A Novel Gaussian Extrapolation Approach for 2D Gel Electrophoresis Saturated Protein Spots

Analysis of images obtained from two-dimensional gel electrophoresis (2D-GE) is a topic of utmost importance in bioinformatics research, since commercial and academic software available currently has proven to be neither completely effective nor fully automatic, often requiring manual revision and refinement of computer generated matches. In this work, we present an effective technique for the detection and the reconstruction of over-saturated protein spots. Firstly, the algorithm reveals overexposed areas, where spots may be truncated, and plateau regions caused by smeared and overlapping spots. Next, it reconstructs the correct distribution of pixel values in these overexposed areas and plateau regions, using a two-dimensional least-squares fitting based on a generalized Gaussian distribution. Pixel correction in saturated and smeared spots allows more accurate quantification, providing more reliable image analysis results. The method is validated for processing highly exposed 2D-GE images, comparing reconstructed spots with the corresponding non-saturated image, demonstrating that the algorithm enables correct spot quantificatio

Image Analysis Workflow for 2-D Electrophoresis Gels Based on ImageJ

A number of commercial software packages are currently available to perform digital two-dimensional electrophoresis (2D-GE) gel analysis. However, both the high cost of the commercial packages and the unavailability of a standard data analysis workflow, have prompted several groups to develop freeware systems to perform certain steps of gel analysis. Unfortunately, to the best of our knowledge none of them offer a package that performs all the steps envisaged in a 2D-GE gel analysis. Here we describe an ImageJ-based procedure, able to manage all the steps of a 2D-GE gel analysis. ImageJ is a free available image processing and analysis application developed by National Institutes of Health (NIH) and widely used in different life sciences fields as medical imaging, microscopy, western blotting and PAGE. Nevertheless no one has yet developed a procedure enabled to compare spots on 2D-GE gels. We collected all used ImageJ tools in a plug-in that allows us to perform the whole 2D-GE analysis. To test it, we performed a set of 2D-GE experiments on plasma samples from 9 patients victims of acute myocardial infarction and 8 controls, and we compared the results obtained by our procedure to those obtained using a widely diffuse commercial package, finding similar performance

Computational Methods on Study of Differentially Expressed Proteins in Maize Proteomes Associated with Resistance to Aflatoxin Accumulation

Plant breeders have focused on improving maize resistance to Aspergillus flavus infection and aflatoxin accumulation by breeding with genotypes having the desirable traits. Various maize inbred lines have been developed for the breeding of resistance. Identification of differentially expressed proteins among such maize inbred lines will facilitate the development of gene markers and expedite the breeding process. Computational biology and proteomics approaches on the investigation of differentially expressed proteins were explored in this research. The major research objectives included 1) application of computational methods in homology and comparative modeling to study 3D protein structures and identify single nucleotide polymorphisms (SNPs) involved in changes of protein structures and functions, which can in turn increase the efficiency of the development of DNA markers; 2) investigation of methods on total protein profiling including purification, separation, visualization, and computational analysis at the proteome level. Special research goals were set on the development of open source computational methods using Matlab image processing tools to quantify and compare protein expression levels visualized by 2D protein electrophoresis gel techniques

A Novel Gaussian Extrapolation Approach for 2D Gel Electrophoresis Saturated Protein Spots

Analysis of images obtained from two-dimensional gel electrophoresis (2D-GE) is a topic of utmost importance in bioinformatics research, since commercial and academic software available currently has proven to be neither completely effective nor fully automatic, often requiring manual revision and refinement of computer generated matches. In this work, we present an effective technique for the detection and the reconstruction of over-saturated protein spots. Firstly, the algorithm reveals overexposed areas, where spots may be truncated, and plateau regions caused by smeared and overlapping spots. Next, it reconstructs the correct distribution of pixel values in these overexposed areas and plateau regions, using a two-dimensional least-squares fitting based on a generalized Gaussian distribution. Pixel correction in saturated and smeared spots allows more accurate quantification, providing more reliable image analysis results. The method is validated for processing highly exposed 2D-GE images, comparing reconstructed spots with the corresponding non-saturated image, demonstrating that the algorithm enables correct spot quantification

Image Analysis Workflow for 2-D Electrophoresis Gels Based on ImageJ

A number of commercial software packages are currently available to perform digital two-dimensional electrophoresis (2D-GE) gel analysis. However, both the high cost of the commercial packages and the unavailability of a standard data analysis workflow, have prompted several groups to develop freeware systems to perform certain steps of gel analysis. Unfortunately, to the best of our knowledge none of them offer a package that performs all the steps envisaged in a 2D-GE gel analysis. Here we describe an ImageJ-based procedure, able to manage all the steps of a 2D-GE gel analysis. ImageJ is a free available image processing and analysis application developed by National Institutes of Health (NIH) and widely used in different life sciences fields as medical imaging, microscopy, western blotting and PAGE. Nevertheless no one has yet developed a procedure enabled to compare spots on 2D-GE gels. We collected all used ImageJ tools in a plug-in that allows us to perform the whole 2D-GE analysis. To test it, we performed a set of 2D-GE experiments on plasma samples from 9 patients victims of acute myocardial infarction and 8 controls, and we compared the results obtained by our procedure to those obtained using a widely diffuse commercial package, finding similar performances

