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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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Spot boundaries produced by segmentation (a) and subsequent modeling (b)
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Fig9: Spot boundaries produced by segmentation (a) and subsequent modeling (b)

Mentions: The goals of this step are to find the spot positions, find their surrounding boundary, and determine their quantities. There are two basic approaches that are used in current software: image segmentation and model-based quantitation. The segmentation approach partitions the image into nonoverlapping segments, essentially classifying each pixel as belonging to a certain spot, or as being part of the background between spots. Spot boundaries and quantities are then derived from the spot’s pixels. The segmentation of the image can take various characteristics of the image into account: raw intensity, slope, and classification of pixels in the surrounding region. The advantage of this approach is that the image is clearly separated into spots and “nonspot” areas which are easy to assess by a user. If the software allows editing of spot boundaries, then any desired spot shape can, in principle, be obtained. Model-based approaches try to model a spot’s intensity as a Gaussian normal distribution or some variant thereof. A spot’s quantity and boundaries are then derived from the model (Fig. 8). The use of a Gaussian is motivated by the “3-D shape” of spots (Fig. 8) and by general considerations on diffusion processes in the gel. Model-based approaches limit the range of possible spot shapes, thus leading to more “natural” outlines. On the other hand, irregular spot shapes are poorly represented by simple models. Spot models can be used in the subsequent quantitation, with overlapping spots being represented as the sum of multiple single-spot models. Delta2D offers a hybrid between segmentation and modeling: starting with segmentation, spots are modeled as Gaussians, and their nonoverlapping boundaries are derived from the models (Fig. 9).Fig. 8


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)

Spot boundaries produced by segmentation (a) and subsequent modeling (b)
© Copyright Policy
Related In: Results  -  Collection

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

Fig9: Spot boundaries produced by segmentation (a) and subsequent modeling (b)
Mentions: The goals of this step are to find the spot positions, find their surrounding boundary, and determine their quantities. There are two basic approaches that are used in current software: image segmentation and model-based quantitation. The segmentation approach partitions the image into nonoverlapping segments, essentially classifying each pixel as belonging to a certain spot, or as being part of the background between spots. Spot boundaries and quantities are then derived from the spot’s pixels. The segmentation of the image can take various characteristics of the image into account: raw intensity, slope, and classification of pixels in the surrounding region. The advantage of this approach is that the image is clearly separated into spots and “nonspot” areas which are easy to assess by a user. If the software allows editing of spot boundaries, then any desired spot shape can, in principle, be obtained. Model-based approaches try to model a spot’s intensity as a Gaussian normal distribution or some variant thereof. A spot’s quantity and boundaries are then derived from the model (Fig. 8). The use of a Gaussian is motivated by the “3-D shape” of spots (Fig. 8) and by general considerations on diffusion processes in the gel. Model-based approaches limit the range of possible spot shapes, thus leading to more “natural” outlines. On the other hand, irregular spot shapes are poorly represented by simple models. Spot models can be used in the subsequent quantitation, with overlapping spots being represented as the sum of multiple single-spot models. Delta2D offers a hybrid between segmentation and modeling: starting with segmentation, spots are modeled as Gaussians, and their nonoverlapping boundaries are derived from the models (Fig. 9).Fig. 8

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