New algorithmic approaches to protein spot detection and pattern matching in two-dimensional electrophoresis gel databases

Pleißner, Klaus‐Peter; Hoffmann, Frank; Kriegel, Klaus; Wenk, Carola; Wegner, Susan; Sahlström, Anders; Oswald, H. R.; Alt, Helmut; Fleck, Eckart

doi:10.1002/(sici)1522-2683(19990101)20:4/5<755::aid-elps755>3.0.co;2-6

Cited by 78 publications

(42 citation statements)

References 30 publications

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“…Other articles deal with general aspects of such algorithms, such as point pattern matching, reproducibility, matching efficiency etc. [5][6][7][8][9][10]. We will review in detail the steps required for analyzing and matching data obtained from control and treated samples run in separate gels and subsequently subjected to matching via different software packages.…”

Section: Introductionmentioning

confidence: 99%

Numerical approaches for quantitative analysis of two‐dimensional maps: A review of commercial software and home‐made systems

et al. 2005

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The present review attempts to cover a number of methods that have appeared in the last few years for performing quantitative proteome analysis. However, due to the large number of methods described for both electrophoretic and chromatographic approaches, we have limited this review to conventional two-dimensional (2-D) map analysis which couples orthogonally a charge-based step (isoelectric focusing) to a size-based separation step (sodium dodecyl sulfateelectrophoresis). The first and oldest method applied to 2-D map data reduction is based on statistical analysis performed on sets of gels via powerful software packages, such as Melanie, PDQuest, Z3 and Z4000, Phoretix and Progenesis. This method calls for separately running a number of replicas for control and treated samples. The two sets of data are then merged and compared via a number of software packages which we describe. In addition to commerciallyavailable systems, a number of home made approaches for 2-D map comparison have been recently described and are also reviewed. They are based on fuzzyfication of the digitized 2-D gel image coupled to linear discriminant analysis, three-way principal component analysis or a combination of principal component analysis and soft-independent modeling of class analogy. These statistical tools appear to perform well in differential proteomic studies.

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Section: Introductionmentioning

confidence: 99%

Numerical approaches for quantitative analysis of two‐dimensional maps: A review of commercial software and home‐made systems

et al. 2005

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“…Several methods have been suggested to tackle with 2D-GE image analysis such as stepwise thresholding [4], edge detection [5] and watersheds [6]. Stepwise thresholding applies an increasing threshold on the 2D-GE image of interest, starting from the lowest intensity level which can be associated with a protein spot.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised 2D gel electrophoresis image segmentation based on active contours

Savelonas

Mylona

Maroulis

2012

Pattern Recognition

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This work introduces a novel active contour-based scheme for unsupervised segmentation of protein spots in two-dimensional gel electrophoresis (2D-GE) images. The proposed segmentation scheme is the first to exploit the attractive properties of the active contour formulation in order to cope with crucial issues in 2D-GE image analysis, including the presence of noise, streaks, multiplets and faint spots. In addition, it is unsupervised, providing an alternate to the laborious, error-prone process of manual editing, which is required in state-of-the-art 2D-GE image analysis software packages. It is based on the formation of a spottargeted level-set surface, as well as of morphologically-derived active contour energy terms, used to guide active contour initialization and evolution, respectively. The experimental results on real and synthetic 2D-GE images demonstrate that the proposed scheme results in more plausible spot boundaries and outperforms all commercial software packages in terms of segmentation quality.

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“…However, they are susceptible to spurious segmentation results in the presence of noise and breaks in spot boundaries [8]. Watersheds cope well with noise, artifacts and inhomogeneous background but often result in oversegmentation, posing the need for additional post-processing [9], [10].…”

Section: Introductionmentioning

confidence: 99%

An automatically initialized level-set approach for the segmentation of proteomics images

Savelonas

Mylona

Maroulis

2011

2011 10th International Workshop on Biomedical Engineering

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Abstract-This work introduces an automatically initialized level-set approach for the segmentation of proteomics images. Level-set methods are natural candidates for this segmentation task, since their topological adaptability is a prerequisite to identify the boundaries of thousands of protein spots present. However, they require manual initialization, whereas their convergence is highly sensitive to the position of the initial contour. The proposed initialization process is based on the formation of a level-set surface of multiple cones, which are centered at regional intensity maxima positions, associated with protein spots. This initialization process, as is the case with the subsequent contour evolution, is aided by means of histogram equalization and morphological processing. The experimental results indicate that the proposed level-set approach facilitates quicker convergence than the one obtained by the straightforward application of the Chan-Vese model. In addition, the identified spot boundaries are more plausible than the ones obtained by the application of state-of-the-art proteomics image analysis software.

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New algorithmic approaches to protein spot detection and pattern matching in two-dimensional electrophoresis gel databases

Cited by 78 publications

References 30 publications

Numerical approaches for quantitative analysis of two‐dimensional maps: A review of commercial software and home‐made systems

Numerical approaches for quantitative analysis of two‐dimensional maps: A review of commercial software and home‐made systems

Unsupervised 2D gel electrophoresis image segmentation based on active contours

An automatically initialized level-set approach for the segmentation of proteomics images

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