Whole-transciptome analysis of [psi+] budding yeast via cDNA microarrays

Introduction: Prions of yeast present a novel analytical challenge in terms of both initial characterization and in vitro manipulation as models for human disease research. Presently, few robust analysis strategies have been successfully implemented which enable the efficient study of prion behavior in vivo. This study sought to evaluate the utilization of conventional dual-channel cDNA microarrays for the surveillance of transcriptomic regulation patterns by the [PSI+] yeast prion relative to an identical prion deficient yeast variant, [psi-]. Methods: A data analysis and normalization workflow strategy was developed and applied to cDNA array images, yielded quality-regulated expression ratios for a subset of genes exhibiting statistical congruence across multiple experimental repetitions and nested hybridization events. The significant gene list was analyzed using classical analytical approaches including several clustering-based methods and singular value decomposition. To add biological meaning to the differential expression data in hand, functional annotation using the Gene Ontology as well as several pathway-mapping approaches was conducted. Finally, the expression patterns observed were queried against all publicly curated microarray data performed using S. cerevisiae in order to discover similar expression behavior across a vast array of experimental conditions. Results: These data collectively implicate a low-level of overall genomic regulation as a result of the [PSI+] state, where the maximum statistically significant degree of differential expression was less than ±1 Log2(FC) in all cases. Notwithstanding, the [PSI+] differential expression was localized to several specific classes of structural elements and cellular functions, implying under homeostatic conditions significant up or down regulation is likely unnecessary but possible in those specific systems if environmental conditions warranted. As a result of these findings additional work pertaining to this system should include controlled insult to both yeast variants of differing environmental properties to promote a potential [PSI+] regulatory response coupled with co-surveillance of these conditions using transcriptomic and proteomic analysis methodologies

GridWeaver: A Fully-Automatic System for Microarray Image Analysis Using Fast Fourier Transforms

Experiments using microarray technology generate large amounts of image data that are used in the analysis of genetic function. An important stage in the analysis is the determination of relative intensities of spots on the images generated. This paper presents GridWeaver, a program that reads in images from a microarray experiment, automatically locates subgrids and spots in the images, and then determines the spot intensities needed in the analysis of gene function. Automatic gridding is performed by running Fast Fourier Transforms on pixel intensity sums. Tests on several data sets show that the program responds well even on images that have significant noise, both random and systemic

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

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

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

Gene Expression Analysis Methods on Microarray Data a A Review

In recent years a new type of experiments are changing the way that biologists and other specialists analyze many problems. These are called high throughput experiments and the main difference with those that were performed some years ago is mainly in the quantity of the data obtained from them. Thanks to the technology known generically as microarrays, it is possible to study nowadays in a single experiment the behavior of all the genes of an organism under different conditions. The data generated by these experiments may consist from thousands to millions of variables and they pose many challenges to the scientists who have to analyze them. Many of these are of statistical nature and will be the center of this review. There are many types of microarrays which have been developed to answer different biological questions and some of them will be explained later. For the sake of simplicity we start with the most well known ones: expression microarrays

Involvement of genes and non-coding RNAs in cancer: profiling using microarrays

MicroRNAs (miRNAs) are small noncoding RNAs (ncRNAs, RNAs that do not code for proteins) that regulate the expression of target genes. MiRNAs can act as tumor suppressor genes or oncogenes in human cancers. Moreover, a large fraction of genomic ultraconserved regions (UCRs) encode a particular set of ncRNAs whose expression is altered in human cancers. Bioinformatics studies are emerging as important tools to identify associations between miRNAs/ncRNAs and CAGRs (Cancer Associated Genomic Regions). ncRNA profiling, the use of highly parallel devices like microarrays for expression, public resources like mapping, expression, functional databases, and prediction algorithms have allowed the identification of specific signatures associated with diagnosis, prognosis and response to treatment of human tumors

Gene Copy Number Variation in Natural Populations of Plasmodium falciparum

Gene copy number variants (CNVs), which consist of gene deletions and amplifications contribute to the great diversity in the Plasmodium falciparum genome. CNVs may influence the expression of genes and hence may affect important parasite phenotypes such as virulence, drug resistance, persistence and transmissibility. The hypothesis underlying the studies in this thesis is that CNVs may be important for adaptation of the parasite to its variable environments. To investigate this hypothesis, a population wide survey of CNVs in 183 fresh field isolates from four populations with different transmission intensities was conducted. To detect CNVs, comparative genome hybridization was performed using a 70mer microarray. This is the first large scale survey for CNVs in natural populations of parasites. A total of 98 different CNVs, consisting of 225 genes, were identified. Various systematic aspects that could affect detection of CNVs were explored and the population of origin of the isolate was found to be the only factor that affects CNV detection. Some of these CNVs showed high differentiation in frequency between populations suggestive of the action of directional selection. Other CNVs showed no or low differentiation in frequencies between populations, indicative of action of neutral evolutionary processes. Validation of the CNVs identified using microarrays was done using whole genome sequencing. Very low concordance was observed between the CNVs identified by the two technologies. These differences may be attributed to technical and analytic differences between the two technologies. Furthermore, the effect of CNVs on gene expression levels was analysed. A number of CNVs were found to be significantly associated (positively or negatively) with the expression levels of genes located inside and also outside the CNVs