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

    94 Citations (Scopus)

    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

    Fingerprint

    Proteomics
    Bottom-up
    Peptides
    Proteins
    Protein
    Labels
    Model-based
    Confidence Measure
    Missing Values
    Imputation
    Mass Spectrometry
    Statistical Models
    Statistical Model
    Mass spectrometry
    Filtering
    Simulation Study
    Framework
    Alternatives
    Model
    Estimate

    ASJC Scopus subject areas

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

    Cite this

    Karpievitch, Y., Stanley, J., Taverner, T., Huang, J., Adkins, J. N., Ansong, C., ... 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

    A statistical framework for protein quantitation in bottom-up MS-based proteomics. / Karpievitch, Yuliya; Stanley, Jeff; Taverner, Thomas; Huang, Jianhua; Adkins, Joshua N.; Ansong, Charles; Heffron, Fred; Metz, Thomas O.; Qian, Wei Jun; Yoon, Hyunjin; Smith, Richard D.; Dabney, Alan R.

    In: Bioinformatics, Vol. 25, No. 16, 08.2009, p. 2028-2034.

    Research output: Contribution to journalArticle

    Karpievitch, Y, Stanley, J, Taverner, T, Huang, J, Adkins, JN, Ansong, C, Heffron, F, Metz, TO, Qian, WJ, Yoon, H, Smith, RD & Dabney, AR 2009, 'A statistical framework for protein quantitation in bottom-up MS-based proteomics', Bioinformatics, vol. 25, no. 16, pp. 2028-2034. https://doi.org/10.1093/bioinformatics/btp362
    Karpievitch Y, Stanley J, Taverner T, Huang J, Adkins JN, Ansong C et al. A statistical framework for protein quantitation in bottom-up MS-based proteomics. Bioinformatics. 2009 Aug;25(16):2028-2034. https://doi.org/10.1093/bioinformatics/btp362
    Karpievitch, Yuliya ; Stanley, Jeff ; Taverner, Thomas ; Huang, Jianhua ; Adkins, Joshua N. ; Ansong, Charles ; Heffron, Fred ; Metz, Thomas O. ; Qian, Wei Jun ; Yoon, Hyunjin ; Smith, Richard D. ; Dabney, Alan R. / A statistical framework for protein quantitation in bottom-up MS-based proteomics. In: Bioinformatics. 2009 ; Vol. 25, No. 16. pp. 2028-2034.
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