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Journal articles on the topic 'Oligonucleotide microarray'

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Published: 4 June 2021

Last updated: 1 February 2022

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1

Kyselková, M., J. Kopecký, M. Ságová-Marečková, G. L. Grundmann, and Y. Moënne-Loccoz. "Oligonucleotide microarray methodology for taxonomic and functional monitoringof microbial community composition." Plant, Soil and Environment 55, No. 9 (October 14, 2009): 379–88. http://dx.doi.org/10.17221/140/2009-pse.

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Microarray analysis is a cultivation-independent, high-throughput technology that can be used for direct and simultaneous identification of microorganisms in complex environmental samples. This review summarizes current methodologies for oligonucleotide microarrays used in microbial ecology. It deals with probe design, microarray manufacturing, sample preparation and labeling, and data handling, as well as with the key features of microarray analysis such as specificity, sensitivity and quantification potential. Microarray analysis has been validated as an effective approach to describe the composition and dynamics of taxonomic and functional microbial communities, in environments including soil, compost, sediment, air or humans. It is now part of the technical arsenal available to address key issues in microbial community ecology, ranging from biogeography to ecosystem functioning.

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2

Chizhikov, Vladimir, Avraham Rasooly, Konstantin Chumakov, and Dan D. Levy. "Microarray Analysis of Microbial Virulence Factors." Applied and Environmental Microbiology 67, no. 7 (July 1, 2001): 3258–63. http://dx.doi.org/10.1128/aem.67.7.3258-3263.2001.

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ABSTRACT Hybridization with oligonucleotide microchips (microarrays) was used for discrimination among strains of Escherichia coli and other pathogenic enteric bacteria harboring various virulence factors. Oligonucleotide microchips are miniature arrays of gene-specific oligonucleotide probes immobilized on a glass surface. The combination of this technique with the amplification of genetic material by PCR is a powerful tool for the detection of and simultaneous discrimination among food-borne human pathogens. The presence of six genes (eaeA, slt-I,slt-II, fliC, rfbE, andipaH) encoding bacterial antigenic determinants and virulence factors of bacterial strains was monitored by multiplex PCR followed by hybridization of the denatured PCR product to the gene-specific oligonucleotides on the microchip. The assay was able to detect these virulence factors in 15 Salmonella,Shigella, and E. coli strains. The results of the chip analysis were confirmed by hybridization of radiolabeled gene-specific probes to genomic DNA from bacterial colonies. In contrast, gel electrophoretic analysis of the multiplex PCR products used for the microarray analysis produced ambiguous results due to the presence of unexpected and uncharacterized bands. Our results suggest that microarray analysis of microbial virulence factors might be very useful for automated identification and characterization of bacterial pathogens.

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3

Russell, R. "Designing microarray oligonucleotide probes." Briefings in Bioinformatics 4, no. 4 (January 1, 2003): 361–67. http://dx.doi.org/10.1093/bib/4.4.361.

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4

Zhang, Yongqing, Antonio Ferreira, Cheng Cheng, Yongchun Wu, and Jiong Zhang. "Modeling Oligonucleotide Microarray Signals." Applied Bioinformatics 5, no. 3 (2006): 151–60. http://dx.doi.org/10.2165/00822942-200605030-00003.

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5

Koslov, I. A., F. Kaper, L. Zhou, and M. S. Chee. "Microarray based oligonucleotide synthesis." Nucleic Acids Symposium Series 52, no. 1 (September 1, 2008): 723. http://dx.doi.org/10.1093/nass/nrn365.

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6

Lehner, Angelika, Alexander Loy, Thomas Behr, Helga Gaenge, Wolfgang Ludwig, Michael Wagner, and Karl-Heinz Schleifer. "Oligonucleotide microarray for identification ofEnterococcusspecies." FEMS Microbiology Letters 246, no. 1 (May 2005): 133–42. http://dx.doi.org/10.1016/j.femsle.2005.04.002.

