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Sample records for rat microarray analysis

  1. Microarray analysis of thioacetamide-treated type 1 diabetic rats

    SciTech Connect

    Devi, Sachin S.; Mehendale, Harihara M. . E-mail: mehendale@ulm.edu

    2006-04-01

    It is well known that diabetes imparts high sensitivity to numerous hepatotoxicants. Previously, we have shown that a normally non-lethal dose of thioacetamide (TA, 300 mg/kg) causes 90% mortality in type 1 diabetic (DB) rats due to inhibited tissue repair allowing progression of liver injury. On the other hand, DB rats exposed to 30 mg TA/kg exhibit delayed tissue repair and delayed recovery from injury. The objective of this study was to investigate the mechanism of impaired tissue repair and progression of liver injury in TA-treated DB rats by using cDNA microarray. Gene expression pattern was examined at 0, 6, and 12 h after TA challenge, and selected mechanistic leads from microarray experiments were confirmed by real-time RT-PCR and further investigated at protein level over the time course of 0 to 36 h after TA treatment. Diabetic condition itself increased gene expression of proteases and decreased gene expression of protease inhibitors. Administration of 300 mg TA/kg to DB rats further elevated gene expression of proteases and suppressed gene expression of protease inhibitors, explaining progression of liver injury in DB rats after TA treatment. Inhibited expression of genes involved in cell division cycle (cyclin D1, IGFBP-1, ras, E2F) was observed after exposure of DB rats to 300 mg TA/kg, explaining inhibited tissue repair in these rats. On the other hand, DB rats receiving 30 mg TA/kg exhibit delayed expression of genes involved in cell division cycle, explaining delayed tissue repair in these rats. In conclusion, impaired cyclin D1 signaling along with increased proteases and decreased protease inhibitors may explain impaired tissue repair that leads to progression of liver injury initiated by TA in DB rats.

  2. A High Phosphorus Diet Affects Lipid Metabolism in Rat Liver: A DNA Microarray Analysis

    PubMed Central

    Chun, Sunwoo; Bamba, Takeshi; Suyama, Tatsuya; Ishijima, Tomoko; Fukusaki, Eiichiro; Abe, Keiko; Nakai, Yuji

    2016-01-01

    A high phosphorus (HP) diet causes disorders of renal function, bone metabolism, and vascular function. We previously demonstrated that DNA microarray analysis is an appropriate method to comprehensively evaluate the effects of a HP diet on kidney dysfunction such as calcification, fibrillization, and inflammation. We reported that type IIb sodium-dependent phosphate transporter is significantly up-regulated in this context. In the present study, we performed DNA microarray analysis to investigate the effects of a HP diet on the liver, which plays a pivotal role in energy metabolism. DNA microarray analysis was performed with total RNA isolated from the livers of rats fed a control diet (containing 0.3% phosphorus) or a HP diet (containing 1.2% phosphorus). Gene Ontology analysis of differentially expressed genes (DEGs) revealed that the HP diet induced down-regulation of genes involved in hepatic amino acid catabolism and lipogenesis, while genes related to fatty acid β-oxidation process were up-regulated. Although genes related to fatty acid biosynthesis were down-regulated in HP diet-fed rats, genes important for the elongation and desaturation reactions of omega-3 and -6 fatty acids were up-regulated. Concentrations of hepatic arachidonic acid and eicosapentaenoic acid were increased in HP diet-fed rats. These essential fatty acids activate peroxisome proliferator-activated receptor alpha (PPARα), a transcription factor for fatty acid β-oxidation. Evaluation of the upstream regulators of DEGs using Ingenuity Pathway Analysis indicated that PPARα was activated in the livers of HP diet-fed rats. Furthermore, the serum concentration of fibroblast growth factor 21, a hormone secreted from the liver that promotes fatty acid utilization in adipose tissue as a PPARα target gene, was higher (p = 0.054) in HP diet-fed rats than in control diet-fed rats. These data suggest that a HP diet enhances energy expenditure through the utilization of free fatty acids

  3. A High Phosphorus Diet Affects Lipid Metabolism in Rat Liver: A DNA Microarray Analysis.

    PubMed

    Chun, Sunwoo; Bamba, Takeshi; Suyama, Tatsuya; Ishijima, Tomoko; Fukusaki, Eiichiro; Abe, Keiko; Nakai, Yuji

    2016-01-01

    A high phosphorus (HP) diet causes disorders of renal function, bone metabolism, and vascular function. We previously demonstrated that DNA microarray analysis is an appropriate method to comprehensively evaluate the effects of a HP diet on kidney dysfunction such as calcification, fibrillization, and inflammation. We reported that type IIb sodium-dependent phosphate transporter is significantly up-regulated in this context. In the present study, we performed DNA microarray analysis to investigate the effects of a HP diet on the liver, which plays a pivotal role in energy metabolism. DNA microarray analysis was performed with total RNA isolated from the livers of rats fed a control diet (containing 0.3% phosphorus) or a HP diet (containing 1.2% phosphorus). Gene Ontology analysis of differentially expressed genes (DEGs) revealed that the HP diet induced down-regulation of genes involved in hepatic amino acid catabolism and lipogenesis, while genes related to fatty acid β-oxidation process were up-regulated. Although genes related to fatty acid biosynthesis were down-regulated in HP diet-fed rats, genes important for the elongation and desaturation reactions of omega-3 and -6 fatty acids were up-regulated. Concentrations of hepatic arachidonic acid and eicosapentaenoic acid were increased in HP diet-fed rats. These essential fatty acids activate peroxisome proliferator-activated receptor alpha (PPARα), a transcription factor for fatty acid β-oxidation. Evaluation of the upstream regulators of DEGs using Ingenuity Pathway Analysis indicated that PPARα was activated in the livers of HP diet-fed rats. Furthermore, the serum concentration of fibroblast growth factor 21, a hormone secreted from the liver that promotes fatty acid utilization in adipose tissue as a PPARα target gene, was higher (p = 0.054) in HP diet-fed rats than in control diet-fed rats. These data suggest that a HP diet enhances energy expenditure through the utilization of free fatty acids

  4. Gene expression microarray analysis of early oxygen-induced retinopathy in the rat.

    PubMed

    Tea, Melinda; Fogarty, Rhys; Brereton, Helen M; Michael, Michael Z; Van der Hoek, Mark B; Tsykin, Anna; Coster, Douglas J; Williams, Keryn A

    2009-12-12

    Different inbred strains of rat differ in their susceptibility to oxygen-induced retinopathy (OIR), an animal model of human retinopathy of prematurity. We examined gene expression in Sprague-Dawley (susceptible) and Fischer 344 (resistant) neonatal rats after 3 days exposure to cyclic hyperoxia or room air, using Affymetrix rat Genearrays. False discovery rate analysis was used to identify differentially regulated genes. Such genes were then ranked by fold change and submitted to the online database, DAVID. The Sprague-Dawley list returned the term "response to hypoxia," absent from the Fischer 344 output. Manual analysis indicated that many genes known to be upregulated by hypoxia-inducible factor-1alpha were downregulated by cyclic hyperoxia. Quantitative real-time RT-PCR analysis of Egln3, Bnip3, Slc16a3, and Hk2 confirmed the microarray results. We conclude that combined methodologies are required for adequate dissection of the pathophysiology of strain susceptibility to OIR in the rat. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12177-009-9041-7) contains supplementary material, which is available to authorized users.

  5. Immune and inflammatory gene signature in rat cerebrum in subarachnoid hemorrhage with microarray analysis.

    PubMed

    Lee, Chu-I; Chou, An-Kuo; Lin, Ching-Chih; Chou, Chia-Hua; Loh, Joon-Khim; Lieu, Ann-Shung; Wang, Chih-Jen; Huang, Chi-Ying F; Howng, Shen-Long; Hong, Yi-Ren

    2012-01-01

    Cerebral vasospasm following subarachnoid hemorrhage (SAH) has been studied in terms of a contraction of the major cerebral arteries, but the effect of cerebrum tissue in SAH is not yet well understood. To gain insight into the biology of SAH-expressing cerebrum, we employed oligonucleotide microarrays to characterize the gene expression profiles of cerebrum tissue at the early stage of SAH. Functional gene expression in the cerebrum was analyzed 2 h following stage 1-hemorrhage in Sprague-Dawley rats. mRNA was investigated by performing microarray and quantitative real-time PCR analyses, and protein expression was determined by Western blot analysis. In this study, 18 upregulated and 18 downregulated genes displayed at least a 1.5-fold change. Five genes were verified by real-time PCR, including three upregulated genes [prostaglandin E synthase (PGES), CD14 antigen, and tissue inhibitor of metalloproteinase 1 (TIMP1)] as well as two downregulated genes [KRAB-zinc finger protein-2 (KZF-2) and γ-aminobutyric acid B receptor 1 (GABA B receptor)]. Notably, there were functional implications for the three upregulated genes involved in the inflammatory SAH process. However, the mechanisms leading to decreased KZF-2 and GABA B receptor expression in SAH have never been characterized. We conclude that oligonucleotide microarrays have the potential for use as a method to identify candidate genes associated with SAH and to provide novel investigational targets, including genes involved in the immune and inflammatory response. Furthermore, understanding the regulation of MMP9/TIMP1 during the early stages of SAH may elucidate the pathophysiological mechanisms in SAH rats.

  6. Microarray analysis in rat liver slices correctly predicts in vivo hepatotoxicity

    SciTech Connect

    Elferink, M.G.L. Olinga, P.; Draaisma, A.L.; Merema, M.T.; Bauerschmidt, S.; Polman, J.; Schoonen, W.G.; Groothuis, G.M.M.

    2008-06-15

    The microarray technology, developed for the simultaneous analysis of a large number of genes, may be useful for the detection of toxicity in an early stage of the development of new drugs. The effect of different hepatotoxins was analyzed at the gene expression level in the rat liver both in vivo and in vitro. As in vitro model system the precision-cut liver slice model was used, in which all liver cell types are present in their natural architecture. This is important since drug-induced toxicity often is a multi-cellular process involving not only hepatocytes but also other cell types such as Kupffer and stellate cells. As model toxic compounds lipopolysaccharide (LPS, inducing inflammation), paracetamol (necrosis), carbon tetrachloride (CCl{sub 4}, fibrosis and necrosis) and gliotoxin (apoptosis) were used. The aim of this study was to validate the rat liver slice system as in vitro model system for drug-induced toxicity studies. The results of the microarray studies show that the in vitro profiles of gene expression cluster per compound and incubation time, and when analyzed in a commercial gene expression database, can predict the toxicity and pathology observed in vivo. Each toxic compound induces a specific pattern of gene expression changes. In addition, some common genes were up- or down-regulated with all toxic compounds. These data show that the rat liver slice system can be an appropriate tool for the prediction of multi-cellular liver toxicity. The same experiments and analyses are currently performed for the prediction of human specific toxicity using human liver slices.

  7. Microarray analysis of diet-induced alterations in gene expression in the ACI rat prostate.

    PubMed

    Reyes, Niradiz; Iatropoulos, Michael; Mittelman, Abraham; Geliebter, Jan

    2002-08-01

    The natural history of prostate cancer is a multistage process that involves the transition from normal tissue to subclinical cancer, with progression to carcinoma in situ and eventually metastatic disease. Evidence suggests that a high-fat diet plays a critical role in the biology and progression of the disease. ACI rats were maintained for two generations on high beef fat or control diets for 18 months. Affymetrix microarrays were used to analyze the mRNA expression levels in the dorsolateral prostates of rats on the different diets. Approximately 4752 genes and expressed sequence tag (EST) were expressed in the prostates of rats on either diet. Twenty-seven genes were upregulated and 28 genes downregulated in the high beef fat diet. Data analysis indicated that a high beef fat diet affects the expression of genes involved in inflammation, glucose and fatty acid metabolism, androgen metabolism, potential tumor suppression and protein kinase activity, as well as intracellular and extracellular matrix molecules, growth factors and androgen responsive genes. Results from these and future studies will lead to a better understanding of the effect of diet on gene expression in the prostate and facilitate the rational design and assessment of potential dietary programs for prostate cancer prevention.

  8. Microarray analysis of pancreatic gene expression during biotin repletion in biotin-deficient rats.

    PubMed

    Dakshinamurti, Krishnamurti; Bagchi, Rushita A; Abrenica, Bernard; Czubryt, Michael P

    2015-12-01

    Biotin is a B vitamin involved in multiple metabolic pathways. In humans, biotin deficiency is relatively rare but can cause dermatitis, alopecia, and perosis. Low biotin levels occur in individuals with type-2 diabetes, and supplementation with biotin plus chromium may improve blood sugar control. The acute effect on pancreatic gene expression of biotin repletion following chronic deficiency is unclear, therefore we induced biotin deficiency in adult male rats by feeding them a 20% raw egg white diet for 6 weeks. Animals were then randomized into 2 groups: one group received a single biotin supplement and returned to normal chow lacking egg white, while the second group remained on the depletion diet. After 1 week, pancreata were removed from biotin-deficient (BD) and biotin-repleted (BR) animals and RNA was isolated for microarray analysis. Biotin depletion altered gene expression in a manner indicative of inflammation, fibrosis, and defective pancreatic function. Conversely, biotin repletion activated numerous repair and anti-inflammatory pathways, reduced fibrotic gene expression, and induced multiple genes involved in pancreatic endocrine and exocrine function. A subset of the results was confirmed by quantitative real-time PCR analysis, as well as by treatment of pancreatic AR42J cells with biotin. The results indicate that biotin repletion, even after lengthy deficiency, results in the rapid induction of repair processes in the pancreas.

  9. Gene expression profiles in the rat streptococcal cell wall-induced arthritis model identified using microarray analysis.

    PubMed

    Rioja, Inmaculada; Clayton, Chris L; Graham, Simon J; Life, Paul F; Dickson, Marion C

    2005-01-01

    Experimental arthritis models are considered valuable tools for delineating mechanisms of inflammation and autoimmune phenomena. Use of microarray-based methods represents a new and challenging approach that allows molecular dissection of complex autoimmune diseases such as arthritis. In order to characterize the temporal gene expression profile in joints from the reactivation model of streptococcal cell wall (SCW)-induced arthritis in Lewis (LEW/N) rats, total RNA was extracted from ankle joints from naive, SCW injected, or phosphate buffered saline injected animals (time course study) and gene expression was analyzed using Affymetrix oligonucleotide microarray technology (RAE230A). After normalization and statistical analysis of data, 631 differentially expressed genes were sorted into clusters based on their levels and kinetics of expression using Spotfire profile search and K-mean cluster analysis. Microarray-based data for a subset of genes were validated using real-time PCR TaqMan analysis. Analysis of the microarray data identified 631 genes (441 upregulated and 190 downregulated) that were differentially expressed (Delta > 1.8, P < 0.01), showing specific levels and patterns of gene expression. The genes exhibiting the highest fold increase in expression on days -13.8, -13, or 3 were involved in chemotaxis, inflammatory response, cell adhesion and extracellular matrix remodelling. Transcriptome analysis identified 10 upregulated genes (Delta > 5), which have not previously been associated with arthritis pathology and are located in genomic regions associated with autoimmune disease. The majority of the downregulated genes were associated with metabolism, transport and regulation of muscle development. In conclusion, the present study describes the temporal expression of multiple disease-associated genes with potential pathophysiological roles in the reactivation model of SCW-induced arthritis in Lewis (LEW/N) rat. These findings improve our understanding of

  10. Gene expression profiles in the rat streptococcal cell wall-induced arthritis model identified using microarray analysis

    PubMed Central

    Rioja, Inmaculada; Clayton, Chris L; Graham, Simon J; Life, Paul F; Dickson, Marion C

    2005-01-01

    Experimental arthritis models are considered valuable tools for delineating mechanisms of inflammation and autoimmune phenomena. Use of microarray-based methods represents a new and challenging approach that allows molecular dissection of complex autoimmune diseases such as arthritis. In order to characterize the temporal gene expression profile in joints from the reactivation model of streptococcal cell wall (SCW)-induced arthritis in Lewis (LEW/N) rats, total RNA was extracted from ankle joints from naïve, SCW injected, or phosphate buffered saline injected animals (time course study) and gene expression was analyzed using Affymetrix oligonucleotide microarray technology (RAE230A). After normalization and statistical analysis of data, 631 differentially expressed genes were sorted into clusters based on their levels and kinetics of expression using Spotfire® profile search and K-mean cluster analysis. Microarray-based data for a subset of genes were validated using real-time PCR TaqMan® analysis. Analysis of the microarray data identified 631 genes (441 upregulated and 190 downregulated) that were differentially expressed (Delta > 1.8, P < 0.01), showing specific levels and patterns of gene expression. The genes exhibiting the highest fold increase in expression on days -13.8, -13, or 3 were involved in chemotaxis, inflammatory response, cell adhesion and extracellular matrix remodelling. Transcriptome analysis identified 10 upregulated genes (Delta > 5), which have not previously been associated with arthritis pathology and are located in genomic regions associated with autoimmune disease. The majority of the downregulated genes were associated with metabolism, transport and regulation of muscle development. In conclusion, the present study describes the temporal expression of multiple disease-associated genes with potential pathophysiological roles in the reactivation model of SCW-induced arthritis in Lewis (LEW/N) rat. These findings improve our

  11. Gene profiling of the rat medial collateral ligament during early healing using microarray analysis

    PubMed Central

    Chamberlain, Connie S.; Brounts, Sabrina H.; Sterken, David G.; Rolnick, Kevin I.; Baer, Geoffrey S.

    2011-01-01

    Ligament heals in a synchronized and complex series of events. The remodeling process may last months or years. Experimental evidence suggests the damaged ligament does not recover its normal functional properties. Specific mechanisms to prevent scar formation and to regenerate the original mechanical function remain elusive but likely involve regulation of creeping substitution. Creeping substitution creates a larger hypercellular, hypervascular, and disorganized granulation tissue mass that results in an inefficient and nonregenerative wound healing process for the ligament. Control of creeping substitution may limit the extent of this tissue compromise and reduce the time necessary for healing. The objective of this study is to better understand the mechanism behind scar formation by identifying the extracellular matrix factors and other unique genes of interest differentially expressed during rat ligament healing via microarray. For this study, rat medial collateral ligaments were either surgically transected or left intact. Ligaments were collected at day 3 or 7 postinjury and used for microarray, quantitative PCR, and/or immunohistochemistry. Results were compared with the normal intact ligament. We demonstrate that early ligament healing is characterized by the modulation of several inflammatory and extracellular matrix factors during the first week of injury. Specifically, a number of matrix metalloproteinases and collagens are differentially and significantly expressed during early ligament healing. Additionally, we demonstrate the modulation of three novel genes, periostin, collagen-triple helix repeat containing-1, and serine protease 35 in our ligament healing model. Together, control of granulation tissue creeping substitution and subsequent downstream scar formation is likely to involve these factors. PMID:21596919

  12. Melatonin or ramelteon therapy differentially affects hepatic gene expression profiles after haemorrhagic shock in rat--A microarray analysis.

    PubMed

    Kleber, Astrid; Ruf, Christian G; Wolf, Alexander; Fink, Tobias; Glas, Michael; Wolf, Beate; Volk, Thomas; Abend, Michael; Mathes, Alexander M

    2015-10-01

    Melatonin has been demonstrated to reduce liver damage in different models of stress. However, there is only limited information on the impact of this hormone on hepatic gene expression. The aim of this study was, to investigate the influence of melatonin or the melatonergic agonist ramelteon on hepatic gene expression profiles after haemorrhagic shock using a whole genome microarray analysis. Male Sprague-Dawley rats (200-300 g, n=4/group) underwent haemorrhagic shock (mean arterial pressure 35±5 mmHg). After 90 min of shock, animals were resuscitated with shed blood and Ringer's and treated with vehicle (5% dimethyl sulfoxide), melatonin or ramelteon (each 1.0 mg/kg intravenously). Sham-operated animals were treated likewise but did not undergo haemorrhage. After 2 h of reperfusion, the liver was harvested, and a whole genome microarray analysis was performed. Functional gene expression profiles were determined using the Panther® classification system; promising candidate genes were evaluated by quantitative polymerase chain reaction (PCR). Microarray and PCR data showed a good correlation (r(2)=0.84). A strong influence of melatonin on receptor mediated signal transduction was revealed using the functional gene expression profile analysis, whereas ramelteon mainly influenced transcription factors. Shock-induced upregulation of three candidate genes with relevant functions for hepatocytes (ppp1r15a, dusp5, rhoB) was significantly reduced by melatonin (p<0.05 vs. shock/vehicle), but not by ramelteon. Two genes previously known as haemorrhage-induced (il1b, s100a8) were transcriptionally repressed by both drugs. Melatonin and ramelteon appear to induce specific hepatic gene expression profiles after haemorrhagic shock in rats. The observed differences between both substances are likely to be attributable to a distinct mechanism of action in these agents. Copyright © 2015 Elsevier Inc. All rights reserved.

  13. A microarray gene analysis of peripheral whole blood in normal adult male rats after long-term GH gene therapy.

    PubMed

    Qin, Ying; Tian, Ya-Ping

    2010-06-01

    The main aims of this study were to determine the effects of GH gene abuse/misuse in normal animals and to discover genes that could be used as candidate biomarkers for the detection of GH gene therapy abuse/misuse in humans. We determined the global gene expression profile of peripheral whole blood from normal adult male rats after long-term GH gene therapy using CapitalBio 27 K Rat Genome Oligo Arrays. Sixty one genes were found to be differentially expressed in GH gene-treated rats 24 weeks after receiving GH gene therapy, at a two-fold higher or lower level compared to the empty vector group (p < 0.05). These genes were mainly associated with angiogenesis, oncogenesis, apoptosis, immune networks, signaling pathways, general metabolism, type I diabetes mellitus, carbon fixation, cell adhesion molecules, and cytokine-cytokine receptor interaction. The results imply that exogenous GH gene expression in normal subjects is likely to induce cellular changes in the metabolism, signal pathways and immunity. A real-time qRT-PCR analysis of a selection of the genes confirmed the microarray data. Eight differently expressed genes were selected as candidate biomarkers from among these 61 genes. These 8 showed five-fold higher or lower expression levels after the GH gene transduction (p < 0.05). They were then validated in real-time PCR experiments using 15 single-treated blood samples and 10 control blood samples. In summary, we detected the gene expression profiles of rat peripheral whole blood after long-term GH gene therapy and screened eight genes as candidate biomarkers based on the microarray data. This will contribute to an increased mechanistic understanding of the effects of chronic GH gene therapy abuse/misuse in normal subjects.

  14. Sex-Related Differences in Rat Choroid Plexus and Cerebrospinal Fluid: A cDNA Microarray and Proteomic Analysis.

    PubMed

    Quintela, T; Marcelino, H; Deery, M J; Feret, R; Howard, J; Lilley, K S; Albuquerque, T; Gonçalves, I; Duarte, A C; Santos, C R A

    2016-01-01

    The choroid plexus (CP) epithelium is a unique structure in the brain that forms an interface between the peripheral blood and the cerebrospinal fluid (CSF), which is mostly produced by the CP itself. Because the CP transcriptome is regulated by the sex hormone background, the present study compared gene/protein expression profiles in the CP and CSF from male and female rats aiming to better understand sex-related differences in CP functions and brain physiology. We used data previously obtained by cDNA microarrays to compare the CP transcriptome between male and female rats, and complemented these data with the proteomic analysis of the CSF of castrated and sham-operated males and females. Microarray analysis showed that 17 128 and 17 002 genes are expressed in the male and female CP, which allowed the functional annotation of 141 and 134 pathways, respectively. Among the most expressed genes, canonical pathways associated with mitochondrial dysfunctions and oxidative phosphorylation were the most prominent, whereas the most relevant molecular and cellular functions annotated were protein synthesis, cellular growth and proliferation, cell death and survival, molecular transport, and protein trafficking. No significant differences were found between males and females regarding these pathways. Seminal functions of the CP differentially regulated between sexes were circadian rhythm signalling, as well as several canonical pathways related to stem cell differentiation, metabolism and the barrier function of the CP. The proteomic analysis identified five down-regulated proteins in the CSF samples from male rats compared to females and seven proteins exhibiting marked variation in the CSF of gonadectomised males compared to sham animals, whereas no differences were found between sham and ovariectomised females. These data clearly show sex-related differences in CP gene expression and CSF protein composition that may impact upon neurological diseases.

  15. Effect of leucine uptake on hepatic and skeletal muscle gene expression in rats: a microarray analysis

    PubMed Central

    Cheon, Wookwang

    2015-01-01

    [Purpose] This study was performed to explore the physiological functions of leucine by exploring genes with leucine-dependent variability using DNA microarray. [Methods] Sprague-Dawley rats (n = 20) were separated into a HPD (30% High Protein Diet, n = 10) group and a NPD (0% Non Protein Diet, n = 10) group and fed a protein diet for 2 weeks. At the end of the 2-week period, the rats were fasted for 12-16 hours, further separated into subgroups within the HPD (Saline, n = 5, Leucine, n = 5) and NPD (Saline, n = 5, Leucine, n = 5) groups and administered with a leucine solution. The liver and muscles were harvested after 2 hours for RNA extraction. RNA purification from the isolated muscles and target gene identification using DNA chip were performed. The target gene was determined based on the results of the DNA chip experiment, and mRNA expression of the target gene was analyzed using Real-Time PCR. [Results] In the skeletal muscle, 27 genes were upregulated while 52 genes were down regulated after leucine administration in the NPD group. In the liver, 160 genes were up-regulated while 126 were down-regulated. The per2 gene was one of the genes with leucine-dependent induction in muscles and liver. [Conclusion] This study was performed to explore the physiological functions of leucine, however, a large number of genes showed variability. Therefore, it was difficult to definitively identify the genes linked with a particular physiological function. Various nutritional effects of leucine were observed. High variability in cytokines, receptors, and various membrane proteins were observed, which suggests that leucine functions as more than a nutrient. The interpretation may depend on investigators’ perspectives, therefore, discussion with relevant experts and the BCAA (Branched-Chain Amino Acids) society may be needed for effective utilization of this data. PMID:26244133

  16. PhyloChip microarray analysis reveals altered gastrointestinal microbial communities in a rat model of colonic hypersensitivity

    SciTech Connect

    Nelson, T.A.; Holmes, S.; Alekseyenko, A.V.; Shenoy, M.; DeSantis, T.; Wu, C.H.; Andersen, G.L.; Winston, J.; Sonnenburg, J.; Pasricha, P.J.; Spormann, A.

    2010-12-01

    Irritable bowel syndrome (IBS) is a chronic, episodic gastrointestinal disorder that is prevalent in a significant fraction of western human populations; and changes in the microbiota of the large bowel have been implicated in the pathology of the disease. Using a novel comprehensive, high-density DNA microarray (PhyloChip) we performed a phylogenetic analysis of the microbial community of the large bowel in a rat model in which intracolonic acetic acid in neonates was used to induce long lasting colonic hypersensitivity and decreased stool water content and frequency, representing the equivalent of human constipation-predominant IBS. Our results revealed a significantly increased compositional difference in the microbial communities in rats with neonatal irritation as compared with controls. Even more striking was the dramatic change in the ratio of Firmicutes relative to Bacteroidetes, where neonatally irritated rats were enriched more with Bacteroidetes and also contained a different composition of species within this phylum. Our study also revealed differences at the level of bacterial families and species. The PhyloChip is a useful and convenient method to study enteric microflora. Further, this rat model system may be a useful experimental platform to study the causes and consequences of changes in microbial community composition associated with IBS.

  17. PhyloChip microarray analysis reveals altered gastrointestinal microbial communities in a rat model of colonic hypersensitivity

    PubMed Central

    NELSON, T. A.; HOLMES, S.; ALEKSEYENKO, A. V.; SHENOY, M.; DESANTIS, T.; WU, C. H.; ANDERSEN, G. L.; WINSTON, J.; SONNENBURG, J.; PASRICHA, P. J.; SPORMANN, A.

    2012-01-01

    Background Irritable bowel syndrome (IBS) is a chronic, episodic gastrointestinal disorder that is prevalent in a significant fraction of western human populations; and changes in the microbiota of the large bowel have been implicated in the pathology of the disease. Methods Using a novel comprehensive, high-density DNA microarray (PhyloChip) we performed a phylogenetic analysis of the microbial community of the large bowel in a rat model in which intracolonic acetic acid in neonates was used to induce long lasting colonic hypersensitivity and decreased stool water content and frequency, representing the equivalent of human constipation-predominant IBS. Key Results Our results revealed a significantly increased compositional difference in the microbial communities in rats with neonatal irritation as compared with controls. Even more striking was the dramatic change in the ratio of Firmicutes relative to Bacteroidetes, where neonatally irritated rats were enriched more with Bacteroidetes and also contained a different composition of species within this phylum. Our study also revealed differences at the level of bacterial families and species. Conclusions & Inferences The PhyloChip is a useful and convenient method to study enteric microflora. Further, this rat model system may be a useful experimental platform to study the causes and consequences of changes in microbial community composition associated with IBS. PMID:21129126

  18. Analysis of the effects of sex hormone background on the rat choroid plexus transcriptome by cDNA microarrays.

    PubMed

    Quintela, Telma; Gonçalves, Isabel; Carreto, Laura C; Santos, Manuel A S; Marcelino, Helena; Patriarca, Filipa M; Santos, Cecília R A

    2013-01-01

    The choroid plexus (CP) are highly vascularized branched structures that protrude into the ventricles of the brain, and form a unique interface between the blood and the cerebrospinal fluid (CSF), the blood-CSF barrier, that are the main site of production and secretion of CSF. Sex hormones are widely recognized as neuroprotective agents against several neurodegenerative diseases, and the presence of sex hormones cognate receptors suggest that it may be a target for these hormones. In an effort to provide further insight into the neuroprotective mechanisms triggered by sex hormones we analyzed gene expression differences in the CP of female and male rats subjected to gonadectomy, using microarray technology. In gonadectomized female and male animals, 3045 genes were differentially expressed by 1.5-fold change, compared to sham controls. Analysis of the CP transcriptome showed that the top-five pathways significantly regulated by the sex hormone background are olfactory transduction, taste transduction, metabolism, steroid hormone biosynthesis and circadian rhythm pathways. These results represent the first overview of global expression changes in CP of female and male rats induced by gonadectomy and suggest that sex hormones are implicated in pathways with central roles in CP functions and CSF homeostasis.

  19. Molecular basis for impaired collateral artery growth in the spontaneously hypertensive rat: insight from microarray analysis

    PubMed Central

    Unthank, Joseph L; McClintick, Jeanette N; Labarrere, Carlos A; Li, Lang; DiStasi, Matthew R; Miller, Steven J

    2013-01-01

    Analysis of global gene expression in mesenteric control and collateral arteries was used to investigate potential molecules, pathways, and mechanisms responsible for impaired collateral growth in the Spontaneously Hypertensive Rat (SHR). A fundamental difference was observed in overall gene expression pattern in SHR versus Wistar Kyoto (WKY) collaterals; only 6% of genes altered in collaterals were similar between rat strains. Ingenuity® Pathway Analysis (IPA) identified major differences between WKY and SHR in networks and biological functions related to cell growth and proliferation and gene expression. In SHR control arteries, several mechano-sensitive and redox-dependent transcription regulators were downregulated including JUN (−5.2×, P = 0.02), EGR1 (−4.1×, P = 0.01), and NFĸB1 (−1.95×, P = 0.04). Predicted binding sites for NFĸB and AP-1 were present in genes altered in WKY but not SHR collaterals. Immunostaining showed increased NFĸB nuclear translocation in collateral arteries of WKY and apocynin-treated SHR, but not in untreated SHR. siRNA for the p65 subunit suppressed collateral growth in WKY, confirming a functional role of NFkB. Canonical pathways identified by IPA in WKY but not SHR included nitric oxide and renin–angiotensin system signaling. The angiotensin type 1 receptor (AGTR1) exhibited upregulation in WKY collaterals, but downregulation in SHR; pharmacological blockade of AGTR1 with losartan prevented collateral luminal expansion in WKY. Together, these results suggest that collateral growth impairment results from an abnormality in a fundamental regulatory mechanism that occurs at a level between signal transduction and gene transcription and implicate redox-dependent modulation of mechano-sensitive transcription factors such as NFĸB as a potential mechanism. PMID:24303120

  20. Microarray analysis of rat immune responses to liver fluke infection following vaccination with Fasciola hepatica phosphoglycerate kinase.

    PubMed

    Wesołowska, Agnieszka; Jaros, Sławomir; Norbury, Luke J; Jaros, Dorota; Zygner, Wojciech; Wędrychowicz, Halina

    2013-05-01

    Fasciolosis is a considerable veterinary problem, causing significant economic losses to livestock production and the food industry. Research in the area of Fasciola hepatica infection immunology is necessary to improve our knowledge about immunological mechanism evoked by the parasite and to develop new control strategies against liver fluke. In this present paper we analyzed the expression levels of cytokines in rats infected with F. hepatica following immunization with F. hepatica phosphoglycerate kinase - a novel vaccine antigen. Immune response analysis using microarray was undertaken six weeks after infection. Expression levels of INF-γ and IL-4, which are characteristic cytokines secreted during Th1-like and Th2-like immune responses, respectively, were unchanged in vaccinated animals as compared to control animals. This indicates the vaccine did not influence the major modulation of immune responses typically observed during Fasciola infections, however, other subtle but significant variations were observed that indicated altered inflammatory and possibly T helper cell responses. A significant rise in IL-12α chain expression levels was observed. Expression levels of TNF-α and some related molecules, such as ADAM17, FasL, CD40 and TRAF3 were also elevated. Expression levels of molecules involved in IL-1 signaling pathways were reduced, although a rise in IL-1α expression was noted. Copyright © 2013 Elsevier Inc. All rights reserved.

  1. Differential gene expression from microarray analysis distinguishes woven and lamellar bone formation in the rat ulna following mechanical loading.

    PubMed

    McKenzie, Jennifer A; Bixby, Elise C; Silva, Matthew J

    2011-01-01

    Formation of woven and lamellar bone in the adult skeleton can be induced through mechanical loading. Although much is known about the morphological appearance and structural properties of the newly formed bone, the molecular responses to loading are still not well understood. The objective of our study was to use a microarray to distinguish the molecular responses between woven and lamellar bone formation induced through mechanical loading. Rat forelimb loading was completed in a single bout to induce the formation of woven bone (WBF loading) or lamellar bone (LBF loading). A set of normal (non-loaded) rats were used as controls. Microarrays were performed at three timepoints after loading: 1 hr, 1 day and 3 days. Confirmation of microarray results was done for a select group of genes using quantitative real-time PCR (qRT-PCR). The micorarray identified numerous genes and pathways that were differentially regulated for woven, but not lamellar bone formation. Few changes in gene expression were evident comparing lamellar bone formation to normal controls. A total of 395 genes were differentially expressed between formation of woven and lamellar bone 1 hr after loading, while 5883 and 5974 genes were differentially expressed on days 1 and 3, respectively. Results suggest that not only are the levels of expression different for each type of bone formation, but that distinct pathways are activated only for woven bone formation. A strong early inflammatory response preceded an increase in angiogenic and osteogenic gene expression for woven bone formation. Furthermore, at later timepoints there was evidence of bone resorption after WBF loading. In summary, the vast coverage of the microarray offers a comprehensive characterization of the early differences in expression between woven and lamellar bone formation.

  2. DNA microarray integromics analysis platform.

    PubMed

    Waller, Tomasz; Gubała, Tomasz; Sarapata, Krzysztof; Piwowar, Monika; Jurkowski, Wiktor

    2015-01-01

    The study of interactions between molecules belonging to different biochemical families (such as lipids and nucleic acids) requires specialized data analysis methods. This article describes the DNA Microarray Integromics Analysis Platform, a unique web application that focuses on computational integration and analysis of "multi-omics" data. Our tool supports a range of complex analyses, including - among others - low- and high-level analyses of DNA microarray data, integrated analysis of transcriptomics and lipidomics data and the ability to infer miRNA-mRNA interactions. We demonstrate the characteristics and benefits of the DNA Microarray Integromics Analysis Platform using two different test cases. The first test case involves the analysis of the nutrimouse dataset, which contains measurements of the expression of genes involved in nutritional problems and the concentrations of hepatic fatty acids. The second test case involves the analysis of miRNA-mRNA interactions in polysaccharide-stimulated human dermal fibroblasts infected with porcine endogenous retroviruses. The DNA Microarray Integromics Analysis Platform is a web-based graphical user interface for "multi-omics" data management and analysis. Its intuitive nature and wide range of available workflows make it an effective tool for molecular biology research. The platform is hosted at https://lifescience.plgrid.pl/.

  3. Microarray analysis and description of SMR1 gene in rat penis in a post-radical prostatectomy model of erectile dysfunction.

    PubMed

    User, Herbert M; Zelner, David J; McKenna, Kevin E; McVary, Kevin T

    2003-07-01

    We focused on the post-radical prostatectomy model to advance the understanding of neurogenic erectile dysfunction. We attempted to identify previously undescribed molecular changes via gene discovery methods using GeneChip (Affymetrix, Santa Clara, California) microarray technology. Five male adult 120-day-old Sprague-Dawley rats underwent bilateral cavernous nerve neurectomy. Five age matched controls were prepared simultaneously. The penises were harvested on postoperative day 2 and snap frozen in liquid nitrogen. RNA was prepared and pooled into cut and uncut groups. Synthesis of cRNA was performed according to the GeneChip technical manual. Microarray analysis was performed on a U34A Rat Array (Affymetrix). This array has approximately 8,800 gene probe sets, approximately 6,600 known genes and approximately 2,200 estimated sequence transcripts. Dramatic results were found during GeneChip microarray expression analysis. A total of 126 candidate genes were noted to be altered based on the magnitude of expression change using rigorous statistical criteria, including 47 that were down-regulated and 79 that were up-regulated. Among the many significant changes seen 1 dominant class of genes was the submandibular rat genes. Submandibular rat 1 (SMR1) was down-regulated 82.5 fold. Other genes in this family were down-regulated 226 and 90 times. This result was confirmed by reverse transcriptase-polymerase chain reaction and Western blot analyses. These assays verified decreases in SMR1 at multiple time points after surgery. Impressive and previously unrecognized genetic changes are being intensely investigated as they are being unmasked by GeneChip technology. We have identified and begun the investigation of 1 interesting family of genes, namely submandibular gland proteins. The role of SMR as a clinically relevant change in penile and/or urethral function following cavernous nerve injury is speculative.

  4. High Phosphorus Diet-Induced Changes in NaPi-IIb Phosphate Transporter Expression in the Rat Kidney: DNA Microarray Analysis

    PubMed Central

    Suyama, Tatsuya; Okada, Shinji; Ishijima, Tomoko; Iida, Kota; Abe, Keiko; Nakai, Yuji

    2012-01-01

    The mechanism by which phosphorus levels are maintained in the body was investigated by analyzing changes in gene expression in the rat kidney following administration of a high phosphorus (HP) diet. Male Wistar rats were divided into two groups and fed a diet containing 0.3% (control) or 1.2% (HP) phosphorous for 24 days. Phosphorous retention was not significantly increased in HP rats, but fractional excretion of phosphorus was significantly increased in the HP group compared to controls, with an excessive amount of the ingested phosphorus being passed through the body. DNA microarray analysis of kidney tissue from both groups revealed changes in gene expression profile induced by a HP diet. Among the genes that were upregulated, Gene Ontology (GO) terms related to ossification, collagen fibril organization, and inflammation and immune response were significantly enriched. In particular, there was significant upregulation of type IIb sodium-dependent phosphate transporter (NaPi-IIb) in the HP rat kidney compared to control rats. This upregulation was confirmed by in situ hybridization. Distinct signals for NaPi-IIb in both the cortex and medulla of the kidney were apparent in the HP group, while the corresponding signals were much weaker in the control group. Immunohistochemical analysis showed that NaPi-IIb localized to the basolateral side of kidney epithelial cells surrounding the urinary duct in HP rats but not in control animals. These data suggest that NaPi-IIb is upregulated in the kidney in response to the active excretion of phosphate in HP diet-fed rats. PMID:22235299

  5. Automated Microarray Image Analysis Toolbox for MATLAB

    SciTech Connect

    White, Amanda M.; Daly, Don S.; Willse, Alan R.; Protic, Miroslava; Chandler, Darrell P.

    2005-09-01

    The Automated Microarray Image Analysis (AMIA) Toolbox for MATLAB is a flexible, open-source microarray image analysis tool that allows the user to customize analysis of sets of microarray images. This tool provides several methods of identifying and quantify spot statistics, as well as extensive diagnostic statistics and images to identify poor data quality or processing. The open nature of this software allows researchers to understand the algorithms used to provide intensity estimates and to modify them easily if desired.

  6. Analysis of DNA microarray expression data.

    PubMed

    Simon, Richard

    2009-06-01

    DNA microarrays are powerful tools for studying biological mechanisms and for developing prognostic and predictive classifiers for identifying the patients who require treatment and are best candidates for specific treatments. Because microarrays produce so much data from each specimen, they offer great opportunities for discovery and great dangers or producing misleading claims. Microarray based studies require clear objectives for selecting cases and appropriate analysis methods. Effective analysis of microarray data, where the number of measured variables is orders of magnitude greater than the number of cases, requires specialized statistical methods which have recently been developed. Recent literature reviews indicate that serious problems of analysis exist a substantial proportion of publications. This manuscript attempts to provide a non-technical summary of the key principles of statistical design and analysis for studies that utilize microarray expression profiling.

  7. Mutational analysis using oligonucleotide microarrays

    PubMed Central

    Hacia, J.; Collins, F.

    1999-01-01

    The development of inexpensive high throughput methods to identify individual DNA sequence differences is important to the future growth of medical genetics. This has become increasingly apparent as epidemiologists, pathologists, and clinical geneticists focus more attention on the molecular basis of complex multifactorial diseases. Such undertakings will rely upon genetic maps based upon newly discovered, common, single nucleotide polymorphisms. Furthermore, candidate gene approaches used in identifying disease associated genes necessitate screening large sequence blocks for changes tracking with the disease state. Even after such genes are isolated, large scale mutational analyses will often be needed for risk assessment studies to define the likely medical consequences of carrying a mutated gene.
This review concentrates on the use of oligonucleotide arrays for hybridisation based comparative sequence analysis. Technological advances within the past decade have made it possible to apply this technology to many different aspects of medical genetics. These applications range from the detection and scoring of single nucleotide polymorphisms to mutational analysis of large genes. Although we discuss published scientific reports, unpublished work from the private sector12 could also significantly affect the future of this technology.


Keywords: mutational analysis; oligonucleotide microarrays; DNA chips PMID:10528850

  8. Dietary olive oil and menhaden oil mitigate induction of lipogenesis in hyperinsulinemic corpulent JCR:LA-cp rats: microarray analysis of lipid-related gene expression.

    PubMed

    Deng, Xiong; Elam, Marshall B; Wilcox, Henry G; Cagen, Lauren M; Park, Edwards A; Raghow, Rajendra; Patel, Divyen; Kumar, Poonam; Sheybani, Ali; Russell, James C

    2004-12-01

    In the corpulent James C. Russell corpulent (JCR:LA-cp) rat, hyperinsulinemia leads to induction of lipogenic enzymes via enhanced expression of sterol-regulatory-binding protein (SREBP)-1c. This results in increased hepatic lipid production and hypertriglyceridemia. Information regarding down-regulation of SREBP-1c and lipogenic enzymes by dietary fatty acids in this model is limited. We therefore assessed de novo hepatic lipogenesis and hepatic and plasma lipids in corpulent JCR rats fed diets enriched in olive oil or menhaden oil. Using microarray and Northern analysis, we determined the effect of these diets on expression of mRNA for lipogenic enzymes and other proteins related to lipid metabolism. In corpulent JCR:LA-cp rats, both the olive oil and menhaden oil diets reduced expression of SREBP-1c, with concomitant reductions in hepatic triglyceride content, lipogenesis, and expression of enzymes related to lipid synthesis. Unexpectedly, expression of many peroxisomal proliferator-activated receptor-dependent enzymes mediating fatty acid oxidation was increased in livers of corpulent JCR rats. The menhaden oil diet further increased expression of these enzymes. Induction of SREBP-1c by insulin is dependent on liver x receptor (LXR)alpha. Although hepatic expression of mRNA for LXR itself was not increased in corpulent rats, expression of Cyp7a1, an LXR-responsive gene, was increased, suggesting increased LXR activity. Expression of mRNA encoding fatty acid translocase and ATP-binding cassette subfamily DALD member 3 was also increased in livers of corpulent JCR rats, indicating a potential role for these fatty acid transporters in the pathogenesis of disordered lipid metabolism in obesity. This study clearly demonstrates that substitution of dietary polyunsaturated fatty acid for carbohydrate in the corpulent JCR:LA-cp rat reduces de novo lipogenesis, at least in part, by reducing hepatic expression of SREBP-1c and that strategies directed toward reducing

  9. MARS: Microarray analysis, retrieval, and storage system

    PubMed Central

    Maurer, Michael; Molidor, Robert; Sturn, Alexander; Hartler, Juergen; Hackl, Hubert; Stocker, Gernot; Prokesch, Andreas; Scheideler, Marcel; Trajanoski, Zlatko

    2005-01-01

    Background Microarray analysis has become a widely used technique for the study of gene-expression patterns on a genomic scale. As more and more laboratories are adopting microarray technology, there is a need for powerful and easy to use microarray databases facilitating array fabrication, labeling, hybridization, and data analysis. The wealth of data generated by this high throughput approach renders adequate database and analysis tools crucial for the pursuit of insights into the transcriptomic behavior of cells. Results MARS (Microarray Analysis and Retrieval System) provides a comprehensive MIAME supportive suite for storing, retrieving, and analyzing multi color microarray data. The system comprises a laboratory information management system (LIMS), a quality control management, as well as a sophisticated user management system. MARS is fully integrated into an analytical pipeline of microarray image analysis, normalization, gene expression clustering, and mapping of gene expression data onto biological pathways. The incorporation of ontologies and the use of MAGE-ML enables an export of studies stored in MARS to public repositories and other databases accepting these documents. Conclusion We have developed an integrated system tailored to serve the specific needs of microarray based research projects using a unique fusion of Web based and standalone applications connected to the latest J2EE application server technology. The presented system is freely available for academic and non-profit institutions. More information can be found at . PMID:15836795

  10. Regulation of gene expression in brain tissues of rats repeatedly treated by the highly abused opioid agonist, oxycodone: microarray profiling and gene mapping analysis.

    PubMed

    Hassan, Hazem E; Myers, Alan L; Lee, Insong J; Chen, Hegang; Coop, Andrew; Eddington, Natalie D

    2010-01-01

    Although oxycodone is the most often used opioid agonist, it remains one of the most understudied drugs. We used microarray analysis to better understand the global changes in gene expression in brain tissues of rats repeatedly treated with oxycodone. Many genes were significantly regulated by oxycodone (e.g., Fkbp5, Per2, Rt1.Dalpha, Slc16a1, and Abcg2). Validation of the microarray data by quantitative real-time-polymerase chain reaction (Q-PCR) indicated that there was a strong significant correlation (r = 0.979, p < 0.0000001) between the Q-PCR and the microarray data. Using MetaCore (a computational platform), many biological processes were identified [e.g., organic anion transport (p = 7.251 x 10(-4)) and regulation of immune response (p = 5.090 x 10(-4))]. Among the regulated genes, Abcg2 mRNA was up-regulated by 2.1-fold, which was further confirmed by immunoblotting (1.8-fold up-regulation). Testing the Abcg2 affinity status of oxycodone using an Abcg2 ATPase assay suggests that oxycodone behaves as an Abcg2 substrate only at higher concentrations (> or = 500 microM). Furthermore, brain uptake studies demonstrated that oxycodone-induced Abcg2 up-regulation resulted in a significant (p < 0.05) decrease (approximately 2-fold) in brain/plasma ratios of mitoxantrone. These results highlight markers/mediators of neuronal responses and identify regulatory pathways involved in the pharmacological action of oxycodone. These results also identify genes that potentially modulate tolerance, dependence, immune response, and drug-drug interactions. Finally, our findings suggest that oxycodone-induced up-regulation of Abcg2 enhanced the efflux of the Abcg2 substrate, mitoxantrone, limiting its brain accumulation and resulting in an undesirable drug-drug interaction. Extrapolating these results to other Abcg2 substrates (e.g., daunorubicin and doxorubicin) indicates that the brain uptake of these agents may be affected if they are administered concomitantly with

  11. Metric learning for DNA microarray data analysis

    NASA Astrophysics Data System (ADS)

    Takeuchi, Ichiro; Nakagawa, Masao; Seto, Masao

    2009-12-01

    In many microarray studies, gene set selection is an important preliminary step for subsequent main task such as tumor classification, cancer subtype identification, etc. In this paper, we investigate the possibility of using metric learning as an alternative to gene set selection. We develop a simple metric learning algorithm aiming to use it for microarray data analysis. Exploiting a property of the algorithm, we introduce a novel approach for extending the metric learning to be adaptive. We apply the algorithm to previously studied microarray data on malignant lymphoma subtype identification.

  12. Microarray analysis in pulmonary hypertension.

    PubMed

    Hoffmann, Julia; Wilhelm, Jochen; Olschewski, Andrea; Kwapiszewska, Grazyna

    2016-07-01

    Microarrays are a powerful and effective tool that allows the detection of genome-wide gene expression differences between controls and disease conditions. They have been broadly applied to investigate the pathobiology of diverse forms of pulmonary hypertension, namely group 1, including patients with idiopathic pulmonary arterial hypertension, and group 3, including pulmonary hypertension associated with chronic lung diseases such as chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis. To date, numerous human microarray studies have been conducted to analyse global (lung homogenate samples), compartment-specific (laser capture microdissection), cell type-specific (isolated primary cells) and circulating cell (peripheral blood) expression profiles. Combined, they provide important information on development, progression and the end-stage disease. In the future, system biology approaches, expression of noncoding RNAs that regulate coding RNAs, and direct comparison between animal models and human disease might be of importance.

  13. Microarray analysis in pulmonary hypertension

    PubMed Central

    Hoffmann, Julia; Wilhelm, Jochen; Olschewski, Andrea

    2016-01-01

    Microarrays are a powerful and effective tool that allows the detection of genome-wide gene expression differences between controls and disease conditions. They have been broadly applied to investigate the pathobiology of diverse forms of pulmonary hypertension, namely group 1, including patients with idiopathic pulmonary arterial hypertension, and group 3, including pulmonary hypertension associated with chronic lung diseases such as chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis. To date, numerous human microarray studies have been conducted to analyse global (lung homogenate samples), compartment-specific (laser capture microdissection), cell type-specific (isolated primary cells) and circulating cell (peripheral blood) expression profiles. Combined, they provide important information on development, progression and the end-stage disease. In the future, system biology approaches, expression of noncoding RNAs that regulate coding RNAs, and direct comparison between animal models and human disease might be of importance. PMID:27076594

  14. The Impact of Photobleaching on Microarray Analysis

    PubMed Central

    von der Haar, Marcel; Preuß, John-Alexander; von der Haar, Kathrin; Lindner, Patrick; Scheper, Thomas; Stahl, Frank

    2015-01-01

    DNA-Microarrays have become a potent technology for high-throughput analysis of genetic regulation. However, the wide dynamic range of signal intensities of fluorophore-based microarrays exceeds the dynamic range of a single array scan by far, thus limiting the key benefit of microarray technology: parallelization. The implementation of multi-scan techniques represents a promising approach to overcome these limitations. These techniques are, in turn, limited by the fluorophores’ susceptibility to photobleaching when exposed to the scanner’s laser light. In this paper the photobleaching characteristics of cyanine-3 and cyanine-5 as part of solid state DNA microarrays are studied. The effects of initial fluorophore intensity as well as laser scanner dependent variables such as the photomultiplier tube’s voltage on bleaching and imaging are investigated. The resulting data is used to develop a model capable of simulating the expected degree of signal intensity reduction caused by photobleaching for each fluorophore individually, allowing for the removal of photobleaching-induced, systematic bias in multi-scan procedures. Single-scan applications also benefit as they rely on pre-scans to determine the optimal scanner settings. These findings constitute a step towards standardization of microarray experiments and analysis and may help to increase the lab-to-lab comparability of microarray experiment results. PMID:26378589

  15. Microarray Data Analysis and Mining Tools

    PubMed Central

    Selvaraj, Saravanakumar; Natarajan, Jeyakumar

    2011-01-01

    Microarrays are one of the latest breakthroughs in experimental molecular biology that allow monitoring the expression levels of tens of thousands of genes simultaneously. Arrays have been applied to studies in gene expression, genome mapping, SNP discrimination, transcription factor activity, toxicity, pathogen identification and many other applications. In this paper we concentrate on discussing various bioinformatics tools used for microarray data mining tasks with its underlying algorithms, web resources and relevant reference. We emphasize this paper mainly for digital biologists to get an aware about the plethora of tools and programs available for microarray data analysis. First, we report the common data mining applications such as selecting differentially expressed genes, clustering, and classification. Next, we focused on gene expression based knowledge discovery studies such as transcription factor binding site analysis, pathway analysis, protein- protein interaction network analysis and gene enrichment analysis. PMID:21584183

  16. Microarray data analysis and mining tools.

    PubMed

    Selvaraj, Saravanakumar; Natarajan, Jeyakumar

    2011-04-22

    Microarrays are one of the latest breakthroughs in experimental molecular biology that allow monitoring the expression levels of tens of thousands of genes simultaneously. Arrays have been applied to studies in gene expression, genome mapping, SNP discrimination, transcription factor activity, toxicity, pathogen identification and many other applications. In this paper we concentrate on discussing various bioinformatics tools used for microarray data mining tasks with its underlying algorithms, web resources and relevant reference. We emphasize this paper mainly for digital biologists to get an aware about the plethora of tools and programs available for microarray data analysis. First, we report the common data mining applications such as selecting differentially expressed genes, clustering, and classification. Next, we focused on gene expression based knowledge discovery studies such as transcription factor binding site analysis, pathway analysis, protein- protein interaction network analysis and gene enrichment analysis.

  17. Sex-related gene expression profiles in the adrenal cortex in the mature rat: Microarray analysis with emphasis on genes involved in steroidogenesis

    PubMed Central

    TREJTER, MARCIN; HOCHOL, ANNA; TYCZEWSKA, MARIANNA; ZIOLKOWSKA, AGNIESZKA; JOPEK, KAROL; SZYSZKA, MARTA; MALENDOWICZ, LUDWIK K; RUCINSKI, MARCIN

    2015-01-01

    Notable sex-related differences exist in mammalian adrenal cortex structure and function. In adult rats, the adrenal weight and the average volume of zona fasciculata cells of females are larger and secrete greater amounts of corticosterone than those of males. The molecular bases of these sex-related differences are poorly understood. In this study, to explore the molecular background of these differences, we defined zone- and sex-specific transcripts in adult male and female (estrous cycle phase) rats. Twelve-week-old rats of both genders were used and samples were taken from the zona glomerulosa (ZG) and zona fasciculata/reticularis (ZF/R) zones. Transcriptome identification was carried out using the Affymetrix® Rat Gene 1.1 ST Array. The microarray data were compared by fold change with significance according to moderated t-statistics. Subsequently, we performed functional annotation clustering using the Gene Ontology (GO) and Database for Annotation, Visualization and Integrated Discovery (DAVID). In the first step, we explored differentially expressed transcripts in the adrenal ZG and ZF/R. The number of differentially expressed transcripts was notably higher in the female than in the male rats (702 vs. 571). The differentially expressed genes which were significantly enriched included genes involved in steroid hormone metabolism, and their expression levels in the ZF/R of adult female rats were significantly higher compared with those in the male rats. In the female ZF/R, when compared with that of the males, prevailing numbers of genes linked to cell fraction, oxidation/reduction processes, response to nutrients and to extracellular stimuli or steroid hormone stimuli were downregulated. The microarray data for key genes involved directly in steroidogenesis were confirmed by qPCR. Thus, when compared with that of the males, in the female ZF/R, higher expression levels of genes involved directly in steroid hormone synthesis were accompanied by lower

  18. Sex-related gene expression profiles in the adrenal cortex in the mature rat: microarray analysis with emphasis on genes involved in steroidogenesis.

    PubMed

    Trejter, Marcin; Hochol, Anna; Tyczewska, Marianna; Ziolkowska, Agnieszka; Jopek, Karol; Szyszka, Marta; Malendowicz, Ludwik K; Rucinski, Marcin

    2015-03-01

    Notable sex-related differences exist in mammalian adrenal cortex structure and function. In adult rats, the adrenal weight and the average volume of zona fasciculata cells of females are larger and secrete greater amounts of corticosterone than those of males. The molecular bases of these sex-related differences are poorly understood. In this study, to explore the molecular background of these differences, we defined zone- and sex-specific transcripts in adult male and female (estrous cycle phase) rats. Twelve-week-old rats of both genders were used and samples were taken from the zona glomerulosa (ZG) and zona fasciculata/reticularis (ZF/R) zones. Transcriptome identification was carried out using the Affymetrix(®) Rat Gene 1.1 ST Array. The microarray data were compared by fold change with significance according to moderated t-statistics. Subsequently, we performed functional annotation clustering using the Gene Ontology (GO) and Database for Annotation, Visualization and Integrated Discovery (DAVID). In the first step, we explored differentially expressed transcripts in the adrenal ZG and ZF/R. The number of differentially expressed transcripts was notably higher in the female than in the male rats (702 vs. 571). The differentially expressed genes which were significantly enriched included genes involved in steroid hormone metabolism, and their expression levels in the ZF/R of adult female rats were significantly higher compared with those in the male rats. In the female ZF/R, when compared with that of the males, prevailing numbers of genes linked to cell fraction, oxidation/reduction processes, response to nutrients and to extracellular stimuli or steroid hormone stimuli were downregulated. The microarray data for key genes involved directly in steroidogenesis were confirmed by qPCR. Thus, when compared with that of the males, in the female ZF/R, higher expression levels of genes involved directly in steroid hormone synthesis were accompanied by lower

  19. Comparative microarray analysis and pulmonary changes in Brown Norway rats exposed to ovalbumin and concentrated air particulates.

    PubMed

    Heidenfelder, Brooke L; Reif, David M; Harkema, Jack R; Cohen Hubal, Elaine A; Hudgens, Edward E; Bramble, Lori A; Wagner, James G; Morishita, Masako; Keeler, Gerald J; Edwards, Stephen W; Gallagher, Jane E

    2009-03-01

    The interaction between air particulates and genetic susceptibility has been implicated in the pathogenesis of asthma. The overall objective of this study was to determine the effects of inhalation exposure to environmentally relevant concentrated air particulates (CAPs) on the lungs of ovalbumin (ova) sensitized and challenged Brown Norway rats. Changes in gene expression were compared with lung tissue histopathology, morphometry, and biochemical and cellular parameters in bronchoalveolar lavage fluid (BALF). Ova challenge was responsible for the preponderance of gene expression changes, related largely to inflammation. CAPs exposure alone resulted in no significant gene expression changes, but CAPs and ova-exposed rodents exhibited an enhanced effect relative to ova alone with differentially expressed genes primarily related to inflammation and airway remodeling. Gene expression data was consistent with the biochemical and cellular analyses of the BALF, the pulmonary pathology, and morphometric changes when comparing the CAPs-ova group to the air-saline or CAPs-saline group. However, the gene expression data were more sensitive than the BALF cell type and number for assessing the effects of CAPs and ova versus the ova challenge alone. In addition, the gene expression results provided some additional insight into the TGF-beta-mediated molecular processes underlying these changes. The broad-based histopathology and functional genomic analyses demonstrate that exposure to CAPs exacerbates rodents with allergic inflammation induced by an allergen and suggests that asthmatics may be at increased risk for air pollution effects.

  20. Microarray analysis of erythromycin resistance determinants.

    PubMed

    Volokhov, D; Chizhikov, V; Chumakov, K; Rasooly, A

    2003-01-01

    To develop a DNA microarray for analysis of genes encoding resistance determinants to erythromycin and the related macrolide, lincosamide and streptogramin B (MLS) compounds. We developed an oligonucleotide microarray containing seven oligonucleotide probes (oligoprobes) for each of the six genes (ermA, ermB, ermC, ereA, ereB and msrA/B) that account for more than 98% of MLS resistance in Staphylococcus aureus clinical isolates. The microarray was used to test reference and clinical S. aureus and Streptococcus pyrogenes strains. Target genes from clinical strains were amplified and fluorescently labelled using multiplex PCR target amplification. The microarray assay correctly identified the MLS resistance genes in the reference strains and clinical isolates of S. aureus, and the results were confirmed by direct DNA sequence analysis. Of 18 S. aureus clinical strains tested, 11 isolates carry MLS determinants. One gene (ermC) was found in all 11 clinical isolates tested, and two others, ermA and msrA/B, were found in five or more isolates. Indeed, eight (72%) of 11 clinical isolate strains contained two or three MLS resistance genes, in one of the three combinations (ermA with ermC, ermC with msrA/B, ermA with ermC and msrA/B). Oligonucleotide microarray can detect and identify the six MLS resistance determinants analysed in this study. Our results suggest that microarray-based detection of microbial antibiotic resistance genes might be a useful tool for identifying antibiotic resistance determinants in a wide range of bacterial strains, given the high homology among microbial MLS resistance genes.

  1. MICROARRAY DATA ANALYSIS USING MULTIPLE STATISTICAL MODELS

    EPA Science Inventory

    Microarray Data Analysis Using Multiple Statistical Models

    Wenjun Bao1, Judith E. Schmid1, Amber K. Goetz1, Ming Ouyang2, William J. Welsh2,Andrew I. Brooks3,4, ChiYi Chu3,Mitsunori Ogihara3,4, Yinhe Cheng5, David J. Dix1. 1National Health and Environmental Effects Researc...

  2. MICROARRAY DATA ANALYSIS USING MULTIPLE STATISTICAL MODELS

    EPA Science Inventory

    Microarray Data Analysis Using Multiple Statistical Models

    Wenjun Bao1, Judith E. Schmid1, Amber K. Goetz1, Ming Ouyang2, William J. Welsh2,Andrew I. Brooks3,4, ChiYi Chu3,Mitsunori Ogihara3,4, Yinhe Cheng5, David J. Dix1. 1National Health and Environmental Effects Researc...

  3. Microarrays

    ERIC Educational Resources Information Center

    Plomin, Robert; Schalkwyk, Leonard C.

    2007-01-01

    Microarrays are revolutionizing genetics by making it possible to genotype hundreds of thousands of DNA markers and to assess the expression (RNA transcripts) of all of the genes in the genome. Microarrays are slides the size of a postage stamp that contain millions of DNA sequences to which single-stranded DNA or RNA can hybridize. This…

  4. Microarrays

    ERIC Educational Resources Information Center

    Plomin, Robert; Schalkwyk, Leonard C.

    2007-01-01

    Microarrays are revolutionizing genetics by making it possible to genotype hundreds of thousands of DNA markers and to assess the expression (RNA transcripts) of all of the genes in the genome. Microarrays are slides the size of a postage stamp that contain millions of DNA sequences to which single-stranded DNA or RNA can hybridize. This…

  5. Analysis of High-Throughput ELISA Microarray Data

    SciTech Connect

    White, Amanda M.; Daly, Don S.; Zangar, Richard C.

    2011-02-23

    Our research group develops analytical methods and software for the high-throughput analysis of quantitative enzyme-linked immunosorbent assay (ELISA) microarrays. ELISA microarrays differ from DNA microarrays in several fundamental aspects and most algorithms for analysis of DNA microarray data are not applicable to ELISA microarrays. In this review, we provide an overview of the steps involved in ELISA microarray data analysis and how the statistically sound algorithms we have developed provide an integrated software suite to address the needs of each data-processing step. The algorithms discussed are available in a set of open-source software tools (http://www.pnl.gov/statistics/ProMAT).

  6. Bioinformatics/biostatistics: microarray analysis.

    PubMed

    Eichler, Gabriel S

    2012-01-01

    The quantity and complexity of the molecular-level data generated in both research and clinical settings require the use of sophisticated, powerful computational interpretation techniques. It is for this reason that bioinformatic analysis of complex molecular profiling data has become a fundamental technology in the development of personalized medicine. This chapter provides a high-level overview of the field of bioinformatics and outlines several, classic bioinformatic approaches. The highlighted approaches can be aptly applied to nearly any sort of high-dimensional genomic, proteomic, or metabolomic experiments. Reviewed technologies in this chapter include traditional clustering analysis, the Gene Expression Dynamics Inspector (GEDI), GoMiner (GoMiner), Gene Set Enrichment Analysis (GSEA), and the Learner of Functional Enrichment (LeFE).

  7. Alteration of Gene Expression Profile in Kidney of Spontaneously Hypertensive Rats Treated with Protein Hydrolysate of Blue Mussel (Mytilus edulis) by DNA Microarray Analysis

    PubMed Central

    Feng, Junli; Dai, Zhiyuan; Zhang, Yanping; Meng, Lu; Ye, Jian; Ma, Xuting

    2015-01-01

    Marine organisms are rich sources of bioactive components, which are often reported to have antihypertensive effects. However, the underlying mechanisms have yet to be fully identified. The aim of this study was to investigate the antihypertensive effect of enzymatic hydrolysis of blue mussel protein (HBMP) in rats. Peptides with in vitro ACE inhibitory activity were purified from HBMP by ultrafiltration, gel filtration chromatography and reversed-phase high performance liquid chromatography. And the amino acid sequences of isolated peptides were estimated to be Val-Trp, Leu-Gly-Trp, and Met-Val-Trp-Thr. To study its in vivo action, spontaneously hypertensive rats (SHRs) were orally administration with high- or low-dose of HBMP for 28 days. Major components of the renin-angiotensin (RAS) system in serum of SHRs from different groups were analyzed, and gene expression profiling were performed in the kidney of SHRs, using the Whole Rat Genome Oligonucleotide Microarray. Results indicated although genes involved in RAS system were not significantly altered, those related to blood coagulation system, cytokine and growth factor, and fatty acids metabolism were remarkablely changed. Several genes which were seldom reported to be implicated in pathogenesis of hypertension also showed significant expression alterations after oral administration of HBMP. These data provided valuable information for our understanding of the molecular mechanisms that underlie the potential antihypertensive activities of HBMP, and will contribute towards increased value-added utilization of blue mussel protein. PMID:26517713

  8. Integrated analysis of microarray data and gene function information.

    PubMed

    Cui, Yan; Zhou, Mi; Wong, Wing Hung

    2004-01-01

    Microarray data should be interpreted in the context of existing biological knowledge. Here we present integrated analysis of microarray data and gene function classification data using homogeneity analysis. Homogeneity analysis is a graphical multivariate statistical method for analyzing categorical data. It converts categorical data into graphical display. By simultaneously quantifying the microarray-derived gene groups and gene function categories, it captures the complex relations between biological information derived from microarray data and the existing knowledge about the gene function. Thus, homogeneity analysis provides a mathematical framework for integrating the analysis of microarray data and the existing biological knowledge.

  9. Integrated microfluidic biochips for DNA microarray analysis.

    PubMed

    Liu, Robin Hui; Dill, Kilian; Fuji, H Sho; McShea, Andy

    2006-03-01

    A fully integrated and self-contained microfluidic biochip device has been developed to automate the fluidic handling steps required to perform a gene expression study of the human leukemia cell line (K-562). The device consists of a DNA microarray semiconductor chip with 12,000 features and a microfluidic cartridge that consists of microfluidic pumps, mixers, valves, fluid channels and reagent storage chambers. Microarray hybridization and subsequent fluidic handling and reactions (including a number of washing and labeling steps) were performed in this fully automated and miniature device before fluorescent image scanning of the microarray chip. Electrochemical micropumps were integrated in the cartridge to provide pumping of liquid solutions. A micromixing technique based on gas bubbling generated by electrochemical micropumps was developed. Low-cost check valves were implemented in the cartridge to prevent cross-talk of the stored reagents. A single-color transcriptional analysis of K-562 cells with a series of calibration controls (spiked-in controls) was performed to characterize this new platform with regard to sensitivity, specificity and dynamic range. The device detected sample RNAs with a concentration as low as 0.375 pM. Detection was quantitative over more than 3 orders of magnitude. Experiments also demonstrated that chip-to-chip variability was low, indicating that the integrated microfluidic devices eliminate manual fluidic handling steps that can be a significant source of variability in genomic analysis.

  10. Digital microarray analysis for digital artifact genomics

    NASA Astrophysics Data System (ADS)

    Jaenisch, Holger; Handley, James; Williams, Deborah

    2013-06-01

    We implement a Spatial Voting (SV) based analogy of microarray analysis for digital gene marker identification in malware code sections. We examine a famous set of malware formally analyzed by Mandiant and code named Advanced Persistent Threat (APT1). APT1 is a Chinese organization formed with specific intent to infiltrate and exploit US resources. Manidant provided a detailed behavior and sting analysis report for the 288 malware samples available. We performed an independent analysis using a new alternative to the traditional dynamic analysis and static analysis we call Spatial Analysis (SA). We perform unsupervised SA on the APT1 originating malware code sections and report our findings. We also show the results of SA performed on some members of the families associated by Manidant. We conclude that SV based SA is a practical fast alternative to dynamics analysis and static analysis.

  11. Microarray analysis of gene expression in liver, adipose tissue and skeletal muscle in response to chronic dietary administration of NDGA to high-fructose fed dyslipidemic rats.

    PubMed

    Zhang, Haiyan; Shen, Wen-Jun; Li, Yihang; Bittner, Alex; Bittner, Stefanie; Tabassum, Juveria; Cortez, Yuan F; Kraemer, Fredric B; Azhar, Salman

    2016-01-01

    Nordihydroguaiaretic acid (NDGA), the main metabolite of Creosote Bush, has been shown to have profound effects on the core components of metabolic syndrome, including lowering of blood glucose, free fatty acids and triglyceride levels, attenuating elevated blood pressure in several rodent models of dyslipidemia, and improving body weight, insulin resistance, diabetes and hypertension. In the present study, a high-fructose diet fed rat model of hypertriglyceridemia, dyslipidemia, insulin resistance and hepatic steatosis was employed to investigate the global transcriptional changes in the lipid metabolizing pathways in three insulin sensitive tissues: liver, skeletal muscle and adipose tissue in response to chronic dietary administration of NDGA. Sprague-Dawley male rats (SD) were fed a chow (control) diet, high-fructose diet (HFrD) or HFrD supplemented with NDGA (2.5 g/kg diet) for eight weeks. Dietary administration of NDGA decreased plasma levels of TG, glucose, and insulin, and attenuated hepatic TG accumulation. DNA microarray expression profiling indicated that dietary administration of NDGA upregulated the expression of certain genes involved in fatty acid oxidation and their transcription regulator, PPARα, decreased the expression of a number of lipogenic genes and relevant transcription factors, and differentially impacted the genes of fatty acid transporters, acetyl CoA synthetases, elongases, fatty acid desaturases and lipid clearance proteins in liver, skeletal muscle and adipose tissues. These findings suggest that NDGA ameliorates hypertriglyceridemia and steatosis primarily by inhibiting lipogenesis and enhancing fatty acid catabolism in three major insulin responsive tissues by altering the expression of key enzyme genes and transcription factors involved in de novo lipogenesis and fatty acid oxidation.

  12. Meta-analysis of incomplete microarray studies.

    PubMed

    Zollinger, Alix; Davison, Anthony C; Goldstein, Darlene R

    2015-10-01

    Meta-analysis of microarray studies to produce an overall gene list is relatively straightforward when complete data are available. When some studies lack information-providing only a ranked list of genes, for example-it is common to reduce all studies to ranked lists prior to combining them. Since this entails a loss of information, we consider a hierarchical Bayes approach to meta-analysis using different types of information from different studies: the full data matrix, summary statistics, or ranks. The model uses an informative prior for the parameter of interest to aid the detection of differentially expressed genes. Simulations show that the new approach can give substantial power gains compared with classical meta-analysis and list aggregation methods. A meta-analysis of 11 published studies with different data types identifies genes known to be involved in ovarian cancer and shows significant enrichment.

  13. Microarray Analysis Reveals Increased Expression of Matrix Metalloproteases and Cytokines of Interleukin-20 Subfamily in the Kidneys of Neonate Rats Underwent Unilateral Ureteral Obstruction: A Potential Role of IL-24 in the Regulation of Inflammation and Tissue Remodeling.

