Cellular neural networks, Navier-Stokes equation and microarray image reconstruction

Copyright @ 2011 IEEE.Although the last decade has witnessed a great deal of improvements achieved for the microarray technology, many major developments in all the main stages of this technology, including image processing, are still needed. Some hardware implementations of microarray image processing have been proposed in the literature and proved to be promising alternatives to the currently available software systems. However, the main drawback of those proposed approaches is the unsuitable addressing of the quantification of the gene spot in a realistic way without any assumption about the image surface. Our aim in this paper is to present a new image-reconstruction algorithm using the cellular neural network that solves the Navier–Stokes equation. This algorithm offers a robust method for estimating the background signal within the gene-spot region. The MATCNN toolbox for Matlab is used to test the proposed method. Quantitative comparisons are carried out, i.e., in terms of objective criteria, between our approach and some other available methods. It is shown that the proposed algorithm gives highly accurate and realistic measurements in a fully automated manner within a remarkably efficient time

Microarray image processing: A novel neural network framework

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Due to the vast success of bioengineering techniques, a series of large-scale analysis tools has been developed to discover the functional organization of cells. Among them, cDNA microarray has emerged as a powerful technology that enables biologists to cDNA microarray technology has enabled biologists to study thousands of genes simultaneously within an entire organism, and thus obtain a better understanding of the gene interaction and regulation mechanisms involved. Although microarray technology has been developed so as to offer high tolerances, there exists high signal irregularity through the surface of the microarray image. The imperfection in the microarray image generation process causes noises of many types, which contaminate the resulting image. These errors and noises will propagate down through, and can significantly affect, all subsequent processing and analysis. Therefore, to realize the potential of such technology it is crucial to obtain high quality image data that would indeed reflect the underlying biology in the samples. One of the key steps in extracting information from a microarray image is segmentation: identifying which pixels within an image represent which gene. This area of spotted microarray image analysis has received relatively little attention relative to the advances in proceeding analysis stages. But, the lack of advanced image analysis, including the segmentation, results in sub-optimal data being used in all downstream analysis methods. Although there is recently much research on microarray image analysis with many methods have been proposed, some methods produce better results than others. In general, the most effective approaches require considerable run time (processing) power to process an entire image. Furthermore, there has been little progress on developing sufficiently fast yet efficient and effective algorithms the segmentation of the microarray image by using a highly sophisticated framework such as Cellular Neural Networks (CNNs). It is, therefore, the aim of this thesis to investigate and develop novel methods processing microarray images. The goal is to produce results that outperform the currently available approaches in terms of PSNR, k-means and ICC measurements.Aleppo University, Syri

Bioinformatics framework for genotyping microarray data analysis

Functional genomics is a flourishing science enabled by recent technological breakthroughs in high-throughput instrumentation and microarray data analysis. Genotyping microarrays establish the genotypes of DNA sequences containing single nucleotide polymorphisms (SNPs), and can help biologists probe the functions of different genes and/or construct complex gene interaction networks. The enormous amount of data from these experiments makes it infeasible to perform manual processing to obtain accurate and reliable results in daily routines. Advanced algorithms as well as an integrated software toolkit are needed to help perform reliable and fast data analysis. The author developed a MatlabTM based software package, called TIMDA (a Toolkit for Integrated Genotyping Microarray Data Analysis), for fully automatic, accurate and reliable genotyping microarray data analysis. The author also developed new algorithms for image processing and genotype-calling. The modular design of TIMDA allows satisfactory extensibility and maintainability. TIMDA is open source (URL: http://timda.SF.net and can be easily customized by users to meet their particular needs. The quality and reproducibility of results in image processing and genotype-calling and the ease of customization indicate that TIMDA is a useful package for genomics research

