Evidence map›Paper›PMID 32244411›Full record

ArticleMetabolites2020

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

Anton Klåvus, Marietta Kokla, Stefania Noerman, Ville M Koistinen, Marjo Tuomainen, Iman Zarei, Topi Meuronen, Merja R Häkkinen, Soile Rummukainen, Ambrin Farizah Babu and 6 more

Abstract read
In one paragraph

Article in Metabolites, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers.

0numbers the graph read from it
0cells of the map it votes in
93citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

93 citing papers in PubMed.

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  7. MiR-940 Suppresses Ferroptosis by Controlling Expression of Key Regulatory Genes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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33 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Anton KlåvusUniversity of Eastern Finland, Department of Clinical Nutrition and Public Health, 70210 Kuopio, Finland.
Marietta KoklaUniversity of Eastern Finland, Department of Clinical Nutrition and Public Health, 70210 Kuopio, Finland.
Stefania NoermanUniversity of Eastern Finland, Department of Clinical Nutrition and Public Health, 70210 Kuopio, Finland.
Ville M KoistinenUniversity of Eastern Finland, Department of Clinical Nutrition and Public Health, 70210 Kuopio, Finland.
Marjo TuomainenUniversity of Eastern Finland, Department of Clinical Nutrition and Public Health, 70210 Kuopio, Finland.
Iman ZareiUniversity of Eastern Finland, Department of Clinical Nutrition and Public Health, 70210 Kuopio, Finland.
Topi MeuronenUniversity of Eastern Finland, Department of Clinical Nutrition and Public Health, 70210 Kuopio, Finland.
Merja R HäkkinenUniversity of Eastern Finland, School of Pharmacy, 70210 Kuopio, Finland.
Soile RummukainenUniversity of Eastern Finland, School of Pharmacy, 70210 Kuopio, Finland.
Ambrin Farizah BabuUniversity of Eastern Finland, Department of Clinical Nutrition and Public Health, 70210 Kuopio, Finland.
Taisa SallinenUniversity of Eastern Finland, Department of Clinical Nutrition and Public Health, 70210 Kuopio, Finland.
Olli KärkkäinenUniversity of Eastern Finland, School of Pharmacy, 70210 Kuopio, Finland.
Jussi PaananenUniversity of Eastern Finland, Institute of Biomedicine, 70210 Kuopio, Finland.
David BroadhurstCentre for Integrative Metabolomics & Computational Biology, School of Science, Edith Cowan University, Joondalup 6027, Australia.
Carl BruniusChalmers University of Technology, Department of Biology and Biological Engineering, 41296 Gothenburg, Sweden.
Kati HanhinevaUniversity of Eastern Finland, Department of Clinical Nutrition and Public Health, 70210 Kuopio, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

computational statisticalLC–MSmass spectrometrymetabolic profilingmetabolomicspathway analysissupervised learningunsupervised learning

Identifiers

PMID32244411
PMCPMC7240970

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.