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Bioinformatics 2009 25(12):i154-i160; doi:10.1093/bioinformatics/btp233
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Proteome coverage prediction with infinite Markov models

Manfred Claassen 1,2,3,4,*, Ruedi Aebersold 2,4,5,6 and Joachim M. Buhmann 1,4,*

1Department of Computer Science, 2Institute of Molecular Systems Biology, ETH Zurich, 3Life Science Zurich PhD Program on Systems Biology of Complex Diseases, 4Competence Center for Systems Physiology and Metabolic Diseases, Zurich, Switzerland, 5Institute for Systems Biology, Seattle, WA, USA and 6Faculty of Science, University of Zurich, Zurich, Switzerland

*To whom correspondence should be addressed.


  Abstract

Motivation: Liquid chromatography tandem mass spectrometry (LC-MS/MS) is the predominant method to comprehensively characterize complex protein mixtures such as samples from prefractionated or complete proteomes. In order to maximize proteome coverage for the studied sample, i.e. identify as many traceable proteins as possible, LC-MS/MS experiments are typically repeated extensively and the results combined. Proteome coverage prediction is the task of estimating the number of peptide discoveries of future LC-MS/MS experiments. Proteome coverage prediction is important to enhance the design of efficient proteomics studies. To date, there does not exist any method to reliably estimate the increase of proteome coverage at an early stage.

Results: We propose an extended infinite Markov model DiriSim to extrapolate the progression of proteome coverage based on a small number of already performed LC-MS/MS experiments. The method explicitly accounts for the uncertainty of peptide identifications. We tested DiriSim on a set of 37 LC-MS/MS experiments of a complete proteome sample and demonstrated that DiriSim correctly predicts the coverage progression already from a small subset of experiments. The predicted progression enabled us to specify maximal coverage for the test sample. We demonstrated that quality requirements on the final proteome map impose an upper bound on the number of useful experiment repetitions and limit the achievable proteome coverage.

Contact: manfredc{at}inf.ethz.ch; jbuhmann{at}inf.ethz.ch


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