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

• Published in: Bioinformatics 2009-08, Vol.25 (16), p.2028-2034
• Main 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.
• Format: Article
• Language: English
• Subjects: Biological and medical sciences; Databases, Protein; Fundamental and applied biological sciences. Psychology; General aspects; Mass Spectrometry - methods; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Models, Statistical; Original Papers; Proteins - analysis; Proteome - analysis; Proteomics - methods
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btp362

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/). Contact: [e-mail] Supplementary information: Supplementary data are available at Bioinformatics online.