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Journal articles on the topic 'DNA microarrays gene expression'

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Published: 4 June 2021
Last updated: 11 February 2022

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1

Russell, S. "DNA Microarrays: Gene Expression Applications." Heredity 89, no. 5 (October 28, 2002): 402. http://dx.doi.org/10.1038/sj.hdy.6800150.

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2

Harrington, Christina A., Carsten Rosenow, and Jacques Retief. "Monitoring gene expression using DNA microarrays." Current Opinion in Microbiology 3, no. 3 (June 2000): 285–91. http://dx.doi.org/10.1016/s1369-5274(00)00091-6.

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3

Wu, Shu-Hsing, Katrina Ramonell, Jeremy Gollub, and Shauna Somerville. "Plant gene expression profiling with DNA microarrays." Plant Physiology and Biochemistry 39, no. 11 (November 2001): 917–26. http://dx.doi.org/10.1016/s0981-9428(01)01322-5.

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King, Hadley C., and Animesh A. Sinha. "Gene Expression Profile Analysis by DNA Microarrays." JAMA 286, no. 18 (November 14, 2001): 2280. http://dx.doi.org/10.1001/jama.286.18.2280.

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Ramaswamy, Sridhar, and Todd R. Golub. "DNA Microarrays in Clinical Oncology." Journal of Clinical Oncology 20, no. 7 (April 1, 2002): 1932–41. http://dx.doi.org/10.1200/jco.2002.20.7.1932.

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ABSTRACT: Aberrant gene expression is critical for tumor initiation and progression. However, we lack a comprehensive understanding of all genes that are aberrantly expressed in human cancer. Recently, DNA microarrays have been used to obtain global views of human cancer gene expression and to identify genetic markers that might be important for diagnosis and therapy. We review clinical applications of these novel tools, discuss some important recent studies, identify promising avenues of research in this emerging field of study, and discuss the likely impact that expression profiling will have on clinical oncology.
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Chinnaivan, Kavitha M., Arun Sreekumar, Walter M. Whitehouse, Naresh T. Gunaratnam, Stuart A. Winston, and Arul M. Chinnaiyan. "Profiling gene expression in atherosclerosis using DNA microarrays." Journal of the American College of Cardiology 39 (March 2002): 233. http://dx.doi.org/10.1016/s0735-1097(02)81038-5.

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7

Weindruch, Richard, Tsuyoshi Kayo, Cheol-Koo Lee, and Tomas A. Prolla. "Gene expression profiling of aging using DNA microarrays." Mechanisms of Ageing and Development 123, no. 2-3 (January 2002): 177–93. http://dx.doi.org/10.1016/s0047-6374(01)00344-x.

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8

Haviv, Izhak, and Ian G. Campbell. "DNA microarrays for assessing ovarian cancer gene expression." Molecular and Cellular Endocrinology 191, no. 1 (May 2002): 121–26. http://dx.doi.org/10.1016/s0303-7207(02)00063-1.

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9

Landgraf, Jeff, Ellen Wisman, Robert Schaffer, Monica Accerbi, Vernadette Simon, Matt Larson, and Pam Green. "Gene expression profiling in Arabidopsis using DNA microarrays." Nature Genetics 23, S3 (November 1999): 56. http://dx.doi.org/10.1038/14344.

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Arcellana-Panlilio, Mayi, and Stephen M. Robbins. "I. Global gene expression profiling using DNA microarrays." American Journal of Physiology-Gastrointestinal and Liver Physiology 282, no. 3 (March 1, 2002): G397—G402. http://dx.doi.org/10.1152/ajpgi.00519.2001.

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Having the complete human genomic sequence poses a new challenge: to use genomic structural information to display and analyze biological processes on a genome-wide scale to assign gene function. DNA microarrays are a miniaturized, ordered arrangement of nucleic acid fragments from individual genes located at defined positions on a solid support, enabling the analysis of thousands of genes in parallel by specific hybridization. This review describes technical aspects, discusses relevant applications, and suggests factors affecting the use of this technology and how it fits in the grand scheme of meeting the needs of the postgenomic era.
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Travensolo, Regiane F., Lucia M. Carareto-Alves, Maria V. C. G. Costa, Tiago J. S. Lopes, Emanuel Carrilho, and Eliana G. M. Lemos. "Xylella fastidiosa gene expression analysis by DNA microarrays." Genetics and Molecular Biology 32, no. 2 (May 1, 2009): 340–53. http://dx.doi.org/10.1590/s1415-47572009005000038.

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Whipple, Mark Eliot, and Winston Patrick Kuo. "DNA Microarrays in Otolaryngology-Head and Neck Surgery." Otolaryngology–Head and Neck Surgery 127, no. 3 (September 2002): 196–204. http://dx.doi.org/10.1067/mhn.2002.127383.