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7

Goji, Noriko, Trevor MacMillan, and Kingsley Kwaku Amoako. "A New Generation Microarray for the Simultaneous Detection and Identification ofYersinia pestisandBacillus anthracisin Food." Journal of Pathogens 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/627036.

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The use of microarrays as a multiple analytic system has generated increased interest and provided a powerful analytical tool for the simultaneous detection of pathogens in a single experiment. A wide array of applications for this technology has been reported. A low density oligonucleotide microarray was generated from the genetic sequences ofY. pestisandB. anthracisand used to fabricate a microarray chip. The new generation chip, consisting of 2,240 spots in 4 quadrants with the capability of stripping/rehybridization, was designated as “Y-PESTIS/B-ANTHRACIS 4x2K Array.” The chip was tested for specificity using DNA from a panel of bacteria that may be potentially present in food. In all, 37 uniqueY. pestis-specific and 83B. anthracis-specific probes were identified. The microarray assay distinguishedY. pestisandB. anthracisfrom the other bacterial species tested and correctly identified theY. pestis-specific oligonucleotide probes using DNA extracted from experimentally inoculated milk samples. Using a whole genome amplification method, the assay was able to detect as low as 1 ng genomic DNA as the start sample. The results suggest that oligonucleotide microarray can specifically detect and identifyY. pestisandB. anthracisand may be a potentially useful diagnostic tool for detecting and confirming the organisms in food during a bioterrorism event.

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8

Kingsley, Mark T., Timothy M. Straub, Douglas R. Call, Don S. Daly, Sharon C. Wunschel, and Darrell P. Chandler. "Fingerprinting Closely Related Xanthomonas Pathovars with Random Nonamer Oligonucleotide Microarrays." Applied and Environmental Microbiology 68, no. 12 (December 2002): 6361–70. http://dx.doi.org/10.1128/aem.68.12.6361-6370.2002.

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ABSTRACT Current bacterial DNA-typing methods are typically based on gel-based fingerprinting methods. As such, they access a limited complement of genetic information and many independent restriction enzymes or probes are required to achieve statistical rigor and confidence in the resulting pattern of DNA fragments. Furthermore, statistical comparison of gel-based fingerprints is complex and nonstandardized. To overcome these limitations of gel-based microbial DNA fingerprinting, we developed a prototype, 47-probe microarray consisting of randomly selected nonamer oligonucleotides. Custom image analysis algorithms and statistical tools were developed to automatically extract fingerprint profiles from microarray images. The prototype array and new image analysis algorithms were used to analyze 14 closely related Xanthomonas pathovars. Of the 47 probes on the prototype array, 10 had diagnostic value (based on a chi-squared test) and were used to construct statistically robust microarray fingerprints. Analysis of the microarray fingerprints showed clear differences between the 14 test organisms, including the separation of X. oryzae strains 43836 and 49072, which could not be resolved by traditional gel electrophoresis of REP-PCR amplification products. The proof-of-application study described here represents an important first step to high-resolution bacterial DNA fingerprinting with microarrays. The universal nature of the nonamer fingerprinting microarray and data analysis methods developed here also forms a basis for method standardization and application to the forensic identification of other closely related bacteria.

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9

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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10

Ali Syed, Haider, and David W. Threadgill. "Enhanced oligonucleotide microarray labeling and hybridization." BioTechniques 41, no. 6 (December 2006): 685–86. http://dx.doi.org/10.2144/000112290.

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11

Sidorov, I. A., D. A. Hosack, D. Gee, J. Yang, M. C. Cam, R. A. Lempicki, and D. S. Dimitrov. "Oligonucleotide microarray data distribution and normalization." Information Sciences 146, no. 1-4 (October 2002): 67–73. http://dx.doi.org/10.1016/s0020-0255(02)00215-3.

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12

Carvalho, Benilton S., and Rafael A. Irizarry. "A framework for oligonucleotide microarray preprocessing." Bioinformatics 26, no. 19 (August 5, 2010): 2363–67. http://dx.doi.org/10.1093/bioinformatics/btq431.