    PubMed

    Pap, Domonkos; Sziksz, Erna; Kiss, Zoltán; Rokonay, Réka; Veres-Székely, Apor; Lippai, Rita; Takács, István Márton; Kis, Éva; Fekete, Andrea; Reusz, György; Vannay, Adam; Szabó, Attila J

    2017-01-01

    Congenital obstructive nephropathy (CON) is the main cause of pediatric chronic kidney diseases leading to renal fibrosis. High morbidity and limited treatment opportunities of CON urge the better understanding of the underlying molecular mechanisms. To identify the differentially expressed genes, microarray analysis was performed on the kidney samples of neonatal rats underwent unilateral ureteral obstruction (UUO). Microarray results were then validated by real-time RT-PCR and bioinformatics analysis was carried out to identify the relevant genes, functional groups and pathways involved in the pathomechanism of CON. Renal expression of matrix metalloproteinase (MMP)-12 and interleukin (IL)-24 were evaluated by real-time RT-PCR, flow cytometry and immunohistochemical analysis. Effect of the main profibrotic factors on the expression of MMP-12 and IL-24 was investigated on HK-2 and HEK-293 cell lines. Finally, the effect of IL-24 treatment on the expression of pro-inflammatory cytokines and MMPs were tested in vitro. Microarray analysis revealed 880 transcripts showing >2.0-fold change following UUO, enriched mainly in immune response related processes. The most up-regulated genes were MMPs and members of IL-20 cytokine subfamily, including MMP-3, MMP-7, MMP-12, IL-19 and IL-24. We found that while TGF-β treatment inhibits the expression of MMP-12 and IL-24, H2O2 or PDGF-B treatment induce the epithelial expression of MMP-12. We demonstrated that IL-24 treatment decreases the expression of IL-6 and MMP-3 in the renal epithelial cells. This study provides an extensive view of UUO induced changes in the gene expression profile of the developing kidney and describes novel molecules, which may play significant role in the pathomechanism of CON. © 2017 The Author(s)Published by S. Karger AG, Basel.

  14. Software and tools for microarray data analysis.

    PubMed

    Mehta, Jai Prakash; Rani, Sweta

    2011-01-01

    A typical microarray experiment results in series of images, depending on the experimental design and number of samples. Software analyses the images to obtain the intensity at each spot and quantify the expression for each transcript. This is followed by normalization, and then various data analysis techniques are applied on the data. The whole analysis pipeline requires a large number of software to accurately handle the massive amount of data. Fortunately, there are large number of freely available and commercial software to churn the massive amount of data to manageable sets of differentially expressed genes, functions, and pathways. This chapter describes the software and tools which can be used to analyze the gene expression data right from the image analysis to gene list, ontology, and pathways.

  15. Analysis of Mycobacterium leprae gene expression using DNA microarray.

    PubMed

    Akama, Takeshi; Tanigawa, Kazunari; Kawashima, Akira; Wu, Huhehasi; Ishii, Norihisa; Suzuki, Koichi

    2010-10-01

    Mycobacterium leprae, the causative agent of leprosy, does not grow under in vitro condition, making molecular analysis of this bacterium difficult. For this reason, bacteriological information regarding M. leprae gene function is limited compared with other mycobacterium species. In this study, we performed DNA microarray analysis to clarify the RNA expression profile of the Thai53 strain of M. leprae grown in footpads of hypertensive nude rats (SHR/NCrj-rnu). Of 1605 M. leprae genes, 315 showed signal intensity twofold higher than the median. These genes include Acyl-CoA metabolic enzymes and drug metabolic enzymes, which might be related to the virulence of M. leprae. In addition, consecutive RNA expression profile and in silico analyses enabled identification of possible operons within the M. leprae genome. The present results will shed light on M. leprae gene function and further our understanding of the pathogenesis of leprosy.

  16. Ontology-Based Analysis of Microarray Data.

    PubMed

    Giuseppe, Agapito; Milano, Marianna

    2016-01-01

    The importance of semantic-based methods and algorithms for the analysis and management of biological data is growing for two main reasons. From a biological side, knowledge contained in ontologies is more and more accurate and complete, from a computational side, recent algorithms are using in a valuable way such knowledge. Here we focus on semantic-based management and analysis of protein interaction networks referring to all the approaches of analysis of protein-protein interaction data that uses knowledge encoded into biological ontologies. Semantic approaches for studying high-throughput data have been largely used in the past to mine genomic and expression data. Recently, the emergence of network approaches for investigating molecular machineries has stimulated in a parallel way the introduction of semantic-based techniques for analysis and management of network data. The application of these computational approaches to the study of microarray data can broad the application scenario of them and simultaneously can help the understanding of disease development and progress.

  17. Phospholipidosis in rats treated with amiodarone: serum biochemistry and whole genome micro-array analysis supporting the lipid traffic jam hypothesis and the subsequent rise of the biomarker BMP.

    PubMed

    Mesens, Natalie; Desmidt, Miek; Verheyen, Geert R; Starckx, Sofie; Damsch, Siegrid; De Vries, Ronald; Verhemeldonck, Marc; Van Gompel, Jacky; Lampo, Ann; Lammens, Lieve

    2012-04-01

    To provide mechanistic insight in the induction of phospholipidosis and the appearance of the proposed biomarker di-docosahexaenoyl (C22:6)-bis(monoacylglycerol) phosphate (BMP), rats were treated with 150 mg/kg amiodarone for 12 consecutive days and analyzed at three different time points (day 4, 9, and 12). Biochemical analysis of the serum revealed a significant increase in cholesterol and phospholipids at the three time points. Bio-analysis on the serum and urine detected a time-dependent increase in BMP, as high as 10-fold compared to vehicle-treated animals on day 12. Paralleling these increases, micro-array analysis on the liver of treated rats identified cholesterol biosynthesis and glycerophospholipid metabolism as highly modulated pathways. This modulation indicates that during phospholipidosis-induction interactions take place between the cationic amphiphilic drug and phospholipids at the level of BMP-rich internal membranes of endosomes, impeding cholesterol sorting and leading to an accumulation of internal membranes, converting into multilamellar bodies. This process shows analogy to Niemann-Pick disease type C (NPC). Whereas the NPC-induced lipid traffic jam is situated at the cholesterol sorting proteins NPC1 and NPC2, the amiodarone-induced traffic jam is thought to be located at the BMP level, demonstrating its role in the mechanism of phospholipidosis-induction and its significance for use as a biomarker.

  18. Tissue Microarray Analysis Applied to Bone Diagenesis

    PubMed Central

    Mello, Rafael Barrios; Silva, Maria Regina Regis; Alves, Maria Teresa Seixas; Evison, Martin Paul; Guimarães, Marco Aurelio; Francisco, Rafaella Arrabaca; Astolphi, Rafael Dias; Iwamura, Edna Sadayo Miazato

    2017-01-01

    Taphonomic processes affecting bone post mortem are important in forensic, archaeological and palaeontological investigations. In this study, the application of tissue microarray (TMA) analysis to a sample of femoral bone specimens from 20 exhumed individuals of known period of burial and age at death is described. TMA allows multiplexing of subsamples, permitting standardized comparative analysis of adjacent sections in 3-D and of representative cross-sections of a large number of specimens. Standard hematoxylin and eosin, periodic acid-Schiff and silver methenamine, and picrosirius red staining, and CD31 and CD34 immunohistochemistry were applied to TMA sections. Osteocyte and osteocyte lacuna counts, percent bone matrix loss, and fungal spheroid element counts could be measured and collagen fibre bundles observed in all specimens. Decalcification with 7% nitric acid proceeded more rapidly than with 0.5 M EDTA and may offer better preservation of histological and cellular structure. No endothelial cells could be detected using CD31 and CD34 immunohistochemistry. Correlation between osteocytes per lacuna and age at death may reflect reported age-related responses to microdamage. Methodological limitations and caveats, and results of the TMA analysis of post mortem diagenesis in bone are discussed, and implications for DNA survival and recovery considered. PMID:28051148

  19. Transcriptome Analysis of Zebrafish Embryogenesis Using Microarrays

    PubMed Central

    Mathavan, Sinnakaruppan; Lee, Serene G. P; Mak, Alicia; Miller, Lance D; Murthy, Karuturi Radha Krishna; Govindarajan, Kunde R; Tong, Yan; Wu, Yi Lian; Lam, Siew Hong; Yang, Henry; Ruan, Yijun; Korzh, Vladimir; Gong, Zhiyuan; Liu, Edison T; Lufkin, Thomas

    2005-01-01

    Zebrafish (Danio rerio) is a well-recognized model for the study of vertebrate developmental genetics, yet at the same time little is known about the transcriptional events that underlie zebrafish embryogenesis. Here we have employed microarray analysis to study the temporal activity of developmentally regulated genes during zebrafish embryogenesis. Transcriptome analysis at 12 different embryonic time points covering five different developmental stages (maternal, blastula, gastrula, segmentation, and pharyngula) revealed a highly dynamic transcriptional profile. Hierarchical clustering, stage-specific clustering, and algorithms to detect onset and peak of gene expression revealed clearly demarcated transcript clusters with maximum gene activity at distinct developmental stages as well as co-regulated expression of gene groups involved in dedicated functions such as organogenesis. Our study also revealed a previously unidentified cohort of genes that are transcribed prior to the mid-blastula transition, a time point earlier than when the zygotic genome was traditionally thought to become active. Here we provide, for the first time to our knowledge, a comprehensive list of developmentally regulated zebrafish genes and their expression profiles during embryogenesis, including novel information on the temporal expression of several thousand previously uncharacterized genes. The expression data generated from this study are accessible to all interested scientists from our institute resource database (http://giscompute.gis.a-star.edu.sg/~govind/zebrafish/data_download.html). PMID:16132083

  20. Autocorrelation analysis reveals widespread spatial biases in microarray experiments

    PubMed Central

    Koren, Amnon; Tirosh, Itay; Barkai, Naama

    2007-01-01

    Background DNA microarrays provide the ability to interrogate multiple genes in a single experiment and have revolutionized genomic research. However, the microarray technology suffers from various forms of biases and relatively low reproducibility. A particular source of false data has been described, in which non-random placement of gene probes on the microarray surface is associated with spurious correlations between genes. Results In order to assess the prevalence of this effect and better understand its origins, we applied an autocorrelation analysis of the relationship between chromosomal position and expression level to a database of over 2000 individual yeast microarray experiments. We show that at least 60% of these experiments exhibit spurious chromosomal position-dependent gene correlations, which nonetheless appear in a stochastic manner within each experimental dataset. Using computer simulations, we show that large spatial biases caused in the microarray hybridization step and independently of printing procedures can exclusively account for the observed spurious correlations, in contrast to previous suggestions. Our data suggest that such biases may generate more than 15% false data per experiment. Importantly, spatial biases are expected to occur regardless of microarray design and over a wide range of microarray platforms, organisms and experimental procedures. Conclusions Spatial biases comprise a major source of noise in microarray studies; revision of routine experimental practices and normalizations to account for these biases may significantly and comprehensively improve the quality of new as well as existing DNA microarray data. PMID:17565680

  1. Autocorrelation analysis reveals widespread spatial biases in microarray experiments.

    PubMed

    Koren, Amnon; Tirosh, Itay; Barkai, Naama

    2007-06-12

    DNA microarrays provide the ability to interrogate multiple genes in a single experiment and have revolutionized genomic research. However, the microarray technology suffers from various forms of biases and relatively low reproducibility. A particular source of false data has been described, in which non-random placement of gene probes on the microarray surface is associated with spurious correlations between genes. In order to assess the prevalence of this effect and better understand its origins, we applied an autocorrelation analysis of the relationship between chromosomal position and expression level to a database of over 2000 individual yeast microarray experiments. We show that at least 60% of these experiments exhibit spurious chromosomal position-dependent gene correlations, which nonetheless appear in a stochastic manner within each experimental dataset. Using computer simulations, we show that large spatial biases caused in the microarray hybridization step and independently of printing procedures can exclusively account for the observed spurious correlations, in contrast to previous suggestions. Our data suggest that such biases may generate more than 15% false data per experiment. Importantly, spatial biases are expected to occur regardless of microarray design and over a wide range of microarray platforms, organisms and experimental procedures. Spatial biases comprise a major source of noise in microarray studies; revision of routine experimental practices and normalizations to account for these biases may significantly and comprehensively improve the quality of new as well as existing DNA microarray data.

  2. Automatic Spot Identification for High Throughput Microarray Analysis

    PubMed Central

    Wu, Eunice; Su, Yan A.; Billings, Eric; Brooks, Bernard R.; Wu, Xiongwu

    2013-01-01

    High throughput microarray analysis has great potential in scientific research, disease diagnosis, and drug discovery. A major hurdle toward high throughput microarray analysis is the time and effort needed to accurately locate gene spots in microarray images. An automatic microarray image processor will allow accurate and efficient determination of spot locations and sizes so that gene expression information can be reliably extracted in a high throughput manner. Current microarray image processing tools require intensive manual operations in addition to the input of grid parameters to correctly and accurately identify gene spots. This work developed a method, herein called auto-spot, to automate the spot identification process. Through a series of correlation and convolution operations, as well as pixel manipulations, this method makes spot identification an automatic and accurate process. Testing with real microarray images has demonstrated that this method is capable of automatically extracting subgrids from microarray images and determining spot locations and sizes within each subgrid, regardless of variations in array patterns and background noises. With this method, we are one step closer to the goal of high throughput microarray analysis. PMID:24298393

  3. DNA microarray unravels rapid changes in transcriptome of MK-801 treated rat brain

    PubMed Central

    Kobayashi, Yuka; Kulikova, Sofya P; Shibato, Junko; Rakwal, Randeep; Satoh, Hiroyuki; Pinault, Didier; Masuo, Yoshinori

    2015-01-01

    AIM: To investigate the impact of MK-801 on gene expression patterns genome wide in rat brain regions. METHODS: Rats were treated with an intraperitoneal injection of MK-801 [0.08 (low-dose) and 0.16 (high-dose) mg/kg] or NaCl (vehicle control). In a first series of experiment, the frontoparietal electrocorticogram was recorded 15 min before and 60 min after injection. In a second series of experiments, the whole brain of each animal was rapidly removed at 40 min post-injection, and different regions were separated: amygdala, cerebral cortex, hippocampus, hypothalamus, midbrain and ventral striatum on ice followed by DNA microarray (4 × 44 K whole rat genome chip) analysis. RESULTS: Spectral analysis revealed that a single systemic injection of MK-801 significantly and selectively augmented the power of baseline gamma frequency (30-80 Hz) oscillations in the frontoparietal electroencephalogram. DNA microarray analysis showed the largest number (up- and down- regulations) of gene expressions in the cerebral cortex (378), midbrain (376), hippocampus (375), ventral striatum (353), amygdala (301), and hypothalamus (201) under low-dose (0.08 mg/kg) of MK-801. Under high-dose (0.16 mg/kg), ventral striatum (811) showed the largest number of gene expression changes. Gene expression changes were functionally categorized to reveal expression of genes and function varies with each brain region. CONCLUSION: Acute MK-801 treatment increases synchrony of baseline gamma oscillations, and causes very early changes in gene expressions in six individual rat brain regions, a first report. PMID:26629322

  4. A Microarray Study of Middle Cerebral Occlusion Rat Brain with Acupuncture Intervention

    PubMed Central

    Zhang, Chao; Wen, Yan; Fan, Xiaonong; Yang, Sha; Tian, Guang; Zhou, Xueyi; Chen, Yaqiong; Meng, Zhihong

    2015-01-01

    Microarray analysis was used to investigate the changes of gene expression of ischemic stroke and acupuncture intervention in middle cerebral artery occlusion (MCAo) rat brain. Results showed that acupuncture intervention had a remarkable improvement in neural deficit score, cerebral blood flow, and cerebral infarction volume of MCAo rats. Microarray analysis showed that a total of 627 different expression genes were regulated in ischemic stroke. 417 genes were upregulated and 210 genes were downregulated. A total of 361 different expression genes were regulated after acupuncture intervention. Three genes were upregulated and 358 genes were downregulated. The expression of novel genes after acupuncture intervention, including Tph1 and Olr883, was further analyzed by Real-Time Quantitative Polymerase Chain Reaction (RT-PCR). Upregulation of Tph1 and downregulation of Olr883 indicated that the therapeutic effect of acupuncture for ischemic stroke may be closely related to the suppression of poststroke depression and regulation of olfactory transduction. In conclusion, the present study may enrich our understanding of the multiple pathological process of ischemic brain injury and indicate possible mechanisms of acupuncture on ischemic stroke. PMID:25861363

  5. Oligonucleotide microarray identifies genes differentially expressed during tumorigenesis of DMBA-induced pancreatic cancer in rats.

    PubMed

    Guo, Jun-Chao; Li, Jian; Yang, Ying-Chi; Zhou, Li; Zhang, Tai-Ping; Zhao, Yu-Pei

    2013-01-01

    The extremely dismal prognosis of pancreatic cancer (PC) is attributed, at least in part, to lack of early diagnosis. Therefore, identifying differentially expressed genes in multiple steps of tumorigenesis of PC is of great interest. In the present study, a 7,12-dimethylbenzanthraene (DMBA)-induced PC model was established in male Sprague-Dawley rats. The gene expression profile was screened using an oligonucleotide microarray, followed by real-time quantitative polymerase chain reaction (qRT-PCR) and immunohistochemical staining validation. A total of 661 differentially expressed genes were identified in stages of pancreatic carcinogenesis. According to GO classification, these genes were involved in multiple molecular pathways. Using two-way hierarchical clustering analysis, normal pancreas, acute and chronic pancreatitis, PanIN, early and advanced pancreatic cancer were completely discriminated. Furthermore, 11 upregulated and 142 downregulated genes (probes) were found by Mann-Kendall trend Monotone test, indicating homologous genes of rat and human. The qRT-PCR and immunohistochemistry analysis of CXCR7 and UBe2c, two of the identified genes, confirmed the microarray results. In human PC cell lines, knockdown of CXCR7 resulted in decreased migration and invasion. Collectively, our data identified several promising markers and therapeutic targets of PC based on a comprehensive screening and systemic validation.

  6. High confidence rule mining for microarray analysis.

    PubMed

    McIntosh, Tara; Chawla, Sanjay

    2007-01-01

    We present an association rule mining method for mining high confidence rules, which describe interesting gene relationships from microarray datasets. Microarray datasets typically contain an order of magnitude more genes than experiments, rendering many data mining methods impractical as they are optimised for sparse datasets. A new family of row-enumeration rule mining algorithms have emerged to facilitate mining in dense datasets. These algorithms rely on pruning infrequent relationships to reduce the search space by using the support measure. This major shortcoming results in the pruning of many potentially interesting rules with low support but high confidence. We propose a new row-enumeration rule mining method, MaxConf, to mine high confidence rules from microarray data. MaxConf is a support-free algorithm which directly uses the confidence measure to effectively prune the search space. Experiments on three microarray datasets show that MaxConf outperforms support-based rule mining with respect to scalability and rule extraction. Furthermore, detailed biological analyses demonstrate the effectiveness of our approach -- the rules discovered by MaxConf are substantially more interesting and meaningful compared with support-based methods.

  7. Zeptosens' protein microarrays: a novel high performance microarray platform for low abundance protein analysis.

    PubMed

    Pawlak, Michael; Schick, Eginhard; Bopp, Martin A; Schneider, Michael J; Oroszlan, Peter; Ehrat, Markus

    2002-04-01

    Protein microarrays are considered an enabling technology, which will significantly expand the scope of current protein expression and protein interaction analysis. Current technologies, such as two-dimensional gel electrophoresis (2-DE) in combination with mass spectrometry, allowing the identification of biologically relevant proteins, have a high resolving power, but also considerable limitations. As was demonstrated by Gygi et al. (Proc. Nat. Acad. Sci. USA 2000,97, 9390-9395), most spots in 2-DE, observed from whole cell extracts, are from high abundance proteins, whereas low abundance proteins, such as signaling molecules or kinases, are only poorly represented. Protein microarrays are expected to significantly expedite the discovery of new markers and targets of pharmaceutical interest, and to have the potential for high-throughput applications. Key factors to reach this goal are: high read-out sensitivity for quantification also of low abundance proteins, functional analysis of proteins, short assay analysis times, ease of handling and the ability to integrate a variety of different targets and new assays. Zeptosens has developed a revolutionary new bioanalytical system based on the proprietary planar waveguide technology which allows us to perform multiplexed, quantitative biomolecular interaction analysis with highest sensitivity in a microarray format upon utilizing the specific advantages of the evanescent field fluorescence detection. The analytical system, comprising an ultrasensitive fluorescence reader and microarray chips with integrated microfluidics, enables the user to generate a multitude of high fidelity data in applications such as protein expression profiling or investigating protein-protein interactions. In this paper, the important factors for developing high performance protein microarray systems, especially for targeting low abundant messengers of relevant biological information, will be discussed and the performance of the system will

  8. A Human Lectin Microarray for Sperm Surface Glycosylation Analysis.

    PubMed

    Sun, Yangyang; Cheng, Li; Gu, Yihua; Xin, Aijie; Wu, Bin; Zhou, Shumin; Guo, Shujuan; Liu, Yin; Diao, Hua; Shi, Huijuan; Wang, Guangyu; Tao, Sheng-Ce

    2016-09-01

    Glycosylation is one of the most abundant and functionally important protein post-translational modifications. As such, technology for efficient glycosylation analysis is in high demand. Lectin microarrays are a powerful tool for such investigations and have been successfully applied for a variety of glycobiological studies. However, most of the current lectin microarrays are primarily constructed from plant lectins, which are not well suited for studies of human glycosylation because of the extreme complexity of human glycans. Herein, we constructed a human lectin microarray with 60 human lectin and lectin-like proteins. All of the lectins and lectin-like proteins were purified from yeast, and most showed binding to human glycans. To demonstrate the applicability of the human lectin microarray, human sperm were probed on the microarray and strong bindings were observed for several lectins, including galectin-1, 7, 8, GalNAc-T6, and ERGIC-53 (LMAN1). These bindings were validated by flow cytometry and fluorescence immunostaining. Further, mass spectrometry analysis showed that galectin-1 binds several membrane-associated proteins including heat shock protein 90. Finally, functional assays showed that binding of galectin-8 could significantly enhance the acrosome reaction within human sperms. To our knowledge, this is the first construction of a human lectin microarray, and we anticipate it will find wide use for a range of human or mammalian studies, alone or in combination with plant lectin microarrays. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

  9. A Human Lectin Microarray for Sperm Surface Glycosylation Analysis *

    PubMed Central

    Sun, Yangyang; Cheng, Li; Gu, Yihua; Xin, Aijie; Wu, Bin; Zhou, Shumin; Guo, Shujuan; Liu, Yin; Diao, Hua; Shi, Huijuan; Wang, Guangyu; Tao, Sheng-ce

    2016-01-01

    Glycosylation is one of the most abundant and functionally important protein post-translational modifications. As such, technology for efficient glycosylation analysis is in high demand. Lectin microarrays are a powerful tool for such investigations and have been successfully applied for a variety of glycobiological studies. However, most of the current lectin microarrays are primarily constructed from plant lectins, which are not well suited for studies of human glycosylation because of the extreme complexity of human glycans. Herein, we constructed a human lectin microarray with 60 human lectin and lectin-like proteins. All of the lectins and lectin-like proteins were purified from yeast, and most showed binding to human glycans. To demonstrate the applicability of the human lectin microarray, human sperm were probed on the microarray and strong bindings were observed for several lectins, including galectin-1, 7, 8, GalNAc-T6, and ERGIC-53 (LMAN1). These bindings were validated by flow cytometry and fluorescence immunostaining. Further, mass spectrometry analysis showed that galectin-1 binds several membrane-associated proteins including heat shock protein 90. Finally, functional assays showed that binding of galectin-8 could significantly enhance the acrosome reaction within human sperms. To our knowledge, this is the first construction of a human lectin microarray, and we anticipate it will find wide use for a range of human or mammalian studies, alone or in combination with plant lectin microarrays. PMID:27364157

  10. Analysis of Microarray and RNA-seq Expression Profiling Data.

    PubMed

    Hung, Jui-Hung; Weng, Zhiping

    2017-03-01

    Gene expression profiling refers to the simultaneous measurement of the expression levels of a large number of genes (often all genes in a genome), typically in multiple experiments spanning a variety of cell types, treatments, or environmental conditions. Expression profiling is accomplished by assaying mRNA levels with microarrays or next-generation sequencing technologies (RNA-seq). This introduction describes normalization and analysis of data generated from microarray or RNA-seq experiments.

  11. Advanced spot quality analysis in two-colour microarray experiments

    PubMed Central

    Yatskou, Mikalai; Novikov, Eugene; Vetter, Guillaume; Muller, Arnaud; Barillot, Emmanuel; Vallar, Laurent; Friederich, Evelyne

    2008-01-01

    Background Image analysis of microarrays and, in particular, spot quantification and spot quality control, is one of the most important steps in statistical analysis of microarray data. Recent methods of spot quality control are still in early age of development, often leading to underestimation of true positive microarray features and, consequently, to loss of important biological information. Therefore, improving and standardizing the statistical approaches of spot quality control are essential to facilitate the overall analysis of microarray data and subsequent extraction of biological information. Findings We evaluated the performance of two image analysis packages MAIA and GenePix (GP) using two complementary experimental approaches with a focus on the statistical analysis of spot quality factors. First, we developed control microarrays with a priori known fluorescence ratios to verify the accuracy and precision of the ratio estimation of signal intensities. Next, we developed advanced semi-automatic protocols of spot quality evaluation in MAIA and GP and compared their performance with available facilities of spot quantitative filtering in GP. We evaluated these algorithms for standardised spot quality analysis in a whole-genome microarray experiment assessing well-characterised transcriptional modifications induced by the transcription regulator SNAI1. Using a set of RT-PCR or qRT-PCR validated microarray data, we found that the semi-automatic protocol of spot quality control we developed with MAIA allowed recovering approximately 13% more spots and 38% more differentially expressed genes (at FDR = 5%) than GP with default spot filtering conditions. Conclusion Careful control of spot quality characteristics with advanced spot quality evaluation can significantly increase the amount of confident and accurate data resulting in more meaningful biological conclusions. PMID:18798985

  12. Challenges for MicroRNA Microarray Data Analysis

    PubMed Central

    Wang, Bin; Xi, Yaguang

    2013-01-01

    Microarray is a high throughput discovery tool that has been broadly used for genomic research. Probe-target hybridization is the central concept of this technology to determine the relative abundance of nucleic acid sequences through fluorescence-based detection. In microarray experiments, variations of expression measurements can be attributed to many different sources that influence the stability and reproducibility of microarray platforms. Normalization is an essential step to reduce non-biological errors and to convert raw image data from multiple arrays (channels) to quality data for further analysis. In general, for the traditional microarray analysis, most established normalization methods are based on two assumptions: (1) the total number of target genes is large enough (>10,000); and (2) the expression level of the majority of genes is kept constant. However, microRNA (miRNA) arrays are usually spotted in low density, due to the fact that the total number of miRNAs is less than 2,000 and the majority of miRNAs are weakly or not expressed. As a result, normalization methods based on the above two assumptions are not applicable to miRNA profiling studies. In this review, we discuss a few representative microarray platforms on the market for miRNA profiling and compare the traditional methods with a few novel strategies specific for miRNA microarrays. PMID:24163754

  13. ProMAT: protein microarray analysis tool

    SciTech Connect

    White, Amanda M.; Daly, Don S.; Varnum, Susan M.; Anderson, Kevin K.; Bollinger, Nikki; Zangar, Richard C.

    2006-04-04

    Summary: ProMAT is a software tool for statistically analyzing data from ELISA microarray experiments. The software estimates standard curves, sample protein concentrations and their uncertainties for multiple assays. ProMAT generates a set of comprehensive figures for assessing results and diagnosing process quality. The tool is available for Windows or Mac, and is distributed as open-source Java and R code. Availability: ProMAT is available at http://www.pnl.gov/statistics/ProMAT. ProMAT requires Java version 1.5.0 and R version 1.9.1 (or more recent versions) which are distributed with the tool.

  14. On the Statics for Micro-Array Data Analysis

    NASA Astrophysics Data System (ADS)

    Urushibara, Tomoko; Akasaka, Shizu; Ito, Makiko; Suzuki, Tomonori; Miyazaki, Satoru

    2010-01-01

    Recently after human genome sequence has been determined almost perfectly, more and more researchers have been studying genes in detail. Therefore, we are sure that accumulated gene information for human will be getting more important in the near future to develop customized medicine and to make gene interactions clear. Among plenty of information, micro array might be one of the most important analysis method for genes because it is the technique that can get big amount of the gene expressions data from one time experiment and also can be used for DNA isolation. To get the novel knowledge from micro array data, we need to enrich statistical tools for its data analysis. So far, many mathematical theories and definition have been proposing. However, many of those proposals are tested with strict conditions or customized to data for specific species. In this paper, we reviewed existing typical statistical methods for micro array analysis and discussed the repeatability of the analysis, construction the guideline with more general procedure. First we analyzed the micro array data for TG rats, with statistical methods of family-wise error rate (FWER) control approach and False Discovery Rate (FDR) control approach. As existing report, no significantly different gene could be detected with FWER control approach. On the other hand, we could find several genes significantly with FDR control approach even q=0.5. To find out the reliability of FDR control approach with micro array conditions, we have analyzed 2 more pieces of data from Gene Expression Omnibus (GEO) public database on the web site with SAM in addition to FWER and FDR control approaches. We could find a certain number of significantly different genes with BH method and SAM in the case of q=0.05. However, we have to note that the number and kinds of detected genes are different when we compare our result with the one from the published paper. Even if the same approach is used to analyze the same micro array

  15. WebArray: an online platform for microarray data analysis

    PubMed Central

    Xia, Xiaoqin; McClelland, Michael; Wang, Yipeng

    2005-01-01

    Background Many cutting-edge microarray analysis tools and algorithms, including commonly used limma and affy packages in Bioconductor, need sophisticated knowledge of mathematics, statistics and computer skills for implementation. Commercially available software can provide a user-friendly interface at considerable cost. To facilitate the use of these tools for microarray data analysis on an open platform we developed an online microarray data analysis platform, WebArray, for bench biologists to utilize these tools to explore data from single/dual color microarray experiments. Results The currently implemented functions were based on limma and affy package from Bioconductor, the spacings LOESS histogram (SPLOSH) method, PCA-assisted normalization method and genome mapping method. WebArray incorporates these packages and provides a user-friendly interface for accessing a wide range of key functions of limma and others, such as spot quality weight, background correction, graphical plotting, normalization, linear modeling, empirical bayes statistical analysis, false discovery rate (FDR) estimation, chromosomal mapping for genome comparison. Conclusion WebArray offers a convenient platform for bench biologists to access several cutting-edge microarray data analysis tools. The website is freely available at . It runs on a Linux server with Apache and MySQL. PMID:16371165

  16. A microarray whole-genome gene expression dataset in a rat model of inflammatory corneal angiogenesis.

    PubMed

    Mukwaya, Anthony; Lindvall, Jessica M; Xeroudaki, Maria; Peebo, Beatrice; Ali, Zaheer; Lennikov, Anton; Jensen, Lasse Dahl Ejby; Lagali, Neil

    2016-11-22

    In angiogenesis with concurrent inflammation, many pathways are activated, some linked to VEGF and others largely VEGF-independent. Pathways involving inflammatory mediators, chemokines, and micro-RNAs may play important roles in maintaining a pro-angiogenic environment or mediating angiogenic regression. Here, we describe a gene expression dataset to facilitate exploration of pro-angiogenic, pro-inflammatory, and remodelling/normalization-associated genes during both an active capillary sprouting phase, and in the restoration of an avascular phenotype. The dataset was generated by microarray analysis of the whole transcriptome in a rat model of suture-induced inflammatory corneal neovascularisation. Regions of active capillary sprout growth or regression in the cornea were harvested and total RNA extracted from four biological replicates per group. High quality RNA was obtained for gene expression analysis using microarrays. Fold change of selected genes was validated by qPCR, and protein expression was evaluated by immunohistochemistry. We provide a gene expression dataset that may be re-used to investigate corneal neovascularisation, and may also have implications in other contexts of inflammation-mediated angiogenesis.

  17. A microarray whole-genome gene expression dataset in a rat model of inflammatory corneal angiogenesis

    PubMed Central

    Mukwaya, Anthony; Lindvall, Jessica M.; Xeroudaki, Maria; Peebo, Beatrice; Ali, Zaheer; Lennikov, Anton; Jensen, Lasse Dahl Ejby; Lagali, Neil

    2016-01-01

    In angiogenesis with concurrent inflammation, many pathways are activated, some linked to VEGF and others largely VEGF-independent. Pathways involving inflammatory mediators, chemokines, and micro-RNAs may play important roles in maintaining a pro-angiogenic environment or mediating angiogenic regression. Here, we describe a gene expression dataset to facilitate exploration of pro-angiogenic, pro-inflammatory, and remodelling/normalization-associated genes during both an active capillary sprouting phase, and in the restoration of an avascular phenotype. The dataset was generated by microarray analysis of the whole transcriptome in a rat model of suture-induced inflammatory corneal neovascularisation. Regions of active capillary sprout growth or regression in the cornea were harvested and total RNA extracted from four biological replicates per group. High quality RNA was obtained for gene expression analysis using microarrays. Fold change of selected genes was validated by qPCR, and protein expression was evaluated by immunohistochemistry. We provide a gene expression dataset that may be re-used to investigate corneal neovascularisation, and may also have implications in other contexts of inflammation-mediated angiogenesis. PMID:27874850

  18. Cortical Auditory Deafferentation Induces Long-Term Plasticity in the Inferior Colliculus of Adult Rats: Microarray and qPCR Analysis

    PubMed Central

    Clarkson, Cheryl; Herrero-Turrión, M. Javier; Merchán, Miguel A.

    2012-01-01

    The cortico-collicular pathway is a bilateral excitatory projection from the cortex to the inferior colliculus (IC). It is asymmetric and predominantly ipsilateral. Using microarrays and RT-qPCR we analyzed changes in gene expression in the IC after unilateral lesions of the auditory cortex, comparing the ICs ipsi- and contralateral to the lesioned side. At 15 days after surgery there were mainly changes in gene expression in the IC ipsilateral to the lesion. Regulation primarily involved inflammatory cascade genes, suggesting a direct effect of degeneration rather than a neuronal plastic reorganization. Ninety days after the cortical lesion the ipsilateral IC showed a significant up-regulation of genes involved in apoptosis and axonal regeneration combined with a down-regulation of genes involved in neurotransmission, synaptic growth, and gap junction assembly. In contrast, the contralateral IC at 90 days post-lesion showed an up-regulation in genes primarily related to neurotransmission, cell proliferation, and synaptic growth. There was also a down-regulation in autophagy and neuroprotection genes. These findings suggest that the reorganization in the IC after descending pathway deafferentation is a long-term process involving extensive changes in gene expression regulation. Regulated genes are involved in many different neuronal functions, and the number and gene rearrangement profile seems to depend on the density of loss of the auditory cortical inputs. PMID:23233834

  19. Comparative analysis of genomic signal processing for microarray data clustering.

    PubMed

    Istepanian, Robert S H; Sungoor, Ala; Nebel, Jean-Christophe

    2011-12-01

    Genomic signal processing is a new area of research that combines advanced digital signal processing methodologies for enhanced genetic data analysis. It has many promising applications in bioinformatics and next generation of healthcare systems, in particular, in the field of microarray data clustering. In this paper we present a comparative performance analysis of enhanced digital spectral analysis methods for robust clustering of gene expression across multiple microarray data samples. Three digital signal processing methods: linear predictive coding, wavelet decomposition, and fractal dimension are studied to provide a comparative evaluation of the clustering performance of these methods on several microarray datasets. The results of this study show that the fractal approach provides the best clustering accuracy compared to other digital signal processing and well known statistical methods.

  20. Short time-series microarray analysis: Methods and challenges

    PubMed Central

    Wang, Xuewei; Wu, Ming; Li, Zheng; Chan, Christina

    2008-01-01

    The detection and analysis of steady-state gene expression has become routine. Time-series microarrays are of growing interest to systems biologists for deciphering the dynamic nature and complex regulation of biosystems. Most temporal microarray data only contain a limited number of time points, giving rise to short-time-series data, which imposes challenges for traditional methods of extracting meaningful information. To obtain useful information from the wealth of short-time series data requires addressing the problems that arise due to limited sampling. Current efforts have shown promise in improving the analysis of short time-series microarray data, although challenges remain. This commentary addresses recent advances in methods for short-time series analysis including simplification-based approaches and the integration of multi-source information. Nevertheless, further studies and development of computational methods are needed to provide practical solutions to fully exploit the potential of this data. PMID:18605994

  1. Microarray analysis of papillary thyroid cancers in Korean.

    PubMed

    Kim, Hyun Sook; Kim, Do Hyung; Kim, Ji Yeon; Jeoung, Nam Ho; Lee, In Kyu; Bong, Jin Gu; Jung, Eui Dal

    2010-12-01

    Papillary thyroid cancer (PTC) is the most common malignancy of the thyroid gland. It involves several molecular mechanisms. The BRAF V600E mutation has been identified as the most common genetic abnormality in PTC. Moreover, it is known to be more prevalent in Korean PTC patients than in patients from other countries. We investigated distinct genetic profiles in Korean PTC through cDNA microarray analysis. Transcriptional profiles of five PTC samples and five paired normal thyroid tissue samples were generated using cDNA microarrays. The tumors were genotyped for BRAF mutations. The results of the cDNA microarray gene expression analysis were confirmed by real-time PCR and immunohistochemistry analysis of 35 PTC patients. Four of the five patients whose PTC tissues were subjected to microarray analysis were found to carry the BRAF V600E mutation. Microarrays analysis of the five PTC tissue samples showed the expression of 96 genes to be increased and that of 16 genes decreased. Real-time reverse transcription-polymerase chain reaction (RT-PCR) confirmed increased expression of SLC34A2, TM7SF4, COMP, KLK7, and KCNJ2 and decreased expression of FOXA2, SLC4A4, LYVE-1, and TFCP2L1 in PTC compared with normal tissue. Of these genes, TFCP2L1, LYVE-1, and KLK7 were previously unidentified in PTC microarray analysis. Notably, Foxa2 activity in PTC was reduced, as shown by its cytoplasmic localization, in immunohistochemical analyses. These findings demonstrate both similarities and differences between our results and previous reports. In Korean cases of PTC, Foxa2 activity was reduced with its cytoplasmic accumulation. Further studies are needed to confirm the relationship between FOXA2 and BRAF mutations in Korean cases of PTC.

  2. Optimized T7 amplification system for microarray analysis.

    PubMed

    Pabón, C; Modrusan, Z; Ruvolo, M V; Coleman, I M; Daniel, S; Yue, H; Arnold, L J

    2001-10-01

    Glass cDNA microarray technologies offer a highly parallel approach for profiling expressed gene sequences in disease-relevant tissues. However, standard hybridization and detection protocols are insufficient for milligram quantities of tissue, such as those derived from needle biopsies. Amplification systems utilizing T7 RNA polymerase can provide multiple cRNA copies from mRNA transcripts, permitting microarray studies with reduced sample inputs. Here, we describe an optimized T7-based amplification system for microarray analysis that yields between 200- and 700-fold amplification. This system was evaluated with both mRNA and total RNA samples and provided microarray sensitivity and precision that are comparable to our standard production process without amplification. The size distributions of amplified cRNA ranged from 200 bp to 4 kb and were similar to original mRNA profiles. These amplified cRNA samples were fluorescently labeled by reverse transcription and hybridized to microarrays comprising approximately 10,000 cDNA targets using a dual-channel format. Replicate hybridization experiments were conducted with the same and different tissues in each channel to assess the sensitivity and precision of differential expression ratios. Statistical analysis of differential expression ratios showed the lower limit of detection to be about 2-fold within and between amplified data sets, and about 3-fold when comparing amplified data to unamplified data (99.5% confidence).

  3. Microarray-based characterization of differential gene expression during vocal fold wound healing in rats.

    PubMed

    Welham, Nathan V; Ling, Changying; Dawson, John A; Kendziorski, Christina; Thibeault, Susan L; Yamashita, Masaru

    2015-03-01

    The vocal fold (VF) mucosa confers elegant biomechanical function for voice production but is susceptible to scar formation following injury. Current understanding of VF wound healing is hindered by a paucity of data and is therefore often generalized from research conducted in skin and other mucosal systems. Here, using a previously validated rat injury model, expression microarray technology and an empirical Bayes analysis approach, we generated a VF-specific transcriptome dataset to better capture the system-level complexity of wound healing in this specialized tissue. We measured differential gene expression at 3, 14 and 60 days post-injury compared to experimentally naïve controls, pursued functional enrichment analyses to refine and add greater biological definition to the previously proposed temporal phases of VF wound healing, and validated the expression and localization of a subset of previously unidentified repair- and regeneration-related genes at the protein level. Our microarray dataset is a resource for the wider research community and has the potential to stimulate new hypotheses and avenues of investigation, improve biological and mechanistic insight, and accelerate the identification of novel therapeutic targets.

  4. Microarray analysis of DNA replication timing.

    PubMed

    Karnani, Neerja; Taylor, Christopher M; Dutta, Anindya

    2009-01-01

    Although all of the DNA in an eukaryotic cell replicates during the S-phase of cell cycle, there is a significant difference in the actual time in S-phase when a given chromosomal segment replicates. Methods are described here for generation of high-resolution temporal maps of DNA replication in synchronized human cells. This method does not require amplification of DNA before microarray hybridization and so avoids errors introduced during PCR. A major advantage of using this procedure is that it facilitates finer dissection of replication time in S-phase. Also, it helps delineate chromosomal regions that undergo biallelic or asynchronous replication, which otherwise are difficult to detect at a genome-wide scale by existing methods. The continuous TR50 (time of completion of 50% replication) maps of replication across chromosomal segments identify regions that undergo acute transitions in replication timing. These transition zones can play a significant role in identifying insulators that separate chromosomal domains with different chromatin modifications.

  5. Microarray analysis in the archaeon Halobacterium salinarum strain R1.

    PubMed

    Twellmeyer, Jens; Wende, Andy; Wolfertz, Jan; Pfeiffer, Friedhelm; Panhuysen, Markus; Zaigler, Alexander; Soppa, Jörg; Welzl, Gerhard; Oesterhelt, Dieter

    2007-10-24

    Phototrophy of the extremely halophilic archaeon Halobacterium salinarum was explored for decades. The research was mainly focused on the expression of bacteriorhodopsin and its functional properties. In contrast, less is known about genome wide transcriptional changes and their impact on the physiological adaptation to phototrophy. The tool of choice to record transcriptional profiles is the DNA microarray technique. However, the technique is still rarely used for transcriptome analysis in archaea. We developed a whole-genome DNA microarray based on our sequence data of the Hbt. salinarum strain R1 genome. The potential of our tool is exemplified by the comparison of cells growing under aerobic and phototrophic conditions, respectively. We processed the raw fluorescence data by several stringent filtering steps and a subsequent MAANOVA analysis. The study revealed a lot of transcriptional differences between the two cell states. We found that the transcriptional changes were relatively weak, though significant. Finally, the DNA microarray data were independently verified by a real-time PCR analysis. This is the first DNA microarray analysis of Hbt. salinarum cells that were actually grown under phototrophic conditions. By comparing the transcriptomics data with current knowledge we could show that our DNA microarray tool is well applicable for transcriptome analysis in the extremely halophilic archaeon Hbt. salinarum. The reliability of our tool is based on both the high-quality array of DNA probes and the stringent data handling including MAANOVA analysis. Among the regulated genes more than 50% had unknown functions. This underlines the fact that haloarchaeal phototrophy is still far away from being completely understood. Hence, the data recorded in this study will be subject to future systems biology analysis.

  6. Microarray Analysis in the Archaeon Halobacterium salinarum Strain R1

    PubMed Central

    Twellmeyer, Jens; Wende, Andy; Wolfertz, Jan; Pfeiffer, Friedhelm; Panhuysen, Markus; Zaigler, Alexander; Soppa, Jörg; Welzl, Gerhard; Oesterhelt, Dieter

    2007-01-01

    Background Phototrophy of the extremely halophilic archaeon Halobacterium salinarum was explored for decades. The research was mainly focused on the expression of bacteriorhodopsin and its functional properties. In contrast, less is known about genome wide transcriptional changes and their impact on the physiological adaptation to phototrophy. The tool of choice to record transcriptional profiles is the DNA microarray technique. However, the technique is still rarely used for transcriptome analysis in archaea. Methodology/Principal Findings We developed a whole-genome DNA microarray based on our sequence data of the Hbt. salinarum strain R1 genome. The potential of our tool is exemplified by the comparison of cells growing under aerobic and phototrophic conditions, respectively. We processed the raw fluorescence data by several stringent filtering steps and a subsequent MAANOVA analysis. The study revealed a lot of transcriptional differences between the two cell states. We found that the transcriptional changes were relatively weak, though significant. Finally, the DNA microarray data were independently verified by a real-time PCR analysis. Conclusion/Significance This is the first DNA microarray analysis of Hbt. salinarum cells that were actually grown under phototrophic conditions. By comparing the transcriptomics data with current knowledge we could show that our DNA microarray tool is well applicable for transcriptome analysis in the extremely halophilic archaeon Hbt. salinarum. The reliability of our tool is based on both the high-quality array of DNA probes and the stringent data handling including MAANOVA analysis. Among the regulated genes more than 50% had unknown functions. This underlines the fact that haloarchaeal phototrophy is still far away from being completely understood. Hence, the data recorded in this study will be subject to future systems biology analysis. PMID:17957248

  7. Identification of candidate genes in osteoporosis by integrated microarray analysis.

    PubMed

    Li, J J; Wang, B Q; Fei, Q; Yang, Y; Li, D

    2016-12-01

    In order to screen the altered gene expression profile in peripheral blood mononuclear cells of patients with osteoporosis, we performed an integrated analysis of the online microarray studies of osteoporosis. We searched the Gene Expression Omnibus (GEO) database for microarray studies of peripheral blood mononuclear cells in patients with osteoporosis. Subsequently, we integrated gene expression data sets from multiple microarray studies to obtain differentially expressed genes (DEGs) between patients with osteoporosis and normal controls. Gene function analysis was performed to uncover the functions of identified DEGs. A total of three microarray studies were selected for integrated analysis. In all, 1125 genes were found to be significantly differentially expressed between osteoporosis patients and normal controls, with 373 upregulated and 752 downregulated genes. Positive regulation of the cellular amino metabolic process (gene ontology (GO): 0033240, false discovery rate (FDR) = 1.00E + 00) was significantly enriched under the GO category for biological processes, while for molecular functions, flavin adenine dinucleotide binding (GO: 0050660, FDR = 3.66E-01) and androgen receptor binding (GO: 0050681, FDR = 6.35E-01) were significantly enriched. DEGs were enriched in many osteoporosis-related signalling pathways, including those of mitogen-activated protein kinase (MAPK) and calcium. Protein-protein interaction (PPI) network analysis showed that the significant hub proteins contained ubiquitin specific peptidase 9, X-linked (Degree = 99), ubiquitin specific peptidase 19 (Degree = 57) and ubiquitin conjugating enzyme E2 B (Degree = 57). Analysis of gene function of identified differentially expressed genes may expand our understanding of fundamental mechanisms leading to osteoporosis. Moreover, significantly enriched pathways, such as MAPK and calcium, may involve in osteoporosis through osteoblastic differentiation and bone formation.Cite this article: J. J

  8. Independent component analysis of Alzheimer's DNA microarray gene expression data

    PubMed Central

    Kong, Wei; Mou, Xiaoyang; Liu, Qingzhong; Chen, Zhongxue; Vanderburg, Charles R; Rogers, Jack T; Huang, Xudong

    2009-01-01

    Background Gene microarray technology is an effective tool to investigate the simultaneous activity of multiple cellular pathways from hundreds to thousands of genes. However, because data in the colossal amounts generated by DNA microarray technology are usually complex, noisy, high-dimensional, and often hindered by low statistical power, their exploitation is difficult. To overcome these problems, two kinds of unsupervised analysis methods for microarray data: principal component analysis (PCA) and independent component analysis (ICA) have been developed to accomplish the task. PCA projects the data into a new space spanned by the principal components that are mutually orthonormal to each other. The constraint of mutual orthogonality and second-order statistics technique within PCA algorithms, however, may not be applied to the biological systems studied. Extracting and characterizing the most informative features of the biological signals, however, require higher-order statistics. Results ICA is one of the unsupervised algorithms that can extract higher-order statistical structures from data and has been applied to DNA microarray gene expression data analysis. We performed FastICA method on DNA microarray gene expression data from Alzheimer's disease (AD) hippocampal tissue samples and consequential gene clustering. Experimental results showed that the ICA method can improve the clustering results of AD samples and identify significant genes. More than 50 significant genes with high expression levels in severe AD were extracted, representing immunity-related protein, metal-related protein, membrane protein, lipoprotein, neuropeptide, cytoskeleton protein, cellular binding protein, and ribosomal protein. Within the aforementioned categories, our method also found 37 significant genes with low expression levels. Moreover, it is worth noting that some oncogenes and phosphorylation-related proteins are expressed in low levels. In comparison to the PCA and support

  9. Regularized gene selection in cancer microarray meta-analysis.

    PubMed

    Ma, Shuangge; Huang, Jian

    2009-01-01

    In cancer studies, it is common that multiple microarray experiments are conducted to measure the same clinical outcome and expressions of the same set of genes. An important goal of such experiments is to identify a subset of genes that can potentially serve as predictive markers for cancer development and progression. Analyses of individual experiments may lead to unreliable gene selection results because of the small sample sizes. Meta analysis can be used to pool multiple experiments, increase statistical power, and achieve more reliable gene selection. The meta analysis of cancer microarray data is challenging because of the high dimensionality of gene expressions and the differences in experimental settings amongst different experiments. We propose a Meta Threshold Gradient Descent Regularization (MTGDR) approach for gene selection in the meta analysis of cancer microarray data. The MTGDR has many advantages over existing approaches. It allows different experiments to have different experimental settings. It can account for the joint effects of multiple genes on cancer, and it can select the same set of cancer-associated genes across multiple experiments. Simulation studies and analyses of multiple pancreatic and liver cancer experiments demonstrate the superior performance of the MTGDR. The MTGDR provides an effective way of analyzing multiple cancer microarray studies and selecting reliable cancer-associated genes.

  10. Microarray data analysis for differential expression: a tutorial.

    PubMed

    Suárez, Erick; Burguete, Ana; Mclachlan, Geoffrey J

    2009-06-01

    DNA microarray is a technology that simultaneously evaluates quantitative measurements for the expression of thousands of genes. DNA microarrays have been used to assess gene expression between groups of cells of different organs or different populations. In order to understand the role and function of the genes, one needs the complete information about their mRNA transcripts and proteins. Unfortunately, exploring the protein functions is very difficult, due to their unique 3-dimentional complicated structure. To overcome this difficulty, one may concentrate on the mRNA molecules produced by the gene expression. In this paper, we describe some of the methods for preprocessing data for gene expression and for pairwise comparison from genomic experiments. Previous studies to assess the efficiency of different methods for pairwise comparisons have found little agreement in the lists of significant genes. Finally, we describe the procedures to control false discovery rates, sample size approach for these experiments, and available software for microarray data analysis. This paper is written for those professionals who are new in microarray data analysis for differential expression and want to have an overview of the specific steps or the different approaches for this sort of analysis.

  11. Bioinformatics and Microarray Data Analysis on the Cloud.

    PubMed

    Calabrese, Barbara; Cannataro, Mario

    2016-01-01

    High-throughput platforms such as microarray, mass spectrometry, and next-generation sequencing are producing an increasing volume of omics data that needs large data storage and computing power. Cloud computing offers massive scalable computing and storage, data sharing, on-demand anytime and anywhere access to resources and applications, and thus, it may represent the key technology for facing those issues. In fact, in the recent years it has been adopted for the deployment of different bioinformatics solutions and services both in academia and in the industry. Although this, cloud computing presents several issues regarding the security and privacy of data, that are particularly important when analyzing patients data, such as in personalized medicine. This chapter reviews main academic and industrial cloud-based bioinformatics solutions; with a special focus on microarray data analysis solutions and underlines main issues and problems related to the use of such platforms for the storage and analysis of patients data.

  12. Microarray analysis of gene expression in medicinal plant research.

    PubMed

    Youns, M; Efferth, T; Hoheisel, J D

    2009-10-01

    Expression profiling analysis offers great opportunities for the identification of novel molecular targets, drug discovery, development, and validation. The beauty of microarray analysis of gene expression is that it can be used to screen the expression of tens of thousands of genes in parallel and to identify appropriate molecular targets for therapeutic intervention. Toward identifying novel therapeutic options, natural products, notably from medicinal plants used in traditional Chinese medicine (TCM), have been thoroughly investigated. Increased knowledge of the molecular mechanisms of TCM-derived drugs could be achieved through application of modern molecular technologies including transcript profiling. In the present review, we introduce a brief introduction to the field of microarray technology and disclose its role in target identification and validation. Moreover, we provide examples for applications regarding molecular target discovery in medicinal plants derived TCM. This could be an attractive strategy for the development of novel and improved therapeutics.

  13. Macrophage Gene Expression Associated with Remodeling of the Prepartum Rat Cervix: Microarray and Pathway Analyses

    PubMed Central

    Dobyns, Abigail E.; Goyal, Ravi; Carpenter, Lauren Grisham; Freeman, Tom C.; Longo, Lawrence D.; Yellon, Steven M.

    2015-01-01

    As the critical gatekeeper for birth, prepartum remodeling of the cervix is associated with increased resident macrophages (Mφ), proinflammatory processes, and extracellular matrix degradation. This study tested the hypothesis that expression of genes unique to Mφs characterizes the prepartum from unremodeled nonpregnant cervix. Perfused cervix from prepartum day 21 postbreeding (D21) or nonpregnant (NP) rats, with or without Mφs, had RNA extracted and whole genome microarray analysis performed. By subtractive analyses, expression of 194 and 120 genes related to Mφs in the cervix from D21 rats were increased and decreased, respectively. In both D21 and NP groups, 158 and 57 Mφ genes were also more or less up- or down-regulated, respectively. Mφ gene expression patterns were most strongly correlated within groups and in 5 major clustering patterns. In the cervix from D21 rats, functional categories and canonical pathways of increased expression by Mφ gene related to extracellular matrix, cell proliferation, differentiation, as well as cell signaling. Pathways were characteristic of inflammation and wound healing, e.g., CD163, CD206, and CCR2. Signatures of only inflammation pathways, e.g., CSF1R, EMR1, and MMP12 were common to both D21 and NP groups. Thus, a novel and complex balance of Mφ genes and clusters differentiated the degraded extracellular matrix and cellular genomic activities in the cervix before birth from the unremodeled state. Predicted Mφ activities, pathways, and networks raise the possibility that expression patterns of specific genes characterize and promote prepartum remodeling of the cervix for parturition at term and with preterm labor. PMID:25811906

  14. Macrophage gene expression associated with remodeling of the prepartum rat cervix: microarray and pathway analyses.

    PubMed

    Dobyns, Abigail E; Goyal, Ravi; Carpenter, Lauren Grisham; Freeman, Tom C; Longo, Lawrence D; Yellon, Steven M

    2015-01-01

    As the critical gatekeeper for birth, prepartum remodeling of the cervix is associated with increased resident macrophages (Mφ), proinflammatory processes, and extracellular matrix degradation. This study tested the hypothesis that expression of genes unique to Mφs characterizes the prepartum from unremodeled nonpregnant cervix. Perfused cervix from prepartum day 21 postbreeding (D21) or nonpregnant (NP) rats, with or without Mφs, had RNA extracted and whole genome microarray analysis performed. By subtractive analyses, expression of 194 and 120 genes related to Mφs in the cervix from D21 rats were increased and decreased, respectively. In both D21 and NP groups, 158 and 57 Mφ genes were also more or less up- or down-regulated, respectively. Mφ gene expression patterns were most strongly correlated within groups and in 5 major clustering patterns. In the cervix from D21 rats, functional categories and canonical pathways of increased expression by Mφ gene related to extracellular matrix, cell proliferation, differentiation, as well as cell signaling. Pathways were characteristic of inflammation and wound healing, e.g., CD163, CD206, and CCR2. Signatures of only inflammation pathways, e.g., CSF1R, EMR1, and MMP12 were common to both D21 and NP groups. Thus, a novel and complex balance of Mφ genes and clusters differentiated the degraded extracellular matrix and cellular genomic activities in the cervix before birth from the unremodeled state. Predicted Mφ activities, pathways, and networks raise the possibility that expression patterns of specific genes characterize and promote prepartum remodeling of the cervix for parturition at term and with preterm labor.

  15. MAGMA: analysis of two-channel microarrays made easy.

    PubMed

    Rehrauer, Hubert; Zoller, Stefan; Schlapbach, Ralph

    2007-07-01

    The web application MAGMA provides a simple and intuitive interface to identify differentially expressed genes from two-channel microarray data. While the underlying algorithms are not superior to those of similar web applications, MAGMA is particularly user friendly and can be used without prior training. The user interface guides the novice user through the most typical microarray analysis workflow consisting of data upload, annotation, normalization and statistical analysis. It automatically generates R-scripts that document MAGMA's entire data processing steps, thereby allowing the user to regenerate all results in his local R installation. The implementation of MAGMA follows the model-view-controller design pattern that strictly separates the R-based statistical data processing, the web-representation and the application logic. This modular design makes the application flexible and easily extendible by experts in one of the fields: statistical microarray analysis, web design or software development. State-of-the-art Java Server Faces technology was used to generate the web interface and to perform user input processing. MAGMA's object-oriented modular framework makes it easily extendible and applicable to other fields and demonstrates that modern Java technology is also suitable for rather small and concise academic projects. MAGMA is freely available at www.magma-fgcz.uzh.ch.

  16. MAGMA: analysis of two-channel microarrays made easy

    PubMed Central

    Rehrauer, Hubert; Zoller, Stefan; Schlapbach, Ralph

    2007-01-01

    The web application MAGMA provides a simple and intuitive interface to identify differentially expressed genes from two-channel microarray data. While the underlying algorithms are not superior to those of similar web applications, MAGMA is particularly user friendly and can be used without prior training. The user interface guides the novice user through the most typical microarray analysis workflow consisting of data upload, annotation, normalization and statistical analysis. It automatically generates R-scripts that document MAGMA's entire data processing steps, thereby allowing the user to regenerate all results in his local R installation. The implementation of MAGMA follows the model-view-controller design pattern that strictly separates the R-based statistical data processing, the web-representation and the application logic. This modular design makes the application flexible and easily extendible by experts in one of the fields: statistical microarray analysis, web design or software development. State-of-the-art Java Server Faces technology was used to generate the web interface and to perform user input processing. MAGMA's object-oriented modular framework makes it easily extendible and applicable to other fields and demonstrates that modern Java technology is also suitable for rather small and concise academic projects. MAGMA is freely available at www.magma-fgcz.uzh.ch. PMID:17517778

  17. Analysis of variance of microarray data.

    PubMed

    Ayroles, Julien F; Gibson, Greg

    2006-01-01

    Analysis of variance (ANOVA) is an approach used to identify differentially expressed genes in complex experimental designs. It is based on testing for the significance of the magnitude of effect of two or more treatments taking into account the variance within and between treatment classes. ANOVA is a highly flexible analytical approach that allows investigators to simultaneously assess the contributions of multiple factors to gene expression variation, including technical (dye, batch) effects and biological (sex, genotype, drug, time) ones, as well as interactions between factors. This chapter provides an overview of the theory of linear mixture modeling and the sequence of steps involved in fitting gene-specific models and discusses essential features of experimental design. Commercial and open-source software for performing ANOVA is widely available.

  18. Identification of candidate genes in osteoporosis by integrated microarray analysis

    PubMed Central

    Li, J. J.; Wang, B. Q.; Yang, Y.; Li, D.

    2016-01-01

    Objectives In order to screen the altered gene expression profile in peripheral blood mononuclear cells of patients with osteoporosis, we performed an integrated analysis of the online microarray studies of osteoporosis. Methods We searched the Gene Expression Omnibus (GEO) database for microarray studies of peripheral blood mononuclear cells in patients with osteoporosis. Subsequently, we integrated gene expression data sets from multiple microarray studies to obtain differentially expressed genes (DEGs) between patients with osteoporosis and normal controls. Gene function analysis was performed to uncover the functions of identified DEGs. Results A total of three microarray studies were selected for integrated analysis. In all, 1125 genes were found to be significantly differentially expressed between osteoporosis patients and normal controls, with 373 upregulated and 752 downregulated genes. Positive regulation of the cellular amino metabolic process (gene ontology (GO): 0033240, false discovery rate (FDR) = 1.00E + 00) was significantly enriched under the GO category for biological processes, while for molecular functions, flavin adenine dinucleotide binding (GO: 0050660, FDR = 3.66E-01) and androgen receptor binding (GO: 0050681, FDR = 6.35E-01) were significantly enriched. DEGs were enriched in many osteoporosis-related signalling pathways, including those of mitogen-activated protein kinase (MAPK) and calcium. Protein-protein interaction (PPI) network analysis showed that the significant hub proteins contained ubiquitin specific peptidase 9, X-linked (Degree = 99), ubiquitin specific peptidase 19 (Degree = 57) and ubiquitin conjugating enzyme E2 B (Degree = 57). Conclusion Analysis of gene function of identified differentially expressed genes may expand our understanding of fundamental mechanisms leading to osteoporosis. Moreover, significantly enriched pathways, such as MAPK and calcium, may involve in osteoporosis through osteoblastic differentiation and

  19. Microarray Analysis of Pneumococcal Gene Expression during Invasive Disease

    PubMed Central

    Orihuela, Carlos J.; Radin, Jana N.; Sublett, Jack E.; Gao, Geli; Kaushal, Deepak; Tuomanen, Elaine I.

    2004-01-01

    Streptococcus pneumoniae is a leading cause of invasive bacterial disease. This is the first study to examine the expression of S. pneumoniae genes in vivo by using whole-genome microarrays available from The Institute for Genomic Research. Total RNA was collected from pneumococci isolated from infected blood, infected cerebrospinal fluid, and bacteria attached to a pharyngeal epithelial cell line in vitro. Microarray analysis of pneumococcal genes expressed in these models identified body site-specific patterns of expression for virulence factors, transporters, transcription factors, translation-associated proteins, metabolism, and genes with unknown function. Contributions to virulence predicted for several unknown genes with enhanced expression in vivo were confirmed by insertion duplication mutagenesis and challenge of mice with the mutants. Finally, we cross-referenced our results with previous studies that used signature-tagged mutagenesis and differential fluorescence induction to identify genes that are potentially required by a broad range of pneumococcal strains for invasive disease. PMID:15385455

  20. Chromatin immunoprecipitation and microarray-based analysis of protein location

    PubMed Central

    Lee, Tong Ihn; Johnstone, Sarah E; Young, Richard A

    2010-01-01

    Genome-wide location analysis, also known as ChIP-Chip, combines chromatin immunoprecipitation and DNA microarray analysis to identify protein-DNA interactions that occur in living cells. Protein-DNA interactions are captured in vivo by chemical crosslinking. Cell lysis, DNA fragmentation and immunoaffinity purification of the desired protein will co-purify DNA fragments that are associated with that protein. The enriched DNA population is then labeled, combined with a differentially labeled reference sample and applied to DNA microarrays to detect enriched signals. Various computational and bioinformatic approaches are then applied to normalize the enriched and reference channels, to connect signals to the portions of the genome that are represented on the DNA microarrays, to provide confidence metrics and to generate maps of protein-genome occupancy. Here, we describe the experimental protocols that we use from crosslinking of cells to hybridization of labeled material, together with insights into the aspects of these protocols that influence the results. These protocols require approximately 1 week to complete once sufficient numbers of cells have been obtained, and have been used to produce robust, high-quality ChIP-chip results in many different cell and tissue types. PMID:17406303

  1. Automated target preparation for microarray-based gene expression analysis.

    PubMed

    Raymond, Frédéric; Metairon, Sylviane; Borner, Roland; Hofmann, Markus; Kussmann, Martin

    2006-09-15

    DNA microarrays have rapidly evolved toward a platform for massively paralleled gene expression analysis. Despite its widespread use, the technology has been criticized to be vulnerable to technical variability. Addressing this issue, recent comparative, interplatform, and interlaboratory studies have revealed that, given defined procedures for "wet lab" experiments and data processing, a satisfactory reproducibility and little experimental variability can be achieved. In view of these advances in standardization, the requirement for uniform sample preparation becomes evident, especially if a microarray platform is used as a facility, i.e., by different users working in the laboratory. While one option to reduce technical variability is to dedicate one laboratory technician to all microarray studies, we have decided to automate the entire RNA sample preparation implementing a liquid handling system coupled to a thermocycler and a microtiter plate reader. Indeed, automated RNA sample preparation prior to chip analysis enables (1) the reduction of experimentally caused result variability, (2) the separation of (important) biological variability from (undesired) experimental variation, and (3) interstudy comparison of gene expression results. Our robotic platform can process up to 24 samples in parallel, using an automated sample preparation method that produces high-quality biotin-labeled cRNA ready to be hybridized on Affymetrix GeneChips. The results show that the technical interexperiment variation is less pronounced than with manually prepared samples. Moreover, experiments using the same starting material showed that the automated process yields a good reproducibility between samples.

  2. Linkage and microarray analyses of susceptibility genes in ACI/Seg rats: a model for prostate cancers in the aged.

    PubMed

    Yamashita, Satoshi; Suzuki, Shugo; Nomoto, Tomoko; Kondo, Yasushi; Wakazono, Kuniko; Tsujino, Yoshimi; Sugimura, Takashi; Shirai, Tomoyuki; Homma, Yukio; Ushijima, Toshikazu

    2005-04-01

    ACI/Seg (ACI) rats develop prostate cancers spontaneously with aging, similar to humans. Here, to identify genes involved in prostate cancer susceptibility, we did linkage analysis and oligonucleotide microarray analysis. Linkage analysis was done using 118 effective rats, and prostate cancer susceptibility 1 (Pcs1), whose ACI allele dominantly induced prostate cancers, was mapped on chromosome 19 [logarithm of odds (LOD) score of 5.0]. PC resistance 1 (Pcr1), whose ACI allele dominantly and paradoxically suppressed the size of prostate cancers, was mapped on chromosome 2 (LOD score of 5.0). When linkage analysis was done in 51 rats with single or no macroscopic testicular tumors, which had larger prostates and higher testosterone levels than those with bilateral testicular tumors, Pcs2 and Pcr2 were mapped on chromosomes 20 and 1, respectively. By oligonucleotide microarray analysis with 8,800 probe sets and confirmation by quantitative reverse transcription-PCR, only two genes within these four loci were found to be differentially expressed >1.8-fold. Membrane metalloendopeptidase (Mme), known to inhibit androgen-independent growth of prostate cancers, on Pcr1 was expressed 2.0- to 5.5-fold higher in the ACI prostate, in accordance with its paradoxical effect. Cdkn1a on Pcs2 was expressed 1.5- to 4.5-fold lower in the ACI prostate. Additionally, genes responsible for testicular tumors and unilateral renal agenesis were mapped on chromosomes 11 and 14, respectively. These results showed that prostate cancer susceptibility of ACI rats involves at least four loci, and suggested Mme and Cdkn1a as candidates for Pcr1 and Pcs2.

  3. Microarray analysis of gene expression profiles in ripening pineapple fruits.

    PubMed

    Koia, Jonni H; Moyle, Richard L; Botella, Jose R

    2012-12-18

    Pineapple (Ananas comosus) is a tropical fruit crop of significant commercial importance. Although the physiological changes that occur during pineapple fruit development have been well characterized, little is known about the molecular events that occur during the fruit ripening process. Understanding the molecular basis of pineapple fruit ripening will aid the development of new varieties via molecular breeding or genetic modification. In this study we developed a 9277 element pineapple microarray and used it to profile gene expression changes that occur during pineapple fruit ripening. Microarray analyses identified 271 unique cDNAs differentially expressed at least 1.5-fold between the mature green and mature yellow stages of pineapple fruit ripening. Among these 271 sequences, 184 share significant homology with genes encoding proteins of known function, 53 share homology with genes encoding proteins of unknown function and 34 share no significant homology with any database accession. Of the 237 pineapple sequences with homologs, 160 were up-regulated and 77 were down-regulated during pineapple fruit ripening. DAVID Functional Annotation Cluster (FAC) analysis of all 237 sequences with homologs revealed confident enrichment scores for redox activity, organic acid metabolism, metalloenzyme activity, glycolysis, vitamin C biosynthesis, antioxidant activity and cysteine peptidase activity, indicating the functional significance and importance of these processes and pathways during pineapple fruit development. Quantitative real-time PCR analysis validated the microarray expression results for nine out of ten genes tested. This is the first report of a microarray based gene expression study undertaken in pineapple. Our bioinformatic analyses of the transcript profiles have identified a number of genes, processes and pathways with putative involvement in the pineapple fruit ripening process. This study extends our knowledge of the molecular basis of pineapple fruit

  4. Microarray analysis of gene expression profiles in ripening pineapple fruits

    PubMed Central

    2012-01-01

    Background Pineapple (Ananas comosus) is a tropical fruit crop of significant commercial importance. Although the physiological changes that occur during pineapple fruit development have been well characterized, little is known about the molecular events that occur during the fruit ripening process. Understanding the molecular basis of pineapple fruit ripening will aid the development of new varieties via molecular breeding or genetic modification. In this study we developed a 9277 element pineapple microarray and used it to profile gene expression changes that occur during pineapple fruit ripening. Results Microarray analyses identified 271 unique cDNAs differentially expressed at least 1.5-fold between the mature green and mature yellow stages of pineapple fruit ripening. Among these 271 sequences, 184 share significant homology with genes encoding proteins of known function, 53 share homology with genes encoding proteins of unknown function and 34 share no significant homology with any database accession. Of the 237 pineapple sequences with homologs, 160 were up-regulated and 77 were down-regulated during pineapple fruit ripening. DAVID Functional Annotation Cluster (FAC) analysis of all 237 sequences with homologs revealed confident enrichment scores for redox activity, organic acid metabolism, metalloenzyme activity, glycolysis, vitamin C biosynthesis, antioxidant activity and cysteine peptidase activity, indicating the functional significance and importance of these processes and pathways during pineapple fruit development. Quantitative real-time PCR analysis validated the microarray expression results for nine out of ten genes tested. Conclusions This is the first report of a microarray based gene expression study undertaken in pineapple. Our bioinformatic analyses of the transcript profiles have identified a number of genes, processes and pathways with putative involvement in the pineapple fruit ripening process. This study extends our knowledge of the

  5. D-MaPs - DNA-microarray projects: Web-based software for multi-platform microarray analysis

    PubMed Central

    2009-01-01

    The web application D-Maps provides a user-friendly interface to researchers performing studies based on microarrays. The program was developed to manage and process one- or two-color microarray data obtained from several platforms (currently, GeneTAC, ScanArray, CodeLink, NimbleGen and Affymetrix). Despite the availability of many algorithms and many software programs designed to perform microarray analysis on the internet, these usually require sophisticated knowledge of mathematics, statistics and computation. D-maps was developed to overcome the requirement of high performance computers or programming experience. D-Maps performs raw data processing, normalization and statistical analysis, allowing access to the analyzed data in text or graphical format. An original feature presented by D-Maps is GEO (Gene Expression Omnibus) submission format service. The D-MaPs application was already used for analysis of oligonucleotide microarrays and PCR-spotted arrays (one- and two-color, laser and light scanner). In conclusion, D-Maps is a valuable tool for microarray research community, especially in the case of groups without a bioinformatic core. PMID:21637530

  6. D-MaPs - DNA-microarray projects: Web-based software for multi-platform microarray analysis.

    PubMed

    Carazzolle, Marcelo F; Herig, Taís S; Deckmann, Ana C; Pereira, Gonçalo A G

    2009-07-01

    The web application D-Maps provides a user-friendly interface to researchers performing studies based on microarrays. The program was developed to manage and process one- or two-color microarray data obtained from several platforms (currently, GeneTAC, ScanArray, CodeLink, NimbleGen and Affymetrix). Despite the availability of many algorithms and many software programs designed to perform microarray analysis on the internet, these usually require sophisticated knowledge of mathematics, statistics and computation. D-maps was developed to overcome the requirement of high performance computers or programming experience. D-Maps performs raw data processing, normalization and statistical analysis, allowing access to the analyzed data in text or graphical format. An original feature presented by D-Maps is GEO (Gene Expression Omnibus) submission format service. The D-MaPs application was already used for analysis of oligonucleotide microarrays and PCR-spotted arrays (one- and two-color, laser and light scanner). In conclusion, D-Maps is a valuable tool for microarray research community, especially in the case of groups without a bioinformatic core.

  7. Administered chrysanthemum flower oil attenuates hyperuricemia: mechanism of action as revealed by DNA microarray analysis.

    PubMed

    Honda, Shinichi; Kawamoto, Seiji; Tanaka, Hozumi; Kishida, Hideyuki; Kitagawa, Masayasu; Nakai, Yuji; Abe, Keiko; Hirata, Dai

    2014-01-01

    We applied Chrysanthemum flower oil (CFO) to a hyperuricemia model by feeding rats a hyperuricemia-inducing diet (HID) and investigated its effect on serum uric acid (SUA) levels and its mode of action. CFO is the oily fraction that contains polyphenols derived from chrysanthemum flowers. Oral administration of CFO to HID-fed rats significantly decreased their SUA levels. It also inhibited xanthine oxidase activities in the liver and increased urine uric acid levels. The effects of CFO on the renal gene expressions that accompanied the induction of hyperuricemia were comprehensively confirmed by DNA microarray analysis. The analysis showed up-regulation of those genes for uric acid excretion by CFO administration. These results suggest that CFO suppresses the increase in SUA levels via two mechanisms: suppression of uric acid production by inhibition of xanthine oxidase in the liver and acceleration of its excretion by up-regulation of uric acid transporter genes in the kidney.

