A Statistical Framework for Protein Quantitation in Bottom-Up MS-Based Proteomics

Journal Article · Sat Aug 15 04:00:00 EDT 2009 · Bioinformatics, 25(16):2028-2034

DOI:https://doi.org/10.1093/bioinformatics/btp362· OSTI ID:989040

Karpievitch, Yuliya; Stanley, Jeffrey R; Taverner, Thomas; Huang, Jianhua; Adkins, Joshua N; Ansong, Charles; Heffron, Fred; Metz, Thomas O; Qian, Weijun; Yoon, Hyunjin; Smith, Richard D; Dabney, Alan R

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 opensource proteomics platform DAnTE (http://omics.pnl.gov/software/). Contact: [e-mail] Supplementary information: Supplementary data are available at Bioinformatics online.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 989040
• Report Number(s): PNNL-SA-70100; 400412000
• Journal Information: Bioinformatics, 25(16):2028-2034, Journal Name: Bioinformatics, 25(16):2028-2034 Journal Issue: 16 Vol. 25; ISSN 1460-2059; ISSN 1367-4803
• Country of Publication: United States
• Language: English

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
99 GENERAL AND MISCELLANEOUS
ALGORITHMS
PEPTIDES
PROTEINS
ROLLING
SIMULATION
STATISTICAL MODELS