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13

Piper, Matthew D. W., Pascale Daran-Lapujade, Christoffer Bro, Birgitte Regenberg, Steen Knudsen, Jens Nielsen, and Jack T. Pronk. "Reproducibility of Oligonucleotide Microarray Transcriptome Analyses." Journal of Biological Chemistry 277, no. 40 (July 16, 2002): 37001–8. http://dx.doi.org/10.1074/jbc.m204490200.

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14

Taib, Ziad. "Statistical analysis of oligonucleotide microarray data." Comptes Rendus Biologies 327, no. 3 (March 2004): 175–80. http://dx.doi.org/10.1016/j.crvi.2003.05.003.

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15

Bae, Jin-Woo, Sung-Keun Rhee, Ja Ryeong Park, Won-Hyong Chung, Young-Do Nam, Insun Lee, Hongik Kim, and Yong-Ha Park. "Development and Evaluation of Genome-Probing Microarrays for Monitoring Lactic Acid Bacteria." Applied and Environmental Microbiology 71, no. 12 (December 2005): 8825–35. http://dx.doi.org/10.1128/aem.71.12.8825-8835.2005.

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ABSTRACT The genome-probing microarray (GPM) was developed for quantitative, high-throughput monitoring of community dynamics in lactic acid bacteria (LAB) fermentation through the deposit of 149 microbial genomes as probes on a glass slide. Compared to oligonucleotide microarrays, the specificity of GPM was remarkably increased to a species-specific level. GPM possesses about 10- to 100-fold higher sensitivity (2.5 ng of genomic DNA) than the currently used 50-mer oligonucleotide microarrays. Since signal variation between the different genomes was very low compared to that of cDNA or oligonucleotide-based microarrays, the capacity of global quantification of microbial genomes could also be observed in GPM hybridization. In order to assess the applicability of GPMs, LAB community dynamics were monitored during the fermentation of kimchi, a traditional Korean food. In this work, approximately 100 diverse LAB species could be quantitatively analyzed as actively involved in kimchi fermentation.

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16

Taroncher-Oldenburg, Gaspar, Erin M. Griner, Chris A. Francis, and Bess B. Ward. "Oligonucleotide Microarray for the Study of Functional Gene Diversity in the Nitrogen Cycle in the Environment." Applied and Environmental Microbiology 69, no. 2 (February 2003): 1159–71. http://dx.doi.org/10.1128/aem.69.2.1159-1171.2003.

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ABSTRACT The analysis of functional diversity and its dynamics in the environment is essential for understanding the microbial ecology and biogeochemistry of aquatic systems. Here we describe the development and optimization of a DNA microarray method for the detection and quantification of functional genes in the environment and report on their preliminary application to the study of the denitrification gene nirS in the Choptank River-Chesapeake Bay system. Intergenic and intragenic resolution constraints were determined by an oligonucleotide (70-mer) microarray approach. Complete signal separation was achieved when comparing unrelated genes within the nitrogen cycle (amoA, nifH, nirK, and nirS) and detecting different variants of the same gene, nirK, corresponding to organisms with two different physiological modes, ammonia oxidizers and denitrifying halobenzoate degraders. The limits of intragenic resolution were investigated with a microarray containing 64 nirS sequences comprising 14 cultured organisms and 50 clones obtained from the Choptank River in Maryland. The nirS oligonucleotides covered a range of sequence identities from approximately 40 to 100%. The threshold values for specificity were determined to be 87% sequence identity and a target-to-probe perfect match-to-mismatch binding free-energy ratio of 0.56. The lower detection limit was 10 pg of DNA (equivalent to approximately 107 copies) per target per microarray. Hybridization patterns on the microarray differed between sediment samples from two stations in the Choptank River, implying important differences in the composition of the denitirifer community along an environmental gradient of salinity, inorganic nitrogen, and dissolved organic carbon. This work establishes a useful set of design constraints (independent of the target gene) for the implementation of functional gene microarrays for environmental applications.

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17

Lee, Ju Seok, Joon Jin Song, Russell Deaton, and Jin-Woo Kim. "Assessing the Detection Capacity of Microarrays as Bio/Nanosensing Platforms." BioMed Research International 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/310461.

