Limits...
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: Software-based image analysis is a crucial step in the biological interpretation of two-dimensional gel electrophoresis 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.

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

PCA of 54 gels from 11 patients. Gels are color coded according to sample (sample a: shades of blue; sample b: shades of red). Notice how replicate gels are grouped closely together. We have chosen the projection onto the second and third principal components because it shows a good separation between samples
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2279157&req=5

Fig15: PCA of 54 gels from 11 patients. Gels are color coded according to sample (sample a: shades of blue; sample b: shades of red). Notice how replicate gels are grouped closely together. We have chosen the projection onto the second and third principal components because it shows a good separation between samples

Mentions: As with clustering, PCA can be done for gels or expression profiles. In the first variant, each gel image is considered as a vector with coordinates given by the spot intensities on that gel. For example, an experiment with 24 gels from sample A and 24 gels from sample B and 1,500 spots on each gel would be modeled as a set (or point cloud) of 48 vectors in 1,500-dimensional space. The goal of PCA is then to find a projection of the point cloud in two- or three-dimensional space such that as much as possible the variation of the point cloud is preserved. One hopes that the gels from different samples will be in separate regions of the resulting diagram (Fig. 15). The principal components can then be interpreted as “typical spot patterns” or “eigengels.” Their coordinates can be analyzed to determine which spots are contributing most to the variance, making them candidates for protein identification and biological interpretation.Fig. 15


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)

PCA of 54 gels from 11 patients. Gels are color coded according to sample (sample a: shades of blue; sample b: shades of red). Notice how replicate gels are grouped closely together. We have chosen the projection onto the second and third principal components because it shows a good separation between samples
© Copyright Policy
Related In: Results  -  Collection

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

Fig15: PCA of 54 gels from 11 patients. Gels are color coded according to sample (sample a: shades of blue; sample b: shades of red). Notice how replicate gels are grouped closely together. We have chosen the projection onto the second and third principal components because it shows a good separation between samples
Mentions: As with clustering, PCA can be done for gels or expression profiles. In the first variant, each gel image is considered as a vector with coordinates given by the spot intensities on that gel. For example, an experiment with 24 gels from sample A and 24 gels from sample B and 1,500 spots on each gel would be modeled as a set (or point cloud) of 48 vectors in 1,500-dimensional space. The goal of PCA is then to find a projection of the point cloud in two- or three-dimensional space such that as much as possible the variation of the point cloud is preserved. One hopes that the gels from different samples will be in separate regions of the resulting diagram (Fig. 15). The principal components can then be interpreted as “typical spot patterns” or “eigengels.” Their coordinates can be analyzed to determine which spots are contributing most to the variance, making them candidates for protein identification and biological interpretation.Fig. 15

Bottom Line: Software-based image analysis is a crucial step in the biological interpretation of two-dimensional gel electrophoresis 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.

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