eastern Finland
Merja R. Häkkinen
University of Eastern Finland
Soile Rummukainen
University of Eastern Finland
Ambrin Farizah Babu
University of Eastern Finland
Taisa Sallinen
University of Eastern Finland
Olli Kärkkäinen
University of Eastern Finland
Jussi Paananen
University of Eastern Finland
David Broadhurst
Edith Cowan University
Carl Brunius
Chalmers, Biology and Biological Engineering, Food and Nutrition Science
Kati Hanhineva
University of Eastern Finland
University of Turku
Chalmers, Biology and Biological Engineering, Food and Nutrition Science
Metabolites
2218-1989 (ISSN)
Vol. 10 4 135Subject Categories
Cell and Molecular Biology
DOI
10.3390/metabo10040135