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Microarray is one of the most powerful detection systems with multiplexing and high throughput capability. It has significant potential as a versatile biosensing platform for environmental monitoring, pathogen detection, medical therapeutics, and drug screening to name a few. To date, however, microarray applications are still limited to preliminary screening of genome-scale transcription profiling or gene ontology analysis. Expanding the utility of microarrays as a detection tool for various biological and biomedical applications requires information about performance such as the limits of detection and quantification, which are considered as an essential information to decide the detection sensitivity of sensing devices. Here we present a calibration design that integrates detection limit theory and linear dynamic range to obtain a performance index of microarray detection platform using oligonucleotide arrays as a model system. Two different types of limits of detection and quantification are proposed by the prediction or tolerance interval for two common cyanine fluorescence dyes, Cy3 and Cy5. Besides oligonucleotide, the proposed method can be generalized to other microarray formats with various biomolecules such as complementary DNA, protein, peptide, carbohydrate, tissue, or other small biomolecules. Also, it can be easily applied to other fluorescence dyes for further dye chemistry improvement.

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18

Maynard, Christine, Frédéric Berthiaume, Karine Lemarchand, Josée Harel, Pierre Payment, Paul Bayardelle, Luke Masson, and Roland Brousseau. "Waterborne Pathogen Detection by Use of Oligonucleotide-Based Microarrays." Applied and Environmental Microbiology 71, no. 12 (December 2005): 8548–57. http://dx.doi.org/10.1128/aem.71.12.8548-8557.2005.

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ABSTRACT A small-oligonucleotide microarray prototype was designed with probes specific for the universal 16S rRNA and cpn60 genes of several pathogens that are usually encountered in wastewaters. In addition to these two targets, wecE-specific oligonucleotide probes were included in the microarray to enhance its discriminating power within the Enterobacteriaceae family. Universal PCR primers were used to amplify variable regions of 16S rRNA, cpn60, and wecE genes directly in Escherichia coli and Salmonella enterica serovar Typhimurium genomic DNA mixtures (binary); E. coli, S. enterica serovar Typhimurium, and Yersinia enterocolitica genomic DNA mixtures (ternary); or wastewater total DNA. Amplified products were fluorescently labeled and hybridized on the prototype chip. The detection sensitivity for S. enterica serovar Typhimurium was estimated to be on the order of 0.1% (104 S. enterica genomes) of the total DNA for the combination of PCR followed by microarray hybridization. The sensitivity of the prototype could be increased by hybridizing amplicons generated by PCR targeting genes specific for a bacterial subgroup, such as wecE genes, instead of universal taxonomic amplicons. However, there was evidence of PCR bias affecting the detection limits of a given pathogen as increasing amounts of a different pathogen were spiked into the test samples. These results demonstrate the feasibility of using DNA microarrays in the detection of waterborne pathogens within mixed populations but also raise the problem of PCR bias in such experiments.

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19

Morey, Jeanine S., James C. Ryan, and Frances M. Van Dolah. "Microarray validation: factors influencing correlation between oligonucleotide microarrays and real-time PCR." Biological Procedures Online 8, no. 1 (December 2006): 175–93. http://dx.doi.org/10.1251/bpo126.

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20

Bavykin, Sergei G., James P. Akowski, Vladimir M. Zakhariev, Viktor E. Barsky, Alexander N. Perov, and Andrei D. Mirzabekov. "Portable System for Microbial Sample Preparation and Oligonucleotide Microarray Analysis." Applied and Environmental Microbiology 67, no. 2 (February 1, 2001): 922–28. http://dx.doi.org/10.1128/aem.67.2.922-928.2001.

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ABSTRACT 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 discriminateEscherichia 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.

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Moon, W. C., C. H. Noh, M. Oh, N. H. Sung, and T. H. Uhm. "Multiplex oligonucleotide microarray analysis of bladder cancers." European Urology Supplements 2, no. 1 (February 2003): 113. http://dx.doi.org/10.1016/s1569-9056(03)80448-7.

