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Article Dans Une Revue Data in Brief Année : 2016

Spiked proteomic standard dataset for testing label-free quantitative software and statistical methods

Claire Ramus
  • Fonction : Auteur
Agnès Hovasse
Anne-Marie Hesse
  • Fonction : Auteur
Christophe Bruley
  • Fonction : Auteur
Jérôme Garin
  • Fonction : Auteur
Myriam Ferro
  • Fonction : Auteur
Yohann Couté

Résumé

This data article describes a controlled, spiked proteomic dataset for which the “ground truth” of variant proteins is known. It is based on the LC-MS analysis of samples composed of a fixed background of yeast lysate and different spiked amounts of the UPS1 mixture of 48 recombinant proteins. It can be used to objectively evaluate bioinformatic pipelines for label-free quantitative analysis, and their ability to detect variant proteins with good sensitivity and low false discovery rate in large-scale proteomic studies. More specifically, it can be useful for tuning software tools parameters, but also testing new algorithms for label-free quantitative analysis, or for evaluation of downstream statistical methods. The raw MS files can be downloaded from ProteomeXchange with identifier http://www.ebi.ac.uk/pride/archive/projects/PXD001819. Starting from some raw files of this dataset, we also provide here some processed data obtained through various bioinformatics tools (including MaxQuant, Skyline, MFPaQ, IRMa-hEIDI and Scaffold) in different workflows, to exemplify the use of such data in the context of software benchmarking, as discussed in details in the accompanying manuscript [1]. The experimental design used here for data processing takes advantage of the different spike levels introduced in the samples composing the dataset, and processed data are merged in a single file to facilitate the evaluation and illustration of software tools results for the detection of variant proteins with different absolute expression levels and fold change values.
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Dates et versions

hal-03080142 , version 1 (17-12-2020)

Identifiants

Citer

Claire Ramus, Agnès Hovasse, Marlène Marcellin, Anne-Marie Hesse, Emmanuelle Mouton-Barbosa, et al.. Spiked proteomic standard dataset for testing label-free quantitative software and statistical methods. Data in Brief, 2016, 6, pp.286-294. ⟨10.1016/j.dib.2015.11.063⟩. ⟨hal-03080142⟩
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