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

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

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.

Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: Nature Portfolio 2022-05-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-022-30094-0

Internet

https://doi.org/10.1038/s41467-022-30094-0

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