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OBJECTIVES: Our goal was to review the technologies underlying DNA microarrays and to explore their use in otolaryngology-head and neck surgery. STUDY DESIGN: The current literature relating to microarray technology and methodology is reviewed, specifically the use of DNA microarrays to characterize gene expression. Bioinformatics involves computational and statistical methods to extract, organize, and analyze the huge amounts of data produced by microarray experiments. The means by which these techniques are being applied to otolaryngology-head and neck surgery are outlined. RESULTS: Microarray technologies are having a substantial impact on biomedical research, including many areas relevant to otolaryngology-head and neck surgery. CONCLUSIONS: DNA microarrays allow for the simultaneous investigationof thousands of individual genes in a single experiment. In the coming years, the application of these technologies to clinical medicine should allow for unprecedented methods ofdiagnosis and treatment. SIGNIFICANCE: These highly parallel experimental techniques promise to revolutionize gene discovery, disease characterization, and drug development.
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13

Widłak, Piotr. "DNA microarrays, a novel approach in studies of chromatin structure." Acta Biochimica Polonica 51, no. 1 (March 31, 2004): 1–8. http://dx.doi.org/10.18388/abp.2004_3592.

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The DNA microarray technology delivers an experimental tool that allows surveying expression of genetic information on a genome-wide scale at the level of single genes--for the new field termed functional genomics. Gene expression profiling--the primary application of DNA microarrays technology--generates monumental amounts of information concerning the functioning of genes, cells and organisms. However, the expression of genetic information is regulated by a number of factors that cannot be directly targeted by standard gene expression profiling. The genetic material of eukaryotic cells is packed into chromatin which provides the compaction and organization of DNA for replication, repair and recombination processes, and is the major epigenetic factor determining the expression of genetic information. Genomic DNA can be methylated and this modification modulates interactions with proteins which change the functional status of genes. Both chromatin structure and transcriptional activity are affected by the processes of replication, recombination and repair. Modified DNA microarray technology could be applied to genome-wide study of epigenetic factors and processes that modulate the expression of genetic information. Attempts to use DNA microarrays in studies of chromatin packing state, chromatin/DNA-binding protein distribution and DNA methylation pattern on a genome-wide scale are briefly reviewed in this paper.
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Chavan, Preeti, Kalpana Joshi, and Bhushan Patwardhan. "DNA Microarrays in Herbal Drug Research." Evidence-Based Complementary and Alternative Medicine 3, no. 4 (2006): 447–57. http://dx.doi.org/10.1093/ecam/nel075.

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Natural products are gaining increased applications in drug discovery and development. Being chemically diverse they are able to modulate several targets simultaneously in a complex system. Analysis of gene expression becomes necessary for better understanding of molecular mechanisms. Conventional strategies for expression profiling are optimized for single gene analysis. DNA microarrays serve as suitable high throughput tool for simultaneous analysis of multiple genes. Major practical applicability of DNA microarrays remains in DNA mutation and polymorphism analysis. This review highlights applications of DNA microarrays in pharmacodynamics, pharmacogenomics, toxicogenomics and quality control of herbal drugs and extracts.
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15

KURELLA, MANJULA, LI-LI HSIAO, TAKUMI YOSHIDA, JEFFREY D. RANDALL, GARY CHOW, SATINDER S. SARANG, RODERICK V. JENSEN, and STEVEN R. GULLANS. "DNA Microarray Analysis of Complex Biologic Processes." Journal of the American Society of Nephrology 12, no. 5 (May 2001): 1072–78. http://dx.doi.org/10.1681/asn.v1251072.

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Abstract. DNA microarrays, or gene chips, allow surveys of gene expression, (i.e., mRNA expression) in a highly parallel and comprehensive manner. The pattern of gene expression produced, known as the expression profile, depicts the subset of gene transcripts expressed in a cell or tissue. At its most fundamental level, the expression profile can address qualitatively which genes are expressed in disease states. However, with the aid of bioinformatics tools such as cluster analysis, self-organizing maps, and principle component analysis, more sophisticated questions can be answered. Microarrays can be used to characterize the functions of novel genes, identify genes in a biologic pathway, analyze genetic variation, and identify therapeutic drug targets. Moreover, the expression profile can be used as a tissue or disease “fingerprint.” This review details the fabrication of arrays, data management tools, and applications of microarrays to the field of renal research and the future of clinical practice.
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16

Rosenfeld, Simon. "Do DNA Microarrays Tell the Story of Gene Expression?" Gene Regulation and Systems Biology 4 (January 2010): GRSB.S4657. http://dx.doi.org/10.4137/grsb.s4657.

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17

Ockers, Sandra, Douglas K. Price, and William D. Figg. "DNA microarrays: Tissue removal and processing affects gene expression." Cancer Biology & Therapy 5, no. 12 (December 31, 2006): 1608–9. http://dx.doi.org/10.4161/cbt.5.12.3547.

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18

van Hal, Nicole L. W., Oscar Vorst, Adèle M. M. L. van Houwelingen, Esther J. Kok, Ad Peijnenburg, Asaph Aharoni, Arjen J. van Tunen, and Jaap Keijer. "The application of DNA microarrays in gene expression analysis." Journal of Biotechnology 78, no. 3 (March 2000): 271–80. http://dx.doi.org/10.1016/s0168-1656(00)00204-2.

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19

Benson, M., P. A. Svensson, B. Carlsson, M. Jernås, J. Reinholdt, L. O. Cardell, and L. Carlsson. "DNA microarrays to study gene expression in allergic airways." Clinical & Experimental Allergy 32, no. 2 (February 2002): 301–8. http://dx.doi.org/10.1046/j.1365-2222.2002.01300.x.

