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

Albaum S, Hahne H, Otto A, Haußmann U, Becher D, Poetsch A, Goesmann A, Nattkemper TW (2011)
Proteome Science 9(1): 30.

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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 [1,2] 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 webcite.
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Zeitschriftentitel
Proteome Science
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9
Zeitschriftennummer
1
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30
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Article Processing Charge funded by the Deutsche Forschungsgemeinschaft and the Open Access Publication Fund of Bielefeld University.
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Albaum S, Hahne H, Otto A, et al. A guide through the computational analysis of isotope-labeled mass spectrometry-based quantitative proteomics data: an application study. Proteome Science. 2011;9(1):30.
Albaum, S., Hahne, H., Otto, A., Haußmann, U., Becher, D., Poetsch, A., Goesmann, A., et al. (2011). A guide through the computational analysis of isotope-labeled mass spectrometry-based quantitative proteomics data: an application study. Proteome Science, 9(1), 30. doi:10.1186/1477-5956-9-30
Albaum, S., Hahne, H., Otto, A., Haußmann, U., Becher, D., Poetsch, A., Goesmann, A., and 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, 30.
Albaum, S., et al., 2011. A guide through the computational analysis of isotope-labeled mass spectrometry-based quantitative proteomics data: an application study. Proteome Science, 9(1), p 30.
S. Albaum, et al., “A guide through the computational analysis of isotope-labeled mass spectrometry-based quantitative proteomics data: an application study”, Proteome Science, vol. 9, 2011, pp. 30.
Albaum, S., Hahne, H., Otto, A., Haußmann, U., Becher, D., Poetsch, A., Goesmann, A., Nattkemper, T.W.: A guide through the computational analysis of isotope-labeled mass spectrometry-based quantitative proteomics data: an application study. Proteome Science. 9, 30 (2011).
Albaum, Stefan, Hahne, H., Otto, A., Haußmann, U., Becher, D., Poetsch, A., Goesmann, Alexander, and Nattkemper, Tim Wilhelm. “A guide through the computational analysis of isotope-labeled mass spectrometry-based quantitative proteomics data: an application study”. Proteome Science 9.1 (2011): 30.
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2012-01-27T10:59:06Z

7 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

Coupling enrichment methods with proteomics for understanding and treating disease.
Kumar A, Baycin-Hizal D, Shiloach J, Bowen MA, Betenbaugh MJ., Proteomics Clin Appl 9(1-2), 2015
PMID: 25523641
Protein turnover quantification in a multilabeling approach: from data calculation to evaluation.
Trötschel C, Albaum SP, Wolff D, Schröder S, Goesmann A, Nattkemper TW, Poetsch A., Mol Cell Proteomics 11(8), 2012
PMID: 22493176
FunSys: Software for functional analysis of prokaryotic transcriptome and proteome.
de Sá P, Pinto A, Ramos RT, Coimbra N, Baraúna R, Dall'agnol H, Carneiro A, Ranieri A, Valadares A, Azevedo V, Schneider MP, Barh D, Silva A., Bioinformation 8(11), 2012
PMID: 22829724

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