Automatic analysis of 2D polyacrylamide gels in the diagnosis of DNA polymorphisms

Introduction: The analysis of polyacrylamide gels is currently carried out manually or automatically. In the automatic method, there are limitations related to the acceptable degree of distortion of lane and band continuity. The available software cannot deal satisfactorily with this type of situations. Therefore, the paper presents an original image analysis method devoid of the aforementioned drawbacks.Material: This paper examines polyacrylamide gel images from Li-Cor DNA Sequencer 4300S resulting from the use of the electrophoretic separation of DNA fragments. The acquired images have a resolution dependent on the length of the analysed DNA fragments and typically it is MG×NG=3806×1027 pixels. The images are saved in TIFF format with a grayscale resolution of 16 bits/pixel. The presented image analysis method was performed on gel images resulting from the analysis of DNA methylome profiling in plants exposed to drought stress, carried out with the MSAP (Methylation Sensitive Amplification Polymorphism) technique.Results: The results of DNA polymorphism analysis were obtained in less than one second for the Intel Core™ 2 Quad CPU [email protected], 8GB RAM. In comparison with other known methods, specificity was 0.95, sensitivity = 0.94 and AUC (Area Under Curve) = 0.98.Conclusions: It is possible to carry out this method of DNA polymorphism analysis on distorted images of polyacrylamide gels. The method is fully automatic and does not require any operator intervention. Compared with other methods, it produces the best results and the resulting image is easy to interpret. The presented method of measurement is used in the practical analysis of polyacrylamide gels in the Department of Genetics at the University of Silesia in Katowice, Poland

Analyzing 2D gel images using a two-component empirical bayes model

<p>Abstract</p> <p>Background</p> <p>Two-dimensional polyacrylomide gel electrophoresis (2D gel, 2D PAGE, 2-DE) is a powerful tool for analyzing the proteome of a organism. Differential analysis of 2D gel images aims at finding proteins that change under different conditions, which leads to large-scale hypothesis testing as in microarray data analysis. Two-component empirical Bayes (EB) models have been widely discussed for large-scale hypothesis testing and applied in the context of genomic data. They have not been implemented for the differential analysis of 2D gel data. In the literature, the mixture and null densities of the test statistics are estimated separately. The estimation of the mixture density does not take into account assumptions about the null density. Thus, there is no guarantee that the estimated null component will be no greater than the mixture density as it should be.</p> <p>Results</p> <p>We present an implementation of a two-component EB model for the analysis of 2D gel images. In contrast to the published estimation method, we propose to estimate the mixture and null densities simultaneously using a constrained estimation approach, which relies on an iteratively re-weighted least-squares algorithm. The assumption about the null density is naturally taken into account in the estimation of the mixture density. This strategy is illustrated using a set of 2D gel images from a factorial experiment. The proposed approach is validated using a set of simulated gels.</p> <p>Conclusions</p> <p>The two-component EB model is a very useful for large-scale hypothesis testing. In proteomic analysis, the theoretical null density is often not appropriate. We demonstrate how to implement a two-component EB model for analyzing a set of 2D gel images. We show that it is necessary to estimate the mixture density and empirical null component simultaneously. The proposed constrained estimation method always yields valid estimates and more stable results. The proposed estimation approach proposed can be applied to other contexts where large-scale hypothesis testing occurs.</p

Texture analysis in gel electrophoresis images using an integrative kernel-based approach

[Abstract] Texture information could be used in proteomics to improve the quality of the image analysis of proteins separated on a gel. In order to evaluate the best technique to identify relevant textures, we use several different kernel-based machine learning techniques to classify proteins in 2-DE images into spot and noise. We evaluate the classification accuracy of each of these techniques with proteins extracted from ten 2-DE images of different types of tissues and different experimental conditions. We found that the best classification model was FSMKL, a data integration method using multiple kernel learning, which achieved AUROC values above 95% while using a reduced number of features. This technique allows us to increment the interpretability of the complex combinations of textures and to weight the importance of each particular feature in the final model. In particular the Inverse Difference Moment exhibited the highest discriminating power. A higher value can be associated with an homogeneous structure as this feature describes the homogeneity; the larger the value, the more symmetric. The final model is performed by the combination of different groups of textural features. Here we demonstrated the feasibility of combining different groups of textures in 2-DE image analysis for spot detection.Instituto de Salud Carlos III; PI13/00280United Kingdom. Medical Research Council; G10000427, MC_UU_12013/8Galicia. Consellería de Economía e Industria; 10SIN105004P