  8. [Design and realization of a microarray data analysis platform].

    PubMed

    Sun, Xian-He; Guo, Yun-Bo; Liu, Na; Ma, Li; Deng, Qin-Kai

    2011-04-01

    To design a platform for microarray data analysis and processing in the browser/server mode running in Linux operating system. Based on the Apache HTTP server, the platform, programmed with Perl language, integrated R language and Bioconductor packages for processing and analysis of the input data of oligonucleotide arrays and two-color spotted arrays. Users were allowed to submit data and parameter configurations to the platform via the web page, and the results of analysis were also returned via the web page. With an easy operation and high performance, the platform fulfilled the functions of processing, quality assessment, biological annotation and statistical analysis of the data from oligonucleotide arrays and two-color spotted arrays. Using the platform, we analyzed the gene expression profiles in Mtb-stimulated macrophages of three clinical phenotypes, namely latent TB (LTB), pulmonary (PTB) and meningeal (TBM), and obtained valuable clues for identifying tuberculosis susceptibility genes. We also analyzed the effect of INH treatment on Mycobacterium tuberculosis gene expression in various dormancy models, such as hypoxia and KatG mutant, and found that a set of genes responded to INH treatment during exponential growth but not in dormancy, and that the overall number of differentially regulated genes was reduced in the cells in low metabolic state. The platform we have constructed integrates comprehensive resources, and with a user-friendly interface, allows direct result visualization to facilitate microarray data analysis.

  9. Microarray analysis of R-gene-mediated resistance to viruses.

    PubMed

    Ishihara, Takeaki; Sato, Yukiyo; Takahashi, Hideki

    2015-01-01

    The complex process for host-plant resistance to viruses is precisely regulated by a number of genes and signaling compounds. Thus, global gene expression analysis can provide a powerful tool to grasp the complex molecular network for resistance to viruses. The procedures for comparative global gene expression profiling of virus-resistant and control plants by microarray analysis include RNA extraction, cDNA synthesis, cRNA labeling, hybridization, array scanning, and data mining steps. There are several platforms for the microarray analysis. Commercial services for the steps from cDNA synthesis to array scanning are now widely available; however, the data manipulation step is highly dependent on the experimental design and research focus. The protocols presented here are optimized for analyzing global gene expression during the R gene-conferred defense response using commercial oligonucleotide-based arrays. We also demonstrate a technique to screen for differentially expressed genes using Excel software and a simple Internet tool-based data mining approach for characterizing the identified genes.

  10. Supervised group Lasso with applications to microarray data analysis

    PubMed Central

    Ma, Shuangge; Song, Xiao; Huang, Jian

    2007-01-01

    Background A tremendous amount of efforts have been devoted to identifying genes for diagnosis and prognosis of diseases using microarray gene expression data. It has been demonstrated that gene expression data have cluster structure, where the clusters consist of co-regulated genes which tend to have coordinated functions. However, most available statistical methods for gene selection do not take into consideration the cluster structure. Results We propose a supervised group Lasso approach that takes into account the cluster structure in gene expression data for gene selection and predictive model building. For gene expression data without biological cluster information, we first divide genes into clusters using the K-means approach and determine the optimal number of clusters using the Gap method. The supervised group Lasso consists of two steps. In the first step, we identify important genes within each cluster using the Lasso method. In the second step, we select important clusters using the group Lasso. Tuning parameters are determined using V-fold cross validation at both steps to allow for further flexibility. Prediction performance is evaluated using leave-one-out cross validation. We apply the proposed method to disease classification and survival analysis with microarray data. Conclusion We analyze four microarray data sets using the proposed approach: two cancer data sets with binary cancer occurrence as outcomes and two lymphoma data sets with survival outcomes. The results show that the proposed approach is capable of identifying a small number of influential gene clusters and important genes within those clusters, and has better prediction performance than existing methods. PMID:17316436

  11. DNA Microarray Data Analysis: A Novel Biclustering Algorithm Approach

    NASA Astrophysics Data System (ADS)

    Tchagang, Alain B.; Tewfik, Ahmed H.

    2006-12-01

    Biclustering algorithms refer to a distinct class of clustering algorithms that perform simultaneous row-column clustering. Biclustering problems arise in DNA microarray data analysis, collaborative filtering, market research, information retrieval, text mining, electoral trends, exchange analysis, and so forth. When dealing with DNA microarray experimental data for example, the goal of biclustering algorithms is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this study, we develop novel biclustering algorithms using basic linear algebra and arithmetic tools. The proposed biclustering algorithms can be used to search for all biclusters with constant values, biclusters with constant values on rows, biclusters with constant values on columns, and biclusters with coherent values from a set of data in a timely manner and without solving any optimization problem. We also show how one of the proposed biclustering algorithms can be adapted to identify biclusters with coherent evolution. The algorithms developed in this study discover all valid biclusters of each type, while almost all previous biclustering approaches will miss some.

  12. Microarray analysis of mRNAs: experimental design and data analysis fundamentals.

    PubMed

    Mehta, Jai Prakash

    2011-01-01

    Microarray technology has made it possible to quantify gene expression of thousands of genes in a single experiment. With the technological advancement, it is now possible to quantify expression of all known genes using a single microarray chip. With this volume of data and the possibility of improper quantification of expression beyond our control, the challenge lies in appropriate experimental design and the data analysis.This chapter describes the different types of experimental design for experiments involving microarray analysis (with their specific advantages and disadvantages). It considers the optimum number of replicates for a particular type of experiment. Additionally, this chapter describes the fundamentals of data analysis and the data analysis pipeline to be followed in most common types of microarray experiment.

  13. Structural analysis of hepatitis C RNA genome using DNA microarrays

    PubMed Central

    Martell, María; Briones, Carlos; de Vicente, Aránzazu; Piron, María; Esteban, Juan I.; Esteban, Rafael; Guardia, Jaime; Gómez, Jordi

    2004-01-01

    Many studies have tried to identify specific nucleotide sequences in the quasispecies of hepatitis C virus (HCV) that determine resistance or sensitivity to interferon (IFN) therapy, unfortunately without conclusive results. Although viral proteins represent the most evident phenotype of the virus, genomic RNA sequences determine secondary and tertiary structures which are also part of the viral phenotype and can be involved in important biological roles. In this work, a method of RNA structure analysis has been developed based on the hybridization of labelled HCV transcripts to microarrays of complementary DNA oligonucleotides. Hybridizations were carried out at non-denaturing conditions, using appropriate temperature and buffer composition to allow binding to the immobilized probes of the RNA transcript without disturbing its secondary/tertiary structural motifs. Oligonucleotides printed onto the microarray covered the entire 5′ non-coding region (5′NCR), the first three-quarters of the core region, the E2–NS2 junction and the first 400 nt of the NS3 region. We document the use of this methodology to analyse the structural degree of a large region of HCV genomic RNA in two genotypes associated with different responses to IFN treatment. The results reported here show different structural degree along the genome regions analysed, and differential hybridization patterns for distinct genotypes in NS2 and NS3 HCV regions. PMID:15247323

  14. Exon Microarray Analysis of Human Dorsolateral Prefrontal Cortex in Alcoholism

    PubMed Central

    Manzardo, Ann M.; Gunewardena, Sumedha; Wang, Kun; Butler, Merlin G.

    2014-01-01

    Background Alcohol abuse is associated with cellular and biochemical disturbances that impact upon protein and nucleic acid synthesis, brain development, function and behavioral responses. To further characterize the genetic influences in alcoholism and the effects of alcohol consumption on gene expression, we used a highly sensitive exon microarray to examine mRNA expression in human frontal cortex of alcoholics and control males. Methods Messenger RNA was isolated from the dorsolateral prefrontal cortex (dlPFC, Brodmann area 9) of 7 adult Alcoholic (6 males, 1 female, mean age 48 years) and 7 matched controls. Affymetrix Human Exon 1.0 ST Array was performed according to standard procedures and the results analyzed at the gene level. Microarray findings were validated using qRT-PCR, and the ontology of disturbed genes characterized using Ingenuity Pathway Analysis (IPA). Results Decreased mRNA expression was observed for genes involved in cellular adhesion (e.g., CTNNA3, ITGA2), transport (e.g., TF, ABCA8), nervous system development (e.g., LRP2, UGT8, GLDN) and signaling (e.g., RASGRP, LGR5) with influence over lipid and myelin synthesis (e.g., ASPA, ENPP2, KLK6). IPA identified disturbances in network functions associated with neurological disease, and development including cellular assembly and organization impacting on psychological disorders. Conclusions Our data in alcoholism support a reduction in expression of dlPFC mRNA for genes involved with neuronal growth, differentiation and signaling that targets white matter of the brain. PMID:24890784

  15. Portable system for microbial sample preparation and oligonucleotide microarray analysis.

    SciTech Connect

    Bavykin, S. G.; Akowski, J. P.; Zakhariev, V. M.; Barsky, V. E.; Mirzabekov, A. D.; Perov, A. N.; Biochip Technology Center; Engelhardt Inst. of Molecular Biology

    2001-02-01

    We have developed a three-component system for microbial identification that consists of (i) a universal syringe-operated silica minicolumn for successive DNA and RNA isolation, fractionation, fragmentation, fluorescent labeling, and removal of excess free label and short oligonucleotides; (ii) microarrays of immobilized oligonucleotide probes for 16S rRNA identification; and (iii) a portable battery-powered device for imaging the hybridization of fluorescently labeled RNA fragments with the arrays. The minicolumn combines a guanidine thiocyanate method of nucleic acid isolation with a newly developed hydroxyl radical-based technique for DNA and RNA labeling and fragmentation. DNA and RNA can also be fractionated through differential binding of double- and single-stranded forms of nucleic acids to the silica. The procedure involves sequential washing of the column with different solutions. No vacuum filtration steps, phenol extraction, or centrifugation is required. After hybridization, the overall fluorescence pattern is captured as a digital image or as a Polaroid photo. This three-component system was used to discriminate Escherichia coli, Bacillus subtilis, Bacillus thuringiensis, and human HL60 cells. The procedure is rapid: beginning with whole cells, it takes approximately 25 min to obtain labeled DNA and RNA samples and an additional 25 min to hybridize and acquire the microarray image using a stationary image analysis system or the portable imager.

  16. Assessment of gene set analysis methods based on microarray data.

    PubMed

    Alavi-Majd, Hamid; Khodakarim, Soheila; Zayeri, Farid; Rezaei-Tavirani, Mostafa; Tabatabaei, Seyyed Mohammad; Heydarpour-Meymeh, Maryam

    2014-01-25

    Gene set analysis (GSA) incorporates biological information into statistical knowledge to identify gene sets differently expressed between two or more phenotypes. It allows us to gain an insight into the functional working mechanism of cells beyond the detection of differently expressed gene sets. In order to evaluate the competence of GSA approaches, three self-contained GSA approaches with different statistical methods were chosen; Category, Globaltest and Hotelling's T(2) together with their assayed power to identify the differences expressed via simulation and real microarray data. The Category does not take care of the correlation structure, while the other two deal with correlations. In order to perform these methods, R and Bioconductor were used. Furthermore, venous thromboembolism and acute lymphoblastic leukemia microarray data were applied. The results of three GSAs showed that the competence of these methods depends on the distribution of gene expression in a dataset. It is very important to assay the distribution of gene expression data before choosing the GSA method to identify gene sets differently expressed between phenotypes. On the other hand, assessment of common genes among significant gene sets indicated that there was a significant agreement between the result of GSA and the findings of biologists. © 2013 Elsevier B.V. All rights reserved.

  17. Analysis of recursive gene selection approaches from microarray data.

    PubMed

    Li, Fan; Yang, Yiming

    2005-10-01

    Finding a small subset of most predictive genes from microarray for disease prediction is a challenging problem. Support vector machines (SVMs) have been found to be successful with a recursive procedure in selecting important genes for cancer prediction. However, it is not well understood how much of the success depends on the choice of the specific classifier and how much on the recursive procedure. We answer this question by examining multiple classifers [SVM, ridge regression (RR) and Rocchio] with feature selection in recursive and non-recursive settings on three DNA microarray datasets (ALL-AML Leukemia data, Breast Cancer data and GCM data). We found recursive RR most effective. On the AML-ALL dataset, it achieved zero error rate on the test set using only three genes (selected from over 7000), which is more encouraging than the best published result (zero error rate using 8 genes by recursive SVM). On the Breast Cancer dataset and the two largest categories of the GCM dataset, the results achieved by recursive RR are also very encouraging. A further analysis of the experimental results shows that different classifiers penalize redundant features to different extent and this property plays an important role in the recursive feature selection process. RR classifier tends to penalize redundant features to a much larger extent than the SVM does. This may be the reason why recursive RR has a better performance in selecting genes.

  18. Using Kepler for Tool Integration in Microarray Analysis Workflows.

    PubMed

    Gan, Zhuohui; Stowe, Jennifer C; Altintas, Ilkay; McCulloch, Andrew D; Zambon, Alexander C

    Increasing numbers of genomic technologies are leading to massive amounts of genomic data, all of which requires complex analysis. More and more bioinformatics analysis tools are being developed by scientist to simplify these analyses. However, different pipelines have been developed using different software environments. This makes integrations of these diverse bioinformatics tools difficult. Kepler provides an open source environment to integrate these disparate packages. Using Kepler, we integrated several external tools including Bioconductor packages, AltAnalyze, a python-based open source tool, and R-based comparison tool to build an automated workflow to meta-analyze both online and local microarray data. The automated workflow connects the integrated tools seamlessly, delivers data flow between the tools smoothly, and hence improves efficiency and accuracy of complex data analyses. Our workflow exemplifies the usage of Kepler as a scientific workflow platform for bioinformatics pipelines.

  19. [Software development in data analysis and mining for cDNA microarray].

    PubMed

    Wu, Bin; Wang, Jianguo; Wang, Miqu

    2007-12-01

    Data analysis and mining is a key issue to microarray technology and is usually implemented through software development. This paper summarizes the state-of-art software development in cDNA microarray data analysis and mining. The updated software developments are discussed in three stages: data inquisition from cDNA microarray tests, statistical treatment of cDNA data and data mining from gene network.

  20. An Introduction to MAMA (Meta-Analysis of MicroArray data) System.

    PubMed

    Zhang, Zhe; Fenstermacher, David

    2005-01-01

    Analyzing microarray data across multiple experiments has been proven advantageous. To support this kind of analysis, we are developing a software system called MAMA (Meta-Analysis of MicroArray data). MAMA utilizes a client-server architecture with a relational database on the server-side for the storage of microarray datasets collected from various resources. The client-side is an application running on the end user's computer that allows the user to manipulate microarray data and analytical results locally. MAMA implementation will integrate several analytical methods, including meta-analysis within an open-source framework offering other developers the flexibility to plug in additional statistical algorithms.

  1. Optimization of Cyanine Dye Stability and Analysis of FRET Interaction on DNA Microarrays.

    PubMed

    von der Haar, Marcel; Heuer, Christopher; Pähler, Martin; von der Haar, Kathrin; Lindner, Patrick; Scheper, Thomas; Stahl, Frank

    2016-11-30

    The application of DNA microarrays for high throughput analysis of genetic regulation is often limited by the fluorophores used as markers. The implementation of multi-scan techniques is limited by the fluorophores' susceptibility to photobleaching when exposed to the scanner laser light. This paper presents combined mechanical and chemical strategies which enhance the photostability of cyanine 3 and cyanine 5 as part of solid state DNA microarrays. These strategies are based on scanning the microarrays while the hybridized DNA is still in an aqueous solution with the presence of a reductive/oxidative system (ROXS). Furthermore, the experimental setup allows for the analysis and eventual normalization of Förster-resonance-energy-transfer (FRET) interaction of cyanine-3/cyanine-5 dye combinations on the microarray. These findings constitute a step towards standardization of microarray experiments and analysis and may help to increase the comparability of microarray experiment results between labs.

  2. Optimization of Cyanine Dye Stability and Analysis of FRET Interaction on DNA Microarrays

    PubMed Central

    von der Haar, Marcel; Heuer, Christopher; Pähler, Martin; von der Haar, Kathrin; Lindner, Patrick; Scheper, Thomas; Stahl, Frank

    2016-01-01

    The application of DNA microarrays for high throughput analysis of genetic regulation is often limited by the fluorophores used as markers. The implementation of multi-scan techniques is limited by the fluorophores’ susceptibility to photobleaching when exposed to the scanner laser light. This paper presents combined mechanical and chemical strategies which enhance the photostability of cyanine 3 and cyanine 5 as part of solid state DNA microarrays. These strategies are based on scanning the microarrays while the hybridized DNA is still in an aqueous solution with the presence of a reductive/oxidative system (ROXS). Furthermore, the experimental setup allows for the analysis and eventual normalization of Förster-resonance-energy-transfer (FRET) interaction of cyanine-3/cyanine-5 dye combinations on the microarray. These findings constitute a step towards standardization of microarray experiments and analysis and may help to increase the comparability of microarray experiment results between labs. PMID:27916881

  3. Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes

    PubMed Central

    Warnat, Patrick; Eils, Roland; Brors, Benedikt

    2005-01-01

    Background The extensive use of DNA microarray technology in the characterization of the cell transcriptome is leading to an ever increasing amount of microarray data from cancer studies. Although similar questions for the same type of cancer are addressed in these different studies, a comparative analysis of their results is hampered by the use of heterogeneous microarray platforms and analysis methods. Results In contrast to a meta-analysis approach where results of different studies are combined on an interpretative level, we investigate here how to directly integrate raw microarray data from different studies for the purpose of supervised classification analysis. We use median rank scores and quantile discretization to derive numerically comparable measures of gene expression from different platforms. These transformed data are then used for training of classifiers based on support vector machines. We apply this approach to six publicly available cancer microarray gene expression data sets, which consist of three pairs of studies, each examining the same type of cancer, i.e. breast cancer, prostate cancer or acute myeloid leukemia. For each pair, one study was performed by means of cDNA microarrays and the other by means of oligonucleotide microarrays. In each pair, high classification accuracies (> 85%) were achieved with training and testing on data instances randomly chosen from both data sets in a cross-validation analysis. To exemplify the potential of this cross-platform classification analysis, we use two leukemia microarray data sets to show that important genes with regard to the biology of leukemia are selected in an integrated analysis, which are missed in either single-set analysis. Conclusion Cross-platform classification of multiple cancer microarray data sets yields discriminative gene expression signatures that are found and validated on a large number of microarray samples, generated by different laboratories and microarray technologies

  4. The application of phenotypic microarray analysis to anti-fungal drug development.

    PubMed

    Greetham, Darren; Lappin, David F; Rajendran, Ranjith; O'Donnell, Lindsay; Sherry, Leighann; Ramage, Gordon; Nile, Christopher

    2017-03-01

    Candida albicans metabolic activity in the presence and absence of acetylcholine was measured using phenotypic microarray analysis. Acetylcholine inhibited C. albicans biofilm formation by slowing metabolism independent of biofilm forming capabilities. Phenotypic microarray analysis can therefore be used for screening compound libraries for novel anti-fungal drugs and measuring antifungal resistance.

  5. Experimental Approaches to Microarray Analysis of Tumor Samples

    ERIC Educational Resources Information Center

    Furge, Laura Lowe; Winter, Michael B.; Meyers, Jacob I.; Furge, Kyle A.

    2008-01-01

    Comprehensive measurement of gene expression using high-density nucleic acid arrays (i.e. microarrays) has become an important tool for investigating the molecular differences in clinical and research samples. Consequently, inclusion of discussion in biochemistry, molecular biology, or other appropriate courses of microarray technologies has…

  6. Experimental Approaches to Microarray Analysis of Tumor Samples

    ERIC Educational Resources Information Center

    Furge, Laura Lowe; Winter, Michael B.; Meyers, Jacob I.; Furge, Kyle A.

    2008-01-01

    Comprehensive measurement of gene expression using high-density nucleic acid arrays (i.e. microarrays) has become an important tool for investigating the molecular differences in clinical and research samples. Consequently, inclusion of discussion in biochemistry, molecular biology, or other appropriate courses of microarray technologies has…

  7. Exon microarray analysis of human dorsolateral prefrontal cortex in alcoholism.

    PubMed

    Manzardo, Ann M; Gunewardena, Sumedha; Wang, Kun; Butler, Merlin G

    2014-06-01

    Alcohol abuse is associated with cellular and biochemical disturbances that impact upon protein and nucleic acid synthesis, brain development, function, and behavioral responses. To further characterize the genetic influences in alcoholism and the effects of alcohol consumption on gene expression, we used a highly sensitive exon microarray to examine mRNA expression in human frontal cortex of alcoholics and control males. Messenger RNA was isolated from the dorsolateral prefrontal cortex (dlPFC; Brodmann area 9) of 7 adult alcoholic (6 males, 1 female, mean age 49 years) and 7 matched controls. Affymetrix Human Exon 1.0 ST array was performed according to standard procedures and the results analyzed at the gene level. Microarray findings were validated using quantitative reverse transcription polymerase chain reaction, and the ontology of disturbed genes characterized using Ingenuity Pathway Analysis (IPA). Decreased mRNA expression was observed for genes involved in cellular adhesion (e.g., CTNNA3, ITGA2), transport (e.g., TF, ABCA8), nervous system development (e.g., LRP2, UGT8, GLDN), and signaling (e.g., RASGRP3, LGR5) with influence over lipid and myelin synthesis (e.g., ASPA, ENPP2, KLK6). IPA identified disturbances in network functions associated with neurological disease and development including cellular assembly and organization impacting on psychological disorders. Our data in alcoholism support a reduction in expression of dlPFC mRNA for genes involved with neuronal growth, differentiation, and signaling that targets white matter of the brain. Copyright © 2014 by the Research Society on Alcoholism.

  8. Application of microarray analysis on computer cluster and cloud platforms.

    PubMed

    Bernau, C; Boulesteix, A-L; Knaus, J

    2013-01-01

    Analysis of recent high-dimensional biological data tends to be computationally intensive as many common approaches such as resampling or permutation tests require the basic statistical analysis to be repeated many times. A crucial advantage of these methods is that they can be easily parallelized due to the computational independence of the resampling or permutation iterations, which has induced many statistics departments to establish their own computer clusters. An alternative is to rent computing resources in the cloud, e.g. at Amazon Web Services. In this article we analyze whether a selection of statistical projects, recently implemented at our department, can be efficiently realized on these cloud resources. Moreover, we illustrate an opportunity to combine computer cluster and cloud resources. In order to compare the efficiency of computer cluster and cloud implementations and their respective parallelizations we use microarray analysis procedures and compare their runtimes on the different platforms. Amazon Web Services provide various instance types which meet the particular needs of the different statistical projects we analyzed in this paper. Moreover, the network capacity is sufficient and the parallelization is comparable in efficiency to standard computer cluster implementations. Our results suggest that many statistical projects can be efficiently realized on cloud resources. It is important to mention, however, that workflows can change substantially as a result of a shift from computer cluster to cloud computing.

  9. GENEVESTIGATOR. Arabidopsis Microarray Database and Analysis Toolbox1[w

    PubMed Central

    Zimmermann, Philip; Hirsch-Hoffmann, Matthias; Hennig, Lars; Gruissem, Wilhelm

    2004-01-01

    High-throughput gene expression analysis has become a frequent and powerful research tool in biology. At present, however, few software applications have been developed for biologists to query large microarray gene expression databases using a Web-browser interface. We present GENEVESTIGATOR, a database and Web-browser data mining interface for Affymetrix GeneChip data. Users can query the database to retrieve the expression patterns of individual genes throughout chosen environmental conditions, growth stages, or organs. Reversely, mining tools allow users to identify genes specifically expressed during selected stresses, growth stages, or in particular organs. Using GENEVESTIGATOR, the gene expression profiles of more than 22,000 Arabidopsis genes can be obtained, including those of 10,600 currently uncharacterized genes. The objective of this software application is to direct gene functional discovery and design of new experiments by providing plant biologists with contextual information on the expression of genes. The database and analysis toolbox is available as a community resource at https://www.genevestigator.ethz.ch. PMID:15375207

  10. A comparative analysis of DNA barcode microarray feature size

    PubMed Central

    Ammar, Ron; Smith, Andrew M; Heisler, Lawrence E; Giaever, Guri; Nislow, Corey

    2009-01-01

    Background Microarrays are an invaluable tool in many modern genomic studies. It is generally perceived that decreasing the size of microarray features leads to arrays with higher resolution (due to greater feature density), but this increase in resolution can compromise sensitivity. Results We demonstrate that barcode microarrays with smaller features are equally capable of detecting variation in DNA barcode intensity when compared to larger feature sizes within a specific microarray platform. The barcodes used in this study are the well-characterized set derived from the Yeast KnockOut (YKO) collection used for screens of pooled yeast (Saccharomyces cerevisiae) deletion mutants. We treated these pools with the glycosylation inhibitor tunicamycin as a test compound. Three generations of barcode microarrays at 30, 8 and 5 μm features sizes independently identified the primary target of tunicamycin to be ALG7. Conclusion We show that the data obtained with 5 μm feature size is of comparable quality to the 30 μm size and propose that further shrinking of features could yield barcode microarrays with equal or greater resolving power and, more importantly, higher density. PMID:19825181

  11. Design and analysis of mismatch probes for long oligonucleotide microarrays

    SciTech Connect

    Deng, Ye; He, Zhili; Van Nostrand, Joy D.; Zhou, Jizhong

    2008-08-15

    Nonspecific hybridization is currently a major concern with microarray technology. One of most effective approaches to estimating nonspecific hybridizations in oligonucleotide microarrays is the utilization of mismatch probes; however, this approach has not been used for longer oligonucleotide probes. Here, an oligonucleotide microarray was constructed to evaluate and optimize parameters for 50-mer mismatch probe design. A perfect match (PM) and 28 mismatch (MM) probes were designed for each of ten target genes selected from three microorganisms. The microarrays were hybridized with synthesized complementary oligonucleotide targets at different temperatures (e.g., 42, 45 and 50 C). In general, the probes with evenly distributed mismatches were more distinguishable than those with randomly distributed mismatches. MM probes with 3, 4 and 5 mismatched nucleotides were differentiated for 50-mer oligonucleotide probes hybridized at 50, 45 and 42 C, respectively. Based on the experimental data generated from this study, a modified positional dependent nearest neighbor (MPDNN) model was constructed to adjust the thermodynamic parameters of matched and mismatched dimer nucleotides in the microarray environment. The MM probes with four flexible positional mismatches were designed using the newly established MPDNN model and the experimental results demonstrated that the redesigned MM probes could yield more consistent hybridizations. Conclusions: This study provides guidance on the design of MM probes for long oligonucleotides (e.g., 50 mers). The novel MPDNN model has improved the consistency for long MM probes, and this modeling method can potentially be used for the prediction of oligonucleotide microarray hybridizations.

  12. Microarray Technology for Major Chemical Contaminants Analysis in Food: Current Status and Prospects

    PubMed Central

    Zhang, Zhaowei; Li, Peiwu; Hu, Xiaofeng; Zhang, Qi; Ding, Xiaoxia; Zhang, Wen

    2012-01-01

    Chemical contaminants in food have caused serious health issues in both humans and animals. Microarray technology is an advanced technique suitable for the analysis of chemical contaminates. In particular, immuno-microarray approach is one of the most promising methods for chemical contaminants analysis. The use of microarrays for the analysis of chemical contaminants is the subject of this review. Fabrication strategies and detection methods for chemical contaminants are discussed in detail. Application to the analysis of mycotoxins, biotoxins, pesticide residues, and pharmaceutical residues is also described. Finally, future challenges and opportunities are discussed. PMID:23012541

  13. Growth hormone regulation of rat liver gene expression assessed by SSH and microarray.

    PubMed

    Gardmo, Cissi; Swerdlow, Harold; Mode, Agneta

    2002-04-25

    The sexually dimorphic secretion of growth hormone (GH) that prevails in the rat leads to a sex-differentiated expression of GH target genes, particularly in the liver. We have used subtractive suppressive hybridization (SSH) to search for new target genes induced by the female-characteristic, near continuous, pattern of GH secretion. Microarrays and dot-blot hybridizations were used in an attempt to confirm differential ratios of expression of obtained SSH clones. Out of 173 unique SSH clones, 41 could be verified as differentially expressed. Among these, we identified 17 known genes not previously recognized as differentially regulated by the sex-specific GH pattern. Additional SSH clones may also represent genes subjected to sex-specific GH regulation since only transcripts abundantly expressed could be verified. Optimized analyses, specific for each gene, are required to fully characterize the degree of differential expression.

  14. ArrayNinja: An Open Source Platform for Unified Planning and Analysis of Microarray Experiments

    PubMed Central

    Dickson, B.M.; Cornett, E.M.; Ramjan, Z.; Rothbart, S.B.

    2017-01-01

    Microarray-based proteomic platforms have emerged as valuable tools for studying various aspects of protein function, particularly in the field of chromatin biochemistry. Microarray technology itself is largely unrestricted in regard to printable material and platform design, and efficient multidimensional optimization of assay parameters requires fluidity in the design and analysis of custom print layouts. This motivates the need for streamlined software infrastructure that facilitates the combined planning and analysis of custom microarray experiments. To this end, we have developed ArrayNinja as a portable, open source, and interactive application that unifies the planning and visualization of microarray experiments and provides maximum flexibility to end users. Array experiments can be planned, stored to a private database, and merged with the imaged results for a level of data interaction and centralization that is not currently attainable with available microarray informatics tools. PMID:27423857

  15. Microarray analysis of Xenopus endoderm expressing Ptf1a

    PubMed Central

    Bilogan, Cassandra K.; Horb, Marko E.

    2012-01-01

    Pancreas specific transcription factor 1a (Ptf1a), a bHLH transcription factor, has two temporally distinct functions during pancreas development; initially it is required for early specification of the entire pancreas, while later it is required for proper differentiation and maintenance of only acinar cells. The importance of Ptf1a function was revealed by the fact that loss of Ptf1a leads to pancreas agenesis in humans. While Ptf1a is one of the most important pancreatic transcription factors, little is known about the differences between the regulatory networks it controls during initial specification of the pancreas as opposed to acinar cell development, and to date no comprehensive analysis of its downstream targets has been published. In this paper, we use Xenopus embryos to identify putative downstream targets of Ptf1a. We isolated anterior endoderm tissue overexpressing Ptf1a at two early stages, NF32 and NF36, and compared their gene expression profiles using microarrays. Our results revealed that Ptf1a regulates genes with a wide variety of functions, providing insight into the complexity of the regulatory network required for pancreas specification. PMID:22815262

  16. Gene Expression Signature in Endemic Osteoarthritis by Microarray Analysis

    PubMed Central

    Wang, Xi; Ning, Yujie; Zhang, Feng; Yu, Fangfang; Tan, Wuhong; Lei, Yanxia; Wu, Cuiyan; Zheng, Jingjing; Wang, Sen; Yu, Hanjie; Li, Zheng; Lammi, Mikko J.; Guo, Xiong

    2015-01-01

    Kashin-Beck Disease (KBD) is an endemic osteochondropathy with an unknown pathogenesis. Diagnosis of KBD is effective only in advanced cases, which eliminates the possibility of early treatment and leads to an inevitable exacerbation of symptoms. Therefore, we aim to identify an accurate blood-based gene signature for the detection of KBD. Previously published gene expression profile data on cartilage and peripheral blood mononuclear cells (PBMCs) from adults with KBD were compared to select potential target genes. Microarray analysis was conducted to evaluate the expression of the target genes in a cohort of 100 KBD patients and 100 healthy controls. A gene expression signature was identified using a training set, which was subsequently validated using an independent test set with a minimum redundancy maximum relevance (mRMR) algorithm and support vector machine (SVM) algorithm. Fifty unique genes were differentially expressed between KBD patients and healthy controls. A 20-gene signature was identified that distinguished between KBD patients and controls with 90% accuracy, 85% sensitivity, and 95% specificity. This study identified a 20-gene signature that accurately distinguishes between patients with KBD and controls using peripheral blood samples. These results promote the further development of blood-based genetic biomarkers for detection of KBD. PMID:25997002

  17. Glycan microarray analysis of Candida glabrata adhesin ligand specificity.

    PubMed

    Zupancic, Margaret L; Frieman, Matthew; Smith, David; Alvarez, Richard A; Cummings, Richard D; Cormack, Brendan P

    2008-05-01

    The Candida glabrata genome encodes at least 23 members of the EPA (epithelial adhesin) family responsible for mediating adherence to host cells. To better understand the mechanism by which the Epa proteins contribute to pathogenesis, we have used glycan microarray analysis to characterize their carbohydrate-binding specificities. Using Saccharomyces cerevisiae strains surface-expressing the N-terminal ligand-binding domain of the Epa proteins, we found that the three Epa family members functionally identified as adhesins in Candida glabrata (Epa1, Epa6 and Epa7) bind to ligands containing a terminal galactose residue. However, the specificity of the three proteins for glycans within this class varies, with Epa6 having a broader specificity range than Epa1 or Epa7. This result is intriguing given the close homology between Epa6 and Epa7, which are 92% identical at the amino acid level. We have mapped a five-amino-acid region within the N-terminal ligand-binding domain that accounts for the difference in specificity of Epa6 and Epa7 and show that these residues contribute to adherence to both epithelial and endothelial cell lines in vitro.

  18. Xylella fastidiosa gene expression analysis by DNA microarrays

    PubMed Central

    2009-01-01

    Xylella fastidiosa genome sequencing has generated valuable data by identifying genes acting either on metabolic pathways or in associated pathogenicity and virulence. Based on available information on these genes, new strategies for studying their expression patterns, such as microarray technology, were employed. A total of 2,600 primer pairs were synthesized and then used to generate fragments using the PCR technique. The arrays were hybridized against cDNAs labeled during reverse transcription reactions and which were obtained from bacteria grown under two different conditions (liquid XDM2 and liquid BCYE). All data were statistically analyzed to verify which genes were differentially expressed. In addition to exploring conditions for X. fastidiosa genome-wide transcriptome analysis, the present work observed the differential expression of several classes of genes (energy, protein, amino acid and nucleotide metabolism, transport, degradation of substances, toxins and hypothetical proteins, among others). The understanding of expressed genes in these two different media will be useful in comprehending the metabolic characteristics of X. fastidiosa, and in evaluating how important certain genes are for the functioning and survival of these bacteria in plants. PMID:21637690

  19. Microarray analysis of human epithelial cell responses to bacterial interaction.

    PubMed

    Mans, Jeffrey J; Lamont, Richard J; Handfield, Martin

    2006-09-01

    Host-pathogen interactions are inherently complex and dynamic. The recent use of human microarrays has been invaluable to monitor the effects of various bacterial and viral pathogens upon host cell gene expression programs. This methodology has allowed the host response transcriptome of several cell lines to be studied on a global scale. To this point, the great majority of reports have focused on the response of immune cells, including macrophages and dendritic cells. These studies revealed that the immune response to microbial pathogens is tailored to different microbial challenges. Conversely, the paradigm for epithelial cells has--until recently--held that the epithelium mostly served as a relatively passive physical barrier to infection. It is now generally accepted that the epithelial barrier contributes more actively to signaling events in the immune response. In light of this shift, this review will compare transcriptional profiling data from studies that involved host-pathogen interactions occurring with epithelial cells. Experiments that defined both a common core response, as well as pathogen-specific host responses will be discussed. This review will also summarize the contributions that transcriptional profiling analysis has made to our understanding of bacterial physio-pathogensis of infection. This will include a discussion of how host transcriptional responses can be used to infer the function of virulence determinants from bacterial pathogens interacting with epithelial mucosa. In particular, we will expand upon the lessons that have been learned from gastro-intestinal and oral pathogens, as well as from members of the commensal flora.

  20. Microarray analysis of transcripts with elevated expressions in the rat medial or lateral habenula suggest fast GABAergic excitation in the medial habenula and habenular involvement in the regulation of feeding and energy balance.

    PubMed

    Wagner, Franziska; Bernard, René; Derst, Christian; French, Leon; Veh, Rüdiger W

    2016-12-01

    In vertebrates the "anti-reward-system" mainly is represented by the habenula and its medial (MHb) and especially lateral (LHb) complexes. Considerable knowledge has accumulated concerning subnuclear structures and connectivities of MHb and LHb subnuclei. The present investigation aimed to obtain novel information, whether MHb or LHb or their subnuclei display field-characteristic gene products, which may shed light on biological functions of these areas. Unfortunately this was not the case. Microarray analysis of mRNAs in microdissected habenular and thalamic control areas yielded expression values of 17,745 RNAs representing protein-coding genes, to which annotated gene names could be assigned. High relative values of genes with known expression in MHb, LHb or thalamus in the corresponding areas indicated a high precision of the microdissection procedure. Note that the present report emphasizes differences between and not absolute expression values in the selected regions. The present investigation disclosed that the LHb genetically is much closer related to the thalamus as compared to the MHb. The results presented here focuse on gene transcripts related to major transmitter systems, catecholamines and neuropeptides. Quite surprisingly, our data indicate potentially inhibitory effects of acetylcholine and glutamate in the habenula. In addition, the absence of the K-Cl co-transporter 2 supports a largely excitatory role of GABAergic transmission especially in the MHb. Furthermore, several G-protein related receptors (Gpr83, Gpr139, Gpr149, Gpr151, Gpr158) and many neuropeptides related to feeding are differentially expressed in the habenular region, indicating that its involvement in the regulation of food consumption and energy expenditure may have been underestimated so far.

  1. A genome-wide 20 K citrus microarray for gene expression analysis

    PubMed Central

    Martinez-Godoy, M Angeles; Mauri, Nuria; Juarez, Jose; Marques, M Carmen; Santiago, Julia; Forment, Javier; Gadea, Jose

    2008-01-01

    Background Understanding of genetic elements that contribute to key aspects of citrus biology will impact future improvements in this economically important crop. Global gene expression analysis demands microarray platforms with a high genome coverage. In the last years, genome-wide EST collections have been generated in citrus, opening the possibility to create new tools for functional genomics in this crop plant. Results We have designed and constructed a publicly available genome-wide cDNA microarray that include 21,081 putative unigenes of citrus. As a functional companion to the microarray, a web-browsable database [1] was created and populated with information about the unigenes represented in the microarray, including cDNA libraries, isolated clones, raw and processed nucleotide and protein sequences, and results of all the structural and functional annotation of the unigenes, like general description, BLAST hits, putative Arabidopsis orthologs, microsatellites, putative SNPs, GO classification and PFAM domains. We have performed a Gene Ontology comparison with the full set of Arabidopsis proteins to estimate the genome coverage of the microarray. We have also performed microarray hybridizations to check its usability. Conclusion This new cDNA microarray replaces the first 7K microarray generated two years ago and allows gene expression analysis at a more global scale. We have followed a rational design to minimize cross-hybridization while maintaining its utility for different citrus species. Furthermore, we also provide access to a website with full structural and functional annotation of the unigenes represented in the microarray, along with the ability to use this site to directly perform gene expression analysis using standard tools at different publicly available servers. Furthermore, we show how this microarray offers a good representation of the citrus genome and present the usefulness of this genomic tool for global studies in citrus by using it to

  2. A genome-wide 20 K citrus microarray for gene expression analysis.

    PubMed

    Martinez-Godoy, M Angeles; Mauri, Nuria; Juarez, Jose; Marques, M Carmen; Santiago, Julia; Forment, Javier; Gadea, Jose

    2008-07-03

    Understanding of genetic elements that contribute to key aspects of citrus biology will impact future improvements in this economically important crop. Global gene expression analysis demands microarray platforms with a high genome coverage. In the last years, genome-wide EST collections have been generated in citrus, opening the possibility to create new tools for functional genomics in this crop plant. We have designed and constructed a publicly available genome-wide cDNA microarray that include 21,081 putative unigenes of citrus. As a functional companion to the microarray, a web-browsable database 1 was created and populated with information about the unigenes represented in the microarray, including cDNA libraries, isolated clones, raw and processed nucleotide and protein sequences, and results of all the structural and functional annotation of the unigenes, like general description, BLAST hits, putative Arabidopsis orthologs, microsatellites, putative SNPs, GO classification and PFAM domains. We have performed a Gene Ontology comparison with the full set of Arabidopsis proteins to estimate the genome coverage of the microarray. We have also performed microarray hybridizations to check its usability. This new cDNA microarray replaces the first 7K microarray generated two years ago and allows gene expression analysis at a more global scale. We have followed a rational design to minimize cross-hybridization while maintaining its utility for different citrus species. Furthermore, we also provide access to a website with full structural and functional annotation of the unigenes represented in the microarray, along with the ability to use this site to directly perform gene expression analysis using standard tools at different publicly available servers. Furthermore, we show how this microarray offers a good representation of the citrus genome and present the usefulness of this genomic tool for global studies in citrus by using it to catalogue genes expressed in

  3. Statistical approaches for the analysis of DNA methylation microarray data.

    PubMed

    Siegmund, Kimberly D

    2011-06-01

    Following the rapid development and adoption in DNA methylation microarray assays, we are now experiencing a growth in the number of statistical tools to analyze the resulting large-scale data sets. As is the case for other microarray applications, biases caused by technical issues are of concern. Some of these issues are old (e.g., two-color dye bias and probe- and array-specific effects), while others are new (e.g., fragment length bias and bisulfite conversion efficiency). Here, I highlight characteristics of DNA methylation that suggest standard statistical tools developed for other data types may not be directly suitable. I then describe the microarray technologies most commonly in use, along with the methods used for preprocessing and obtaining a summary measure. I finish with a section describing downstream analyses of the data, focusing on methods that model percentage DNA methylation as the outcome, and methods for integrating DNA methylation with gene expression or genotype data.

  4. Issues in the analysis of oligonucleotide tiling microarrays for transcript mapping

    NASA Technical Reports Server (NTRS)

    Royce, Thomas E.; Rozowsky, Joel S.; Bertone, Paul; Samanta, Manoj; Stolc, Viktor; Weissman, Sherman; Snyder, Michael; Gerstein, Mark

    2005-01-01

    Traditional microarrays use probes complementary to known genes to quantitate the differential gene expression between two or more conditions. Genomic tiling microarray experiments differ in that probes that span a genomic region at regular intervals are used to detect the presence or absence of transcription. This difference means the same sets of biases and the methods for addressing them are unlikely to be relevant to both types of experiment. We introduce the informatics challenges arising in the analysis of tiling microarray experiments as open problems to the scientific community and present initial approaches for the analysis of this nascent technology.

  5. Issues in the analysis of oligonucleotide tiling microarrays for transcript mapping

    NASA Technical Reports Server (NTRS)

    Royce, Thomas E.; Rozowsky, Joel S.; Bertone, Paul; Samanta, Manoj; Stolc, Viktor; Weissman, Sherman; Snyder, Michael; Gerstein, Mark

    2005-01-01

    Traditional microarrays use probes complementary to known genes to quantitate the differential gene expression between two or more conditions. Genomic tiling microarray experiments differ in that probes that span a genomic region at regular intervals are used to detect the presence or absence of transcription. This difference means the same sets of biases and the methods for addressing them are unlikely to be relevant to both types of experiment. We introduce the informatics challenges arising in the analysis of tiling microarray experiments as open problems to the scientific community and present initial approaches for the analysis of this nascent technology.

  6. Gene profile in the spleen under massive partial hepatectomy using complementary DNA microarray and pathway analysis.

    PubMed

    Arakawa, Yusuke; Shimada, Mitsuo; Utsunomiya, Tohru; Imura, Satoru; Morine, Yuji; Ikemoto, Tetsuya; Mori, Hiroki; Kanamoto, Mami; Iwahashi, Shuichi; Saito, Yu; Takasu, Chie

    2014-08-01

    In general, the spleen is one of the abdominal organs connected by the portal system, and a splenectomy improves hepatic functions in the settings of partial hepatectomy (Hx) for portal hypertensive cases or living donor liver transplantation with excessive portal vein flow. Those precise mechanisms remain still unclear; therefore, we investigated the DNA expression profile in the spleen after 90% Hx in rats using complementary DNA microarray and pathway analysis. Messenger RNAs (mRNAs) were prepared from three rat spleens at each time point (0, 3, and 6 h after 90% Hx). Using the gene chip, mRNA was hybridized to Affymetrix GeneChip Rat Genome 230 2.0 Array (Affymetrix®) and pathway analysis was done with Ingenuity Pathway Analysis (IPA®). We determined the 3-h or 6-h/0-h ratio to assess the influence of Hx, and cut-off values were set at more than 2.0-fold or less than 1/2 (0.5)-fold. Chemokine activity-related genes including Cxcl1 (GRO1) and Cxcl2 (MIP-2) related pathway were upregulated in the spleen. Also, immediate early response genes including early growth response-1 (EGR1), FBJ murine osteosarcoma (FOS) and activating transcription factor 3 (ATF3) related pathway were upregulated in the spleen. We concluded that in the spleen the expression of numerous inflammatory-related genes would occur after 90% Hx. The spleen could take a harmful role and provide a negative impact during post Hx phase due to the induction of chemokine and transcription factors including GRO1 and EGR1. © 2014 Journal of Gastroenterology and Hepatology Foundation and Wiley Publishing Asia Pty Ltd.

  7. Microarray gene expression analysis of uterosacral ligaments in uterine prolapse.

    PubMed

    Ak, Handan; Zeybek, Burak; Atay, Sevcan; Askar, Niyazi; Akdemir, Ali; Aydin, Hikmet Hakan

    2016-11-01

    Pelvic organ prolapse (POP) is a major health problem that impairs the quality of life with a wide clinical spectrum. Since the uterosacral ligaments provide primary support for the uterus and the upper vagina, we hypothesize that the disruption of these ligaments may lead to a loss of support and eventually contribute to POP. In this study, we therefore investigated whether there are any differences in the transcription profile of uterosacral ligaments in patients with POP when compared to those of the control samples. Seventeen women with POP and 8 non-POP controls undergoing hysterectomy for benign conditions were included in the study. Affymetrix® Gene Chip microarrays (Human Hu 133 plus 2.0) were used for whole genome gene expression profiling analysis. There was 1 significantly down-regulated gene, NKX2-3 in patients with POP compared to the controls (p=4.28464e-013). KIF11 gene was found to be significantly down-regulated in patients with ≥3 deliveries compared to patients with <3 deliveries (p=0.0156237). UGT1A1 (p=2.43388e-005), SCARB1 (p=1.19001e-006) and NKX2-3 (p=2.17966e-013) genes were found to be significantly down-regulated in the premenopausal patients compared to the premenopausal controls. UGT1A1 gene was also found to be significantly down-regulated in the post menopausal patients compared to the postmenopausal controls (p=0.0005). This study provides evidence for a significant down-regulation of the genes that take role in cell cycle, proliferation and embryonic development along with cell adhesion process on the development of POP for the first time. Copyright © 2016 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

  8. Differential analysis for high density tiling microarray data.

    PubMed

    Ghosh, Srinka; Hirsch, Heather A; Sekinger, Edward A; Kapranov, Philipp; Struhl, Kevin; Gingeras, Thomas R

    2007-09-24

    High density oligonucleotide tiling arrays are an effective and powerful platform for conducting unbiased genome-wide studies. The ab initio probe selection method employed in tiling arrays is unbiased, and thus ensures consistent sampling across coding and non-coding regions of the genome. These arrays are being increasingly used to study the associated processes of transcription, transcription factor binding, chromatin structure and their association. Studies of differential expression and/or regulation provide critical insight into the mechanics of transcription and regulation that occurs during the developmental program of a cell. The time-course experiment, which comprises an in-vivo system and the proposed analyses, is used to determine if annotated and un-annotated portions of genome manifest coordinated differential response to the induced developmental program. We have proposed a novel approach, based on a piece-wise function - to analyze genome-wide differential response. This enables segmentation of the response based on protein-coding and non-coding regions; for genes the methodology also partitions differential response with a 5' versus 3' versus intra-genic bias. The algorithm built upon the framework of Significance Analysis of Microarrays, uses a generalized logic to define regions/patterns of coordinated differential change. By not adhering to the gene-centric paradigm, discordant differential expression patterns between exons and introns have been identified at a FDR of less than 12 percent. A co-localization of differential binding between RNA Polymerase II and tetra-acetylated histone has been quantified at a p-value < 0.003; it is most significant at the 5' end of genes, at a p-value < 10-13. The prototype R code has been made available as supplementary material [see Additional file 1].

  9. Speeding up the Consensus Clustering methodology for microarray data analysis

    PubMed Central

    2011-01-01

    Background The inference of the number of clusters in a dataset, a fundamental problem in Statistics, Data Analysis and Classification, is usually addressed via internal validation measures. The stated problem is quite difficult, in particular for microarrays, since the inferred prediction must be sensible enough to capture the inherent biological structure in a dataset, e.g., functionally related genes. Despite the rich literature present in that area, the identification of an internal validation measure that is both fast and precise has proved to be elusive. In order to partially fill this gap, we propose a speed-up of Consensus (Consensus Clustering), a methodology whose purpose is the provision of a prediction of the number of clusters in a dataset, together with a dissimilarity matrix (the consensus matrix) that can be used by clustering algorithms. As detailed in the remainder of the paper, Consensus is a natural candidate for a speed-up. Results Since the time-precision performance of Consensus depends on two parameters, our first task is to show that a simple adjustment of the parameters is not enough to obtain a good precision-time trade-off. Our second task is to provide a fast approximation algorithm for Consensus. That is, the closely related algorithm FC (Fast Consensus) that would have the same precision as Consensus with a substantially better time performance. The performance of FC has been assessed via extensive experiments on twelve benchmark datasets that summarize key features of microarray applications, such as cancer studies, gene expression with up and down patterns, and a full spectrum of dimensionality up to over a thousand. Based on their outcome, compared with previous benchmarking results available in the literature, FC turns out to be among the fastest internal validation methods, while retaining the same outstanding precision of Consensus. Moreover, it also provides a consensus matrix that can be used as a dissimilarity matrix

  10. DNA Microarray Analysis of Human Monocytes Early Response Genes upon Infection with Rickettsia rickettsii

    DTIC Science & Technology

    2004-11-15

    DNA Microarray Analysis of Human Monocytes Early Response Genes upon Infection with Rickettsia rickettsii Chien-Chung Chao Rickettsiae Diseases...TITLE AND SUBTITLE DNA Microarray Analysis of Human Monocytes Early Response Genes upon Infection with Rickettsia rickettsii 5a. CONTRACT NUMBER 5b...ANSI Std Z39-18 Rickettsiae • Gram negative coccobacillary bacteria • Obligate intracellular organisms • Arthropod-borne • Cause febrile diseases (mild

  11. Matrix Factorization Methods Applied in Microarray Data Analysis

    PubMed Central

    Kossenkov, Andrew V.

    2010-01-01

    Numerous methods have been applied to microarray data to group genes into clusters that show similar expression patterns. These methods assign each gene to a single group, which does not reflect the widely held view among biologists that most, if not all, genes in eukaryotes are involved in multiple biological processes and therefore will be multiply regulated. Here, we review several methods that have been developed that are capable of identifying patterns of behavior in transcriptional response and assigning genes to multiple patterns. Broadly speaking, these methods define a series of mathematical approaches to matrix factorization with different approaches to the fitting of the model to the data. We focus on these methods in contrast to traditional clustering methods applied to microarray data, which assign one gene to one cluster. PMID:20376923

  12. EMAAS: An extensible grid-based Rich Internet Application for microarray data analysis and management

    PubMed Central

    Barton, G; Abbott, J; Chiba, N; Huang, DW; Huang, Y; Krznaric, M; Mack-Smith, J; Saleem, A; Sherman, BT; Tiwari, B; Tomlinson, C; Aitman, T; Darlington, J; Game, L; Sternberg, MJE; Butcher, SA

    2008-01-01

    Background Microarray experimentation requires the application of complex analysis methods as well as the use of non-trivial computer technologies to manage the resultant large data sets. This, together with the proliferation of tools and techniques for microarray data analysis, makes it very challenging for a laboratory scientist to keep up-to-date with the latest developments in this field. Our aim was to develop a distributed e-support system for microarray data analysis and management. Results EMAAS (Extensible MicroArray Analysis System) is a multi-user rich internet application (RIA) providing simple, robust access to up-to-date resources for microarray data storage and analysis, combined with integrated tools to optimise real time user support and training. The system leverages the power of distributed computing to perform microarray analyses, and provides seamless access to resources located at various remote facilities. The EMAAS framework allows users to import microarray data from several sources to an underlying database, to pre-process, quality assess and analyse the data, to perform functional analyses, and to track data analysis steps, all through a single easy to use web portal. This interface offers distance support to users both in the form of video tutorials and via live screen feeds using the web conferencing tool EVO. A number of analysis packages, including R-Bioconductor and Affymetrix Power Tools have been integrated on the server side and are available programmatically through the Postgres-PLR library or on grid compute clusters. Integrated distributed resources include the functional annotation tool DAVID, GeneCards and the microarray data repositories GEO, CELSIUS and MiMiR. EMAAS currently supports analysis of Affymetrix 3' and Exon expression arrays, and the system is extensible to cater for other microarray and transcriptomic platforms. Conclusion EMAAS enables users to track and perform microarray data management and analysis tasks

  13. Copasetic analysis: a framework for the blind analysis of microarray imagery.

    PubMed

    Fraser, K; O'Neill, P; Wang, Z; Liu, X

    2004-06-01

    From its conception, bioinformatics has been a multidisciplinary field which blends domain expert knowledge with new and existing processing techniques, all of which are focused on a common goal. Typically, these techniques have focused on the direct analysis of raw microarray image data. Unfortunately, this fails to utilise the image's full potential and in practice, this results in the lab technician having to guide the analysis algorithms. This paper presents a dynamic framework that aims to automate the process of microarray image analysis using a variety of techniques. An overview of the entire framework process is presented, the robustness of which is challenged throughout with a selection of real examples containing varying degrees of noise. The results show the potential of the proposed framework in its ability to determine slide layout accurately and perform analysis without prior structural knowledge. The algorithm achieves approximately, a 1 to 3 dB improved peak signal-to-noise ratio compared to conventional processing techniques like those implemented in GenePix when used by a trained operator. As far as the authors are aware, this is the first time such a comprehensive framework concept has been directly applied to the area of microarray image analysis.

  14. Evaluation of a gene information summarization system by users during the analysis process of microarray datasets.

    PubMed

    Yang, Jianji; Cohen, Aaron; Hersh, William

    2009-02-05

    Summarization of gene information in the literature has the potential to help genomics researchers translate basic research into clinical benefits. Gene expression microarrays have been used to study biomarkers for disease and discover novel types of therapeutics and the task of finding information in journal articles on sets of genes is common for translational researchers working with microarray data. However, manually searching and scanning the literature references returned from PubMed is a time-consuming task for scientists. We built and evaluated an automatic summarizer of information on genes studied in microarray experiments. The Gene Information Clustering and Summarization System (GICSS) is a system that integrates two related steps of the microarray data analysis process: functional gene clustering and gene information gathering. The system evaluation was conducted during the process of genomic researchers analyzing their own experimental microarray datasets. The clusters generated by GICSS were validated by scientists during their microarray analysis process. In addition, presenting sentences in the abstract provided significantly more important information to the users than just showing the title in the default PubMed format. The evaluation results suggest that GICSS can be useful for researchers in genomic area. In addition, the hybrid evaluation method, partway between intrinsic and extrinsic system evaluation, may enable researchers to gauge the true usefulness of the tool for the scientists in their natural analysis workflow and also elicit suggestions for future enhancements. GICSS can be accessed online at: http://ir.ohsu.edu/jianji/index.html.

  15. Genomewide expression analysis in amino acid-producing bacteria using DNA microarrays.

    PubMed

    Polen, Tino; Wendisch, Volker F

    2004-01-01

    DNA microarray technology has become an important research tool for biotechnology and microbiology. It is now possible to characterize genetic diversity and gene expression in a genomewide manner. DNA microarrays have been applied extensively to study the biology of many bacteria including Escherichia coli, but only recently have they been developed for the Gram-positive Corynebacterium glutamicum. Both bacteria are widely used for biotechnological amino acid production. In this article, in addition to the design and generation of microarrays as well as their use in hybridization experiments and subsequent data analysis, we describe recent applications of DNA microarray technology regarding amino acid production in C. glutamicum and E. coli. We also discuss the impact of functional genomics studies on fundamental as well as applied aspects of amino acid production with C. glutamicum and E. coli.

  16. Microarray analysis of p-anisaldehyde-induced transcriptome of Saccharomyces cerevisiae.

    PubMed

    Yu, Lu; Guo, Na; Yang, Yi; Wu, Xiuping; Meng, Rizeng; Fan, Junwen; Ge, Fa; Wang, Xuelin; Liu, Jingbo; Deng, Xuming

    2010-03-01

    p-Anisaldehyde (4-methoxybenzaldehyde), an extract from Pimpinella anisum L. seeds, is a potential novel preservative. To reveal the possible action mechanism of p-anisaldehyde against microorganisms, yeast-based commercial oligonucleotide microarrays were used to analyze the genome-wide transcriptional changes in response to p-anisaldehyde. Quantitative real-time RT-PCR was performed for selected genes to verify the microarray results. We interpreted our microarray data with the clustering tool, T-profiler. Analysis of microarray data revealed that p-anisaldehyde induced the expression of genes related to sulphur assimilation, aromatic aldehydes metabolism, and secondary metabolism, which demonstrated that the addition of p-anisaldehyde may influence the normal metabolism of aromatic aldehydes. This genome-wide transcriptomics approach revealed first insights into the response of Saccharomyces cerevisiae (S. cerevisiae) to p-anisaldehyde challenge.

  17. BASE--2nd generation software for microarray data management and analysis.

    PubMed

    Vallon-Christersson, Johan; Nordborg, Nicklas; Svensson, Martin; Häkkinen, Jari

    2009-10-12

    Microarray experiments are increasing in size and samples are collected asynchronously over long time. Available data are re-analysed as more samples are hybridized. Systematic use of collected data requires tracking of biomaterials, array information, raw data, and assembly of annotations. To meet the information tracking and data analysis challenges in microarray experiments we reimplemented and improved BASE version 1.2. The new BASE presented in this report is a comprehensive annotable local microarray data repository and analysis application providing researchers with an efficient information management and analysis tool. The information management system tracks all material from biosource, via sample and through extraction and labelling to raw data and analysis. All items in BASE can be annotated and the annotations can be used as experimental factors in downstream analysis. BASE stores all microarray experiment related data regardless if analysis tools for specific techniques or data formats are readily available. The BASE team is committed to continue improving and extending BASE to make it usable for even more experimental setups and techniques, and we encourage other groups to target their specific needs leveraging on the infrastructure provided by BASE. BASE is a comprehensive management application for information, data, and analysis of microarray experiments, available as free open source software at http://base.thep.lu.se under the terms of the GPLv3 license.

  18. THEME: a web tool for loop-design microarray data analysis.

    PubMed

    Chen, Chaang-Ray; Shu, Wun-Yi; Tsai, Min-Lung; Cheng, Wei-Chung; Hsu, Ian C

    2012-02-01

    A number of recent studies have shown that loop-design is more efficient than reference control design. Data analysis for loop-design microarray experiments is commonly undertaken using linear models and statistical tests. These techniques require specialized knowledge in statistical programming. However, limited loop-design web-based tools are available. We have developed the THEME (Tsing Hua Engine of Microarray Experiment) that exploits all necessary data analysis tools for loop-design microarray studies. THEME allows users to construct linear models and to apply multiple user-defined statistical tests of hypotheses for detection of DEG (differentially expressed genes). Users can modify entries of design matrix for experimental design as well as that of contrast matrix for statistical tests of hypotheses. The output of multiple user-defined statistical tests of hypotheses, DEG lists, can be cross-validated. The web platform provides data assessment and visualization tools that significantly assist users when evaluating the performance of microarray experimental procedures. THEME is also a MIAME (Minimal Information About a Microarray Experiment) compliant system, which enables users to export formatted files for GEO (Gene Expression Omnibus) submission. THEME offers comprehensive web services to biologists for data analysis of loop-design microarray experiments. This web-based resource is especially useful for core facility service as well as collaboration projects when researchers are not at the same site. Data analysis procedures, starting from uploading raw data files to retrieving DEG lists, can be flexibly operated with natural workflows. These features make THEME a reliable and powerful on-line system for data analysis of loop-design microarrays. The THEME server is available at http://metadb.bmes.nthu.edu.tw/theme/. Copyright © 2011 Elsevier Ltd. All rights reserved.

  19. Interactive molecular networks obtained by computer-aided conversion of microarray data from brains of alcohol-drinking rats.

    PubMed

    Matthäus, F; Smith, V A; Fogtman, A; Sommer, W H; Leonardi-Essmann, F; Lourdusamy, A; Reimers, M A; Spanagel, R; Gebicke-Haerter, P J

    2009-05-01

    Lists of differentially expressed genes in a disease have become increasingly more comprehensive with improvements on all technical levels. Despite statistical cutoffs of 99% or 95% confidence intervals, the number of genes can rise to several hundreds or even thousands, which is barely amenable to a researcher's understanding. This report describes some ways of processing those data by mathematical algorithms. Gene lists obtained from 53 microarrays (two brain regions (amygdala and caudate putamen), three rat strains drinking alcohol or being abstinent) have been used. They resulted from analyses on Affymetrix chips and encompassed approximately 6 000 genes that passed our quality filters. They have been subjected to four mathematical ways of processing: (a) basic statistics, (b) principal component analysis, (c) hierarchical clustering, and (d) introduction into Bayesian networks. It turns out, by using the p-values or the log-ratios, that they best subdivide into brain areas, followed by a fairly good discrimination into the rat strains and the least good discrimination into alcohol-drinking vs. abstinent. Nevertheless, despite the fact that the relation to alcohol-drinking was the weakest signal, attempts have been made to integrate the genes related to alcohol-drinking into Bayesian networks to learn more about their inter-relationships. The study shows, that the tools employed here are extremely useful for (a) quality control of datasets, (b) for constructing interactive (molecular) networks, but (c) have limitations in integration of larger numbers into the networks. The study also shows that it is often pivotal to balance out the number of experimental conditions with the number of animals.

  20. Fully automated analysis of multi-resolution four-channel micro-array genotyping data

    NASA Astrophysics Data System (ADS)

    Abbaspour, Mohsen; Abugharbieh, Rafeef; Podder, Mohua; Tebbutt, Scott J.

    2006-03-01

    We present a fully-automated and robust microarray image analysis system for handling multi-resolution images (down to 3-micron with sizes up to 80 MBs per channel). The system is developed to provide rapid and accurate data extraction for our recently developed microarray analysis and quality control tool (SNP Chart). Currently available commercial microarray image analysis applications are inefficient, due to the considerable user interaction typically required. Four-channel DNA microarray technology is a robust and accurate tool for determining genotypes of multiple genetic markers in individuals. It plays an important role in the state of the art trend where traditional medical treatments are to be replaced by personalized genetic medicine, i.e. individualized therapy based on the patient's genetic heritage. However, fast, robust, and precise image processing tools are required for the prospective practical use of microarray-based genetic testing for predicting disease susceptibilities and drug effects in clinical practice, which require a turn-around timeline compatible with clinical decision-making. In this paper we have developed a fully-automated image analysis platform for the rapid investigation of hundreds of genetic variations across multiple genes. Validation tests indicate very high accuracy levels for genotyping results. Our method achieves a significant reduction in analysis time, from several hours to just a few minutes, and is completely automated requiring no manual interaction or guidance.

  1. Probe-Level Analysis of Expression Microarrays Characterizes Isoform-Specific Degradation during Mouse Oocyte Maturation

    PubMed Central

    Salisbury, Jesse; Hutchison, Keith W.; Wigglesworth, Karen; Eppig, John J.; Graber, Joel H.

    2009-01-01

    Background Gene expression microarrays have provided many insights into changes in gene expression patterns between different tissue types, developmental stages, and disease states. Analyses of these data focused primarily measuring the relative abundance of transcripts of a gene, while treating most or all transcript isoforms as equivalent. Differences in the selection between transcript isoforms can, however, represent critical changes to either the protein product or the posttranscriptional regulation of the transcript. Novel analyses on existing microarray data provide fresh insights and new interpretations into transcriptome-wide changes in expression. Methodology A probe-level analysis of existing gene expression arrays revealed differences in mRNA processing, primarily affecting the 3′-untranslated region. Working with the example of microarrays drawn from a transcriptionally silent period of mouse oocyte development, probe-level analysis (implemented here as rmodel) identified genes whose transcript isoforms have differing stabilities. Comparison of micorarrays measuring cDNA generated from oligo-dT and random primers revealed further differences in the polyadenylation status of some transcripts. Additional analysis provided evidence for sequence-targeted cleavage, including putative targeting sequences, as one mechanism of degradation for several hundred transcripts in the maturing oocyte. Conclusions The capability of probe-level analysis to elicit novel findings from existing expression microarray data was demonstrated. The characterization of differences in stability between transcript isoforms in maturing mouse oocytes provided some mechanistic details of degradation. Similar analysis of existing archives of expression microarray data will likely provide similar discoveries. PMID:19834616

  2. Variance estimation in the analysis of microarray data.

    PubMed

    Wang, Yuedong; Ma, Yanyuan; Carroll, Raymond J

    2009-04-01

    Microarrays are one of the most widely used high throughput technologies. One of the main problems in the area is that conventional estimates of the variances that are required in the t-statistic and other statistics are unreliable owing to the small number of replications. Various methods have been proposed in the literature to overcome this lack of degrees of freedom problem. In this context, it is commonly observed that the variance increases proportionally with the intensity level, which has led many researchers to assume that the variance is a function of the mean. Here we concentrate on estimation of the variance as a function of an unknown mean in two models: the constant coefficient of variation model and the quadratic variance-mean model. Because the means are unknown and estimated with few degrees of freedom, naive methods that use the sample mean in place of the true mean are generally biased because of the errors-in-variables phenomenon. We propose three methods for overcoming this bias. The first two are variations on the theme of the so-called heteroscedastic simulation-extrapolation estimator, modified to estimate the variance function consistently. The third class of estimators is entirely different, being based on semiparametric information calculations. Simulations show the power of our methods and their lack of bias compared with the naive method that ignores the measurement error. The methodology is illustrated by using microarray data from leukaemia patients.

  3. Microarray analysis of microRNA expression in the developing mammalian brain

    PubMed Central

    Miska, Eric A; Alvarez-Saavedra, Ezequiel; Townsend, Matthew; Yoshii, Akira; Šestan, Nenad; Rakic, Pasko; Constantine-Paton, Martha; Horvitz, H Robert

    2004-01-01

    Background MicroRNAs are a large new class of tiny regulatory RNAs found in nematodes, plants, insects and mammals. MicroRNAs are thought to act as post-transcriptional modulators of gene expression. In invertebrates microRNAs have been implicated as regulators of developmental timing, neuronal differentiation, cell proliferation, programmed cell death and fat metabolism. Little is known about the roles of microRNAs in mammals. Results We isolated 18-26 nucleotide RNAs from developing rat and monkey brains. From the sequences of these RNAs and the sequences of the rat and human genomes we determined which of these small RNAs are likely to have derived from stem-loop precursors typical of microRNAs. Next, we developed a microarray technology suitable for detecting microRNAs and printed a microRNA microarray representing 138 mammalian microRNAs corresponding to the sequences of the microRNAs we cloned as well as to other known microRNAs. We used this microarray to determine the profile of microRNAs expressed in the developing mouse brain. We observed a temporal wave of expression of microRNAs, suggesting that microRNAs play important roles in the development of the mammalian brain. Conclusion We describe a microarray technology that can be used to analyze the expression of microRNAs and of other small RNAs. MicroRNA microarrays offer a new tool that should facilitate studies of the biological roles of microRNAs. We used this method to determine the microRNA expression profile during mouse brain development and observed a temporal wave of gene expression of sequential classes of microRNAs. PMID:15345052

  4. GeneXplorer: an interactive web application for microarray data visualization and analysis.

    PubMed

    Rees, Christian A; Demeter, Janos; Matese, John C; Botstein, David; Sherlock, Gavin

    2004-10-01

    When publishing large-scale microarray datasets, it is of great value to create supplemental websites where either the full data, or selected subsets corresponding to figures within the paper, can be browsed. We set out to create a CGI application containing many of the features of some of the existing standalone software for the visualization of clustered microarray data. We present GeneXplorer, a web application for interactive microarray data visualization and analysis in a web environment. GeneXplorer allows users to browse a microarray dataset in an intuitive fashion. It provides simple access to microarray data over the Internet and uses only HTML and JavaScript to display graphic and annotation information. It provides radar and zoom views of the data, allows display of the nearest neighbors to a gene expression vector based on their Pearson correlations and provides the ability to search gene annotation fields. The software is released under the permissive MIT Open Source license, and the complete documentation and the entire source code are freely available for download from CPAN http://search.cpan.org/dist/Microarray-GeneXplorer/.

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

    PubMed Central

    Argraves, Gary L; Jani, Saurin; Barth, Jeremy L; Argraves, W Scott

    2005-01-01

    Background Numerous microarray analysis programs have been created through the efforts of Open Source software development projects. Providing browser-based interfaces that allow these programs to be executed over the Internet enhances the applicability and utility of these analytic software tools. Results Here we present ArrayQuest, a web-based DNA microarray analysis process controller. Key features of ArrayQuest are that (1) it is capable of executing numerous analysis programs such as those written in R, BioPerl and C++; (2) new analysis programs can be added to ArrayQuest Methods Library at the request of users or developers; (3) input DNA microarray data can be selected from public databases (i.e., the Medical University of South Carolina (MUSC) DNA Microarray Database or Gene Expression Omnibus (GEO)) or it can be uploaded to the ArrayQuest center-point web server into a password-protected area; and (4) analysis jobs are distributed across computers configured in a backend cluster. To demonstrate the utility of ArrayQuest we have populated the methods library with methods for analysis of Affymetrix DNA microarray data. Conclusion ArrayQuest enables browser-based implementation of DNA microarray data analysis programs that can be executed on a Linux-based platform. Importantly, ArrayQuest is a platform that will facilitate the distribution and implementation of new analysis algorithms and is therefore of use to both developers of analysis applications as well as users. ArrayQuest is freely available for use at . PMID:16321157

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

    PubMed

    Argraves, Gary L; Jani, Saurin; Barth, Jeremy L; Argraves, W Scott

    2005-12-01

    Numerous microarray analysis programs have been created through the efforts of Open Source software development projects. Providing browser-based interfaces that allow these programs to be executed over the Internet enhances the applicability and utility of these analytic software tools. Here we present ArrayQuest, a web-based DNA microarray analysis process controller. Key features of ArrayQuest are that (1) it is capable of executing numerous analysis programs such as those written in R, BioPerl and C++; (2) new analysis programs can be added to ArrayQuest Methods Library at the request of users or developers; (3) input DNA microarray data can be selected from public databases (i.e., the Medical University of South Carolina (MUSC) DNA Microarray Database or Gene Expression Omnibus (GEO)) or it can be uploaded to the ArrayQuest center-point web server into a password-protected area; and (4) analysis jobs are distributed across computers configured in a backend cluster. To demonstrate the utility of ArrayQuest we have populated the methods library with methods for analysis of Affymetrix DNA microarray data. ArrayQuest enables browser-based implementation of DNA microarray data analysis programs that can be executed on a Linux-based platform. Importantly, ArrayQuest is a platform that will facilitate the distribution and implementation of new analysis algorithms and is therefore of use to both developers of analysis applications as well as users. ArrayQuest is freely available for use at http://proteogenomics.musc.edu/arrayquest.html.

  7. Enhancing Interdisciplinary Mathematics and Biology Education: A Microarray Data Analysis Course Bridging These Disciplines

    PubMed Central

    Evans, Irene M.

    2010-01-01

    BIO2010 put forth the goal of improving the mathematical educational background of biology students. The analysis and interpretation of microarray high-dimensional data can be very challenging and is best done by a statistician and a biologist working and teaching in a collaborative manner. We set up such a collaboration and designed a course on microarray data analysis. We started using Genome Consortium for Active Teaching (GCAT) materials and Microarray Genome and Clustering Tool software and added R statistical software along with Bioconductor packages. In response to student feedback, one microarray data set was fully analyzed in class, starting from preprocessing to gene discovery to pathway analysis using the latter software. A class project was to conduct a similar analysis where students analyzed their own data or data from a published journal paper. This exercise showed the impact that filtering, preprocessing, and different normalization methods had on gene inclusion in the final data set. We conclude that this course achieved its goals to equip students with skills to analyze data from a microarray experiment. We offer our insight about collaborative teaching as well as how other faculty might design and implement a similar interdisciplinary course. PMID:20810954

  8. Enhancing interdisciplinary mathematics and biology education: a microarray data analysis course bridging these disciplines.

