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

Fröhlich, Klemens; Brombacher, Eva; Fahrner, Matthias; Vogele, Daniel; Kook, Lucas; Pinter, Niko; Bronsert, Peter; Timme-Bronsert, Sylvia; Schmidt, Alexander; Bärenfaller, Katja; Kreutz, Clemens; Schilling, Oliver

doi:10.1038/s41467-022-30094-0

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-31214

Publication type:	Article in scientific journal
Type of review:	Open peer review
Title:	Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity
Authors:	Fröhlich, Klemens Brombacher, Eva Fahrner, Matthias Vogele, Daniel Kook, Lucas Pinter, Niko Bronsert, Peter Timme-Bronsert, Sylvia Schmidt, Alexander Bärenfaller, Katja Kreutz, Clemens Schilling, Oliver
et. al:	No
DOI:	10.1038/s41467-022-30094-0 10.21256/zhaw-31214
Published in:	Nature Communications
Volume(Issue):	13
Issue:	1
Page(s):	2622
Issue Date:	12-May-2022
Publisher / Ed. Institution:	Nature Publishing Group
ISSN:	2041-1723
Language:	English
Subjects:	Human; Proteome; Software; Workflow; Benchmarking; Proteomics
Subject (DDC):	005: Computer programming, programs and data
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.
URI:	https://digitalcollection.zhaw.ch/handle/11475/31214
Fulltext version:	Published version
License (according to publishing contract):	CC BY 4.0: Attribution 4.0 International
Departement:	School of Engineering
Organisational Unit:	Institute of Data Analysis and Process Design (IDP)
Appears in collections:	Publikationen School of Engineering

Files in This Item:

File	Description	Size	Format
2022_Frohlich-etal_Analysis-strategies-for-data-independent-acquisition-proteomics.pdf		2.14 MB	Adobe PDF	View/Open

Show full item record

Fröhlich, K., Brombacher, E., Fahrner, M., Vogele, D., Kook, L., Pinter, N., Bronsert, P., Timme-Bronsert, S., Schmidt, A., Bärenfaller, K., Kreutz, C., & Schilling, O. (2022). Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity. Nature Communications, 13(1), 2622. https://doi.org/10.1038/s41467-022-30094-0

Fröhlich, K. et al. (2022) ‘Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity’, Nature Communications, 13(1), p. 2622. Available at: https://doi.org/10.1038/s41467-022-30094-0.

K. Fröhlich et al., “Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity,” Nature Communications, vol. 13, no. 1, p. 2622, May 2022, doi: 10.1038/s41467-022-30094-0.

FRÖHLICH, Klemens, Eva BROMBACHER, Matthias FAHRNER, Daniel VOGELE, Lucas KOOK, Niko PINTER, Peter BRONSERT, Sylvia TIMME-BRONSERT, Alexander SCHMIDT, Katja BÄRENFALLER, Clemens KREUTZ und Oliver SCHILLING, 2022. Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity. Nature Communications. 12 Mai 2022. Bd. 13, Nr. 1, S. 2622. DOI 10.1038/s41467-022-30094-0

Fröhlich, Klemens, Eva Brombacher, Matthias Fahrner, Daniel Vogele, Lucas Kook, Niko Pinter, Peter Bronsert, et al. 2022. “Benchmarking of Analysis Strategies for Data-Independent Acquisition Proteomics Using a Large-Scale Dataset Comprising Inter-Patient Heterogeneity.” Nature Communications 13 (1): 2622. https://doi.org/10.1038/s41467-022-30094-0.

Fröhlich, Klemens, et al. “Benchmarking of Analysis Strategies for Data-Independent Acquisition Proteomics Using a Large-Scale Dataset Comprising Inter-Patient Heterogeneity.” Nature Communications, vol. 13, no. 1, May 2022, p. 2622, https://doi.org/10.1038/s41467-022-30094-0.