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20

Karakach, Tobias K., Robert M. Flight, Susan E. Douglas, and Peter D. Wentzell. "An introduction to DNA microarrays for gene expression analysis." Chemometrics and Intelligent Laboratory Systems 104, no. 1 (November 2010): 28–52. http://dx.doi.org/10.1016/j.chemolab.2010.04.003.

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21

Ju, Xin-Sheng, and Martin Zenke. "Gene expression profiling of dendritic cells by DNA microarrays." Immunobiology 209, no. 1-2 (August 2004): 155–61. http://dx.doi.org/10.1016/j.imbio.2004.02.005.

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22

Valente, Eduardo, and Miguel Rocha. "Integrating data from heterogeneous DNA microarray platforms." Journal of Integrative Bioinformatics 12, no. 4 (December 1, 2015): 39–55. http://dx.doi.org/10.1515/jib-2015-281.

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Summary DNA microarrays are one of the most used technologies for gene expression measurement. However, there are several distinct microarray platforms, from different manufacturers, each with its own measurement protocol, resulting in data that can hardly be compared or directly integrated. Data integration from multiple sources aims to improve the assertiveness of statistical tests, reducing the data dimensionality problem. The integration of heterogeneous DNA microarray platforms comprehends a set of tasks that range from the re-annotation of the features used on gene expression, to data normalization and batch effect elimination. In this work, a complete methodology for gene expression data integration and application is proposed, which comprehends a transcript-based re-annotation process and several methods for batch effect attenuation. The integrated data will be used to select the best feature set and learning algorithm for a brain tumor classification case study. The integration will consider data from heterogeneous Agilent and Affymetrix platforms, collected from public gene expression databases, such as The Cancer Genome Atlas and Gene Expression Omnibus.
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23

Wang, Wei, Larry Reitzer, David A. Rasko, Melanie M. Pearson, Robert J. Blick, Cassie Laurence, and Eric J. Hansen. "Metabolic Analysis of Moraxella catarrhalis and the Effect of Selected In Vitro Growth Conditions on Global Gene Expression." Infection and Immunity 75, no. 10 (July 9, 2007): 4959–71. http://dx.doi.org/10.1128/iai.00073-07.

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ABSTRACT The nucleotide sequence from the genome of Moraxella catarrhalis ATCC 43617 was annotated and used both to assess the metabolic capabilities and limitations of this bacterium and to design probes for a DNA microarray. An absence of gene products for utilization of exogenous carbohydrates was noteworthy and could be correlated with published phenotypic data. Gene products necessary for aerobic energy generation were present, as were a few gene products generally ascribed to anaerobic systems. Enzymes for synthesis of all amino acids except proline and arginine were present. M. catarrhalis DNA microarrays containing 70-mer oligonucleotide probes were designed from the genome-derived nucleotide sequence data. Analysis of total RNA extracted from M. catarrhalis ATCC 43617 cells grown under iron-replete and iron-restricted conditions was used to establish the utility of these DNA microarrays. These DNA microarrays were then used to analyze total RNA from M. catarrhalis cells grown in a continuous-flow biofilm system and in the planktonic state. The genes whose expression was most dramatically increased by growth in the biofilm state included those encoding a nitrate reductase, a nitrite reductase, and a nitric oxide reductase. Real-time reverse transcriptase PCR analysis was used to validate these DNA microarray results. These results indicate that growth of M. catarrhalis in a biofilm results in increased expression of gene products which can function not only in energy generation but also in resisting certain elements of the innate immune response.
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24

Yuvaraj, K., and D. Manjula. "A performance analysis of clustering based algorithms for the microarray gene expression data." International Journal of Engineering & Technology 7, no. 2.21 (April 20, 2018): 201. http://dx.doi.org/10.14419/ijet.v7i2.21.12172.

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Current advancements in microarray technology permit simultaneous observing of the expression levels of huge number of genes over various time points. Microarrays have obtained amazing implication in the field of bioinformatics. It includes an ordered set of huge different Deoxyribonucleic Acid (DNA) sequences that can be used to measure both DNA as well as Ribonucleic Acid (RNA) dissimilarities. The Gene Expression (GE) summary aids in understanding the basic cause of gene activities, the growth of genes, determining recent disorders like cancer and as well analysing their molecular pharmacology. Clustering is a significant tool applied for analyzing such microarray gene expression data. It has developed into a greatest part of gene expression analysis. Grouping the genes having identical expression patterns is known as gene clustering. A number of clustering algorithms have been applied for the analysis of microarray gene expression data. The aim of this paper is to analyze the precision level of the microarray data by using various clustering algorithms.
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GERHOLD, DAVID, MEIQING LU, JIAN XU, CHRISTOPHER AUSTIN, C. THOMAS CASKEY, and THOMAS RUSHMORE. "Monitoring expression of genes involved in drug metabolism and toxicology using DNA microarrays." Physiological Genomics 5, no. 4 (April 27, 2001): 161–70. http://dx.doi.org/10.1152/physiolgenomics.2001.5.4.161.

