title: Comparative evaluation of label-free quantification methods for shotgun proteomics.
abstract: BACKGROUND
  Label-free quantification (LFQ) is a popular strategy for shotgun proteomics. A variety of LFQ algorithms have been developed recently. However, a comprehensive comparison of the most commonly used LFQ methods is still rare, in part due to a lack of clear metrics for their evaluation and an annotated and quantitatively well-characterized data set.
  
  
  METHODS
  Five LFQ methods were compared: spectral counting based algorithms SIN, emPAI, and NSAF, and approaches relying on the extracted ion chromatogram (XIC) intensities, MaxLFQ and Quanti. We used three criteria for performance evaluation: coefficient of variation (CV) of protein abundances between replicates; analysis of variance (ANOVA); and the root-mean-square error of logarithmized calculated concentration ratios, referred to as standard quantification error (SQE). Comparison was performed using a quantitatively annotated publicly available data set.
  
  
  RESULTS
  The best results in terms of inter-replicate reproducibility were observed for MaxLFQ and NSAF, although they exhibited larger standard quantification errors. Using NSAF, all quantitatively annotated proteins were correctly identified in the Bonferronni-corrected results of the ANOVA test. SINwas found to be the most accurate in terms of SQE. Finally, the current implementations of XIC-based LFQ methods did not outperform the methods based on spectral counting for the data set used in this study.
  
  
  CONCLUSIONS
  Surprisingly, the performances of XIC-based approaches measured using three independent metrics were found to be comparable with more straightforward and simple MS/MS-based spectral counting approaches. The study revealed no clear leader among the latter. Copyright © 2017 John Wiley & Sons, Ltd.
authors.affiliation: Institute for Energy Problems of Chemical Physics, Russian Academy of Sciences, 119334, Moscow, Russia.|Moscow Institute of Physics and Technology (State University), 141700, Dolgoprudny, Russia.
authors.forename: Julia A
authors.initials: JA
authors.lastname: Bubis
authors.affiliation: Institute for Energy Problems of Chemical Physics, Russian Academy of Sciences, 119334, Moscow, Russia.|Moscow Institute of Physics and Technology (State University), 141700, Dolgoprudny, Russia.
authors.forename: Lev I
authors.initials: LI
authors.lastname: Levitsky
authors.affiliation: Institute for Energy Problems of Chemical Physics, Russian Academy of Sciences, 119334, Moscow, Russia.|Moscow Institute of Physics and Technology (State University), 141700, Dolgoprudny, Russia.
authors.forename: Mark V
authors.initials: MV
authors.lastname: Ivanov
authors.affiliation: Institute for Energy Problems of Chemical Physics, Russian Academy of Sciences, 119334, Moscow, Russia.
authors.forename: Irina A
authors.initials: IA
authors.lastname: Tarasova
authors.affiliation: Institute for Energy Problems of Chemical Physics, Russian Academy of Sciences, 119334, Moscow, Russia.|Moscow Institute of Physics and Technology (State University), 141700, Dolgoprudny, Russia.
authors.forename: Mikhail V
authors.identifier: http://orcid.org/0000-0001-9572-3452
authors.initials: MV
authors.lastname: Gorshkov
mesh_terms: D000465:Algorithms; D010446:Peptide Fragments / Q000032:analysis / Q000737:chemistry; D020543:Proteome / Q000032:analysis* / Q000737:chemistry; D040901:Proteomics / Q000379:methods* / Q000592:standards; D015203:Reproducibility of Results; D029701:Saccharomyces cerevisiae Proteins / Q000032:analysis / Q000737:chemistry; D053719:Tandem Mass Spectrometry / Q000379:methods* / Q000592:standards
chemical_list: D010446:Peptide Fragments; D020543:Proteome; D029701:Saccharomyces cerevisiae Proteins