    PubMed

    Tra, Yolande V; Evans, Irene M

    2010-01-01

    BIO2010 put forth the goal of improving the mathematical educational background of biology students. The analysis and interpretation of microarray high-dimensional data can be very challenging and is best done by a statistician and a biologist working and teaching in a collaborative manner. We set up such a collaboration and designed a course on microarray data analysis. We started using Genome Consortium for Active Teaching (GCAT) materials and Microarray Genome and Clustering Tool software and added R statistical software along with Bioconductor packages. In response to student feedback, one microarray data set was fully analyzed in class, starting from preprocessing to gene discovery to pathway analysis using the latter software. A class project was to conduct a similar analysis where students analyzed their own data or data from a published journal paper. This exercise showed the impact that filtering, preprocessing, and different normalization methods had on gene inclusion in the final data set. We conclude that this course achieved its goals to equip students with skills to analyze data from a microarray experiment. We offer our insight about collaborative teaching as well as how other faculty might design and implement a similar interdisciplinary course.

  9. Classification of a large microarray data set: Algorithm comparison and analysis of drug signatures

    PubMed Central

    Natsoulis, Georges; El Ghaoui, Laurent; Lanckriet, Gert R.G.; Tolley, Alexander M.; Leroy, Fabrice; Dunlea, Shane; Eynon, Barrett P.; Pearson, Cecelia I.; Tugendreich, Stuart; Jarnagin, Kurt

    2005-01-01

    A large gene expression database has been produced that characterizes the gene expression and physiological effects of hundreds of approved and withdrawn drugs, toxicants, and biochemical standards in various organs of live rats. In order to derive useful biological knowledge from this large database, a variety of supervised classification algorithms were compared using a 597-microarray subset of the data. Our studies show that several types of linear classifiers based on Support Vector Machines (SVMs) and Logistic Regression can be used to derive readily interpretable drug signatures with high classification performance. Both methods can be tuned to produce classifiers of drug treatments in the form of short, weighted gene lists which upon analysis reveal that some of the signature genes have a positive contribution (act as “rewards” for the class-of-interest) while others have a negative contribution (act as “penalties”) to the classification decision. The combination of reward and penalty genes enhances performance by keeping the number of false positive treatments low. The results of these algorithms are combined with feature selection techniques that further reduce the length of the drug signatures, an important step towards the development of useful diagnostic biomarkers and low-cost assays. Multiple signatures with no genes in common can be generated for the same classification end-point. Comparison of these gene lists identifies biological processes characteristic of a given class. PMID:15867433

  10. Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient.

    PubMed

    Yao, Jianchao; Chang, Chunqi; Salmi, Mari L; Hung, Yeung Sam; Loraine, Ann; Roux, Stanley J

    2008-06-18

    Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson correlation coefficient and the standard deviation (SD)-weighted correlation coefficient are the two most widely-used correlations as the similarity metrics in clustering microarray data. However, these two correlations are not optimal for analyzing replicated microarray data generated by most laboratories. An effective correlation coefficient is needed to provide statistically sufficient analysis of replicated microarray data. In this study, we describe a novel correlation coefficient, shrinkage correlation coefficient (SCC), that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. The value of SCC is revealed by its comparison with two other correlation coefficients that are currently the most widely-used (Pearson correlation coefficient and SD-weighted correlation coefficient) using statistical measures on both synthetic expression data as well as real gene expression data from Saccharomyces cerevisiae. Two leading clustering methods, hierarchical and k-means clustering were applied for the comparison. The comparison indicated that using SCC achieves better clustering performance. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. Functional analysis suggested that some of the genetic mechanisms that control germination in such diverse plant lineages as mosses and angiosperms are also conserved among ferns. This study shows that SCC is an alternative to the Pearson

  11. Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient

    PubMed Central

    Yao, Jianchao; Chang, Chunqi; Salmi, Mari L; Hung, Yeung Sam; Loraine, Ann; Roux, Stanley J

    2008-01-01

    Background Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson correlation coefficient and the standard deviation (SD)-weighted correlation coefficient are the two most widely-used correlations as the similarity metrics in clustering microarray data. However, these two correlations are not optimal for analyzing replicated microarray data generated by most laboratories. An effective correlation coefficient is needed to provide statistically sufficient analysis of replicated microarray data. Results In this study, we describe a novel correlation coefficient, shrinkage correlation coefficient (SCC), that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. The value of SCC is revealed by its comparison with two other correlation coefficients that are currently the most widely-used (Pearson correlation coefficient and SD-weighted correlation coefficient) using statistical measures on both synthetic expression data as well as real gene expression data from Saccharomyces cerevisiae. Two leading clustering methods, hierarchical and k-means clustering were applied for the comparison. The comparison indicated that using SCC achieves better clustering performance. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. Functional analysis suggested that some of the genetic mechanisms that control germination in such diverse plant lineages as mosses and angiosperms are also conserved among ferns. Conclusion This study shows that SCC is

  12. Microarray analysis for a comprehensive immunological-status evaluation during cancer vaccine immune monitoring.

    PubMed

    Monsurrò, Vladia; Marincola, Francesco M

    2011-01-01

    Anticancer immune responses can be enhanced by immune intervention that promotes complex biological mechanisms involving several cellular populations. The classical immune monitoring for biological-based cancer clinical trials is often based on single-cell analysis. However, the overall effect could be lost by such a reductionist approach explaining the lack of correlation among clinical and immunological endpoints often reported. Microarray technology could give the possibility of studying in a multiparametric setting the immune therapy effects. The application of microarray is leading to an improved understanding of the immune responses to tumor immunotherapy. In fact, analysis of cancer vaccine-induced host responses using microarrays is proposed as valuable alternative to the standard cell-based methods. This paper shows successful examples of how high-throughput gene expression profiling contributed to the understanding of anticancer immune responses during biological therapy, introducing as well the integrative platforms that allow the network analysis in molecular biology studies.

  13. Gene expression analysis of perennial ryegrass (Lolium perenne) using cDNA microarrays

    NASA Astrophysics Data System (ADS)

    Ong, Eng-Kok; Sawbridge, Tim; Webster, Tracie; Emmerling, Michael; Nguyen, Nga; Nunan, Katrina; O'Neill, Matthew; O'Toole, Fiona; Rhodes, Carolyn; Simmonds, Jason; Tian, Pei; Wearne, Katherine; Winkworth, Amanda; Spangenberg, German

    2003-07-01

    Perennial ryegrass (Lolium perenne) is a major forage grass of temperate pastures. A genomics program has been undertaken generating over 52,000 expressed sequence tags (ESTs). Cluster analysis of the ESTs identified approximately 14,600 ryegrass unigenes. In this report, we described the application of ryegrass unigene cDNAs to produce ryegrass 15K microarray. Fifteen microarray hybridisations were performed with labeled total RNA isolated from a variety of plant organs and developmental stages. In a proof of concept, gene expression profiling of ryegrass ESTs using the 15K unigene microarrays has been established using several known genes and two cluster analysis approaches (parallel coordinate planes plot and hierarchical clustering). The expression profile of the known genes (e.g. rubisco and invertase) corresponds well with published data. The microarray expression profile of a ryegrass putative root specific kinase gene was also verified with Northern blotting. This combination of DNA microarray hybridisations and cluster analysis can be applied as a tool for the identification of novel sequences of unknown function.

  14. Microarray analysis of potential genes in the pathogenesis of recurrent oral ulcer.

    PubMed

    Han, Jingying; He, Zhiwei; Li, Kun; Hou, Lu

    2015-01-01

    Recurrent oral ulcer seriously threatens patients' daily life and health. This study investigated potential genes and pathways that participate in the pathogenesis of recurrent oral ulcer by high throughput bioinformatic analysis. RT-PCR and Western blot were applied to further verify screened interleukins effect. Recurrent oral ulcer related genes were collected from websites and papers, and further found out from Human Genome 280 6.0 microarray data. Each pathway of recurrent oral ulcer related genes were got through chip hybridization. RT-PCR was applied to test four recurrent oral ulcer related genes to verify the microarray data. Data transformation, scatter plot, clustering analysis, and expression pattern analysis were used to analyze recurrent oral ulcer related gene expression changes. Recurrent oral ulcer gene microarray was successfully established. Microarray showed that 551 genes involved in recurrent oral ulcer activity and 196 genes were recurrent oral ulcer related genes. Of them, 76 genes up-regulated, 62 genes down-regulated, and 58 genes up-/down-regulated. Total expression level up-regulated 752 times (60%) and down-regulated 485 times (40%). IL-2 plays an important role in the occurrence, development and recurrence of recurrent oral ulcer on the mRNA and protein levels. Gene microarray can be used to analyze potential genes and pathways in recurrent oral ulcer. IL-2 may be involved in the pathogenesis of recurrent oral ulcer.

  15. Automating dChip: toward reproducible sharing of microarray data analysis.

    PubMed

    Li, Cheng

    2008-05-08

    During the past decade, many software packages have been developed for analysis and visualization of various types of microarrays. We have developed and maintained the widely used dChip as a microarray analysis software package accessible to both biologist and data analysts. However, challenges arise when dChip users want to analyze large number of arrays automatically and share data analysis procedures and parameters. Improvement is also needed when the dChip user support team tries to identify the causes of reported analysis errors or bugs from users. We report here implementation and application of the dChip automation module. Through this module, dChip automation files can be created to include menu steps, parameters, and data viewpoints to run automatically. A data-packaging function allows convenient transfer from one user to another of the dChip software, microarray data, and analysis procedures, so that the second user can reproduce the entire analysis session of the first user. An analysis report file can also be generated during an automated run, including analysis logs, user comments, and viewpoint screenshots. The dChip automation module is a step toward reproducible research, and it can prompt a more convenient and reproducible mechanism for sharing microarray software, data, and analysis procedures and results. Automation data packages can also be used as publication supplements. Similar automation mechanisms could be valuable to the research community if implemented in other genomics and bioinformatics software packages.

  16. Microarray analysis of circular RNA expression patterns in polarized macrophages

    PubMed Central

    Zhang, Yingying; Zhang, Yao; Li, Xueqin; Zhang, Mengying; Lv, Kun

    2017-01-01

    Circular RNAs (circRNAs) are generated from diverse genomic locations and are a new player in the regulation of post-transcriptional gene expression. Recent studies have revealed that circRNAs play a crucial role in fine-tuning the level of microRNA (miRNA)-mediated regulation of gene expression by sequestering miRNAs. The interaction of circRNAs with disease-associated miRNAs suggests that circRNAs are important in the pathology of disease. However, the effects and roles of circRNAs in macrophage polarization have yet to be explored. In the present study, we performed a circRNA microarray to compare the circRNA expression profiles of bone marrow-derived macrophages (BMDMs) under two distinct polarizing conditions (M1 macrophages induced by interferon-γ and LPS stimulation, and M2 macrophages induced by interleukin-4 stimulation). Our results showed that a total of 189 circRNAs were differentially expressed between M1 and M2 macrophages. Differentially expressed circRNAs with a high fold-change were selected for validation by RT-qPCR: circRNA-003780, circRNA-010056, and circRNA-010231 were upregulated and circRNA-003424, circRNA-013630, circRNA-001489 and circRNA-018127 were downregulated (fold-change >4, P<0.05) in M1 compared to M2, which was found to correlate with the microarray data. Furthermore, the most differentially expressed circRNAs within all the comparisons were annotated in detail with circRNA/miRNA interaction information using miRNA target prediction software. In conclusion, the present study provides novel insight into the role of circRNAs in macrophage differentiation and polarization. PMID:28075448

  17. Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data.

    PubMed

    Tan, Qihua; Thomassen, Mads; Burton, Mark; Mose, Kristian Fredløv; Andersen, Klaus Ejner; Hjelmborg, Jacob; Kruse, Torben

    2017-06-06

    Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

  18. A process for analysis of microarray comparative genomics hybridisation studies for bacterial genomes

    PubMed Central

    Carter, Ben; Wu, Guanghui; Woodward, Martin J; Anjum, Muna F

    2008-01-01

    Background Microarray based comparative genomic hybridisation (CGH) experiments have been used to study numerous biological problems including understanding genome plasticity in pathogenic bacteria. Typically such experiments produce large data sets that are difficult for biologists to handle. Although there are some programmes available for interpretation of bacterial transcriptomics data and CGH microarray data for looking at genetic stability in oncogenes, there are none specifically to understand the mosaic nature of bacterial genomes. Consequently a bottle neck still persists in accurate processing and mathematical analysis of these data. To address this shortfall we have produced a simple and robust CGH microarray data analysis process that may be automated in the future to understand bacterial genomic diversity. Results The process involves five steps: cleaning, normalisation, estimating gene presence and absence or divergence, validation, and analysis of data from test against three reference strains simultaneously. Each stage of the process is described and we have compared a number of methods available for characterising bacterial genomic diversity, for calculating the cut-off between gene presence and absence or divergence, and shown that a simple dynamic approach using a kernel density estimator performed better than both established, as well as a more sophisticated mixture modelling technique. We have also shown that current methods commonly used for CGH microarray analysis in tumour and cancer cell lines are not appropriate for analysing our data. Conclusion After carrying out the analysis and validation for three sequenced Escherichia coli strains, CGH microarray data from 19 E. coli O157 pathogenic test strains were used to demonstrate the benefits of applying this simple and robust process to CGH microarray studies using bacterial genomes. PMID:18230148

  19. EMMA 2--a MAGE-compliant system for the collaborative analysis and integration of microarray data.

    PubMed

    Dondrup, Michael; Albaum, Stefan P; Griebel, Thasso; Henckel, Kolja; Jünemann, Sebastian; Kahlke, Tim; Kleindt, Christiane K; Küster, Helge; Linke, Burkhard; Mertens, Dominik; Mittard-Runte, Virginie; Neuweger, Heiko; Runte, Kai J; Tauch, Andreas; Tille, Felix; Pühler, Alfred; Goesmann, Alexander

    2009-02-06

    Understanding transcriptional regulation by genome-wide microarray studies can contribute to unravel complex relationships between genes. Attempts to standardize the annotation of microarray data include the Minimum Information About a Microarray Experiment (MIAME) recommendations, the MAGE-ML format for data interchange, and the use of controlled vocabularies or ontologies. The existing software systems for microarray data analysis implement the mentioned standards only partially and are often hard to use and extend. Integration of genomic annotation data and other sources of external knowledge using open standards is therefore a key requirement for future integrated analysis systems. The EMMA 2 software has been designed to resolve shortcomings with respect to full MAGE-ML and ontology support and makes use of modern data integration techniques. We present a software system that features comprehensive data analysis functions for spotted arrays, and for the most common synthesized oligo arrays such as Agilent, Affymetrix and NimbleGen. The system is based on the full MAGE object model. Analysis functionality is based on R and Bioconductor packages and can make use of a compute cluster for distributed services. Our model-driven approach for automatically implementing a full MAGE object model provides high flexibility and compatibility. Data integration via SOAP-based web-services is advantageous in a distributed client-server environment as the collaborative analysis of microarray data is gaining more and more relevance in international research consortia. The adequacy of the EMMA 2 software design and implementation has been proven by its application in many distributed functional genomics projects. Its scalability makes the current architecture suited for extensions towards future transcriptomics methods based on high-throughput sequencing approaches which have much higher computational requirements than microarrays.

  20. The Use of Atomic Force Microscopy for 3D Analysis of Nucleic Acid Hybridization on Microarrays.

    PubMed

    Dubrovin, E V; Presnova, G V; Rubtsova, M Yu; Egorov, A M; Grigorenko, V G; Yaminsky, I V

    2015-01-01

    Oligonucleotide microarrays are considered today to be one of the most efficient methods of gene diagnostics. The capability of atomic force microscopy (AFM) to characterize the three-dimensional morphology of single molecules on a surface allows one to use it as an effective tool for the 3D analysis of a microarray for the detection of nucleic acids. The high resolution of AFM offers ways to decrease the detection threshold of target DNA and increase the signal-to-noise ratio. In this work, we suggest an approach to the evaluation of the results of hybridization of gold nanoparticle-labeled nucleic acids on silicon microarrays based on an AFM analysis of the surface both in air and in liquid which takes into account of their three-dimensional structure. We suggest a quantitative measure of the hybridization results which is based on the fraction of the surface area occupied by the nanoparticles.

  1. Immunohistochemistry - microarray analysis of patients with peritoneal metastases of appendiceal or colorectal origin.

    PubMed

    Green, Danielle E; Jayakrishnan, Thejus T; Hwang, Michael; Pappas, Sam G; Gamblin, T Clark; Turaga, Kiran K

    2014-01-01

    The value of immunohistochemistry (IHC)-microarray analysis of pathological specimens in the management of patients is controversial, although preliminary data suggest potential benefit. We describe the characteristics of patients undergoing a commercially available IHC-microarray method in patients with peritoneal metastases (PM) and the feasibility of this technique in this population. We retrospectively analyzed consecutive patients with pathologically confirmed PM from appendiceal or colorectal primary who underwent Caris Molecular Intelligence(™) testing. IHC, microarray, FISH, and mutational analysis were included and stratified by Peritoneal Carcinomatosis Index (PCI) score, histology, and treatment characteristics. Statistical analysis was performed using non-parametric tests. Our study included 5 patients with appendiceal and 11 with colorectal PM. The median age of patients was 51 (IQR 39-65) years, with 11 (68%) female. The median PCI score of the patients was 17 (IQR 10-25). Hyperthermic intra-peritoneal chemoperfusion was performed in 4 (80%) patients with appendiceal primary tumors and 4 (36%) with colorectal primary. KRAS mutations were encountered in 40% of appendiceal vs. 30% colorectal tumors, while BRAF mutations were seen in 40% of colorectal PM and none of the patients with appendiceal PM (p = 0.06). IHC biomarker expression was not significantly different between the two primaries. Sufficient tumor for microarray analysis was found in 44% (n = 7) patients, which was not associated with previous use of chemotherapy (p > 0.20 for 5-FU/LV, Irinotecan and Oxaliplatin). In a small sample of patients with PM, the feasibility and results of IHC-microarray staining based on a commercially available test is reported. The apparent high incidence of the BRAF mutation in patients with PM may potentially offer opportunities for novel therapeutics and suggest that IHC-microarray is a method that can be used in this population.

  2. Comparison of Gene Expression in Peri-implant Soft Tissue and Oral Mucosal Tissue by Microarray Analysis.

    PubMed

    Makabe, Yasushi; Sasaki, Hodaka; Mori, Gentaro; Sekine, Hideshi; Yoshinari, Masao; Yajima, Yasutomo

    2015-01-01

    Implant placement entails disruption of the epithelial continuity, which can lead to various complications. Therefore, the area of mucosal penetration is of particular interest clinically. The goal of the present study was to compare gene expression in peri-implant soft tissue (PIST) with that in oral mucosal tissue (OMT) using microarray analysis, and to investigate which genes were specifically expressed in PIST. The bilateral upper first molars were extracted from 4-week-old rats and titanium alloy implants placed only in the left-side extraction sockets. Four weeks after surgery, samples were harvested from the left-side PIST and right-side OMT and total RNA samples isolated. Microarray analysis was used to compare gene expression in PIST and OMT, which was then confirmed using quantitative real-time polymerase chain reaction. Immunohistochemical staining was also performed to confirm protein level expression. The number of genes expressed with more than a twofold change in PIST compared with OMT was 1,102, of which 750 genes were upregulated and 352 genes were downregulated. The messenger RNA (mRNA) expression of three selected genes-Ceacam1, Ifitm1, and MUC4-were more significantly expressed in PIST than in OMT(P < .01). Immunohistochemical localization of CEACAM1, IFITM1, and MUC4 was observed in PIST, but no immunoreaction was recognized in OMT. The result of microarray analysis showed that, because of implant placement, 750 genes were upregulated in PIST compared with OMT. CEACAM1, IFITM1, and MUC4 were specifically upregulated in PIST.

  3. A protein microarray-based analysis of S-nitrosylation

    PubMed Central

    Foster, Matthew W.; Forrester, Michael T.; Stamler, Jonathan S.

    2009-01-01

    The ubiquitous cellular influence of nitric oxide (NO) is exerted substantially through protein S-nitrosylation. Whereas NO is highly promiscuous, physiological S-nitrosylation is typically restricted to one or very few Cys residue(s) in target proteins. The molecular basis for this specificity may derive from properties of the target protein, the S-nitrosylating species, or both. Here, we describe a protein microarray-based approach to investigate determinants of S-nitrosylation by biologically relevant low-mass S-nitrosothiols (SNOs). We identify large sets of yeast and human target proteins, among which those with active-site Cys thiols residing at N termini of α-helices or within catalytic loops were particularly prominent. However, S-nitrosylation varied substantially even within these families of proteins (e.g., papain-related Cys-dependent hydrolases and rhodanese/Cdc25 phosphatases), suggesting that neither secondary structure nor intrinsic nucleophilicity of Cys thiols was sufficient to explain specificity. Further analyses revealed a substantial influence of NO-donor stereochemistry and structure on efficiency of S-nitrosylation as well as an unanticipated and important role for allosteric effectors. Thus, high-throughput screening and unbiased proteome coverage reveal multifactorial determinants of S-nitrosylation (which may be overlooked in alternative proteomic analyses), and support the idea that target specificity can be achieved through rational design of S-nitrosothiols. PMID:19864628

  4. DNA microarray analysis of genes differentially expressed in adipocyte differentiation.

    PubMed

    Yin, Chunyan; Xiao, Yanfeng; Zhang, Wei; Xu, Erdi; Liu, Weihua; Yi, Xiaoqing; Chang, Ming

    2014-06-01

    In the present study, the human liposarcoma cell line SW872 was used to identify global changes in gene expression profiles occurring during adipogenesis. We further explored some of the genes expressed during the late phase of adipocyte differentiation. These genes may play a major role in promoting excessive proliferation and accumulation of lipid droplets, which contribute to the development of obesity. By using microarray-based technology, we examined differential gene expression in early differentiated adipocytes and late differentiated adipocytes. Validated genes exhibited a greater than or equal to 10-fold increase in the late phase of adipocyte differentiation by polymerase chain reaction (RT-PCR). Compared with undifferentiated preadipocytes, we found that 763 genes were increased in early differentiated adipocytes, and 667 genes were increased in later differentiated adipocytes. Furthermore, 21 genes were found being expressed 10-fold higher in the late phase of adipocyte differentiation. The results were in accordance with the RTPCR test, which validated 11 genes, namely, CIDEC, PID1, LYRM1, ADD1, PPAR?2, ANGPTL4, ADIPOQ, ACOX1, FIP1L1, MAP3K2 and PEX14. Most of these genes were found being expressed in the later phase of adipocyte differentiation involved in obesity-related diseases. The findings may help to better understand the mechanism of obesity and related diseases.

  5. Microarray Analysis of the Microflora of Root Caries in Elderly

    PubMed Central

    Preza, Dorita; Olsen, Ingar; Willumsen, Tiril; Boches, Susan K.; Cotton, Sean L.; Grinde, Bjørn; Paster, Bruce J.

    2009-01-01

    Purpose The present study used a new 16S rRNA-based microarray with probes for over 300 bacterial species better define the bacterial profiles of healthy root surfaces and root caries (RC) in the elderly. Materials Supragingival plaque was collected from 20 healthy subjects (Controls) and from healthy and carious roots and carious dentin from 21 RC subjects (Patients). Results Collectively, 179 bacterial species and species groups were detected. A higher bacterial diversity was observed in the Controls as compared to Patients. Lactobacillus casei/paracasei/rhamnosus and Pseudoramibacter alactolyticus were notably associated with most root caries samples. Streptococcus mutans was detected more frequently in the infected dentin than in the other samples, but the difference was not significant. Actinomyces were found more frequently in Controls. Conclusion Actinomyces and S. mutans may play a limited role as pathogens of RC. The results from this study were in agreement with those of our previous study based on 16S rRNA gene sequencing with 72% of the species being detected with both methods. PMID:19039610

  6. Microarray analysis reveals differential gene expression in hybrid sunflower species

    PubMed Central

    LAI, ZHAO; GROSS, BRIANA L.; YIZOU; ANDREWS, JUSTEN; RIESEBERG, LOREN H.

    2008-01-01

    This paper describes the creation of a cDNA microarray for annual sunflowers and its use to elucidate patterns of gene expression in Helianthus annuus, Helianthus petiolaris, and the homoploid hybrid species Helianthus deserticola. The array comprises 3743 ESTs (expressed sequence tags) representing approximately 2897 unique genes. It has an average clone/EST identity rate of 91%, is applicable across species boundaries within the annual sunflowers, and shows patterns of gene expression that are highly reproducible according to real-time RT–PCR (reverse transcription–polymerase chain reaction) results. Overall, 12.8% of genes on the array showed statistically significant differential expression across the three species. Helianthus deserticola displayed transgressive, or extreme, expression for 58 genes, with roughly equal numbers exhibiting up- or down-regulation relative to both parental species. Transport-related proteins were strongly over-represented among the transgressively expressed genes, which makes functional sense given the extreme desert floor habitat of H. deserticola. The potential adaptive value of differential gene expression was evaluated for five genes in two populations of early generation (BC2) hybrids between the parental species grown in the H. deserticola habitat. One gene (a G protein-coupled receptor) had a significant association with fitness and maps close to a QTL controlling traits that may be adaptive in the desert habitat. PMID:16626449

  7. Microarray Analysis of Microbiota of Gingival Lesions in Noma Patients

    PubMed Central

    Huyghe, Antoine; François, Patrice; Mombelli, Andrea; Tangomo, Manuela; Girard, Myriam; Baratti-Mayer, Denise; Bolivar, Ignacio; Pittet, Didier; Schrenzel, Jacques

    2013-01-01

    Noma (cancrum oris) is a gangrenous disease of unknown etiology affecting the maxillo-facial region of young children in extremely limited resource countries. In an attempt to better understand the microbiological events occurring during this disease, we used phylogenetic and low-density microarrays targeting the 16S rRNA gene to characterize the gingival flora of acute noma and acute necrotizing gingivitis (ANG) lesions, and compared them to healthy control subjects of the same geographical and social background. Our observations raise doubts about Fusobacterium necrophorum, a previously suspected causative agent of noma, as this species was not associated with noma lesions. Various oral pathogens were more abundant in noma lesions, notably Atopobium spp., Prevotella intermedia, Peptostreptococcus spp., Streptococcus pyogenes and Streptococcus anginosus. On the other hand, pathogens associated with periodontal diseases such as Aggregatibacter actinomycetemcomitans, Capnocytophaga spp., Porphyromonas spp. and Fusobacteriales were more abundant in healthy controls. Importantly, the overall loss of bacterial diversity observed in noma samples as well as its homology to that of ANG microbiota supports the hypothesis that ANG might be the immediate step preceding noma. PMID:24086784

  8. A protein microarray-based analysis of S-nitrosylation.

    PubMed

    Foster, Matthew W; Forrester, Michael T; Stamler, Jonathan S

    2009-11-10

    The ubiquitous cellular influence of nitric oxide (NO) is exerted substantially through protein S-nitrosylation. Whereas NO is highly promiscuous, physiological S-nitrosylation is typically restricted to one or very few Cys residue(s) in target proteins. The molecular basis for this specificity may derive from properties of the target protein, the S-nitrosylating species, or both. Here, we describe a protein microarray-based approach to investigate determinants of S-nitrosylation by biologically relevant low-mass S-nitrosothiols (SNOs). We identify large sets of yeast and human target proteins, among which those with active-site Cys thiols residing at N termini of alpha-helices or within catalytic loops were particularly prominent. However, S-nitrosylation varied substantially even within these families of proteins (e.g., papain-related Cys-dependent hydrolases and rhodanese/Cdc25 phosphatases), suggesting that neither secondary structure nor intrinsic nucleophilicity of Cys thiols was sufficient to explain specificity. Further analyses revealed a substantial influence of NO-donor stereochemistry and structure on efficiency of S-nitrosylation as well as an unanticipated and important role for allosteric effectors. Thus, high-throughput screening and unbiased proteome coverage reveal multifactorial determinants of S-nitrosylation (which may be overlooked in alternative proteomic analyses), and support the idea that target specificity can be achieved through rational design of S-nitrosothiols.

  9. A probabilistic framework for microarray data analysis: fundamental probability models and statistical inference.

    PubMed

    Ogunnaike, Babatunde A; Gelmi, Claudio A; Edwards, Jeremy S

    2010-05-21

    Gene expression studies generate large quantities of data with the defining characteristic that the number of genes (whose expression profiles are to be determined) exceed the number of available replicates by several orders of magnitude. Standard spot-by-spot analysis still seeks to extract useful information for each gene on the basis of the number of available replicates, and thus plays to the weakness of microarrays. On the other hand, because of the data volume, treating the entire data set as an ensemble, and developing theoretical distributions for these ensembles provides a framework that plays instead to the strength of microarrays. We present theoretical results that under reasonable assumptions, the distribution of microarray intensities follows the Gamma model, with the biological interpretations of the model parameters emerging naturally. We subsequently establish that for each microarray data set, the fractional intensities can be represented as a mixture of Beta densities, and develop a procedure for using these results to draw statistical inference regarding differential gene expression. We illustrate the results with experimental data from gene expression studies on Deinococcus radiodurans following DNA damage using cDNA microarrays.

  10. Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB.

    PubMed

    Chatziioannou, Aristotelis; Moulos, Panagiotis; Kolisis, Fragiskos N

    2009-10-27

    The microarray data analysis realm is ever growing through the development of various tools, open source and commercial. However there is absence of predefined rational algorithmic analysis workflows or batch standardized processing to incorporate all steps, from raw data import up to the derivation of significantly differentially expressed gene lists. This absence obfuscates the analytical procedure and obstructs the massive comparative processing of genomic microarray datasets. Moreover, the solutions provided, heavily depend on the programming skills of the user, whereas in the case of GUI embedded solutions, they do not provide direct support of various raw image analysis formats or a versatile and simultaneously flexible combination of signal processing methods. We describe here Gene ARMADA (Automated Robust MicroArray Data Analysis), a MATLAB implemented platform with a Graphical User Interface. This suite integrates all steps of microarray data analysis including automated data import, noise correction and filtering, normalization, statistical selection of differentially expressed genes, clustering, classification and annotation. In its current version, Gene ARMADA fully supports 2 coloured cDNA and Affymetrix oligonucleotide arrays, plus custom arrays for which experimental details are given in tabular form (Excel spreadsheet, comma separated values, tab-delimited text formats). It also supports the analysis of already processed results through its versatile import editor. Besides being fully automated, Gene ARMADA incorporates numerous functionalities of the Statistics and Bioinformatics Toolboxes of MATLAB. In addition, it provides numerous visualization and exploration tools plus customizable export data formats for seamless integration by other analysis tools or MATLAB, for further processing. Gene ARMADA requires MATLAB 7.4 (R2007a) or higher and is also distributed as a stand-alone application with MATLAB Component Runtime. Gene ARMADA provides a

  11. Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB

    PubMed Central

    Chatziioannou, Aristotelis; Moulos, Panagiotis; Kolisis, Fragiskos N

    2009-01-01

    Background The microarray data analysis realm is ever growing through the development of various tools, open source and commercial. However there is absence of predefined rational algorithmic analysis workflows or batch standardized processing to incorporate all steps, from raw data import up to the derivation of significantly differentially expressed gene lists. This absence obfuscates the analytical procedure and obstructs the massive comparative processing of genomic microarray datasets. Moreover, the solutions provided, heavily depend on the programming skills of the user, whereas in the case of GUI embedded solutions, they do not provide direct support of various raw image analysis formats or a versatile and simultaneously flexible combination of signal processing methods. Results We describe here Gene ARMADA (Automated Robust MicroArray Data Analysis), a MATLAB implemented platform with a Graphical User Interface. This suite integrates all steps of microarray data analysis including automated data import, noise correction and filtering, normalization, statistical selection of differentially expressed genes, clustering, classification and annotation. In its current version, Gene ARMADA fully supports 2 coloured cDNA and Affymetrix oligonucleotide arrays, plus custom arrays for which experimental details are given in tabular form (Excel spreadsheet, comma separated values, tab-delimited text formats). It also supports the analysis of already processed results through its versatile import editor. Besides being fully automated, Gene ARMADA incorporates numerous functionalities of the Statistics and Bioinformatics Toolboxes of MATLAB. In addition, it provides numerous visualization and exploration tools plus customizable export data formats for seamless integration by other analysis tools or MATLAB, for further processing. Gene ARMADA requires MATLAB 7.4 (R2007a) or higher and is also distributed as a stand-alone application with MATLAB Component Runtime

  12. Microarray and KOG analysis of Acanthamoeba healyi genes up-regulated by mouse-brain passage.

    PubMed

    Moon, Eun-Kyung; Xuan, Ying-Hua; Kong, Hyun-Hee

    2014-08-01

    Long-term cultivation in a laboratory could reduce the virulence of Acanthamoeba. To identify virulence factors of Acanthamoeba, the authors compared the transcription profiles of long-term cultivated Acanthamoeba healyi (OLD) and three times mouse-brain passaged A. healyi (MBP) using microarray analysis and eukaryotic orthologous group (KOG) assignments. Microarray analysis revealed that 601 genes were up-regulated by mouse-brain passage. The results of real-time PCR of 8 randomly selected genes up-regulated in the MBP strain confirmed microarray analysis findings. KOG assignments showed relatively higher percentages of the MBP strain up-regulated genes in T article (signal transduction mechanism), O article (posttranslational modification, protein turnover, chaperones), C article (energy production and conversion), and J article (translation, ribosomal structure and biogenesis). In particular, the MBP strain showed higher expressions of cysteine protease and metalloprotease. A comparison of KOG assignments by microarray analysis and previous EST (expressed sequence tags) analysis showed similar populations of up-regulated genes. These results provide important information regarding the identification of virulence factors of pathogenic Acanthamoeba.

  13. ADOA3R as a therapeutic target in experimental colitis: proof by validated high-density oligonucleotide microarray analysis.

    PubMed

    Guzman, Jorge; Yu, Jun Ge; Suntres, Zacharias; Bozarov, Andrey; Cooke, Helen; Javed, Najma; Auer, Herbert; Palatini, Jeff; Hassanain, Hamdy H; Cardounel, Arturo J; Javed, Asad; Grants, Iveta; Wunderlich, Jacqueline E; Christofi, Fievos L

    2006-08-01

    Adenosine A3 receptors (ADOA3Rs) are emerging as novel purinergic targets for treatment of inflammatory diseases. Our goal was to assess the protective effect of the ADOA3R agonist N(6)-(3-iodobenzyl)-adenosine-5-N-methyluronamide (IB-MECA) on gene dysregulation and injury in a rat chronic model of 2,4,6-trinitrobenzene sulfonic acid (TNBS)--induced colitis. It was necessary to develop and validate a microarray technique for testing the protective effects of purine-based drugs in experimental inflammatory bowel disease. High-density oligonucleotide microarray analysis of gene dysregulation was assessed in colons from normal, TNBS-treated (7 days), and oral IB-MECA-treated rats (1.5 mg/kg b.i.d.) using a rat RNU34 neural GeneChip of 724 genes and SYBR green polymerase chain reaction. Analysis included clinical evaluation, weight loss assessment, and electron paramagnetic resonance imaging/spin-trap monitoring of free radicals. Remarkable colitis-induced gene dysregulation occurs in the most exceptional cluster of 5.4% of the gene pool, revealing 2 modes of colitis-related dysregulation. Downregulation occurs in membrane transporter, mitogen-activated protein (MAP) kinase, and channel genes. Upregulation occurs in chemokine, cytokine/inflammatory, stress, growth factor, intracellular signaling, receptor, heat shock protein, retinoid metabolism, neural, remodeling, and redox-sensitive genes. Oral IB-MECA prevented dysregulation in 92% of these genes, histopathology, gut injury, and weight loss. IB-MECA or adenosine suppressed elevated free radicals in ex vivo inflamed gut. Oral IB-MECA blocked the colitis-induced upregulation (90% of genes tested (33 of 37 genes). We conclude that our validated high

  14. Polypyrrole-peptide microarray for biomolecular interaction analysis by SPR imaging

    PubMed Central

    Villiers, Marie-Bernadette; Cortès, Sandra; Brakha, Carine; Marche, Patrice; Roget, André; Livache, Thierry

    2009-01-01

    Nowadays, high-throughput analysis of biological events is a great challenge which could take benefit of the recent development of microarray devices. The great potential of such technology is related to the availability of a chip bearing a large set of probes, stable and easy to obtain, and suitable for ligand binding detection. Here, we described a new method based on polypyrrole chemistry and allowing the covalent immobilization of peptides in a microarray format and on a gold surface compatible with the use of Surface Plasmon Resonance. This technique is then illustrated by the detection and characterization of antibodies induced by hepatitis C virus and present in patients’serums. PMID:19649603

  15. Microarray Meta-Analysis and Cross-Platform Normalization: Integrative Genomics for Robust Biomarker Discovery

    PubMed Central

    Walsh, Christopher J.; Hu, Pingzhao; Batt, Jane; Dos Santos, Claudia C.

    2015-01-01

    The diagnostic and prognostic potential of the vast quantity of publicly-available microarray data has driven the development of methods for integrating the data from different microarray platforms. Cross-platform integration, when appropriately implemented, has been shown to improve reproducibility and robustness of gene signature biomarkers. Microarray platform integration can be conceptually divided into approaches that perform early stage integration (cross-platform normalization) versus late stage data integration (meta-analysis). A growing number of statistical methods and associated software for platform integration are available to the user, however an understanding of their comparative performance and potential pitfalls is critical for best implementation. In this review we provide evidence-based, practical guidance to researchers performing cross-platform integration, particularly with an objective to discover biomarkers. PMID:27600230

  16. Microarray Meta-Analysis and Cross-Platform Normalization: Integrative Genomics for Robust Biomarker Discovery.

    PubMed

    Walsh, Christopher J; Hu, Pingzhao; Batt, Jane; Santos, Claudia C Dos

    2015-08-21

    The diagnostic and prognostic potential of the vast quantity of publicly-available microarray data has driven the development of methods for integrating the data from different microarray platforms. Cross-platform integration, when appropriately implemented, has been shown to improve reproducibility and robustness of gene signature biomarkers. Microarray platform integration can be conceptually divided into approaches that perform early stage integration (cross-platform normalization) versus late stage data integration (meta-analysis). A growing number of statistical methods and associated software for platform integration are available to the user, however an understanding of their comparative performance and potential pitfalls is critical for best implementation. In this review we provide evidence-based, practical guidance to researchers performing cross-platform integration, particularly with an objective to discover biomarkers.

  17. A renewed approach to the nonparametric analysis of replicated microarray experiments.

    PubMed

    Jung, Klaus; Quast, Karsten; Gannoun, Ali; Urfer, Wolfgang

    2006-04-01

    DNA-microarrays find broad employment in biochemical research. This technology allows the monitoring of the expression levels of thousands of genes at the same time. Often, the goal of a microarray study is to find differentially expressed genes in two different types of tissue, for example normal and cancerous. Multiple hypothesis testing is a useful statistical tool for such studies. One approach using multiple hypothesis testing is nonparametric analysis for replicated microarray experiments. In this paper we present an improved version of this method. We also show how p-values are calculated for all significant genes detected with this testing procedure. All algorithms were implemented in an R-package, and instructions on it's use are included. The package can be downloaded at http://www.statistik.unidortmund.de/de/content/einrichtungen/lehrstuehle/personen/jung.html

  18. Identification of key genes associated with cervical cancer by comprehensive analysis of transcriptome microarray and methylation microarray

    PubMed Central

    LIU, MING-YAN; ZHANG, HONG; HU, YUAN-JING; CHEN, YU-WEI; ZHAO, XIAO-NAN

    2016-01-01

    Cervical cancer is the second most commonly diagnosed type of cancer and the third leading cause of cancer-associated mortality in women. The current study aimed to determine the genes associated with cervical cancer development. Microarray data (GSE55940 and GSE46306) were downloaded from Gene Expression Omnibus. Overlapping genes between the differentially expressed genes (DEGs) in GSE55940 (identified by Limma package) and the differentially methylated genes were screened. Gene Ontology (GO) enrichment analysis was subsequently performed for these genes using the ToppGene database. In GSE55940, 91 downregulated and 151 upregulated DEGs were identified. In GSE46306, 561 overlapping differentially methylated genes were obtained through the differential methylation analysis at the CpG site level, CpG island level and gene level. A total of 5 overlapping genes [dipeptidyl peptidase 4 (DPP4); endothelin 3 (EDN3); fibroblast growth factor 14 (FGF14); tachykinin, precursor 1 (TAC1); and wingless-type MMTV integration site family, member 16 (WNT16)] between the 561 overlapping differentially methylated genes and the 242 DEGs were identified, which were downregulated and hypermethylated simultaneously in cervical cancer samples. Enriched GO terms were receptor binding (involving DPP4, EDN3, FGF14, TAC1 and WNT16), ameboidal-type cell migration (DPP4, EDN3 and TAC1), mitogen-activated protein kinase cascade (FGF14, EDN3 and WNT16) and cell proliferation (EDN3, WNT16, DPP4 and TAC1). These results indicate that DPP4, EDN3, FGF14, TAC1 and WNT16 may be involved in the pathogenesis of cervical cancer. PMID:27347167

  19. Microarray analysis of radiation response genes in primary human fibroblasts

    SciTech Connect

    Kis, Enikoe; Szatmari, Tuende; Keszei, Marton; Farkas, Robert; Esik, Olga; Lumniczky, Katalin; Falus, Andras; Safrany, Geza . E-mail: safrany@hp.osski.hu

    2006-12-01

    Purpose: To identify radiation-induced early transcriptional responses in primary human fibroblasts and understand cellular pathways leading to damage correction. Methods and Materials: Primary human fibroblast cell lines were irradiated with 2 Gy {gamma}-radiation and RNA isolated 2 h later. Radiation-induced transcriptional alterations were investigated with microarrays covering the entire human genome. Time- and dose dependent radiation responses were studied by quantitative real-time polymerase chain reaction (RT-PCR). Results: About 200 genes responded to ionizing radiation on the transcriptional level in primary human fibroblasts. The expression profile depended on individual genetic backgrounds. Thirty genes (28 up- and 2 down-regulated) responded to radiation in identical manner in all investigated cells. Twenty of these consensus radiation response genes were functionally categorized: most of them belong to the DNA damage response (GADD45A, BTG2, PCNA, IER5), regulation of cell cycle and cell proliferation (CDKN1A, PPM1D, SERTAD1, PLK2, PLK3, CYR61), programmed cell death (BBC3, TP53INP1) and signaling (SH2D2A, SLIC1, GDF15, THSD1) pathways. Four genes (SEL10, FDXR, CYP26B1, OR11A1) were annotated to other functional groups. Many of the consensus radiation response genes are regulated by, or regulate p53. Time- and dose-dependent expression profiles of selected consensus genes (CDKN1A, GADD45A, IER5, PLK3, CYR61) were investigated by quantitative RT-PCR. Transcriptional alterations depended on the applied dose, and on the time after irradiation. Conclusions: The data presented here could help in the better understanding of early radiation responses and the development of biomarkers to identify radiation susceptible individuals.

  20. MICROARRAY ANALYSIS OF DICHLOROACETIC ACID-INDUCED CHANGES IN GENE EXPRESSION

    EPA Science Inventory


    MICROARRAY ANALYSIS OF DICHLOROACETIC ACID-INDUCED CHANGES IN GENE EXPRESSION

    Dichloroacetic acid (DCA) is a major by-product of water disinfection by chlorination. Several studies have demonstrated the hepatocarcinogenicity of DCA in rodents when administered in dri...

  1. Diagnostic Yield of Chromosomal Microarray Analysis in an Autism Primary Care Practice: Which Guidelines to Implement?

    ERIC Educational Resources Information Center

    McGrew, Susan G.; Peters, Brittany R.; Crittendon, Julie A.; Veenstra-VanderWeele, Jeremy

    2012-01-01

    Genetic testing is recommended for patients with ASD; however specific recommendations vary by specialty. American Academy of Pediatrics and American Academy of Neurology guidelines recommend G-banded karyotype and Fragile X DNA. The American College of Medical Genetics recommends Chromosomal Microarray Analysis (CMA). We determined the yield of…

  2. The Utility of Chromosomal Microarray Analysis in Developmental and Behavioral Pediatrics

    ERIC Educational Resources Information Center

    Beaudet, Arthur L.

    2013-01-01

    Chromosomal microarray analysis (CMA) has emerged as a powerful new tool to identify genomic abnormalities associated with a wide range of developmental disabilities including congenital malformations, cognitive impairment, and behavioral abnormalities. CMA includes array comparative genomic hybridization (CGH) and single nucleotide polymorphism…

  3. Diagnostic Yield of Chromosomal Microarray Analysis in an Autism Primary Care Practice: Which Guidelines to Implement?

    ERIC Educational Resources Information Center

    McGrew, Susan G.; Peters, Brittany R.; Crittendon, Julie A.; Veenstra-VanderWeele, Jeremy

    2012-01-01

    Genetic testing is recommended for patients with ASD; however specific recommendations vary by specialty. American Academy of Pediatrics and American Academy of Neurology guidelines recommend G-banded karyotype and Fragile X DNA. The American College of Medical Genetics recommends Chromosomal Microarray Analysis (CMA). We determined the yield of…

  4. Enhancing Interdisciplinary Mathematics and Biology Education: A Microarray Data Analysis Course Bridging These Disciplines

    ERIC Educational Resources Information Center

    Tra, Yolande V.; Evans, Irene M.

    2010-01-01

    "BIO2010" put forth the goal of improving the mathematical educational background of biology students. The analysis and interpretation of microarray high-dimensional data can be very challenging and is best done by a statistician and a biologist working and teaching in a collaborative manner. We set up such a collaboration and designed a course on…

  5. The Utility of Chromosomal Microarray Analysis in Developmental and Behavioral Pediatrics

    ERIC Educational Resources Information Center

    Beaudet, Arthur L.

    2013-01-01

    Chromosomal microarray analysis (CMA) has emerged as a powerful new tool to identify genomic abnormalities associated with a wide range of developmental disabilities including congenital malformations, cognitive impairment, and behavioral abnormalities. CMA includes array comparative genomic hybridization (CGH) and single nucleotide polymorphism…

  6. Parents' Perceptions of the Usefulness of Chromosomal Microarray Analysis for Children with Autism Spectrum Disorders

    ERIC Educational Resources Information Center

    Reiff, Marian; Giarelli, Ellen; Bernhardt, Barbara A.; Easley, Ebony; Spinner, Nancy B.; Sankar, Pamela L.; Mulchandani, Surabhi

    2015-01-01

    Clinical guidelines recommend chromosomal microarray analysis (CMA) for all children with autism spectrum disorders (ASDs). We explored the test's perceived usefulness among parents of children with ASD who had undergone CMA, and received a result categorized as pathogenic, variant of uncertain significance, or negative. Fifty-seven parents…

  7. Parents' Perceptions of the Usefulness of Chromosomal Microarray Analysis for Children with Autism Spectrum Disorders

    ERIC Educational Resources Information Center

    Reiff, Marian; Giarelli, Ellen; Bernhardt, Barbara A.; Easley, Ebony; Spinner, Nancy B.; Sankar, Pamela L.; Mulchandani, Surabhi

    2015-01-01

    Clinical guidelines recommend chromosomal microarray analysis (CMA) for all children with autism spectrum disorders (ASDs). We explored the test's perceived usefulness among parents of children with ASD who had undergone CMA, and received a result categorized as pathogenic, variant of uncertain significance, or negative. Fifty-seven parents…

  8. Enhancing Interdisciplinary Mathematics and Biology Education: A Microarray Data Analysis Course Bridging These Disciplines

    ERIC Educational Resources Information Center

    Tra, Yolande V.; Evans, Irene M.

    2010-01-01

    "BIO2010" put forth the goal of improving the mathematical educational background of biology students. The analysis and interpretation of microarray high-dimensional data can be very challenging and is best done by a statistician and a biologist working and teaching in a collaborative manner. We set up such a collaboration and designed a course on…

  9. Scanometric analysis of DNA microarrays using DNA intercalator-conjugated gold nanoparticles.

    PubMed

    Cho, Hyunmin; Jung, Juyeon; Chung, Bong Hyun

    2012-08-07

    We introduce a scanometric detection method for the analysis of DNA microarrays using DNA intercalator-conjugated gold nanoparticles that can be analyzed with the naked eye or with an optical scanner after the enhancement of the AuNPs. Moreover, we successfully detected a hemagglutinin-subtyping DNA array using this method.

  10. Multivariate curve resolution for hyperspectral image analysis :applications to microarray technology.

    SciTech Connect

    Van Benthem, Mark Hilary; Sinclair, Michael B.; Haaland, David Michael; Martinez, M. Juanita (University of New Mexico, Albuquerque, NM); Timlin, Jerilyn Ann; Werner-Washburne, Margaret C. (University of New Mexico, Albuquerque, NM); Aragon, Anthony D. (University of New Mexico, Albuquerque, NM)

    2003-01-01

    Multivariate curve resolution (MCR) using constrained alternating least squares algorithms represents a powerful analysis capability for a quantitative analysis of hyperspectral image data. We will demonstrate the application of MCR using data from a new hyperspectral fluorescence imaging microarray scanner for monitoring gene expression in cells from thousands of genes on the array. The new scanner collects the entire fluorescence spectrum from each pixel of the scanned microarray. Application of MCR with nonnegativity and equality constraints reveals several sources of undesired fluorescence that emit in the same wavelength range as the reporter fluorphores. MCR analysis of the hyperspectral images confirms that one of the sources of fluorescence is due to contaminant fluorescence under the printed DNA spots that is spot localized. Thus, traditional background subtraction methods used with data collected from the current commercial microarray scanners will lead to errors in determining the relative expression of low-expressed genes. With the new scanner and MCR analysis, we generate relative concentration maps of the background, impurity, and fluorescent labels over the entire image. Since the concentration maps of the fluorescent labels are relatively unaffected by the presence of background and impurity emissions, the accuracy and useful dynamic range of the gene expression data are both greatly improved over those obtained by commercial microarray scanners.

  11. MICROARRAY ANALYSIS OF DICHLOROACETIC ACID-INDUCED CHANGES IN GENE EXPRESSION

    EPA Science Inventory


    MICROARRAY ANALYSIS OF DICHLOROACETIC ACID-INDUCED CHANGES IN GENE EXPRESSION

    Dichloroacetic acid (DCA) is a major by-product of water disinfection by chlorination. Several studies have demonstrated the hepatocarcinogenicity of DCA in rodents when administered in dri...

  12. Candidate genes for the progression of malignant gliomas identified by microarray analysis.

    PubMed

    Bozinov, Oliver; Köhler, Sylvia; Samans, Birgit; Benes, Ludwig; Miller, Dorothea; Ritter, Markus; Sure, Ulrich; Bertalanffy, Helmut

    2008-01-01

    Malignant astrocytomas of World Health Organization (WHO) grade III or IV have a reduced median survival time, and possible pathways have been described for the progression of anaplastic astrocytomas and glioblastomas, but the molecular basis of malignant astrocytoma progression is still poorly understood. Microarray analysis provides the chance to accelerate studies by comparison of the expression of thousands of genes in these tumours and consequently identify targeting genes. We compared the transcriptional profile of 4,608 genes in tumours of 15 patients including 6 anaplastic astrocytomas (WHO grade III) and 9 glioblastomas (WHO grade IV) using microarray analysis. The microarray data were corroborated by real-time reverse transcription-polymerase chain reaction analysis of two selected genes. We identified 166 gene alterations with a fold change of 2 and higher whose mRNA levels differed (absolute value of the t statistic of 1.96) between the two malignant glioma groups. Further analyses confirmed same transcription directions for Olig2 and IL-13Ralpha2 in anaplastic astrocytomas as compared to glioblastomas. Microarray analyses with a close binary question reveal numerous interesting candidate genes, which need further histochemical testing after selection for confirmation. IL-13Ralpha2 and Olig2 have been identified and confirmed to be interesting candidate genes whose differential expression likely plays a role in malignant progression of astrocytomas.

  13. Gene and noncoding RNA regulation underlying photoreceptor protection: microarray study of dietary antioxidant saffron and photobiomodulation in rat retina

    PubMed Central

    Zhu, Yuan; Valter, Krisztina; Bisti, Silvia; Eells, Janis; Stone, Jonathan

    2010-01-01

    Purpose To identify the genes and noncoding RNAs (ncRNAs) involved in the neuroprotective actions of a dietary antioxidant (saffron) and of photobiomodulation (PBM). Methods We used a previously published assay of photoreceptor damage, in which albino Sprague Dawley rats raised in dim cyclic illumination (12 h 5 lux, 12 h darkness) were challenged by 24 h exposure to bright (1,000 lux) light. Experimental groups were protected against light damage by pretreatment with dietary saffron (1 mg/kg/day for 21 days) or PBM (9 J/cm2 at the eye, daily for 5 days). RNA from one eye of four animals in each of the six experimental groups (control, light damage [LD], saffron, PBM, saffronLD, and PBMLD) was hybridized to Affymetrix rat genome ST arrays. Quantitative real-time PCR analysis of 14 selected genes was used to validate the microarray results. Results LD caused the regulation of 175 entities (genes and ncRNAs) beyond criterion levels (p<0.05 in comparison with controls, fold-change >2). PBM pretreatment reduced the expression of 126 of these 175 LD-regulated entities below criterion; saffron pretreatment reduced the expression of 53 entities (50 in common with PBM). In addition, PBM pretreatment regulated the expression of 67 entities not regulated by LD, while saffron pretreatment regulated 122 entities not regulated by LD (48 in common with PBM). PBM and saffron, given without LD, regulated genes and ncRNAs beyond criterion levels, but in lesser numbers than during their protective action. A high proportion of the entities regulated by LD (>90%) were known genes. By contrast, ncRNAs were prominent among the entities regulated by PBM and saffron in their neuroprotective roles (73% and 62%, respectively). Conclusions Given alone, saffron and (more prominently) PBM both regulated significant numbers of genes and ncRNAs. Given before retinal exposure to damaging light, thus while exerting their neuroprotective action, they regulated much larger numbers of entities

  14. Array2BIO: A Comprehensive Suite of Utilities for the Analysis of Microarray Data

    SciTech Connect

    Loots, G G; Chain, P G; Mabery, S; Rasley, A; Garcia, E; Ovcharenko, I

    2006-02-13

    We have developed an integrative and automated toolkit for the analysis of Affymetrix microarray data, named Array2BIO. It identifies groups of coexpressed genes using two complementary approaches--comparative analysis of signal versus control microarrays and clustering analysis of gene expression across different conditions. The identified genes are assigned to functional categories based on the Gene Ontology classification, and a detection of corresponding KEGG protein interaction pathways. Array2BIO reliably handles low-expressor genes and provides a set of statistical methods to quantify the odds of observations, including the Benjamini-Hochberg and Bonferroni multiple testing corrections. Automated interface with the ECR Browser provides evolutionary conservation analysis of identified gene loci while the interconnection with Creme allows high-throughput analysis of human promoter regions and prediction of gene regulatory elements that underlie the observed expression patterns. Array2BIO is publicly available at http://array2bio.dcode.org.

  15. Robust gene selection methods using weighting schemes for microarray data analysis.

    PubMed

    Kang, Suyeon; Song, Jongwoo

    2017-09-02

    A common task in microarray data analysis is to identify informative genes that are differentially expressed between two different states. Owing to the high-dimensional nature of microarray data, identification of significant genes has been essential in analyzing the data. However, the performances of many gene selection techniques are highly dependent on the experimental conditions, such as the presence of measurement error or a limited number of sample replicates. We have proposed new filter-based gene selection techniques, by applying a simple modification to significance analysis of microarrays (SAM). To prove the effectiveness of the proposed method, we considered a series of synthetic datasets with different noise levels and sample sizes along with two real datasets. The following findings were made. First, our proposed methods outperform conventional methods for all simulation set-ups. In particular, our methods are much better when the given data are noisy and sample size is small. They showed relatively robust performance regardless of noise level and sample size, whereas the performance of SAM became significantly worse as the noise level became high or sample size decreased. When sufficient sample replicates were available, SAM and our methods showed similar performance. Finally, our proposed methods are competitive with traditional methods in classification tasks for microarrays. The results of simulation study and real data analysis have demonstrated that our proposed methods are effective for detecting significant genes and classification tasks, especially when the given data are noisy or have few sample replicates. By employing weighting schemes, we can obtain robust and reliable results for microarray data analysis.

  16. Microarray Cluster Analysis of Irradiated Growth Plate Zones Following Laser Microdissection

    SciTech Connect

    Damron, Timothy A. Zhang Mingliang; Pritchard, Meredith R.; Middleton, Frank A.; Horton, Jason A.; Margulies, Bryan M.; Strauss, Judith A.; Farnum, Cornelia E.; Spadaro, Joseph A.

    2009-07-01

    Purpose: Genes and pathways involved in early growth plate chondrocyte recovery after fractionated irradiation were sought as potential targets for selective radiorecovery modulation. Materials and Methods: Three groups of six 5-week male Sprague-Dawley rats underwent fractionated irradiation to the right tibiae over 5 days, totaling 17.5 Gy, and then were killed at 7, 11, and 16 days after the first radiotherapy fraction. The growth plates were collected from the proximal tibiae bilaterally and subsequently underwent laser microdissection to separate reserve, perichondral, proliferative, and hypertrophic zones. Differential gene expression was analyzed between irradiated right and nonirradiated left tibia using RAE230 2.0 GeneChip microarray, compared between zones and time points and subjected to functional pathway cluster analysis with real-time polymerase chain reaction to confirm selected results. Results: Each zone had a number of pathways showing enrichment after the pattern of hypothesized importance to growth plate recovery, yet few met the strictest criteria. The proliferative and hypertrophic zones showed both the greatest number of genes with a 10-fold right/left change at 7 days after initiation of irradiation and enrichment of the most functional pathways involved in bone, cartilage, matrix, or skeletal development. Six genes confirmed by real-time polymerase chain reaction to have early upregulation included insulin-like growth factor 2, procollagen type I alpha 2, matrix metallopeptidase 9, parathyroid hormone receptor 1, fibromodulin, and aggrecan 1. Conclusions: Nine overlapping pathways in the proliferative and hypertrophic zones (skeletal development, ossification, bone remodeling, cartilage development, extracellular matrix structural constituent, proteinaceous extracellular matrix, collagen, extracellular matrix, and extracellular matrix part) may play key roles in early growth plate radiorecovery.

  17. Gene microarray data analysis using parallel point-symmetry-based clustering.

    PubMed

    Sarkar, Anasua; Maulik, Ujjwal

    2015-01-01

    Identification of co-expressed genes is the central goal in microarray gene expression analysis. Point-symmetry-based clustering is an important unsupervised learning technique for recognising symmetrical convex- or non-convex-shaped clusters. To enable fast clustering of large microarray data, we propose a distributed time-efficient scalable approach for point-symmetry-based K-Means algorithm. A natural basis for analysing gene expression data using symmetry-based algorithm is to group together genes with similar symmetrical expression patterns. This new parallel implementation also satisfies linear speedup in timing without sacrificing the quality of clustering solution on large microarray data sets. The parallel point-symmetry-based K-Means algorithm is compared with another new parallel symmetry-based K-Means and existing parallel K-Means over eight artificial and benchmark microarray data sets, to demonstrate its superiority, in both timing and validity. The statistical analysis is also performed to establish the significance of this message-passing-interface based point-symmetry K-Means implementation. We also analysed the biological relevance of clustering solutions.

  18. Informatics Enhanced SNP Microarray Analysis of 30 Miscarriage Samples Compared to Routine Cytogenetics

    PubMed Central

    Lathi, Ruth B.; Loring, Megan; Massie, Jamie A. M.; Demko, Zachary P.; Johnson, David; Sigurjonsson, Styrmir; Gemelos, George; Rabinowitz, Matthew

    2012-01-01

    Purpose The metaphase karyotype is often used as a diagnostic tool in the setting of early miscarriage; however this technique has several limitations. We evaluate a new technique for karyotyping that uses single nucleotide polymorphism microarrays (SNP). This technique was compared in a blinded, prospective fashion, to the traditional metaphase karyotype. Methods Patients undergoing dilation and curettage for first trimester miscarriage between February and August 2010 were enrolled. Samples of chorionic villi were equally divided and sent for microarray testing in parallel with routine cytogenetic testing. Results Thirty samples were analyzed, with only four discordant results. Discordant results occurred when the entire genome was duplicated or when a balanced rearrangement was present. Cytogenetic karyotyping took an average of 29 days while microarray-based karytoyping took an average of 12 days. Conclusions Molecular karyotyping of POC after missed abortion using SNP microarray analysis allows for the ability to detect maternal cell contamination and provides rapid results with good concordance to standard cytogenetic analysis. PMID:22403611

  19. Application of Equilibrium Models of Solution Hybridization to Microarray Design and Analysis

    PubMed Central

    Gharaibeh, Raad Z.; Newton, Joshua M.; Weller, Jennifer W.; Gibas, Cynthia J.

    2010-01-01

    Background The probe percent bound value, calculated using multi-state equilibrium models of solution hybridization, is shown to be useful in understanding the hybridization behavior of microarray probes having 50 nucleotides, with and without mismatches. These longer oligonucleotides are in widespread use on microarrays, but there are few controlled studies of their interactions with mismatched targets compared to 25-mer based platforms. Principal Findings 50-mer oligonucleotides with centrally placed single, double and triple mismatches were spotted on an array. Over a range of target concentrations it was possible to discriminate binding to perfect matches and mismatches, and the type of mismatch could be predicted accurately in the concentration midrange (100 pM to 200 pM) using solution hybridization modeling methods. These results have implications for microarray design, optimization and analysis methods. Conclusions Our results highlight the importance of incorporating biophysical factors in both the design and the analysis of microarrays. Use of the probe “percent bound” value predicted by equilibrium models of hybridization is confirmed to be important for predicting and interpreting the behavior of long oligonucleotide arrays, as has been shown for short oligonucleotide arrays. PMID:20548788

  20. Goober: a fully integrated and user-friendly microarray data management and analysis solution for core labs and bench biologists.

    PubMed

    Luo, Wen; Gudipati, Murali; Jung, Kevin; Chen, Mao; Marschke, Keith B

    2009-08-23

    Despite the large number of software tools developed to address different areas of microarray data analysis, very few offer an all-in-one solution with little learning curve. For microarray core labs, there are even fewer software packages available to help with their routine but critical tasks, such as data quality control (QC) and inventory management. We have developed a simple-to-use web portal to allow bench biologists to analyze and query complicated microarray data and related biological pathways without prior training. Both experiment-based and gene-based analysis can be easily performed, even for the first-time user, through the intuitive multi-layer design and interactive graphic links. While being friendly to inexperienced users, most parameters in Goober can be easily adjusted via drop-down menus to allow advanced users to tailor their needs and perform more complicated analysis. Moreover, we have integrated graphic pathway analysis into the website to help users examine microarray data within the relevant biological content. Goober also contains features that cover most of the common tasks in microarray core labs, such as real time array QC, data loading, array usage and inventory tracking. Overall, Goober is a complete microarray solution to help biologists instantly discover valuable information from a microarray experiment and enhance the quality and productivity of microarray core labs. The whole package is freely available at http://sourceforge.net/projects/goober. A demo web server is available at http://www.goober-array.org.

  1. Comparative analysis of gene expression by microarray analysis of male and female flowers of Asparagus officinalis.

    PubMed

    Gao, Wu-Jun; Li, Shu-Fen; Zhang, Guo-Jun; Wang, Ning-Na; Deng, Chuan-Liang; Lu, Long-Dou

    2013-01-01

    To identify rapidly a number of genes probably involved in sex determination and differentiation of the dioecious plant Asparagus officinalis, gene expression profiles in early flower development for male and female plants were investigated by microarray assay with 8,665 probes. In total, 638 male-biased and 543 female-biased genes were identified. These genes with biased-expression for male and female were involved in a variety of processes associated with molecular functions, cellular components, and biological processes, suggesting that a complex mechanism underlies the sex development of asparagus. Among the differentially expressed genes involved in the reproductive process, a number of genes associated with floral development were identified. Reverse transcription-PCR was performed for validation, and the results were largely consistent with those obtained by microarray analysis. The findings of this study might contribute to understanding of the molecular mechanisms of sex determination and differentiation in dioecious asparagus and provide a foundation for further studies of this plant.

  2. MiMiR – an integrated platform for microarray data sharing, mining and analysis

    PubMed Central

    Tomlinson, Chris; Thimma, Manjula; Alexandrakis, Stelios; Castillo, Tito; Dennis, Jayne L; Brooks, Anthony; Bradley, Thomas; Turnbull, Carly; Blaveri, Ekaterini; Barton, Geraint; Chiba, Norie; Maratou, Klio; Soutter, Pat; Aitman, Tim; Game, Laurence

    2008-01-01

    Background Despite considerable efforts within the microarray community for standardising data format, content and description, microarray technologies present major challenges in managing, sharing, analysing and re-using the large amount of data generated locally or internationally. Additionally, it is recognised that inconsistent and low quality experimental annotation in public data repositories significantly compromises the re-use of microarray data for meta-analysis. MiMiR, the Microarray data Mining Resource was designed to tackle some of these limitations and challenges. Here we present new software components and enhancements to the original infrastructure that increase accessibility, utility and opportunities for large scale mining of experimental and clinical data. Results A user friendly Online Annotation Tool allows researchers to submit detailed experimental information via the web at the time of data generation rather than at the time of publication. This ensures the easy access and high accuracy of meta-data collected. Experiments are programmatically built in the MiMiR database from the submitted information and details are systematically curated and further annotated by a team of trained annotators using a new Curation and Annotation Tool. Clinical information can be annotated and coded with a clinical Data Mapping Tool within an appropriate ethical framework. Users can visualise experimental annotation, assess data quality, download and share data via a web-based experiment browser called MiMiR Online. All requests to access data in MiMiR are routed through a sophisticated middleware security layer thereby allowing secure data access and sharing amongst MiMiR registered users prior to publication. Data in MiMiR can be mined and analysed using the integrated EMAAS open source analysis web portal or via export of data and meta-data into Rosetta Resolver data analysis package. Conclusion The new MiMiR suite of software enables systematic and

  3. A Grid-based solution for management and analysis of microarrays in distributed experiments

    PubMed Central

    Porro, Ivan; Torterolo, Livia; Corradi, Luca; Fato, Marco; Papadimitropoulos, Adam; Scaglione, Silvia; Schenone, Andrea; Viti, Federica

    2007-01-01

    Several systems have been presented in the last years in order to manage the complexity of large microarray experiments. Although good results have been achieved, most systems tend to lack in one or more fields. A Grid based approach may provide a shared, standardized and reliable solution for storage and analysis of biological data, in order to maximize the results of experimental efforts. A Grid framework has been therefore adopted due to the necessity of remotely accessing large amounts of distributed data as well as to scale computational performances for terabyte datasets. Two different biological studies have been planned in order to highlight the benefits that can emerge from our Grid based platform. The described environment relies on storage services and computational services provided by the gLite Grid middleware. The Grid environment is also able to exploit the added value of metadata in order to let users better classify and search experiments. A state-of-art Grid portal has been implemented in order to hide the complexity of framework from end users and to make them able to easily access available services and data. The functional architecture of the portal is described. As a first test of the system performances, a gene expression analysis has been performed on a dataset of Affymetrix GeneChip® Rat Expression Array RAE230A, from the ArrayExpress database. The sequence of analysis includes three steps: (i) group opening and image set uploading, (ii) normalization, and (iii) model based gene expression (based on PM/MM difference model). Two different Linux versions (sequential and parallel) of the dChip software have been developed to implement the analysis and have been tested on a cluster. From results, it emerges that the parallelization of the analysis process and the execution of parallel jobs on distributed computational resources actually improve the performances. Moreover, the Grid environment have been tested both against the possibility of

  4. Genomic Imbalances in Neonates With Birth Defects: High Detection Rates by Using Chromosomal Microarray Analysis

    PubMed Central

    Lu, Xin-Yan; Phung, Mai T.; Shaw, Chad A.; Pham, Kim; Neil, Sarah E.; Patel, Ankita; Sahoo, Trilochan; Bacino, Carlos A.; Stankiewicz, Pawel; Lee Kang, Sung-Hae; Lalani, Seema; Chinault, A. Craig; Lupski, James R.; Cheung, Sau W.; Beaudet, Arthur L.

    2009-01-01

    OBJECTIVES Our aim was to determine the frequency of genomic imbalances in neonates with birth defects by using targeted array-based comparative genomic hybridization, also known as chromosomal microarray analysis. METHODS Between March 2006 and September 2007, 638 neonates with various birth defects were referred for chromosomal microarray analysis. Three consecutive chromosomal microarray analysis versions were used: bacterial artificial chromosome-based versions V5 and V6 and bacterial artificial chromosome emulated oligonucleotide-based version V6 Oligo. Each version had targeted but increasingly extensive genomic coverage and interrogated >150 disease loci with enhanced coverage in genomic rearrangement-prone pericentromeric and subtelomeric regions. RESULTS Overall, 109 (17.1%) patients were identified with clinically significant abnormalities with detection rates of 13.7%, 16.6%, and 19.9% on V5, V6, and V6 Oligo, respectively. The majority of these abnormalities would not be defined by using karyotype analysis. The clinically significant detection rates by use of chromosomal microarray analysis for various clinical indications were 66.7% for “possible chromosomal abnormality” ± “others” (other clinical indications), 33.3% for ambiguous genitalia ± others, 27.1% for dysmorphic features + multiple congenital anomalies ± others, 24.6% for dysmorphic features ± others, 21.8% for congenital heart disease ± others, 17.9% for multiple congenital anomalies ± others, and 9.5% for the patients referred for others that were different from the groups defined. In all, 16 (2.5%) patients had chromosomal aneuploidies, and 81 (12.7%) patients had segmental aneusomies including common microdeletion or microduplication syndromes and other genomic disorders. Chromosomal mosaicism was found in 12 (1.9%) neonates. CONCLUSIONS Chromosomal microarray analysis is a valuable clinical diagnostic tool that allows precise and rapid identification of genomic imbalances

  5. A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability.

    PubMed

    Sontrop, Herman M J; Moerland, Perry D; van den Ham, René; Reinders, Marcel J T; Verhaegh, Wim F J

    2009-11-26

    Large discrepancies in signature composition and outcome concordance have been observed between different microarray breast cancer expression profiling studies. This is often ascribed to differences in array platform as well as biological variability. We conjecture that other reasons for the observed discrepancies are the measurement error associated with each feature and the choice of preprocessing method. Microarray data are known to be subject to technical variation and the confidence intervals around individual point estimates of expression levels can be wide. Furthermore, the estimated expression values also vary depending on the selected preprocessing scheme. In microarray breast cancer classification studies, however, these two forms of feature variability are almost always ignored and hence their exact role is unclear. We have performed a comprehensive sensitivity analysis of microarray breast cancer classification under the two types of feature variability mentioned above. We used data from six state of the art preprocessing methods, using a compendium consisting of eight different datasets, involving 1131 hybridizations, containing data from both one and two-color array technology. For a wide range of classifiers, we performed a joint study on performance, concordance and stability. In the stability analysis we explicitly tested classifiers for their noise tolerance by using perturbed expression profiles that are based on uncertainty information directly related to the preprocessing methods. Our results indicate that signature composition is strongly influenced by feature variability, even if the array platform and the stratification of patient samples are identical. In addition, we show that there is often a high level of discordance between individual class assignments for signatures constructed on data coming from different preprocessing schemes, even if the actual signature composition is identical. Feature variability can have a strong impact on

  6. Electronic microarray analysis of 16S rDNA amplicons for bacterial detection.

    PubMed

    Barlaan, Edward A; Sugimori, Miho; Furukawa, Seiji; Takeuchi, Kazuhisa

    2005-01-12

    Electronic microarray technology is a potential alternative in bacterial detection and identification. However, conditions for bacterial detection by electronic microarray need optimization. Using the NanoChip electronic microarray, we investigated eight marine bacterial species. Based on the 16S rDNA sequences of these species, we constructed primers, reporter probes, and species-specific capture probes. We carried out two separate analyses for longer (533 bp) and shorter (350 and 200 bp) amplified products (amplicons). To detect simultaneously the hybridization signals for the 350- and 200-bp amplicons, we designed a common reporter probe from an overlapping sequence within both fragments. We developed methods to optimize detection of hybridization signals for processing the DNA chips. A matrix analysis was performed for different bacterial species and complementary capture probes on electronic microarrays. Results showed that, when using the longer amplicon, not all bacterial targets hybridized with the complementary capture probes, which was characterized by the presence of false-positive signals. However, with the shorter amplicons, all bacterial species were correctly and completely detected using the constructed complementary capture probes.