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22

Brettschneider, Julia, François Collin, Benjamin M. Bolstad, and Terence P. Speed. "Quality Assessment for Short Oligonucleotide Microarray Data." Technometrics 50, no. 3 (August 2008): 241–64. http://dx.doi.org/10.1198/004017008000000334.

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23

Liu, E. T. "Representational oligonucleotide microarray analysis (ROMA) in pharmacogenomics." Pharmacogenomics Journal 4, no. 2 (March 25, 2004): 74–76. http://dx.doi.org/10.1038/sj.tpj.6500232.

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24

Mulders, Geert CWM, Gerard T. Barkema, and Enrico Carlon. "Inverse Langmuir method for oligonucleotide microarray analysis." BMC Bioinformatics 10, no. 1 (2009): 64. http://dx.doi.org/10.1186/1471-2105-10-64.

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25

Geller, S. C., J. P. Gregg, P. Hagerman, and D. M. Rocke. "Transformation and normalization of oligonucleotide microarray data." Bioinformatics 19, no. 14 (September 22, 2003): 1817–23. http://dx.doi.org/10.1093/bioinformatics/btg245.

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26

Svensen, Nina, Juan José Díaz-Mochón, and Mark Bradley. "Microarray Generation of Thousand-Member Oligonucleotide Libraries." PLoS ONE 6, no. 9 (September 23, 2011): e24906. http://dx.doi.org/10.1371/journal.pone.0024906.

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27

Chandler, D. P., O. Alferov, B. Chernov, D. S. Daly, J. Golova, A. Perov, M. Protic, et al. "Diagnostic Oligonucleotide Microarray Fingerprinting of Bacillus Isolates." Journal of Clinical Microbiology 44, no. 1 (January 1, 2006): 244–50. http://dx.doi.org/10.1128/jcm.44.1.244-250.2006.

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28

Rouillard, J. M., and E. Gulari. "OligoArrayDb: pangenomic oligonucleotide microarray probe sets database." Nucleic Acids Research 37, Database (January 1, 2009): D938—D941. http://dx.doi.org/10.1093/nar/gkn761.

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29

Jacak, Jaroslaw, Jan Hesse, Gerhard Regl, Thomas Eichberger, Fritz Aberger, Stefan Howorka, Leila Muresan, Annemarie Frischauf, and Gerhard J. Schütz. "Oligonucleotide Microarray Analysis with Single Molecule Sensitivity." Biophysical Journal 96, no. 3 (February 2009): 313a. http://dx.doi.org/10.1016/j.bpj.2008.12.1567.

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30

Garrido, Patricia, Miguel Blanco, Mercedes Moreno-Paz, Carlos Briones, Ghizlane Dahbi, Jesús Blanco, Jorge Blanco, and Víctor Parro. "STEC-EPEC Oligonucleotide Microarray: A New Tool for Typing Genetic Variants of the LEE Pathogenicity Island of Human and Animal Shiga Toxin–Producing Escherichia coli (STEC) and Enteropathogenic E. coli (EPEC) Strains." Clinical Chemistry 52, no. 2 (February 1, 2006): 192–201. http://dx.doi.org/10.1373/clinchem.2005.059766.

