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Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity

Author

Listed:
  • Klemens Fröhlich

    (Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
    University of Freiburg
    University of Freiburg)

  • Eva Brombacher

    (University of Freiburg
    University of Freiburg
    Faculty of Medicine and Medical Center – University of Freiburg
    University of Freiburg)

  • Matthias Fahrner

    (Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
    University of Freiburg
    University of Freiburg)

  • Daniel Vogele

    (Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
    University of Freiburg)

  • Lucas Kook

    (University of Zurich
    Zurich University of Applied Sciences)

  • Niko Pinter

    (Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg)

  • Peter Bronsert

    (Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
    German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ)
    Tumorbank Comprehensive Cancer Center Freiburg, Medical Center University of Freiburg)

  • Sylvia Timme-Bronsert

    (Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
    Tumorbank Comprehensive Cancer Center Freiburg, Medical Center University of Freiburg)

  • Alexander Schmidt

    (University of Basel)

  • Katja Bärenfaller

    (University of Zurich, and Swiss Institute of Bioinformatics (SIB))

  • Clemens Kreutz

    (Faculty of Medicine and Medical Center – University of Freiburg
    University of Freiburg)

  • Oliver Schilling

    (Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
    German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ)
    BIOSS Centre for Biological Signaling Studies, University of Freiburg)

Abstract

Numerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking. We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best.

Suggested Citation

  • Klemens Fröhlich & Eva Brombacher & Matthias Fahrner & Daniel Vogele & Lucas Kook & Niko Pinter & Peter Bronsert & Sylvia Timme-Bronsert & Alexander Schmidt & Katja Bärenfaller & Clemens Kreutz & Oliv, 2022. "Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30094-0
    DOI: 10.1038/s41467-022-30094-0
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    References listed on IDEAS

    as
    1. Yi Yang & Guoquan Yan & Siyuan Kong & Mengxi Wu & Pengyuan Yang & Weiqian Cao & Liang Qiao, 2021. "GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    2. Brian C. Searle & Kristian E. Swearingen & Christopher A. Barnes & Tobias Schmidt & Siegfried Gessulat & Bernhard Küster & Mathias Wilhelm, 2020. "Generating high quality libraries for DIA MS with empirically corrected peptide predictions," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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    6. Yi Yang & Xiaohui Liu & Chengpin Shen & Yu Lin & Pengyuan Yang & Liang Qiao, 2020. "In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
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    Cited by:

    1. Fengchao Yu & Guo Ci Teo & Andy T. Kong & Klemens Fröhlich & Ginny Xiaohe Li & Vadim Demichev & Alexey I. Nesvizhskii, 2023. "Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

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