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Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and

Zhucui Li1, Yan Lu2, Yufeng Guo3

  • 1University of Chinese Academy of Sciences, Beijing 100049, China; iHuman Institute, ShanghaiTech University, Shanghai 201210, China; Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.

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Summary

This study evaluated untargeted metabolomics software for data analysis. MZmine 2 showed superior quantification accuracy and biomarker identification, suggesting its use for reliable metabolomics results.

Keywords:
Data processing softwareDiscriminating marker selectionFeature detectionFeature quantificationUntargeted metabolomics

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Area of Science:

  • Analytical Chemistry
  • Bioinformatics
  • Metabolomics

Background:

  • Untargeted metabolomics data analysis is challenging due to large datasets.
  • Existing software for processing high-resolution mass spectrometry data lacks comprehensive evaluation.
  • A standardized benchmark dataset is needed to assess software performance.

Purpose of the Study:

  • To comprehensively evaluate five untargeted metabolomics software packages.
  • To compare their performance in feature detection, quantification, and marker selection.
  • To guide users in selecting appropriate tools and improve bioinformatics software.

Main Methods:

  • Acquired a benchmark dataset from standard mixtures (1100 compounds).
  • Evaluated MS-Dial, MZmine 2, XCMS, MarkerView, and Compound Discoverer.
  • Assessed performance using the benchmark and a real-case metabolomics dataset.

Main Results:

  • All evaluated software showed similar feature detection performance.
  • Significant differences were observed in relative quantification accuracy.
  • MZmine 2 demonstrated superior quantification accuracy and more reliable biomarker identification.

Conclusions:

  • MZmine 2 is recommended for accurate quantification and biomarker discovery in untargeted metabolomics.
  • Combined usage of two software packages can increase confidence in biomarker identification.
  • Findings will aid in improving bioinformatics tools and interpreting metabolomics data.