An Automated Dna Strands Detection System Featuring 32-Bit Arm7tdmi Microcontroller And Vga-Cmos Digital Image Sensor.

Genetic DNA recognition is a routine experiment for detecting the origin of the species. Electrophoresis is one of the processes for such detection which has been used extensively. Pengecaman genetik DNA ialah eksperimen rutin untuk mengesan asal usul sesuatu spesis. Proses electrophoresis ialah salah satu proses pengecaman yang digunakan secara meluas

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

Automated DNA Fragments Recognition and Sizing through AFM Image Processing

This paper presents an automated algorithm to determine DNA fragment size from atomic force microscope images and to extract the molecular profiles. The sizing of DNA fragments is a widely used procedure for investigating the physical properties of individual or protein-bound DNA molecules. Several atomic force microscope (AFM) real and computer-generated images were tested for different pixel and fragment sizes and for different background noises. The automated approach minimizes processing time with respect to manual and semi-automated DNA sizing. Moreover, the DNA molecule profile recognition can be used to perform further structural analysis. For computer-generated images, the root mean square error incurred by the automated algorithm in the length estimation is 0.6% for a 7.8 nm image pixel size and 0.34% for a 3.9 nm image pixel size. For AFM real images we obtain a distribution of lengths with a standard deviation of 2.3% of mean and a measured average length very close to the real one, with an error around 0.33%

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 Platform for Fast Detection of Let-7 Micro RNA Using Polyaniline Fluorescence and Image Analysis Techniques

The project describes a new strategy for transducing hybridization events through modulating intrinsic properties of the electroconductive polymer polyaniline (PANI). When DNA based probes electrostatically interact with PANI, its fluorescence properties are increased, a phenomenon that can be enhanced by UV irradiation. Hybridization of target nucleic acids results in dissociation of probes causing PANI fluorescence to return to basal levels. By monitoring restoration of base PANI fluorescence as little as 10-11 M (10 pM) of target oligonucleotides could be detected within 15 minutes of hybridization. Detection of complementary oligos was specific, with introduction of a single mismatch failing to form a target-probe duplex that would dissociate from PANI. Furthermore, this approach is robust and is capable of detecting specific RNAs in extracts from animals. This sensor system improves on previously reported strategies by transducing highly specific probe dissociation events through intrinsic properties of a conducting polymer without the need for additional labels. The change in fluorescence property of PANI by oligo immobilization and hybridization with mimic let-7 is measured by fluorescence microscope and the image analyzed by MATLAB. A heuristic algorithm determines color threshold of the fluorescent active image. This image segmentation helps to determine the average pixel intensity representing the active image foreground of PANI fluorescence triggered by DNA immobilization and hybridization process. This would help us to quantify response of PANI based biosensor for detecting micro RNA let-7

image analysis and processing with applications in proteomics and medicine

This thesis introduces unsupervised image analysis algorithms for the segmentation of several types of images, with an emphasis on proteomics and medical images. Τhe presented algorithms are tailored upon the principles of deformable models and more specific region-based active contours. Two different objectives are pursued. The first is the core issue of unsupervised parameterization in image segmentation, whereas the second is the formulation of a complete model for the segmentation of proteomics images, which is the first to exploit the appealing attributes of active contours. The first major contribution of this thesis is a novel framework for the automated parameterization of region-based active contours. The presented framework aims to endow segmentation results with objectivity and robustness as well as to set domain users free from the cumbersome and time-consuming process of empirical adjustment. It is applicable on various medical imaging modalities and remains insensitive on alterations in the settings of the acquisition devices. The experimental results demonstrate that the presented framework maintains a segmentation quality which is comparable to the one obtained with empirical parameterization. The second major contribution of this thesis is an unsupervised active contour-based model for the segmentation of proteomics images. The presented model copes with crucial issues in 2D-GE image analysis including streaks, artifacts, faint and overlapping spots. In addition, it provides 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. The experimental results demonstrate that the presented model outperforms 2D-GE image analysis software packages in terms of detection and segmentation quantity metrics