Journal Article

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

Yuliya Karpievitch, Jeff Stanley, Thomas Taverner, Jianhua Huang, Joshua N. Adkins, Charles Ansong, Fred Heffron, Thomas O. Metz, Wei-Jun Qian, Hyunjin Yoon, Richard D. Smith and Alan R. Dabney

in Bioinformatics

Volume 25, issue 16, pages 2028-2034
Published in print August 2009 | ISSN: 1367-4803
Published online June 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp362
A statistical framework for protein quantitation in bottom-up MS-based proteomics

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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/).

Contact: adabney@stat.tamu.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5807 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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