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Abstract Background: Shiga toxin–producing Escherichia coli (STEC) and enteropathogenic E. coli (EPEC) are important emerging pathogens that can cause a severe and sometimes fatal illness. Differentiation of eae, tir, espA, espD, and espB gene variants of the locus of enterocyte effacement (LEE) pathogenicity island represents an important tool for typing in routine diagnostics as well as in pathogenesis, epidemiologic, clonal, and immunologic studies. Methods: Type-specific oligonucleotide microarrays and a PCR scheme were designed and constructed for the detection and typing of genetic variants of the LEE genes. Oligonucleotide probes were tested for their specificity against the corresponding type strain by microarray hybridization using fluorescent DNA, either PCR-amplified (single, multiplex, long-range), chromosomal, or amplified chromosomal DNA. Results: The PCR scheme and the oligonucleotide microarray allowed us to distinguish 16 variants (α1, α2, β1, β2, γ1, γ2/θ, δ/κ, ε, ζ, η, ι, λ, μ, ν, ξ, ο) of the eae gene, 4 variants (α1, β1, γ1, γ2/θ) of the tir gene, 4 variants (α1, β1, β2, γ1) of the espA gene, 3 variants (α1, β1, γ1) of the espB gene, and 3 variants (α1, β1, γ1) of the espD gene. We found a total of 12 different combinations of tir, espA, espB, and espD genes among the 25 typed strains. Conclusions: The PCR scheme and the oligonucleotide microarray described are effective tools to rapidly screen multiple virulence genes and their variants in E. coli strains isolated from human and animal infections. The results demonstrate the great genetic diversity among LEE genes of human and animal STEC and EPEC strains.

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Bystricka, D., O. Lenz, I. Mraz, L. Piherova, S. Kmoch, and M. Sip. "Oligonucleotide-based microarray: A new improvement in microarray detection of plant viruses." Journal of Virological Methods 128, no. 1-2 (September 2005): 176–82. http://dx.doi.org/10.1016/j.jviromet.2005.04.009.

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Burden, Conrad J., Yvonne E. Pittelkow, and Susan R. Wilson. "Statistical Analysis of Adsorption Models for Oligonucleotide Microarrays." Statistical Applications in Genetics and Molecular Biology 3, no. 1 (January 9, 2004): 1–27. http://dx.doi.org/10.2202/1544-6115.1095.

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Recent analyses have shown that the relationship between intensity measurements from high density oligonucleotide microarrays and known concentration is non linear. Thus many measurements of so-called gene expression are neither measures of transcript nor mRNA concentration as might be expected.Intensity as measured in such microarrays is a measurement of fluorescent dye attached to probe-target duplexes formed during hybridization of a sample to the probes on the microarray. We develop several dynamic adsorption models relating fluorescent dye intensity to target RNA concentration, the simplest of which is the equilibrium Langmuir isotherm, or hyperbolic response function. Using data from the Affymerix HG-U95A Latin Square experiment, we evaluate various physical models, including equilibrium and non-equilibrium models, by applying maximum likelihood methods. We show that for these data, equilibrium Langmuir isotherms with probe dependent parameters are appropriate. We describe how probe sequence information may then be used to estimate the parameters of the Langmuir isotherm in order to provide an improved measure of absolute target concentration.

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Girard, Laurie D., Karel Boissinot, Régis Peytavi, Maurice Boissinot, and Michel G. Bergeron. "Structured oligonucleotides for target indexing to allow single-vessel PCR amplification and solid support microarray hybridization." Analyst 140, no. 3 (2015): 912–21. http://dx.doi.org/10.1039/c4an01352b.

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A structured oligonucleotide is irreversibly digested in the presence of its complementary target during PCR, releasing a short oligonucleotide tag for microarray hybridization in a single vessel and single reaction mixture.

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34

Kahlke, Tim, Paavo Jumppanen, Ralf Westram, Guy C. G. Abell, and Levente Bodrossy. "ProbeSpec: batch specificity testing and visualization of oligonucleotide probe sets implemented in ARB." F1000Research 7 (December 6, 2018): 1901. http://dx.doi.org/10.12688/f1000research.16905.1.

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High-throughput molecular methods such as quantitative polymerase chain reaction (qPCR) and environmental microarrays are cost-effective methods for semi-quantitative assessment of bacterial community structure and the identification of specific target organisms. Both techniques rely on short nucleotide sequences, so-called oligonucleotide probes, which require high specificity to the organisms in question to avoid cross-hybridization with non-target taxa. However, designing oligonucleotide probes for novel taxa or marker genes that show sufficient phylogenetic sensitivity and specificity is often time- and labor-intensive, as each probe has to be in-silico tested for its specificity and sensitivity. Here we present ProbeSpec, to our knowledge the first batch sensitivity and specificity estimation and visualization tool for oligonucleotide probes integrated into the widely used ARB software. Using ProbeSpec’s interactive “mismatch threshold” and “clade marked threshold” we were able to reduce the development time of highly specific probes for a recently published environmental oligonucleotide microarray from several months to one week.