Automating the processing of cDNA microarray images

This work is concerned with the development of an automatic image processing tool for DNA microarray images. This paper proposes, implements and tests a new tool for cDNA image analysis. The DNAs are imaged as thousands of circularly shaped objects (spots) on the microarray image and the purpose of this tool is to correctly address their location, segment the pixels belonging to spots and extract the quality features of each spot. Techniques used for addressing, segmentation and feature extraction of spots are described in detail. The results obtained with the proposed tool are systematically compared with conventional cDNA microarray analysis software tools

Microarray image processing : a novel neural network framework

Due to the vast success of bioengineering techniques, a series of large-scale analysis tools has been developed to discover the functional organization of cells. Among them, cDNA microarray has emerged as a powerful technology that enables biologists to cDNA microarray technology has enabled biologists to study thousands of genes simultaneously within an entire organism, and thus obtain a better understanding of the gene interaction and regulation mechanisms involved. Although microarray technology has been developed so as to offer high tolerances, there exists high signal irregularity through the surface of the microarray image. The imperfection in the microarray image generation process causes noises of many types, which contaminate the resulting image. These errors and noises will propagate down through, and can significantly affect, all subsequent processing and analysis. Therefore, to realize the potential of such technology it is crucial to obtain high quality image data that would indeed reflect the underlying biology in the samples. One of the key steps in extracting information from a microarray image is segmentation: identifying which pixels within an image represent which gene. This area of spotted microarray image analysis has received relatively little attention relative to the advances in proceeding analysis stages. But, the lack of advanced image analysis, including the segmentation, results in sub-optimal data being used in all downstream analysis methods. Although there is recently much research on microarray image analysis with many methods have been proposed, some methods produce better results than others. In general, the most effective approaches require considerable run time (processing) power to process an entire image. Furthermore, there has been little progress on developing sufficiently fast yet efficient and effective algorithms the segmentation of the microarray image by using a highly sophisticated framework such as Cellular Neural Networks (CNNs). It is, therefore, the aim of this thesis to investigate and develop novel methods processing microarray images. The goal is to produce results that outperform the currently available approaches in terms of PSNR, k-means and ICC measurements.EThOS - Electronic Theses Online ServiceAleppo University, SyriaGBUnited Kingdo

New microarray image segmentation using Segmentation Based Contours method

The goal of the research developed in this dissertation is to develop a more accurate segmentation method for Affymetrix microarray images. The Affymetrix microarray biotechnologies have become increasingly important in the biomedical research field. Affymetrix microarray images are widely used in disease diagnostics and disease control. They are capable of monitoring the expression levels of thousands of genes simultaneously. Hence, scientists can get a deep understanding on genomic regulation, interaction and expression by using such tools. We also introduce a novel Affymetrix microarray image simulation model and how the Affymetrix microarray image is simulated by using this model. This simulation model embraces all realistic biological characteristics and experimental preparation characteristics, which could have different impacts on the quality of microarray image during the real microarray experiment. The most important aspect is that this model could provide the ground true information, which allows us to have a deep understanding on different segmentation algorithms performance. After the simulation, the new proposed segmentation algorithm Segmentation Based Contours (SBC) method is presented as well as the modifications of the Active Contours Without the Edges (ACWE) method. By modifying the ACWE method with higher order finite difference scheme and fast scheme, we establish the new segmentation algorithm Segmentation Based Contours method. In the end, we compare the gene signal values obtained from the new proposed algorithm Segmentation Based Contours method and the best currently known method. This gene expression signal comparison is more meaningful in gene expression analysis, since it represents the whole gene expression level rather than the small transcripts hybridization abundance level. Different types of experimental comparison results will be presented to show that the new proposed Segmentation Based Contours method is more efficient and accurate

Gene expression profiling in prepubertal and adult male mice using cDNA and oligonucleotide microarrays

