Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Marietta Kokla
* ,
Anton Klåvus
* ,
Stefania Noerman
,
Ville M. Koistinen
ORCID logo ,
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
*
Version 1 : Received: 31 January 2020 / Approved: 3 February 2020 / Online: 3 February 2020 (05:54:14 CET)

How to cite: Kokla, M.; Klåvus, A.; 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. Preprints 2020, 2020020019. https://doi.org/10.20944/preprints202002.0019.v1 Kokla, M.; Klåvus, A.; 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. Preprints 2020, 2020020019. https://doi.org/10.20944/preprints202002.0019.v1

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 enables 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.

Supplementary and Associated Material

https://github.com/antonvsdata/notame: R package for the workflow
https://github.com/antonvsdata/wranglr: Wranglr web application

Keywords

metabolomics; LC-MS; mass spectrometry; metabolic profiling; computational; statistical; unsupervised learning; supervised learning; pathway analysis

Subject

Biology and Life Sciences, Endocrinology and Metabolism

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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