Genomics | Bioinformatics
Statistical approach to absolute protein quantification
Sarah Gerster*, Peter Bühlmann
*Corresponding author: Sarah Gerster
Seminar für Statistik, Zürich, Switzerland
F1000Posters 2011, 2: 1402 (poster) [ENGLISH]
Poster [470.51 KB]
Presented at
19th Annual International Conference on Intelligent Systems for Molecular Biology and 10th European Conference on Computational Biology 2011 (ISMB/ECCB),
17 - 19 Jul 2011, R01
A major goal in proteomics is the comprehensive and accurate description of a proteome. Proteomics provides additional insights into biological systems that cannot be provided by genomic or transcriptomic approaches (Aebersold and Mann, 2003). In particular, proteomics holds great promise for the identification of biomarkers capable of accurately predicting disease already at a very early stage.
The method of choice for the analysis of complex protein mixtures is shotgun proteomics. Proteins are identified and quantified based on experimentally measured peptides. While several probabilistic models exist for the identification of proteins, label-free quantification is often done in a deterministic way.
We propose a statistical approach to protein quantification with three main advantages. (i) Peptide intensities are modeled as random quantities, accounting for the uncertainty of these measurements, (ii) our Markov-type model for bipartite graphs ensures transparent propagation of the uncertainties and reproducible results and (iii) the problem of peptides mapping to several protein sequences (often neglected in other models) is addressed automatically according to our statistical model.
The performance of our model is shown on two synthetic control datasets and compared to the results of two common approaches for protein quantification (Silva et al., 2006; Lu et al., 2007).
No relevant conflicts of interest declared.
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