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Related Experiment Video

Updated: Dec 25, 2025

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
07:34

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

Anton Klåvus1, Marietta Kokla1, Stefania Noerman1

  • 1University of Eastern Finland, Department of Clinical Nutrition and Public Health, 70210 Kuopio, Finland.

Metabolites
|April 5, 2020
PubMed
Summary

This protocol introduces notame, a workflow for non-targeted metabolomics analysis using liquid chromatography-mass spectrometry. It details methods for data production, preprocessing, and statistical analysis to identify significant metabolic findings.

Keywords:
LC–MScomputational statisticalmass spectrometrymetabolic profilingmetabolomicspathway analysissupervised learningunsupervised learning

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Last Updated: Dec 25, 2025

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Analytical Chemistry

Background:

  • Metabolomics generates large datasets requiring integrated expertise for meaningful discovery.
  • Non-targeted metabolic profiling is crucial for identifying novel biomarkers and biological insights.

Purpose of the Study:

  • To introduce 'notame', a comprehensive analytical workflow for non-targeted metabolomics.
  • To provide detailed protocols for data acquisition, preprocessing, and statistical analysis in nutritional metabolomics.

Main Methods:

  • Utilizing liquid chromatography-mass spectrometry (LC-MS) for metabolic profiling.
  • Implementing robust statistical methods for analyzing complex metabolomics data.
  • Describing step-by-step protocols for data production, preprocessing, and compound identification.

Main Results:

  • The 'notame' workflow enables coherent, high-quality data generation for metabolomics research.
  • The protocol facilitates the discovery of robust and biologically significant metabolic findings.
  • Detailed methods support the identification and interpretation of key metabolites.

Conclusions:

  • The 'notame' workflow offers a standardized approach to non-targeted metabolomics.
  • This protocol enhances the efficiency and reliability of nutritional metabolomics studies.
  • It empowers researchers to derive deeper biological insights from complex metabolic data.