U.S. Department of Energy

Pacific Northwest National Laboratory

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

TitleA statistical framework for protein quantitation in bottom-up MS-based proteomics.
Publication TypeJournal Article
Year of Publication2009
AuthorsKarpievitch Y, Stanley J, Taverner T, Huang J, Adkins JN, Ansong C, Heffron F, Metz TO, Qian W-J, Yoon H, Smith RD, Dabney AR
KeywordsDatabases, Protein, Mass Spectrometry, Models, Statistical, Proteins, Proteome, Proteomics

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/).

Alternate JournalBioinformatics
PubMed ID19535538
PubMed Central IDPMC2723007
Grant ListDK070146 / DK / NIDDK NIH HHS / United States
R01 AI022933-24 / AI / NIAID NIH HHS / United States
R25-CA-90301 / CA / NCI NIH HHS / United States
Y1-AI-4894-01 / AI / NIAID NIH HHS / United States
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