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The state of the art in the analysis of two-dimensional gel electrophoresis images.

Berth M, Moser FM, Kolbe M, Bernhardt J - Appl. Microbiol. Biotechnol. (2007)

Bottom Line: Recent significant advances in image processing methods combined with powerful computing hardware have enabled the routine analysis of large experiments.Challenges for analysis software as well as good practices are highlighted.We close with an overview of visualization and presentation methods (proteome maps) and current challenges in the field.

View Article: PubMed Central - PubMed

Affiliation: DECODON GmbH, Rathenau-Strasse 49a, 17489 Greifswald, Germany.

ABSTRACT
Software-based image analysis is a crucial step in the biological interpretation of two-dimensional gel electrophoresis experiments. Recent significant advances in image processing methods combined with powerful computing hardware have enabled the routine analysis of large experiments. We cover the process starting with the imaging of 2-D gels, quantitation of spots, creation of expression profiles to statistical expression analysis followed by the presentation of results. Challenges for analysis software as well as good practices are highlighted. We emphasize image warping and related methods that are able to overcome the difficulties that are due to varying migration positions of spots between gels. Spot detection, quantitation, normalization, and the creation of expression profiles are described in detail. The recent development of consensus spot patterns and complete expression profiles enables one to take full advantage of statistical methods for expression analysis that are well established for the analysis of DNA microarray experiments. We close with an overview of visualization and presentation methods (proteome maps) and current challenges in the field.

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Proteome maps with spot color coding. a Stress proteome map of B. subtilis 168 (compare Tam le et al. 2006). Spots were color coded according to their induced expression in response to different stress factors. b Proteome map of B. subtilis 168 in a glucose starvation time course experiment. Spots were color coded according to the growth phase
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Fig17: Proteome maps with spot color coding. a Stress proteome map of B. subtilis 168 (compare Tam le et al. 2006). Spots were color coded according to their induced expression in response to different stress factors. b Proteome map of B. subtilis 168 in a glucose starvation time course experiment. Spots were color coded according to the growth phase

Mentions: In proteome maps, color can be used for encoding the association of a protein to a regulatory group (Voigt et al. 2006). In Fig. 17a, proteins above a defined induction factor were grouped by showing them in the same color. If several induction groups are displayed in parallel, Venn diagrams for defining the protein set’s color are used. The presented stress proteome map shows 15 combinations of expression behavior in response to four analyzed stimuli. In general, color coding of protein subsets can be applied for any way of allocating spots to (possibly overlapping) categories.Fig. 17


The state of the art in the analysis of two-dimensional gel electrophoresis images.

Berth M, Moser FM, Kolbe M, Bernhardt J - Appl. Microbiol. Biotechnol. (2007)

Proteome maps with spot color coding. a Stress proteome map of B. subtilis 168 (compare Tam le et al. 2006). Spots were color coded according to their induced expression in response to different stress factors. b Proteome map of B. subtilis 168 in a glucose starvation time course experiment. Spots were color coded according to the growth phase
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2279157&req=5

Fig17: Proteome maps with spot color coding. a Stress proteome map of B. subtilis 168 (compare Tam le et al. 2006). Spots were color coded according to their induced expression in response to different stress factors. b Proteome map of B. subtilis 168 in a glucose starvation time course experiment. Spots were color coded according to the growth phase
Mentions: In proteome maps, color can be used for encoding the association of a protein to a regulatory group (Voigt et al. 2006). In Fig. 17a, proteins above a defined induction factor were grouped by showing them in the same color. If several induction groups are displayed in parallel, Venn diagrams for defining the protein set’s color are used. The presented stress proteome map shows 15 combinations of expression behavior in response to four analyzed stimuli. In general, color coding of protein subsets can be applied for any way of allocating spots to (possibly overlapping) categories.Fig. 17

Bottom Line: Recent significant advances in image processing methods combined with powerful computing hardware have enabled the routine analysis of large experiments.Challenges for analysis software as well as good practices are highlighted.We close with an overview of visualization and presentation methods (proteome maps) and current challenges in the field.

View Article: PubMed Central - PubMed

Affiliation: DECODON GmbH, Rathenau-Strasse 49a, 17489 Greifswald, Germany.

ABSTRACT
Software-based image analysis is a crucial step in the biological interpretation of two-dimensional gel electrophoresis experiments. Recent significant advances in image processing methods combined with powerful computing hardware have enabled the routine analysis of large experiments. We cover the process starting with the imaging of 2-D gels, quantitation of spots, creation of expression profiles to statistical expression analysis followed by the presentation of results. Challenges for analysis software as well as good practices are highlighted. We emphasize image warping and related methods that are able to overcome the difficulties that are due to varying migration positions of spots between gels. Spot detection, quantitation, normalization, and the creation of expression profiles are described in detail. The recent development of consensus spot patterns and complete expression profiles enables one to take full advantage of statistical methods for expression analysis that are well established for the analysis of DNA microarray experiments. We close with an overview of visualization and presentation methods (proteome maps) and current challenges in the field.

Show MeSH
Related in: MedlinePlus