  7. Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships

    PubMed Central

    2010-01-01

    Background The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. Results In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. Conclusion High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data. PMID:20122245

  8. Microarray analysis of gene expression during early development: a cautionary overview.

    PubMed

    Robert, Claude

    2010-12-01

    The rise of the 'omics' technologies started nearly a decade ago and, among them, transcriptomics has been used successfully to contrast gene expression in mammalian oocytes and early embryos. The scarcity of biological material that early developmental stages provide is the prime reason why the field of transcriptomics is becoming more and more popular with reproductive biologists. The potential to amplify scarce mRNA samples and generate the necessary amounts of starting material enables the relative measurement of RNA abundance of thousands of candidates simultaneously. So far, microarrays have been the most commonly used high-throughput method in this field. Microarray platforms can be found in a wide variety of formats, from cDNA collections to long or short oligo probe sets. These platforms generate large amounts of data that require the integration of comparative RNA abundance values in the physiological context of early development for their full benefit to be appreciated. Unfortunately, significant discrepancies between datasets suggest that direct comparison between studies is difficult and often not possible. We have investigated the sample-handling steps leading to the generation of microarray data produced from prehatching embryo samples and have identified key steps that significantly impact the downstream results. This review provides a discussion on the best methods for the preparation of samples from early embryos for microarray analysis and focuses on the challenges that impede dataset comparisons from different platforms and the reasons why methodological benchmarking performed using somatic cells may not apply to the atypical nature of prehatching development.

  9. Comprehensive analysis of correlation coefficients estimated from pooling heterogeneous microarray data

    PubMed Central

    2013-01-01

    Background The synthesis of information across microarray studies has been performed by combining statistical results of individual studies (as in a mosaic), or by combining data from multiple studies into a large pool to be analyzed as a single data set (as in a melting pot of data). Specific issues relating to data heterogeneity across microarray studies, such as differences within and between labs or differences among experimental conditions, could lead to equivocal results in a melting pot approach. Results We applied statistical theory to determine the specific effect of different means and heteroskedasticity across 19 groups of microarray data on the sign and magnitude of gene-to-gene Pearson correlation coefficients obtained from the pool of 19 groups. We quantified the biases of the pooled coefficients and compared them to the biases of correlations estimated by an effect-size model. Mean differences across the 19 groups were the main factor determining the magnitude and sign of the pooled coefficients, which showed largest values of bias as they approached ±1. Only heteroskedasticity across the pool of 19 groups resulted in less efficient estimations of correlations than did a classical meta-analysis approach of combining correlation coefficients. These results were corroborated by simulation studies involving either mean differences or heteroskedasticity across a pool of N > 2 groups. Conclusions The combination of statistical results is best suited for synthesizing the correlation between expression profiles of a gene pair across several microarray studies. PMID:23822712

  10. A non-parametric meta-analysis approach for combining independent microarray datasets: application using two microarray datasets pertaining to chronic allograft nephropathy.

    PubMed

    Kong, Xiangrong; Mas, Valeria; Archer, Kellie J

    2008-02-26

    With the popularity of DNA microarray technology, multiple groups of researchers have studied the gene expression of similar biological conditions. Different methods have been developed to integrate the results from various microarray studies, though most of them rely on distributional assumptions, such as the t-statistic based, mixed-effects model, or Bayesian model methods. However, often the sample size for each individual microarray experiment is small. Therefore, in this paper we present a non-parametric meta-analysis approach for combining data from independent microarray studies, and illustrate its application on two independent Affymetrix GeneChip studies that compared the gene expression of biopsies from kidney transplant recipients with chronic allograft nephropathy (CAN) to those with normal functioning allograft. The simulation study comparing the non-parametric meta-analysis approach to a commonly used t-statistic based approach shows that the non-parametric approach has better sensitivity and specificity. For the application on the two CAN studies, we identified 309 distinct genes that expressed differently in CAN. By applying Fisher's exact test to identify enriched KEGG pathways among those genes called differentially expressed, we found 6 KEGG pathways to be over-represented among the identified genes. We used the expression measurements of the identified genes as predictors to predict the class labels for 6 additional biopsy samples, and the predicted results all conformed to their pathologist diagnosed class labels. We present a new approach for combining data from multiple independent microarray studies. This approach is non-parametric and does not rely on any distributional assumptions. The rationale behind the approach is logically intuitive and can be easily understood by researchers not having advanced training in statistics. Some of the identified genes and pathways have been reported to be relevant to renal diseases. Further study on the

  11. VARAN: a web server for variability analysis of DNA microarray experiments.

    PubMed

    Golfier, G; Dang, M Tran; Dauphinot, L; Graison, E; Rossier, J; Potier, M-C

    2004-07-10

    Here, we describe a tool for VARiability Analysis of DNA microarrays experiments (VARAN), a freely available Web server that performs a signal intensity based analysis of the log2 expression ratio variability deduced from DNA microarray data (one or two channels). Two modules are proposed: VARAN generator to compute a sliding windows analysis of the experimental variability (mean and SD) and VARAN analyzer to compare experimental data with an asymptotic variability model previously built with the generator module from control experiments. Both modules provide normalized intensity signals with five possible methods, log ratio values and a list of genes showing significant variations between conditions. http://www.bionet.espci.fr/varan/ http://www.bionet.espci.fr/varan/help.html

  12. Improving gene set analysis of microarray data by SAM-GS

    PubMed Central

    Dinu, Irina; Potter, John D; Mueller, Thomas; Liu, Qi; Adewale, Adeniyi J; Jhangri, Gian S; Einecke, Gunilla; Famulski, Konrad S; Halloran, Philip; Yasui, Yutaka

    2007-01-01

    Background Gene-set analysis evaluates the expression of biological pathways, or a priori defined gene sets, rather than that of individual genes, in association with a binary phenotype, and is of great biologic interest in many DNA microarray studies. Gene Set Enrichment Analysis (GSEA) has been applied widely as a tool for gene-set analyses. We describe here some critical problems with GSEA and propose an alternative method by extending the individual-gene analysis method, Significance Analysis of Microarray (SAM), to gene-set analyses (SAM-GS). Results Using a mouse microarray dataset with simulated gene sets, we illustrate that GSEA gives statistical significance to gene sets that have no gene associated with the phenotype (null gene sets), and has very low power to detect gene sets in which half the genes are moderately or strongly associated with the phenotype (truly-associated gene sets). SAM-GS, on the other hand, performs very well. The two methods are also compared in the analyses of three real microarray datasets and relevant pathways, the diverging results of which clearly show advantages of SAM-GS over GSEA, both statistically and biologically. In a microarray study for identifying biological pathways whose gene expressions are associated with p53 mutation in cancer cell lines, we found biologically relevant performance differences between the two methods. Specifically, there are 31 additional pathways identified as significant by SAM-GS over GSEA, that are associated with the presence vs. absence of p53. Of the 31 gene sets, 11 actually involve p53 directly as a member. A further 6 gene sets directly involve the extrinsic and intrinsic apoptosis pathways, 3 involve the cell-cycle machinery, and 3 involve cytokines and/or JAK/STAT signaling. Each of these 12 gene sets, then, is in a direct, well-established relationship with aspects of p53 signaling. Of the remaining 8 gene sets, 6 have plausible, if less well established, links with p53. Conclusion We

  13. GEPAS, a web-based tool for microarray data analysis and interpretation

    PubMed Central

    Tárraga, Joaquín; Medina, Ignacio; Carbonell, José; Huerta-Cepas, Jaime; Minguez, Pablo; Alloza, Eva; Al-Shahrour, Fátima; Vegas-Azcárate, Susana; Goetz, Stefan; Escobar, Pablo; Garcia-Garcia, Francisco; Conesa, Ana; Montaner, David; Dopazo, Joaquín

    2008-01-01

    Gene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for microarray data analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing microarray data analysis arena. GEPAS offers diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://www.gepas.org. PMID:18508806

  14. Transcriptome analysis in non-model species: a new method for the analysis of heterologous hybridization on microarrays.

    PubMed

    Degletagne, Cyril; Keime, Céline; Rey, Benjamin; de Dinechin, Marc; Forcheron, Fabien; Chuchana, Paul; Jouventin, Pierre; Gautier, Christian; Duchamp, Claude

    2010-05-31

    Recent developments in high-throughput methods of analyzing transcriptomic profiles are promising for many areas of biology, including ecophysiology. However, although commercial microarrays are available for most common laboratory models, transcriptome analysis in non-traditional model species still remains a challenge. Indeed, the signal resulting from heterologous hybridization is low and difficult to interpret because of the weak complementarity between probe and target sequences, especially when no microarray dedicated to a genetically close species is available. We show here that transcriptome analysis in a species genetically distant from laboratory models is made possible by using MAXRS, a new method of analyzing heterologous hybridization on microarrays. This method takes advantage of the design of several commercial microarrays, with different probes targeting the same transcript. To illustrate and test this method, we analyzed the transcriptome of king penguin pectoralis muscle hybridized to Affymetrix chicken microarrays, two organisms separated by an evolutionary distance of approximately 100 million years. The differential gene expression observed between different physiological situations computed by MAXRS was confirmed by real-time PCR on 10 genes out of 11 tested. MAXRS appears to be an appropriate method for gene expression analysis under heterologous hybridization conditions.

  15. Transcriptome analysis in non-model species: a new method for the analysis of heterologous hybridization on microarrays

    PubMed Central

    2010-01-01

    Background Recent developments in high-throughput methods of analyzing transcriptomic profiles are promising for many areas of biology, including ecophysiology. However, although commercial microarrays are available for most common laboratory models, transcriptome analysis in non-traditional model species still remains a challenge. Indeed, the signal resulting from heterologous hybridization is low and difficult to interpret because of the weak complementarity between probe and target sequences, especially when no microarray dedicated to a genetically close species is available. Results We show here that transcriptome analysis in a species genetically distant from laboratory models is made possible by using MAXRS, a new method of analyzing heterologous hybridization on microarrays. This method takes advantage of the design of several commercial microarrays, with different probes targeting the same transcript. To illustrate and test this method, we analyzed the transcriptome of king penguin pectoralis muscle hybridized to Affymetrix chicken microarrays, two organisms separated by an evolutionary distance of approximately 100 million years. The differential gene expression observed between different physiological situations computed by MAXRS was confirmed by real-time PCR on 10 genes out of 11 tested. Conclusions MAXRS appears to be an appropriate method for gene expression analysis under heterologous hybridization conditions. PMID:20509979

  16. A rapid automatic processing platform for bead label-assisted microarray analysis: application for genetic hearing-loss mutation detection.

    PubMed

    Zhu, Jiang; Song, Xiumei; Xiang, Guangxin; Feng, Zhengde; Guo, Hongju; Mei, Danyang; Zhang, Guohao; Wang, Dong; Mitchelson, Keith; Xing, Wanli; Cheng, Jing

    2014-04-01

    Molecular diagnostics using microarrays are increasingly being used in clinical diagnosis because of their high throughput, sensitivity, and accuracy. However, standard microarray processing takes several hours and involves manual steps during hybridization, slide clean up, and imaging. Here we describe the development of an integrated platform that automates these individual steps as well as significantly shortens the processing time and improves reproducibility. The platform integrates such key elements as a microfluidic chip, flow control system, temperature control system, imaging system, and automated analysis of clinical results. Bead labeling of microarray signals required a simple imaging system and allowed continuous monitoring of the microarray processing. To demonstrate utility, the automated platform was used to genotype hereditary hearing-loss gene mutations. Compared with conventional microarray processing procedures, the platform increases the efficiency and reproducibility of hybridization, speeding microarray processing through to result analysis. The platform also continuously monitors the microarray signals, which can be used to facilitate optimization of microarray processing conditions. In addition, the modular design of the platform lends itself to development of simultaneous processing of multiple microfluidic chips. We believe the novel features of the platform will benefit its use in clinical settings in which fast, low-complexity molecular genetic testing is required.

  17. Microarray and qPCR Analyses of Wallerian Degeneration in Rat Sciatic Nerves

    PubMed Central

    Yi, Sheng; Tang, Xin; Yu, Jun; Liu, Jie; Ding, Fei; Gu, Xiaosong

    2017-01-01

    Wallerian degeneration occurs immediately following injury to mammal peripheral nerves. To better understand the molecular events occurring during Wallerian degeneration, a rat model of sciatic nerve transection was used to assess differentially expressed genes at 0.5, 1, 6, 12, 24 h, 4 days, 1, 2, 3, and 4 weeks post nerve injury (PNI). Hierarchical clustering, Euclidean distance matrix, and principal component analysis (PCA) collectively suggested three distinct phases within the post-injury period of 4 weeks. Gene ontology (GO) analysis suggested that phase I (0–6 h PNI), phase II (6–24 h PNI), and phase III (4 days to 4 weeks) were associated with acute response to injury, preformation of Wallerian degeneration, and complete execution of Wallerian degeneration, respectively. Critical signaling pathways and transcriptional factor networks responsible for the regulation of Wallerian degeneration were further identified and integrated using Kyoto Enrichment of Genes and Genomes (KEGG) pathway analysis and Ingenuity Pathway Analysis (IPA), respectively. Our results may help to elucidate some molecular mechanisms of gene regulation associated with Wallerian degeneration that occurs after traumatic injury to peripheral nerve axons in mammals. PMID:28239339

  18. Comparison of High-Level Microarray Analysis Methods in the Context of Result Consistency.

    PubMed

    Chrominski, Kornel; Tkacz, Magdalena

    2015-01-01

    When we were asked for help with high-level microarray data analysis (on Affymetrix HGU-133A microarray), we faced the problem of selecting an appropriate method. We wanted to select a method that would yield "the best result" (detected as many "really" differentially expressed genes (DEGs) as possible, without false positives and false negatives). However, life scientists could not help us--they use their "favorite" method without special argumentation. We also did not find any norm or recommendation. Therefore, we decided to examine it for our own purpose. We considered whether the results obtained using different methods of high-level microarray data analyses--Significant Analysis of Microarrays, Rank Products, Bland-Altman, Mann-Whitney test, T test and the Linear Models for Microarray Data--would be in agreement. Initially, we conducted a comparative analysis of the results on eight real data sets from microarray experiments (from the Array Express database). The results were surprising. On the same array set, the set of DEGs by different methods were significantly different. We also applied the methods to artificial data sets and determined some measures that allow the preparation of the overall scoring of tested methods for future recommendation. We found a very low level concordance of results from tested methods on real array sets. The number of common DEGs (detected by all six methods on fixed array sets, checked on eight array sets) ranged from 6 to 433 (22,283 total array readings). Results on artificial data sets were better than those on the real data. However, they were not fully satisfying. We scored tested methods on accuracy, recall, precision, f-measure and Matthews correlation coefficient. Based on the overall scoring, the best methods were SAM and LIMMA. We also found TT to be acceptable. The worst scoring was MW. Based on our study, we recommend: 1. Carefully taking into account the need for study when choosing a method, 2. Making high

  19. [Diagnosis of a case with Williams-Beuren syndrome with nephrocalcinosis using chromosome microarray analysis].

    PubMed

    Jin, S J; Liu, M; Long, W J; Luo, X P

    2016-12-02

    Objective: To explore the clinical phenotypes and the genetic cause for a boy with unexplained growth retardation, nephrocalcinosis, auditory anomalies and multi-organ/system developmental disorders. Method: Routine G-banding and chromosome microarray analysis were applied to a child with unexplained growth retardation, nephrocalcinosis, auditory anomalies and multi-organ/system developmental disorders treated in the Department of Pediatrics of Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology in September 2015 and his parents to conduct the chromosomal karyotype analysis and the whole genome scanning. Deleted genes were searched in the Decipher and NCBI databases, and their relationships with the clinical phenotypes were analyzed. Result: A six-month-old boy was refered to us because of unexplained growth retardation and feeding intolerance.The affected child presented with abnormal manifestation such as special face, umbilical hernia, growth retardation, hypothyroidism, congenital heart disease, right ear sensorineural deafness, hypercalcemia and nephrocalcinosis. The child's karyotype was 46, XY, 16qh(+) , and his parents' karyotypes were normal. Chromosome microarray analysis revealed a 1 436 kb deletion on the 7q11.23(72701098_74136633) region of the child. This region included 23 protein-coding genes, which were reported to be corresponding to Williams-Beuren syndrome and its certain clinical phenotypes. His parents' results of chromosome microarray analysis were normal. Conclusion: A boy with characteristic manifestation of Williams-Beuren syndrome and rare nephrocalcinosis was diagnosed using chromosome microarray analysis. The deletion on the 7q11.23 might be related to the clinical phenotypes of Williams-Beuren syndrome, yet further studies are needed.

  20. Systematic analysis of microarray datasets to identify Parkinson's disease-associated pathways and genes

    PubMed Central

    Feng, Yinling; Wang, Xuefeng

    2017-01-01

    In order to investigate commonly disturbed genes and pathways in various brain regions of patients with Parkinson's disease (PD), microarray datasets from previous studies were collected and systematically analyzed. Different normalization methods were applied to microarray datasets from different platforms. A strategy combining gene co-expression networks and clinical information was adopted, using weighted gene co-expression network analysis (WGCNA) to screen for commonly disturbed genes in different brain regions of patients with PD. Functional enrichment analysis of commonly disturbed genes was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Co-pathway relationships were identified with Pearson's correlation coefficient tests and a hypergeometric distribution-based test. Common genes in pathway pairs were selected out and regarded as risk genes. A total of 17 microarray datasets from 7 platforms were retained for further analysis. Five gene coexpression modules were identified, containing 9,745, 736, 233, 101 and 93 genes, respectively. One module was significantly correlated with PD samples and thus the 736 genes it contained were considered to be candidate PD-associated genes. Functional enrichment analysis demonstrated that these genes were implicated in oxidative phosphorylation and PD. A total of 44 pathway pairs and 52 risk genes were revealed, and a risk gene pathway relationship network was constructed. Eight modules were identified and were revealed to be associated with PD, cancers and metabolism. A number of disturbed pathways and risk genes were unveiled in PD, and these findings may help advance understanding of PD pathogenesis. PMID:28098893

  1. Application of phylogenetic microarray analysis to discriminate sources of fecal pollution.

    PubMed

    Dubinsky, Eric A; Esmaili, Laleh; Hulls, John R; Cao, Yiping; Griffith, John F; Andersen, Gary L

    2012-04-17

    Conventional methods for fecal source tracking typically use single biomarkers to systematically identify or exclude sources. High-throughput DNA sequence analysis can potentially identify all sources of microbial contaminants in a single test by measuring the total diversity of fecal microbial communities. In this study, we used phylogenetic microarray analysis to determine the comprehensive suite of bacteria that define major sources of fecal contamination in coastal California. Fecal wastes were collected from 42 different populations of humans, birds, cows, horses, elk, and pinnipeds. We characterized bacterial community composition using a DNA microarray that probes for 16S rRNA genes of 59,316 different bacterial taxa. Cluster analysis revealed strong differences in community composition among fecal wastes from human, birds, pinnipeds, and grazers. Actinobacteria, Bacilli, and many Gammaproteobacteria taxa discriminated birds from mammalian sources. Diverse families within the Clostridia and Bacteroidetes taxa discriminated human wastes, grazers, and pinnipeds from each other. We found 1058 different bacterial taxa that were unique to either human, grazing mammal, or bird fecal wastes. These OTUs can serve as specific identifier taxa for these sources in environmental waters. Two field tests in marine waters demonstrate the capacity of phylogenetic microarray analysis to track multiple sources with one test.

  2. Gene expression analysis: teaching students to do 30,000 experiments at once with microarray.

    PubMed

    Carvalho, Felicia I; Johns, Christopher; Gillespie, Marc E

    2012-01-01

    Genome scale experiments routinely produce large data sets that require computational analysis, yet there are few student-based labs that illustrate the design and execution of these experiments. In order for students to understand and participate in the genomic world, teaching labs must be available where students generate and analyze large data sets. We present a microarray-based gene expression analysis experiment that is tailored for undergraduate students. The methods in this article describe an expression analysis experiment that can also be applied to CGH and SNP experiments. Factors such as technical difficulty, duration, cost, and availability of materials and equipments are considered in the lab design. The microarray teaching lab is performed in two sessions. The first is an introductory wet bench exercise that allows students to master the basic technical skills. The second builds on the concepts and skills with students acquiring and analyzing the microarray data. This lab exercise familiarizes students with large-scale data experiments and introduces them to the initial analysis steps. Copyright © 2011 Wiley Periodicals, Inc.

  3. Microarray analysis of differentially expressed genes between cysts and trophozoites of Acanthamoeba castellanii.

    PubMed

    Moon, Eun-Kyung; Xuan, Ying-Hua; Chung, Dong-Il; Hong, Yeonchul; Kong, Hyun-Hee

    2011-12-01

    Acanthamoeba infection is difficult to treat because of the resistance property of Acanthamoeba cyst against the host immune system, diverse antibiotics, and therapeutic agents. To identify encystation mediating factors of Acanthamoeba, we compared the transcription profile between cysts and trophozoites using microarray analysis. The DNA chip was composed of 12,544 genes based on expressed sequence tag (EST) from an Acanthamoeba ESTs database (DB) constructed in our laboratory, genetic information of Acanthamoeba from TBest DB, and all of Acanthamoeba related genes registered in the NCBI. Microarray analysis indicated that 701 genes showed higher expression than 2 folds in cysts than in trophozoites, and 859 genes were less expressed in cysts than in trophozoites. The results of real-time PCR analysis of randomly selected 9 genes of which expression was increased during cyst formation were coincided well with the microarray results. Eukaryotic orthologous groups (KOG) analysis showed an increment in T article (signal transduction mechanisms) and O article (posttranslational modification, protein turnover, and chaperones) whereas significant decrement of C article (energy production and conversion) during cyst formation. Especially, cystein proteinases showed high expression changes (282 folds) with significant increases in real-time PCR, suggesting a pivotal role of this proteinase in the cyst formation of Acanthamoeba. The present study provides important clues for the identification and characterization of encystation mediating factors of Acanthamoeba.

  4. New molecular phenotypes in the dst mutants of Arabidopsis revealed by DNA microarray analysis.

    PubMed

    Pérez-Amador, M A; Lidder, P; Johnson, M A; Landgraf, J; Wisman, E; Green, P J

    2001-12-01

    In this study, DNA microarray analysis was used to expand our understanding of the dst1 mutant of Arabidopsis. The dst (downstream) mutants were isolated originally as specifically increasing the steady state level and the half-life of DST-containing transcripts. As such, txhey offer a unique opportunity to study rapid sequence-specific mRNA decay pathways in eukaryotes. These mutants show a threefold to fourfold increase in mRNA abundance for two transgenes and an endogenous gene, all containing DST elements, when examined by RNA gel blot analysis; however, they show no visible aberrant phenotype. Here, we use DNA microarrays to identify genes with altered expression levels in dst1 compared with the parental plants. In addition to verifying the increase in the transgene mRNA levels, which were used to isolate these mutants, we were able to identify new genes with altered mRNA abundance in dst1. RNA gel blot analysis confirmed the microarray data for all genes tested and also was used to catalog the first molecular differences in gene expression between the dst1 and dst2 mutants. These differences revealed previously unknown molecular phenotypes for the dst mutants that will be helpful in future analyses. Cluster analysis of genes altered in dst1 revealed new coexpression patterns that prompt new hypotheses regarding the nature of the dst1 mutation and a possible role of the DST-mediated mRNA decay pathway in plants.

  5. Systematic analysis of microarray datasets to identify Parkinson's disease‑associated pathways and genes.

    PubMed

    Feng, Yinling; Wang, Xuefeng

    2017-03-01

    In order to investigate commonly disturbed genes and pathways in various brain regions of patients with Parkinson's disease (PD), microarray datasets from previous studies were collected and systematically analyzed. Different normalization methods were applied to microarray datasets from different platforms. A strategy combining gene co‑expression networks and clinical information was adopted, using weighted gene co‑expression network analysis (WGCNA) to screen for commonly disturbed genes in different brain regions of patients with PD. Functional enrichment analysis of commonly disturbed genes was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Co‑pathway relationships were identified with Pearson's correlation coefficient tests and a hypergeometric distribution‑based test. Common genes in pathway pairs were selected out and regarded as risk genes. A total of 17 microarray datasets from 7 platforms were retained for further analysis. Five gene coexpression modules were identified, containing 9,745, 736, 233, 101 and 93 genes, respectively. One module was significantly correlated with PD samples and thus the 736 genes it contained were considered to be candidate PD‑associated genes. Functional enrichment analysis demonstrated that these genes were implicated in oxidative phosphorylation and PD. A total of 44 pathway pairs and 52 risk genes were revealed, and a risk gene pathway relationship network was constructed. Eight modules were identified and were revealed to be associated with PD, cancers and metabolism. A number of disturbed pathways and risk genes were unveiled in PD, and these findings may help advance understanding of PD pathogenesis.

  6. Blind source separation and the analysis of microarray data.

    PubMed

    Chiappetta, P; Roubaud, M C; Torrésani, B

    2004-01-01

    We develop an approach for the exploratory analysis of gene expression data, based upon blind source separation techniques. This approach exploits higher-order statistics to identify a linear model for (logarithms of) expression profiles, described as linear combinations of "independent sources." As a result, it yields "elementary expression patterns" (the "sources"), which may be interpreted as potential regulation pathways. Further analysis of the so-obtained sources show that they are generally characterized by a small number of specific coexpressed or antiexpressed genes. In addition, the projections of the expression profiles onto the estimated sources often provides significant clustering of conditions. The algorithm relies on a large number of runs of "independent component analysis" with random initializations, followed by a search of "consensus sources." It then provides estimates for independent sources, together with an assessment of their robustness. The results obtained on two datasets (namely, breast cancer data and Bacillus subtilis sulfur metabolism data) show that some of the obtained gene families correspond to well known families of coregulated genes, which validates the proposed approach.

  7. Statistical analysis of efficient unbalanced factorial designs for two-color microarray experiments.

    PubMed

    Tempelman, Robert J

    2008-01-01

    Experimental designs that efficiently embed a fixed effects treatment structure within a random effects design structure typically require a mixed-model approach to data analyses. Although mixed model software tailored for the analysis of two-color microarray data is increasingly available, much of this software is generally not capable of correctly analyzing the elaborate incomplete block designs that are being increasingly proposed and used for factorial treatment structures. That is, optimized designs are generally unbalanced as it pertains to various treatment comparisons, with different specifications of experimental variability often required for different treatment factors. This paper uses a publicly available microarray dataset, as based upon an efficient experimental design, to demonstrate a proper mixed model analysis of a typical unbalanced factorial design characterized by incomplete blocks and hierarchical levels of variability.

  8. Statistical Analysis of Efficient Unbalanced Factorial Designs for Two-Color Microarray Experiments

    PubMed Central

    Tempelman, Robert J.

    2008-01-01

    Experimental designs that efficiently embed a fixed effects treatment structure within a random effects design structure typically require a mixed-model approach to data analyses. Although mixed model software tailored for the analysis of two-color microarray data is increasingly available, much of this software is generally not capable of correctly analyzing the elaborate incomplete block designs that are being increasingly proposed and used for factorial treatment structures. That is, optimized designs are generally unbalanced as it pertains to various treatment comparisons, with different specifications of experimental variability often required for different treatment factors. This paper uses a publicly available microarray dataset, as based upon an efficient experimental design, to demonstrate a proper mixed model analysis of a typical unbalanced factorial design characterized by incomplete blocks and hierarchical levels of variability. PMID:18584033

  9. Integrated Microfluidic Devices for Automated Microarray-Based Gene Expression and Genotyping Analysis

    NASA Astrophysics Data System (ADS)

    Liu, Robin H.; Lodes, Mike; Fuji, H. Sho; Danley, David; McShea, Andrew

    Microarray assays typically involve multistage sample processing and fluidic handling, which are generally labor-intensive and time-consuming. Automation of these processes would improve robustness, reduce run-to-run and operator-to-operator variation, and reduce costs. In this chapter, a fully integrated and self-contained microfluidic biochip device that has been developed to automate the fluidic handling steps for microarray-based gene expression or genotyping analysis is presented. The device consists of a semiconductor-based CustomArray® chip with 12,000 features and a microfluidic cartridge. The CustomArray was manufactured using a semiconductor-based in situ synthesis technology. The micro-fluidic cartridge consists of microfluidic pumps, mixers, valves, fluid channels, and reagent storage chambers. Microarray hybridization and subsequent fluidic handling and reactions (including a number of washing and labeling steps) were performed in this fully automated and miniature device before fluorescent image scanning of the microarray chip. Electrochemical micropumps were integrated in the cartridge to provide pumping of liquid solutions. A micromixing technique based on gas bubbling generated by electrochemical micropumps was developed. Low-cost check valves were implemented in the cartridge to prevent cross-talk of the stored reagents. Gene expression study of the human leukemia cell line (K562) and genotyping detection and sequencing of influenza A subtypes have been demonstrated using this integrated biochip platform. For gene expression assays, the microfluidic CustomArray device detected sample RNAs with a concentration as low as 0.375 pM. Detection was quantitative over more than three orders of magnitude. Experiment also showed that chip-to-chip variability was low indicating that the integrated microfluidic devices eliminate manual fluidic handling steps that can be a significant source of variability in genomic analysis. The genotyping results showed

  10. Long non-coding RNA expression profiles in gallbladder carcinoma identified using microarray analysis

    PubMed Central

    Wang, Jiwen; Liu, Han; Shen, Xiaokun; Wang, Yueqi; Zhang, Dexiang; Shen, Sheng; Suo, Tao; Pan, Hongtao; Ming, Yue; Ding, Kan; Liu, Houbao

    2017-01-01

    Gallbladder carcinoma (GBC) is the most common biliary tract cancer and exhibits poor patient prognosis. Previous studies have identified that long non-coding RNAs (lncRNAs) serve important regulatory roles in cancer biology. Alterations in lncRNAs are associated with several types of cancer. However, the contribution of lncRNAs to GBC remains unclear. To investigate the lncRNAs that are potentially involved in GBC, lncRNA profiles were identified in three pairs of human GBC and corresponding peri-carcinomatous tissue samples using microarray analysis. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to validate the microarray data. In order to elucidate potential functions, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, and network analysis were used to determine relevant signaling pathways. Abundant RNA probes were used, and 1,758 lncRNAs and 1,254 mRNAs were detected to be differentially expressed by the microarray. Compared with para-carcinoma tissue, numerous lncRNAs were markedly upregulated or downregulated in GBC. The results demonstrated that the lncRNAs that were downregulated in GBC were more numerous compared with the lncRNAs that were upregulated. Among them, RP11-152P17.2-006 was the most upregulated, whereas CTA-941F9.9 was the most downregulated. The RT-qPCR results were consistent with the microarray data. Pathway analysis indicated that five pathways corresponded to the differentially expressed transcripts. It was demonstrated that lncRNA expression in GBC was markedly altered, and a series of novel lncRNAs associated with GBC were identified. The results of the present study suggest that the functions of lncRNAs are important in GBC development and progression. PMID:28529578

  11. DNA microarray analysis of the liver of mice treated with cobalt chloride.

    PubMed

    Matsumoto, Kanako; Fujishiro, Hitomi; Satoh, Masahiko; Himeno, Seiichiro

    2010-12-01

    To investigate the in vivo effects of cobalt chloride on gene expression at early time points, DNA microarray analysis was performed on the liver of mice injected subcutaneously with cobalt chloride. The liver tissue samples were taken 0.5, 1, and 3 hr after injection. Of the 14 genes up-regulated at 0.5 hr after injection, 7 are related to immunological responses, and 4 of the 7 were found to be involved in the activation of interferon.

  12. Long non-coding RNA expression profiles in gallbladder carcinoma identified using microarray analysis.

    PubMed

    Wang, Jiwen; Liu, Han; Shen, Xiaokun; Wang, Yueqi; Zhang, Dexiang; Shen, Sheng; Suo, Tao; Pan, Hongtao; Ming, Yue; Ding, Kan; Liu, Houbao

    2017-05-01

    Gallbladder carcinoma (GBC) is the most common biliary tract cancer and exhibits poor patient prognosis. Previous studies have identified that long non-coding RNAs (lncRNAs) serve important regulatory roles in cancer biology. Alterations in lncRNAs are associated with several types of cancer. However, the contribution of lncRNAs to GBC remains unclear. To investigate the lncRNAs that are potentially involved in GBC, lncRNA profiles were identified in three pairs of human GBC and corresponding peri-carcinomatous tissue samples using microarray analysis. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to validate the microarray data. In order to elucidate potential functions, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, and network analysis were used to determine relevant signaling pathways. Abundant RNA probes were used, and 1,758 lncRNAs and 1,254 mRNAs were detected to be differentially expressed by the microarray. Compared with para-carcinoma tissue, numerous lncRNAs were markedly upregulated or downregulated in GBC. The results demonstrated that the lncRNAs that were downregulated in GBC were more numerous compared with the lncRNAs that were upregulated. Among them, RP11-152P17.2-006 was the most upregulated, whereas CTA-941F9.9 was the most downregulated. The RT-qPCR results were consistent with the microarray data. Pathway analysis indicated that five pathways corresponded to the differentially expressed transcripts. It was demonstrated that lncRNA expression in GBC was markedly altered, and a series of novel lncRNAs associated with GBC were identified. The results of the present study suggest that the functions of lncRNAs are important in GBC development and progression.

  13. Microarray analysis reveals altered circulating microRNA expression in mice infected with Coxsackievirus B3

    PubMed Central

    Sun, Chaoyu; Tong, Lei; Zhao, Wenran; Wang, Yan; Meng, Yuan; Lin, Lexun; Liu, Bingchen; Zhai, Yujia; Zhong, Zhaohua; Li, Xueqi

    2016-01-01

    Coxsackievirus B3 (CVB3) is a common causative agent in the development of inflammatory cardiomyopathy. However, whether the expression of peripheral blood microRNAs (miRNAs) is altered in this process is unknown. The present study investigated changes to miRNA expression in the peripheral blood of CVB3-infected mice. Utilizing miRNA microarray technology, differential miRNA expression was examined between normal and CVB3-infected mice. The present results suggest that specific miRNAs were differentially expressed in the peripheral blood of mice infected with CVB3, varying with infection duration. Using miRNA microarray analysis, a total of 96 and 89 differentially expressed miRNAs were identified in the peripheral blood of mice infected with CVB3 for 3 and 6 days, respectively. Quantitative polymerase chain reaction was used to validate differentially expressed miRNAs, revealing a consistency of these results with the miRNA microarray analysis results. The biological functions of the differentially expressed miRNAs were then predicted by bioinformatics analysis. The potential biological roles of differentially expressed miRNAs included hypertrophic cardiomyopathy, dilated cardiomyopathy and arrhythmogenic right ventricular cardiomyopathy. These results may provide important insights into the mechanisms responsible for the progression of CVB3 infection. PMID:27698715

  14. Bayesian Survival Analysis of High-Dimensional Microarray Data for Mantle Cell Lymphoma Patients.

    PubMed

    Moslemi, Azam; Mahjub, Hossein; Saidijam, Massoud; Poorolajal, Jalal; Soltanian, Ali Reza

    2016-01-01

    Survival time of lymphoma patients can be estimated with the help of microarray technology. In this study, with the use of iterative Bayesian Model Averaging (BMA) method, survival time of Mantle Cell Lymphoma patients (MCL) was estimated and in reference to the findings, patients were divided into two high- risk and low-risk groups. In this study, gene expression data of MCL patients were used in order to select a subset of genes for survival analysis with microarray data, using the iterative BMA method. To evaluate the performance of the method, patients were divided into high-risk and low-risk based on their scores. Performance prediction was investigated using the log-rank test. The bioconductor package "iterativeBMAsurv" was applied with R statistical software for classification and survival analysis. In this study, 25 genes associated with survival for MCL patients were identified across 132 selected models. The maximum likelihood estimate coefficients of the selected genes and the posterior probabilities of the selected models were obtained from training data. Using this method, patients could be separated into high-risk and low-risk groups with high significance (p<0.001). The iterative BMA algorithm has high precision and ability for survival analysis. This method is capable of identifying a few predictive variables associated with survival, among many variables in a set of microarray data. Therefore, it can be used as a low-cost diagnostic tool in clinical research.

  15. System Level Meta-analysis of Microarray Datasets for Elucidation of Diabetes Mellitus Pathobiology.

    PubMed

    Saxena, Aditya; Sachin, Kumar; Bhatia, Ashok Kumar

    2017-06-01

    Type 2 diabetes (T2D) is a common multi-factorial disease that is primarily ac-counted to ineffective insulin action in lowering blood glucose level and later escalates to impaired insu-lin secretion by pancreatic β cells. Deregulation in insulin signaling to its target organs is attributed to this disease phenotype. Various genome-wide microarray studies from multiple insulin responsive tis-sues have been conducted in past but due to inherent noise in microarray data and heterogeneity in dis-ease etiology; reproduction of prioritized pathways/genes is very low across various studies. In this study, we aim to identify consensus signaling and metabolic pathways through system level meta-analysis of multiple expression-sets to elucidate T2D pathobiology. We used 'R', an open source statistical environment, which is routinely used for Microarray data analysis particularly using special sets of packages available at Bioconductor. We primarily focused on gene-set analysis methods to elucidate various aspects of T2D. Literature-based evidences have shown the success of our approach in exploring various known aspects of diabetes pathophysiology. Our study stressed the need to develop novel bioinformatics workflows to advance our understanding further in insulin signaling.

  16. GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data

    PubMed Central

    Vaquerizas, Juan M.; Conde, Lucía; Yankilevich, Patricio; Cabezón, Amaya; Minguez, Pablo; Díaz-Uriarte, Ramón; Al-Shahrour, Fátima; Herrero, Javier; Dopazo, Joaquín

    2005-01-01

    The Gene Expression Profile Analysis Suite, GEPAS, has been running for more than three years. With >76 000 experiments analysed during the last year and a daily average of almost 300 analyses, GEPAS can be considered a well-established and widely used platform for gene expression microarray data analysis. GEPAS is oriented to the analysis of whole series of experiments. Its design and development have been driven by the demands of the biomedical community, probably the most active collective in the field of microarray users. Although clustering methods have obviously been implemented in GEPAS, our interest has focused more on methods for finding genes differentially expressed among distinct classes of experiments or correlated to diverse clinical outcomes, as well as on building predictors. There is also a great interest in CGH-arrays which fostered the development of the corresponding tool in GEPAS: InSilicoCGH. Much effort has been invested in GEPAS for developing and implementing efficient methods for functional annotation of experiments in the proper statistical framework. Thus, the popular FatiGO has expanded to a suite of programs for functional annotation of experiments, including information on transcription factor binding sites, chromosomal location and tissues. The web-based pipeline for microarray gene expression data, GEPAS, is available at . PMID:15980548

  17. Analysis and modelling of septic shock microarray data using Singular Value Decomposition.

    PubMed

    Allanki, Srinivas; Dixit, Madhulika; Thangaraj, Paul; Sinha, Nandan Kumar

    2017-06-01

    Being a high throughput technique, enormous amounts of microarray data has been generated and there arises a need for more efficient techniques of analysis, in terms of speed and accuracy. Finding the differentially expressed genes based on just fold change and p-value might not extract all the vital biological signals that occur at a lower gene expression level. Besides this, numerous mathematical models have been generated to predict the clinical outcome from microarray data, while very few, if not none, aim at predicting the vital genes that are important in a disease progression. Such models help a basic researcher narrow down and concentrate on a promising set of genes which leads to the discovery of gene-based therapies. In this article, as a first objective, we have used the lesser known and used Singular Value Decomposition (SVD) technique to build a microarray data analysis tool that works with gene expression patterns and intrinsic structure of the data in an unsupervised manner. We have re-analysed a microarray data over the clinical course of Septic shock from Cazalis et al. (2014) and have shown that our proposed analysis provides additional information compared to the conventional method. As a second objective, we developed a novel mathematical model that predicts a set of vital genes in the disease progression that works by generating samples in the continuum between health and disease, using a simple normal-distribution-based random number generator. We also verify that most of the predicted genes are indeed related to septic shock. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. Use of microarray analysis to unveil transcription factor and gene networks contributing to Beta cell dysfunction and apoptosis.

    PubMed

    Eizirik, Decio L; Kutlu, Burak; Rasschaert, Joanne; Darville, Martine; Cardozo, Alessandra K

    2003-11-01

    The beta cell fate following immune-mediated damage depends on an intricate pattern of dozens of genes up- or downregulated in parallel and/or sequentially. We are utilizing microarray analysis to clarify the pattern of gene expression in primary rat beta cells exposed to the proapoptotic cytokines, IL-1beta and/or IFN-gamma. The picture emerging from these experiments is that beta cells are not passive bystanders of their own destruction. On the contrary, beta cells respond to damage by activating diverse networks of transcription factors and genes that may either lead to apoptosis or preserve viability. Of note, cytokine-exposed beta cells produce and release chemokines that may contribute to the homing and activation of T cells and macrophages during insulitis. Several of the effects of cytokines depend on the activation of the transcription factor, NF-kappaB. NF-kappaB blocking prevents cytokine-induced beta cell death, and characterization of NF-kappaB-dependent genes by microarray analysis indicated that this transcription factor controls diverse networks of transcription factors and effector genes that are relevant for maintenance of beta cell differentiated status, cytosolic and ER calcium homeostasis, attraction of mononuclear cells, and apoptosis. Identification of this and additional "transcription factor networks" is being pursued by cluster analysis of gene expression in insulin-producing cells exposed to cytokines for different time periods. Identification of complex gene patterns poses a formidable challenge, but is now technically feasible. These accumulating evidences may finally unveil the molecular mechanisms regulating the beta cell "decision" to undergo or not apoptosis in early T1DM.

  19. Outcome-Driven Cluster Analysis with Application to Microarray Data.

    PubMed

    Hsu, Jessie J; Finkelstein, Dianne M; Schoenfeld, David A

    2015-01-01

    One goal of cluster analysis is to sort characteristics into groups (clusters) so that those in the same group are more highly correlated to each other than they are to those in other groups. An example is the search for groups of genes whose expression of RNA is correlated in a population of patients. These genes would be of greater interest if their common level of RNA expression were additionally predictive of the clinical outcome. This issue arose in the context of a study of trauma patients on whom RNA samples were available. The question of interest was whether there were groups of genes that were behaving similarly, and whether each gene in the cluster would have a similar effect on who would recover. For this, we develop an algorithm to simultaneously assign characteristics (genes) into groups of highly correlated genes that have the same effect on the outcome (recovery). We propose a random effects model where the genes within each group (cluster) equal the sum of a random effect, specific to the observation and cluster, and an independent error term. The outcome variable is a linear combination of the random effects of each cluster. To fit the model, we implement a Markov chain Monte Carlo algorithm based on the likelihood of the observed data. We evaluate the effect of including outcome in the model through simulation studies and describe a strategy for prediction. These methods are applied to trauma data from the Inflammation and Host Response to Injury research program, revealing a clustering of the genes that are informed by the recovery outcome.

  20. Chipster: user-friendly analysis software for microarray and other high-throughput data

    PubMed Central

    2011-01-01

    Background The growth of high-throughput technologies such as microarrays and next generation sequencing has been accompanied by active research in data analysis methodology, producing new analysis methods at a rapid pace. While most of the newly developed methods are freely available, their use requires substantial computational skills. In order to enable non-programming biologists to benefit from the method development in a timely manner, we have created the Chipster software. Results Chipster (http://chipster.csc.fi/) brings a powerful collection of data analysis methods within the reach of bioscientists via its intuitive graphical user interface. Users can analyze and integrate different data types such as gene expression, miRNA and aCGH. The analysis functionality is complemented with rich interactive visualizations, allowing users to select datapoints and create new gene lists based on these selections. Importantly, users can save the performed analysis steps as reusable, automatic workflows, which can also be shared with other users. Being a versatile and easily extendable platform, Chipster can be used for microarray, proteomics and sequencing data. In this article we describe its comprehensive collection of analysis and visualization tools for microarray data using three case studies. Conclusions Chipster is a user-friendly analysis software for high-throughput data. Its intuitive graphical user interface enables biologists to access a powerful collection of data analysis and integration tools, and to visualize data interactively. Users can collaborate by sharing analysis sessions and workflows. Chipster is open source, and the server installation package is freely available. PMID:21999641

  1. Nutrient control of gene expression in Drosophila: microarray analysis of starvation and sugar-dependent response

    PubMed Central

    Zinke, Ingo; Schütz, Christina S.; Katzenberger, Jörg D.; Bauer, Matthias; Pankratz, Michael J.

    2002-01-01

    We have identified genes regulated by starvation and sugar signals in Drosophila larvae using whole-genome microarrays. Based on expression profiles in the two nutrient conditions, they were organized into different categories that reflect distinct physiological pathways mediating sugar and fat metabolism, and cell growth. In the category of genes regulated in sugar-fed, but not in starved, animals, there is an upregulation of genes encoding key enzymes of the fat biosynthesis pathway and a downregulation of genes encoding lipases. The highest and earliest activated gene upon sugar ingestion is sugarbabe, a zinc finger protein that is induced in the gut and the fat body. Identification of potential targets using microarrays suggests that sugarbabe functions to repress genes involved in dietary fat breakdown and absorption. The current analysis provides a basis for studying the genetic mechanisms underlying nutrient signalling. PMID:12426388

  2. Membrane gene ontology bias in sequencing and microarray obtained by housekeeping-gene analysis.

    PubMed

    Zhang, Yijuan; Akintola, Oluwafemi S; Liu, Ken J A; Sun, Bingyun

    2016-01-10

    Microarray (MA) and high-throughput sequencing are two commonly used detection systems for global gene expression profiling. Although these two systems are frequently used in parallel, the differences in their final results have not been examined thoroughly. Transcriptomic analysis of housekeeping (HK) genes provides a unique opportunity to reliably examine the technical difference between these two systems. We investigated here the structure, genome location, expression quantity, microarray probe coverage, as well as biological functions of differentially identified human HK genes by 9 MA and 6 sequencing studies. These in-depth analyses allowed us to discover, for the first time, a subset of transcripts encoding membrane, cell surface and nuclear proteins that were prone to differential identification by the two platforms. We hope that the discovery can aid the future development of these technologies for comprehensive transcriptomic studies. Copyright © 2015 Elsevier B.V. All rights reserved.

  3. Nano rolling-circle amplification for enhanced SERS hot spots in protein microarray analysis.

    PubMed

    Yan, Juan; Su, Shao; He, Shijiang; He, Yao; Zhao, Bin; Wang, Dongfang; Zhang, Honglu; Huang, Qing; Song, Shiping; Fan, Chunhai

    2012-11-06

    Although "hot spots" have been proved to contribute to surface enhanced Raman scattering (SERS), less attention was paid to increase the number of the "hot spot" to directly enhance the Raman signals in bioanalytical systems. Here we report a new strategy based on nano rolling-circle amplification (nanoRCA) and nano hyperbranched rolling-circle amplification (nanoHRCA) to increase "hot spot" groups for protein microarrays. First, protein and ssDNA are coassembled on gold nanoparticles, making the assembled probe have both binding ability and hybridization ability. Second, the ssDNAs act as primers to initiate in situ RCA reaction to produced long ssDNAs. Third, a large number of SERS probes are loaded on the long ssDNA templetes, allowing thousands of SERS probes involved in each biomolecular recognition event. The strategy offered high-efficiency Raman enhancement and could detect less than 10 zeptomolar protein molecules in protein microarray analysis.

  4. BayGO: Bayesian analysis of ontology term enrichment in microarray data.

    PubMed

    Vêncio, Ricardo Z N; Koide, Tie; Gomes, Suely L; Pereira, Carlos A de B

    2006-02-23

    The search for enriched (aka over-represented or enhanced) ontology terms in a list of genes obtained from microarray experiments is becoming a standard procedure for a system-level analysis. This procedure tries to summarize the information focussing on classification designs such as Gene Ontology, KEGG pathways, and so on, instead of focussing on individual genes. Although it is well known in statistics that association and significance are distinct concepts, only the former approach has been used to deal with the ontology term enrichment problem. BayGO implements a Bayesian approach to search for enriched terms from microarray data. The R source-code is freely available at http://blasto.iq.usp.br/~tkoide/BayGO in three versions: Linux, which can be easily incorporated into pre-existent pipelines; Windows, to be controlled interactively; and as a web-tool. The software was validated using a bacterial heat shock response dataset, since this stress triggers known system-level responses. The Bayesian model accounts for the fact that, eventually, not all the genes from a given category are observable in microarray data due to low intensity signal, quality filters, genes that were not spotted and so on. Moreover, BayGO allows one to measure the statistical association between generic ontology terms and differential expression, instead of working only with the common significance analysis.

  5. Microarray analysis of differentially expressed genes engaged in fruit development between Prunus mume and Prunus armeniaca.

    PubMed

    Li, Xiaoying; Korir, Nicholas Kibet; Liu, Lili; Shangguan, Lingfei; Wang, Yuzhu; Han, Jian; Chen, Ming; Fang, Jinggui

    2012-11-15

    Microarray analysis is a technique that can be employed to provide expression profiles of single genes and new insights to elucidate the biological mechanisms responsible for fruit development. To evaluate expression of genes mostly engaged in fruit development between Prunus mume and Prunus armeniaca, we first identified differentially expressed transcripts along the entire fruit life cycle by using microarrays spotted with 10,641 ESTs collected from P. mume and other Prunus EST sequences. A total of 1418 ESTs were selected after quality control of microarray spots and analysis for differential gene expression patterns during fruit development of P. mume and P. Armeniaca. From these, 707 up-regulated and 711 down-regulated genes showing more than two-fold differences in expression level were annotated by GO based on biological processes, molecular functions and cellular components. These differentially expressed genes were found to be involved in several important pathways of carbohydrate, galactose, and starch and sucrose metabolism as well as in biosynthesis of other secondary metabolites via KEGG. This could provide detailed information on the fruit quality differences during development and ripening of these two species. With the results obtained, we provide a practical database for comprehensive understanding of molecular events during fruit development and also lay a theoretical foundation for the cloning of genes regulating in a series of important rate-limiting enzymes involved in vital metabolic pathways during fruit development. Copyright © 2012 Elsevier GmbH. All rights reserved.

  6. BayGO: Bayesian analysis of ontology term enrichment in microarray data

    PubMed Central

    Vêncio, Ricardo ZN; Koide, Tie; Gomes, Suely L; de B Pereira, Carlos A

    2006-01-01

    Background The search for enriched (aka over-represented or enhanced) ontology terms in a list of genes obtained from microarray experiments is becoming a standard procedure for a system-level analysis. This procedure tries to summarize the information focussing on classification designs such as Gene Ontology, KEGG pathways, and so on, instead of focussing on individual genes. Although it is well known in statistics that association and significance are distinct concepts, only the former approach has been used to deal with the ontology term enrichment problem. Results BayGO implements a Bayesian approach to search for enriched terms from microarray data. The R source-code is freely available at in three versions: Linux, which can be easily incorporated into pre-existent pipelines; Windows, to be controlled interactively; and as a web-tool. The software was validated using a bacterial heat shock response dataset, since this stress triggers known system-level responses. Conclusion The Bayesian model accounts for the fact that, eventually, not all the genes from a given category are observable in microarray data due to low intensity signal, quality filters, genes that were not spotted and so on. Moreover, BayGO allows one to measure the statistical association between generic ontology terms and differential expression, instead of working only with the common significance analysis. PMID:16504085

  7. Application of microarray analysis of foodborne Salmonella in poultry production: a review.

    PubMed

    Ricke, Steven C; Khatiwara, Anita; Kwon, Young Min

    2013-09-01

    Salmonellosis in the United States is one of the most costly foodborne diseases. Given that Salmonella can originate from a wide variety of environments, reduction of this organism at all stages of poultry production is critical. Salmonella species can encounter various environmental stress conditions that can dramatically influence their survival and virulence. Previous knowledge of Salmonella species genomic regulation of metabolism and physiology in relation to poultry is based on limited information of a few well-characterized genes. Consequently, although there is some information about environmental signals that control Salmonella growth and pathogenesis, much still remains unknown. Advancements in DNA sequencing technologies revolutionized the way bacteria were studied and molecular tools such as microarrays have subsequently been used for comprehensive transcriptomic analysis of Salmonella. With microarray analysis, the expression levels of each single gene in the Salmonella genome can be directly assessed and previously unknown genetic systems that are required for Salmonella growth and survival in the poultry production cycle can be elucidated. This represents an opportunity for development of novel approaches for limiting Salmonella establishment in all phases of poultry production. In this review, recent advances in transcriptome-microarray technologies that are facilitating a better understanding of Salmonella biology in poultry production and processing are discussed.

  8. Single exon-resolution targeted chromosomal microarray analysis of known and candidate intellectual disability genes

    PubMed Central

    Tucker, Tracy; Zahir, Farah R; Griffith, Malachi; Delaney, Allen; Chai, David; Tsang, Erica; Lemyre, Emmanuelle; Dobrzeniecka, Sylvia; Marra, Marco; Eydoux, Patrice; Langlois, Sylvie; Hamdan, Fadi F; Michaud, Jacques L; Friedman, Jan M

    2014-01-01

    Intellectual disability affects about 3% of individuals globally, with∼50% idiopathic. We designed an exonic-resolution array targeting all known submicroscopic chromosomal intellectual disability syndrome loci, causative genes for intellectual disability, and potential candidate genes, all genes encoding glutamate receptors and epigenetic regulators. Using this platform, we performed chromosomal microarray analysis on 165 intellectual disability trios (affected child and both normal parents). We identified and independently validated 36 de novo copy-number changes in 32 trios. In all, 67% of the validated events were intragenic, involving only exon 1 (which includes the promoter sequence according to our design), exon 1 and adjacent exons, or one or more exons excluding exon 1. Seventeen of the 36 copy-number variants involve genes known to cause intellectual disability. Eleven of these, including seven intragenic variants, are clearly pathogenic (involving STXBP1, SHANK3 (3 patients), IL1RAPL1, UBE2A, NRXN1, MEF2C, CHD7, 15q24 and 9p24 microdeletion), two are likely pathogenic (PI4KA, DCX), two are unlikely to be pathogenic (GRIK2, FREM2), and two are unclear (ARID1B, 15q22 microdeletion). Twelve individuals with genomic imbalances identified by our array were tested with a clinical microarray, and six had a normal result. We identified de novo copy-number variants within genes not previously implicated in intellectual disability and uncovered pathogenic variation of known intellectual disability genes below the detection limit of standard clinical diagnostic chromosomal microarray analysis. PMID:24253858

  9. Single exon-resolution targeted chromosomal microarray analysis of known and candidate intellectual disability genes.

    PubMed

    Tucker, Tracy; Zahir, Farah R; Griffith, Malachi; Delaney, Allen; Chai, David; Tsang, Erica; Lemyre, Emmanuelle; Dobrzeniecka, Sylvia; Marra, Marco; Eydoux, Patrice; Langlois, Sylvie; Hamdan, Fadi F; Michaud, Jacques L; Friedman, Jan M

    2014-06-01

    Intellectual disability affects about 3% of individuals globally, with∼50% idiopathic. We designed an exonic-resolution array targeting all known submicroscopic chromosomal intellectual disability syndrome loci, causative genes for intellectual disability, and potential candidate genes, all genes encoding glutamate receptors and epigenetic regulators. Using this platform, we performed chromosomal microarray analysis on 165 intellectual disability trios (affected child and both normal parents). We identified and independently validated 36 de novo copy-number changes in 32 trios. In all, 67% of the validated events were intragenic, involving only exon 1 (which includes the promoter sequence according to our design), exon 1 and adjacent exons, or one or more exons excluding exon 1. Seventeen of the 36 copy-number variants involve genes known to cause intellectual disability. Eleven of these, including seven intragenic variants, are clearly pathogenic (involving STXBP1, SHANK3 (3 patients), IL1RAPL1, UBE2A, NRXN1, MEF2C, CHD7, 15q24 and 9p24 microdeletion), two are likely pathogenic (PI4KA, DCX), two are unlikely to be pathogenic (GRIK2, FREM2), and two are unclear (ARID1B, 15q22 microdeletion). Twelve individuals with genomic imbalances identified by our array were tested with a clinical microarray, and six had a normal result. We identified de novo copy-number variants within genes not previously implicated in intellectual disability and uncovered pathogenic variation of known intellectual disability genes below the detection limit of standard clinical diagnostic chromosomal microarray analysis.

  10. Integration of microarray analysis into the clinical diagnosis of hematological malignancies: How much can we improve cytogenetic testing?

    PubMed

    Peterson, Jess F; Aggarwal, Nidhi; Smith, Clayton A; Gollin, Susanne M; Surti, Urvashi; Rajkovic, Aleksandar; Swerdlow, Steven H; Yatsenko, Svetlana A

    2015-08-07

    To evaluate the clinical utility, diagnostic yield and rationale of integrating microarray analysis in the clinical diagnosis of hematological malignancies in comparison with classical chromosome karyotyping/fluorescence in situ hybridization (FISH). G-banded chromosome analysis, FISH and microarray studies using customized CGH and CGH+SNP designs were performed on 27 samples from patients with hematological malignancies. A comprehensive comparison of the results obtained by three methods was conducted to evaluate benefits and limitations of these techniques for clinical diagnosis. Overall, 89.7% of chromosomal abnormalities identified by karyotyping/FISH studies were also detectable by microarray. Among 183 acquired copy number alterations (CNAs) identified by microarray, 94 were additional findings revealed in 14 cases (52%), and at least 30% of CNAs were in genomic regions of diagnostic/prognostic significance. Approximately 30% of novel alterations detected by microarray were >20 Mb in size. Balanced abnormalities were not detected by microarray; however, of the 19 apparently "balanced" rearrangements, 55% (6/11) of recurrent and 13% (1/8) of non-recurrent translocations had alterations at the breakpoints discovered by microarray. Microarray technology enables accurate, cost-effective and time-efficient whole-genome analysis at a resolution significantly higher than that of conventional karyotyping and FISH. Array-CGH showed advantage in identification of cryptic imbalances and detection of clonal aberrations in population of non-dividing cancer cells and samples with poor chromosome morphology. The integration of microarray analysis into the cytogenetic diagnosis of hematologic malignancies has the potential to improve patient management by providing clinicians with additional disease specific and potentially clinically actionable genomic alterations.

  11. Integration of microarray analysis into the clinical diagnosis of hematological malignancies: How much can we improve cytogenetic testing?

    PubMed Central

    Peterson, Jess F.; Aggarwal, Nidhi; Smith, Clayton A.; Gollin, Susanne M.; Surti, Urvashi; Rajkovic, Aleksandar; Swerdlow, Steven H.; Yatsenko, Svetlana A.

    2015-01-01

    Purpose To evaluate the clinical utility, diagnostic yield and rationale of integrating microarray analysis in the clinical diagnosis of hematological malignancies in comparison with classical chromosome karyotyping/fluorescence in situ hybridization (FISH). Methods G-banded chromosome analysis, FISH and microarray studies using customized CGH and CGH+SNP designs were performed on 27 samples from patients with hematological malignancies. A comprehensive comparison of the results obtained by three methods was conducted to evaluate benefits and limitations of these techniques for clinical diagnosis. Results Overall, 89.7% of chromosomal abnormalities identified by karyotyping/FISH studies were also detectable by microarray. Among 183 acquired copy number alterations (CNAs) identified by microarray, 94 were additional findings revealed in 14 cases (52%), and at least 30% of CNAs were in genomic regions of diagnostic/prognostic significance. Approximately 30% of novel alterations detected by microarray were >20 Mb in size. Balanced abnormalities were not detected by microarray; however, of the 19 apparently “balanced” rearrangements, 55% (6/11) of recurrent and 13% (1/8) of non-recurrent translocations had alterations at the breakpoints discovered by microarray. Conclusion Microarray technology enables accurate, cost-effective and time-efficient whole-genome analysis at a resolution significantly higher than that of conventional karyotyping and FISH. Array-CGH showed advantage in identification of cryptic imbalances and detection of clonal aberrations in population of non-dividing cancer cells and samples with poor chromosome morphology. The integration of microarray analysis into the cytogenetic diagnosis of hematologic malignancies has the potential to improve patient management by providing clinicians with additional disease specific and potentially clinically actionable genomic alterations. PMID:26299921

  12. Whole Genome Microarray Analysis of Gene Expression in Prader–Willi Syndrome

    PubMed Central

    Bittel, Douglas C.; Kibiryeva, Nataliya; Sell, Susan M.; Strong, Theresa V.; Butler, Merlin G.

    2017-01-01

    Prader–Willi syndrome (PWS) is caused by loss of function of paternally expressed genes in the 15q11-q13 region and a paucity of data exists on transcriptome variation. To further characterize genetic alterations in this classic obesity syndrome using whole genome microarrays to analyze gene expression, microarray and quantitative RT-PCR analysis were performed using RNA isolated from lymphoblastoid cells from PWS male subjects (four with 15q11-q13 deletion and three with UPD) and three age and cognition matched nonsyndromic comparison males. Of more than 47,000 probes examined in the microarray, 23,383 were detectable and 323 had significantly different expression in the PWS lymphoblastoid cells relative to comparison cells, 14 of which were related to neurodevelopment and function. As expected, there was no evidence of expression of paternally expressed genes from the 15q11-q13 region (e.g., SNRPN) in the PWS cells. Alterations in expression of serotonin receptor genes (e.g., HTR2B) and genes involved in eating behavior and obesity (ADIPOR2, MC2R, HCRT, OXTR) were noted. Other genes of interest with reduced expression in PWS subjects included STAR (a key regulator of steroid synthesis) and SAG (an arrestin family member which desensitizes G-protein-coupled receptors). Quantitative RT-PCR for SAG, OXTR, STAR, HCRT, and HTR2B using RNA isolated from their lymphoblastoid cells and available brain tissue (frontal cortex) from separate individuals with PWS and control subjects and normalized to GAPD gene expression levels validated our microarray gene expression data. Our analysis identified previously unappreciated changes in gene expression which may contribute to the clinical manifestations seen in PWS. PMID:17236194

  13. Predicting microRNA targets in time-series microarray experiments via functional data analysis.

    PubMed

    Parker, Brian J; Wen, Jiayu

    2009-01-30

    MicroRNA (miRNA) target prediction is an important component in understanding gene regulation. One approach is computational: searching nucleotide sequences for miRNA complementary base pairing. An alternative approach explored in this paper is the use of gene expression profiles from time-series microarray experiments to aid in miRNA target prediction. This requires distinguishing genuine targets from genes that are secondarily down-regulated as part of the same regulatory module. We use a functional data analytic (FDA) approach, FDA being a subfield of statistics that extends standard multivariate techniques to datasets with predictor and/or response variables that are functional. In a miR-124 transfection experiment spanning 120 hours, for genes with measurably down-regulated mRNA, exploratory functional data analysis showed differences in expression profiles over time between directly and indirectly down-regulated genes, such as response latency and biphasic response for direct miRNA targets. For prediction, an FDA approach was shown to effectively classify direct miR-124 targets from time-series microarray data (accuracy 88%; AUC 0.96), providing better performance than multivariate approaches. Exploratory FDA analysis can reveal interesting aspects of dynamic microarray miRNA studies. Predictive FDA models can be applied where computational miRNA target predictors fail or are unreliable, e.g. when there is a lack of evolutionary conservation, and can provide posterior probabilities to provide additional confirmatory evidence to validate candidate miRNA targets computationally predicted using sequence information. This approach would be applicable to the investigation of other miRNAs and suggests that dynamic microarray studies at a higher time resolution could reveal further details on miRNA regulation.

  14. Transcript-Specific Expression Profiles Derived from Sequence-Based Analysis of Standard Microarrays

    PubMed Central

    Moll, Anton G.; Lindenmeyer, Maja T.; Kretzler, Matthias; Nelson, Peter J.; Zimmer, Ralf; Cohen, Clemens D.

    2009-01-01

    Background Alternative mRNA processing mechanisms lead to multiple transcripts (i.e. splice isoforms) of a given gene which may have distinct biological functions. Microarrays like Affymetrix GeneChips measure mRNA expression of genes using sets of nucleotide probes. Until recently probe sets were not designed for transcript specificity. Nevertheless, the re-analysis of established microarray data using newly defined transcript-specific probe sets may provide information about expression levels of specific transcripts. Methodology/Principal Findings In the present study alignment of probe sequences of the Affymetrix microarray HG-U133A with Ensembl transcript sequences was performed to define transcript-specific probe sets. Out of a total of 247,965 perfect match probes, 95,008 were designated “transcript-specific”, i.e. showing complete sequence alignment, no cross-hybridization, and transcript-, not only gene-specificity. These probes were grouped into 7,941 transcript-specific probe sets and 15,619 gene-specific probe sets, respectively. The former were used to differentiate 445 alternative transcripts of 215 genes. For selected transcripts, predicted by this analysis to be differentially expressed in the human kidney, confirmatory real-time RT-PCR experiments were performed. First, the expression of two specific transcripts of the genes PPM1A (PP2CA_HUMAN and P35813) and PLG (PLMN_HUMAN and Q5TEH5) in human kidneys was determined by the transcript-specific array analysis and confirmed by real-time RT-PCR. Secondly, disease-specific differential expression of single transcripts of PLG and ABCA1 (ABCA1_HUMAN and Q5VYS0_HUMAN) was computed from the available array data sets and confirmed by transcript-specific real-time RT-PCR. Conclusions Transcript-specific analysis of microarray experiments can be employed to study gene-regulation on the transcript level using conventional microarray data. In this study, predictions based on sufficient probe set size and

  15. CoPub: a literature-based keyword enrichment tool for microarray data analysis

    PubMed Central

    Frijters, Raoul; Heupers, Bart; van Beek, Pieter; Bouwhuis, Maurice; van Schaik, René; de Vlieg, Jacob; Polman, Jan; Alkema, Wynand

    2008-01-01

    Medline is a rich information source, from which links between genes and keywords describing biological processes, pathways, drugs, pathologies and diseases can be extracted. We developed a publicly available tool called CoPub that uses the information in the Medline database for the biological interpretation of microarray data. CoPub allows batch input of multiple human, mouse or rat genes and produces lists of keywords from several biomedical thesauri that are significantly correlated with the set of input genes. These lists link to Medline abstracts in which the co-occurring input genes and correlated keywords are highlighted. Furthermore, CoPub can graphically visualize differentially expressed genes and over-represented keywords in a network, providing detailed insight in the relationships between genes and keywords, and revealing the most influential genes as highly connected hubs. CoPub is freely accessible at http://services.nbic.nl/cgi-bin/copub/CoPub.pl. PMID:18442992

  16. Extreme value distribution based gene selection criteria for discriminant microarray data analysis using logistic regression.

    PubMed

    Li, Wentian; Sun, Fengzhu; Grosse, Ivo

    2004-01-01

    One important issue commonly encountered in the analysis of microarray data is to decide which and how many genes should be selected for further studies. For discriminant microarray data analyses based on statistical models, such as the logistic regression models, gene selection can be accomplished by a comparison of the maximum likelihood of the model given the real data, L(D|M), and the expected maximum likelihood of the model given an ensemble of surrogate data with randomly permuted label, L(D(0)|M). Typically, the computational burden for obtaining L(D(0)M) is immense, often exceeding the limits of available computing resources by orders of magnitude. Here, we propose an approach that circumvents such heavy computations by mapping the simulation problem to an extreme-value problem. We present the derivation of an asymptotic distribution of the extreme-value as well as its mean, median, and variance. Using this distribution, we propose two gene selection criteria, and we apply them to two microarray datasets and three classification tasks for illustration.

  17. Microarray Meta-Analysis of RNA-Binding Protein Functions in Alternative Polyadenylation

    PubMed Central

    Hu, Wenchao; Liu, Yuting; Yan, Jun

    2014-01-01

    Alternative polyadenylation (APA) is a post-transcriptional mechanism to generate diverse mRNA transcripts with different 3′UTRs from the same gene. In this study, we systematically searched for the APA events with differential expression in public mouse microarray data. Hundreds of genes with over-represented differential APA events and the corresponding experiments were identified. We further revealed that global APA differential expression occurred prevalently in tissues such as brain comparing to peripheral tissues, and biological processes such as development, differentiation and immune responses. Interestingly, we also observed widespread differential APA events in RNA-binding protein (RBP) genes such as Rbm3, Eif4e2 and Elavl1. Given the fact that RBPs are considered as the main regulators of differential APA expression, we constructed a co-expression network between APAs and RBPs using the microarray data. Further incorporation of CLIP-seq data of selected RBPs showed that Nova2 represses and Mbnl1 promotes the polyadenylation of closest poly(A) sites respectively. Altogether, our study is the first microarray meta-analysis in a mammal on the regulation of APA by RBPs that integrated massive mRNA expression data under a wide-range of biological conditions. Finally, we present our results as a comprehensive resource in an online website for the research community. PMID:24622240

  18. Array painting: a protocol for the rapid analysis of aberrant chromosomes using DNA microarrays

    PubMed Central

    Gribble, Susan M; Ng, Bee Ling; Prigmore, Elena; Fitzgerald, Tomas; Carter, Nigel P

    2012-01-01

    Aarray painting is a technique that uses microarray technology to rapidly map chromosome translocation breakpoints. previous methods to map translocation breakpoints have used fluorescence in situ hybridization (FIsH) and have consequently been labor-intensive, time-consuming and restricted to the low breakpoint resolution imposed by the use of metaphase chromosomes. array painting combines the isolation of derivative chromosomes (chromosomes with translocations) and high-resolution microarray analysis to refine the genomic location of translocation breakpoints in a single experiment. In this protocol, we describe array painting by isolation of derivative chromosomes using a MoFlo flow sorter, amplification of these derivatives using whole-genome amplification and hybridization onto commercially available oligonucleotide microarrays. although the sorting of derivative chromosomes is a specialized procedure requiring sophisticated equipment, the amplification, labeling and hybridization of Dna is straightforward, robust and can be completed within 1 week. the protocol described produces good quality data; however, array painting is equally achievable using any combination of the available alternative methodologies for chromosome isolation, amplification and hybridization. PMID:19893508

  19. Microarray gene expression profiling analysis combined with bioinformatics in multiple sclerosis.

    PubMed

    Liu, Mingyuan; Hou, Xiaojun; Zhang, Ping; Hao, Yong; Yang, Yiting; Wu, Xiongfeng; Zhu, Desheng; Guan, Yangtai

    2013-05-01

    Multiple sclerosis (MS) is the most prevalent demyelinating disease and the principal cause of neurological disability in young adults. Recent microarray gene expression profiling studies have identified several genetic variants contributing to the complex pathogenesis of MS, however, expressional and functional studies are still required to further understand its molecular mechanism. The present study aimed to analyze the molecular mechanism of MS using microarray analysis combined with bioinformatics techniques. We downloaded the gene expression profile of MS from Gene Expression Omnibus (GEO) and analysed the microarray data using the differentially coexpressed genes (DCGs) and links package in R and Database for Annotation, Visualization and Integrated Discovery. The regulatory impact factor (RIF) algorithm was used to measure the impact factor of transcription factor. A total of 1,297 DCGs between MS patients and healthy controls were identified. Functional annotation indicated that these DCGs were associated with immune and neurological functions. Furthermore, the RIF result suggested that IKZF1, BACH1, CEBPB, EGR1, FOS may play central regulatory roles in controlling gene expression in the pathogenesis of MS. Our findings confirm the presence of multiple molecular alterations in MS and indicate the possibility for identifying prognostic factors associated with MS pathogenesis.

  20. RL-SAGE and microarray analysis of the rice transcriptome after Rhizoctonia solani infection.

    PubMed

    Venu, R C; Jia, Yulin; Gowda, Malali; Jia, Melissa H; Jantasuriyarat, Chatchawan; Stahlberg, Eric; Li, Huameng; Rhineheart, Andrew; Boddhireddy, Prashanth; Singh, Pratibha; Rutger, Neil; Kudrna, David; Wing, Rod; Nelson, James C; Wang, Guo-Liang

    2007-10-01

    Sheath blight caused by the fungal pathogen Rhizoctonia solani is an emerging problem in rice production worldwide. To elucidate the molecular basis of rice defense to the pathogen, RNA isolated from R. solani-infected leaves of Jasmine 85 was used for both RL-SAGE library construction and microarray hybridization. RL-SAGE sequence analysis identified 20,233 and 24,049 distinct tags from the control and inoculated libraries, respectively. Nearly half of the significant tags (> or =2 copies) from both libraries matched TIGR annotated genes and KOME full-length cDNAs. Among them, 42% represented sense and 7% antisense transcripts, respectively. Interestingly, 60% of the library-specific (> or =10 copies) and differentially expressed (>4.0-fold change) tags were novel transcripts matching genomic sequence but not annotated genes. About 70% of the genes identified in the SAGE libraries showed similar expression patterns (up or down-regulated) in the microarray data obtained from three biological replications. Some candidate RL-SAGE tags and microarray genes were located in known sheath blight QTL regions. The expression of ten differentially expressed RL-SAGE tags was confirmed with RT-PCR. The defense genes associated with resistance to R. solani identified in this study are useful genomic materials for further elucidation of the molecular basis of the defense response to R. solani and fine mapping of target sheath blight QTLs.