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Oligonucleotide DNA microarrays were investigated for utility in measuring global expression profiles of drug metabolism genes. This study was performed to investigate the feasibility of using microarray technology to minimize the long, expensive process of testing drug candidates for safety in animals. In an evaluation of hybridization specificity, microarray technology from Affymetrix distinguished genes up to a threshold of ∼90% DNA identity. Oligonucleotides representing human cytochrome P-450 gene CYP3A5 showed heterologous hybridization to CYP3A4 and CYP3A7 RNAs. These genes could be clearly distinguished by selecting a subset of oligonucleotides that hybridized selectively to CYP3A5. Further validation of the technology was performed by measuring gene expression profiles in livers of rats treated with vehicle, 3-methylcholanthrene (3MC), phenobarbital, dexamethasone, or clofibrate and by confirming data for six genes using quantitative RT-PCR. Responses of drug metabolism genes, including CYPs, epoxide hydrolases ( EHs), UDP-glucuronosyl transferases ( UGTs), glutathione sulfotransferases ( GSTs), sulfotransferases ( STs), drug transporter genes, and peroxisomal genes, to these well-studied compounds agreed well with, and extended, published observations. Additional gene regulatory responses were noted that characterize metabolic effects or stress responses to these compounds. Thus microarray technology can provide a facile overview of gene expression responses relevant to drug metabolism and toxicology.
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26

Kang, Seung-Hoon, Jianqiang Huang, Han-Na Lee, Yoon-Ah Hur, Stanley N. Cohen, and Eung-Soo Kim. "Interspecies DNA Microarray Analysis Identifies WblA as a Pleiotropic Down-Regulator of Antibiotic Biosynthesis in Streptomyces." Journal of Bacteriology 189, no. 11 (April 6, 2007): 4315–19. http://dx.doi.org/10.1128/jb.01789-06.

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ABSTRACT Using Streptomyces coelicolor microarrays to discover regulators of gene expression in other Streptomyces species, we identified wblA, a whiB-like gene encoding a putative transcription factor, as a down-regulator of doxorubicin biosynthesis in Streptomyces peucetius. Further analysis revealed that wblA functions pleiotropically to control antibiotic production and morphological differentiation in streptomycetes. Our results reveal a novel biological role for wblA and show the utility of interspecies microarray analysis for the investigation of streptomycete gene expression.
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27

Dennis, Philip, Elizabeth A. Edwards, Steven N. Liss, and Roberta Fulthorpe. "Monitoring Gene Expression in Mixed Microbial Communities by Using DNA Microarrays." Applied and Environmental Microbiology 69, no. 2 (February 2003): 769–78. http://dx.doi.org/10.1128/aem.69.2.769-778.2003.

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ABSTRACT A DNA microarray to monitor the expression of bacterial metabolic genes within mixed microbial communities was designed and tested. Total RNA was extracted from pure and mixed cultures containing the 2,4-dichlorophenoxyacetic acid (2,4-D)-degrading bacterium Ralstonia eutropha JMP134, and the inducing agent 2,4-D. Induction of the 2,4-D catabolic genes present in this organism was readily detected 4, 7, and 24 h after the addition of 2,4-D. This strain was diluted into a constructed mixed microbial community derived from a laboratory scale sequencing batch reactor. Induction of two of five 2,4-D catabolic genes (tfdA and tfdC) from populations of JMP134 as low as 105 cells/ml was clearly detected against a background of 108 cells/ml. Induction of two others (tfdB and tfdE) was detected from populations of 106 cells/ml in the same background; however, the last gene, tfdF, showed no significant induction due to high variability. In another experiment, the induction of resin acid degradative genes was statistically detectable in sludge-fed pulp mill effluent exposed to dehydroabietic acid in batch experiments. We conclude that microarrays will be useful tools for the detection of bacterial gene expression in wastewaters and other complex systems.
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Hayden, Patrick S., Ashraf El-Meanawy, Jeffrey R. Schelling, and John R. Sedor. "DNA expression analysis: serial analysis of gene expression, microarrays and kidney disease." Current Opinion in Nephrology and Hypertension 12, no. 4 (July 2003): 407–14. http://dx.doi.org/10.1097/00041552-200307000-00009.

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29

Liesegang, Thomas J. "Gene expression profile analysis by DNA microarrays. Promise and pitfalls." American Journal of Ophthalmology 133, no. 5 (May 2002): 739. http://dx.doi.org/10.1016/s0002-9394(02)01446-0.

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30

Luo, Jun, William B. Isaacs, Jeffrey M. Trent, and David J. Duggan. "Looking Beyond Morphology: Cancer Gene Expression Profiling Using DNA Microarrays." Cancer Investigation 21, no. 6 (January 2003): 937–49. http://dx.doi.org/10.1081/cnv-120025096.

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31

Hayes, D. Neil, Stefano Monti, Giovanni Parmigiani, C. Blake Gilks, Katsuhiko Naoki, Arindam Bhattacharjee, Mark A. Socinski, Charles Perou, and Matthew Meyerson. "Gene Expression Profiling Reveals Reproducible Human Lung Adenocarcinoma Subtypes in Multiple Independent Patient Cohorts." Journal of Clinical Oncology 24, no. 31 (November 1, 2006): 5079–90. http://dx.doi.org/10.1200/jco.2005.05.1748.

