ArrayQuest: a web resource for the analysis of DNA microarray data

DNA Microarray Technique Current Analytical Methods of DNA Microarray Data Advantages and Disadvantages Current Application of DNA Microarray Technique in Medicine A Novel Method for DNA Microarray Data Analysis Future Directions.

Zeroing in on the “holy grail”

‘‘Genomics in Endocrinology’’ edited by Stuart Handwerger, M.D. and Bruce J. Aronow, Ph.D. presents a series of short articles by hormonal researchers who utilized DNA microarray techniques to analyze the expression of diverse genes in different hormone-producing and hormoneresponsive tissues. In addition, gene expression was monitored in diseased tissues using the DNA microarray approach. This monograph represents a state-of-the-art summary of the present use of DNA microarray analyses in hormonal research for readers interested in this topic. The interesting chapters by 40 some authors represent key DNA microarray findings in the field and provide a useful resource for readers to get an overview of this area of research. In addition, the introductory chapter by the editors provides a useful lead-in to the general approach, including data analyses, statistical consideration, data storage and validation, as well as potential pitfalls. The major advantage of the DNA microarray approach is certainly the high-throughput, genome-wide analyses and the identification of candidate genes important for physiological processes of interest. Although many of the chapters deal with a general survey of the expression of multiple genes, few chapters used DNA microarray data to provide molecular classification of different disease states. Of particular interest, several chapters described more advanced use of DNA microarray data, including analyses of promoter regions of co-expressed genes, identification of candidate genes related to a particular disease, advanced chromatin immuno-precipitation microarray for genes regulated by transcriptional factors, and attempts to reveal gene pathways based on expression data. The overall field is progressing from the straightforward description of gene expression patterns to the formulation of new hypotheses based on gene expression to allow a better understanding of role of genes in physiology and pathophysiology. A more appropriate title for the present monograph is probably ‘‘Transcriptomics of hormone-producing and hormone-responsive tissues’’ due to the emphasis on DNA microarray analyses and a lack of information on genomic topics such as single nucleotide polymorphism, gene copy numbers, genome-wide analyses of the phylogenetic relationship of hormone and receptor genes, identification of new hormone and receptor genes, and other genomic issues.

Objective To investigate the genes associated with tuberous scleroses complex and refractory epilepsy with bioinformatics methods to provide new ideas for the basic research and clinical treatment of epilepsy. Methods The microarray data of tuberous scleroses complex and refractory epilepsy were downloaded from the Gene Expression Omnibus (GEO) database and analyzed by bioinformatics methods using String, KEGG and Panther databases.Results Of all the 100 differentially expressed genes, 47 were found to encode proteins with interactions; they involved to multiple biological pathways, biological processes and molecular functions.Conclusion The pathogenesis of tuberous scleroses complex and refractory epilepsy involves multiple genes, such as GFAP, ANXA 2 and S100A 10. Key words: Tuberous scleroses complex; Epilepsy; DNA microarray; Bioinformatics

