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

Pleiner, Klaus-Peter; Hoffmann, Frank; Kriegel, Klaus; Wenk, Carola; Wegner, Susan; Sahlstrm, Anders; Oswald, H. R.; Alt, Helmut G.; Fleck, Eckart

doi:10.1002/9783527613489.ch22

Cited by 7 publications

(14 citation statements)

References 31 publications

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“…It may be implemented by flooding the landscape, which results in catchment basins separated by ridges, the so-called watersheds. In our experiments we adopt the approach of [5] where the watershed transform is applied to the gradient magnitude image of a gel image. Thus, spots and homogeneous background regions are associated with the catchment basins while their borders form the watersheds.…”

Section: Watershed Detectormentioning

confidence: 99%

“…A more sophisticated and widely used approach for spot detection is based on the watershed transformation [5,7]. Its main idea is borrowed from geography and defines an algorithm for segmenting a landscape into valleys and ridges to analyze its overall topology [31].…”

Section: Watershed Detectormentioning

confidence: 99%

“…For registration of gel images, protein spots have typically been used as features (e.g. [5][6][7]) as they are detected anyway to identify proteins. However, for the registration process there is no need to restrict potential types of features to spots, especially as even commercial programs show large variance in spot detection [8].…”

Section: Generalmentioning

confidence: 99%

“…features that appear to yield a perfect base for gel registration. There is a large variety of common techniques for detecting spots in literature, like intensity level and flow analysis [14,15], Laplacians [6], watersheds [5,7], morphological operators [16], or parametric spot models like 2-D Gaussians [17]. Also, more complex algorithms have been proposed, e.g., based on Markov Random Fields [18].…”

Section: Related Workmentioning

confidence: 99%

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Robust features for 2‐DE gel image registration

Möller

Posch

2009

Electrophoresis

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Proteomics is a rapidly growing field of modern biology. Since quantitative data of proteins involved in dynamic processes of living organisms are essential for understanding the basics of life, techniques like 2-DE and related procedures for automatic data interpretation are at the heart of this research field. They are strongly required to enable analysis and interpretation of the emerging amount of available data. Analyzing and interpreting gel image data usually requires the comparison of gels from different experiments and, thus, a prior registration of gels. This can be accomplished using featureless, feature-based or hybrid registration approaches combining both techniques. Recently, the latter ones have shown high performance, and it is undoubtful that in general robust and reliable features are an essential ingredient and valuable source of information for high-quality image registration. In this paper we provide a thorough overview and elaborate analysis of the capabilities of available feature detectors for gel image registration. Particularly, a detailed and extensive comparative study is presented where common spot-specific detectors are included as well as image-content independent detectors that were not applied to the task of gel image registration until now. The study incorporates tests on several thousand synthetically deformed images from different experimental conditions. As a result it provides valuable quantitative data allowing for direct objective comparisons of various detectors, and is well suited to guide the design of new registration algorithms.

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Section: Watershed Detectormentioning

confidence: 99%

Section: Watershed Detectormentioning

confidence: 99%

Section: Generalmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Robust features for 2‐DE gel image registration

Möller

Posch

2009

Electrophoresis

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“…The ultimate objective of a 2‐DE‐based proteomic study is to discover, through statistical analysis of matched spot expression profiles, proteins that can be used to discriminate reliably between two states of interest in a biological system (biomarkers discovery). The 2‐DE gel image analysis pipeline starts with image pre‐processing (noise suppression, background correction and artefacts removal), followed by segmentation (spot boundaries detection), quantification (spot volume estimation) 2, 4 and finally matching of corresponding spots across multiple gels (using feature‐ or intensity‐based methods) 5–9.…”

Section: Introductionmentioning

confidence: 99%

Improving 2‐DE gel image denoising using contourlets

Tsakanikas

Μανωλάκος

2009

Proteomics

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One of the most commonly used methods for protein separation is 2-DE. After 2-DE gel scanning, images with a plethora of spot features emerge that are usually contaminated by inherent noise. The objective of the denoising process is to remove noise to the extent that the true spots are recovered correctly and accurately i.e. without introducing distortions leading to the detection of false-spot features. In this paper we propose and justify the use of the contourlet transform as a tool for 2-DE gel images denoising. We compare its effectiveness with state-of-the-art methods such as wavelets-based multiresolution image analysis and spatial filtering. We show that contourlets not only achieve better average S/N performance than wavelets and spatial filters, but also preserve better spot boundaries and faint spots and alter less the intensities of informative spot features, leading to more accurate spot volume estimation and more reliable spot detection, operations that are essential to differential expression proteomics for biomarkers discovery.

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Hierarchical, model‐based merging of multiple fragments for improved three‐dimensional segmentation of nuclei

et al. 2004

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Background: Automated segmentation of fluorescently labeled cell nuclei in three-dimensional confocal images is essential for numerous studies, e.g., spatiotemporal fluorescence in situ hybridization quantification of immediate early gene transcription. High accuracy and automation levels are required in high-throughput and large-scale studies. Common sources of segmentation error include tight clustering and fragmentation of nuclei. Previous regionbased methods are limited because they perform merging of two nuclear fragments at a time. To achieve higher accuracy without sacrificing scale, more sophisticated yet computationally efficient algorithms are needed. Methods: A recursive tree-based algorithm that can consider multiple object fragments simultaneously is described. Starting with oversegmented data, it searches efficiently for the optimal merging pattern guided by a quantitative scoring criterion based on object modeling. Computation is bounded by limiting the depth of the merging tree.

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

Cited by 7 publications

References 31 publications

Robust features for 2‐DE gel image registration

Robust features for 2‐DE gel image registration

Improving 2‐DE gel image denoising using contourlets

Hierarchical, model‐based merging of multiple fragments for improved three‐dimensional segmentation of nuclei

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