Introduction

BACKGROUND: Untargeted metabolomics generates a huge amount of data. Software packages for automated data processing are crucial to successfully process these data. A variety of such software packages exist, but the outcome of data processing strongly depends on algorithm parameter settings. If they are not carefully chosen, suboptimal parameter settings can easily lead to biased results. Therefore, parameter settings also require optimization. Several parameter optimization approaches have already been proposed, but a software package for parameter optimization which is free of intricate experimental labeling steps, fast and widely applicable is still missing. RESULTS: We implemented the software package IPO ('Isotopologue Parameter Optimization') which is fast and free of labeling steps, and applicable to data from different kinds of samples and data from different methods of liquid chromatography - high resolution mass spectrometry and data from different instruments. IPO optimizes XCMS peak picking parameters by using natural, stable (13)C isotopic peaks to calculate a peak picking score. Retention time correction is optimized by minimizing relative retention time differences within peak groups. Grouping parameters are optimized by maximizing the number of peak groups that show one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiments, and the resulting scores are evaluated using response surface models. IPO was tested on three different data sets, each consisting of a training set and test set. IPO resulted in an increase of reliable groups (146% - 361%), a decrease of non-reliable groups (3% - 8%) and a decrease of the retention time deviation to one third. CONCLUSIONS: IPO was successfully applied to data derived from liquid chromatography coupled to high resolution mass spectrometry from three studies with different sample types and different chromatographic methods and devices. We were also able to show the potential of IPO to increase the reliability of metabolomics data. The source code is implemented in R, tested on Linux and Windows and it is freely available for download at https://github.com/glibiseller/IPO . The training sets and test sets can be downloaded from https://health.joanneum.at/IPO .

Publications

  1. IPO: a tool for automated optimization of XCMS parameters.
    Cite this
    Libiseller G, Dvorzak M, Kleb U, Gander E, Eisenberg T, Madeo F, Neumann S, Trausinger G, Sinner F, Pieber T, Magnes C, 2015-01-01 - BMC bioinformatics

Credits

  1. Gunnar Libiseller
    Developer

    Joanneum Research Forschungsgesellschaft m.b.H., HEALTH

  2. Michaela Dvorzak
    Developer

    Joanneum Research Forschungsgesellschaft m.b.H., POLICIES

  3. Ulrike Kleb
    Developer

    Joanneum Research Forschungsgesellschaft m.b.H., POLICIES

  4. Edgar Gander
    Developer

    Joanneum Research Forschungsgesellschaft m.b.H., HEALTH

  5. Tobias Eisenberg
    Developer

    Institute of Molecular Biosciences, NAWI Graz

  6. Frank Madeo
    Developer

    BioTechMed Graz, 8010

  7. Steffen Neumann
    Developer

    Department of Stress- and Developmental Biology, Leibniz Institute of Plant Biochemistry

  8. Gert Trausinger
    Developer

    Joanneum Research Forschungsgesellschaft m.b.H., HEALTH

  9. Frank Sinner
    Developer

    Department of Internal Medicine, Medical University of Graz

  10. Thomas Pieber
    Developer

    Department of Internal Medicine, Medical University of Graz

  11. Christoph Magnes
    Investigator

    Joanneum Research Forschungsgesellschaft m.b.H., HEALTH

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Summary
AccessionBT006617
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Submitted ByChristoph Magnes