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A statistical framework for protein quantitation in bottom-up MS-based proteomics
journal contribution
posted on 2023-05-17, 14:20 authored by Karpievitch, Y, Stanley, J, Taverner, T, Huang, J, Adkins, JN, Ansong, C, Heffron, F, Metz, TO, Qian, W-J, Yoon, H, Smith, RD, Dabney, ARMotivation: 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/).
History
Publication title
BioinformaticsVolume
25Issue
16Pagination
2028-2034ISSN
1367-4803Department/School
School of Natural SciencesPublisher
Oxford Univ PressPlace of publication
Great Clarendon St, Oxford, England, Ox2 6DpRights statement
Copyright 2009 the authors.Repository Status
- Restricted