Metabolomics

, 12:98 | Cite as

Improved metabolite identification with MIDAS and MAGMa through MS/MS spectral dataset-driven parameter optimization

  • Dries Verdegem
  • Diether Lambrechts
  • Peter Carmeliet
  • Bart Ghesquière
Original Article

Abstract

Introduction

LC–MS/MS based untargeted metabolomics is evoking high interests in the metabolomics and broader biology community for its potential to uncover the contribution of unanticipated metabolic pathways to phenotypic observations. The major challenge for this methodology is making the computational metabolite identification as reliable as possible in order to reduce subsequent target candidate validation to a minimum. Metabolite library matching techniques based on precise masses and fragment mass patterns have become the de facto method in the field. However, in the literature the original methods are often under-validated, making it complicated to judge their intrinsic value.

Objectives

We aimed to demonstrate that large MS/MS metabolite spectral libraries can be used not only to validate and compare, but also to improve the methods.

Methods

Several computational tools for metabolite identification (MAGMa, CFM-ID, MetFrag, MIDAS) were applied on a large MS/MS dataset derived from Metlin. Their performance was first compared and for the two best-performing tools (MAGMa and MIDAS), the performance was then improved by applying a parameter fine-tuning procedure.

Results

We confirmed MIDAS and MAGMa as the state-of-the-art freely available tools for metabolite identification. Moreover, we were able to identify optimized working parameters, engendering an improvement in their performance. For MAGMa, dynamic, metabolite-dependent optimized parameters were obtained using machine learning techniques.

Conclusion

We were able to achieve an incremental increase in the identification accuracy of MIDAS and MAGMa. A wrapper script (MAGMa+) capable of calling MAGMa with tailored parameters is made available for download.

Keywords

Untargeted metabolomics Metabolite identification MAGMa Method comparison Method optimization Machine learning 

Notes

Acknowledgments

The authors wish to thank Marco Saerens and Pascal Francq (UCLouvain) and Yorick Poels, Matthieu Moisse and Bram Boeckx (VIB – KU Leuven) for providing computing power and technical support for this research.

Funding

This study was supported by a Federal Government Belgium grant (IUAP P7/03), long-term structural Methusalem funding by the Flemish Government, grants from the Research Foundation Flanders (FWO), the Foundation Leducq Transatlantic Network (ARTEMIS), Foundation against Cancer, an ERC Advanced Research Grant (EU-ERC269073), an ERC Consolidator Grant (RCN:191, 995), an AXA Research Fund, and by VIB.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Supplementary material

11306_2016_1036_MOESM1_ESM.docx (90 kb)
Supplementary material 1 (DOCX 89 kb)

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Dries Verdegem
    • 1
    • 2
  • Diether Lambrechts
    • 3
  • Peter Carmeliet
    • 2
  • Bart Ghesquière
    • 1
  1. 1.Metabolomics Expertise Center, Vesalius Research Center (VRC)VIB, KU Leuven - University of LeuvenLouvainBelgium
  2. 2.Laboratory of Angiogenesis and Neurovascular Link, Vesalius Research Center (VRC), Department of OncologyVIB, KU Leuven - University of LeuvenLouvainBelgium
  3. 3.Laboratory for Translational Genetics, Vesalius Research Center (VRC), Department of OncologyVIB, KU Leuven - University of LeuvenLouvainBelgium

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