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

Klemens Fröhlich, Eva Brombacher, Matthias Fahrner, Daniel Vogele, Lucas Kook, Niko Pinter, Peter Bronsert, Sylvia Timme-Bronsert, Alexander Schmidt, Katja Bärenfaller, Clemens Kreutz and Oliver Schilling ()
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Klemens Fröhlich: Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
Eva Brombacher: University of Freiburg
Matthias Fahrner: Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
Daniel Vogele: Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
Lucas Kook: University of Zurich
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
Sylvia Timme-Bronsert: Medical Center—University of Freiburg, Faculty of Medicine, 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
Oliver Schilling: Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-30094-0

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