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Purpose Published reports suggest that DNA microarrays identify clinically meaningful subtypes of lung adenocarcinomas not recognizable by other routine tests. This report is an investigation of the reproducibility of the reported tumor subtypes. Methods Three independent cohorts of patients with lung cancer were evaluated using a variety of DNA microarray assays. Using the integrative correlations method, a subset of genes was selected, the reliability of which was acceptable across the different DNA microarray platforms. Tumor subtypes were selected using consensus clustering and genes distinguishing subtypes were identified using the weighted difference statistic. Gene lists were compared across cohorts using centroids and gene set enrichment analysis. Results Cohorts of 31, 72, and 128 adenocarcinomas were generated for a total of 231 microarrays, each with 2,553 reliable genes. Three adenocarcinoma subtypes were identified in each cohort. These were named bronchioid, squamoid, and magnoid according to their respective correlations with gene expression patterns from histologically defined bronchioalveolar carcinoma, squamous cell carcinoma, and large-cell carcinoma. Tumor subtypes were distinguishable by many hundreds of genes, and lists generated in one cohort were predictive of tumor subtypes in the two other cohorts. Tumor subtypes correlated with clinically relevant covariates, including stage-specific survival and metastatic pattern. Most notably, bronchioid tumors were correlated with improved survival in early-stage disease, whereas squamoid tumors were associated with better survival in advanced disease. Conclusion DNA microarray analysis of lung adenocarcinomas identified reproducible tumor subtypes which differ significantly in clinically important behaviors such as stage-specific survival.
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32

Weidenhammer, Elaine M., Brenda F. Kahl, Ling Wang, Larry Wang, Melanie Duhon, Jo Ann Jackson, Matthew Slater, and Xiao Xu. "Multiplexed, Targeted Gene Expression Profiling and Genetic Analysis on Electronic Microarrays." Clinical Chemistry 48, no. 11 (November 1, 2002): 1873–82. http://dx.doi.org/10.1093/clinchem/48.11.1873.

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Abstract Background: Electronic microarrays comprise independent microelectrode test sites that can be electronically biased positive or negative, or left neutral, to move and concentrate charged molecules such as DNA and RNA to one or more test sites. We developed a protocol for multiplexed gene expression profiling of mRNA targets that uses electronic field-facilitated hybridization on electronic microarrays. Methods: A multiplexed, T7 RNA polymerase-mediated amplification method was used for expression profiling of target mRNAs from total cellular RNA; targets were detected by hybridization to sequence-specific capture oligonucleotides on electronic microarrays. Activation of individual test sites on the electronic microarray was used to target hybridization to designated subsets of sites and allow comparisons of target concentrations in different samples. We used multiplexed amplification and electronic field-facilitated hybridization to analyze expression of a model set of 10 target genes in the U937 cell line during lipopolysaccharide-mediated differentiation. Performance of multiple genetic analyses (single-nucleotide polymorphism detection, gene expression profiling, and splicing isoform detection) on a single electronic microarray was demonstrated using the ApoE and ApoER2 genes as a model system. Results: Targets were detected after a 2-min hybridization reaction. With noncomplementary capture probes, no signal was detectable. Twofold changes in target concentration were detectable throughout the (∼64-fold) range of concentrations tested. Levels of 10 targets were analyzed side by side across seven time points. By confining electronic activation to subsets of test sites, polymorphism detection, expression profiling, and splicing isoform analysis were performed on a single electronic microarray. Conclusions: Microelectronic array technology provides specific target detection and quantification with advantages over currently available methodologies for targeted gene expression profiling and combinatorial genomics testing.
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Calvo-Dmgz, D., J. F. Gálvez, D. Glez-Peña, S. Gómez-Meire, and F. Fdez-Riverola. "Using Variable Precision Rough Set for Selection and Classification of Biological Knowledge Integrated in DNA Gene Expression." Journal of Integrative Bioinformatics 9, no. 3 (December 1, 2012): 1–17. http://dx.doi.org/10.1515/jib-2012-199.

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Summary DNA microarrays have contributed to the exponential growth of genomic and experimental data in the last decade. This large amount of gene expression data has been used by researchers seeking diagnosis of diseases like cancer using machine learning methods. In turn, explicit biological knowledge about gene functions has also grown tremendously over the last decade. This work integrates explicit biological knowledge, provided as gene sets, into the classication process by means of Variable Precision Rough Set Theory (VPRS). The proposed model is able to highlight which part of the provided biological knowledge has been important for classification. This paper presents a novel model for microarray data classification which is able to incorporate prior biological knowledge in the form of gene sets. Based on this knowledge, we transform the input microarray data into supergenes, and then we apply rough set theory to select the most promising supergenes and to derive a set of easy interpretable classification rules. The proposed model is evaluated over three breast cancer microarrays datasets obtaining successful results compared to classical classification techniques. The experimental results shows that there are not significat differences between our model and classical techniques but it is able to provide a biological-interpretable explanation of how it classifies new samples.
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Virtanen, Carl, and Mark Takahashi. "Muscling in on microarrays." Applied Physiology, Nutrition, and Metabolism 33, no. 1 (February 2008): 124–29. http://dx.doi.org/10.1139/h07-150.

