Benchmarking quantitative label-free LC-MS data processing workflows using a complex spiked proteomic standard dataset.
Fiche publication
Date publication
janvier 2016
Journal
Journal of proteomics
Auteurs
Membres identifiés du Cancéropôle Est :
Dr CIANFERANI Sarah, Mme SCHAEFFER-REISS Christine, Dr VAN DORSSELAER Alain, Dr CARAPITO Christine
Tous les auteurs :
Ramus C, Hovasse A, Marcellin M, Hesse AM, Mouton-Barbosa E, Bouyssié D, Vaca S, Carapito C, Chaoui K, Bruley C, Garin J, Cianférani S, Ferro M, Van Dorssaeler A, Burlet-Schiltz O, Schaeffer C, Couté Y, Gonzalez de Peredo A
Lien Pubmed
Résumé
Proteomic workflows based on nanoLC-MS/MS data-dependent-acquisition analysis have progressed tremendously in recent years. High-resolution and fast sequencing instruments have enabled the use of label-free quantitative methods, based either on spectral counting or on MS signal analysis, which appear as an attractive way to analyze differential protein expression in complex biological samples. However, the computational processing of the data for label-free quantification still remains a challenge. Here, we used a proteomic standard composed of an equimolar mixture of 48 human proteins (Sigma UPS1) spiked at different concentrations into a background of yeast cell lysate to benchmark several label-free quantitative workflows, involving different software packages developed in recent years. This experimental design allowed to finely assess their performances in terms of sensitivity and false discovery rate, by measuring the number of true and false-positive (respectively UPS1 or yeast background proteins found as differential). The spiked standard dataset has been deposited to the ProteomeXchange repository with the identifier PXD001819 and can be used to benchmark other label-free workflows, adjust software parameter settings, improve algorithms for extraction of the quantitative metrics from raw MS data, or evaluate downstream statistical methods.
Mots clés
Benchmarking, methods, Chromatography, Liquid, standards, Mass Spectrometry, standards, Proteome, analysis, Reproducibility of Results, Sensitivity and Specificity, Software, Software Validation, Staining and Labeling, Workflow
Référence
J Proteomics. 2016 Jan;132:51-62