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


Karpievitch, Y and Stanley, J and Taverner, T and Huang, J and Adkins, JN and Ansong, C and Heffron, F and Metz, TO and Qian, W-J and Yoon, H and Smith, RD and Dabney, AR, A statistical framework for protein quantitation in bottom-up MS-based proteomics, Bioinformatics, 25, (16) pp. 2028-2034. ISSN 1367-4803 (2009) [Refereed Article]

Copyright Statement

Copyright 2009 the authors.

DOI: 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 (

Item Details

Item Type:Refereed Article
Research Division:Mathematical Sciences
Research Group:Statistics
Research Field:Biostatistics
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the mathematical sciences
UTAS Author:Karpievitch, Y (Dr Yuliya Karpievitch)
ID Code:80883
Year Published:2009
Web of Science® Times Cited:116
Deposited By:Mathematics and Physics
Deposited On:2012-11-14
Last Modified:2015-06-22

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