A statistical framework for protein quantitation in bottom-up MS-based proteomics

Yuliya Karpievitch, Jeff Stanley, Thomas Taverner, Jianhua Huang, Joshua N. Adkins, Charles Ansong, Fred Heffron, Thomas O. Metz, Wei Jun Qian, Hyunjin Yoon, Richard D. Smith, Alan R. Dabney

    Research output: Contribution to journalArticle

    101 Scopus citations

    Abstract

    Motivation: Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level. Results: We present a statistical model that carefully accounts for informative missingness in peak intensities and allows unbiased, model-based, protein-level estimation and inference. The model is applicable to both label-based and label-free quantitation experiments. We also provide automated, model-based, algorithms for filtering of proteins and peptides as well as imputation of missing values. Two LC/MS datasets are used to illustrate the methods. In simulation studies, our methods are shown to achieve substantially more discoveries than standard alternatives.

    Original languageEnglish (US)
    Pages (from-to)2028-2034
    Number of pages7
    JournalBioinformatics
    Volume25
    Issue number16
    DOIs
    StatePublished - Aug 2009

    ASJC Scopus subject areas

    • Statistics and Probability
    • Biochemistry
    • Molecular Biology
    • Computer Science Applications
    • Computational Theory and Mathematics
    • Computational Mathematics

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  • Cite this

    Karpievitch, Y., Stanley, J., Taverner, T., Huang, J., Adkins, J. N., Ansong, C., Heffron, F., Metz, T. O., Qian, W. J., Yoon, H., Smith, R. D., & Dabney, A. R. (2009). A statistical framework for protein quantitation in bottom-up MS-based proteomics. Bioinformatics, 25(16), 2028-2034. https://doi.org/10.1093/bioinformatics/btp362