2020
DOI: 10.1101/2020.11.03.365585
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Extensive and accurate benchmarking of DIA acquisition methods and software tools using a complex proteomic standard

Abstract: In the past few years, LC-MS/MS in DIA mode has become a strategy of choice for deep coverage of complex proteomes and accurate quantification of low abundant species. However, there is still no consensus in the literature on the best acquisition parameters and processing tools to use. We present here the largest benchmark of DIA proteomic workflows on Orbitrap instruments ever published. Using a complex proteomic standard, we tested 36 workflows including 4 different acquisition schemes and 6 different softwa… Show more

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Cited by 6 publications
(9 citation statements)
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“…pdresults file of Proteome Discoverer. Detailed parameters used to generate both spectral libraries are listed in the supplementary table S1 of the Gotti et al article [1] .…”
Section: Experimental Design Materials and Methodsmentioning
confidence: 99%
See 2 more Smart Citations
“…pdresults file of Proteome Discoverer. Detailed parameters used to generate both spectral libraries are listed in the supplementary table S1 of the Gotti et al article [1] .…”
Section: Experimental Design Materials and Methodsmentioning
confidence: 99%
“…The dataset provided in this article has been initially generated with the aim to benchmark DIA acquisition methods and software tools [1] . As shown on Fig.…”
Section: Data Descriptionmentioning
confidence: 99%
See 1 more Smart Citation
“…An overview over available parameters and a short description is provided at https://nfco.re/diaproteomics. The default parametrization has been benchmarked multiple times in the past 23,37 . It involves spectral library assay generation with the six most intense b-and y-ion transitions falling into the precursor mass range of 400 to 1200 m/z and a fragment mass range of 350 to 2000 m/z.…”
Section: Parametrizationmentioning
confidence: 99%
“…sequence and concentration) are added to 'background' peptides with likewise known properties. To mimic the complexity encountered in realistic settings often different organisms are added in combination to create benchmark datasets 4 . These benchmark datasets are valuable tools for controlling and optimizing different aspects of data acquisition and analysis, including LC-MS/MS parameters, library generation, analysis software parameters, data preprocessing and statistical analysis for detecting differentially abundant proteins.…”
Section: Introductionmentioning
confidence: 99%