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Adaptations that are the result of exercise require a multitude of changes at the level of gene expression. The mechanisms involved in regulating these changes are many, and can occur at various points in the pathways that affect gene expression. The completion of the human genome sequence, along with the genomes of related species, has provided an enormous amount of information to help dissect and understand these pathways. High-throughput methods, such as DNA microarrays, were the first on the scene to take advantage of this wealth of information. A new generation of microarrays has now taken the next step in revealing the mechanisms controlling gene expression. Analysis of the regulation of gene expression can now be profiled in a high-throughput fashion. However, the application of this technology has yet to be fully realized in the exercise physiology community. This review will highlight some of the latest advances in microarrays and briefly discuss some potential applications to the field of exercise physiology.
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35

Álvarez, Patricia, Pilar Sáenz, David Arteta, Antonio Martínez, Miguel Pocoví, Laureano Simón, and Pilar Giraldo. "Transcriptional Profiling of Hematologic Malignancies with a Low-Density DNA Microarray." Clinical Chemistry 53, no. 2 (February 1, 2007): 259–67. http://dx.doi.org/10.1373/clinchem.2006.075887.

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Abstract Background: High-density microarrays are powerful tools for expression analysis of thousands of genes simultaneously; however, experience with low-density microarrays in gene expression studies has been limited. Methods: We developed an optimized procedure for gene expression analysis based on a microarray containing 538 oligonucleotides and used this procedure to analyze neoplastic cell lines and whole-blood samples from healthy individuals and patients with different hematologic neoplasias. Hierarchical clustering and the Welch t-test with adjusted P values were used for data analysis. Results: This procedure detects 0.2 fmol of mRNA and generates a linear response of 2 orders of magnitude, with CV values of <20% for hybridization and label replicates. We found statistically significant differences between Jurkat and U937 cell lines, between blood samples from 15 healthy donors and 59 chronic lymphocytic leukemia (CLL) samples, and between 6 acute myeloid leukemia patients and 4 myelodysplastic syndrome patients. A classification system constructed from the expression data predicted healthy or CLL status from a whole-blood sample with a 97% success rate. Conclusion: Transcriptional profiling of whole-blood samples was carried out without any cellular or sample manipulation before RNA extraction. This gene expression analysis procedure uncovered statistically significant differences associated with different hematologic neoplasias and made possible the construction of a classification system that predicts the healthy or CLL status from a whole-blood sample.
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Koczor, Christopher A., Eva K. Lee, Rebecca A. Torres, Amy Boyd, J. David Vega, Karan Uppal, Fan Yuan, Earl J. Fields, Allen M. Samarel, and William Lewis. "Detection of differentially methylated gene promoters in failing and nonfailing human left ventricle myocardium using computation analysis." Physiological Genomics 45, no. 14 (July 15, 2013): 597–605. http://dx.doi.org/10.1152/physiolgenomics.00013.2013.

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Human dilated cardiomyopathy (DCM) is characterized by congestive heart failure and altered myocardial gene expression. Epigenetic changes, including DNA methylation, are implicated in the development of DCM but have not been studied extensively. Clinical human DCM and nonfailing control left ventricle samples were individually analyzed for DNA methylation and expressional changes. Expression microarrays were used to identify 393 overexpressed and 349 underexpressed genes in DCM (GEO accession number: GSE43435 ). Gene promoter microarrays were utilized for DNA methylation analysis, and the resulting data were analyzed by two different computational methods. In the first method, we utilized subtractive analysis of DNA methylation peak data to identify 158 gene promoters exhibiting DNA methylation changes that correlated with expression changes. In the second method, a two-stage approach combined a particle swarm optimization feature selection algorithm and a discriminant analysis via mixed integer programming classifier to identify differentially methylated gene promoters. This analysis identified 51 hypermethylated promoters and six hypomethylated promoters in DCM with 100% cross-validation accuracy in the group assignment. Generation of a composite list of genes identified by subtractive analysis and two-stage computation analysis revealed four genes that exhibited differential DNA methylation by both methods in addition to altered gene expression. Computationally identified genes ( AURKB, BTNL9, CLDN5, and TK1) define a central set of differentially methylated gene promoters that are important in classifying DCM. These genes have no previously reported role in DCM. This study documents that rigorous computational analysis applied to microarray analysis of healthy and diseased human heart samples helps to define clinically relevant DNA methylation and expressional changes in DCM.
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37

Mao, Mao, Matt C. Biery, Sumire V. Kobayashi, Terry Ward, Greg Schimmack, Julja Burchard, Janell M. Schelter, Hongyue Dai, Yudong D. He, and Peter S. Linsley. "T lymphocyte activation gene identification by coregulated expression on DNA microarrays." Genomics 83, no. 6 (June 2004): 989–99. http://dx.doi.org/10.1016/j.ygeno.2003.12.019.

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38

Johnson, Claire, Frank Burslem, and Jerry Lanfear. "Gene expression profiling of wound healing in keratinocytes using DNA microarrays." Nature Genetics 23, S3 (November 1999): 54. http://dx.doi.org/10.1038/14336.

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39

Peano, Clelia, Silvio Bicciato, Giorgio Corti, Francesco Ferrari, Ermanno Rizzi, Raoul JP Bonnal, Roberta Bordoni, et al. "Complete gene expression profiling of Saccharopolyspora erythraea using GeneChip DNA microarrays." Microbial Cell Factories 6, no. 1 (2007): 37. http://dx.doi.org/10.1186/1475-2859-6-37.

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40

Oshlack, A., A. E. Chabot, G. K. Smyth, and Y. Gilad. "Using DNA microarrays to study gene expression in closely related species." Bioinformatics 23, no. 10 (March 23, 2007): 1235–42. http://dx.doi.org/10.1093/bioinformatics/btm111.

