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Related Experiment Video

Updated: Dec 8, 2025

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Comparison of Three Untargeted Data Processing Workflows for Evaluating LC-HRMS Metabolomics Data.

Selina Hemmer1, Sascha K Manier1, Svenja Fischmann2

  • 1Department of Experimental and Clinical Toxicology, Institute of Experimental and Clinical Pharmacology and Toxicology, Center for Molecular Signaling (PZMS), Saarland University, 66421 Homburg, Germany.

Metabolites
|September 24, 2020
PubMed
Summary

Comparing liquid chromatography-high resolution mass spectrometry (LC-HRMS) data processing workflows is vital for accurate untargeted metabolomics. This study evaluated commercial and open-source tools, finding Compound Discoverer user-friendly for small studies, while R offers flexibility for complex analyses.

Keywords:
A-CHMINACALC-HRMSdata processingfeature detectionuntargeted metabolomics

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Area of Science:

  • Metabolomics
  • Analytical Chemistry
  • Biotechnology

Background:

  • Untargeted metabolomics relies on accurate processing of liquid chromatography-high resolution mass spectrometry (LC-HRMS) data.
  • Minimizing false positives in LC-HRMS data is critical for reliable biological interpretation.
  • Various commercial and open-source software solutions exist for LC-HRMS data analysis.

Purpose of the Study:

  • To compare the performance of three distinct LC-HRMS data processing workflows.
  • To evaluate commercial (Compound Discoverer 3.1) and open-source (XCMS Online/MetaboAnalyst 4.0, R) approaches.
  • To assess workflows based on feature identification, peak quality, and statistical outcomes.

Main Methods:

  • Standardized datasets were generated using pooled human liver microsomes incubated with A-CHMINACA.
  • LC-HRMS analysis was conducted using normal- and reversed-phase chromatography with positive/negative mode full scan MS.
  • MS/MS spectra were acquired for significant features in a separate run.

Main Results:

  • Compound Discoverer 3.1 demonstrated ease of use, suitable for simpler metabolomics studies.
  • XCMS Online/MetaboAnalyst 4.0 and R offered extensive customization and flexibility.
  • The R workflow required advanced programming skills but provided high adaptability for complex research questions.

Conclusions:

  • Workflow selection depends on study complexity and user expertise.
  • Commercial software offers accessibility for routine analyses.
  • Open-source solutions provide greater flexibility for advanced and customized metabolomics research.