Variations in gene expression are the basis of differences in cell and tissue function, response to DNA damaging agents, susceptibility to genetic disease, and cellular differentiation. The purpose of this dissertation research was to characterize variation in basal gene expression among adult mouse tissues for selected stress response, DNA repair and damage control genes and to utilize variation in temporal gene expression patterns to identify candidate genes associated with germ cell differentiation from mitosis through meiosis in the prepubertal mouse testis. To accomplish these goals, high throughput analyses of gene expression were performed using custom cDNA and random oligonucleotide microarrays. CDNA microarray technology was optimized by evaluating the effects of multiple hybridization and image analysis methodologies on the magnitude of background-subtracted hybridization signal intensities. The results showed that hybridizing lower probe quantities in a buffer developed at Lawrence Livermore National Laboratory to tryptone-blocked microarrays improved signal intensities. In addition, the error in expression ratio measurements was significantly reduced when microarray images were preprocessed. A custom cDNA microarray comprised of 417 genes and enriched for stress response, DNA repair, and damage control genes was used to investigate basal gene expression differences among adult mouse testis, brain, liver, spleen, and heart. Genes with functions related to stress response exhibited the most variation in expression among tissues whereas DNA repair-associated gene expression varied the least. Random oligonucleotide microarrays comprised of ∼10,000 genes were used to profile changes in gene expression during the first wave of spermatogenesis in the prepubertal mouse testis. Approximately 550 genes were differentially expressed as male germ cells differentiated from spermatogonia to primary spermatocytes. These findings suggest that the 313 unannotated sequences and 178 genes with known functions in other biological pathways have spermatogenesis-associated roles. This dissertation research showed that microarrays are a useful tool for quantitating the expression of large numbers of genes in parallel under normal physiological conditions and during differentiation. It has also provided candidate genes for future investigations of the molecular mechanisms underlying (1) tissue-specific DNA damage response and genetic disease susceptibility and (2) cellular differentiation during the onset and progression of spermatogenesis

Differential expression and detection of transcripts in sweetpotato (Ipomoea batatas (L.) Lam.) using cDNA microarrays

Microarray protocols were developed for sweetpotato (Ipomoea batatas (L.) Lam.) and then used to study issues of importance in sweetpotato physiology and production. The effect of replication number and image analysis software was compared with results obtained by quantitative real-time PCR. The results indicated that reliable results could be obtained using six replicates and UCSF Spot image analysis software. These methodologies were employed to elucidate aspects of sweetpotato development, physiology and response to virus infection. Storage root formation is the most economically important process in sweetpotato development. Gene expression levels were compared between fibrous and storage roots of the cultivar Jewel. Sucrose synthase, ADP-glucose pyrophosphorylase, and fructokinase were up-regulated in storage roots, while hexokinase was not differentially expressed. A variety of transcription factors were differentially expressed as well as several auxin-related genes. The orange flesh color of sweetpotato is due to β-carotene stored in chromoplasts of root cells. β-carotene is important because of its role in human health. To elucidate biosynthesis and storage of β-carotene in sweetpotato roots, microarray analysis was used to investigate genes differentially expressed between ‘White Jewel’ and ‘Jewel’ storage roots. β-carotene content calculated for ‘Jewel’ and ‘White Jewel’ were 20.66 mg/100 g fresh weight (FW) and 1.68 mg/100 g FW, respectively. Isopentenyl diphosphate isomerase was down-regulated in ‘White Jewel’, but three other genes in the β-carotene biosynthetic pathway were not differentially expressed. Several genes associated with chloroplasts were differentially expressed, indicating probable differences in chromoplast development of ‘White Jewel’ and ‘Jewel’. Sweet potato virus disease (SPVD) is caused by the co-infection of plants with a potyvirus, Sweet potato feathery mottle virus (SPFMV), and a crinivirus, Sweet potato chlorotic stunt virus (SPCSV). Expression analysis revealed that the number of differentially expressed genes in plants infected with SPFMV alone and SPCSV alone compared to virus-tested plants was only three and 14, respectively. In contrast, more than 200 genes from various functional categories were differentially expressed between virus-tested and SPVD-affected plants. Microarray analysis has proved to be a useful tool to study important aspects of sweetpotato physiology and production