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Related Experiment Video

Updated: Jun 17, 2026

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
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Modular comparison of untargeted metabolomics processing steps.

Markus Aigensberger1, Christoph Bueschl2, Ezequias Castillo-Lopez3

  • 1Christian Doppler Laboratory for Innovative Gut Health Concepts of Livestock, Austria; BOKU University, Vienna, Dept. IFA-Tulln, Institute of Bioanalytics and Agro-Metabolomics, Tulln, Austria.

Analytica Chimica Acta
|January 9, 2025
PubMed
Summary
This summary is machine-generated.

Choosing the right untargeted metabolomics software is crucial, as different tools yield varied results. MS-DIAL showed the most similarity to manual integration, and optimizing data processing improves accuracy.

Keywords:
Anion exchange chromatographyData treatmentMass spectrometryMetabolomicsProcessing

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Area of Science:

  • Metabolomics
  • Analytical Chemistry
  • Bioinformatics

Background:

  • Untargeted metabolomics demands robust data processing for reliable insights.
  • Software tool selection significantly impacts analysis outcomes.
  • Evaluation of four common metabolomics workflows was performed.

Purpose of the Study:

  • To comprehensively evaluate four metabolomics data processing workflows.
  • To compare the performance of XCMS, Compound Discoverer, MS-DIAL, and MZmine.
  • To assess post-processing strategies for untargeted metabolomics data.

Main Methods:

  • Applied four software tools (XCMS, Compound Discoverer, MS-DIAL, MZmine) to bovine saliva data.
  • Analyzed anion exchange chromatography coupled to high resolution mass spectrometry data.
  • Investigated missing value imputation, transformation, scaling, and filtering strategies.

Main Results:

  • Significant differences in feature detection and overlap were observed; only ~8% of features were common across all tools.
  • MS-DIAL exhibited the highest similarity to manual integration, followed by XCMS and MZmine.
  • Imputation with small values for low-abundance features, auto scaling, and low blank filtering thresholds enhanced data quality.

Conclusions:

  • Workflow selection critically influences untargeted metabolomics results.
  • MS-DIAL offers reliable feature detection, comparable to manual integration.
  • Optimized post-processing, particularly for missing values and filtering, is essential for accurate metabolomics analysis.