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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 2009 the authors.
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/).
|Item Type:||Refereed Article|
|Research Division:||Mathematical Sciences|
|Objective Division:||Expanding Knowledge|
|Objective Group:||Expanding knowledge|
|Objective Field:||Expanding knowledge in the mathematical sciences|
|UTAS Author:||Karpievitch, Y (Dr Yuliya Karpievitch)|
|Web of Science® Times Cited:||116|
|Deposited By:||Mathematics and Physics|
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