ABSTRACT Motivation: Quantitative mass spectrometry-based proteomics requires protein-level estimates and confidence measures. Challenges include the presence of low-quality or incorrectly identified peptides and widespread, informative, missing data. Furthermore, models are required for rolling peptide-level information up to the protein level. Results: We present a statistical model for protein abundance in terms of peptide peak intensities, applicable to both label-based and label-free quantitation experiments. The model allows for both random and censoring missingness mechanisms and provides naturally for protein-level estimates and confidence measures. The model is also used to derive automated filtering and imputation routines. Three LC-MS datasets are used to illustrate the methods. Availability: The software has been made available in the open-source proteomics platform DAnTE (Polpitiya et al. (2008)) (http://omics.pnl.gov/software/). Contact: adabney@stat.tamu.edu
Revised: August 3, 2010 |
Published: August 15, 2009
Citation
Karpievitch Y., J.R. Stanley, T. Taverner, J. Huang, J.N. Adkins, C. Ansong, and F. Heffron, et al. 2009.A statistical framework for protein quantitation in bottom-up MS-based proteomics.Bioinformatics 25, no. 16:2028-2034.PNNL-SA-66740.doi:10.1093/bioinformatics/btp362