Zeranol (Z) is a nonsteroidal mycotoxin with potent estrogenic activity. Z is one of six growth promoters approved by the FDA for use in the U.S. beef industry to accelerate weight gain, improve feed efficiency and increase the lean meat-to-fat ratio. Recently, the safety meat products from Z-implanted beef has been questioned on the basis that increased exposure of bioactive Z residues may have adverse health implications. Our previous study showed that serum, meat and meat extracts derived from beef cattle implanted with Z significantly stimulated the proliferation of primary cultured human normal and cancerous breast epithelial cells, primary cultured human breast pre-adipocytes, and human breast cancer cell lines. However, little is known about the mechanisms of Z stimulation on the growth of different types of breast cells. To systematically investigate gene expression patterns in primary cultured human breast cancer epithelial cells (PCHBCECs) after Z treatment, we performed DNA microarray analysis. After PCHBCECs were isolated from breast cancer tissue, they were treated with 30 nM Z or 0.1% DMSO, as a vehicle control, for 48 hr, and then mRNA was isolated and purified for gene expression analysis using the Human Genome U133 Plus 2.0 Array TM (Affymetrix Co. Ltd) in the Microarray Shared Resources at The Ohio State University Comprehensive Cancer Center. DNA microarray data were generated on the basis of the criteria of signal intensity and signal ratio. Our results showed that 10 genes were down-regulated and 25 genes were up-regulated in PCHBCECs treated with 30 nM Z as compared to the controls. Among the genes regulated by Z were, limb-bud and heart (LBH) and annexin-1 (ANXA1) which were decreased 2.27 and 2 fold, respectively and human HMG-CoA synthase 1 (HMGCS1) and methyltransferase like 7A (METTL7A) which were up-regulated to 3.8 and 3.25 fold, respectively compared with the controls. Our preliminary data also showed that Z decreased the expression of several tumor suppressor genes, such as protein tyrosine phosphatase γ (PTPγ), p16, p21, and increased DNMT1 expression in PCHBCECs which suggests epigenetic modification of Z in their promoter regions. Our results indicate that long term consumption of beef products with low level of Z or its metabolites might have adverse health effects on human breast. Further validation of our DNA microarray results is in progress in our laboratory (Supported by NIH R01Grant ES 015212). Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 33. doi:10.1158/1538-7445.AM2011-33

The real-time system of the JT-60 has a cascade structure with the major and the minor control loops. The multi-variable feedback control is adopted in the major control loop in order to adjust non-linear effects which will appear in the high-/beta/region. By applying the concept of 'phase shift control' to the control program of the major control loop, a flexible tokamak discharge control is expected to be performed with taking account of machine protection.

DNA microarray technology is a fundamental tool in gene expression data analysis. The collection of datasets from the technology has underscored the need for quantitative analytical tools to examine such data. Due to the large number of genes and complex gene regulation networks, clustering is a useful exploratory technique for analyzing these data. Many clustering algorithms have been proposed to analyze microarray gene expression data, but very few of them evaluate the quality of the clusters. In this paper, a novel cluster analysis technique has been proposed without considering number of clusters a priori. The method computes a similarity measurement function based on which the clusters are merged and subsequently splits a cluster by computing the degree of separation of the cluster. The process of splitting and merging performs iteratively until the cluster validity index (i.e. DB index) degrades. The experimental result shows that the proposed cluster analysis technique gives comparable results on gene cancer dataset with existing methods. This study may help raise relevant issues in the extraction of meaningful biological information from microarray expression data. Keywords- DNA microarray data, Clustering technique, Cluster validity index, Similarity measurement, Splitting and Merging, Cancer data analysis

During the past decade, the advent of new molecular techniques has led to enormous progress in biology, notably with the development of DNA microarray technology. This technology allows monitoring simultaneously the expression of thousands of genes from a given organism. DNA microarrays have been used in a variety of applications, including the characterization of bacteria in biological samples. In this thesis, two distinct DNA microarray approaches for the characterization of bacterial flora are introduced. The first approach, termed phylogenetic microarrays, consists in a probe set recognizing specific sequence signatures for each node of the bacterial phylogenetic tree. This strategy, based on sequence information, allows extending the scope of microarrays to the whole bacterial kingdom and detecting both known and unknown microorganisms in biological samples. Moreover, phylogenetic microarrays permit detection of a broader bacterial diversity compared to the classical cloning-sequencing approach. The second strategy consists in a low-density 16S rRNA gene microarray specifically designed to monitor bacterial species found in the gingival flora of African children suffering from noma, a devastating disease of unknown etiology. In an attempt to identify the causative agent(s) of noma, these two methodologies were applied for the characterization of the gingival flora of children suffering from the disease. Observations made during this study allowed exonerating Fusobacterium necrophorum, considered by some experts as the causative agent of noma. Moreover, various oral pathogens were recovered in higher abundance in noma lesions, notably Atopobium spp., Peptostreptococcus spp., Prevotella intermedia, Streptococcus pyogenes and Streptococcus anginosus. In addition, noma lesions exhibited a lower bacterial diversity compared to acute necrotizing gingivitis (ANG), thus supporting a previous hypothesis that ANG might precede acute noma. The accomplished work, while giving better insights on the bacteriology of noma, demonstrates the power of the two developed approaches to explore and systematically characterize complex microbial communities.