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35

Mah, Nancy, Anders Thelin, Tim Lu, Susanna Nikolaus, Tanja Kühbacher, Yesim Gurbuz, Holger Eickhoff, et al. "A comparison of oligonucleotide and cDNA-based microarray systems." Physiological Genomics 16, no. 3 (February 13, 2004): 361–70. http://dx.doi.org/10.1152/physiolgenomics.00080.2003.

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Large-scale public data mining will become more common as public release of microarray data sets becomes a corequisite for publication. Therefore, there is an urgent need to clarify whether data from different microarray platforms are comparable. To assess the compatibility of microarray data, results were compared from the two main types of high-throughput microarray expression technologies, namely, an oligonucleotide-based and a cDNA-based platform, using RNA obtained from complex tissue (human colonic mucosa) of five individuals. From 715 sequence-verified genes represented on both platforms, 64% of the genes matched in “present” or “absent” calls made by both platforms. Calls were influenced by spurious signals caused by Alu repeats in cDNA clones, clone annotation errors, or matched probes that were designed to different regions of the gene; however, these factors could not completely account for the level of call discordance observed. Expression levels in sequence-verified, platform-overlapping genes were not related, as demonstrated by weakly positive rank order correlation. This study demonstrates that there is only moderate overlap in the results from the two array systems. This fact should be carefully considered when performing large-scale analyses on data originating from different microarray platforms.

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36

Tadesse, Mahlet, Qiang He, Raymond Carroll, and Kenneth Ramos. "Comparison of High-Density Short Oligonucleotide Microarray Platforms." Current Bioinformatics 2, no. 3 (September 1, 2007): 203–13. http://dx.doi.org/10.2174/157489307781662132.

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37

Sip, Miroslav, Dagmar Bystricka, Stanislav Kmoch, Lenka Noskova, Hana Hartmannova, and Petr Dedic. "Detection of viral infections by an oligonucleotide microarray." Journal of Virological Methods 165, no. 1 (April 2010): 64–70. http://dx.doi.org/10.1016/j.jviromet.2010.01.004.

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Kim, Il-Jin, Hio Chung Kang, Sang Geun Jang, Sun-A. Ahn, Hyun-Ju Yoon, and Jae-Gahb Park. "Development and Applications of a BRAF Oligonucleotide Microarray." Journal of Molecular Diagnostics 9, no. 1 (February 2007): 55–63. http://dx.doi.org/10.2353/jmoldx.2007.060072.

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Lindsey, Rebecca L., Jonathan G. Frye, Paula J. Fedorka-Cray, Timothy J. Welch, and Richard J. Meinersmann. "An oligonucleotide microarray to characterize multidrug resistant plasmids." Journal of Microbiological Methods 81, no. 2 (May 2010): 96–100. http://dx.doi.org/10.1016/j.mimet.2010.01.024.

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Xia, Xiao-Qin, Zhenyu Jia, Steffen Porwollik, Fred Long, Claudia Hoemme, Kai Ye, Carsten Müller-Tidow, Michael McClelland, and Yipeng Wang. "Evaluating oligonucleotide properties for DNA microarray probe design." Nucleic Acids Research 38, no. 11 (March 17, 2010): e121-e121. http://dx.doi.org/10.1093/nar/gkq039.

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Wang, Lih-Chiann, Dean Huang, Ming-Chu Cheng, Shu-Hwae Lee, and Ching-Ho Wang. "H5 avian influenza virus pathotyping using oligonucleotide microarray." Journal of Virological Methods 220 (August 2015): 39–42. http://dx.doi.org/10.1016/j.jviromet.2015.04.006.