  1. Neural network analysis of lymphoma microarray data: prognosis and diagnosis near-perfect

    PubMed Central

    O'Neill, Michael C; Song, Li

    2003-01-01

    Background Microarray chips are being rapidly deployed as a major tool in genomic research. To date most of the analysis of the enormous amount of information provided on these chips has relied on clustering techniques and other standard statistical procedures. These methods, particularly with regard to cancer patient prognosis, have generally been inadequate in providing the reduced gene subsets required for perfect classification. Results Networks trained on microarray data from DLBCL lymphoma patients have, for the first time, been able to predict the long-term survival of individual patients with 100% accuracy. Other networks were able to distinguish DLBCL lymphoma donors from other donors, including donors with other lymphomas, with 99% accuracy. Differentiating the trained network can narrow the gene profile to less than three dozen genes for each classification. Conclusions Here we show that artificial neural networks are a superior tool for digesting microarray data both with regard to making distinctions based on the data and with regard to providing very specific reference as to which genes were most important in making the correct distinction in each case. PMID:12697066

  2. Global gene expression analysis of two Streptococcus thermophilus bacteriophages using DNA microarray.

    PubMed

    Duplessis, Martin; Russell, W Michael; Romero, Dennis A; Moineau, Sylvain

    2005-09-30

    A custom microarray was developed to study the temporal gene expression of the two groups of phages infecting the Gram-positive lactic acid bacterium Streptococcus thermophilus. The complete genomic sequence of the virulent cos-type phage DT1 (34,815 bp) and the pac-type phage 2972 (34,704 bp) were used for the construction of the microarray. Gene expression was measured at nine time intervals (0, 2, 7, 12, 17, 22, 27, 32 and 37 min) during phage infection and an expression curve was determined for each gene. Each phage gene was then classified into one of the three traditional transcription classes and these data were used to generate the complete transcriptional map of DT1 and 2972. Phage DT1 possesses 18 early genes, 12 middle genes and 12 late-expressed genes whereas 2972 has 16 early, 11 middle and 14 late genes. The trends of the phage gene expression profiles were also confirmed by slot blot hybridizations. Significant differences were observed when comparing the transcriptional maps of DT1 and 2972 with those already available for the S. thermophilus phages Sfi19 and Sfi21. To our knowledge, this report presents the first complete transcription analysis of bacteriophages infecting Gram-positive bacteria using the DNA microarray technology.

  3. Differential modulation of Bordetella pertussis virulence genes as evidenced by DNA microarray analysis.

    PubMed

    Hot, D; Antoine, R; Renauld-Mongénie, G; Caro, V; Hennuy, B; Levillain, E; Huot, L; Wittmann, G; Poncet, D; Jacob-Dubuisson, F; Guyard, C; Rimlinger, F; Aujame, L; Godfroid, E; Guiso, N; Quentin-Millet, M-J; Lemoine, Y; Locht, C

    2003-07-01

    The production of most factors involved in Bordetella pertussis virulence is controlled by a two-component regulatory system termed BvgA/S. In the Bvg+ phase virulence-activated genes (vags) are expressed, and virulence-repressed genes (vrgs) are down-regulated. The expression of these genes can also be modulated by MgSO(4) or nicotinic acid. In this study we used microarrays to analyse the influence of BvgA/S or modulation on the expression of nearly 200 selected genes. With the exception of one vrg, all previously known vags and vrgs were correctly assigned as such, and the microarray analyses identified several new vags and vrgs, including genes coding for putative autotransporters, two-component systems, extracellular sigma factors, the adenylate cyclase accessory genes cyaBDE, and two genes coding for components of a type III secretion system. For most of the new vrgs and vags the results of the microarray analyses were confirmed by RT-PCR analysis and/or lacZfusions. The degree of regulation and modulation varied between genes, and showed a continuum from strongly BvgA/S-activated genes to strongly BvgA/S-repressed genes. The microarray analyses also led to the identification of a subset of vags and vrgs that are differentially regulated and modulated by MgSO(4) or nicotinic acid, indicating that these genes may be targets for multiple regulatory circuits. For example, the expression of bilA, a gene predicted to encode an intimin-like protein, was found to be activated by BvgA/S and up-modulated by nicotinic acid. Furthermore, surprisingly, in the strain analysed here, which produces only type 2 fimbriae, the fim3 gene was identified as a vrg, while fim2 was confirmed to be a vag.

  4. Factorial microarray analysis of zebra mussel (Dreissena polymorpha: Dreissenidae, Bivalvia) adhesion.

    PubMed

    Xu, Wei; Faisal, Mohamed

    2010-05-28

    The zebra mussel (Dreissena polymorpha) has been well known for its expertise in attaching to substances under the water. Studies in past decades on this underwater adhesion focused on the adhesive protein isolated from the byssogenesis apparatus of the zebra mussel. However, the mechanism of the initiation, maintenance, and determination of the attachment process remains largely unknown. In this study, we used a zebra mussel cDNA microarray previously developed in our lab and a factorial analysis to identify the genes that were involved in response to the changes of four factors: temperature (Factor A), current velocity (Factor B), dissolved oxygen (Factor C), and byssogenesis status (Factor D). Twenty probes in the microarray were found to be modified by one of the factors. The transcription products of four selected genes, DPFP-BG20_A01, EGP-BG97/192_B06, EGP-BG13_G05, and NH-BG17_C09 were unique to the zebra mussel foot based on the results of quantitative reverse transcription PCR (qRT-PCR). The expression profiles of these four genes under the attachment and non-attachment were also confirmed by qRT-PCR and the result is accordant to that from microarray assay. The in situ hybridization with the RNA probes of two identified genes DPFP-BG20_A01 and EGP-BG97/192_B06 indicated that both of them were expressed by a type of exocrine gland cell located in the middle part of the zebra mussel foot. The results of this study suggested that the changes of D. polymorpha byssogenesis status and the environmental factors can dramatically affect the expression profiles of the genes unique to the foot. It turns out that the factorial design and analysis of the microarray experiment is a reliable method to identify the influence of multiple factors on the expression profiles of the probesets in the microarray; therein it provides a powerful tool to reveal the mechanism of zebra mussel underwater attachment.

  5. Factorial microarray analysis of zebra mussel (Dreissena polymorpha: Dreissenidae, Bivalvia) adhesion

    PubMed Central

    2010-01-01

    Background The zebra mussel (Dreissena polymorpha) has been well known for its expertise in attaching to substances under the water. Studies in past decades on this underwater adhesion focused on the adhesive protein isolated from the byssogenesis apparatus of the zebra mussel. However, the mechanism of the initiation, maintenance, and determination of the attachment process remains largely unknown. Results In this study, we used a zebra mussel cDNA microarray previously developed in our lab and a factorial analysis to identify the genes that were involved in response to the changes of four factors: temperature (Factor A), current velocity (Factor B), dissolved oxygen (Factor C), and byssogenesis status (Factor D). Twenty probes in the microarray were found to be modified by one of the factors. The transcription products of four selected genes, DPFP-BG20_A01, EGP-BG97/192_B06, EGP-BG13_G05, and NH-BG17_C09 were unique to the zebra mussel foot based on the results of quantitative reverse transcription PCR (qRT-PCR). The expression profiles of these four genes under the attachment and non-attachment were also confirmed by qRT-PCR and the result is accordant to that from microarray assay. The in situ hybridization with the RNA probes of two identified genes DPFP-BG20_A01 and EGP-BG97/192_B06 indicated that both of them were expressed by a type of exocrine gland cell located in the middle part of the zebra mussel foot. Conclusions The results of this study suggested that the changes of D. polymorpha byssogenesis status and the environmental factors can dramatically affect the expression profiles of the genes unique to the foot. It turns out that the factorial design and analysis of the microarray experiment is a reliable method to identify the influence of multiple factors on the expression profiles of the probesets in the microarray; therein it provides a powerful tool to reveal the mechanism of zebra mussel underwater attachment. PMID:20509938

  6. Development of a novel multiplex DNA microarray for Fusarium graminearum and analysis of azole fungicide responses

    PubMed Central

    2011-01-01

    Background The toxigenic fungal plant pathogen Fusarium graminearum compromises wheat production worldwide. Azole fungicides play a prominent role in controlling this pathogen. Sequencing of its genome stimulated the development of high-throughput technologies to study mechanisms of coping with fungicide stress and adaptation to fungicides at a previously unprecedented precision. DNA-microarrays have been used to analyze genome-wide gene expression patterns and uncovered complex transcriptional responses. A recently developed one-color multiplex array format allowed flexible, effective, and parallel examinations of eight RNA samples. Results We took advantage of the 8 × 15 k Agilent format to design, evaluate, and apply a novel microarray covering the whole F. graminearum genome to analyze transcriptional responses to azole fungicide treatment. Comparative statistical analysis of expression profiles uncovered 1058 genes that were significantly differentially expressed after azole-treatment. Quantitative RT-PCR analysis for 31 selected genes indicated high conformity to results from the microarray hybridization. Among the 596 genes with significantly increased transcript levels, analyses using GeneOntology and FunCat annotations detected the ergosterol-biosynthesis pathway genes as the category most significantly responding, confirming the mode-of-action of azole fungicides. Cyp51A, which is one of the three F. graminearum paralogs of Cyp51 encoding the target of azoles, was the most consistently differentially expressed gene of the entire study. A molecular phylogeny analyzing the relationships of the three CYP51 proteins in the context of 38 fungal genomes belonging to the Pezizomycotina indicated that CYP51C (FGSG_11024) groups with a new clade of CYP51 proteins. The transcriptional profiles for genes encoding ABC transporters and transcription factors suggested several involved in mechanisms alleviating the impact of the fungicide. Comparative analyses with

  7. High-content single-cell analysis on-chip using a laser microarray scanner.

    PubMed

    Zhou, Jing; Wu, Yu; Lee, Sang-Kwon; Fan, Rong

    2012-12-07

    High-content cellomic analysis is a powerful tool for rapid screening of cellular responses to extracellular cues and examination of intracellular signal transduction pathways at the single-cell level. In conjunction with microfluidics technology that provides unique advantages in sample processing and precise control of fluid delivery, it holds great potential to transform lab-on-a-chip systems for high-throughput cellular analysis. However, high-content imaging instruments are expensive, sophisticated, and not readily accessible. Herein, we report on a laser scanning cytometry approach that exploits a bench-top microarray scanner as an end-point reader to perform rapid and automated fluorescence imaging of cells cultured on a chip. Using high-content imaging analysis algorithms, we demonstrated multiplexed measurements of morphometric and proteomic parameters from all single cells. Our approach shows the improvement of both sensitivity and dynamic range by two orders of magnitude as compared to conventional epifluorescence microscopy. We applied this technology to high-throughput analysis of mesenchymal stem cells on an extracellular matrix protein array and characterization of heterotypic cell populations. This work demonstrates the feasibility of a laser microarray scanner for high-content cellomic analysis and opens up new opportunities to conduct informative cellular analysis and cell-based screening in the lab-on-a-chip systems.

  8. Extending the Interpretation of Gene Profiling Microarray Experiments to Pathway Analysis Through the Use of Gene Ontology Terms

    NASA Astrophysics Data System (ADS)

    Chatziioannou, Aristotelis; Moulos, Panagiotis

    Microarray technology allows the survey of gene expression at a global level by measuring mRNA abundance. However, the grand complexity characterizing a microarray experiment entails the development of computationally powerful tools apt for probing the biological problem studied. Here we propose a suite for flexible, adaptable to a wide range of possible needs of the biological end-user, data-driven interpretation of microarray experiments. The suite is implemented in MATLAB and is making use of two modules, able to perform all steps of typical microarray data analysis starting from data standardization and normalization up to statistical selection and pathway analysis utilizing Gene Ontology Term annotations for the species genomes interrogated, whereas due to its modular structure it is scalable thus enabling the incorporation or its seamless assembly with other existing tools.

  9. Development of a prostate cDNA microarray and statistical gene expression analysis package.

    PubMed

    Carlisle, A J; Prabhu, V V; Elkahloun, A; Hudson, J; Trent, J M; Linehan, W M; Williams, E D; Emmert-Buck, M R; Liotta, L A; Munson, P J; Krizman, D B

    2000-05-01

    A cDNA microarray comprising 5184 different cDNAs spotted onto nylon membrane filters was developed for prostate gene expression studies. The clones used for arraying were identified by cluster analysis of > 35 000 prostate cDNA library-derived expressed sequence tags (ESTs) present in the dbEST database maintained by the National Center for Biotechnology Information. Total RNA from two cell lines, prostate line 8.4 and melanoma line UACC903, was used to make radiolabeled probe for filter hybridizations. The absolute intensity of each individual cDNA spot was determined by phosphorimager scanning and evaluated by a bioinformatics package developed specifically for analysis of cDNA microarray experimentation. Results indicated 89% of the genes showed intensity levels above background in prostate cells compared with only 28% in melanoma cells. Replicate probe preparations yielded results with correlation values ranging from r = 0.90 to 0.93 and coefficient of variation ranging from 16 to 28%. Findings indicate that among others, the keratin 5 and vimentin genes were differentially expressed between these two divergent cell lines. Follow-up northern blot analysis verified these two expression changes, thereby demonstrating the reliability of this system. We report the development of a cDNA microarray system that is sensitive and reliable, demonstrates a low degree of variability, and is capable of determining verifiable gene expression differences between two distinct human cell lines. This system will prove useful for differential gene expression analysis in prostate-derived cells and tissue.

  10. Microarray-Based Gene Expression Analysis for Veterinary Pathologists: A Review.

    PubMed

    Raddatz, Barbara B; Spitzbarth, Ingo; Matheis, Katja A; Kalkuhl, Arno; Deschl, Ulrich; Baumgärtner, Wolfgang; Ulrich, Reiner

    2017-09-01

    High-throughput, genome-wide transcriptome analysis is now commonly used in all fields of life science research and is on the cusp of medical and veterinary diagnostic application. Transcriptomic methods such as microarrays and next-generation sequencing generate enormous amounts of data. The pathogenetic expertise acquired from understanding of general pathology provides veterinary pathologists with a profound background, which is essential in translating transcriptomic data into meaningful biological knowledge, thereby leading to a better understanding of underlying disease mechanisms. The scientific literature concerning high-throughput data-mining techniques usually addresses mathematicians or computer scientists as the target audience. In contrast, the present review provides the reader with a clear and systematic basis from a veterinary pathologist's perspective. Therefore, the aims are (1) to introduce the reader to the necessary methodological background; (2) to introduce the sequential steps commonly performed in a microarray analysis including quality control, annotation, normalization, selection of differentially expressed genes, clustering, gene ontology and pathway analysis, analysis of manually selected genes, and biomarker discovery; and (3) to provide references to publically available and user-friendly software suites. In summary, the data analysis methods presented within this review will enable veterinary pathologists to analyze high-throughput transcriptome data obtained from their own experiments, supplemental data that accompany scientific publications, or public repositories in order to obtain a more in-depth insight into underlying disease mechanisms.

  11. arrayCGHbase: an analysis platform for comparative genomic hybridization microarrays

    PubMed Central

    Menten, Björn; Pattyn, Filip; De Preter, Katleen; Robbrecht, Piet; Michels, Evi; Buysse, Karen; Mortier, Geert; De Paepe, Anne; van Vooren, Steven; Vermeesch, Joris; Moreau, Yves; De Moor, Bart; Vermeulen, Stefan; Speleman, Frank; Vandesompele, Jo

    2005-01-01

    Background The availability of the human genome sequence as well as the large number of physically accessible oligonucleotides, cDNA, and BAC clones across the entire genome has triggered and accelerated the use of several platforms for analysis of DNA copy number changes, amongst others microarray comparative genomic hybridization (arrayCGH). One of the challenges inherent to this new technology is the management and analysis of large numbers of data points generated in each individual experiment. Results We have developed arrayCGHbase, a comprehensive analysis platform for arrayCGH experiments consisting of a MIAME (Minimal Information About a Microarray Experiment) supportive database using MySQL underlying a data mining web tool, to store, analyze, interpret, compare, and visualize arrayCGH results in a uniform and user-friendly format. Following its flexible design, arrayCGHbase is compatible with all existing and forthcoming arrayCGH platforms. Data can be exported in a multitude of formats, including BED files to map copy number information on the genome using the Ensembl or UCSC genome browser. Conclusion ArrayCGHbase is a web based and platform independent arrayCGH data analysis tool, that allows users to access the analysis suite through the internet or a local intranet after installation on a private server. ArrayCGHbase is available at . PMID:15910681

  12. Development of an oligonucleotide-based DNA microarray for transcriptional analysis of Choristoneura fumiferana nucleopolyhedrovirus (CfMNPV) genes.

    PubMed

    Yang, Dan-Hui; Barari, Mehrnoosh; Arif, Basil M; Krell, Peter J

    2007-08-01

    A modified oligonucleotide-based two-channel DNA microarray was developed for characterization of temporal expression profiles of select Choristoneura fumiferana nucleopolyhedrovirus (CfMNPV) ORFs including its 7 unique ORFs. The microarray chip contained oligonucleotide probes for 23 CfMNPV ORFs and their complements as well as five host genes. Total RNA was isolated at different times post infection from Cf203 insect cells infected with CfMNPV. The cDNA was synthesized, fluorescent labelled with Cy3, and co-hybridized to the microarray chips along with Cy5-labelled viral genomic DNA, which served as equimolar reference standards for each probe. Transcription of the 7 CfMNPV unique ORFs was detected using DNA microarray analysis and their temporal expression profiles suggest that they are functional genes. The expression levels of three host genes varied throughout virus infection and therefore were unsuitable for normalization between microarrays. The DNA microarray results were compared to quantitative RT-PCR (qRT-PCR). Transcription of the non-coding (antisense) strands of some of the CfMNPV select genes including the polyhedrin gene, was also detected by array analysis and confirmed by qRT-PCR. The polyhedrin antisense transcript, based on long-range RT-PCR analysis, appeared to be a read-through product of an adjacent ORF in the same orientation as the antisense transcript.

  13. Analysis of microarray leukemia data using an efficient MapReduce-based K-nearest-neighbor classifier.

    PubMed

    Kumar, Mukesh; Rath, Nitish Kumar; Rath, Santanu Kumar

    2016-04-01

    Microarray-based gene expression profiling has emerged as an efficient technique for classification, prognosis, diagnosis, and treatment of cancer. Frequent changes in the behavior of this disease generates an enormous volume of data. Microarray data satisfies both the veracity and velocity properties of big data, as it keeps changing with time. Therefore, the analysis of microarray datasets in a small amount of time is essential. They often contain a large amount of expression, but only a fraction of it comprises genes that are significantly expressed. The precise identification of genes of interest that are responsible for causing cancer are imperative in microarray data analysis. Most existing schemes employ a two-phase process such as feature selection/extraction followed by classification. In this paper, various statistical methods (tests) based on MapReduce are proposed for selecting relevant features. After feature selection, a MapReduce-based K-nearest neighbor (mrKNN) classifier is also employed to classify microarray data. These algorithms are successfully implemented in a Hadoop framework. A comparative analysis is done on these MapReduce-based models using microarray datasets of various dimensions. From the obtained results, it is observed that these models consume much less execution time than conventional models in processing big data.

  14. Comparison of microarray and sage techniques in gene expression analysis of human glioblastoma.

    PubMed

    Kavsan, V M; Dmitrenko, V V; Shostak, K O; Bukreieva, T V; Vitak, N Y; Simirenko, O E; Malisheva, T A; Shamayev, M I; Rozumenko, V D; Zozulya, Y A

    2007-01-01

    To enhance glioblastoma (GB) marker discovery we compared gene expression in GB with human normal brain (NB) by accessing SAGE Genie web site and compared obtained results with published data. Nine GB and five NB SAGE-libraries were analyzed using the Digital Gene Expression Displayer (DGED), the results of DGED were tested by Northern blot analysis and RT-PCR of arbitrary selected genes. Review of available data from the articles on gene expression profiling by microarray-based hybridization showed as few as 35 overlapped genes with increased expression in GB. Some of them were identified in four articles, but most genes in three or even in two investigations. There was found also some differences between SAGE results of GB analysis. Digital Gene Expression Displayer approach revealed 676 genes differentially expressed in GB vs. NB with cut-off ratio: twofold change and P < or = 0.05. Differential expression of selectedgenes obtained by DGED was confirmed by Northern analysis and RT-PCR. Altogether, only 105 of 955 genes presented in published investigations were among the genes obtained by DGED. Comparison of the results obtained by microarrays and SAGE is very complicated because authors present only the most prominent differentially expressed genes. However, even available data give quite poor overlapping of genes revealed by microarrays. Some differences between results obtained by SAGE in different investigations can be explained by high dependence on the statistical methods used. As for now, the best solution to search for molecular tumor markers is to compare all available results and to select only those genes, which significant expression in tumor combined with very low expression in normal tissues was reproduced in several articles. 105 differentially expressed genes, common to both methods, can be included in the list of candidates for the molecular typing of GBs. Some genes, encoded cell surface or extra-cellular proteins may be useful for targeting

  15. Microarray meta-analysis identifies evolutionarily conserved BMP signaling targets in developing long bones.

    PubMed

    Prashar, Paritosh; Yadav, Prem Swaroop; Samarjeet, Fnu; Bandyopadhyay, Amitabha

    2014-05-15

    In vertebrates, BMP signaling has been demonstrated to be sufficient for bone formation in several tissue contexts. This suggests that genes necessary for bone formation are expressed in a BMP signaling dependent manner. However, till date no gene has been reported to be expressed in a BMP signaling dependent manner in bone. Our aim was to identify such genes. On searching the literature we found that several microarray experiments have been conducted where the transcriptome of osteogenic cells in absence and presence of BMP signaling activation have been compared. However, till date, there is no evidence to suggest that any of the genes found to be upregulated in presence of BMP signaling in these microarray analyses is indeed a target of BMP signaling in bone. We wanted to utilize this publicly available information to identify candidate BMP signaling target genes in vivo. We performed a meta-analysis of six such comparable microarray datasets. This analysis and subsequent experiments led to the identification of five targets of BMP signaling in bone that are conserved both in mouse and chick. Of these Lox, Klf10 and Gpr97 are likely to be direct transcriptional targets of BMP signaling pathway. Dpysl3, is a novel BMP signaling target identified in our study. Our data demonstrate that Dpysl3 is important for osteogenic differentiation of mesenchymal cells and is involved in cell secretion. We have demonstrated that the expression of Dpysl3 is co-operatively regulated by BMP signaling and Runx2. Based on our experimental data, in silico analysis of the putative promoter-enhancer regions of Bmp target genes and existing literature, we hypothesize that BMP signaling collaborates with multiple signaling pathways to regulate the expression of a unique set of genes involved in endochondral ossification. Copyright © 2014. Published by Elsevier Inc.

  16. Aberrant Expression Profile of Long Noncoding RNA in Human Sinonasal Squamous Cell Carcinoma by Microarray Analysis

    PubMed Central

    Meng, Ling-zhao; Sun, Jing-wu; Yang, Fan

    2016-01-01

    Objectives. This study aimed to identify aberrantly expressed long noncoding RNAs (lncRNAs) profile of sinonasal squamous cell carcinoma (SSCC) and explore their potential functions. Methods. We investigated lncRNA and mRNA expression in SSCC and paired adjacent noncancerous tissues obtained from 6 patients with microarrays. Gene ontology (GO) analysis and pathway analysis were utilized to investigate the gene function. Gene signal-network and lncRNA-mRNA network were depicted. Quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to validate 5 lncRNAs in a second set of paired SSCC and adjacent noncancerous tissues obtained from 22 additional patients. Results. We identified significantly differentially expressed lncRNAs (n = 3146) and mRNAs (n = 2208) in SSCC relative to noncancerous tissues. The GO annotation indicated that there are some core gene products that may be attributed to the progress of SSCC. The pathway analysis identified many pathways associated with cancer. The results of lncRNA-mRNA network and gene signal-network implied some core lncRNAs/mRNAs might play important roles in SSCC pathogenesis. The results of qRT-PCR showed that all of the 5 lncRNAs were differentially expressed and consistent with the microarray results. Conclusion. Our study is the first screening and analysis of lncRNAs expression profile in SSCC and may offer new insights into pathogenesis of this disease. PMID:28044124

  17. From RNA isolation to microarray analysis: Comparison of methods in FFPE tissues.

    PubMed

    Belder, Nevin; Coskun, Öznur; Doganay Erdogan, Beyza; Ilk, Ozlem; Savas, Berna; Ensari, Arzu; Özdağ, Hilal

    2016-08-01

    Genome-wide gene expression profiling analysis of FFPE tissue samples is indispensable for cancer research and provides the opportunity to evaluate links between molecular and clinical information, however, working with FFPE samples is challenging due to extensive cross-linking, fragmentation and limited quantities of nucleic acid. Thus, processing of FFPE tissue samples from RNA extraction to microarray analysis still needs optimization. In this study, a modified deparaffinization protocol was conducted prior to RNA isolation. Trizol, Qiagen RNeasy FFPE and Arcturus PicoPure RNA Isolation kits were used in parallel to compare their impact on RNA isolation. We also evaluated the effect of two different cRNA/cDNA preparation and labeling protocols with two different array platforms (Affymetrix Human Genome U133 Plus 2.0 and U133_X3P) on the percentage of present calls. Our optimization study shows that the Qiagen RNeasy FFPE kit with modified deparaffinization step gives better results (RNA quantity and quality) than the other two isolation kits. The Ribo-SPIA protocol gave a significantly higher percentage of present calls than the 3' IVT cDNA amplification and labeling system. However, no significant differences were found between the two array platforms. Our study paves the way for future high-throughput transcriptional analysis by optimizing FFPE tissue sample processing from RNA isolation to microarray analysis. Copyright © 2016 Elsevier GmbH. All rights reserved.

  18. Separate-channel analysis of two-channel microarrays: recovering inter-spot information

    PubMed Central

    2013-01-01

    Background Two-channel (or two-color) microarrays are cost-effective platforms for comparative analysis of gene expression. They are traditionally analysed in terms of the log-ratios (M-values) of the two channel intensities at each spot, but this analysis does not use all the information available in the separate channel observations. Mixed models have been proposed to analyse intensities from the two channels as separate observations, but such models can be complex to use and the gain in efficiency over the log-ratio analysis is difficult to quantify. Mixed models yield test statistics for the null distributions can be specified only approximately, and some approaches do not borrow strength between genes. Results This article reformulates the mixed model to clarify the relationship with the traditional log-ratio analysis, to facilitate information borrowing between genes, and to obtain an exact distributional theory for the resulting test statistics. The mixed model is transformed to operate on the M-values and A-values (average log-expression for each spot) instead of on the log-expression values. The log-ratio analysis is shown to ignore information contained in the A-values. The relative efficiency of the log-ratio analysis is shown to depend on the size of the intraspot correlation. A new separate channel analysis method is proposed that assumes a constant intra-spot correlation coefficient across all genes. This approach permits the mixed model to be transformed into an ordinary linear model, allowing the data analysis to use a well-understood empirical Bayes analysis pipeline for linear modeling of microarray data. This yields statistically powerful test statistics that have an exact distributional theory. The log-ratio, mixed model and common correlation methods are compared using three case studies. The results show that separate channel analyses that borrow strength between genes are more powerful than log-ratio analyses. The common correlation analysis

  19. Nonlinear matching measure for the analysis of on-off type DNA microarray images

    NASA Astrophysics Data System (ADS)

    Kim, Jong D.; Park, Misun; Kim, Jongwon

    2003-07-01

    In this paper, we propose a new nonlinear matching measure for automatic analysis of the on-off type DNA microarray images in which the hybridized spots are detected by the template matching method. The targeting spots of HPV DNA chips are designed for genotyping the human papilloma virus(HPV). The proposed measure is obtained by binarythresholding over the whole template region and taking the number of white pixels inside the spotted area. This measure is evaluated in terms of the accuracy of the estimated marker location to show better performance than the normalized covariance.

  20. Diagnostic biomarkers for renal cell carcinoma: selection using novel bioinformatics systems for microarray data analysis

    PubMed Central

    Osunkoya, Adeboye O; Yin-Goen, Qiqin; Phan, John H; Moffitt, Richard A; Stokes, Todd H; Wang, May D; Young, Andrew N

    2009-01-01

    Summary The differential diagnosis of clear cell, papillary and chromophobe renal cell carcinoma is clinically important, because these tumor subtypes are associated with different pathobiology and clinical behavior. For cases in which histopathology is equivocal, immunohistochemistry and quantitative RT-PCR can assist in the differential diagnosis by measuring expression of subtype-specific biomarkers. Several renal tumor biomarkers have been discovered in expression microarray studies. However, due to heterogeneity of gene and protein expression, additional biomarkers are needed for reliable diagnostic classification. We developed novel bioinformatics systems to identify candidate renal tumor biomarkers from the microarray profiles of 45 clear cell, 16 papillary and 10 chromophobe renal cell carcinoma; the microarray data was derived from two independent published studies. The ArrayWiki biocomputing system merged the microarray datasets into a single file, so gene expression could be analyzed from a larger number of tumors. The caCORRECT system removed non-random sources of error from the microarray data, and the omniBioMarker system analyzed data with several gene-ranking algorithms, in order to identify algorithms effective at recognizing previously described renal tumor biomarkers. We predicted these algorithms would also be effective at identifying unknown biomarkers that could be verified by independent methods. We selected six novel candidate biomakers from the omniBioMarker analysis, and verified their differential expression in formalin-fixed paraffin-embedded tissues by quantitative RT-PCR and immunohistochemistry. The candidate biomarkers were carbonic anhydrase IX, ceruloplasmin, schwannomin-interacting protein 1, E74-like factor 3, cytochrome c oxidase subunit 5a and acetyl-CoA acetyltransferase 1. Quantitative RT-PCR was performed on 17 clear cell, 13 papillary and 7 chromophobe renal cell carcinoma. Carbonic anhydrase IX and ceruloplasmin were

  1. High-Throughput Analysis of Serum Antigens Using Sandwich ELISAs on Microarrays

    SciTech Connect

    Servoss, Shannon; Gonzalez, Rachel M.; Varnum, Susan M.; Zangar, Richard C.

    2009-05-11

    Enzyme-linked immunosorbent assay (ELISA) microarrays promise to be a powerful tool for the detection and validation of disease biomarkers. ELISA microarrays are capable of simultaneous detection of many proteins using a small sample volume. Although there are many potential pitfalls to the use of ELISA microarrays, these can be avoided by careful planning of experiments. In this chapter we describe a high-throughput protocol for processing ELISA microarrays that will result in reliable and reproducible data.

  2. Investigating the effect of paralogs on microarray gene-set analysis

    PubMed Central

    2011-01-01

    Background In order to interpret the results obtained from a microarray experiment, researchers often shift focus from analysis of individual differentially expressed genes to analyses of sets of genes. These gene-set analysis (GSA) methods use previously accumulated biological knowledge to group genes into sets and then aim to rank these gene sets in a way that reflects their relative importance in the experimental situation in question. We suspect that the presence of paralogs affects the ability of GSA methods to accurately identify the most important sets of genes for subsequent research. Results We show that paralogs, which typically have high sequence identity and similar molecular functions, also exhibit high correlation in their expression patterns. We investigate this correlation as a potential confounding factor common to current GSA methods using Indygene http://www.cbio.uct.ac.za/indygene, a web tool that reduces a supplied list of genes so that it includes no pairwise paralogy relationships above a specified sequence similarity threshold. We use the tool to reanalyse previously published microarray datasets and determine the potential utility of accounting for the presence of paralogs. Conclusions The Indygene tool efficiently removes paralogy relationships from a given dataset and we found that such a reduction, performed prior to GSA, has the ability to generate significantly different results that often represent novel and plausible biological hypotheses. This was demonstrated for three different GSA approaches when applied to the reanalysis of previously published microarray datasets and suggests that the redundancy and non-independence of paralogs is an important consideration when dealing with GSA methodologies. PMID:21261946

  3. BioconductorBuntu: a Linux distribution that implements a web-based DNA microarray analysis server.

    PubMed

    Geeleher, Paul; Morris, Dermot; Hinde, John P; Golden, Aaron

    2009-06-01

    BioconductorBuntu is a custom distribution of Ubuntu Linux that automatically installs a server-side microarray processing environment, providing a user-friendly web-based GUI to many of the tools developed by the Bioconductor Project, accessible locally or across a network. System installation is via booting off a CD image or by using a Debian package provided to upgrade an existing Ubuntu installation. In its current version, several microarray analysis pipelines are supported including oligonucleotide, dual-or single-dye experiments, including post-processing with Gene Set Enrichment Analysis. BioconductorBuntu is designed to be extensible, by server-side integration of further relevant Bioconductor modules as required, facilitated by its straightforward underlying Python-based infrastructure. BioconductorBuntu offers an ideal environment for the development of processing procedures to facilitate the analysis of next-generation sequencing datasets. BioconductorBuntu is available for download under a creative commons license along with additional documentation and a tutorial from (http://bioinf.nuigalway.ie).

  4. Microarray analysis of differentially expressed genes regulating lipid metabolism during melanoma progression.

    PubMed

    Sumantran, Venil N; Mishra, Pratik; Sudhakar, N

    2015-04-01

    A new hallmark of cancer involves acquisition of a lipogenic phenotype which promotes tumorigenesis. Little is known about lipid metabolism in melanomas. Therefore, we used BRB (Biometrics Research Branch) class comparison tool with multivariate analysis to identify differentially expressed genes in human cutaneous melanomas, compared with benign nevi and normal skin derived from the microarray dataset (GDS1375). The methods were validated by identifying known melanoma biomarkers (CITED1, FGFR2, PTPRF, LICAM, SPP1 and PHACTR1) in our results. Eighteen genes regulating metabolism of fatty acids, lipid second messengers and gangliosides were 2-9 fold upregulated in melanomas of GDS-1375. Out of the 18 genes, 13 were confirmed by KEGG pathway analysis and 10 were also significantly upregulated in human melanoma cell lines of NCI-60 Cell Miner database. Results showed that melanomas upregulated PPARGC1A transcription factor and its target genes regulating synthesis of fatty acids (SCD) and complex lipids (FABP3 and ACSL3). Melanoma also upregulated genes which prevented lipotoxicity (CPT2 and ACOT7) and regulated lipid second messengers, such as phosphatidic acid (AGPAT-4, PLD3) and inositol triphosphate (ITPKB, ITPR3). Genes for synthesis of pro-tumorigenic GM3 and GD3 gangliosides (UGCG, HEXA, ST3GAL5 and ST8SIA1) were also upregulated in melanoma. Overall, the microarray analysis of GDS-1375 dataset indicated that melanomas can become lipogenic by upregulating genes, leading to increase in fatty acid metabolism, metabolism of specific lipid second messengers, and ganglioside synthesis.

  5. Microarray Analysis of Human Liver Cells irradiated by 80MeV/u Carbon Ions

    NASA Astrophysics Data System (ADS)

    Wang, Xiao; Tian, Xiaoling; Kong, Fuquan; Li, Qiang; Jin, Xiaodong; Dai, Zhongying; Zhang, Hong; Yang, Mingjian; Zhao, Kui

    Objective Biological effect of heavy ion beam has the important significance for cancer therapy and space exploring owing its high LET and RBE, low OER, especially forming Bragg spike at the end of the tracks of charged particles. More serious damage for cells are induced by heavy ions and difficult repair than other irradiation such as X-ray and ν-ray . To explore the molecular mechanism of biological effect caused by heavy ionizing radiation (HIR) and to construct the gene expression profile database of HIR-induced human liver cells L02 by microarray analysis. Methods In this study, L02 cells were irradiated by 80MeV/u carbon ions at 5 Gy delivered by HIRFL (Heavy Ion Research Facility in Lanzhou) at room temperature. Total RNAs of cells incubated 6 hours and 24hours after irradiation were extracted with Trizol. Unirradiated cells were used as a control. RNAs were transcripted into cDNA by reverse transcription and labelled with cy5-dCTP and cy3-dCTP respectively. A human genome oligonucleotide set consisting of 5 amino acid-modified 70-mer probes and representing 21,329 well-characterized Homo sapiens genes was selected for microarray analysis and printed on amino-silaned glass slides. Arrays were fabricated using an OmniGrid microarrayer. Only genes whose alteration tendency was consistent in both microarrays were selected as differentially expressed genes. The Affymetrix's short oligonucleotide (25-mer) HG U133A 2.0 array analyses were performed per the manufacturer's instructions. Results Of the 21,329 genes tested, 37 genes showed changes in expression level with ratio higher than 2.0 and lower than 0.5 at 6hrs after irradiation. There were 19 genes showing up-regulation in radiated L02 cells, whereas 18 genes showing down-regulation; At 24hrs after irradiation, 269 genes showed changes in expression level with ratio higher than 2.0 and lower than 0.5. There were 67 genes showing up-regulation in radiated L02 cells, whereas 202 genes showing down

  6. A microarray analysis of gene expression patterns during early phases of newt lens regeneration

    PubMed Central

    Sousounis, Konstantinos; Michel, Christian S.; Bruckskotten, Marc; Maki, Nobuyasu; Borchardt, Thilo; Braun, Thomas; Tsonis, Panagiotis A.

    2013-01-01

    Purpose Notophthalmus viridescens, the red-spotted newt, possesses tremendous regenerative capabilities. Among the tissues and organs newts can regenerate, the lens is regenerated via transdifferentiation of the pigment epithelial cells of the dorsal iris, following complete removal (lentectomy). Under normal conditions, the same cells from the ventral iris are not capable of regenerating. This study aims to further understand the initial signals of lens regeneration. Methods We performed microarray analysis using RNA from a dorsal or ventral iris isolated 1, 3, and 5 days after lentectomy and compared to RNA isolated from an intact iris. This analysis was supported with quantitative real-time polymerase chain reaction (qRT-PCR) of selected genes. Results Microarrays showed 804 spots were differentially regulated 1, 3, and 5 days post-lentectomy in the dorsal and ventral iris. Functional annotation using Gene Ontology revealed interesting terms. Among them, factors related to cell cycle and DNA repair were mostly upregulated, in the microarray, 3 and 5 days post-lentectomy. qRT-PCR for rad1 and vascular endothelial growth factor receptor 1 showed upregulation for the dorsal iris 3 and 5 days post- lentectomy and for the ventral iris 5 days post-lentectomy. Rad1 was also upregulated twofold more in the dorsal iris than in the ventral iris 5 days post-lentectomy (p<0.001). Factors related to redox homeostasis were mostly upregulated in the microarray in all time points and samples. qRT-PCR for glutathione peroxidase 1 also showed upregulation in all time points for the ventral and dorsal iris. For the most part, mitochondrial enzymes were downregulated with the notable exception of cytochrome c–related oxidases that were mostly upregulated at all time points. qRT-PCR for cytochrome c oxidase subunit 2 showed upregulation especially 3 days post-lentectomy for the dorsal and ventral iris (p<0.001). Factors related to extracellular matrix and tissue remodeling showed

  7. Time-Frequency Analysis of Peptide Microarray Data: Application to Brain Cancer Immunosignatures

    PubMed Central

    O’Donnell, Brian; Maurer, Alexander; Papandreou-Suppappola, Antonia; Stafford, Phillip

    2015-01-01

    One of the gravest dangers facing cancer patients is an extended symptom-free lull between tumor initiation and the first diagnosis. Detection of tumors is critical for effective intervention. Using the body’s immune system to detect and amplify tumor-specific signals may enable detection of cancer using an inexpensive immunoassay. Immunosignatures are one such assay: they provide a map of antibody interactions with random-sequence peptides. They enable detection of disease-specific patterns using classic train/test methods. However, to date, very little effort has gone into extracting information from the sequence of peptides that interact with disease-specific antibodies. Because it is difficult to represent all possible antigen peptides in a microarray format, we chose to synthesize only 330,000 peptides on a single immunosignature microarray. The 330,000 random-sequence peptides on the microarray represent 83% of all tetramers and 27% of all pentamers, creating an unbiased but substantial gap in the coverage of total sequence space. We therefore chose to examine many relatively short motifs from these random-sequence peptides. Time-variant analysis of recurrent subsequences provided a means to dissect amino acid sequences from the peptides while simultaneously retaining the antibody–peptide binding intensities. We first used a simple experiment in which monoclonal antibodies with known linear epitopes were exposed to these random-sequence peptides, and their binding intensities were used to create our algorithm. We then demonstrated the performance of the proposed algorithm by examining immunosignatures from patients with Glioblastoma multiformae (GBM), an aggressive form of brain cancer. Eight different frameshift targets were identified from the random-sequence peptides using this technique. If immune-reactive antigens can be identified using a relatively simple immune assay, it might enable a diagnostic test with sufficient sensitivity to detect tumors

  8. Loss of HITS (FAM107B) expression in cancers of multiple organs: tissue microarray analysis.

    PubMed

    Nakajima, Hideo; Koizumi, Keita; Tanaka, Takuji; Ishigaki, Yasuhito; Yoshitake, Yoshino; Yonekura, Hideto; Sakuma, Tsutomu; Fukushima, Toshihiro; Umehara, Hisanori; Ueno, Soichiro; Minamoto, Toshinari; Motoo, Yoshiharu

    2012-10-01

    Family with sequence similarity 107 (FAM107) proteins consist of two subtypes, FAM107A and FAM107B in mammals, possessing a conserved N-terminal domain of unknown function. Recently we found that FAM107B, an 18 kDa nuclear protein, is expressed in a broad range of tissues and is downregulated in gastrointestinal cancer. Because FAM107B expression is amplified by heat-shock stimulation, we designated it heat shock-inducible tumor small protein (HITS). Although data related to FAM107A as a candidate tumor suppressor have been accumulated, little biological information is available for HITS. In the present study, we examined HITS expression using immunohistochemistry with tissue microarrays and performed detailed statistical analyses. By screening a high-density multiple organ tumor and normal tissue microarray, HITS expression was decreased in tumor tissues of the breast, thyroid, testis and uterine cervix as well as the stomach and colon. Further analysis of tissue microarrays of individual organs showed that loss of HITS expression in cancer tissues was statistically significant and commonly observed in distinct organs in a histological type-specific manner. The HITS expression intensity was inversely correlated with the primary tumor size in breast and thyroid cancers. In addition, effects of tetracycline-inducible HITS expression on tumor growth were investigated in vivo. Forced expression of HITS inhibited tumor xenograft proliferation, compared with the mock-treated tumor xenograft model. These results show that loss of HITS expression is a common phenomenon observed in cancers of distinct organs and involved in tumor development and proliferation.

  9. Segment and fit thresholding: a new method for image analysis applied to microarray and immunofluorescence data.

    PubMed

    Ensink, Elliot; Sinha, Jessica; Sinha, Arkadeep; Tang, Huiyuan; Calderone, Heather M; Hostetter, Galen; Winter, Jordan; Cherba, David; Brand, Randall E; Allen, Peter J; Sempere, Lorenzo F; Haab, Brian B

    2015-10-06

    Experiments involving the high-throughput quantification of image data require algorithms for automation. A challenge in the development of such algorithms is to properly interpret signals over a broad range of image characteristics, without the need for manual adjustment of parameters. Here we present a new approach for locating signals in image data, called Segment and Fit Thresholding (SFT). The method assesses statistical characteristics of small segments of the image and determines the best-fit trends between the statistics. Based on the relationships, SFT identifies segments belonging to background regions; analyzes the background to determine optimal thresholds; and analyzes all segments to identify signal pixels. We optimized the initial settings for locating background and signal in antibody microarray and immunofluorescence data and found that SFT performed well over multiple, diverse image characteristics without readjustment of settings. When used for the automated analysis of multicolor, tissue-microarray images, SFT correctly found the overlap of markers with known subcellular localization, and it performed better than a fixed threshold and Otsu's method for selected images. SFT promises to advance the goal of full automation in image analysis.

  10. Microarray and functional analysis of growth phase-dependent gene regulation in Bordetella bronchiseptica.

    PubMed

    Nicholson, Tracy L; Buboltz, Anne M; Harvill, Eric T; Brockmeier, Susan L

    2009-10-01

    Growth phase-dependent gene regulation has recently been demonstrated to occur in Bordetella pertussis, with many transcripts, including known virulence factors, significantly decreasing during the transition from logarithmic to stationary-phase growth. Given that B. pertussis is thought to have derived from a Bordetella bronchiseptica-like ancestor, we hypothesized that growth phase-dependent gene regulation would also occur in B. bronchiseptica. Microarray analysis revealed and quantitative real-time PCR (qRT-PCR) confirmed that growth phase-dependent gene regulation occurs in B. bronchiseptica, resulting in prominent temporal shifts in global gene expression. Two virulence phenotypes associated with these gene expression changes were tested. We found that growth-dependent increases in expression of some type III secretion system (TTSS) genes led to a growth phase-dependent increase in a TTSS-dependent function, cytotoxicity. Although the transcription of genes encoding adhesins previously shown to mediate adherence was decreased in late-log and stationary phases, we found that the adherence of B. bronchiseptica did not decrease in these later phases of growth. Microarray analysis revealed and qRT-PCR confirmed that growth phase-dependent gene regulation occurred in both Bvg(+) and Bvg(-) phase-locked mutants, indicating that growth phase-dependent gene regulation in B. bronchiseptica can function independently from the BvgAS regulatory system.

  11. Identification of Iron Homeostasis Genes Dysregulation Potentially Involved in Retinopathy of Prematurity Pathogenicity by Microarray Analysis

    PubMed Central

    Luo, Xian-qiong; Zhang, Chun-yi; Zhang, Jia-wen; Jiang, Jing-bo; Yin, Ai-hua; Guo, Li; Nie, Chuan; Lu, Xu-zai; Deng, Hua; Zhang, Liang

    2015-01-01

    Retinopathy of prematurity (ROP) is a serious disease of preterm neonates and there are limited systematic studies of the molecular mechanisms underlying ROP. Therefore, here we performed global gene expression profiling in human fetal retinal microvascular endothelial cells (RMECs) under hypoxic conditions in vitro. Aborted fetuses were enrolled and primary RMECs were isolated from eyeballs. Cultivated cells were treated with CoCl2 to induce hypoxia. The dual-color microarray approach was adopted to compare gene expression profiling between treated RMECs and the paired untreated control. The one-class algorithm in significance analysis of microarray (SAM) software was used to screen the differentially expressed genes (DEGs) and quantitative RT-PCR (qRT-PCR) was conducted to validate the results. Gene Ontology was employed for functional enrichment analysis. There were 326 DEGs between the hypoxia-induced group and untreated group. Of these genes, 198 were upregulated in hypoxic RMECs, while the other 128 hits were downregulated. In particular, genes in the iron ion homeostasis pathway were highly enriched under hypoxic conditions. Our study indicates that dysregulation of genes involved in iron homeostasis mediating oxidative damage may be responsible for the mechanisms underlying ROP. The “oxygen plus iron” hypothesis may improve our understanding of ROP pathogenesis. PMID:26557385

  12. Using microarray analysis to evaluate genetic polymorphisms involved in the metabolism of environmental chemicals.

    PubMed

    Ban, Susumu; Kondo, Tomoko; Ishizuka, Mayumi; Sasaki, Seiko; Konishi, Kanae; Washino, Noriaki; Fujita, Syoichi; Kishi, Reiko

    2007-05-01

    The field of molecular biology currently faces the need for a comprehensive method of evaluating individual differences derived from genetic variation in the form of single nucleotide polymorphisms (SNPs). SNPs in human genes are generally considered to be very useful in determining inherited genetic disorders, susceptibility to certain diseases, and cancer predisposition. Quick and accurate discrimination of SNPs is the key characteristic of technology used in DNA diagnostics. For this study, we first developed a DNA microarray and then evaluated its efficacy by determining the detection ability and validity of this method. Using DNA obtained from 380 pregnant Japanese women, we examined 13 polymorphisms of 9 genes, which are associated with the metabolism of environmental chemical compounds found in high frequency among Japanese populations. The ability to detect CYP1A1 I462V, CYP1B1 L432V, GSTP1 I105V and AhR R554K gene polymorphisms was above 98%, and agreement rates when compared with real time PCR analysis methods (kappa values) showed high validity: 0.98 (0.96), 0.97 (0.93), 0.90 (0.81), 0.90 (0.91), respectively. While this DNA microarray analysis should prove important as a method for initial screening, it is still necessary that we find better methods for improving the detection of other gene polymorphisms not part of this study.

  13. Evaluation of Different Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes

    PubMed Central

    Johnstone, Daniel M.; Riveros, Carlos; Heidari, Moones; Graham, Ross M.; Trinder, Debbie; Berretta, Regina; Olynyk, John K.; Scott, Rodney J.; Moscato, Pablo; Milward, Elizabeth A.

    2013-01-01

    While Illumina microarrays can be used successfully for detecting small gene expression changes due to their high degree of technical replicability, there is little information on how different normalization and differential expression analysis strategies affect outcomes. To evaluate this, we assessed concordance across gene lists generated by applying different combinations of normalization strategy and analytical approach to two Illumina datasets with modest expression changes. In addition to using traditional statistical approaches, we also tested an approach based on combinatorial optimization. We found that the choice of both normalization strategy and analytical approach considerably affected outcomes, in some cases leading to substantial differences in gene lists and subsequent pathway analysis results. Our findings suggest that important biological phenomena may be overlooked when there is a routine practice of using only one approach to investigate all microarray datasets. Analytical artefacts of this kind are likely to be especially relevant for datasets involving small fold changes, where inherent technical variation—if not adequately minimized by effective normalization—may overshadow true biological variation. This report provides some basic guidelines for optimizing outcomes when working with Illumina datasets involving small expression changes. PMID:27605185

  14. Segment and Fit Thresholding: A New Method for Image Analysis Applied to Microarray and Immunofluorescence Data

    PubMed Central

    Ensink, Elliot; Sinha, Jessica; Sinha, Arkadeep; Tang, Huiyuan; Calderone, Heather M.; Hostetter, Galen; Winter, Jordan; Cherba, David; Brand, Randall E.; Allen, Peter J.; Sempere, Lorenzo F.; Haab, Brian B.

    2016-01-01

    Certain experiments involve the high-throughput quantification of image data, thus requiring algorithms for automation. A challenge in the development of such algorithms is to properly interpret signals over a broad range of image characteristics, without the need for manual adjustment of parameters. Here we present a new approach for locating signals in image data, called Segment and Fit Thresholding (SFT). The method assesses statistical characteristics of small segments of the image and determines the best-fit trends between the statistics. Based on the relationships, SFT identifies segments belonging to background regions; analyzes the background to determine optimal thresholds; and analyzes all segments to identify signal pixels. We optimized the initial settings for locating background and signal in antibody microarray and immunofluorescence data and found that SFT performed well over multiple, diverse image characteristics without readjustment of settings. When used for the automated analysis of multi-color, tissue-microarray images, SFT correctly found the overlap of markers with known subcellular localization, and it performed better than a fixed threshold and Otsu’s method for selected images. SFT promises to advance the goal of full automation in image analysis. PMID:26339978

  15. Focused Microarray Analysis of Peripheral Mononuclear Blood Cells from Churg–Strauss Syndrome Patients

    PubMed Central

    Tougan, Takahiro; Onda, Hiroaki; Okuzaki, Daisuke; Kobayashi, Shigeto; Hashimoto, Hiroshi; Nojima, Hiroshi

    2008-01-01

    DNA diagnostics are useful but are hampered by difficult ethical issues. Moreover, it cannot provide enough information on the environmental factors that are important for pathogenesis of certain diseases. However, this is not a problem for RNA diagnostics, which evaluate the expression of the gene in question. We here report a novel RNA diagnostics tool that can be employed with peripheral blood mononuclear cells (PBMCs). To establish this tool, we identified 290 genes that are highly expressed in normal PBMCs but not in TIG-1, a normal human fibroblast cell. These genes were entitled PREP after predominantly expressed in PBMC and included 50 uncharacterized genes. We then conducted PREP gene-focused microarray analysis on PBMCs from seven cases of Churg–Strauss syndrome (CSS), which is a small-vessel necrotizing vasculitis. We found that PREP135 (coactosin-like protein), PREP77 (prosaposin), PREP191 (cathepsin D), PREP234 (c-fgr), and PREP136 (lysozyme) were very highly up-regulated in all seven CSS patients. Another 28 genes were also up-regulated, albeit more moderately, and three were down-regulated in all CSS patients. The nature of these up- and down-regulated genes suggest that the immune systems of the patients are activated in response to invading microorganisms. These observations indicate that focused microarray analysis of PBMCs may be a practical, useful, and low-cost bedside diagnostics tool. PMID:18263571

  16. Concordance between RNA-sequencing data and DNA microarray data in transcriptome analysis of proliferative and quiescent fibroblasts

    PubMed Central

    Trost, Brett; Moir, Catherine A.; Gillespie, Zoe E.; Kusalik, Anthony; Mitchell, Jennifer A.; Eskiw, Christopher H.

    2015-01-01

    DNA microarrays and RNA sequencing (RNA-seq) are major technologies for performing high-throughput analysis of transcript abundance. Recently, concerns have been raised regarding the concordance of data derived from the two techniques. Using cDNA libraries derived from normal human foreskin fibroblasts, we measured changes in transcript abundance as cells transitioned from proliferative growth to quiescence using both DNA microarrays and RNA-seq. The internal reproducibility of the RNA-seq data was greater than that of the microarray data. Correlations between the RNA-seq data and the individual microarrays were low, but correlations between the RNA-seq values and the geometric mean of the microarray values were moderate. The two technologies had good agreement when considering probes with the largest (both positive and negative) fold change (FC) values. An independent technique, quantitative reverse-transcription PCR (qRT-PCR), was used to measure the FC of 76 genes between proliferative and quiescent samples, and a higher correlation was observed between the qRT-PCR data and the RNA-seq data than between the qRT-PCR data and the microarray data. PMID:26473061

  17. Systematic analysis of T7 RNA polymerase based in vitro linear RNA amplification for use in microarray experiments.

    PubMed

    Schneider, Jörg; Buness, Andreas; Huber, Wolfgang; Volz, Joachim; Kioschis, Petra; Hafner, Mathias; Poustka, Annemarie; Sültmann, Holger

    2004-04-30

    The requirement of a large amount of high-quality RNA is a major limiting factor for microarray experiments using biopsies. An average microarray experiment requires 10-100 microg of RNA. However, due to their small size, most biopsies do not yield this amount. Several different approaches for RNA amplification in vitro have been described and applied for microarray studies. In most of these, systematic analyses of the potential bias introduced by the enzymatic modifications are lacking. We examined the sources of error introduced by the T7 RNA polymerase based RNA amplification method through hybridisation studies on microarrays and performed statistical analysis of the parameters that need to be evaluated prior to routine laboratory use. The results demonstrate that amplification of the RNA has no systematic influence on the outcome of the microarray experiment. Although variations in differential expression between amplified and total RNA hybridisations can be observed, RNA amplification is reproducible, and there is no evidence that it introduces a large systematic bias. Our results underline the utility of the T7 based RNA amplification for use in microarray experiments provided that all samples under study are equally treated.

  18. Systematic analysis of T7 RNA polymerase based in vitro linear RNA amplification for use in microarray experiments

    PubMed Central

    Schneider, Jörg; Buneß, Andreas; Huber, Wolfgang; Volz, Joachim; Kioschis, Petra; Hafner, Mathias; Poustka, Annemarie; Sültmann, Holger

    2004-01-01

    Background The requirement of a large amount of high-quality RNA is a major limiting factor for microarray experiments using biopsies. An average microarray experiment requires 10–100 μg of RNA. However, due to their small size, most biopsies do not yield this amount. Several different approaches for RNA amplification in vitro have been described and applied for microarray studies. In most of these, systematic analyses of the potential bias introduced by the enzymatic modifications are lacking. Results We examined the sources of error introduced by the T7 RNA polymerase based RNA amplification method through hybridisation studies on microarrays and performed statistical analysis of the parameters that need to be evaluated prior to routine laboratory use. The results demonstrate that amplification of the RNA has no systematic influence on the outcome of the microarray experiment. Although variations in differential expression between amplified and total RNA hybridisations can be observed, RNA amplification is reproducible, and there is no evidence that it introduces a large systematic bias. Conclusions Our results underline the utility of the T7 based RNA amplification for use in microarray experiments provided that all samples under study are equally treated. PMID:15119961

  19. DATE analysis: A general theory of biological change applied to microarray data.

    PubMed

    Rasnick, David

    2009-01-01

    In contrast to conventional data mining, which searches for specific subsets of genes (extensive variables) to correlate with specific phenotypes, DATE analysis correlates intensive state variables calculated from the same datasets. At the heart of DATE analysis are two biological equations of state not dependent on genetic pathways. This result distinguishes DATE analysis from other bioinformatics approaches. The dimensionless state variable F quantifies the relative overall cellular activity of test cells compared to well-chosen reference cells. The variable pi(i) is the fold-change in the expression of the ith gene of test cells relative to reference. It is the fraction phi of the genome undergoing differential expression-not the magnitude pi-that controls biological change. The state variable phi is equivalent to the control strength of metabolic control analysis. For tractability, DATE analysis assumes a linear system of enzyme-connected networks and exploits the small average contribution of each cellular component. This approach was validated by reproducible values of the state variables F, RNA index, and phi calculated from random subsets of transcript microarray data. Using published microarray data, F, RNA index, and phi were correlated with: (1) the blood-feeding cycle of the malaria parasite, (2) embryonic development of the fruit fly, (3) temperature adaptation of Killifish, (4) exponential growth of cultured S. pneumoniae, and (5) human cancers. DATE analysis was applied to aCGH data from the great apes. A good example of the power of DATE analysis is its application to genomically unstable cancers, which have been refractory to data mining strategies.

  20. Overview of Protein Microarrays

    PubMed Central

    Reymond Sutandy, FX; Qian, Jiang; Chen, Chien-Sheng; Zhu, Heng

    2013-01-01

    Protein microarray is an emerging technology that provides a versatile platform for characterization of hundreds of thousands of proteins in a highly parallel and high-throughput way. Two major classes of protein microarrays are defined to describe their applications: analytical and functional protein microarrays. In addition, tissue or cell lysates can also be fractionated and spotted on a slide to form a reverse-phase protein microarray. While the fabrication technology is maturing, applications of protein microarrays, especially functional protein microarrays, have flourished during the past decade. Here, we will first review recent advances in the protein microarray technologies, and then present a series of examples to illustrate the applications of analytical and functional protein microarrays in both basic and clinical research. The research areas will include detection of various binding properties of proteins, study of protein posttranslational modifications, analysis of host-microbe interactions, profiling antibody specificity, and identification of biomarkers in autoimmune diseases. As a powerful technology platform, it would not be surprising if protein microarrays will become one of the leading technologies in proteomic and diagnostic fields in the next decade. PMID:23546620

  1. Pathway-based analysis of microarray and RNAseq data using Pathway Processor 2.0.

    PubMed

    Beltrame, Luca; Bianco, Luca; Fontana, Paolo; Cavalieri, Duccio

    2013-03-01

    The constant improvement of high-throughput technologies has led to a great increase in generated data per single experiment. Pathway analysis is a widespread method to understand experimental results at the system level. Pathway Processor 2.0 is an upgrade over the original Pathway Processor program developed in 2002, extended to support more species, analysis methods, and RNAseq data in addition to microarrays through a simple Web-based interface. The tool can perform two different types of analysis: the first covers the traditional Fisher's Test used by Pathway Processor and topology-aware analyses, which take into account the propagation of changes over the whole structure of a pathway, and the second is a new pathway-based method to investigate differences between phenotypes of interest. Common problems and troubleshooting are also discussed.

  2. GEPAS: a web-based resource for microarray gene expression data analysis

    PubMed Central

    Herrero, Javier; Al-Shahrour, Fátima; Díaz-Uriarte, Ramón; Mateos, Álvaro; Vaquerizas, Juan M.; Santoyo, Javier; Dopazo, Joaquín

    2003-01-01

    We present a web-based pipeline for microarray gene expression profile analysis, GEPAS, which stands for Gene Expression Profile Analysis Suite (http://gepas.bioinfo.cnio.es). GEPAS is composed of different interconnected modules which include tools for data pre-processing, two-conditions comparison, unsupervised and supervised clustering (which include some of the most popular methods as well as home made algorithms) and several tests for differential gene expression among different classes, continuous variables or survival analysis. A multiple purpose tool for data mining, based on Gene Ontology, is also linked to the tools, which constitutes a very convenient way of analysing clustering results. On-line tutorials are available from our main web server (http://bioinfo.cnio.es). PMID:12824345

  3. k-Nearest neighbor models for microarray gene expression analysis and clinical outcome prediction.

    PubMed

    Parry, R M; Jones, W; Stokes, T H; Phan, J H; Moffitt, R A; Fang, H; Shi, L; Oberthuer, A; Fischer, M; Tong, W; Wang, M D

    2010-08-01

    In the clinical application of genomic data analysis and modeling, a number of factors contribute to the performance of disease classification and clinical outcome prediction. This study focuses on the k-nearest neighbor (KNN) modeling strategy and its clinical use. Although KNN is simple and clinically appealing, large performance variations were found among experienced data analysis teams in the MicroArray Quality Control Phase II (MAQC-II) project. For clinical end points and controls from breast cancer, neuroblastoma and multiple myeloma, we systematically generated 463,320 KNN models by varying feature ranking method, number of features, distance metric, number of neighbors, vote weighting and decision threshold. We identified factors that contribute to the MAQC-II project performance variation, and validated a KNN data analysis protocol using a newly generated clinical data set with 478 neuroblastoma patients. We interpreted the biological and practical significance of the derived KNN models, and compared their performance with existing clinical factors.

  4. Microarray Based Gene Expression Analysis of Murine Brown and Subcutaneous Adipose Tissue: Significance with Human

    PubMed Central

    Boparai, Ravneet K.; Kondepudi, Kanthi Kiran; Mantri, Shrikant; Bishnoi, Mahendra

    2015-01-01

    Background Two types of adipose tissues, white (WAT) and brown (BAT) are found in mammals. Increasingly novel strategies are being proposed for the treatment of obesity and its associated complications by altering amount and/or activity of BAT using mouse models. Methodology/Principle Findings The present study was designed to: (a) investigate the differential expression of genes in LACA mice subcutaneous WAT (sWAT) and BAT using mouse DNA microarray, (b) to compare mouse differential gene expression with previously published human data; to understand any inter- species differences between the two and (c) to make a comparative assessment with C57BL/6 mouse strain. In mouse microarray studies, over 7003, 1176 and 401 probe sets showed more than two-fold, five-fold and ten-fold change respectively in differential expression between murine BAT and WAT. Microarray data was validated using quantitative RT-PCR of key genes showing high expression in BAT (Fabp3, Ucp1, Slc27a1) and sWAT (Ms4a1, H2-Ob, Bank1) or showing relatively low expression in BAT (Pgk1, Cox6b1) and sWAT (Slc20a1, Cd74). Multi-omic pathway analysis was employed to understand possible links between the organisms. When murine two fold data was compared with published human BAT and sWAT data, 90 genes showed parallel differential expression in both mouse and human. Out of these 90 genes, 46 showed same pattern of differential expression whereas the pattern was opposite for the remaining 44 genes. Based on our microarray results and its comparison with human data, we were able to identify genes (targets) (a) which can be studied in mouse model systems to extrapolate results to human (b) where caution should be exercised before extrapolation of murine data to human. Conclusion Our study provides evidence for inter species (mouse vs human) differences in differential gene expression between sWAT and BAT. Critical understanding of this data may help in development of novel ways to engineer one form of adipose

  5. Microarray based gene expression analysis of murine brown and subcutaneous adipose tissue: significance with human.

    PubMed

    Baboota, Ritesh K; Sarma, Siddhartha M; Boparai, Ravneet K; Kondepudi, Kanthi Kiran; Mantri, Shrikant; Bishnoi, Mahendra

    2015-01-01

    Two types of adipose tissues, white (WAT) and brown (BAT) are found in mammals. Increasingly novel strategies are being proposed for the treatment of obesity and its associated complications by altering amount and/or activity of BAT using mouse models. The present study was designed to: (a) investigate the differential expression of genes in LACA mice subcutaneous WAT (sWAT) and BAT using mouse DNA microarray, (b) to compare mouse differential gene expression with previously published human data; to understand any inter- species differences between the two and (c) to make a comparative assessment with C57BL/6 mouse strain. In mouse microarray studies, over 7003, 1176 and 401 probe sets showed more than two-fold, five-fold and ten-fold change respectively in differential expression between murine BAT and WAT. Microarray data was validated using quantitative RT-PCR of key genes showing high expression in BAT (Fabp3, Ucp1, Slc27a1) and sWAT (Ms4a1, H2-Ob, Bank1) or showing relatively low expression in BAT (Pgk1, Cox6b1) and sWAT (Slc20a1, Cd74). Multi-omic pathway analysis was employed to understand possible links between the organisms. When murine two fold data was compared with published human BAT and sWAT data, 90 genes showed parallel differential expression in both mouse and human. Out of these 90 genes, 46 showed same pattern of differential expression whereas the pattern was opposite for the remaining 44 genes. Based on our microarray results and its comparison with human data, we were able to identify genes (targets) (a) which can be studied in mouse model systems to extrapolate results to human (b) where caution should be exercised before extrapolation of murine data to human. Our study provides evidence for inter species (mouse vs human) differences in differential gene expression between sWAT and BAT. Critical understanding of this data may help in development of novel ways to engineer one form of adipose tissue to another using murine model with focus on

  6. SegMine workflows for semantic microarray data analysis in Orange4WS

    PubMed Central

    2011-01-01

    Background In experimental data analysis, bioinformatics researchers increasingly rely on tools that enable the composition and reuse of scientific workflows. The utility of current bioinformatics workflow environments can be significantly increased by offering advanced data mining services as workflow components. Such services can support, for instance, knowledge discovery from diverse distributed data and knowledge sources (such as GO, KEGG, PubMed, and experimental databases). Specifically, cutting-edge data analysis approaches, such as semantic data mining, link discovery, and visualization, have not yet been made available to researchers investigating complex biological datasets. Results We present a new methodology, SegMine, for semantic analysis of microarray data by exploiting general biological knowledge, and a new workflow environment, Orange4WS, with integrated support for web services in which the SegMine methodology is implemented. The SegMine methodology consists of two main steps. First, the semantic subgroup discovery algorithm is used to construct elaborate rules that identify enriched gene sets. Then, a link discovery service is used for the creation and visualization of new biological hypotheses. The utility of SegMine, implemented as a set of workflows in Orange4WS, is demonstrated in two microarray data analysis applications. In the analysis of senescence in human stem cells, the use of SegMine resulted in three novel research hypotheses that could improve understanding of the underlying mechanisms of senescence and identification of candidate marker genes. Conclusions Compared to the available data analysis systems, SegMine offers improved hypothesis generation and data interpretation for bioinformatics in an easy-to-use integrated workflow environment. PMID:22029475

  7. SegMine workflows for semantic microarray data analysis in Orange4WS.

    PubMed

    Podpečan, Vid; Lavrač, Nada; Mozetič, Igor; Novak, Petra Kralj; Trajkovski, Igor; Langohr, Laura; Kulovesi, Kimmo; Toivonen, Hannu; Petek, Marko; Motaln, Helena; Gruden, Kristina

    2011-10-26

    In experimental data analysis, bioinformatics researchers increasingly rely on tools that enable the composition and reuse of scientific workflows. The utility of current bioinformatics workflow environments can be significantly increased by offering advanced data mining services as workflow components. Such services can support, for instance, knowledge discovery from diverse distributed data and knowledge sources (such as GO, KEGG, PubMed, and experimental databases). Specifically, cutting-edge data analysis approaches, such as semantic data mining, link discovery, and visualization, have not yet been made available to researchers investigating complex biological datasets. We present a new methodology, SegMine, for semantic analysis of microarray data by exploiting general biological knowledge, and a new workflow environment, Orange4WS, with integrated support for web services in which the SegMine methodology is implemented. The SegMine methodology consists of two main steps. First, the semantic subgroup discovery algorithm is used to construct elaborate rules that identify enriched gene sets. Then, a link discovery service is used for the creation and visualization of new biological hypotheses. The utility of SegMine, implemented as a set of workflows in Orange4WS, is demonstrated in two microarray data analysis applications. In the analysis of senescence in human stem cells, the use of SegMine resulted in three novel research hypotheses that could improve understanding of the underlying mechanisms of senescence and identification of candidate marker genes. Compared to the available data analysis systems, SegMine offers improved hypothesis generation and data interpretation for bioinformatics in an easy-to-use integrated workflow environment.

  8. Statistical Analysis of Microarray Data with Replicated Spots: A Case Study with Synechococcus WH8102

    DOE PAGES

    Thomas, E. V.; Phillippy, K. H.; Brahamsha, B.; ...

    2009-01-01

    Until recently microarray experiments often involved relatively few arrays with only a single representation of each gene on each array. A complete genome microarray with multiple spots per gene (spread out spatially across the array) was developed in order to compare the gene expression of a marine cyanobacterium and a knockout mutant strain in a defined artificial seawater medium. Statistical methods were developed for analysis in the special situation of this case study where there is gene replication within an array and where relatively few arrays are used, which can be the case with current array technology. Due in partmore » to the replication within an array, it was possible to detect very small changes in the levels of expression between the wild type and mutant strains. One interesting biological outcome of this experiment is the indication of the extent to which the phosphorus regulatory system of this cyanobacterium affects the expression of multiple genes beyond those strictly involved in phosphorus acquisition.« less

  9. Transcriptional Profiling of Hydrogen Production Metabolism of Rhodobacter capsulatus under Temperature Stress by Microarray Analysis

    PubMed Central

    Gürgan, Muazzez; Afşar Erkal, Nilüfer; Özgür, Ebru; Gündüz, Ufuk; Eroglu, Inci; Yücel, Meral

    2015-01-01

    Biohydrogen is a clean and renewable form of hydrogen, which can be produced by photosynthetic bacteria in outdoor large-scale photobioreactors using sunlight. In this study, the transcriptional response of Rhodobacter capsulatus to cold (4 °C) and heat (42 °C) stress was studied using microarrays. Bacteria were grown in 30/2 acetate/glutamate medium at 30 °C for 48 h under continuous illumination. Then, cold and heat stresses were applied for two and six hours. Growth and hydrogen production were impaired under both stress conditions. Microarray chips for R. capsulatus were custom designed by Affymetrix (GeneChip®. TR_RCH2a520699F). The numbers of significantly changed genes were 328 and 293 out of 3685 genes under cold and heat stress, respectively. Our results indicate that temperature stress greatly affects the hydrogen production metabolisms of R. capsulatus. Specifically, the expression of genes that participate in nitrogen metabolism, photosynthesis and the electron transport system were induced by cold stress, while decreased by heat stress. Heat stress also resulted in down regulation of genes related to cell envelope, transporter and binding proteins. Transcriptome analysis and physiological results were consistent with each other. The results presented here may aid clarification of the genetic mechanisms for hydrogen production in purple non-sulfur (PNS) bacteria under temperature stress. PMID:26086826

  10. Analysis of ripening-related gene expression in papaya using an Arabidopsis-based microarray

    PubMed Central

    2012-01-01

    Background Papaya (Carica papaya L.) is a commercially important crop that produces climacteric fruits with a soft and sweet pulp that contain a wide range of health promoting phytochemicals. Despite its importance, little is known about transcriptional modifications during papaya fruit ripening and their control. In this study we report the analysis of ripe papaya transcriptome by using a cross-species (XSpecies) microarray technique based on the phylogenetic proximity between papaya and Arabidopsis thaliana. Results Papaya transcriptome analyses resulted in the identification of 414 ripening-related genes with some having their expression validated by qPCR. The transcription profile was compared with that from ripening tomato and grape. There were many similarities between papaya and tomato especially with respect to the expression of genes encoding proteins involved in primary metabolism, regulation of transcription, biotic and abiotic stress and cell wall metabolism. XSpecies microarray data indicated that transcription factors (TFs) of the MADS-box, NAC and AP2/ERF gene families were involved in the control of papaya ripening and revealed that cell wall-related gene expression in papaya had similarities to the expression profiles seen in Arabidopsis during hypocotyl development. Conclusion The cross-species array experiment identified a ripening-related set of genes in papaya allowing the comparison of transcription control between papaya and other fruit bearing taxa during the ripening process. PMID:23256600

  11. Analysing breast cancer microarrays from African Americans using shrinkage-based discriminant analysis.

    PubMed

    Pang, Herbert; Ebisu, Keita; Watanabe, Emi; Sue, Laura Y; Tong, Tiejun

    2010-10-01

    Breast cancer tumours among African Americans are usually more aggressive than those found in Caucasian populations. African-American patients with breast cancer also have higher mortality rates than Caucasian women. A better understanding of the disease aetiology of these breast cancers can help to improve and develop new methods for cancer prevention, diagnosis and treatment. The main goal of this project was to identify genes that help differentiate between oestrogen receptor-positive and -negative samples among a small group of African-American patients with breast cancer. Breast cancer microarrays from one of the largest genomic consortiums were analysed using 13 African-American and 201 Caucasian samples with oestrogen receptor status. We used a shrinkage-based classification method to identify genes that were informative in discriminating between oestrogen receptor-positive and -negative samples. Subset analysis and permutation were performed to obtain a set of genes unique to the African-American population. We identified a set of 156 probe sets, which gave a misclassification rate of 0.16 in distinguishing between oestrogen receptor-positive and -negative patients. The biological relevance of our findings was explored through literature-mining techniques and pathway mapping. An independent dataset was used to validate our findings and we found that the top ten genes mapped onto this dataset gave a misclassification rate of 0.15. The described method allows us best to utilise the information available from small sample size microarray data in the context of ethnic minorities.