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41

Wu, Thomas D. "Analysing gene expression data from DNA microarrays to identify candidate genes." Journal of Pathology 195, no. 1 (2001): 53–65. http://dx.doi.org/10.1002/1096-9896(200109)195:1<53::aid-path891>3.0.co;2-h.

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42

Antonescu, Cristina R., Kai Wu, Guoliang Leon Xing, Manqiu Cao, Yaron Turpaz, Margaret A. Leversha, Earl Hubbell, Robert G. Maki, C. Garrett Miyada, and Raji Pillai. "DNA Copy Number Analysis in Gastrointestinal Stromal Tumors Using Gene Expression Microarrays." Cancer Informatics 6 (January 2008): CIN.S387. http://dx.doi.org/10.4137/cin.s387.

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We report a method, Expression-Microarray Copy Number Analysis (ECNA) for the detection of copy number changes using Affymetrix Human Genome U133 Plus 2.0 arrays, starting with as little as 5 ng input genomic DNA. An analytical approach was developed using DNA isolated from cell lines containing various X-chromosome numbers, and validated with DNA from cell lines with defined deletions and amplifications in other chromosomal locations. We applied this method to examine the copy number changes in DNA from 5 frozen gastrointestinal stromal tumors (GIST). We detected known copy number aberrations consistent with previously published results using conventional or BAC-array CGH, as well as novel changes in GIST tumors. These changes were concordant with results from Affymetrix 100K human SNP mapping arrays. Gene expression data for these GIST samples had previously been generated on U133A arrays, allowing us to explore correlations between chromosomal copy number and RNA expression levels. One of the novel aberrations identified in the GIST samples, a previously unreported gain on 1q21.1 containing the PEX11B gene, was confirmed in this study by FISH and was also shown to have significant differences in expression pattern when compared to a control sample. In summary, we have demonstrated the use of gene expression microarrays for the detection of genomic copy number aberrations in tumor samples. This method may be used to study copy number changes in other species for which RNA expression arrays are available, e.g. other mammals, plants, etc., and for which SNPs have not yet been mapped.
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43

Ebert, Benjamin L., and Todd R. Golub. "Genomic approaches to hematologic malignancies." Blood 104, no. 4 (August 15, 2004): 923–32. http://dx.doi.org/10.1182/blood-2004-01-0274.

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AbstractIn the past several years, experiments using DNA microarrays have contributed to an increasingly refined molecular taxonomy of hematologic malignancies. In addition to the characterization of molecular profiles for known diagnostic classifications, studies have defined patterns of gene expression corresponding to specific molecular abnormalities, oncologic phenotypes, and clinical outcomes. Furthermore, novel subclasses with distinct molecular profiles and clinical behaviors have been identified. In some cases, specific cellular pathways have been highlighted that can be therapeutically targeted. The findings of microarray studies are beginning to enter clinical practice as novel diagnostic tests, and clinical trials are ongoing in which therapeutic agents are being used to target pathways that were identified by gene expression profiling. While the technology of DNA microarrays is becoming well established, genome-wide surveys of gene expression generate large data sets that can easily lead to spurious conclusions. Many challenges remain in the statistical interpretation of gene expression data and the biologic validation of findings. As data accumulate and analyses become more sophisticated, genomic technologies offer the potential to generate increasingly sophisticated insights into the complex molecular circuitry of hematologic malignancies. This review summarizes the current state of discovery and addresses key areas for future research.
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44

Skotheim, Rolf I., Anne Kallioniemi, Bodil Bjerkhagen, Fredrik Mertens, Helge R. Brekke, Outi Monni, Spyro Mousses, et al. "Topoisomerase-IIα Is Upregulated in Malignant Peripheral Nerve Sheath Tumors and Associated With Clinical Outcome." Journal of Clinical Oncology 21, no. 24 (December 15, 2003): 4586–91. http://dx.doi.org/10.1200/jco.2003.07.067.

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Purpose: To identify target genes of clinical significance for patients with malignant peripheral-nerve sheath tumor (MPNST), an aggressive cancer for which no consensus therapy exists. Materials and Methods: Biopsies and clinical data from 51 patients with MPNST were included in this study. Based on our previous research implicating chromosome arm 17q amplification in MPNST, we performed gene expression analyses of 14 MPNSTs using chromosome 17–specific cDNA microarrays. Copy numbers of selected gene probes and centromere probes were then determined by interphase fluorescence in situ hybridization in 16 MPNSTs. Finally, we generated a tissue microarray containing 79 samples from 44 MPNSTs, on which in situ protein expressions of candidate genes were examined and related to clinical end points. Results: Among several deregulated genes found by cDNA microarray analyses, topoisomerase IIα (TOP2A) was the most overexpressed gene in MPNSTs compared with benign neurofibromas. Excess copies of the TOP2A were also seen at the DNA level in 10 of 16 cases, and high expression of the TOP2A protein was seen in 83% of the tumors on the tissue microarray. The TOP2A-expressing tumors were associated with poor cancer-specific survival and presence of metastases. Conclusion: We have identified TOP2A as a target gene in MPNST, using a focused gene expression profiling followed by a DNA copy number evaluation and clinical validation of the encoded protein using a tissue microarray. This study is the first to suggest that TOP2A expression may be a predictive factor for adverse outcome in MPNST.
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Verducci, Joseph S., Vincent F. Melfi, Shili Lin, Zailong Wang, Sashwati Roy, and Chandan K. Sen. "Microarray analysis of gene expression: considerations in data mining and statistical treatment." Physiological Genomics 25, no. 3 (May 16, 2006): 355–63. http://dx.doi.org/10.1152/physiolgenomics.00314.2004.