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Hacia, Joseph G., Keith Edgemon, Nicole Fang, R. Aeryn Mayer, Dominick Sudano, Nathaniel Hunt, and Francis S. Collins. "Oligonucleotide microarray based detection of repetitive sequence changes." Human Mutation 16, no. 4 (2000): 354–63. http://dx.doi.org/10.1002/1098-1004(200010)16:4<354::aid-humu8>3.0.co;2-v.

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Huang, Xi, and Linda D. Hazlett. "Analysis ofPseudomonas aeruginosaCorneal Infection Using an Oligonucleotide Microarray." Investigative Opthalmology & Visual Science 44, no. 8 (August 1, 2003): 3409. http://dx.doi.org/10.1167/iovs.03-0162.

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Vigé, A., C. Gallou-Kabani, M. S. Gross, A. Fabre, C. Junien, and J. P. Jais. "An oligonucleotide microarray for mouse imprinted genes profiling." Cytogenetic and Genome Research 113, no. 1-4 (2006): 253–61. http://dx.doi.org/10.1159/000090840.

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Steibel, J. P., M. Wysocki, J. K. Lunney, A. M. Ramos, Z. L. Hu, M. F. Rothschild, and C. W. Ernst. "Assessment of the swine protein-annotated oligonucleotide microarray." Animal Genetics 40, no. 6 (December 2009): 883–93. http://dx.doi.org/10.1111/j.1365-2052.2009.01928.x.

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Willse, A. "Quantitative oligonucleotide microarray fingerprinting of Salmonella enterica isolates." Nucleic Acids Research 32, no. 5 (March 8, 2004): 1848–56. http://dx.doi.org/10.1093/nar/gkh329.

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Kostina, Elena V., Alexander N. Sinyakov, and Vladimir A. Ryabinin. "A many probes-one spot hybridization oligonucleotide microarray." Analytical and Bioanalytical Chemistry 410, no. 23 (June 22, 2018): 5817–23. http://dx.doi.org/10.1007/s00216-018-1190-8.

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Sergeev, Nikolay, Margaret Distler, Shannon Courtney, Sufian F. Al-Khaldi, Dmitriy Volokhov, Vladimir Chizhikov, and Avraham Rasooly. "Multipathogen oligonucleotide microarray for environmental and biodefense applications." Biosensors and Bioelectronics 20, no. 4 (November 2004): 684–98. http://dx.doi.org/10.1016/j.bios.2004.04.030.

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Klotchenko, S. A., A. V. Vasin, N. T. Sandybaev, M. A. Plotnikova, O. V. Chervyakova, E. A. Smirnova, E. V. Kushnareva, et al. "Oligonucleotide microarray for subtyping of influenza A viruses." Journal of Physics: Conference Series 345 (February 9, 2012): 012041. http://dx.doi.org/10.1088/1742-6596/345/1/012041.

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Miller, Melissa B., and Yi-Wei Tang. "Basic Concepts of Microarrays and Potential Applications in Clinical Microbiology." Clinical Microbiology Reviews 22, no. 4 (October 2009): 611–33. http://dx.doi.org/10.1128/cmr.00019-09.

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Abstract:

SUMMARY The introduction of in vitro nucleic acid amplification techniques, led by real-time PCR, into the clinical microbiology laboratory has transformed the laboratory detection of viruses and select bacterial pathogens. However, the progression of the molecular diagnostic revolution currently relies on the ability to efficiently and accurately offer multiplex detection and characterization for a variety of infectious disease pathogens. Microarray analysis has the capability to offer robust multiplex detection but has just started to enter the diagnostic microbiology laboratory. Multiple microarray platforms exist, including printed double-stranded DNA and oligonucleotide arrays, in situ-synthesized arrays, high-density bead arrays, electronic microarrays, and suspension bead arrays. One aim of this paper is to review microarray technology, highlighting technical differences between them and each platform's advantages and disadvantages. Although the use of microarrays to generate gene expression data has become routine, applications pertinent to clinical microbiology continue to rapidly expand. This review highlights uses of microarray technology that impact diagnostic microbiology, including the detection and identification of pathogens, determination of antimicrobial resistance, epidemiological strain typing, and analysis of microbial infections using host genomic expression and polymorphism profiles.

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