“Notame”: Workflow for non-targeted LC-MS metabolic profiling
Artikel i vetenskaplig tidskrift, 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


Unsupervised learning

Metabolic profiling

Supervised learning


Mass spectrometry


Anton Klåvus

Itä-Suomen Yliopisto

Marietta Kokla

Itä-Suomen Yliopisto

Stefania Noerman

Itä-Suomen Yliopisto

Ville Mikael Koistinen

Itä-Suomen Yliopisto

Marjo Tuomainen

Itä-Suomen Yliopisto

Iman Zarei

Itä-Suomen Yliopisto

Topi Meuronen

Itä-Suomen Yliopisto

Merja R. Häkkinen

Itä-Suomen Yliopisto

Soile Rummukainen

Itä-Suomen Yliopisto

Ambrin Farizah Babu

Itä-Suomen Yliopisto

Taisa Sallinen

Itä-Suomen Yliopisto

Olli Kärkkäinen

Itä-Suomen Yliopisto

Jussi Paananen

Itä-Suomen Yliopisto

David Broadhurst

Edith Cowan University

Carl Brunius

Chalmers, Biologi och bioteknik, Livsmedelsvetenskap

Kati Hanhineva

Itä-Suomen Yliopisto

Turun Yliopisto

Chalmers, Biologi och bioteknik, Livsmedelsvetenskap


2218-1989 (ISSN)

Vol. 10 4 135


Cell- och molekylärbiologi



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