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

Abstract

IntroductionThe 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.MaterialThis 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.ResultsThe results of DNA polymorphism analysis were obtained in less than one second for the Intel Core™ 2 Quad CPU Q9300@2.5GHz, 8GB RAM. In comparison with other known methods, specificity was 0.95, sensitivity = 0.94 and AUC (Area Under Curve) = 0.98.ConclusionsIt 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.

A New Algorithm for Detecting GPN Protein Expression and Overexpression of IDC and ILC Her2+ Subtypes on Polyacrylamide Gels Associated with Breast Cancer

“…Unlike DNA gels, protein gels can contain many bands per sample, making them difficult for the human eye to interpret (see lane 4 in Figure 1). As a result, misinterpretations of protein gels can occur due to errors generated by optical illusions, visual sensitivity, or fatigue [3,[5][6][7].…”

“…Many techniques have been used to remove background noise, such as contourlet and wavelet transforms, top-hat transforms, Gaussian low-pass filters, normalization, intensity shifts, median filters, adaptive thresholding, non-linear Gaussians, Fourier analysis, fuzzy-c-means, and some convolution matrix and image slices to search for regions of interest. These methods have been used individually or in combination to obtain improved and noise-free image profiles to extract gel features [1,[3][4][5]7,8,[10][11][12][13][14][15][16][17][18][19][20]. Several tools have been employed to detect lanes and segmentation of the bands [1,[3][4][5]7,8,[10][11][12][13][14][15][16][18][19][20][21][22].…”

“…These methods have been used individually or in combination to obtain improved and noise-free image profiles to extract gel features [1,[3][4][5]7,8,[10][11][12][13][14][15][16][17][18][19][20]. Several tools have been employed to detect lanes and segmentation of the bands [1,[3][4][5]7,8,[10][11][12][13][14][15][16][18][19][20][21][22]. These techniques involve various methods to improve segmentation by selecting necessary pixels related to background contrast.…”

“…Several tools have been employed to detect lanes and segmentation of the bands [1,[3][4][5]7,8,[10][11][12][13][14][15][16][18][19][20][21][22]. These techniques involve various methods to improve segmentation by selecting necessary pixels related to background contrast.…”