A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics

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 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).

Competing Interest Statement

Chris Hughes and Lee Gethings are employed by Waters Corporation. Nic Bloomfield and Stephen Tate are employed by Sciex.

Footnotes

* https://panoramaweb.org/LFQBenchmark.url

* https://zenodo.org/record/5714380#.YZ49otDMKUk

* http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD028735