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).

Download
OA
Journal Article | Published | English
Author
; ; ; ; ; ; ;
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 [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.
Publishing Year
ISSN
Financial disclosure
Article Processing Charge funded by the Deutsche Forschungsgemeinschaft and the Open Access Publication Fund of Bielefeld University.
PUB-ID

Cite this

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).
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).
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.
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).
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.
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, (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).
Main File(s)
Access Level
OA Open Access
Last Uploaded
2012-01-27 10:59:06

This data publication is cited in the following publications:
This publication cites the following data publications:

4 Citations in Europe PMC

Data provided by 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
FunSys: Software for functional analysis of prokaryotic transcriptome and proteome.
de Sa P, Pinto A, Ramos RT, Coimbra N, Barauna R, Dall'agnol H, Carneiro A, Ranieri A, Valadares A, Azevedo V, Schneider MP, Barh D, Silva A., Bioinformation 8(11), 2012
PMID: 22829724
Protein turnover quantification in a multilabeling approach: from data calculation to evaluation.
Trotschel C, Albaum SP, Wolff D, Schroder S, Goesmann A, Nattkemper TW, Poetsch A., Mol. Cell Proteomics 11(8), 2012
PMID: 22493176

58 References

Data provided by Europe PubMed Central.

Cluster analysis and display of genome-wide expression patterns.
Eisen MB, Spellman PT, Brown PO, Botstein D., Proc. Natl. Acad. Sci. U.S.A. 95(25), 1998
PMID: 9843981
A statistical framework for protein quantitation in bottom-up MS-based proteomics.
Karpievitch Y, Stanley J, Taverner T, Huang J, Adkins JN, Ansong C, Heffron F, Metz TO, Qian WJ, Yoon H, Smith RD, Dabney AR., Bioinformatics 25(16), 2009
PMID: 19535538
Consequences of Failure to Meet Assumptions Underlying the Fixed Effects Analysesof Variance and Covariance
AUTHOR UNKNOWN, 1972
Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.
Keller A, Nesvizhskii AI, Kolker E, Aebersold R., Anal. Chem. 74(20), 2002
PMID: 12403597
A statistical model for identifying proteins by tandem mass spectrometry.
Nesvizhskii AI, Keller A, Kolker E, Aebersold R., Anal. Chem. 75(17), 2003
PMID: 14632076
An easy-to-use Decoy Database Builder software tool, implementing different decoy strategies for false discovery rate calculation in automated MS/MS protein identifications.
Reidegeld KA, Eisenacher M, Kohl M, Chamrad D, Korting G, Bluggel M, Meyer HE, Stephan C., Proteomics 8(6), 2008
PMID: 18338823
A MS data search method for improved 15N-labeled protein identification.
Zhang Y, Webhofer C, Reckow S, Filiou MD, Maccarrone G, Turck CW., Proteomics 9(17), 2009
PMID: 19722194

Export

0 Marked Publications

Open Data PUB

Web of Science

View record in Web of Science®

Sources

PMID: 21663690
PubMed | Europe PMC

Search this title in

Google Scholar