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:965993

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

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: [e-mail]

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 965993
• Report Number(s): PNNL-SA-66740; KP1601010
• Journal Information: Bioinformatics, 25(16):2028-2034, Journal Name: Bioinformatics, 25(16):2028-2034 Journal Issue: 16 Vol. 25
• Country of Publication: United States
• Language: English

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
ABUNDANCE
PEPTIDES
PROTEINS
ROLLING
STATISTICAL MODELS