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DNA microarray represents a powerful tool in biomedical discoveries. Harnessing the potential of this technology depends on the development and appropriate use of data mining and statistical tools. Significant current advances have made microarray data mining more versatile. Researchers are no longer limited to default choices that generate suboptimal results. Conflicting results in repeated experiments can be resolved through attention to the statistical details. In the current dynamic environment, there are many choices and potential pitfalls for researchers who intend to incorporate microarrays as a research tool. This review is intended to provide a simple framework to understand the choices and identify the pitfalls. Specifically, this review article discusses the choice of microarray platform, preprocessing raw data, differential expression and validation, clustering, annotation and functional characterization of genes, and pathway construction in light of emergent concepts and tools.
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Beliaev, Alex S., Dorothea K. Thompson, Matthew W. Fields, Liyou Wu, Douglas P. Lies, Kenneth H. Nealson, and Jizhong Zhou. "Microarray Transcription Profiling of a Shewanella oneidensis etrA Mutant." Journal of Bacteriology 184, no. 16 (August 15, 2002): 4612–16. http://dx.doi.org/10.1128/jb.184.16.4612-4616.2002.

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ABSTRACT DNA microarrays were used to examine the effect of an insertional mutation in the Shewanella oneidensis etrA (electron transport regulator) locus on gene expression under anaerobic conditions. The mRNA levels of 69 genes with documented functions in energy and carbon metabolism, regulation, transport, and other cellular processes displayed significant alterations in transcript abundance in an etrA-mutant genetic background. This is the first microarray study indicating a possible involvement of EtrA in the regulation of gene expression in S. oneidensis MR-1.
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Aittokallio, Tero, Markus Kurki, Olli Nevalainen, Tuomas Nikula, Anne West, and Riitta Lahesmaa. "Computational Strategies for Analyzing Data in Gene Expression Microarray Experiments." Journal of Bioinformatics and Computational Biology 01, no. 03 (October 2003): 541–86. http://dx.doi.org/10.1142/s0219720003000319.

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Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.
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Prasad, Alisha, Syed Mohammad Abid Hasan, Steven Grouchy, and Manas Ranjan Gartia. "DNA microarray analysis using a smartphone to detect the BRCA-1 gene." Analyst 144, no. 1 (2019): 197–205. http://dx.doi.org/10.1039/c8an01020j.

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49

McGrath, Ken C., Rhiannon Mondav, Regina Sintrajaya, Bill Slattery, Susanne Schmidt, and Peer M. Schenk. "Development of an Environmental Functional Gene Microarray for Soil Microbial Communities." Applied and Environmental Microbiology 76, no. 21 (September 17, 2010): 7161–70. http://dx.doi.org/10.1128/aem.03108-09.

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ABSTRACT Functional attributes of microbial communities are difficult to study, and most current techniques rely on DNA- and rRNA-based profiling of taxa and genes, including microarrays containing sequences of known microorganisms. To quantify gene expression in environmental samples in a culture-independent manner, we constructed an environmental functional gene microarray (E-FGA) consisting of 13,056 mRNA-enriched anonymous microbial clones from diverse microbial communities to profile microbial gene transcripts. A new normalization method using internal spot standards was devised to overcome spotting and hybridization bias, enabling direct comparisons of microarrays. To evaluate potential applications of this metatranscriptomic approach for studying microbes in environmental samples, we tested the E-FGA by profiling the microbial activity of agricultural soils with a low or high flux of N2O. A total of 109 genes displayed expression that differed significantly between soils with low and high N2O emissions. We conclude that mRNA-based approaches such as the one presented here may complement existing techniques for assessing functional attributes of microbial communities.
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50

Ma, Chi, and Louis M. Staudt. "Molecular definition of the germinal centre stage of B–cell differentiation." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 356, no. 1405 (January 29, 2001): 83–89. http://dx.doi.org/10.1098/rstb.2000.0752.

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Genomic–scale gene expression analysis provides views of biological processes as a whole that are difficult to obtain using traditional single–gene experimental approaches. In the case of differentiating systems, gene expression profiling can define a stage of differentiation by the characteristic expression of hundreds of genes. Using specialized DNA microarrays termed ‘Lymphochips’, gene expression during mature B–cell differentiation has been defined. Germinal centre B cells represent a stage of differentiation that can be defined by a gene expression signature that is not shared by other highly proliferative B–cell populations such as mitogenically activated peripheral blood B cells. The germinal centre gene expression signature is maintained to a significant degree in lymphoma cell lines derived from this stage of differentiation, demonstrating that this gene expression programme does not require ongoing interactions with other germinal centre cell types. Analysis of representative cDNA libraries prepared from resting and activated peripheral blood B cells, germinal centre centroblasts, centrocytes and tonsillar memory B cells has confirmed and extended the results of DNA microarray gene expression analysis.
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