  12. cDNA Microarray Analysis of Serially Sampled Cervical Cancer Specimens From Patients Treated With Thermochemoradiotherapy

    SciTech Connect

    Borkamo, Erling Dahl; Schem, Baard-Christian; Fluge, Oystein; Bruland, Ove; Dahl, Olav; Mella, Olav

    2009-12-01

    Purpose: To elucidate changes in gene expression after treatment with regional thermochemoradiotherapy in locally advanced squamous cell cervical cancer. Methods and Materials: Tru-Cut biopsy specimens were serially collected from 16 patients. Microarray gene expression levels before and 24 h after the first and second trimodality treatment sessions were compared. Pathway and network analyses were conducted by use of Ingenuity Pathways Analysis (IPA; Ingenuity Systems, Redwood City, CA). Single gene expressions were analyzed by quantitative real-time reverse transcription-polymerase chain reaction. Results: We detected 53 annotated genes that were differentially expressed after trimodality treatment. Central in the three top networks detected by IPA were interferon alfa, interferon beta, and interferon gamma receptor; nuclear factor kappaB; and tumor necrosis factor, respectively. These genes encode proteins that are important in regulation cell signaling, proliferation, gene expression, and immune stimulation. Biological processes over-represented among the 53 genes were fibrosis, tumorigenesis, and immune response. Conclusions: Microarrays showed minor changes in gene expression after thermochemoradiotherapy in locally advanced cervical cancer. We detected 53 differentially expressed genes, mainly involved in fibrosis, tumorigenesis, and immune response. A limitation with the use of serial biopsy specimens was low quality of ribonucleic acid from tumors that respond to highly effective therapy. Another 'key limitation' is timing of the post-treatment biopsy, because 24 h may be too late to adequately assess the impact of hyperthermia on gene expression.

  13. ParaSAM: a parallelized version of the significance analysis of microarrays algorithm

    PubMed Central

    Sharma, Ashok; Zhao, Jieping; Podolsky, Robert; McIndoe, Richard A.

    2010-01-01

    Motivation: Significance analysis of microarrays (SAM) is a widely used permutation-based approach to identifying differentially expressed genes in microarray datasets. While SAM is freely available as an Excel plug-in and as an R-package, analyses are often limited for large datasets due to very high memory requirements. Summary: We have developed a parallelized version of the SAM algorithm called ParaSAM to overcome the memory limitations. This high performance multithreaded application provides the scientific community with an easy and manageable client-server Windows application with graphical user interface and does not require programming experience to run. The parallel nature of the application comes from the use of web services to perform the permutations. Our results indicate that ParaSAM is not only faster than the serial version, but also can analyze extremely large datasets that cannot be performed using existing implementations. Availability:A web version open to the public is available at http://bioanalysis.genomics.mcg.edu/parasam. For local installations, both the windows and web implementations of ParaSAM are available for free at http://www.amdcc.org/bioinformatics/software/parasam.aspx Contact: rmcindoe@mail.mcg.edu Supplementary information: Supplementary Data is available at Bioinformatics online. PMID:20400455

  14. Comparative analysis of amplified and nonamplified RNA for hybridization in cDNA microarray.

    PubMed

    Gomes, Luciana I; Silva, Ricardo L A; Stolf, Beatriz S; Cristo, Elier B; Hirata, Roberto; Soares, Fernando A; Reis, Luiz F L; Neves, E Jordão; Carvalho, Alex F

    2003-10-15

    Limiting amounts of RNA is a major issue in cDNA microarray, especially when one is dealing with fresh tissue samples. Here we describe a protocol based on template switch and T7 amplification that led to efficient and linear amplification of 1300x. Using a glass-array containing 368 genes printed in three or six replicas covering a wide range of expression levels and ratios, we determined quality and reproducibility of the data obtained from one nonamplified and two independently amplified RNAs (aRNA) derived from normal and tumor samples using replicas with dye exchange (dye-swap measurements). Overall, signal-to-noise ratio improved when we used aRNA (1.45-fold for channel 1 and 2.02-fold for channel 2), increasing by 6% the number of spots with meaningful data. Measurements arising from independent aRNA samples showed strong correlation among themselves (r(2)=0.962) and with those from the nonamplified sample (r(2)=0.975), indicating the reproducibility and fidelity of the amplification procedure. Measurement differences, i.e, spots with poor correlation between amplified and nonamplified measurements, did not show association with gene sequence, expression intensity, or expression ratio and can, therefore, be compensated with replication. In conclusion, aRNA can be used routinely in cDNA microarray analysis, leading to improved quality of data with high fidelity and reproducibility.

  15. Analysis of factorial time-course microarrays with application to a clinical study of burn injury

    PubMed Central

    Zhou, Baiyu; Xu, Weihong; Herndon, David; Tompkins, Ronald; Davis, Ronald; Xiao, Wenzhong; Wong, Wing Hung; Toner, Mehmet; Warren, H. Shaw; Schoenfeld, David A.; Rahme, Laurence; McDonald-Smith, Grace P.; Hayden, Douglas; Mason, Philip; Fagan, Shawn; Yu, Yong-Ming; Cobb, J. Perren; Remick, Daniel G.; Mannick, John A.; Lederer, James A.; Gamelli, Richard L.; Silver, Geoffrey M.; West, Michael A.; Shapiro, Michael B.; Smith, Richard; Camp, David G.; Qian, Weijun; Storey, John; Mindrinos, Michael; Tibshirani, Rob; Lowry, Stephen; Calvano, Steven; Chaudry, Irshad; West, Michael A.; Cohen, Mitchell; Moore, Ernest E.; Johnson, Jeffrey; Moldawer, Lyle L.; Baker, Henry V.; Efron, Philip A.; Balis, Ulysses G.J.; Billiar, Timothy R.; Ochoa, Juan B.; Sperry, Jason L.; Miller-Graziano, Carol L.; De, Asit K.; Bankey, Paul E.; Finnerty, Celeste C.; Jeschke, Marc G.; Minei, Joseph P.; Arnoldo, Brett D.; Hunt, John L.; Horton, Jureta; Cobb, J. Perren; Brownstein, Bernard; Freeman, Bradley; Maier, Ronald V.; Nathens, Avery B.; Cuschieri, Joseph; Gibran, Nicole; Klein, Matthew; O’Keefe, Grant

    2010-01-01

    Time-course microarray experiments are capable of capturing dynamic gene expression profiles. It is important to study how these dynamic profiles depend on the multiple factors that characterize the experimental condition under which the time course is observed. Analytic methods are needed to simultaneously handle the time course and factorial structure in the data. We developed a method to evaluate factor effects by pooling information across the time course while accounting for multiple testing and nonnormality of the microarray data. The method effectively extracts gene-specific response features and models their dependency on the experimental factors. Both longitudinal and cross-sectional time-course data can be handled by our approach. The method was used to analyze the impact of age on the temporal gene response to burn injury in a large-scale clinical study. Our analysis reveals that 21% of the genes responsive to burn are age-specific, among which expressions of mitochondria and immunoglobulin genes are differentially perturbed in pediatric and adult patients by burn injury. These new findings in the body’s response to burn injury between children and adults support further investigations of therapeutic options targeting specific age groups. The methodology proposed here has been implemented in R package “TANOVA” and submitted to the Comprehensive R Archive Network at http://www.r-project.org/. It is also available for download at http://gluegrant1.stanford.edu/TANOVA/. PMID:20479259

  16. Hidden variable analysis of transcription factor cooperativity from microarray time courses.

    PubMed

    Cromer, D; Christophides, G K; Stark, J

    2010-03-01

    Gene expression is regulated by transcription factor activity, which can be extremely difficult to measure directly. Previous work has established a method to extract the 'hidden' transcription factor activity profile from microarray data and use it to effectively identify genes that are targets of a single transcription factor. However, most genes are regulated by two or more transcription factors, and so may not be recognised by this method. Here, the authors present a model-based analysis technique which is able to extract two separate 'hidden' transcription factor profiles using microarray data from wild-type and gene knock-down samples. The algorithm can predict targets of each of the transcription factors as well as the amount of cooperative regulation of genes which occurs because of the interaction between the two transcription factors. The authors evaluate this method using simulated data, and show that it is highly effective at classifying genes into categories based on their relative regulation by each of the transcription factors. The authors also show that our method can accurately measure the effectiveness of a gene knock-down when including of a reasonable amount of measurement error.

  17. Microarray analysis of differentially expressed genes in preeclamptic and normal placental tissues.

    PubMed

    Ma, K; Lian, Y; Zhou, S; Hu, R; Xiong, Y; Ting, P; Xiong, Y; Li, X; Wang, X

    2014-01-01

    To detect the candidate genes for preeclampsia (PE). The gene expression profiles in preeclamptic and normal placental tissues were analyzed using cDNA microarray approach and the altered expression of important genes were further confirmed by real-time RT-PCR (reverse transcription polymerase chain reaction) technique. Total RNA was extracted from placental tissues of three cases with severe PE and from three cases with normal pregnancy. After scanning, differentially expressed genes were detected by software. In two experiments (the fluorescent labels were exchanged), a total of 111 differentially expressed genes were detected. In placental tissue ofpreeclamptic pregnancy, 68 differentially expressed genes were up-regulated, and 44 differentially expressed genes were down-regulated. Of these genes, 16 highly differentially expressed genes were confirmed by real-time fluorescent quantitative RT-PCR, and the result showed that the ratio of gene expression differences was comparable to that detected by cDNA microarray. The results of bioinformatic analysis showed that encoding products of differentially expressed genes were correlated to infiltration of placenta trophoblastic cells, immunomodulatory factors, pregnancy-associated plasma protein, signal transduction pathway, and cell adhesion. Further studies on the biological function and regulating mechanism of these genes will provide new clues for better understanding of etiology and pathogenesis of PE.

  18. Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis

    PubMed Central

    2011-01-01

    Background Epidermal Growth Factor (EGF) is a key regulatory growth factor activating many processes relevant to normal development and disease, affecting cell proliferation and survival. Here we use a combined approach to study the EGF dependent transcriptome of HeLa cells by using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer. Results By applying a procedure for cross-platform data meta-analysis based on RankProd and GlobalAncova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We use this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions. Conclusions We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets. This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data. PMID:21699700

  19. ParaSAM: a parallelized version of the significance analysis of microarrays algorithm.

    PubMed

    Sharma, Ashok; Zhao, Jieping; Podolsky, Robert; McIndoe, Richard A

    2010-06-01

    Significance analysis of microarrays (SAM) is a widely used permutation-based approach to identifying differentially expressed genes in microarray datasets. While SAM is freely available as an Excel plug-in and as an R-package, analyses are often limited for large datasets due to very high memory requirements. We have developed a parallelized version of the SAM algorithm called ParaSAM to overcome the memory limitations. This high performance multithreaded application provides the scientific community with an easy and manageable client-server Windows application with graphical user interface and does not require programming experience to run. The parallel nature of the application comes from the use of web services to perform the permutations. Our results indicate that ParaSAM is not only faster than the serial version, but also can analyze extremely large datasets that cannot be performed using existing implementations. A web version open to the public is available at http://bioanalysis.genomics.mcg.edu/parasam. For local installations, both the windows and web implementations of ParaSAM are available for free at http://www.amdcc.org/bioinformatics/software/parasam.aspx.

  20. Analysis of factorial time-course microarrays with application to a clinical study of burn injury.

    PubMed

    Zhou, Baiyu; Xu, Weihong; Herndon, David; Tompkins, Ronald; Davis, Ronald; Xiao, Wenzhong; Wong, Wing Hung; Toner, Mehmet; Warren, H Shaw; Schoenfeld, David A; Rahme, Laurence; McDonald-Smith, Grace P; Hayden, Douglas; Mason, Philip; Fagan, Shawn; Yu, Yong-Ming; Cobb, J Perren; Remick, Daniel G; Mannick, John A; Lederer, James A; Gamelli, Richard L; Silver, Geoffrey M; West, Michael A; Shapiro, Michael B; Smith, Richard; Camp, David G; Qian, Weijun; Storey, John; Mindrinos, Michael; Tibshirani, Rob; Lowry, Stephen; Calvano, Steven; Chaudry, Irshad; West, Michael A; Cohen, Mitchell; Moore, Ernest E; Johnson, Jeffrey; Moldawer, Lyle L; Baker, Henry V; Efron, Philip A; Balis, Ulysses G J; Billiar, Timothy R; Ochoa, Juan B; Sperry, Jason L; Miller-Graziano, Carol L; De, Asit K; Bankey, Paul E; Finnerty, Celeste C; Jeschke, Marc G; Minei, Joseph P; Arnoldo, Brett D; Hunt, John L; Horton, Jureta; Cobb, J Perren; Brownstein, Bernard; Freeman, Bradley; Maier, Ronald V; Nathens, Avery B; Cuschieri, Joseph; Gibran, Nicole; Klein, Matthew; O'Keefe, Grant

    2010-06-01

    Time-course microarray experiments are capable of capturing dynamic gene expression profiles. It is important to study how these dynamic profiles depend on the multiple factors that characterize the experimental condition under which the time course is observed. Analytic methods are needed to simultaneously handle the time course and factorial structure in the data. We developed a method to evaluate factor effects by pooling information across the time course while accounting for multiple testing and nonnormality of the microarray data. The method effectively extracts gene-specific response features and models their dependency on the experimental factors. Both longitudinal and cross-sectional time-course data can be handled by our approach. The method was used to analyze the impact of age on the temporal gene response to burn injury in a large-scale clinical study. Our analysis reveals that 21% of the genes responsive to burn are age-specific, among which expressions of mitochondria and immunoglobulin genes are differentially perturbed in pediatric and adult patients by burn injury. These new findings in the body's response to burn injury between children and adults support further investigations of therapeutic options targeting specific age groups. The methodology proposed here has been implemented in R package "TANOVA" and submitted to the Comprehensive R Archive Network at http://www.r-project.org/. It is also available for download at http://gluegrant1.stanford.edu/TANOVA/.

  1. Functional analysis of differentially expressed genes associated with glaucoma from DNA microarray data.

    PubMed

    Wu, Y; Zang, W D; Jiang, W

    2014-11-11

    Microarray data of astrocytes extracted from the optic nerves of donors with and without glaucoma were analyzed to screen for differentially expressed genes (DEGs). Functional exploration with bioinformatic tools was then used to understand the roles of the identified DEGs in glaucoma. Microarray data were downloaded from the Gene Expression Omnibus (GEO) database, which contains 13 astrocyte samples, 6 from healthy subjects and 7 from patients suffering from glaucoma. Data were pre-processed, and DEGs were screened out using R software packages. Interactions between DEGs were identified, and networks were built using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). GENECODIS was utilized for the functional analysis of the DEGs, and GOTM was used for module division, for which functional annotation was conducted with the Database for Annotation, Visualization, and Integrated Discovery (DAVID). A total of 371 DEGs were identified between glaucoma-associated samples and normal samples. Three modules included in the PPID database were generated with 11, 12, and 2 significant functional annotations, including immune system processes, inflammatory responses, and synaptic vesicle endocytosis, respectively. We found that the most significantly enriched functions for each module were associated with immune function. Several genes that play interesting roles in the development of glaucoma are described; these genes may be potential biomarkers for glaucoma diagnosis or treatment.

  2. DNA microarray analysis on gene candidates possibly related to tetrodotoxin accumulation in pufferfish.

    PubMed

    Feroudj, Holger; Matsumoto, Takuya; Kurosu, Yohei; Kaneko, Gen; Ushio, Hideki; Suzuki, Katsuaki; Kondo, Hidehiro; Hirono, Ikuo; Nagashima, Yuji; Akimoto, Seiji; Usui, Kazushige; Kinoshita, Shigeharu; Asakawa, Shuichi; Kodama, Masaaki; Watabe, Shugo

    2014-01-01

    Pufferfish accumulate tetrodotoxin (TTX) at high levels in liver and ovary through the food chain. However, the mechanisms underlying TTX toxification in pufferfish have been poorly understood. In order to search gene candidates involved in TTX accumulation in the torafugu pufferfish Takifugu rubripes, a custom 4x44k oligonucleotide microarray slide was designed by the Agilent eArray program using oligonucleotide probes of 60 bp in length referring to 42,724 predicted transcripts in the publicly available Fugu genome database. DNA microarray analysis was performed with total RNA samples from the livers of two toxic wild specimens in comparison with those from a nontoxic wild specimen and two nontoxic cultured specimens. The mRNA levels of 1108 transcripts were more than 2-fold higher in the toxic specimens than in the nontoxic specimens. The levels of 613 transcripts were remarkably high, and 16 transcripts encoded by 9 genes were up-regulated more than 10-fold. These genes included those encoding forming structural filaments (keratins) and those related to vitamin D metabolism and immunity. It was also noted that the levels of the transcripts encoding serpin peptidase inhibitor clade C member 1, coagulation factor X precursor, complement C2, C3, C5, C8 precursors, and interleukin-6 receptor were high in the toxic liver samples. Copyright © 2013 Elsevier Ltd. All rights reserved.

  3. Increased power of microarray analysis by use of an algorithm based on a multivariate procedure.

    PubMed

    Krohn, K; Eszlinger, M; Paschke, R; Roeder, I; Schuster, E

    2005-09-01

    The power of microarray analyses to detect differential gene expression strongly depends on the statistical and bioinformatical approaches used for data analysis. Moreover, the simultaneous testing of tens of thousands of genes for differential expression raises the 'multiple testing problem', increasing the probability of obtaining false positive test results. To achieve more reliable results, it is, therefore, necessary to apply adjustment procedures to restrict the family-wise type I error rate (FWE) or the false discovery rate. However, for the biologist the statistical power of such procedures often remains abstract, unless validated by an alternative experimental approach. In the present study, we discuss a multiplicity adjustment procedure applied to classical univariate as well as to recently proposed multivariate gene-expression scores. All procedures strictly control the FWE. We demonstrate that the use of multivariate scores leads to a more efficient identification of differentially expressed genes than the widely used MAS5 approach provided by the Affymetrix software tools (Affymetrix Microarray Suite 5 or GeneChip Operating Software). The practical importance of this finding is successfully validated using real time quantitative PCR and data from spike-in experiments. The R-code of the statistical routines can be obtained from the corresponding author. Schuster@imise.uni-leipzig.de

  4. [Microarray analytic system for multiplex analysis by real-time polymerase chain reaction with reagents immobilized in microreactors].

    PubMed

    Navolotskiĭ, D V; Perchik, A V; Mark'ianov, I A; Ganeev, A A; Sliadnev, M N

    2011-01-01

    A microarray analytic system that uses a silicon chip with immobilized in microreactor test-system for multiplex analysis of DNA by real-time polymerase chain reaction (RT-PCR) was developed and optimized. We suggested the method of immobilization of PCR-components of a test-system, chose the stabilizer, and conducted the optimization of the composition of reaction mixture to achieve permanent stability of a microarray. We conducted optimization of preparation of samples using magnetic sorbent and indicated that, with 2.6 x 10(4) copies/ml, 60 min are necessary to obtain positive identification including time for preparation of model probes. The abilities of the created system were demonstrated on the example of microarray analysis of samples with different content of DNA, low absolute limits of identification (20 DNA copies in microreactor), and high reproducibility of the analysis.

  5. Chromosomal Microarray Analysis of Consecutive Individuals with Autism Spectrum Disorders Using an Ultra-High Resolution Chromosomal Microarray Optimized for Neurodevelopmental Disorders

    PubMed Central

    Ho, Karen S.; Wassman, E. Robert; Baxter, Adrianne L.; Hensel, Charles H.; Martin, Megan M.; Prasad, Aparna; Twede, Hope; Vanzo, Rena J.; Butler, Merlin G.

    2016-01-01

    Copy number variants (CNVs) detected by chromosomal microarray analysis (CMA) significantly contribute to understanding the etiology of autism spectrum disorder (ASD) and other related conditions. In recognition of the value of CMA testing and its impact on medical management, CMA is in medical guidelines as a first-tier test in the evaluation of children with these disorders. As CMA becomes adopted into routine care for these patients, it becomes increasingly important to report these clinical findings. This study summarizes the results of over 4 years of CMA testing by a CLIA-certified clinical testing laboratory. Using a 2.8 million probe microarray optimized for the detection of CNVs associated with neurodevelopmental disorders, we report an overall CNV detection rate of 28.1% in 10,351 consecutive patients, which rises to nearly 33% in cases without ASD, with only developmental delay/intellectual disability (DD/ID) and/or multiple congenital anomalies (MCA). The overall detection rate for individuals with ASD is also significant at 24.4%. The detection rate and pathogenic yield of CMA vary significantly with the indications for testing, age, and gender, as well as the specialty of the ordering doctor. We note discrete differences in the most common recurrent CNVs found in individuals with or without a diagnosis of ASD. PMID:27941670

  6. Chromosomal Microarray Analysis of Consecutive Individuals with Autism Spectrum Disorders Using an Ultra-High Resolution Chromosomal Microarray Optimized for Neurodevelopmental Disorders.

    PubMed

    Ho, Karen S; Wassman, E Robert; Baxter, Adrianne L; Hensel, Charles H; Martin, Megan M; Prasad, Aparna; Twede, Hope; Vanzo, Rena J; Butler, Merlin G

    2016-12-09

    Copy number variants (CNVs) detected by chromosomal microarray analysis (CMA) significantly contribute to understanding the etiology of autism spectrum disorder (ASD) and other related conditions. In recognition of the value of CMA testing and its impact on medical management, CMA is in medical guidelines as a first-tier test in the evaluation of children with these disorders. As CMA becomes adopted into routine care for these patients, it becomes increasingly important to report these clinical findings. This study summarizes the results of over 4 years of CMA testing by a CLIA-certified clinical testing laboratory. Using a 2.8 million probe microarray optimized for the detection of CNVs associated with neurodevelopmental disorders, we report an overall CNV detection rate of 28.1% in 10,351 consecutive patients, which rises to nearly 33% in cases without ASD, with only developmental delay/intellectual disability (DD/ID) and/or multiple congenital anomalies (MCA). The overall detection rate for individuals with ASD is also significant at 24.4%. The detection rate and pathogenic yield of CMA vary significantly with the indications for testing, age, and gender, as well as the specialty of the ordering doctor. We note discrete differences in the most common recurrent CNVs found in individuals with or without a diagnosis of ASD.

  7. Phytoremediation potential of Arabidopsis with reference to acrylamide and microarray analysis of acrylamide-response genes.

    PubMed

    Gao, Jian-Jie; Peng, Ri-He; Zhu, Bo; Wang, Bo; Wang, Li-Juan; Xu, Jing; Sun, Miao; Yao, Quan-Hong

    2015-10-01

    Acrylamide (ACR) is a widely used industrial chemical. However, it is a dangerous compound because it showed neurotoxic effects in humans and act as reproductive toxicant and carcinogen in many animal species. In the environment, acrylamide has high soil mobility and may travel via groundwater. Phytoremediation is an effective method to remove the environmental pollutants, but the mechanism of plant response to acrylamide remains unknown. With the purpose of assessing remediation potentials of plants for acrylamide, we have examined acrylamide uptake by the model plant Arabidopsis grown on contaminated substrates with high performance liquid chromatography (HPLC) analysis. The result revealed that acrylamide could be absorbed and degraded by Arabidopsis. Further microarray analysis showed that 527 transcripts were up-regulated within 2-days under acrylamide exposure condition. We have found many potential acrylamide-induced genes playing a major role in plant metabolism and phytoremediation. Copyright © 2015 Elsevier Inc. All rights reserved.

  8. Cyclin D1 and Ewing's sarcoma/PNET: A microarray analysis.

    PubMed

    Fagone, Paolo; Nicoletti, Ferdinando; Salvatorelli, Lucia; Musumeci, Giuseppe; Magro, Gaetano

    2015-10-01

    Recent immunohistochemical analyses have showed that cyclin D1 is expressed in soft tissue Ewing's sarcoma/peripheral neuroectodermal tumor (PNET) of childhood and adolescents, while it is undetectable in both embryonal and alveolar rhabdomyosarcoma. In the present paper, microarray analysis provided evidence of a significant upregulation of cyclin D1 in Ewing's sarcoma as compared to normal tissues. In addition, we confirmed our previous findings of a significant over-expression of cyclin D1 in Ewing sarcoma as compared to rhabdomyosarcoma. Bioinformatic analysis also allowed to identify some other genes, strongly correlated to cyclin D1, which, although not previously studied in pediatric tumors, could represent novel markers for the diagnosis and prognosis of Ewing's sarcoma/PNET. The data herein provided support not only the use of cyclin D1 as a diagnostic marker of Ewing sarcoma/PNET but also the possibility of using drugs targeting cyclin D1 as potential therapeutic strategies.

  9. Microarray analysis of Neosartorya fischeri using different carbon sources, petroleum asphaltenes and glucose-peptone

    PubMed Central

    Hernández-López, Edna L.; Ramírez-Puebla, Shamayim T.; Vazquez-Duhalt, Rafael

    2015-01-01

    Asphaltenes are considered as the most recalcitrant petroleum fraction and represent a big problem for the recovery, separation and processing of heavy oils and bitumens. Neosartorya fischeri is a saprophytic fungus that is able to grow using asphaltenes as the sole carbon source [1]. We performed transcription profiling using a custom designed microarray with the complete genome from N. fischeri NRRL 181 in order to identify genes related to the transformation of asphaltenes [1]. Data analysis was performed using the genArise software. Results showed that 287 genes were up-regulated and 118 were down-regulated. Here we describe experimental procedures and methods about our dataset (NCBI GEO accession number GSE68146) and describe the data analysis to identify different expression levels in N. fischeri using this recalcitrant carbon source. PMID:26484261

  10. Adaptation of a Bioinformatics Microarray Analysis Workflow for a Toxicogenomic Study in Rainbow Trout

    PubMed Central

    Depiereux, Sophie; De Meulder, Bertrand; Bareke, Eric; Berger, Fabrice; Le Gac, Florence; Depiereux, Eric; Kestemont, Patrick

    2015-01-01

    Sex steroids play a key role in triggering sex differentiation in fish, the use of exogenous hormone treatment leading to partial or complete sex reversal. This phenomenon has attracted attention since the discovery that even low environmental doses of exogenous steroids can adversely affect gonad morphology (ovotestis development) and induce reproductive failure. Modern genomic-based technologies have enhanced opportunities to find out mechanisms of actions (MOA) and identify biomarkers related to the toxic action of a compound. However, high throughput data interpretation relies on statistical analysis, species genomic resources, and bioinformatics tools. The goals of this study are to improve the knowledge of feminisation in fish, by the analysis of molecular responses in the gonads of rainbow trout fry after chronic exposure to several doses (0.01, 0.1, 1 and 10 μg/L) of ethynylestradiol (EE2) and to offer target genes as potential biomarkers of ovotestis development. We successfully adapted a bioinformatics microarray analysis workflow elaborated on human data to a toxicogenomic study using rainbow trout, a fish species lacking accurate functional annotation and genomic resources. The workflow allowed to obtain lists of genes supposed to be enriched in true positive differentially expressed genes (DEGs), which were subjected to over-representation analysis methods (ORA). Several pathways and ontologies, mostly related to cell division and metabolism, sexual reproduction and steroid production, were found significantly enriched in our analyses. Moreover, two sets of potential ovotestis biomarkers were selected using several criteria. The first group displayed specific potential biomarkers belonging to pathways/ontologies highlighted in the experiment. Among them, the early ovarian differentiation gene foxl2a was overexpressed. The second group, which was highly sensitive but not specific, included the DEGs presenting the highest fold change and lowest p

  11. EMMA 2 – A MAGE-compliant system for the collaborative analysis and integration of microarray data

    PubMed Central

    Dondrup, Michael; Albaum, Stefan P; Griebel, Thasso; Henckel, Kolja; Jünemann, Sebastian; Kahlke, Tim; Kleindt, Christiane K; Küster, Helge; Linke, Burkhard; Mertens, Dominik; Mittard-Runte, Virginie; Neuweger, Heiko; Runte, Kai J; Tauch, Andreas; Tille, Felix; Pühler, Alfred; Goesmann, Alexander

    2009-01-01

    Background Understanding transcriptional regulation by genome-wide microarray studies can contribute to unravel complex relationships between genes. Attempts to standardize the annotation of microarray data include the Minimum Information About a Microarray Experiment (MIAME) recommendations, the MAGE-ML format for data interchange, and the use of controlled vocabularies or ontologies. The existing software systems for microarray data analysis implement the mentioned standards only partially and are often hard to use and extend. Integration of genomic annotation data and other sources of external knowledge using open standards is therefore a key requirement for future integrated analysis systems. Results The EMMA 2 software has been designed to resolve shortcomings with respect to full MAGE-ML and ontology support and makes use of modern data integration techniques. We present a software system that features comprehensive data analysis functions for spotted arrays, and for the most common synthesized oligo arrays such as Agilent, Affymetrix and NimbleGen. The system is based on the full MAGE object model. Analysis functionality is based on R and Bioconductor packages and can make use of a compute cluster for distributed services. Conclusion Our model-driven approach for automatically implementing a full MAGE object model provides high flexibility and compatibility. Data integration via SOAP-based web-services is advantageous in a distributed client-server environment as the collaborative analysis of microarray data is gaining more and more relevance in international research consortia. The adequacy of the EMMA 2 software design and implementation has been proven by its application in many distributed functional genomics projects. Its scalability makes the current architecture suited for extensions towards future transcriptomics methods based on high-throughput sequencing approaches which have much higher computational requirements than microarrays. PMID:19200358

  12. Porous Silicon Antibody Microarrays for Quantitative Analysis: Measurement of Free and Total PSA in Clinical Plasma Samples

    PubMed Central

    Tojo, Axel; Malm, Johan; Marko-Varga, György; Lilja, Hans; Laurell, Thomas

    2014-01-01

    The antibody microarrays have become widespread, but their use for quantitative analyses in clinical samples has not yet been established. We investigated an immunoassay based on nanoporous silicon antibody microarrays for quantification of total prostate-specific-antigen (PSA) in 80 clinical plasma samples, and provide quantitative data from a duplex microarray assay that simultaneously quantifies free and total PSA in plasma. To further develop the assay the porous silicon chips was placed into a standard 96-well microtiter plate for higher throughput analysis. The samples analyzed by this quantitative microarray were 80 plasma samples obtained from men undergoing clinical PSA testing (dynamic range: 0.14-44ng/ml, LOD: 0.14ng/ml). The second dataset, measuring free PSA (dynamic range: 0.40-74.9ng/ml, LOD: 0.47ng/ml) and total PSA (dynamic range: 0.87-295ng/ml, LOD: 0.76ng/ml), was also obtained from the clinical routine. The reference for the quantification was a commercially available assay, the ProStatus PSA Free/Total DELFIA. In an analysis of 80 plasma samples the microarray platform performs well across the range of total PSA levels. This assay might have the potential to substitute for the large-scale microtiter plate format in diagnostic applications. The duplex assay paves the way for a future quantitative multiplex assay, which analyses several prostate cancer biomarkers simultaneously. PMID:22921878

  13. Use of expressed sequence tag analysis and cDNA microarrays of the filamentous fungus Aspergillus nidulans.

    PubMed

    Sims, Andrew H; Robson, Geoffrey D; Hoyle, David C; Oliver, Stephen G; Turner, Geoffrey; Prade, Rolf A; Russell, Hugh H; Dunn-Coleman, Nigel S; Gent, Manda E

    2004-02-01

    The use of microarrays in the analysis of gene expression is becoming widespread for many organisms, including yeast. However, although the genomes of a number of filamentous fungi have been fully or partially sequenced, microarray analysis is still in its infancy in these organisms. Here, we describe the construction and validation of microarrays for the fungus Aspergillus nidulans using PCR products from a 4092 EST conidial germination library. An experiment was designed to validate these arrays by monitoring the expression profiles of known genes following the addition of 1% (w/v) glucose to wild-type A. nidulans cultures grown to mid-exponential phase in Vogel's minimal medium with ethanol as the sole carbon source. The profiles of genes showing statistically significant differential expression following the glucose up-shift are presented and an assessment of the quality and reproducibility of the A. nidulans arrays discussed.

  14. Differential co-expression analysis of rheumatoid arthritis with microarray data.

    PubMed

    Wang, Kunpeng; Zhao, Liqiang; Liu, Xuefeng; Hao, Zhenyong; Zhou, Yong; Yang, Chuandong; Li, Hongqiang

    2014-11-01

    The aim of the present study was to investigate the underlying molecular mechanisms of rheumatoid arthritis (RA) using microarray expression profiles from osteoarthritis and RA patients, to improve diagnosis and treatment strategies for the condition. The gene expression profile of GSE27390 was downloaded from Gene Expression Omnibus, including 19 samples from patients with RA (n=9) or osteoarthritis (n=10). Firstly, the differentially expressed genes (DEGs) were obtained with the thresholds of |logFC|>1.0 and P<0.05, using the t‑test method in LIMMA package. Then, differentially co-expressed genes (DCGs) and differentially co-expressed links (DCLs) were screened with q<0.25 by the differential coexpression analysis and differential regulation analysis of gene expression microarray data package. Secondly, pathway enrichment analysis for DCGs was performed by the Database for Annotation, Visualization and Integrated Discovery and the DCLs associated with RA were selected by comparing the obtained DCLs with known transcription factor (TF)-targets in the TRANSFAC database. Finally, the obtained TFs were mapped to the known TF-targets to construct the network using cytoscape software. A total of 1755 DEGs, 457 DCGs and 101988 DCLs were achieved and there were 20 TFs in the obtained six TF-target relations (STAT3-TNF, PBX1‑PLAU, SOCS3-STAT3, GATA1-ETS2, ETS1-ICAM4 and CEBPE‑GATA1) and 457 DCGs. A number of TF-target relations in the constructed network were not within DCLs when the TF and target gene were DCGs. The identified TFs may have an important role in the pathogenesis of RA and have the potential to be used as biomarkers for the development of novel diagnostic and therapeutic strategies for RA.

  15. Expression profile of long non-coding RNAs in colorectal cancer: A microarray analysis.

    PubMed

    Luo, Jia; Xu, Luning; Jiang, Yigui; Zhuo, Dexiang; Zhang, Shengjun; Wu, Lianhui; Xu, Huadong; Huang, Yue

    2016-04-01

    Colorectal cancer (CRC) is one of the most prevalent malignant tumors and the second cause of cancer-related mortality worldwide. Due to increased morbidity and mortality rates, there is an urgent need to understand the pathogenesis of CRC, discover strategies that can improve diagnosis, and ultimately identify therapies targeting this disease. Over the past several years, research into tumor progression mechanisms has been devoted to identifying and understanding various coding and non-coding regions of the genome and how these genetic variants may affect tumorigenesis and progression. Recently, long non-coding RNAs (lncRNAs), which are non‑protein coding transcripts longer than 200 nucleotides, have emerged as a key aspect in tumor pathogenesis. In the present study, we examined the lncRNA and mRNA expression profiles in 4 patients with colon adenocarcinoma, with paired adjacent normal tissues as controls. Microarray data showed that a total of 3,523 lncRNAs and 2,515 mRNAs were consistently differentially expressed in the CRC tissues compared to adjacent normal tissues. Upon comparison of the differentially expressed transcripts between the groups, we identified 22 pathways which were related to the upregulated transcripts and 24 pathways that corresponded to the downregulated transcripts. Gene ontology analysis revealed that the upregulated transcripts were predominantly enriched in DNA metabolic processes, and the downregulated transcripts were predominantly enriched in organic hydroxyl compound metabolic processes. Coding-non-coding gene co-expression analysis showed that these differentially expressed lncRNAs were closely correlated with 'Wnt signaling pathway' components, whose aberrant activation plays a central role in CRC, indicating that a functional correlation exists between them. In conclusion, the results of the microarray and informatic analysis strongly suggest that lncRNA dysregulation is involved in the complicated process of CRC development

  16. The cut-off value for normal nuchal translucency evaluated by chromosomal microarray analysis.

    PubMed

    Maya, Idit; Yacobson, Shiri; Kahana, Sarit; Yeshaya, Josepha; Tenne, Tamar; Agmon-Fishman, Ifaat; Cohen-Vig, Lital; Shohat, Mordechai; Basel-Vanagaite, Lina; Sharony, Reuven

    2017-01-30

    An association between isolated, increased nuchal translucency thickness and pathogenic chromosomal microarray analysis (CMA) has been reported. A recent meta-analysis reported that most studies used a 3.5 mm cut-off value. Considering nuchal translucency distribution and the commonly accepted 5% false positive rate in maternal serum screening, nuchal translucency cut-off levels should be reconsidered. This study evaluated the unique contribution of CMA to the investigation of foetuses with mildly increased nuchal translucency (NT) thickness of 3.0-3.4 mm. This was a retrospective, multicenter study. A single laboratory performed all genetic analyses. Comparative Genomic Hybridization Microarray analysis or Single Nucleotide Polymorphism Array technology was used for CMA. NT was divided into three groups (≤2.9; 3.0-3.4; ≥3.5 mm) and the results were compared, focusing on pregnancies with NT as the only medical indication for CMA at the time of the invasive procedure. If combined first trimester screening (NT and biochemistry) indicated increased risk for common aneuploidies, the case was excluded. CMA results were recorded in 1,588 pregnancies, of which 770 foetuses had NT as a normal or an isolated abnormal finding. Of these, 462 had NT ≤2.9 mm, 170 had NT 3.0-3.4 mm and 138 had NT ≥3.5 mm. Pathogenic copy number variations were found in 1.7%, 7.1%, and 13.0%, respectively. The results suggest that CMA should be part of the investigation in foetuses with isolated, mildly increased NT (3.0-3.4 mm). This article is protected by copyright. All rights reserved.

  17. Detection of Herpesviridae in whole blood by multiplex PCR DNA-based microarray analysis after hematopoietic stem cell transplantation.

    PubMed

    Debaugnies, France; Busson, Laurent; Ferster, Alina; Lewalle, Philippe; Azzi, Nadira; Aoun, Mickael; Verhaegen, Godelieve; Mahadeb, Bhavna; de Marchin, Jérôme; Vandenberg, Olivier; Hallin, Marie

    2014-07-01

    Viral infections are important causes of morbidity and mortality in patients after hematopoietic stem cell transplantation. The monitoring by PCR of Herpesviridae loads in blood samples has become a critical part of posttransplant follow-up, representing mounting costs for the laboratory. In this study, we assessed the clinical performance of the multiplex PCR DNA microarray Clart Entherpex kit for detection of cytomegalovirus (CMV), Epstein-Barr virus (EBV), and human herpesvirus 6 (HHV-6) as a screening test for virological follow-up. Two hundred fifty-five blood samples from 16 transplanted patients, prospectively tested by routine PCR assays, were analyzed by microarray. Routine PCR detected single or multiple viruses in 42% and 10% of the samples, respectively. Microarray detected single or multiple viruses in 34% and 18% of the samples, respectively. Microarray results correlated well with CMV and EBV detections by routine PCR (kappa tests = 0.79 and 0.78, respectively), whereas a weak correlation was observed with HHV-6 (0.43). HHV-7 was also detected in 48 samples by microarray. In conclusion, the microarray is a reliable screening assay for a posttransplant virological follow-up to detect CMV and EBV infections in blood. However, positive samples must be subsequently confirmed and viral loads must be quantified by PCR assays. Limitations were identified regarding HHV-6 detection. Although it is promising, is easy to use as a first-line test, and allows a reduction in the cost of analysis without undue delay in the reporting of the final quantitative result to the clinician, some characteristics of this microarray should be improved, particularly regarding quality control and the targeted virus panel, such that it could then be used as a routine test. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

  18. Microarray-Based Analysis of Differential Gene Expression between Infective and Noninfective Larvae of Strongyloides stercoralis

    PubMed Central

    Ramanathan, Roshan; Varma, Sudhir; Ribeiro, José M. C.; Myers, Timothy G.; Nolan, Thomas J.; Abraham, David; Lok, James B.; Nutman, Thomas B.

    2011-01-01

    Background Differences between noninfective first-stage (L1) and infective third-stage (L3i) larvae of parasitic nematode Strongyloides stercoralis at the molecular level are relatively uncharacterized. DNA microarrays were developed and utilized for this purpose. Methods and Findings Oligonucleotide hybridization probes for the array were designed to bind 3,571 putative mRNA transcripts predicted by analysis of 11,335 expressed sequence tags (ESTs) obtained as part of the Nematode EST project. RNA obtained from S. stercoralis L3i and L1 was co-hybridized to each array after labeling the individual samples with different fluorescent tags. Bioinformatic predictions of gene function were developed using a novel cDNA Annotation System software. We identified 935 differentially expressed genes (469 L3i-biased; 466 L1-biased) having two-fold expression differences or greater and microarray signals with a p value<0.01. Based on a functional analysis, L1 larvae have a larger number of genes putatively involved in transcription (p = 0.004), and L3i larvae have biased expression of putative heat shock proteins (such as hsp-90). Genes with products known to be immunoreactive in S. stercoralis-infected humans (such as SsIR and NIE) had L3i biased expression. Abundantly expressed L3i contigs of interest included S. stercoralis orthologs of cytochrome oxidase ucr 2.1 and hsp-90, which may be potential chemotherapeutic targets. The S. stercoralis ortholog of fatty acid and retinol binding protein-1, successfully used in a vaccine against Ancylostoma ceylanicum, was identified among the 25 most highly expressed L3i genes. The sperm-containing glycoprotein domain, utilized in a vaccine against the nematode Cooperia punctata, was exclusively found in L3i biased genes and may be a valuable S. stercoralis target of interest. Conclusions A new DNA microarray tool for the examination of S. stercoralis biology has been developed and provides new and valuable insights regarding

  19. Parents' perceptions of the usefulness of chromosomal microarray analysis for children with autism spectrum disorders.

    PubMed

    Reiff, Marian; Giarelli, Ellen; Bernhardt, Barbara A; Easley, Ebony; Spinner, Nancy B; Sankar, Pamela L; Mulchandani, Surabhi

    2015-10-01

    Clinical guidelines recommend chromosomal microarray analysis (CMA) for all children with autism spectrum disorders (ASDs). We explored the test's perceived usefulness among parents of children with ASD who had undergone CMA, and received a result categorized as pathogenic, variant of uncertain significance, or negative. Fifty-seven parents participated in a semi-structured telephone interview, and 50 also completed a survey. Most parents reported that CMA was helpful for their child and family. Major themes regarding perceived usefulness were: medical care, educational and behavioral interventions, causal explanation, information for family members, and advancing knowledge. Limits to utility, uncertainties and negative outcomes were also identified. Our findings highlight the importance of considering both health and non-health related utility in genomic testing.

  20. Microarray analysis reveals overlapping and specific transcriptional responses to different plant hormones in rice

    PubMed Central

    Garg, Rohini; Tyagi, Akhilesh K.; Jain, Mukesh

    2012-01-01

    Hormones exert pleiotropic effects on plant growth and development throughout the life cycle. Many of these effects are mediated at molecular level via altering gene expression. In this study, we investigated the exogenous effect of plant hormones, including auxin, cytokinin, abscisic acid, ethylene, salicylic acid and jasmonic acid, on the transcription of rice genes at whole genome level using microarray. Our analysis identified a total of 4171 genes involved in several biological processes, whose expression was altered significantly in the presence of different hormones. Further, 28% of these genes exhibited overlapping transcriptional responses in the presence of any two hormones, indicating crosstalk among plant hormones. In addition, we identified genes showing only a particular hormone-specific response, which can be used as hormone-specific markers. The results of this study will facilitate further studies in hormone biology in rice. PMID:22827941

  1. RNA Expression Microarray Analysis in Mouse Prospermatogonia: Identification of Candidate Epigenetic Modifiers

    PubMed Central

    Lefèvre, Christophe; Mann, Jeffrey R.

    2011-01-01

    The mammalian totipotent and pluripotent lineage exhibits genome-wide dynamics in respect to DNA methylation content. The first phase of global DNA demethylation and de novo remethylation occurs during preimplantation development and gastrulation, respectively, while the second phase occurs in primordial germ cells and primary oocytes/prospermatogonia, respectively. These dynamics are indicative of a comprehensive epigenetic resetting or reprogramming of the genome in preparation for major differentiation events. To gain further insight into the mechanisms driving DNA methylation dynamics and other types of epigenetic modification, we performed an RNA expression microarray analysis of fetal prospermatogonia at the stage when they are undergoing rapid de novo DNA remethylation. We have identified a number of highly or specifically expressed genes which could be important for determining epigenetic change in prospermatogonia. These data provide a useful resource in the discovery of molecular pathways involved in epigenetic reprogramming in the mammalian germ line. PMID:18330932

  2. Identification of Germ Plasm-Associated Transcripts by Microarray Analysis of Xenopus Vegetal Cortex RNA

    PubMed Central

    Cuykendall, Tawny N.; Houston, Douglas W.

    2011-01-01

    RNA localization is a common mechanism for regulating cell structure and function. Localized RNAs in Xenopus oocytes are critical for early development, including germline specification by the germ plasm. Despite the importance of these localized RNAs, only approximately 25 have been identified and fewer are functionally characterized. Using microarrays, we identified a large set of localized RNAs from the vegetal cortex. Overall, our results indicate a minimum of 275 localized RNAs in oocytes, or 2–3% of maternal transcripts, which are in general agreement with previous findings. We further validated vegetal localization for 24 candidates and further characterized three genes expressed in the germ plasm. We identified novel germ plasm expression for reticulon 3.1, exd2 (a novel exonuclease-domain encoding gene), and a putative noncoding RNA. Further analysis of these and other localized RNAs will likely identify new functions of germ plasm and facilitate the identification of cis-acting RNA localization elements. PMID:20503379

  3. Antimicrobial resistance determinant microarray for analysis of multi-drug resistant isolates

    NASA Astrophysics Data System (ADS)

    Taitt, Chris Rowe; Leski, Tomasz; Stenger, David; Vora, Gary J.; House, Brent; Nicklasson, Matilda; Pimentel, Guillermo; Zurawski, Daniel V.; Kirkup, Benjamin C.; Craft, David; Waterman, Paige E.; Lesho, Emil P.; Bangurae, Umaru; Ansumana, Rashid

    2012-06-01

    The prevalence of multidrug-resistant infections in personnel wounded in Iraq and Afghanistan has made it challenging for physicians to choose effective therapeutics in a timely fashion. To address the challenge of identifying the potential for drug resistance, we have developed the Antimicrobial Resistance Determinant Microarray (ARDM) to provide DNAbased analysis for over 250 resistance genes covering 12 classes of antibiotics. Over 70 drug-resistant bacteria from different geographic regions have been analyzed on ARDM, with significant differences in patterns of resistance identified: genes for resistance to sulfonamides, trimethoprim, chloramphenicol, rifampin, and macrolide-lincosamidesulfonamide drugs were more frequently identified in isolates from sources in Iraq/Afghanistan. Of particular concern was the presence of genes responsible for resistance to many of the last-resort antibiotics used to treat war traumaassociated infections.

  4. Component retention in principal component analysis with application to cDNA microarray data

    PubMed Central

    Cangelosi, Richard; Goriely, Alain

    2007-01-01

    Shannon entropy is used to provide an estimate of the number of interpretable components in a principal component analysis. In addition, several ad hoc stopping rules for dimension determination are reviewed and a modification of the broken stick model is presented. The modification incorporates a test for the presence of an "effective degeneracy" among the subspaces spanned by the eigenvectors of the correlation matrix of the data set then allocates the total variance among subspaces. A summary of the performance of the methods applied to both published microarray data sets and to simulated data is given. This article was reviewed by Orly Alter, John Spouge (nominated by Eugene Koonin), David Horn and Roy Varshavsky (both nominated by O. Alter). PMID:17229320

  5. Analysis of Protein Tyrosine Kinase Specificity Using Positional Scanning Peptide Microarrays.

    PubMed

    Deng, Yang; Turk, Benjamin E

    2016-01-01

    Protein tyrosine kinases phosphorylate their substrates within the context of specific consensus sequences surrounding the site of modification. We describe a peptide microarray approach to rapidly determine tyrosine kinase phosphorylation site motifs. This method uses a peptide library that systematically substitutes each of the amino acid residues at multiple positions surrounding a central tyrosine residue. Peptide substrates are synthesized as biotin conjugates for immobilization on avidin-coated slides. Following incubation of the slide with protein kinase and radiolabeled ATP, the relative extent of phosphorylation of each of the peptides is quantified by phosphor imaging. This method allows small quantities of kinase to be analyzed rapidly in parallel, facilitating analysis of large numbers of kinases.

  6. Microarray analysis reveals novel features of the muscle aging process in men and women.

    PubMed

    Liu, Dongmei; Sartor, Maureen A; Nader, Gustavo A; Pistilli, Emidio E; Tanton, Leah; Lilly, Charles; Gutmann, Laurie; IglayReger, Heidi B; Visich, Paul S; Hoffman, Eric P; Gordon, Paul M

    2013-09-01

    To develop a global view of muscle transcriptional differences between older men and women and sex-specific aging, we obtained muscle biopsies from the biceps brachii of young and older men and women and profiled the whole-genome gene expression using microarray. A logistic regression-based method in combination with an intensity-based Bayesian moderated t test was used to identify significant sex- and aging-related gene functional groups. Our analysis revealed extensive sex differences in the muscle transcriptome of older individuals and different patterns of transcriptional changes with aging in men and women. In older women, we observed a coordinated transcriptional upregulation of immune activation, extracellular matrix remodeling, and lipids storage; and a downregulation of mitochondrial biogenesis and function and muscle regeneration. The effect of aging results in sexual dimorphic alterations in the skeletal muscle transcriptome, which may modify the risk for developing musculoskeletal and metabolic diseases in men and women.

  7. Microarray Analysis Reveals Novel Features of the Muscle Aging Process in Men and Women

    PubMed Central

    2013-01-01

    To develop a global view of muscle transcriptional differences between older men and women and sex-specific aging, we obtained muscle biopsies from the biceps brachii of young and older men and women and profiled the whole-genome gene expression using microarray. A logistic regression-based method in combination with an intensity-based Bayesian moderated t test was used to identify significant sex- and aging-related gene functional groups. Our analysis revealed extensive sex differences in the muscle transcriptome of older individuals and different patterns of transcriptional changes with aging in men and women. In older women, we observed a coordinated transcriptional upregulation of immune activation, extracellular matrix remodeling, and lipids storage; and a downregulation of mitochondrial biogenesis and function and muscle regeneration. The effect of aging results in sexual dimorphic alterations in the skeletal muscle transcriptome, which may modify the risk for developing musculoskeletal and metabolic diseases in men and women. PMID:23418191

  8. Microarray Analysis on Gene Regulation by Estrogen, Progesterone and Tamoxifen in Human Endometrial Stromal Cells

    PubMed Central

    Ren, Chun-E; Zhu, Xueqiong; Li, Jinping; Lyle, Christian; Dowdy, Sean; Podratz, Karl C.; Byck, David; Chen, Hai-Bin; Jiang, Shi-Wen

    2015-01-01

    Epithelial stromal cells represent a major cellular component of human uterine endometrium that is subject to tight hormonal regulation. Through cell-cell contacts and/or paracrine mechanisms, stromal cells play a significant role in the malignant transformation of epithelial cells. We isolated stromal cells from normal human endometrium and investigated the morphological and transcriptional changes induced by estrogen, progesterone and tamoxifen. We demonstrated that stromal cells express appreciable levels of estrogen and progesterone receptors and undergo different morphological changes upon hormonal stimulation. Microarray analysis indicated that both estrogen and progesterone induced dramatic alterations in a variety of genes associated with cell structure, transcription, cell cycle, and signaling. However, divergent patterns of changes, and in some genes opposite effects, were observed for the two hormones. A large number of genes are identified as novel targets for hormonal regulation. These hormone-responsive genes may be involved in normal uterine function and the development of endometrial malignancies. PMID:25782154

  9. Chromosomal Microarray Analysis (CMA) a Clinical Diagnostic Tool in the Prenatal and Postnatal Settings.

    PubMed

    Batzir, Nurit Assia; Shohat, Mordechai; Maya, Idit

    2015-09-01

    Chromosomal microarray analysis (CMA) is a technology used for the detection of clinically-significant microdeietions or duplications, with a high sensitivity for submicroscopic aberrations. It is able to detect changes as small as 5-10Kb in size - a resolution up to 1000 times higher than that of conventional karyotyping. CMA is used for uncovering copy number variants (CNVs) thought to play an important role in the pathogenesis of a variety of disorders, primarily neurodevelopmental disorders and congenital anomalies. CMA may be applied in the prenatal or postnatal setting, with unique benefits and limitations in each setting. The growing use of CMA makes it essential for practicing physicians to understand the principles of this technology and be aware of its powers and limitations.

  10. A new locally weighted K-means for cancer-aided microarray data analysis.

    PubMed

    Iam-On, Natthakan; Boongoen, Tossapon

    2012-11-01

    Cancer has been identified as the leading cause of death. It is predicted that around 20-26 million people will be diagnosed with cancer by 2020. With this alarming rate, there is an urgent need for a more effective methodology to understand, prevent and cure cancer. Microarray technology provides a useful basis of achieving this goal, with cluster analysis of gene expression data leading to the discrimination of patients, identification of possible tumor subtypes and individualized treatment. Amongst clustering techniques, k-means is normally chosen for its simplicity and efficiency. However, it does not account for the different importance of data attributes. This paper presents a new locally weighted extension of k-means, which has proven more accurate across many published datasets than the original and other extensions found in the literature.

  11. Parents’ Perceptions of the Usefulness of Chromosomal Microarray Analysis for Children with Autism Spectrum Disorders

    PubMed Central

    Giarelli, Ellen; Bernhardt, Barbara A.; Easley, Ebony; Spinner, Nancy B.; Sankar, Pamela L.; Mulchandani, Surabhi

    2015-01-01

    Clinical guidelines recommend chromosomal microarray analysis (CMA) for all children with autism spectrum disorders (ASDs). We explored the test’s perceived usefulness among parents of children with ASD who had undergone CMA, and received a result categorized as pathogenic, variant of uncertain significance, or negative. Fifty-seven parents participated in a semi-structured telephone interview, and 50 also completed a survey. Most parents reported that CMA was helpful for their child and family. Major themes regarding perceived usefulness were: medical care, educational and behavioral interventions, causal explanation, information for family members, and advancing knowledge. Limits to utility, uncertainties and negative outcomes were also identified. Our findings highlight the importance of considering both health and non-health related utility in genomic testing. PMID:26066358

  12. Current Practice and Utility of Chromosome Microarray Analysis in Infants Undergoing Cardiac Surgery

    PubMed Central

    Buckley, Jason R.; Kavarana, Minoo N.; Chowdhury, Shahryar M.; Scheurer, Mark A.

    2014-01-01

    Objective Traditionally, karyotype and fluorescence in situ hybridization (FISH) were used for cytogenetic testing of infants with congenital heart disease who underwent cardiac surgery at our institution. Recently, chromosome microarray analysis (CMA) has been performed in lieu of the traditional tests. A standardized approach to cytogenetic testing does not exist in this population. The purpose of this study was to assess the utility of CMA based on our current ordering practice. Design We reviewed the records of all infants (< 1 year old) who underwent cardiac surgery at our institution from January 2010 to June 2013. Data included results of all cytogenetic testing performed. Diagnostic yield was calculated as the percentage of significant abnormal results obtained by each test modality. Patients were grouped by classification of congenital heart disease (CHD). Results Two hundred and seventy-five (51%) of 535 infants who underwent cardiac surgery had cytogenetic testing. Of those tested, 154 (56%) had multiple tests performed and at least 18% were redundant or overlapping. The utilization of CMA has increased each year since its implementation. The diagnostic yield for karyotype, FISH and CMA was 10%, 12% and 14% respectively. CMA yield was significantly higher in patients with septal defects (33%, p = 0.01) compared to all other CHD classes. CMA detected abnormalities of unknown clinical significance in 13% of infants tested. Conclusions In our center, redundant cytogenetic testing is frequently performed in infants undergoing cardiac surgery. The utilization of chromosome microarray analysis has increased over time and abnormalities of unknown clinical significance are detected in an important subset of patients. A screening algorithm that risk-stratifies based on classification of CHD and clinical suspicion may provide a practical, data-driven approach to genetic testing in this population and limit unnecessary resource utilization. PMID:25494910

  13. Fast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray data.

    PubMed

    Chang, Chunqi; Ding, Zhi; Hung, Yeung Sam; Fung, Peter Chin Wan

    2008-06-01

    Recently developed network component analysis (NCA) approach is promising for gene regulatory network reconstruction from microarray data. The existing NCA algorithm is an iterative method which has two potential limitations: computational instability and multiple local solutions. The subsequently developed NCA-r algorithm with Tikhonov regularization can help solve the first issue but cannot completely handle the second one. Here we develop a novel Fast Network Component Analysis (FastNCA) algorithm which has an analytical solution that is much faster and does not have the above limitations. Firstly FastNCA is compared to NCA and NCA-r using synthetic data. The reconstruction of FastNCA is more accurate than that of NCA-r and comparable to that of properly converged NCA. FastNCA is not sensitive to the correlation among the input signals, while its performance does degrade a little but not as dramatically as that of NCA. Like NCA, FastNCA is not very sensitive to small inaccuracies in a priori information on the network topology. FastNCA is about several tens times faster than NCA and several hundreds times faster than NCA-r. Then, the method is applied to real yeast cell-cycle microarray data. The activities of the estimated cell-cycle regulators by FastNCA and NCA-r are compared to the semi-quantitative results obtained independently by Lee et al. (2002). It is shown here that there is a greater agreement between the results of FastNCA and Lee's, which is represented by the ratio 23/33, than that between the results of NCA-r and Lee's, which is 14/33. Software and supplementary materials are available from http://www.eee.hku.hk/~cqchang/FastNCA.htm

  14. Microarray analysis identifies candidate genes for key roles in coral development.

    PubMed

    Grasso, Lauretta C; Maindonald, John; Rudd, Stephen; Hayward, David C; Saint, Robert; Miller, David J; Ball, Eldon E

    2008-11-14

    Anthozoan cnidarians are amongst the simplest animals at the tissue level of organization, but are surprisingly complex and vertebrate-like in terms of gene repertoire. As major components of tropical reef ecosystems, the stony corals are anthozoans of particular ecological significance. To better understand the molecular bases of both cnidarian development in general and coral-specific processes such as skeletogenesis and symbiont acquisition, microarray analysis was carried out through the period of early development - when skeletogenesis is initiated, and symbionts are first acquired. Of 5081 unique peptide coding genes, 1084 were differentially expressed (P microarray analysis demonstrates the potential of this approach for investigating many aspects of coral biology, including the effects of stress and disease.

  15. Microarray analysis of altered gene expression in ERbeta-overexpressing HEK293 cells.

    PubMed

    Zhao, Chunyan; Putnik, Milica; Gustafsson, Jan-Ake; Dahlman-Wright, Karin

    2009-10-01

    Estrogen receptors (ERs), ERalpha and ERbeta, mediate estrogen actions in a broad range of target tissues. With the introduction of microarray techniques, a significant understanding has been gained regarding the interplay between the ERalpha and ERbeta in breast cancer cell lines. To gain a more comprehensive understanding of ERbeta-dependent gene regulation independent of ERalpha, we performed microarray analysis on HEK293/mock and HEK293/ERbeta cells. A total of 332 genes was identified as ERbeta-upregulated genes and 210 identified as ERbeta-downregulated genes. ERbeta-induced and ERbeta-repressed genes were involved in cell-cell signaling, morphogenesis, and cell proliferation. The ERbeta repressive effect on genes related to proliferation was further studied by proliferation assays, where ERbeta expression resulted in a significant decrease in cell proliferation. To identify primary ERbeta target genes, we examined a number of ERbeta-regulated genes using chromatin immunoprecipitation assays for regions bound by ERbeta. Our results showed that ERbeta recruitment was significant to regions associated with 12 genes (IL1RAP, TMSB4X, COLEC12, ENPP2, KLRC1, RERG, RGS16, TNNT2, CYR61, FER1L3, FAM108A1, and CYP4X1), suggesting that these genes are likely to be ERbeta primary target genes. This study has provided novel information on the gene regulatory function of ERbeta independent of ERalpha and identified a number of ERbeta primary target genes. The results of Gene Ontology analysis and proliferation assays are consistent with an antiproliferative role of ERbeta independent of ERalpha.

  16. High throughput phenotypic analysis of Mycobacterium tuberculosis and Mycobacterium bovis strains' metabolism using biolog phenotype microarrays.

    PubMed

    Khatri, Bhagwati; Fielder, Mark; Jones, Gareth; Newell, William; Abu-Oun, Manal; Wheeler, Paul R

    2013-01-01

    Tuberculosis is a major human and animal disease of major importance worldwide. Genetically, the closely related strains within the Mycobacterium tuberculosis complex which cause disease are well-characterized but there is an urgent need better to understand their phenotypes. To search rapidly for metabolic differences, a working method using Biolog Phenotype MicroArray analysis was developed. Of 380 substrates surveyed, 71 permitted tetrazolium dye reduction, the readout over 7 days in the method. By looking for ≥5-fold differences in dye reduction, 12 substrates differentiated M. tuberculosis H37Rv and Mycobacterium bovis AF2122/97. H37Rv and a Beijing strain of M. tuberculosis could also be distinguished in this way, as could field strains of M. bovis; even pairs of strains within one spoligotype could be distinguished by 2 to 3 substrates. Cluster analysis gave three clear groups: H37Rv, Beijing, and all the M. bovis strains. The substrates used agreed well with prior knowledge, though an unexpected finding that AF2122/97 gave greater dye reduction than H37Rv with hexoses was investigated further, in culture flasks, revealing that hexoses and Tween 80 were synergistic for growth and used simultaneously rather than in a diauxic fashion. Potential new substrates for growth media were revealed, too, most promisingly N-acetyl glucosamine. Osmotic and pH arrays divided the mycobacteria into two groups with different salt tolerance, though in contrast to the substrate arrays the groups did not entirely correlate with taxonomic differences. More interestingly, these arrays suggested differences between the amines used by the M. tuberculosis complex and enteric bacteria in acid tolerance, with some hydrophobic amino acids being highly effective. In contrast, γ-aminobutyrate, used in the enteric bacteria, had no effect in the mycobacteria. This study proved principle that Phenotype MicroArrays can be used with slow-growing pathogenic mycobacteria and already has

  17. High Throughput Phenotypic Analysis of Mycobacterium tuberculosis and Mycobacterium bovis Strains' Metabolism Using Biolog Phenotype Microarrays

    PubMed Central

    Khatri, Bhagwati; Fielder, Mark; Jones, Gareth; Newell, William; Abu-Oun, Manal; Wheeler, Paul R.

    2013-01-01

    Tuberculosis is a major human and animal disease of major importance worldwide. Genetically, the closely related strains within the Mycobacterium tuberculosis complex which cause disease are well-characterized but there is an urgent need better to understand their phenotypes. To search rapidly for metabolic differences, a working method using Biolog Phenotype MicroArray analysis was developed. Of 380 substrates surveyed, 71 permitted tetrazolium dye reduction, the readout over 7 days in the method. By looking for ≥5-fold differences in dye reduction, 12 substrates differentiated M. tuberculosis H37Rv and Mycobacterium bovis AF2122/97. H37Rv and a Beijing strain of M. tuberculosis could also be distinguished in this way, as could field strains of M. bovis; even pairs of strains within one spoligotype could be distinguished by 2 to 3 substrates. Cluster analysis gave three clear groups: H37Rv, Beijing, and all the M. bovis strains. The substrates used agreed well with prior knowledge, though an unexpected finding that AF2122/97 gave greater dye reduction than H37Rv with hexoses was investigated further, in culture flasks, revealing that hexoses and Tween 80 were synergistic for growth and used simultaneously rather than in a diauxic fashion. Potential new substrates for growth media were revealed, too, most promisingly N-acetyl glucosamine. Osmotic and pH arrays divided the mycobacteria into two groups with different salt tolerance, though in contrast to the substrate arrays the groups did not entirely correlate with taxonomic differences. More interestingly, these arrays suggested differences between the amines used by the M. tuberculosis complex and enteric bacteria in acid tolerance, with some hydrophobic amino acids being highly effective. In contrast, γ-aminobutyrate, used in the enteric bacteria, had no effect in the mycobacteria. This study proved principle that Phenotype MicroArrays can be used with slow-growing pathogenic mycobacteria and already has

  18. Microarray analysis identifies candidate genes for key roles in coral development

    PubMed Central

    Grasso, Lauretta C; Maindonald, John; Rudd, Stephen; Hayward, David C; Saint, Robert; Miller, David J; Ball, Eldon E

    2008-01-01

    Background Anthozoan cnidarians are amongst the simplest animals at the tissue level of organization, but are surprisingly complex and vertebrate-like in terms of gene repertoire. As major components of tropical reef ecosystems, the stony corals are anthozoans of particular ecological significance. To better understand the molecular bases of both cnidarian development in general and coral-specific processes such as skeletogenesis and symbiont acquisition, microarray analysis was carried out through the period of early development – when skeletogenesis is initiated, and symbionts are first acquired. Results Of 5081 unique peptide coding genes, 1084 were differentially expressed (P ≤ 0.05) in comparisons between four different stages of coral development, spanning key developmental transitions. Genes of likely relevance to the processes of settlement, metamorphosis, calcification and interaction with symbionts were characterised further and their spatial expression patterns investigated using whole-mount in situ hybridization. Conclusion This study is the first large-scale investigation of developmental gene expression for any cnidarian, and has provided candidate genes for key roles in many aspects of coral biology, including calcification, metamorphosis and symbiont uptake. One surprising finding is that some of these genes have clear counterparts in higher animals but are not present in the closely-related sea anemone Nematostella. Secondly, coral-specific processes (i.e. traits which distinguish corals from their close relatives) may be analogous to similar processes in distantly related organisms. This first large-scale application of microarray analysis demonstrates the potential of this approach for investigating many aspects of coral biology, including the effects of stress and disease. PMID:19014561

  19. Droplet Microarray Based on Superhydrophobic-Superhydrophilic Patterns for Single Cell Analysis

    PubMed Central

    Jogia, Gabriella E.; Tronser, Tina; Popova, Anna A.; Levkin, Pavel A.

    2016-01-01

    Single-cell analysis provides fundamental information on individual cell response to different environmental cues and is a growing interest in cancer and stem cell research. However, current existing methods are still facing challenges in performing such analysis in a high-throughput manner whilst being cost-effective. Here we established the Droplet Microarray (DMA) as a miniaturized screening platform for high-throughput single-cell analysis. Using the method of limited dilution and varying cell density and seeding time, we optimized the distribution of single cells on the DMA. We established culturing conditions for single cells in individual droplets on DMA obtaining the survival of nearly 100% of single cells and doubling time of single cells comparable with that of cells cultured in bulk cell population using conventional methods. Our results demonstrate that the DMA is a suitable platform for single-cell analysis, which carries a number of advantages compared with existing technologies allowing for treatment, staining and spot-to-spot analysis of single cells over time using conventional analysis methods such as microscopy. PMID:27941668

  20. Meta-analysis of differentially expressed genes in primary Sjogren's syndrome by using microarray.

    PubMed

    Song, Gwan Gyu; Kim, Jae-Hoon; Seo, Young Ho; Choi, Sung Jae; Ji, Jong Dae; Lee, Young Ho

    2014-01-01

    The purpose of this study was to identify differentially expressed (DE) genes and biological processes associated with changes in gene expression in primary Sjogren's syndrome (pSS). We performed a meta-analysis using the INMEX program (integrative meta-analysis of expression data) of publicly available microarray GEO datasets of pSS. We performed Gene Ontology (GO) enrichment analyses and pathway analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG). Three GEO datasets including 37 cases and 33 controls were available for the meta-analysis. We identified 179 genes across the studies which were consistently DE in pSS (146 up-regulated and 33 down-regulated). The up-regulated gene with the largest effect size (ES) (ES = -2.4228) was SELL (selectin L), whose product is required for the binding and subsequent rolling of leucocytes on endothelial cells to facilitate their migration into secondary lymphoid organs and inflammation sites. The most significant enrichment was in the immune response GO category (P = 2.52 × 10(-25)). The most significant pathway in our KEGG analysis was Epstein-Barr virus infection (P = 9.91 × 10(-06)). Our meta-analysis demonstrated genes that were consistently DE and biological pathways associated with gene expression changes with pSS. Copyright © 2013 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved.

  1. The expression profiles of circRNAs in lung tissues from rats with lipopolysaccharide-induced acute respiratory distress syndrome: A microarray study.

    PubMed

    Wan, Qi-Quan; Wu, Di; Ye, Qi-Fa

    2017-08-31

    The development of circular RNA (circRNA) microarray has facilitated the study of the role of circRNAs in regulating gene expression through a circRNA-miRNA-mRNA network. In our study, microarray was performed to detect the expression profiles of circRNAs during lipopolysaccharide (LPS)-induced acute respiratory distress syndrome (ARDS). Twenty rats were randomly divided into 2 groups, the control group and the LPS group, 10 rats in each group. Three rats each from both groups were randomly selected. Using circRNA microarray data, we compared the circRNA expression profiles in lung tissues between these 6 rats. The most differentially expressed circRNA species from these profiles were validated and optimized as ARDS biomarkers and potential therapeutic targets. Overall, 395 and 562 circRNAs were significantly up- and down-regulated in LPS group vs. control group, respectively. Six up-regulated and 4 down-regulated circRNAs from the top 10 candidates were eventually selected to be validated. Among them, only 4 up-regulated circRNAs (mmu_circRNA_19423, rno_circRNA_010489, rno_circRNA_011426, mmu_circRNA_30664) and 1 down-regulated circRNA (rno_circRNA_005564) exhibited significant validation. The 5 highest ranking target miRNAs of these 5 validated circRNAs were predicted according to the miRNA support vector regression method. This is the first study to investigate circRNA expression profile and a large number of aberrantly expressed circRNAs were revealed during ARDS. The significantly over- or under-expressed circRNA may represent a novel biomarker and be developed as a novel therapeutic target for the clinical management of ARDS. The results are preliminary and need to be confirmed in further well-designed studies with larger sample size. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. Microarray analysis of gene expression induced by sexual contact in Schistosoma mansoni

    PubMed Central

    Waisberg, Michael; Lobo, Francisco P; Cerqueira, Gustavo C; Passos, Liana KJ; Carvalho, Omar S; Franco, Glória R; El-Sayed, Najib M

    2007-01-01

    Background The parasitic trematode Schistosoma mansoni is one of the major causative agents of Schistosomiasis, a disease that affects approximately 200 million people, mostly in developing countries. Since much of the pathology is associated with eggs laid by the female worm, understanding the mechanisms involved in oogenesis and sexual maturation is an important step towards the discovery of new targets for effective drug therapy. It is known that the adult female worm only develops fully in the presence of a male worm and that the rates of oviposition and maturation of eggs are significantly increased by mating. In order to study gene transcripts associated with sexual maturation and oviposition, we compared the gene expression profiles of sexually mature and immature parasites using DNA microarrays. Results For each experiment, three amplified RNA microarray hybridizations and their dye swaps were analyzed. Our results show that 265 transcripts are differentially expressed in adult females and 53 in adult males when mature and immature worms are compared. Of the genes differentially expressed, 55% are expressed at higher levels in paired females while the remaining 45% are more expressed in unpaired ones and 56.6% are expressed at higher levels in paired male worms while the remaining 43.4% are more expressed in immature parasites. Real-time RT-PCR analysis validated the microarray results. Several new maturation associated transcripts were identified. Genes that were up-regulated in single-sex females were mostly related to energy generation (i.e. carbohydrate and protein metabolism, generation of precursor metabolites and energy, cellular catabolism, and organelle organization and biogenesis) while genes that were down-regulated related to RNA metabolism, reactive oxygen species metabolism, electron transport, organelle organization and biogenesis and protein biosynthesis. Conclusion Our results confirm previous observations related to gene expression induced

  3. Multiplexed Analysis of Serum Breast and Ovarian Cancer Markers by Means of Suspension Bead-quantum Dot Microarrays

    NASA Astrophysics Data System (ADS)

    Brazhnik, Kristina; Sokolova, Zinaida; Baryshnikova, Maria; Bilan, Regina; Nabiev, Igor; Sukhanova, Alyona

    Multiplexed analysis of cancer markers is crucial for early tumor diagnosis and screening. We have designed lab-on-a-bead microarray for quantitative detection of three breast cancer markers in human serum. Quantum dots were used as bead-bound fluorescent tags for identifying each marker by means of flow cytometry. Antigen-specific beads reliably detected CA 15-3, CEA, and CA 125 in serum samples, providing clear discrimination between the samples with respect to the antigen levels. The novel microarray is advantageous over the routine single-analyte ones due to the simultaneous detection of various markers. Therefore the developed microarray is a promising tool for serum tumor marker profiling.

