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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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By using a consensus spot pattern in Delta2D (a), complete expression profiles (b) are generated. Profiles can be imported into DNA array analysis software (here: TIGR MultiExperiment Viewer, TMEV). With appropriate data transformations and normalization, many approaches for data analysis known from DNA arrays can be used for 2-D-gel-based proteome data. Hierarchical clustering (c) and self-organizing maps (d) group proteins by similarity of their expression profiles. Template matching (e) can be used to find proteins that conform to an expression pattern given by the user. Terrain maps (f) can give a high level overview of a data set where correlations of protein expression profiles are shown as distances in two dimensions, and protein density is shown in the third dimension (height)
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Fig13: By using a consensus spot pattern in Delta2D (a), complete expression profiles (b) are generated. Profiles can be imported into DNA array analysis software (here: TIGR MultiExperiment Viewer, TMEV). With appropriate data transformations and normalization, many approaches for data analysis known from DNA arrays can be used for 2-D-gel-based proteome data. Hierarchical clustering (c) and self-organizing maps (d) group proteins by similarity of their expression profiles. Template matching (e) can be used to find proteins that conform to an expression pattern given by the user. Terrain maps (f) can give a high level overview of a data set where correlations of protein expression profiles are shown as distances in two dimensions, and protein density is shown in the third dimension (height)

Mentions: Currently, all 2-D gel analysis software packages come with some basic internal statistical analysis facilities. The advantage of using these facilities as opposed to external programs is that the analysis of expression profiles is tightly integrated with image analysis. For example, it is easy to see a section of all gels around a given spot that was flagged as being differentially expressed. All packages support the export of expression data in tabular form so more advanced methods can be used. Beyond the image analysis packages, there are a few commercial and noncommercial options for the statistical analysis of 2-D gel data. Genedata’s Expressionist (GeneData, Basel) and DeCyder EDA (GE Healthcare) are products that offer multivariate statistics tailored for 2-D gel image data. General-purpose statistics packages like the free and open-source R (www.r-project.org) have extensive facilities for higher-level methods such as principal component analysis (PCA) and clustering. The R-based BioConductor package (www.bioconductor.org) provides access to a wide variety of data analysis methods and graphics facilities that were developed for microarray data. While these command-line-oriented packages offer great flexibility and control as well as some of the latest methods in the field, their learning curve can be steep. A more interactive and visual approach to data analysis is offered by the open-source TIGR Multiple Experiment Viewer MeV (Saeed et al. 2003; http://www.tigr.org/software/tm4/mev.html). MeV combines interactive visualization of microarray data with a wide choice of analysis methods such as hierarchical clustering, self-organizing maps, and PCA (Fig. 13).Fig. 13


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)

By using a consensus spot pattern in Delta2D (a), complete expression profiles (b) are generated. Profiles can be imported into DNA array analysis software (here: TIGR MultiExperiment Viewer, TMEV). With appropriate data transformations and normalization, many approaches for data analysis known from DNA arrays can be used for 2-D-gel-based proteome data. Hierarchical clustering (c) and self-organizing maps (d) group proteins by similarity of their expression profiles. Template matching (e) can be used to find proteins that conform to an expression pattern given by the user. Terrain maps (f) can give a high level overview of a data set where correlations of protein expression profiles are shown as distances in two dimensions, and protein density is shown in the third dimension (height)
© Copyright Policy
Related In: Results  -  Collection

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

Fig13: By using a consensus spot pattern in Delta2D (a), complete expression profiles (b) are generated. Profiles can be imported into DNA array analysis software (here: TIGR MultiExperiment Viewer, TMEV). With appropriate data transformations and normalization, many approaches for data analysis known from DNA arrays can be used for 2-D-gel-based proteome data. Hierarchical clustering (c) and self-organizing maps (d) group proteins by similarity of their expression profiles. Template matching (e) can be used to find proteins that conform to an expression pattern given by the user. Terrain maps (f) can give a high level overview of a data set where correlations of protein expression profiles are shown as distances in two dimensions, and protein density is shown in the third dimension (height)
Mentions: Currently, all 2-D gel analysis software packages come with some basic internal statistical analysis facilities. The advantage of using these facilities as opposed to external programs is that the analysis of expression profiles is tightly integrated with image analysis. For example, it is easy to see a section of all gels around a given spot that was flagged as being differentially expressed. All packages support the export of expression data in tabular form so more advanced methods can be used. Beyond the image analysis packages, there are a few commercial and noncommercial options for the statistical analysis of 2-D gel data. Genedata’s Expressionist (GeneData, Basel) and DeCyder EDA (GE Healthcare) are products that offer multivariate statistics tailored for 2-D gel image data. General-purpose statistics packages like the free and open-source R (www.r-project.org) have extensive facilities for higher-level methods such as principal component analysis (PCA) and clustering. The R-based BioConductor package (www.bioconductor.org) provides access to a wide variety of data analysis methods and graphics facilities that were developed for microarray data. While these command-line-oriented packages offer great flexibility and control as well as some of the latest methods in the field, their learning curve can be steep. A more interactive and visual approach to data analysis is offered by the open-source TIGR Multiple Experiment Viewer MeV (Saeed et al. 2003; http://www.tigr.org/software/tm4/mev.html). MeV combines interactive visualization of microarray data with a wide choice of analysis methods such as hierarchical clustering, self-organizing maps, and PCA (Fig. 13).Fig. 13

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