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J Proteome Res. 2017 Feb 3;16(2):945-957. doi: 10.1021/acs.jproteome.6b00881. Epub 2017 Jan 3.

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

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Northeastern University , Boston, Massachusetts 02115, United States.
Mugla Sitki Kocman University , 48000 Mugla, Turkey.
Primary Ion, LLC , Old Lyme, Connecticut 06371, United States.
Matrix Science Ltd. , London W1U 7GB, U.K.
Institute for Systems Biology , Seattle, Washington 98109, United States.
Walter and Eliza Hall Institute of Medical Research , Melbourne 3052, Australia.
Pacific Northwest National Laboratory , Richland, Washington 99354, United States.
Department of Chemical and Biomolecular Engineering and Division of Biomedical Engineering, The Hong Kong University of Science and Technology , Clear Water Bay, Hong Kong.
Skirball Institute and Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine , New York, New York 10016, United States.
Center for Proteomics and Metabolomics, Leiden University Medical Center , 2333 ZA Leiden, The Netherlands.
University of California at Davis , Davis, California 95616, United States.
University of Texas Health Science Center at San Antonio , San Antonio, Texas 78229, United States.
University of Washington , Seattle, Washington 98105, United States.


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.


LC−MS/MS; bioinformatics; differential abundance; mass spectrometry; quantitative proteomics; statistics

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