ABRF Proteome Informatics Research Group (iPRG) 2015 Study: Detection of Differentially Abundant Proteins in Label-Free Quantitative LC–MS/MS Experiments

• Published in: Journal of proteome research Vol. 16; no. 2; pp. 945 - 957
• Main Authors: Choi, Meena, Eren-Dogu, Zeynep F, Colangelo, Christopher, Cottrell, John, Hoopmann, Michael R, Kapp, Eugene A, Kim, Sangtae, Lam, Henry, Neubert, Thomas A, Palmblad, Magnus, Phinney, Brett S, Weintraub, Susan T, MacLean, Brendan, Vitek, Olga
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 03.02.2017
• Subjects: Chromatography, Liquid - standards; Data Interpretation, Statistical; Humans; Laboratory Proficiency Testing; Professional Competence; Proteome - isolation & purification; Proteome - standards; Proteomics - instrumentation; Proteomics - methods; Proteomics - standards; Reproducibility of Results; Tandem Mass Spectrometry - standards; Uncertainty; mass spectrometry; LC−MS/MS; quantitative proteomics; bioinformatics; differential abundance; statistics
• DOI: 10.1021/acs.jproteome.6b00881
• ISSN: 1535-3893

Detection of differentially abundant proteins in label-free quantitative shotgun liquid chromatography–tandem mass spectrometry (LC–MS/MS) experiments requires a series of computational steps that identify and quantify LC–MS features. It also requires statistical analyses that distinguish systematic changes in abundance between conditions from artifacts of biological and technical variation. The 2015 study of the Proteome Informatics Research Group (iPRG) of the Association of Biomolecular Resource Facilities (ABRF) aimed to evaluate the effects of the statistical analysis on the accuracy of the results. The study used LC–tandem mass spectra acquired from a controlled mixture, and made the data available to anonymous volunteer participants. The participants used methods of their choice to detect differentially abundant proteins, estimate the associated fold changes, and characterize the uncertainty of the results. The study found that multiple strategies (including the use of spectral counts versus peak intensities, and various software tools) could lead to accurate results, and that the performance was primarily determined by the analysts’ expertise. This manuscript summarizes the outcome of the study, and provides representative examples of good computational and statistical practice. The data set generated as part of this study is publicly available.