Journal of Proteome Research · 2021 · 84 citations · 35 references
Life SciencesBiochemistryMulti-omics IntegrationProteomicsProteomics InformaticsMass Spectrometry Data AnalysisMass Spectrometry-based ProteomicsProtein IdentificationQuantitative ProteomicsBenchmarking Proteomics WorkflowsData Acquisition ParametersData-independent AcquisitionEscherichia ColiInstrument LinearityLinearityOrbitrap Mass SpectrometryQuantitative Proteomics Workflows
Over the past decade, the data-independent acquisition mode has gained popularity for broad coverage of complex proteomes by LC-MS/MS and quantification of low-abundance proteins. However, there is no consensus in the literature on the best data acquisition parameters and processing tools to use for this specific application. Here, we present the most comprehensive comparison of DIA workflows on Orbitrap instruments published so far in the field of proteomics. Using a standard human 48 proteins mixture (UPS1–Sigma) at 8 different concentrations in an E. coli proteome background, we tested 36 workflows including 4 different DIA window acquisition schemes and 6 different software tools (DIA-NN, DIA-Umpire, OpenSWATH, ScaffoldDIA, Skyline, and Spectronaut) with or without the use of a DDA spectral library. On the basis of the number of proteins identified, quantification linearity and reproducibility, as well as sensitivity and specificity in 28 pairwise comparisons of different UPS1 concentrations, we summarize the major considerations and propose guidelines for choosing the DIA workflow best suited for LC-MS/MS proteomic analyses. Our 96 DIA raw files and software outputs have been deposited on ProteomeXchange for testing or developing new DIA processing tools.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Xavier Robin, Natacha Turck, Alexandre Hainard et al. · BMC Bioinformatics · 2011 · 14K citations · Full text
Jürgen Cox, Marco Y. Hein, Christian A. Luber et al. · Molecular & Cellular Proteomics · 2014 · 5.5K citations · Full text