A statistical framework for protein quantitation in bottom-up MS-based proteomics. Academic Article uri icon


  • 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. AVAILABILITY: The software has been made available in the open-source proteomics platform DAnTE (http://omics.pnl.gov/software/).

altmetric score

  • 6

author list (cited authors)

  • Karpievitch, Y., Stanley, J., Taverner, T., Huang, J., Adkins, J. N., Ansong, C., ... Dabney, A. R

citation count

  • 139

complete list of authors

  • 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

publication date

  • January 2009