Abstract

In the last decade, a revolution in liquid chromatography-mass spectrometry (LC-MS) based proteomics was unfolded with the introduction of dozens of novel instruments that incorporate additional data dimensions through innovative acquisition methodologies, in turn inspiring specialized data analysis pipelines. Simultaneously, a growing number of proteomics datasets have been made publicly available through data repositories such as ProteomeXchange, Zenodo and Skyline Panorama. However, developing algorithms to mine this data and assessing the performance on different platforms is currently hampered by the lack of a single benchmark experimental design. Therefore, we acquired a hybrid proteome mixture on different instrument platforms and in all currently available families of data acquisition. Here, we present a comprehensive Data-Dependent and Data-Independent Acquisition (DDA/DIA) dataset acquired using several of the most commonly used current day instrumental platforms. The dataset consists of over 700 LC-MS runs, including adequate replicates allowing robust statistics and covering over nearly 10 different data formats, including scanning quadrupole and ion mobility enabled acquisitions. Datasets are available via ProteomeXchange (PXD028735).

Measurement(s)

Digital Data Repository

Technology Type(s)

Digital Data Repository

Details

Title
A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics
Author
Bart, Van Puyvelde 1   VIAFID ORCID Logo  ; Daled Simon 1 ; Willems Sander 2 ; Gabriels Ralf 3   VIAFID ORCID Logo  ; Gonzalez de Peredo Anne 4   VIAFID ORCID Logo  ; Chaoui Karima 4 ; Mouton-Barbosa Emmanuelle 4 ; Bouyssié, David 4   VIAFID ORCID Logo  ; Boonen, Kurt 5 ; Hughes, Christopher J 6 ; Gethings Lee A 6 ; Perez-Riverol Yasset 7   VIAFID ORCID Logo  ; Bloomfield Nic 8 ; Tate, Stephen 8 ; Schiltz Odile 4 ; Martens Lennart 3   VIAFID ORCID Logo  ; Deforce Dieter 1 ; Dhaenens Maarten 1 

 Ghent University, ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent, Belgium (GRID:grid.5342.0) (ISNI:0000 0001 2069 7798) 
 Max Planck Institute of Biochemistry, Department of Proteomics and Signal Transduction, Martinsried, Germany (GRID:grid.418615.f) (ISNI:0000 0004 0491 845X) 
 VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium (GRID:grid.511525.7); Ghent University, Department of Biomolecular Medicine, Ghent, Belgium (GRID:grid.5342.0) (ISNI:0000 0001 2069 7798) 
 Université de Toulouse, CNRS, UPS, Institut de Pharmacologie et de Biologie Structural (IPBS), Toulouse, France (GRID:grid.15781.3a) (ISNI:0000 0001 0723 035X) 
 VITO Health, Mol, Belgium (GRID:grid.6717.7) (ISNI:0000000120341548); University of Antwerpen, Centre for Proteomics, Antwerp, Belgium (GRID:grid.5284.b) (ISNI:0000 0001 0790 3681) 
 Waters Corporation, Wilmslow, UK (GRID:grid.5284.b) 
 Wellcome Genome Campus, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK (GRID:grid.52788.30) (ISNI:0000 0004 0427 7672) 
 SCIEX, Concord, Ontario, Canada (GRID:grid.292651.b) (ISNI:0000 0004 0641 7691) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20524463
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2645337541
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.