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.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
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Autor*in
Albaum, StefanUniBi ;
Hahne, H.;
Otto, A.;
Haußmann, U.;
Becher, D.;
Poetsch, A.;
Goesmann, AlexanderUniBi ;
Nattkemper, Tim WilhelmUniBi
Einrichtung
Centrum für Biotechnologie > Arbeitsgruppe A. Goesmann
Technische Fakultät > Computational Genomics
Technische Fakultät > AG Biodata Mining
Centrum für Biotechnologie > Institut für Bioinformatik
Centrum für Biotechnologie > Technologieplattformen > Bioinformatics Resource Facility
Centrum für Biotechnologie > Arbeitsgruppe T. Nattkemper
Center of Excellence - Cognitive Interaction Technology CITEC
Technische Fakultät > Computational Genomics
Technische Fakultät > AG Biodata Mining
Centrum für Biotechnologie > Institut für Bioinformatik
Centrum für Biotechnologie > Technologieplattformen > Bioinformatics Resource Facility
Centrum für Biotechnologie > Arbeitsgruppe T. Nattkemper
Center of Excellence - Cognitive Interaction Technology CITEC
Abstract / Bemerkung
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.
Erscheinungsjahr
2011
Zeitschriftentitel
Proteome Science
Band
9
Ausgabe
1
Art.-Nr.
30
ISSN
1477-5956
Finanzierungs-Informationen
Open-Access-Publikationskosten wurden durch die Deutsche Forschungsgemeinschaft und die Universität Bielefeld gefördert.
Page URI
https://pub.uni-bielefeld.de/record/2300063
Zitieren
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. https://doi.org/10.1186/1477-5956-9-30
Albaum, Stefan, Hahne, H., Otto, A., Haußmann, U., Becher, D., Poetsch, A., Goesmann, Alexander, and Nattkemper, Tim Wilhelm. 2011. “A guide through the computational analysis of isotope-labeled mass spectrometry-based quantitative proteomics data: an application study”. Proteome Science 9 (1): 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): 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, : 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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Daten bereitgestellt von European Bioinformatics Institute (EBI)
8 Zitationen in Europe PMC
Daten bereitgestellt von Europe PubMed Central.
An sRNA and Cold Shock Protein Homolog-Based Feedforward Loop Post-transcriptionally Controls Cell Cycle Master Regulator CtrA.
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