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DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics

Author

Listed:
  • Oliver Alka

    (University of Tübingen
    University of Tübingen)

  • Premy Shanthamoorthy

    (University of Toronto
    University of Toronto)

  • Michael Witting

    (Helmholtz Zentrum München
    Helmholtz Zentrum München
    Technical University of Munich)

  • Karin Kleigrewe

    (Technical University of Munich)

  • Oliver Kohlbacher

    (University of Tübingen
    University of Tübingen
    University Hospital Tübingen)

  • Hannes L. Röst

    (University of Toronto
    University of Toronto
    University of Toronto)

Abstract

The extraction of meaningful biological knowledge from high-throughput mass spectrometry data relies on limiting false discoveries to a manageable amount. For targeted approaches in metabolomics a main challenge is the detection of false positive metabolic features in the low signal-to-noise ranges of data-independent acquisition results and their filtering. Another factor is that the creation of assay libraries for data-independent acquisition analysis and the processing of extracted ion chromatograms have not been automated in metabolomics. Here we present a fully automated open-source workflow for high-throughput metabolomics that combines data-dependent and data-independent acquisition for library generation, analysis, and statistical validation, with rigorous control of the false-discovery rate while matching manual analysis regarding quantification accuracy. Using an experimentally specific data-dependent acquisition library based on reference substances allows for accurate identification of compounds and markers from data-independent acquisition data in low concentrations, facilitating biomarker quantification.

Suggested Citation

  • Oliver Alka & Premy Shanthamoorthy & Michael Witting & Karin Kleigrewe & Oliver Kohlbacher & Hannes L. Röst, 2022. "DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29006-z
    DOI: 10.1038/s41467-022-29006-z
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    References listed on IDEAS

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    1. Kerstin Scheubert & Franziska Hufsky & Daniel Petras & Mingxun Wang & Louis-Félix Nothias & Kai Dührkop & Nuno Bandeira & Pieter C. Dorrestein & Sebastian Böcker, 2017. "Significance estimation for large scale metabolomics annotations by spectral matching," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
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    Cited by:

    1. Mingdu Luo & Yandong Yin & Zhiwei Zhou & Haosong Zhang & Xi Chen & Hongmiao Wang & Zheng-Jiang Zhu, 2023. "A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    2. Aivett Bilbao & Nathalie Munoz & Joonhoon Kim & Daniel J. Orton & Yuqian Gao & Kunal Poorey & Kyle R. Pomraning & Karl Weitz & Meagan Burnet & Carrie D. Nicora & Rosemarie Wilton & Shuang Deng & Ziyu , 2023. "PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    3. Zhiqiang Pang & Lei Xu & Charles Viau & Yao Lu & Reza Salavati & Niladri Basu & Jianguo Xia, 2024. "MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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