Cancer is a world-famous deadly disease. According to the World Health Organization (WHO), cancer is the second leading cause of death globally and is responsible for an estimated 9.6 million deaths in 2018. One well-known technique for cancer detection is the DNA microarray technique. DNA microarray technology provides an opportunity for researchers to analyze thousands of gene expression profiles at the same time to determine whether a person has cancer or not. However, one of the problems in DNA microarray data is the large number of features that require feature selection. In overcoming these problems, this study will use the feature selection Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and Chi-Square and use the Naive Bayes classification method. The accuracy results from using feature selection with those that are not will be compared. The accuracy between using the two feature selection methods will also be compared to find which feature selection method is better when combined with the Naive Bayes classification method. To get an overall picture of the performance comparison, this study also considers precision, recall, and F1-score. The best accuracy results obtained were 100% lung cancer data with SVM-RFE and Chi-Square, 99.6% ovarian cancer with SVM-RFE, 93.7% breast cancer with SVM-RFE, and 90% colon cancer with SVM- RFE.

The σB regulon in Staphylococcus aureus and its regulation

We investigate discrete structures and combinatoric modeling of weighted prefix trees for managing and analyzing DNA microarray data. We describe the algorithms to construct the weighted trees for these data. Using these weighted trees with our algorithms, we propose methods to compute the appearance probability of a DNA microarray, to compare the informational distances in the expression of genes between the DNA microarrays, to search the characteristic microarrays and the group of candidate genes suggestive of a pathology.

Objective To prepare the microarrays for detection of HBV genotypes. Method HBV PCR products were hybridized with oligonuleotide probes, which were prestablized on the chip. The hybridized results were colorized by BCIP/NBT.According to the hybridization signal and the corresponding probe sequence, HBV genotypes were deter-mined. Results In the analysis of 106 HBV patients' serum samples using our developed microarray, we identified 37 genotype B, 69 genotype C. Sequencing results of all samples agreed 100% with microarray data. The hybridized signals on gene chips indicated that the sensitivity and specificity of DNA microarray for detecting the HBV genotypos were saris-factory. Conclusions This assay was a quick, simple and effective method for HBV genotyping in clinic. Key words: Hepatitis B virus; DNA microarray; Genotypes

We previously showed that cardiomyocyte-specific G protein-coupled estrogen receptor (GPER) gene deletion leads to sex-specific adverse effects on cardiac structure and function; alterations which may be due to distinct differences in mitochondrial and inflammatory processes between sexes. Here, we provide the results of Gene Set Enrichment Analysis (GSEA) based on the DNA microarray data from GPER-knockout versus GPER-intact (intact) cardiomyocytes. This article contains complete data on the mitochondrial and inflammatory response-related gene expression changes that were significant in GPER knockout versus intact cardiomyocytes from adult male and female mice. The data are supplemental to our original research article “Cardiomyocyte-specific deletion of the G protein-coupled estrogen receptor (GPER) leads to left ventricular dysfunction and adverse remodeling: a sex-specific gene profiling” (Wang et al., 2016) [1]. Data have been deposited to the Gene Expression Omnibus (GEO) database repository with the dataset identifier GSE86843.

Expression profiling: Opportunities and pitfalls and impact on the study and management of allergic diseases

physiological genomics ( PG ) publishes, on a quarterly basis, brief editorial perspectives entitled “Physiological genomics in PG and beyond.” These editorial perspectives provide brief, accessible highlights of the studies published in PG in the most recent quarter, and relate those studies to