A guide through the computational analysis of isotope-labeled mass spectrometry-based quantitative proteomics data: An application study

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Abstract

Background: Mass spectrometry-based proteomics has reached a stage where it is possible to comprehensively analyze the whole proteome of a cell in one experiment. Here, the employment of stable isotopes has become a standard technique to yield relative abundance values of proteins. In recent times, more and more experiments are conducted that depict not only a static image of the up- or down-regulated proteins at a distinct time point but instead compare developmental stages of an organism or varying experimental conditions.Results: Although the scientific questions behind these experiments are of course manifold, there are, nevertheless, two questions that commonly arise: 1) which proteins are differentially regulated regarding the selected experimental conditions, and 2) are there groups of proteins that show similar abundance ratios, indicating that they have a similar turnover? We give advice on how these two questions can be answered and comprehensively compare a variety of commonly applied computational methods and their outcomes.Conclusions: This work provides guidance through the jungle of computational methods to analyze mass spectrometry-based isotope-labeled datasets and recommends an effective and easy-to-use evaluation strategy. We demonstrate our approach with three recently published datasets on Bacillus subtilis 12 and Corynebacterium glutamicum 3. Special focus is placed on the application and validation of cluster analysis methods. All applied methods were implemented within the rich internet application QuPE 4. Results can be found at http://qupe.cebitec.uni-bielefeld.de. © 2011 Albaum et al; licensee BioMed Central Ltd.

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Albaum, S. P., Hahne, H., Otto, A., Haußmann, U., Becher, D., Poetsch, A., … Nattkemper, T. W. (2011). A guide through the computational analysis of isotope-labeled mass spectrometry-based quantitative proteomics data: An application study. Proteome Science, 9. https://doi.org/10.1186/1477-5956-9-30

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