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; Oliver Schilling

doi:10.1038/s41467-022-30094-0

Nature Communications (May 2022)

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,
Oliver Schilling

Affiliations

Klemens Fröhlich: Institute for Surgical Pathology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
Eva Brombacher: Faculty of Biology, University of Freiburg
Matthias Fahrner: Institute for Surgical Pathology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
Daniel Vogele: Institute for Surgical Pathology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
Lucas Kook: Epidemiology, Biostatistics & Prevention Institute, University of Zurich
Niko Pinter: Institute for Surgical Pathology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
Peter Bronsert: Institute for Surgical Pathology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
Sylvia Timme-Bronsert: Institute for Surgical Pathology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
Alexander Schmidt: Proteomics Core Facility, Biozentrum, University of Basel
Katja Bärenfaller: Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, and Swiss Institute of Bioinformatics (SIB)
Clemens Kreutz: Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center – University of Freiburg
Oliver Schilling: Institute for Surgical Pathology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg

DOI: https://doi.org/10.1038/s41467-022-30094-0
Journal volume & issue: Vol. 13, no. 1
pp. 1 – 13

Abstract

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Data independent acquisition (DIA) has been gaining momentum in clinical proteomics. Here, the authors create a benchmark dataset comprising inter-patient heterogeneity to compare popular DIA data analysis workflows for identifying differentially abundant proteins.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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