  4. Microarray analysis of gene expression in acaricide-exposed Rhipcephalus (Boophilus) microplus larvae.

    USDA-ARS?s Scientific Manuscript database

    Acaricide-inducible differential gene expression was studied in larvae of Rhipicephalus (Boophilus) microplus using a microarray-based approach. The acaricides used were: coumaphos, permethrin, ivermectin, and amitraz. The microarrays contained over 13,000 probes, having been derived from a previous...

  5. Microarray analysis of acaricide inducible gene expression in the southern cattle tick, Rhipicephalus (Boophilus) microplus

    USDA-ARS?s Scientific Manuscript database

    Acaricide-inducible differential gene expression was studied in larvae of Rhipicephalus (Boophilus) microplus using a microarray-based approach. The acaricides used were: coumaphos, permethrin, ivermectin, and amitraz. The microarrays contained over 13,000 probes, having been derived from a previous...

  6. Transcriptional analysis of the innate immune response using the avian innate immunity microarray

    USDA-ARS?s Scientific Manuscript database

    The avian innate immunity microarray (AIIM) is a genomics tool designed to study the transcriptional activity of the avian immune response (Cytogenet. Genome Res. 117:139-145, 2007). It is an avian cDNA microarray representing 4,959 avian genes spotted in triplicate. The AIIM contains 25 avian int...

  7. Diagnostic Yield of Chromosomal Microarray Analysis in a Cohort of Patients with Autism Spectrum Disorders from a Highly Consanguineous Population

    ERIC Educational Resources Information Center

    Al-Mamari, Watfa; Al-Saegh, Abeer; Al-Kindy, Adila; Bruwer, Zandre; Al-Murshedi, Fathiya; Al-Thihli, Khalid

    2015-01-01

    Autism Spectrum Disorders are a complicated group of disorders characterized with heterogeneous genetic etiologies. The genetic investigations for this group of disorders have expanded considerably over the past decade. In our study we designed a tired approach and studied the diagnostic yield of chromosomal microarray analysis on patients…

  8. Diagnostic Yield of Chromosomal Microarray Analysis in a Cohort of Patients with Autism Spectrum Disorders from a Highly Consanguineous Population

    ERIC Educational Resources Information Center

    Al-Mamari, Watfa; Al-Saegh, Abeer; Al-Kindy, Adila; Bruwer, Zandre; Al-Murshedi, Fathiya; Al-Thihli, Khalid

    2015-01-01

    Autism Spectrum Disorders are a complicated group of disorders characterized with heterogeneous genetic etiologies. The genetic investigations for this group of disorders have expanded considerably over the past decade. In our study we designed a tired approach and studied the diagnostic yield of chromosomal microarray analysis on patients…

  9. A Meta-Analysis of Microarray Gene Expression in Mouse Stem Cells: Redefining Stemness

    PubMed Central

    Edwards, Yvonne J. K.; Bryson, Kevin; Jones, David T.

    2008-01-01

    Background While much progress has been made in understanding stem cell (SC) function, a complete description of the molecular mechanisms regulating SCs is not yet established. This lack of knowledge is a major barrier holding back the discovery of therapeutic uses of SCs. We investigated the value of a novel meta-analysis of microarray gene expression in mouse SCs to aid the elucidation of regulatory mechanisms common to SCs and particular SC types. Methodology/Principal Findings We added value to previously published microarray gene expression data by characterizing the promoter type likely to regulate transcription. Promoters of up-regulated genes in SCs were characterized in terms of alternative promoter (AP) usage and CpG-richness, with the aim of correlating features known to affect transcriptional control with SC function. We found that SCs have a higher proportion of up-regulated genes using CpG-rich promoters compared with the negative controls. Comparing subsets of SC type with the controls a slightly different story unfolds. The differences between the proliferating adult SCs and the embryonic SCs versus the negative controls are statistically significant. Whilst the difference between the quiescent adult SCs compared with the negative controls is not. On examination of AP usage, no difference was observed between SCs and the controls. However, comparing the subsets of SC type with the controls, the quiescent adult SCs are found to up-regulate a larger proportion of genes that have APs compared to the controls and the converse is true for the proliferating adult SCs and the embryonic SCs. Conclusions/Significance These findings suggest that looking at features associated with control of transcription is a promising future approach for characterizing “stemness” and that further investigations of stemness could benefit from separate considerations of different SC states. For example, “proliferating-stemness” is shown here, in terms of promoter

  10. Global Microarray Analysis of Carbohydrate Use in Alkaliphilic Hemicellulolytic Bacterium Bacillus sp. N16-5

    PubMed Central

    Song, Yajian; Xue, Yanfen; Ma, Yanhe

    2013-01-01

    The alkaliphilic hemicellulolytic bacterium Bacillus sp. N16-5 has a broad substrate spectrum and exhibits the capacity to utilize complex carbohydrates such as galactomannan, xylan, and pectin. In the monosaccharide mixture, sequential utilization by Bacillus sp. N16-5 was observed. Glucose appeared to be its preferential monosaccharide, followed by fructose, mannose, arabinose, xylose, and galactose. Global transcription profiles of the strain were determined separately for growth on six monosaccharides (glucose, fructose, mannose, galactose, arabinose, and xylose) and four polysaccharides (galactomannan, xylan, pectin, and sodium carboxymethylcellulose) using one-color microarrays. Numerous genes potentially related to polysaccharide degradation, sugar transport, and monosaccharide metabolism were found to respond to a specific substrate. Putative gene clusters for different carbohydrates were identified according to transcriptional patterns and genome annotation. Identification and analysis of these gene clusters contributed to pathway reconstruction for carbohydrate utilization in Bacillus sp. N16-5. Several genes encoding putative sugar transporters were highly expressed during growth on specific sugars, suggesting their functional roles. Two phosphoenolpyruvate-dependent phosphotransferase systems were identified as candidate transporters for mannose and fructose, and a major facilitator superfamily transporter was identified as a candidate transporter for arabinose and xylose. Five carbohydrate uptake transporter 1 family ATP-binding cassette transporters were predicted to participate in the uptake of hemicellulose and pectin degradation products. Collectively, microarray data improved the pathway reconstruction involved in carbohydrate utilization of Bacillus sp. N16-5 and revealed that the organism precisely regulates gene transcription in response to fluctuations in energy resources. PMID:23326578

  11. Microarray-based Analysis of Microbial Community RNAs by Whole Community RNA Amplification (WCRA)

    SciTech Connect

    Gao, Haichun; Yang, Zamin Koo; Gentry, Terry; Wu, Liyou; Schadt, Christopher Warren; Zhou, Jizhong

    2007-01-01

    A new approach, termed whole-community RNA amplification (WCRA), was developed to provide sufficient amounts of mRNAs from environmental samples for microarray analysis. This method employs fusion primers (six to nine random nucleotides with an attached T7 promoter) for the first-strand synthesis. The shortest primer (T7N6S) gave the best results in terms of the yield and representativeness of amplification. About 1,200- to 1,800-fold amplification was obtained with amounts of the RNA templates ranging from 10 to 100 ng, and very representative detection was obtained with 50 to 100 ng total RNA. Evaluation with a Shewanella oneidensis {Delta}fur strain revealed that the amplification method which we developed could preserve the original abundance relationships of mRNAs. In addition, to determine whether representative detection of RNAs can be achieved with mixed community samples, amplification biases were evaluated with a mixture containing equal quantities of RNAs (100 ng each) from four bacterial species, and representative amplification was also obtained. Finally, the method which we developed was applied to the active microbial populations in a denitrifying fluidized bed reactor used for denitrification of contaminated groundwater and ethanol-stimulated groundwater samples for uranium reduction. The genes expressed were consistent with the expected functions of the bioreactor and groundwater system, suggesting that this approach is useful for analyzing the functional activities of microbial communities. This is one of the first demonstrations that microarray-based technology can be used to successfully detect the activities of microbial communities from real environmental samples in a high-throughput fashion.

  12. A Pathway Analysis Tool for Analyzing Microarray Data of Species with Low Physiological Information

    PubMed Central

    te Pas, M. F. W.; van Hemert, S.; Hulsegge, B.; Hoekman, A. J. W.; Pool, M. H.; Rebel, J. M. J.; Smits, M. A.

    2008-01-01

    Pathway information provides insight into the biological processes underlying microarray data. Pathway information is widely available for humans and laboratory animals in databases through the internet, but less for other species, for example, livestock. Many software packages use species-specific gene IDs that cannot handle genomics data from other species. We developed a species-independent method to search pathways databases to analyse microarray data. Three PERL scripts were developed that use the names of the genes on the microarray. (1) Add synonyms of gene names by searching the Gene Ontology (GO) database. (2) Search the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database for pathway information using this GO-enriched gene list. (3) Combine the pathway data with the microarray data and visualize the results using color codes indicating regulation. To demonstrate the power of the method, we used a previously reported chicken microarray experiment investigating line-specific reactions to Salmonella infection as an example. PMID:19920988

  13. Comparison of three microarray probe annotation pipelines: differences in strategies and their effect on downstream analysis

    PubMed Central

    Neerincx, Pieter BT; Casel, Pierrot; Prickett, Dennis; Nie, Haisheng; Watson, Michael; Leunissen, Jack AM; Groenen, Martien AM; Klopp, Christophe

    2009-01-01

    Background Reliable annotation linking oligonucleotide probes to target genes is essential for functional biological analysis of microarray experiments. We used the IMAD, OligoRAP and sigReannot pipelines to update the annotation for the ARK-Genomics Chicken 20 K array as part of a joined EADGENE/SABRE workshop. In this manuscript we compare their annotation strategies and results. Furthermore, we analyse the effect of differences in updated annotation on functional analysis for an experiment involving Eimeria infected chickens and finally we propose guidelines for optimal annotation strategies. Results IMAD, OligoRAP and sigReannot update both annotation and estimated target specificity. The 3 pipelines can assign oligos to target specificity categories although with varying degrees of resolution. Target specificity is judged based on the amount and type of oligo versus target-gene alignments (hits), which are determined by filter thresholds that users can adjust based on their experimental conditions. Linking oligos to annotation on the other hand is based on rigid rules, which differ between pipelines. For 52.7% of the oligos from a subset selected for in depth comparison all pipelines linked to one or more Ensembl genes with consensus on 44.0%. In 31.0% of the cases none of the pipelines could assign an Ensembl gene to an oligo and for the remaining 16.3% the coverage differed between pipelines. Differences in updated annotation were mainly due to different thresholds for hybridisation potential filtering of oligo versus target-gene alignments and different policies for expanding annotation using indirect links. The differences in updated annotation packages had a significant effect on GO term enrichment analysis with consensus on only 67.2% of the enriched terms. Conclusion In addition to flexible thresholds to determine target specificity, annotation tools should provide metadata describing the relationships between oligos and the annotation assigned to them

  14. A note on joint versus gene-specific mixed model analysis of microarray gene expression data.

    PubMed

    Hoeschele, Ina; Li, Hua

    2005-04-01

    Currently, linear mixed model analyses of expression microarray experiments are performed either in a gene-specific or global mode. The joint analysis provides more flexibility in terms of how parameters are fitted and estimated and tends to be more powerful than the gene-specific analysis. Here we show how to implement the gene-specific linear mixed model analysis as an exact algorithm for the joint linear mixed model analysis. The gene-specific algorithm is exact, when the mixed model equations can be partitioned into unrelated components: One for all global fixed and random effects and the others for the gene-specific fixed and random effects for each gene separately. This unrelatedness holds under three conditions: (1) any gene must have the same number of replicates or probes on all arrays, but these numbers can differ among genes; (2) the residual variance of the (transformed) expression data must be homogeneous or constant across genes (other variance components need not be homogeneous) and (3) the number of genes in the experiment is large. When these conditions are violated, the gene-specific algorithm is expected to be nearly exact.

  15. TMA Navigator: Network inference, patient stratification and survival analysis with tissue microarray data.

    PubMed

    Lubbock, Alexander L R; Katz, Elad; Harrison, David J; Overton, Ian M

    2013-07-01

    Tissue microarrays (TMAs) allow multiplexed analysis of tissue samples and are frequently used to estimate biomarker protein expression in tumour biopsies. TMA Navigator (www.tmanavigator.org) is an open access web application for analysis of TMA data and related information, accommodating categorical, semi-continuous and continuous expression scores. Non-biological variation, or batch effects, can hinder data analysis and may be mitigated using the ComBat algorithm, which is incorporated with enhancements for automated application to TMA data. Unsupervised grouping of samples (patients) is provided according to Gaussian mixture modelling of marker scores, with cardinality selected by Bayesian information criterion regularization. Kaplan-Meier survival analysis is available, including comparison of groups identified by mixture modelling using the Mantel-Cox log-rank test. TMA Navigator also supports network inference approaches useful for TMA datasets, which often constitute comparatively few markers. Tissue and cell-type specific networks derived from TMA expression data offer insights into the molecular logic underlying pathophenotypes, towards more effective and personalized medicine. Output is interactive, and results may be exported for use with external programs. Private anonymous access is available, and user accounts may be generated for easier data management.

  16. Identification of the dichotomous role of age-related LCK in calorie restriction revealed by integrative analysis of cDNA microarray and interactome.

    PubMed

    Park, Daeui; Lee, Eun Kyeong; Jang, Eun Jee; Jeong, Hyoung Oh; Kim, Byoung-Chul; Ha, Young Mi; Hong, Seong Eui; Yu, Byung Pal; Chung, Hae Young

    2013-08-01

    Among the many experimental paradigms used for the investigation of aging, the calorie restriction (CR) model has been proven to be the most useful in gerontological research. Exploration of the mechanisms underlying CR has produced a wealth of data. To identify key molecules controlled by aging and CR, we integrated data from 84 mouse and rat cDNA microarrays with a protein-protein interaction network. On the basis of this integrative analysis, we selected three genes that are upregulated in aging but downregulated by CR and two genes that are downregulated in aging but upregulated by CR. One of these key molecules is lymphocyte-specific protein tyrosine kinase (LCK). To further confirm this result on LCK, we performed a series of experiments in vitro and in vivo using kidneys obtained from aged ad libitum-fed and CR rats. Our major significant findings are as follows: (1) identification of LCK as a key molecule using integrative analysis; (2) confirmation that the age-related increase in LCK was modulated by CR and that protein tyrosine kinase activity was decreased using a LCK-specific inhibitor; and (3) upregulation of LCK leads to NF-κB activation in a ONOO(-) generation-dependent manner, which is modulated by CR. These results indicate that LCK could be considered a target attenuated by the anti-aging effects of CR. Integrative analysis of cDNA microarray and interactome data are powerful tools for identifying target molecules that are involved in the aging process and modulated by CR.

  17. Methanotrophic bacteria associated to rice roots: the cultivar effect assessed by T-RFLP and microarray analysis.

    PubMed

    Lüke, Claudia; Bodrossy, Levente; Lupotto, Elisabetta; Frenzel, Peter

    2011-10-01

    Rice plants play a key role in regulating methane emissions from paddy fields by affecting both underlying processes: methane production and oxidation. Specific differences were reported for methane oxidation rates; however, studies on the bacterial communities involved are rare. Here, we analysed the methanotrophic community on the roots of 18 different rice cultivars by pmoA-based terminal restriction fragment length polymorphism (T-RFLP) and microarray analysis. Both techniques showed comparable and consistent results revealing a high diversity dominated by type II and type Ib methanotrophs. pmoA microarrays have been successfully used to study methane-oxidizing bacteria in various environments. However, the microarray's full potential resolving community structure has not been exploited yet. Here, we provide an example on how to include this information into multivariate statistics. The analysis revealed a rice cultivar effect on the methanotroph community composition that could be affiliated to the plant genotype. This effect became only significant by including the specific phylogenetic resolution provided by the microarray into the statistical analysis.

  18. Evaluating Japanese patients with the Marfan syndrome using high-throughput microarray-based mutational analysis of fibrillin-1 gene.

    PubMed

    Ogawa, Naomi; Imai, Yasushi; Takahashi, Yuji; Nawata, Kan; Hara, Kazuo; Nishimura, Hiroshi; Kato, Masayoshi; Takeda, Norifumi; Kohro, Takahide; Morita, Hiroyuki; Taketani, Tsuyoshi; Morota, Tetsuro; Yamazaki, Tsutomu; Goto, Jun; Tsuji, Shoji; Takamoto, Shinichi; Nagai, Ryozo; Hirata, Yasunobu

    2011-12-15

    Marfan syndrome (MS) is an inherited connective tissue disorder, and detailed evaluations of multiple organ systems are required for its diagnosis. Genetic testing of the disease-causing fibrillin-1 gene (FBN1) is also important in this diagnostic scheme. The aim of this study was to define the clinical characteristics of Japanese patients with MS and enable the efficient and accurate diagnosis of MS with mutational analysis using a high-throughput microarray-based resequencing system. Fifty-three Japanese probands were recruited, and their clinical characteristics were evaluated using the Ghent criteria. For mutational analysis, an oligonucleotide microarray was designed to interrogate FBN1, and the entire exon and exon-intron boundaries of FBN1 were sequenced. Clinical evaluation revealed more pulmonary phenotypes and fewer skeletal phenotypes in Japanese patients with MS compared to Caucasians. The microarray-based resequencing system detected 35 kinds of mutations, including 23 new mutations. The mutation detection rate for patients who fulfilled the Ghent criteria reached 71%. Of note, splicing mutations accounted for 19% of all mutations, which is more than previously reported. In conclusion, this comprehensive approach successfully detected clinical phenotypes of Japanese patients with MS and demonstrated the usefulness and feasibility of this microarray-based high-throughput resequencing system for mutational analysis of MS. Copyright © 2011 Elsevier Inc. All rights reserved.

  19. Tissue tablet method: an efficient tissue banking procedure applicable to both molecular analysis and frozen tissue microarray.

    PubMed

    Torata, Nobuhiro; Ohuchida, Kenoki; Akagawa, Shin; Cui, Lin; Kozono, Shingo; Mizumoto, Kazuhiro; Aishima, Shinichi; Oda, Yoshinao; Tanaka, Masao

    2014-01-01

    Frozen human tissues are necessary for research purposes, but tissue banking methods have not changed for more than a decade. Many institutions use cryovial tubes or plastic molds with an optimal cutting temperature compound. However, these methods are associated with several problems, such as samples sticking to one another and the need for a larger storing space. We established an efficient tissue freezing and storing procedure ("tissue tablet method") applicable to both molecular analysis and frozen tissue microarray. Tissue samples were chopped into tiny fragments and embedded into tablet-shaped frozen optimal cutting temperature compound using our original tissue-freezing plate. These tablets can be sectioned and stored in cryovial tubes. We compared the tissue quality of tablet-shaped samples with that of conventional optimal cutting temperature blocks and found no significant difference between them. Tissue microarray is a key method to utilize tissue-banking specimens. However, most tissue microarrays require the coring out of cylindrically shaped tissues from formalin-fixed, paraffin-embedded tissue blocks. Antigenic changes and mRNA degradation are frequently observed with formalin-fixed, paraffin-embedded samples. Therefore, we have applied tablet-shaped samples to construct frozen tissue microarrays with our original mounting base. Constructed tissue microarray sections showed good morphology without obvious artifact and good immunohistochemistry and in situ hybridization results. These results suggest that the quality of arrayed samples was sufficiently appropriate for research purposes. In conclusion, the tissue tablet method and frozen tissue microarray procedure can save time, provides easy tissue handling and processing, and satisfies the demands of research methodologies and tissue banking. © 2013.

  20. Characterization and analysis of an industrial strain of Streptomyces bingchenggensis by genome sequencing and gene microarray.

    PubMed

    Wang, Xiang-Jing; Zhang, Bo; Yan, Yi-Jun; An, Jing; Zhang, Ji; Liu, Chong-Xi; Xiang, Wen-Sheng

    2013-11-01

    Streptomyces bingchenggensis is a soil bacterium that produces milbemycins, a family of macrolide antibiotics that are commercially important in crop protection and veterinary medicine. In addition, S. bingchenggensis produces many other natural products including the polyether nanchangmycin and novel cyclic pentapeptides. To identify the gene clusters involved in the biosynthesis of these compounds, and better clarify the biochemical pathways of these gene clusters, the whole genome of S. bingchenggensis was sequenced, and the transcriptome profile was subsequently investigated by microarray. In comparison with other sequenced genomes in Streptomyces, S. bingchenggensis has the largest linear chromosome consisting of 11 936 683 base pairs (bp), with an average GC content of 70.8%. The 10 023 predicted protein-coding sequences include at least 47 gene clusters correlated with the biosynthesis of known or predicted secondary metabolites. Transcriptional analysis demonstrated an extremely high expression level of the milbemycin gene cluster during the entire growth period and a moderately high expression level of the nanchangmycin gene cluster during the initial hours that subsequently decreased. However, other gene clusters appear to be silent. The genome-wide analysis of the secondary metabolite gene clusters in S. bingchenggensis, coupled with transcriptional analysis, will facilitate the rational development of high milbemycins-producing strains as well as the discovery of new natural products.

  1. VennMaster: Area-proportional Euler diagrams for functional GO analysis of microarrays

    PubMed Central

    Kestler, Hans A; Müller, André; Kraus, Johann M; Buchholz, Malte; Gress, Thomas M; Liu, Hongfang; Kane, David W; Zeeberg, Barry R; Weinstein, John N

    2008-01-01

    Background Microarray experiments generate vast amounts of data. The functional context of differentially expressed genes can be assessed by querying the Gene Ontology (GO) database via GoMiner. Directed acyclic graph representations, which are used to depict GO categories enriched with differentially expressed genes, are difficult to interpret and, depending on the particular analysis, may not be well suited for formulating new hypotheses. Additional graphical methods are therefore needed to augment the GO graphical representation. Results We present an alternative visualization approach, area-proportional Euler diagrams, showing set relationships with semi-quantitative size information in a single diagram to support biological hypothesis formulation. The cardinalities of sets and intersection sets are represented by area-proportional Euler diagrams and their corresponding graphical (circular or polygonal) intersection areas. Optimally proportional representations are obtained using swarm and evolutionary optimization algorithms. Conclusion VennMaster's area-proportional Euler diagrams effectively structure and visualize the results of a GO analysis by indicating to what extent flagged genes are shared by different categories. In addition to reducing the complexity of the output, the visualizations facilitate generation of novel hypotheses from the analysis of seemingly unrelated categories that share differentially expressed genes. PMID:18230172

  2. Analysis of Endocrine Disruption in Southern California Coastal Fish Using an Aquatic Multispecies Microarray

    PubMed Central

    Baker, Michael E.; Ruggeri, Barbara; Sprague, L. James; Eckhardt-Ludka, Colleen; Lapira, Jennifer; Wick, Ivan; Soverchia, Laura; Ubaldi, Massimo; Polzonetti-Magni, Alberta Maria; Vidal-Dorsch, Doris; Bay, Steven; Gully, Joseph R.; Reyes, Jesus A.; Kelley, Kevin M.; Schlenk, Daniel; Breen, Ellen C.; Šášik, Roman; Hardiman, Gary

    2009-01-01

    Background Endocrine disruptors include plasticizers, pesticides, detergents, and pharmaceuticals. Turbot and other flatfish are used to characterize the presence of chemicals in the marine environment. Unfortunately, there are relatively few genes of turbot and other flatfish in GenBank, which limits the use of molecular tools such as microarrays and quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) to study disruption of endocrine responses in sentinel fish captured by regulatory agencies. Objectives We fabricated a multigene cross-species microarray as a diagnostic tool to screen the effects of environmental chemicals in fish, for which there is minimal genomic information. The array included genes that are involved in the actions of adrenal and sex steroids, thyroid hormone, and xenobiotic responses. This microarray will provide a sensitive tool for screening for the presence of chemicals with adverse effects on endocrine responses in coastal fish species. Methods We used a custom multispecies microarray to study gene expression in wild hornyhead turbot (Pleuronichthys verticalis) collected from polluted and clean coastal waters and in laboratory male zebrafish (Danio rerio) after exposure to estradiol and 4-nonylphenol. We measured gene-specific expression in turbot liver by qRT-PCR and correlated it to microarray data. Results Microarray and qRT-PCR analyses of livers from turbot collected from polluted areas revealed altered gene expression profiles compared with those from nonaffected areas. Conclusions The agreement between the array data and qRT-PCR analyses validates this multispecies microarray. The microarray measurement of gene expression in zebrafish, which are phylogenetically distant from turbot, indicates that this multispecies microarray will be useful for measuring endocrine responses in other fish. PMID:19270792

  3. Using Ambystoma mexicanum (Mexican Axolotl) Embryos, Chemical Genetics, and Microarray Analysis to Identify Signaling Pathways Associated with Tissue Regeneration

    PubMed Central

    Ponomareva, Larissa V.; Athippozhy, Antony; Thorson, Jon S.; Voss, S. Randal

    2015-01-01

    Amphibian vertebrates are important models in regenerative biology because they present exceptional regenerative capabilities throughout life. However, it takes considerable effort to rear amphibians to juvenile and adult stages for regeneration studies and the relatively large sizes that frogs and salamanders achieve during development make them difficult to use in chemical screens. Here we introduce a new tail regeneration model using late stage Mexican axolotl embryos. We show that axolotl embryos completely regenerate amputated tails in 7 days before they exhaust their yolk supply and begin to feed. Further, we show that axolotl embryos can be efficiently reared in microtiter plates to achieve moderate throughput screening of soluble chemicals to investigate toxicity and identify molecules that alter regenerative outcome. As proof of principle, we identified integration 1 / wingless (Wnt), transforming growth factor beta (Tgf-β), and fibroblast growth factor (Fgf) pathway antagonists that completely block tail regeneration and additional chemicals that significantly affected tail outgrowth. Furthermore, we used microarray analysis to show that inhibition of Wnt signaling broadly affects transcription of genes associated with Wnt, Fgf, Tgf-β, epidermal growth factor (Egf), Notch, nerve growth factor (Ngf), homeotic gene (Hox), rat sarcoma/mitogen-activated protein kinase (Ras/Mapk), myelocytomatosis viral oncogene (Myc), tumor protein 53 (p53), and retinoic acid (RA) pathways. Punctuated changes in the expression of genes known to regulate vertebrate development were observed; this suggests the tail regeneration transcriptional program is hierarchically structured and temporally ordered. Our study establishes the axolotl as a chemical screening model to investigate signaling pathways associated with tissue regeneration. PMID:26092703

  4. Using Ambystoma mexicanum (Mexican axolotl) embryos, chemical genetics, and microarray analysis to identify signaling pathways associated with tissue regeneration.

    PubMed

    Ponomareva, Larissa V; Athippozhy, Antony; Thorson, Jon S; Voss, S Randal

    2015-12-01

    Amphibian vertebrates are important models in regenerative biology because they present exceptional regenerative capabilities throughout life. However, it takes considerable effort to rear amphibians to juvenile and adult stages for regeneration studies, and the relatively large sizes that frogs and salamanders achieve during development make them difficult to use in chemical screens. Here, we introduce a new tail regeneration model using late stage Mexican axolotl embryos. We show that axolotl embryos completely regenerate amputated tails in 7days before they exhaust their yolk supply and begin to feed. Further, we show that axolotl embryos can be efficiently reared in microtiter plates to achieve moderate throughput screening of soluble chemicals to investigate toxicity and identify molecules that alter regenerative outcome. As proof of principle, we identified integration 1 / wingless (Wnt), transforming growth factor beta (Tgf-β), and fibroblast growth factor (Fgf) pathway antagonists that completely block tail regeneration and additional chemicals that significantly affected tail outgrowth. Furthermore, we used microarray analysis to show that inhibition of Wnt signaling broadly affects transcription of genes associated with Wnt, Fgf, Tgf-β, epidermal growth factor (Egf), Notch, nerve growth factor (Ngf), homeotic gene (Hox), rat sarcoma/mitogen-activated protein kinase (Ras/Mapk), myelocytomatosis viral oncogene (Myc), tumor protein 53 (p53), and retinoic acid (RA) pathways. Punctuated changes in the expression of genes known to regulate vertebrate development were observed; this suggests the tail regeneration transcriptional program is hierarchically structured and temporally ordered. Our study establishes the axolotl as a chemical screening model to investigate signaling pathways associated with tissue regeneration. Copyright © 2015 Elsevier Inc. All rights reserved.

  5. Genome-wide expression analysis of hereditary hyperplastic gingivitis in silver foxes (Vulpes vulpes) using canine microarrays.

    PubMed

    Clark, Jo-Anna B J; Booman, Marije; Hudson, Robert C; Marshall, H Dawn

    2014-08-01

    Hereditary hyperplastic gingivitis (HHG) is an autosomal recessive condition found predominantly in farmed silver foxes, first documented in Europe in the 1940s. Hereditary gingival fibromatosis (HGF) is an analogous condition occurring in humans. HGF has a heterogeneous aetiology with emphasis placed on the autosomal dominant forms of inheritance for which there are three known loci: HGF1, HGF2, and HGF3. Among these, only one causative mutation has been determined, in the Son of sevenless homolog 1 (SOS1) gene. The goal of this study was to explore potential molecular or cellular mechanisms underlying HHG by analysis of global gene expression patterns from Affymetrix Canine 2.0 microarrays cross-referenced against candidate genes within the human loci. We conclude that the SOS1 gene involved in HGF1 is not significantly up-regulated in HHG. However, the structurally and functionally similar SOS2 gene is up-regulated in affected foxes, and we propose this as a candidate gene for HHG. At HGF2 we identify RASA1 (rat sarcoma viral p21 protein activator 1) as a candidate gene for HHG, as it is up-regulated in affected foxes and is involved in MAPK signalling. From comparison to the genes within the HGF3 locus, we find evidence for a role of androgens in HHG phenotype severity by differential up-regulation of SRD5A2 in HHG-affected foxes. We hypothesize that the putative mutation occurs upstream of RAS in the extracellular signal-regulated kinase component of MAPK signalling.

  6. Application of genetic algorithms and constructive neural networks for the analysis of microarray cancer data

    PubMed Central

    2014-01-01

    Background Extracting relevant information from microarray data is a very complex task due to the characteristics of the data sets, as they comprise a large number of features while few samples are generally available. In this sense, feature selection is a very important aspect of the analysis helping in the tasks of identifying relevant genes and also for maximizing predictive information. Methods Due to its simplicity and speed, Stepwise Forward Selection (SFS) is a widely used feature selection technique. In this work, we carry a comparative study of SFS and Genetic Algorithms (GA) as general frameworks for the analysis of microarray data with the aim of identifying group of genes with high predictive capability and biological relevance. Six standard and machine learning-based techniques (Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Naive Bayes (NB), C-MANTEC Constructive Neural Network, K-Nearest Neighbors (kNN) and Multilayer perceptron (MLP)) are used within both frameworks using six free-public datasets for the task of predicting cancer outcome. Results Better cancer outcome prediction results were obtained using the GA framework noting that this approach, in comparison to the SFS one, leads to a larger selection set, uses a large number of comparison between genetic profiles and thus it is computationally more intensive. Also the GA framework permitted to obtain a set of genes that can be considered to be more biologically relevant. Regarding the different classifiers used standard feedforward neural networks (MLP), LDA and SVM lead to similar and best results, while C-MANTEC and k-NN followed closely but with a lower accuracy. Further, C-MANTEC, MLP and LDA permitted to obtain a more limited set of genes in comparison to SVM, NB and kNN, and in particular C-MANTEC resulted in the most robust classifier in terms of changes in the parameter settings. Conclusions This study shows that if prediction accuracy is the objective, the GA

  7. Application of genetic algorithms and constructive neural networks for the analysis of microarray cancer data.

    PubMed

    Luque-Baena, Rafael Marcos; Urda, Daniel; Subirats, Jose Luis; Franco, Leonardo; Jerez, Jose M

    2014-05-07

    Extracting relevant information from microarray data is a very complex task due to the characteristics of the data sets, as they comprise a large number of features while few samples are generally available. In this sense, feature selection is a very important aspect of the analysis helping in the tasks of identifying relevant genes and also for maximizing predictive information. Due to its simplicity and speed, Stepwise Forward Selection (SFS) is a widely used feature selection technique. In this work, we carry a comparative study of SFS and Genetic Algorithms (GA) as general frameworks for the analysis of microarray data with the aim of identifying group of genes with high predictive capability and biological relevance. Six standard and machine learning-based techniques (Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Naive Bayes (NB), C-MANTEC Constructive Neural Network, K-Nearest Neighbors (kNN) and Multilayer perceptron (MLP)) are used within both frameworks using six free-public datasets for the task of predicting cancer outcome. Better cancer outcome prediction results were obtained using the GA framework noting that this approach, in comparison to the SFS one, leads to a larger selection set, uses a large number of comparison between genetic profiles and thus it is computationally more intensive. Also the GA framework permitted to obtain a set of genes that can be considered to be more biologically relevant. Regarding the different classifiers used standard feedforward neural networks (MLP), LDA and SVM lead to similar and best results, while C-MANTEC and k-NN followed closely but with a lower accuracy. Further, C-MANTEC, MLP and LDA permitted to obtain a more limited set of genes in comparison to SVM, NB and kNN, and in particular C-MANTEC resulted in the most robust classifier in terms of changes in the parameter settings. This study shows that if prediction accuracy is the objective, the GA-based approach lead to better results

  8. Cancer Classification in Microarray Data using a Hybrid Selective Independent Component Analysis and υ-Support Vector Machine Algorithm

    PubMed Central

    Saberkari, Hamidreza; Shamsi, Mousa; Joroughi, Mahsa; Golabi, Faegheh; Sedaaghi, Mohammad Hossein

    2014-01-01

    Microarray data have an important role in identification and classification of the cancer tissues. Having a few samples of microarrays in cancer researches is always one of the most concerns which lead to some problems in designing the classifiers. For this matter, preprocessing gene selection techniques should be utilized before classification to remove the noninformative genes from the microarray data. An appropriate gene selection method can significantly improve the performance of cancer classification. In this paper, we use selective independent component analysis (SICA) for decreasing the dimension of microarray data. Using this selective algorithm, we can solve the instability problem occurred in the case of employing conventional independent component analysis (ICA) methods. First, the reconstruction error and selective set are analyzed as independent components of each gene, which have a small part in making error in order to reconstruct new sample. Then, some of the modified support vector machine (υ-SVM) algorithm sub-classifiers are trained, simultaneously. Eventually, the best sub-classifier with the highest recognition rate is selected. The proposed algorithm is applied on three cancer datasets (leukemia, breast cancer and lung cancer datasets), and its results are compared with other existing methods. The results illustrate that the proposed algorithm (SICA + υ-SVM) has higher accuracy and validity in order to increase the classification accuracy. Such that, our proposed algorithm exhibits relative improvements of 3.3% in correctness rate over ICA + SVM and SVM algorithms in lung cancer dataset. PMID:25426433

  9. Cancer Classification in Microarray Data using a Hybrid Selective Independent Component Analysis and υ-Support Vector Machine Algorithm.

    PubMed

    Saberkari, Hamidreza; Shamsi, Mousa; Joroughi, Mahsa; Golabi, Faegheh; Sedaaghi, Mohammad Hossein

    2014-10-01

    Microarray data have an important role in identification and classification of the cancer tissues. Having a few samples of microarrays in cancer researches is always one of the most concerns which lead to some problems in designing the classifiers. For this matter, preprocessing gene selection techniques should be utilized before classification to remove the noninformative genes from the microarray data. An appropriate gene selection method can significantly improve the performance of cancer classification. In this paper, we use selective independent component analysis (SICA) for decreasing the dimension of microarray data. Using this selective algorithm, we can solve the instability problem occurred in the case of employing conventional independent component analysis (ICA) methods. First, the reconstruction error and selective set are analyzed as independent components of each gene, which have a small part in making error in order to reconstruct new sample. Then, some of the modified support vector machine (υ-SVM) algorithm sub-classifiers are trained, simultaneously. Eventually, the best sub-classifier with the highest recognition rate is selected. The proposed algorithm is applied on three cancer datasets (leukemia, breast cancer and lung cancer datasets), and its results are compared with other existing methods. The results illustrate that the proposed algorithm (SICA + υ-SVM) has higher accuracy and validity in order to increase the classification accuracy. Such that, our proposed algorithm exhibits relative improvements of 3.3% in correctness rate over ICA + SVM and SVM algorithms in lung cancer dataset.

  10. Integrative meta-analysis of differentially expressed genes in osteoarthritis using microarray technology.

    PubMed

    Wang, Xi; Ning, Yujie; Guo, Xiong

    2015-09-01

    The aim of the present study was to identify differentially expressed (DE) genes in patients with osteoarthritis (OA), and biological processes associated with changes in gene expression that occur in this disease. Using the INMEX (integrative meta‑analysis of expression data) software tool, a meta‑analysis of publicly available microarray Gene Expression Omnibus (GEO) datasets of OA was performed. Gene ontology (GO) enrichment analysis was performed in order to detect enriched functional attributes based on gene‑associated GO terms. Three GEO datasets, containing 137 patients with OA and 52 healthy controls, were included in the meta‑analysis. The analysis identified 85 genes that were consistently differentially expressed in OA (30 genes were upregulated and 55 genes were downregulated). The upregulated gene with the lowest P‑value (P=5.36E‑07) was S‑phase kinase‑associated protein 2, E3 ubiquitin protein ligase (SKP2). The downregulated gene with the lowest P‑value (P=4.42E‑09) was Proline rich 5 like (PRR5L). Among the 210 GO terms that were associated with the set of DE genes, the most significant two enrichments were observed in the GO categories of 'Immune response', with a P‑value of 0.000129438, and 'Immune effectors process', with a P‑value of 0.000288619. The current meta‑analysis identified genes that were consistently DE in OA, in addition to biological pathways associated with changes in gene expression that occur during OA, which may provide insight into the molecular mechanisms underlying the pathogenesis of this disease.

  11. Unique gene expression profile in osteoarthritis synovium compared with cartilage: analysis of publicly accessible microarray datasets.

    PubMed

    Park, Robin; Ji, Jong Dae

    2016-06-01

    The purpose of this study was to identify a gene expression signature in osteoarthritis (OA) synovium and genomic pathways likely to be involved in the pathogenesis of OA. Four publicly accessible microarray studies from synovium of OA patients were integrated, and a transcriptomic and network-based meta-analysis was performed. Based on pathways according to the Kyoto Encyclopedia of Genes and Genomes, functional enrichment analysis was performed. Meta-analysis results of OA synovium were compared to two previously published studies of OA cartilage to determine the relative number of common and specific DEGs of the cartilage and synovium. According to our meta-analysis, a total of 1350 genes were found to be differentially expressed in the synovium of OA patients as compared to that of healthy controls. Pathway analysis found 41 significant pathways in the total DEGs, and 22 and 16 pathways in the upregulated and downregulated DEGs, respectively. Cell adhesion molecules and cytokine-cytokine receptor interaction were the most significant pathway in the upregulated and downregulated DEGs, respectively. Comparison of meta-analysis results of OA synovium with results of two previous studies of OA cartilage identified 85 common genes and 1632 cartilage-specific DEGs and 1265 synovium-specific DEGs in the first study; and 142 common genes, and 856 cartilage-specific DEGs and 1208 synovium-specific DEGs in the second study. Our results show a small overlap between the DEGs of the synovium compared to DEGs of the cartilage, suggesting different pathogenic mechanisms that are specific to the synovium.

  12. Three microarray platforms: an analysis of their concordance in profiling gene expression

    PubMed Central

    Petersen, David; Chandramouli, GVR; Geoghegan, Joel; Hilburn, Joanne; Paarlberg, Jonathon; Kim, Chang Hee; Munroe, David; Gangi, Lisa; Han, Jing; Puri, Raj; Staudt, Lou; Weinstein, John; Barrett, J Carl; Green, Jeffrey; Kawasaki, Ernest S

    2005-01-01

    Background Microarrays for the analysis of gene expression are of three different types: short oligonucleotide (25–30 base), long oligonucleotide (50–80 base), and cDNA (highly variable in length). The short oligonucleotide and cDNA arrays have been the mainstay of expression analysis to date, but long oligonucleotide platforms are gaining in popularity and will probably replace cDNA arrays. As part of a validation study for the long oligonucleotide arrays, we compared and contrasted expression profiles from the three formats, testing RNA from six different cell lines against a universal reference standard. Results The three platforms had 6430 genes in common. In general, correlation of gene expression levels across the platforms was good when defined by concordance in the direction of expression difference (upregulation or downregulation), scatter plot analysis, principal component analysis, cell line correlation or quantitative RT-PCR. The overall correlations (r values) between platforms were in the range 0.7 to 0.8, as determined by analysis of scatter plots. When concordance was measured for expression ratios significant at p-values of <0.05 and at expression threshold levels of 1.5 and 2-fold, the agreement among the platforms was very high, ranging from 93% to 100%. Conclusion Our results indicate that the long oligonucleotide platform is highly suitable for expression analysis and compares favorably with the cDNA and short oligonucleotide varieties. All three platforms can give similar and reproducible results if the criterion is the direction of change in gene expression and minimal emphasis is placed on the magnitude of change. PMID:15876355

  13. Microarray Gene Expression Analysis to Evaluate Cell Type Specific Expression of Targets Relevant for Immunotherapy of Hematological Malignancies.

    PubMed

    Pont, M J; Honders, M W; Kremer, A N; van Kooten, C; Out, C; Hiemstra, P S; de Boer, H C; Jager, M J; Schmelzer, E; Vries, R G; Al Hinai, A S; Kroes, W G; Monajemi, R; Goeman, J J; Böhringer, S; Marijt, W A F; Falkenburg, J H F; Griffioen, M

    2016-01-01

    Cellular immunotherapy has proven to be effective in the treatment of hematological cancers by donor lymphocyte infusion after allogeneic hematopoietic stem cell transplantation and more recently by targeted therapy with chimeric antigen or T-cell receptor-engineered T cells. However, dependent on the tissue distribution of the antigens that are targeted, anti-tumor responses can be accompanied by undesired side effects. Therefore, detailed tissue distribution analysis is essential to estimate potential efficacy and toxicity of candidate targets for immunotherapy of hematological malignancies. We performed microarray gene expression analysis of hematological malignancies of different origins, healthy hematopoietic cells and various non-hematopoietic cell types from organs that are often targeted in detrimental immune responses after allogeneic stem cell transplantation leading to graft-versus-host disease. Non-hematopoietic cells were also cultured in the presence of IFN-γ to analyze gene expression under inflammatory circumstances. Gene expression was investigated by Illumina HT12.0 microarrays and quality control analysis was performed to confirm the cell-type origin and exclude contamination of non-hematopoietic cell samples with peripheral blood cells. Microarray data were validated by quantitative RT-PCR showing strong correlations between both platforms. Detailed gene expression profiles were generated for various minor histocompatibility antigens and B-cell surface antigens to illustrate the value of the microarray dataset to estimate efficacy and toxicity of candidate targets for immunotherapy. In conclusion, our microarray database provides a relevant platform to analyze and select candidate antigens with hematopoietic (lineage)-restricted expression as potential targets for immunotherapy of hematological cancers.

  14. Genomic microarray in fetuses with increased nuchal translucency and normal karyotype: a systematic review and meta-analysis.

    PubMed

    Grande, M; Jansen, F A R; Blumenfeld, Y J; Fisher, A; Odibo, A O; Haak, M C; Borrell, A

    2015-12-01

    To estimate the incremental yield of detecting copy number variants (CNVs) by genomic microarray over karyotyping in fetuses with increased nuchal translucency (NT) diagnosed by first-trimester ultrasound. This was a systematic review conducted in accordance with PRISMA criteria. We searched PubMed, Ovid MEDLINE and Web of Science for studies published between January 2009 and January 2015 that described CNVs in fetuses with increased NT, usually defined as ≥  3.5 mm, and normal karyotype. Search terms included: fetal or prenatal, nuchal translucency or cystic hygroma or ultrasound anomaly, array comparative genomic hybridization or copy number variants, with related search terms. Risk differences were pooled to estimate the overall and stratified microarray incremental yield using RevMan. Quality assessment of included studies was performed using the Quality Assessment tool for Diagnostic Accuracy Studies (QUADAS-2) checklist. Seventeen studies met the inclusion criteria for analysis. Meta-analysis indicated an incremental yield of 5.0% (95% CI, 2.0-8.0%) for the detection of CNVs using microarray when pooling results. Stratified analysis of microarray results demonstrated a 4.0% (95% CI, 2.0-7.0%) incremental yield in cases of isolated NT and 7.0% (95% CI, 2.0-12.0%) when other malformations were present. The most common pathogenic CNVs reported were 22q11.2 deletion, 22q11.2 duplication, 10q26.12q26.3 deletion and 12q21q22 deletion. The pooled prevalence for variants of uncertain significance was 1%. The use of genomic microarray provides a 5.0% incremental yield of detecting CNVs in fetuses with increased NT and normal karyotype. Copyright © 2015 ISUOG. Published by John Wiley & Sons Ltd.

  15. Transcriptomic profiling of long non-coding RNAs in dermatomyositis by microarray analysis

    PubMed Central

    Peng, Qing-Lin; Zhang, Ya-Mei; Yang, Han-Bo; Shu, Xiao-Ming; Lu, Xin; Wang, Guo-Chun

    2016-01-01

    Long non-coding RNAs (lncRNAs) are prevalently transcribed in the genome and have been found to be of functional importance. However, the potential roles of lncRNAs in dermatomyositis (DM) remain unknown. In this study, a lncRNA + mRNA microarray analysis was performed to profile lncRNAs and mRNAs from 15 treatment-naive DM patients and 5 healthy controls. We revealed a total of 1198 lncRNAs (322 up-regulated and 876 down-regulated) and 1213 mRNAs (665 up-regulated and 548 down-regulated) were significantly differentially expressed in DM patients compared with the healthy controls (fold change>2, P < 0.05). Subgrouping DM patients according to the presence of interstitial lung disease and anti-Jo-1 antibody revealed different expression patterns of the lncRNAs. Pathway and gene ontology analysis for the differentially expressed mRNAs confirmed that type 1 interferon signaling was the most significantly dysregulated pathway in all DM subgroups. In addition, distinct pathways that uniquely associated with DM subgroup were also identified. Bioinformatics prediction suggested that linc-DGCR6-1 may be a lncRNA that regulates type 1 interferon-inducible gene USP18, which was found highly expressed in the perifascicular areas of the muscle fibers of DM patients. Our findings provide an overview of aberrantly expressed lncRNAs in DM muscle and further broaden the understanding of DM pathogenesis. PMID:27605457

  16. Chromosomal microarray analysis in a girl with mental retardation and spina bifida.

    PubMed

    Ben Abdallah, Inesse; Hannachi, Hanene; Soyah, Najla; Saad, Ali; Elghezal, Hatem

    2011-01-01

    Chromosomal imbalances comprise a major cause of mental retardation, particularly in association with congenital malformations and dysmorphic features. Chromosomal analysis using banded karyotyping is limited by the low resolution of this technique, and cryptic chromosomal rearrangements cannot be detected. We describe a 6-year-old girl with mental retardation, mild growth, congenital malformation, and facial anomalies. Chromosomal analysis with karyotyping produced normal results. Because the phenotype suggested chromosomal abnormality, microarray comparative genomic hybridization was used to search for a possible cryptic anomaly. A subtelomeric chromosomal imbalance, consisting of partial trisomy 2q35 and partial monosomy 3p26, was detected and confirmed using fluorescence in situ hybridization. This rearrangement was inherited from an equilibrated maternal t(2;3) reciprocal translocation. Comparative genomic hybridization array in similar situations is useful in detecting cryptic chromosomal rearrangements, identifying genes contained in deleted or duplicated regions, establishing a precise phenotype-genotype correlation, and offering unambiguous genetic counseling. Copyright © 2011 Elsevier Inc. All rights reserved.

  17. Multi-Tissue Microarray Analysis Identifies a Molecular Signature of Regeneration

    PubMed Central

    Mercer, Sarah E.; Cheng, Chia-Ho; Atkinson, Donald L.; Krcmery, Jennifer; Guzman, Claudia E.; Kent, David T.; Zukor, Katherine; Marx, Kenneth A.; Odelberg, Shannon J.; Simon, Hans-Georg

    2012-01-01

    The inability to functionally repair tissues that are lost as a consequence of disease or injury remains a significant challenge for regenerative medicine. The molecular and cellular processes involved in complete restoration of tissue architecture and function are expected to be complex and remain largely unknown. Unlike humans, certain salamanders can completely regenerate injured tissues and lost appendages without scar formation. A parsimonious hypothesis would predict that all of these regenerative activities are regulated, at least in part, by a common set of genes. To test this hypothesis and identify genes that might control conserved regenerative processes, we performed a comprehensive microarray analysis of the early regenerative response in five regeneration-competent tissues from the newt Notophthalmus viridescens. Consistent with this hypothesis, we established a molecular signature for regeneration that consists of common genes or gene family members that exhibit dynamic differential regulation during regeneration in multiple tissue types. These genes include members of the matrix metalloproteinase family and its regulators, extracellular matrix components, genes involved in controlling cytoskeleton dynamics, and a variety of immune response factors. Gene Ontology term enrichment analysis validated and supported their functional activities in conserved regenerative processes. Surprisingly, dendrogram clustering and RadViz classification also revealed that each regenerative tissue had its own unique temporal expression profile, pointing to an inherent tissue-specific regenerative gene program. These new findings demand a reconsideration of how we conceptualize regenerative processes and how we devise new strategies for regenerative medicine. PMID:23300656

  18. Transcriptomic profiling of long non-coding RNAs in dermatomyositis by microarray analysis.

    PubMed

    Peng, Qing-Lin; Zhang, Ya-Mei; Yang, Han-Bo; Shu, Xiao-Ming; Lu, Xin; Wang, Guo-Chun

    2016-09-08

    Long non-coding RNAs (lncRNAs) are prevalently transcribed in the genome and have been found to be of functional importance. However, the potential roles of lncRNAs in dermatomyositis (DM) remain unknown. In this study, a lncRNA + mRNA microarray analysis was performed to profile lncRNAs and mRNAs from 15 treatment-naive DM patients and 5 healthy controls. We revealed a total of 1198 lncRNAs (322 up-regulated and 876 down-regulated) and 1213 mRNAs (665 up-regulated and 548 down-regulated) were significantly differentially expressed in DM patients compared with the healthy controls (fold change>2, P < 0.05). Subgrouping DM patients according to the presence of interstitial lung disease and anti-Jo-1 antibody revealed different expression patterns of the lncRNAs. Pathway and gene ontology analysis for the differentially expressed mRNAs confirmed that type 1 interferon signaling was the most significantly dysregulated pathway in all DM subgroups. In addition, distinct pathways that uniquely associated with DM subgroup were also identified. Bioinformatics prediction suggested that linc-DGCR6-1 may be a lncRNA that regulates type 1 interferon-inducible gene USP18, which was found highly expressed in the perifascicular areas of the muscle fibers of DM patients. Our findings provide an overview of aberrantly expressed lncRNAs in DM muscle and further broaden the understanding of DM pathogenesis.

  19. A Comprehensive Comparison of Different Clustering Methods for Reliability Analysis of Microarray Data

    PubMed Central

    Kafieh, Rahele; Mehridehnavi, Alireza

    2013-01-01

    In this study, we considered some competitive learning methods including hard competitive learning and soft competitive learning with/without fixed network dimensionality for reliability analysis in microarrays. In order to have a more extensive view, and keeping in mind that competitive learning methods aim at error minimization or entropy maximization (different kinds of function optimization), we decided to investigate the abilities of mixture decomposition schemes. Therefore, we assert that this study covers the algorithms based on function optimization with particular insistence on different competitive learning methods. The destination is finding the most powerful method according to a pre-specified criterion determined with numerical methods and matrix similarity measures. Furthermore, we should provide an indication showing the intrinsic ability of the dataset to form clusters before we apply a clustering algorithm. Therefore, we proposed Hopkins statistic as a method for finding the intrinsic ability of a data to be clustered. The results show the remarkable ability of Rayleigh mixture model in comparison with other methods in reliability analysis task. PMID:24083134

  20. Quantitative comparison of the HSV-1 and HSV-2 transcriptomes using DNA microarray analysis

    SciTech Connect

    Aguilar, J.S. . E-mail: jsaguila@uci.edu; Devi-Rao, G.V.; Rice, M.K.; Sunabe, J.; Ghazal, P.; Wagner, E.K.

    2006-04-25

    The genomes of human herpes virus type-1 and type-2 share a high degree of sequence identity; yet, they exhibit important differences in pathology in their natural human host as well as in animal host and cell cultures. Here, we report the comparative analysis of the time and relative abundance profiles of the transcription of each virus type (their transcriptomes) using parallel infections and microarray analysis using HSV-1 probes which hybridize with high efficiency to orthologous HSV-2 transcripts. We have confirmed that orthologous transcripts belong to the same kinetic class; however, the temporal pattern of accumulation of 4 transcripts (U{sub L}4, U{sub L}29, U{sub L}30, and U{sub L}31) differs in infections between the two virus types. Interestingly, the protein products of these transcripts are all involved in nuclear organization and viral DNA localization. We discuss the relevance of these findings and whether they may have potential roles in the pathological differences of HSV-1 and HSV-2.

  1. Microarray analysis of human milk cells: persistent high expression of osteopontin during the lactation period

    PubMed Central

    NAGATOMO, T; OHGA, S; TAKADA, H; NOMURA, A; HIKINO, S; IMURA, M; OHSHIMA, K; HARA, T

    2004-01-01

    To continue the search for immunological roles of breast milk, cDNA microarray analysis on cytokines and growth factors was performed for human milk cells. Among the 240 cytokine-related genes, osteopontin (OPN) gene ranked top of the expression. Real-time PCR revealed that the OPN mRNA levels in colostrum cells were approximately 100 times higher than those in PHA-stimulated peripheral blood mononuclear cells (PBMNCs), and 10 000 times higher than those in PB CD14+ cells. The median levels of OPN mRNA in early milk or mature milk cells were more than three times higher than those in colostrum cells. Western blot analysis of human milk showed appreciable expression of full-length and short form proteins of OPN. The concentrations of full-length OPN in early milk or mature milk whey continued to be higher than those in colostrum whey and plasma as assessed by ELISA. The early milk (3–7 days postpartum) contained the highest concentrations of OPN protein, while the late mature milk cells (1 years postpartum) had the highest expression of OPN mRNA of all the lactating periods. The results of immunohistochemical and immunocytochemical staining indicated that OPN-producing epithelial cells and macrophages are found in actively lactating mammary glands. These results suggest that the persistently and extraordinarily high expression of OPN in human milk cells plays a potential role in the immunological development of breast-fed infants. PMID:15373904

  2. Microarray Data Analysis of Space Grown Arabidopsis Leaves for Genes Important in Vascular Patterning

    NASA Technical Reports Server (NTRS)

    Weitzeal, A. J.; Wyatt, S. E.; Parsons-Wingerter, P.

    2016-01-01

    Venation patterning in leaves is a major determinant of photosynthesis efficiency because of its dependency on vascular transport of photoassimilates, water, and minerals. Arabidopsis thaliana grown in microgravity show delayed growth and leaf maturation. Gene expression data from the roots, hypocotyl, and leaves of A. thaliana grown during spaceflight vs. ground control analyzed by Affymetrix microarray are available through NASAs GeneLab (GLDS-7). We analyzed the data for differential expression of genes in leaves resulting from the effects of spaceflight on vascular patterning. Two genes were found by preliminary analysis to be upregulated during spaceflight that may be related to vascular formation. The genes are responsible for coding an ARGOS like protein (potentially affecting cell elongation in the leaves), and an F-boxkelch-repeat protein (possibly contributing to protoxylem specification). Further analysis that will focus on raw data quality assessment and a moderated t-test may further confirm upregulation of the two genes and/or identify other gene candidates. Plants defective in these genes will then be assessed for phenotype by the mapping and quantification of leaf vascular patterning by NASAs VESsel GENeration (VESGEN) software to model specific vascular differences of plants grown in spaceflight.

  3. A Phenotypic microarray analysis of Streptococcus mutans liaS mutant

    PubMed Central

    Zhang, Jiaqin; Biswas, Indranil

    2009-01-01

    Streptococcus mutans, a bioflim-forming gram-positive bacterium that resides in the human oral cavity, is considered to be the primary etiological agent of human dental caries. A cell-envelope stress sensing histidine kinase, LiaS, is considered to be important for expression of virulence factors such as glucan-binding protein C and mutacin production. In this communication, a liaS mutant was subjected to phenotypic microarray (PM) analysis of about 2000 phenotypes that includes utilization of various carbon, nitrogen, phosphate, and sulfur sources; osmolytes; metabolic inhibitors; and susceptibility to toxic compounds, including several types of antibiotics. Compared to the parental strain UA159, the liaS mutant strain (IBS148) was more tolerant to various inhibitors that target protein synthesis, DNA synthesis, and cell-wall biosynthesis. Some of the key findings of the PM analysis were confirmed in independent growth studies and by using antibiotic discs and E-test strips for susceptibility testing. PMID:19118347

  4. Meta-Analysis of Multiple Sclerosis Microarray Data Reveals Dysregulation in RNA Splicing Regulatory Genes.

    PubMed

    Paraboschi, Elvezia Maria; Cardamone, Giulia; Rimoldi, Valeria; Gemmati, Donato; Spreafico, Marta; Duga, Stefano; Soldà, Giulia; Asselta, Rosanna

    2015-09-30

    Abnormalities in RNA metabolism and alternative splicing (AS) are emerging as important players in complex disease phenotypes. In particular, accumulating evidence suggests the existence of pathogenic links between multiple sclerosis (MS) and altered AS, including functional studies showing that an imbalance in alternatively-spliced isoforms may contribute to disease etiology. Here, we tested whether the altered expression of AS-related genes represents a MS-specific signature. A comprehensive comparative analysis of gene expression profiles of publicly-available microarray datasets (190 MS cases, 182 controls), followed by gene-ontology enrichment analysis, highlighted a significant enrichment for differentially-expressed genes involved in RNA metabolism/AS. In detail, a total of 17 genes were found to be differentially expressed in MS in multiple datasets, with CELF1 being dysregulated in five out of seven studies. We confirmed CELF1 downregulation in MS (p=0.0015) by real-time RT-PCRs on RNA extracted from blood cells of 30 cases and 30 controls. As a proof of concept, we experimentally verified the unbalance in alternatively-spliced isoforms in MS of the NFAT5 gene, a putative CELF1 target. In conclusion, for the first time we provide evidence of a consistent dysregulation of splicing-related genes in MS and we discuss its possible implications in modulating specific AS events in MS susceptibility genes.

  5. Microarray Data Analysis of Space Grown Arabidopsis Leaves for Genes Important in Vascular Patterning

    NASA Technical Reports Server (NTRS)

    Weitzeal, A. J.; Wyatt, S. E.; Parsons-Wingerter, P.

    2016-01-01

    Venation patterning in leaves is a major determinant of photosynthesis efficiency because of its dependency on vascular transport of photoassimilates, water, and minerals. Arabidopsis thaliana grown in microgravity show delayed growth and leaf maturation. Gene expression data from the roots, hypocotyl, and leaves of A. thaliana grown during spaceflight vs. ground control analyzed by Affymetrix microarray are available through NASA's GeneLab (GLDS-7). We analyzed the data for differential expression of genes in leaves resulting from the effects of spaceflight on vascular patterning. Two genes were found by preliminary analysis to be upregulated during spaceflight that may be related to vascular formation. The genes are responsible for coding an ARGOS like protein (potentially affecting cell elongation in the leaves), and an F-box/kelch-repeat protein (possibly contributing to protoxylem specification). Further analysis that will focus on raw data quality assessment and a moderated t-test may further confirm upregulation of the two genes and/or identify other gene candidates. Plants defective in these genes will then be assessed for phenotype by the mapping and quantification of leaf vascular patterning by NASA's VESsel GENeration (VESGEN) software to model specific vascular differences of plants grown in spaceflight.

  6. Hyperspectral microscopic analysis of normal, benign and carcinoma microarray tissue sections

    NASA Astrophysics Data System (ADS)

    Maggioni, Mauro; Davis, Gustave L.; Warner, Frederick J.; Geshwind, Frank B.; Coppi, Andreas C.; DeVerse, Richard A.; Coifman, Ronald R.

    2006-02-01

    We apply a unique micro-optoelectromechanical tuned light source and new algorithms to the hyper-spectral microscopic analysis of human colon biopsies. The tuned light prototype (Plain Sight Systems Inc.) transmits any combination of light frequencies, range 440nm 700nm, trans-illuminating H and E stained tissue sections of normal (N), benign adenoma (B) and malignant carcinoma (M) colon biopsies, through a Nikon Biophot microscope. Hyper-spectral photomicrographs, randomly collected 400X magnication, are obtained with a CCD camera (Sensovation) from 59 different patient biopsies (20 N, 19 B, 20 M) mounted as a microarray on a single glass slide. The spectra of each pixel are normalized and analyzed to discriminate among tissue features: gland nuclei, gland cytoplasm and lamina propria/lumens. Spectral features permit the automatic extraction of 3298 nuclei with classification as N, B or M. When nuclei are extracted from each of the 59 biopsies the average classification among N, B and M nuclei is 97.1%; classification of the biopsies, based on the average nuclei classification, is 100%. However, when the nuclei are extracted from a subset of biopsies, and the prediction is made on nuclei in the remaining biopsies, there is a marked decrement in performance to 60% across the 3 classes. Similarly the biopsy classification drops to 54%. In spite of these classification differences, which we believe are due to instrument and biopsy normalization issues, hyper-spectral analysis has the potential to achieve diagnostic efficiency needed for objective microscopic diagnosis.

  7. Expression Profile of Long Noncoding RNAs in Human Earlobe Keloids: A Microarray Analysis

    PubMed Central

    Guo, Liang; Xu, Kai; Yan, Hongbo; Feng, Haifeng

    2016-01-01

    Background. Long noncoding RNAs (lncRNAs) play key roles in a wide range of biological processes and their deregulation results in human disease, including keloids. Earlobe keloid is a type of pathological skin scar, and the molecular pathogenesis of this disease remains largely unknown. Methods. In this study, microarray analysis was used to determine the expression profiles of lncRNAs and mRNAs between 3 pairs of earlobe keloid and normal specimens. Gene Ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to identify the main functions of the differentially expressed genes and earlobe keloid-related pathways. Results. A total of 2068 lncRNAs and 1511 mRNAs were differentially expressed between earlobe keloid and normal tissues. Among them, 1290 lncRNAs and 1092 mRNAs were upregulated, and 778 lncRNAs and 419 mRNAs were downregulated. Pathway analysis revealed that 24 pathways were correlated to the upregulated transcripts, while 11 pathways were associated with the downregulated transcripts. Conclusion. We characterized the expression profiles of lncRNA and mRNA in earlobe keloids and suggest that lncRNAs may serve as diagnostic biomarkers for the therapy of earlobe keloid. PMID:28101509

  8. Microarray analysis after strenuous exercise in peripheral blood mononuclear cells of endurance horses.

    PubMed

    Capomaccio, S; Cappelli, K; Barrey, E; Felicetti, M; Silvestrelli, M; Verini-Supplizi, A

    2010-12-01

    It is known that moderate physical activity may have beneficial effects on health, whereas strenuous effort induces a state resembling inflammation. The molecular mechanisms underlying the cellular response to exercise remain unclear, although it is clear that the immune system plays a key role. It has been hypothesized that the physio-pathological condition that develops in athletes subjected to heavy training is caused by derangement of cellular immune regulation. The purpose of the present study was to obtain information on endurance horse gene transcription under strenuous conditions and to identify candidate genes causing immune system derangement. We performed a wide gene expression scan, using microarray technology, on peripheral blood mononuclear cells of ten horses chosen from high-level participants in national and international endurance races. The use of three different timepoints revealed changes in gene expression when post-effort samples (T1, taken immediately after the race; and T2, taken 24 h after the race) were compared with basal sample (T0, at rest). Statistical analysis showed no differences in gene expression between T0 and T2 samples, indicating complete restoration of homeostasis by 24 h after racing, whereas T1 showed strong modulation of expression, affecting 132 genes (97 upregulated, 35 downregulated). Ingenuity pathway analysis revealed that the main mechanisms and biofunctions involved were significantly associated with immunological and inflammatory responses. Real-time PCR was performed on 26 gene products to validate the array data.

  9. Microarray Analysis of Port Wine Stains Before and After Pulsed Dye Laser Treatment

    PubMed Central

    Laquer, Vivian T.; Hevezi, Peter A.; Albrecht, Huguette; Chen, Tina S.; Zlotnik, Albert; Kelly, Kristen M.

    2014-01-01

    Background and Objectives Neither the pathogenesis of port wine stain (PWS) birthmarks nor tissue effects of pulsed dye laser (PDL) treatment of these lesions is fully understood. There are few published reports utilizing gene expression analysis in human PWS skin. We aim to compare gene expression in PWS before and after PDL, using DNA microarrays that represent most, if not all, human genes to obtain comprehensive molecular profiles of PWS lesions and PDL-associated tissue effects. Materials and Methods Five human subjects had PDL treatment of their PWS. One week later, three biopsies were taken from each subject: normal skin (N); untreated PWS (PWS); PWS post-PDL (PWS + PDL). Samples included two lower extremity lesions, two facial lesions, and one facial nodule. High-quality total RNA isolated from skin biopsies was processed and applied to Affymetrix Human gene 1.0ST microarrays for gene expression analysis. We performed a 16 pair-wise comparison identifying either up- or down-regulated genes between N versus PWS and PWS versus PWS + PDL for four of the donor samples. The PWS nodule (nPWS) was analyzed separately. Results There was significant variation in gene expression profiles between individuals. By doing pair-wise comparisons between samples taken from the same donor, we were able to identify genes that may participate in the formation of PWS lesions and PDL tissue effects. Genes associated with immune, epidermal, and lipid metabolism were up-regulated in PWS skin. The nPWS exhibited more profound differences in gene expression than the rest of the samples, with significant differential expression of genes associated with angiogenesis, tumorigenesis, and inflammation. Conclusion In summary, gene expression profiles from N, PWS, and PWS + PDL demonstrated significant variation within samples from the same donor and between donors. By doing pair-wise comparisons between samples taken from the same donor and comparing these results between donors, we were

  10. Gene set enrichment analysis of microarray data from Pimephales promelas (Rafinesque), a non-mammalian model organism

    PubMed Central

    2011-01-01

    Background Methods for gene-class testing, such as Gene Set Enrichment Analysis (GSEA), incorporate biological knowledge into the analysis and interpretation of microarray data by comparing gene expression patterns to pathways, systems and emergent phenotypes. However, to use GSEA to its full capability with non-mammalian model organisms, a microarray platform must be annotated with human gene symbols. Doing so enables the ability to relate a model organism's gene expression, in response to a given treatment, to potential human health consequences of that treatment. We enhanced the annotation of a microarray platform from a non-mammalian model organism, and then used the GSEA approach in a reanalysis of a study examining the biological significance of acute and chronic methylmercury exposure on liver tissue of fathead minnow (Pimephales promelas). Using GSEA, we tested the hypothesis that fathead livers, in response to methylmercury exposure, would exhibit gene expression patterns similar to diseased human livers. Results We describe an enhanced annotation of the fathead minnow microarray platform with human gene symbols. This resource is now compatible with the GSEA approach for gene-class testing. We confirmed that GSEA, using this enhanced microarray platform, is able to recover results consistent with a previous analysis of fathead minnow exposure to methylmercury using standard analytical approaches. Using GSEA to compare fathead gene expression profiles to human phenotypes, we also found that fathead methylmercury-treated livers exhibited expression profiles that are homologous to human systems & pathways and results in damage that is similar to those of human liver damage associated with hepatocellular carcinoma and hepatitis B. Conclusions This study describes a powerful resource for enabling the use of non-mammalian model organisms in the study of human health significance. Results of microarray gene expression studies involving fathead minnow, typically

  11. Identification of key genes associated with the effect of estrogen on ovarian cancer using microarray analysis.

    PubMed

    Zhang, Shi-tao; Zuo, Chao; Li, Wan-nan; Fu, Xue-qi; Xing, Shu; Zhang, Xiao-ping

    2016-02-01

    To identify key genes related to the effect of estrogen on ovarian cancer. Microarray data (GSE22600) were downloaded from Gene Expression Omnibus. Eight estrogen and seven placebo treatment samples were obtained using a 2 × 2 factorial designs, which contained 2 cell lines (PEO4 and 2008) and 2 treatments (estrogen and placebo). Differentially expressed genes were identified by Bayesian methods, and the genes with P < 0.05 and |log2FC (fold change)| ≥0.5 were chosen as cut-off criterion. Differentially co-expressed genes (DCGs) and differentially regulated genes (DRGs) were, respectively, identified by DCe function and DRsort function in DCGL package. Topological structure analysis was performed on the important transcriptional factors (TFs) and genes in transcriptional regulatory network using tYNA. Functional enrichment analysis was, respectively, performed for DEGs and the important genes using Gene Ontology and KEGG databases. In total, 465 DEGs were identified. Functional enrichment analysis of DEGs indicated that ACVR2B, LTBP1, BMP7 and MYC involved in TGF-beta signaling pathway. The 2285 DCG pairs and 357 DRGs were identified. Topological structure analysis showed that 52 important TFs and 65 important genes were identified. Functional enrichment analysis of the important genes showed that TP53 and MLH1 participated in DNA damage response and the genes (ACVR2B, LTBP1, BMP7 and MYC) involved in TGF-beta signaling pathway. TP53, MLH1, ACVR2B, LTBP1 and BMP7 might participate in the pathogenesis of ovarian cancer.

  12. Identification of potential genes/proteins regulated by Tiam1 in colorectal cancer by microarray analysis and proteome analysis.

    PubMed

    Liu, Li; Wang, Shuang; Zhang, Qingling; Ding, Yanqing

    2008-10-01

    Tiam1 (T-cell lymphoma invasion and metastasis-inducing protein 1), a guanine nucleotide exchange factor that activates Rac, is a colorectal cancer metastasis-related gene. In this study, we aimed to better understand the mechanism underlying Tiam1-mediated metastasis. We applied gene microarray and proteome analysis and compared expression of genes and proteins in a stable Tiam1-silencing colorectal cancer cell line and in a control cell line. Our analysis identified three genes, high-mobility group box1 (HMGB1), annexin IV (ANXA4) and phosphoglycerate mutase 1 (PGAM1) that were associated with Tiam1. Analysis of these proteins, which may be directly or indirectly regulated by Tiam1, may provide insight into the role and mechanism of Tiam1 in colorectal cancer metastasis.

  13. Global gene expression profiling of dimethylnitrosamine-induced liver fibrosis: from pathological and biochemical data to microarray analysis.

    PubMed

    Su, Li-Jen; Hsu, Shih-Lan; Yang, Jyh-Shyue; Tseng, Huei-Hun; Huang, Shiu-Feng; Huang, Chi-Ying F

    2006-01-01

    The development of hepatocellular carcinoma (HCC) is generally preceded by cirrhosis, which occurs at the end stage of fibrosis. This is a common and potentially lethal problem of chronic liver disease in Asia. The development of microarrays permits us to monitor transcriptomes on a genome-wide scale; this has dramatically speeded up a comprehensive understanding of the disease process. Here we used dimethylnitrosamine (DMN), a nongenotoxic hepatotoxin, to induce rat necroinflammatory and hepatic fibrosis. During the 6-week time course, histopathological, biochemical, and quantitative RT-PCR analyses confirmed the incidence of necroinflammatory and hepatic fibrosis in this established rat model system. Using the Affymetrix microarray chip, 256 differentially expressed genes were identified from the liver injury samples. Hierarchical clustering of gene expression using a gene ontology database allowed the identification of several stage-specific characters and functionally related clusters that encode proteins related to metabolism, cell growth/maintenance, and response to external challenge. Among these genes, we classified 44 potential necroinflammatory-related genes and 62 potential fibrosis-related markers or drug targets based on histopathological scores. We also compared the results with other data on well-known markers and various other microarray datasets that are available. In conclusion, we believe that the molecular picture of necroinflammatory and hepatic fibrosis from this study may provide novel biological insights into the development of early liver damage molecular classifiers than can be used for basic research and in clinical applications. A public accessible website is available at http://LiverFibrosis.nchc.org.tw:8080/LF.

  14. Identification of target genes conferring ethanol stress tolerance to Saccharomyces cerevisiae based on DNA microarray data analysis.

    PubMed

    Hirasawa, Takashi; Yoshikawa, Katsunori; Nakakura, Yuki; Nagahisa, Keisuke; Furusawa, Chikara; Katakura, Yoshio; Shimizu, Hiroshi; Shioya, Suteaki

    2007-08-01

    During industrial production process using yeast, cells are exposed to the stress due to the accumulation of ethanol, which affects the cell growth activity and productivity of target products, thus, the ethanol stress-tolerant yeast strains are highly desired. To identify the target gene(s) for constructing ethanol stress tolerant yeast strains, we obtained the gene expression profiles of two strains of Saccharomyces cerevisiae, namely, a laboratory strain and a strain used for brewing Japanese rice wine (sake), in the presence of 5% (v/v) ethanol, using DNA microarray. For the selection of target genes for breeding ethanol stress