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Journal articles on the topic 'Microarray and DNA'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 February 2022

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1

Whipple, Mark Eliot, and Winston Patrick Kuo. "DNA Microarrays in Otolaryngology-Head and Neck Surgery." Otolaryngology–Head and Neck Surgery 127, no. 3 (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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2

Jack, Philippa, and David Boyle. "DNA microarrays for pathogen detection and characterisation." Microbiology Australia 27, no. 2 (2006): 68. http://dx.doi.org/10.1071/ma06068.

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DNA microarrays have three main potential diagnostic uses in clinical microbiology: detection of known pathogens, pathogen typing and novel pathogen discovery. Although DNA microarray platforms offer the ability to screen for a large number of agents in parallel, sensitivity is dependent on the ability to obtain adequate amounts of pathogen nucleic acids from collected samples. In general, high levels of sensitivity require a PCR amplification step using specific primer sets, subsequently reducing the overall scope of the microarray assay. At present, relatively high costs, restricted sample throughput capabilities and validation difficulties are also major factors limiting the implementation of DNA microarray assays in diagnostic microbiology laboratories.

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Call, Douglas R., Marlene K. Bakko, Melissa J. Krug, and Marilyn C. Roberts. "Identifying Antimicrobial Resistance Genes with DNA Microarrays." Antimicrobial Agents and Chemotherapy 47, no. 10 (2003): 3290–95. http://dx.doi.org/10.1128/aac.47.10.3290-3295.2003.

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ABSTRACT We developed and tested a glass-based microarray suitable for detecting multiple tetracycline (tet) resistance genes. Microarray probes for 17 tet genes, the β-lactamase bla TEM-1 gene, and a 16S ribosomal DNA gene (Escherichia coli) were generated from known controls by PCR. The resulting products (ca. 550 bp) were applied as spots onto epoxy-silane-derivatized, Teflon-masked slides by using a robotic spotter. DNA was extracted from test strains, biotinylated, hybridized overnight to individual microarrays at 65°C, and detected with Tyramide Signal Amplification, Alexa Fluor 546, and a microarray scanner. Using a detection threshold of 3× the standard deviation, we correctly identified tet genes carried by 39 test strains. Nine additional strains were not known to harbor any genes represented on the microarray, and these strains were negative for all 17 tet probes as expected. We verified that R741a, which was originally thought to carry a novel tet gene, tet(I), actually harbored a tet(G) gene. Microarray technology has the potential for screening a large number of different antibiotic resistance genes by the relatively low-cost methods outlined in this paper.

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Guo, Qingbin M. "DNA microarray and cancer." Current Opinion in Oncology 15, no. 1 (2003): 36–43. http://dx.doi.org/10.1097/00001622-200301000-00005.

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5

Davies, S. W., and D. A. Seale. "DNA Microarray Stochastic Model." IEEE Transactions on Nanobioscience 4, no. 3 (2005): 248–54. http://dx.doi.org/10.1109/tnb.2005.853665.

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6

Jia, Kun, Miao Yu, Gui-Hong Zhang, et al. "Detection and identification of Mycobacterium tuberculosis and Mycobacterium bovis from clinical species using DNA microarrays." Journal of Veterinary Diagnostic Investigation 24, no. 1 (2011): 156–60. http://dx.doi.org/10.1177/1040638711417141.

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The objectives of the current study were to evaluate the use of DNA microarray for the rapid and direct detection of Mycobacterium tuberculosis and Mycobacterium bovis in bovine milk, blood, and pharyngeal swab samples, and to compare the use of DNA microarrays with current molecular detection techniques. The present study describes a microarray assay based on mtp40 and pncA gene sequences, which can be used to detect M. tuberculosis and M. bovis species. Each probe was spotted onto a silylated glass slide with an arrayer and used for hybridization with fluorescently labeled DNA derived from amplified DNA samples. The detection limit for mycobacterial DNA using this DNA microarray method was 50 fg (5 tubercle bacilli). Mycobacterium tuberculosis and/or M. bovis was detected in 7.1% (24/336) of the cattle specimens using the DNA microarray compared to 6.0% (20/336) using culture methods. Mixed infections were detected in 3 animals using the DNA microarray method, whereas the mixed infections were detected in 2 animals using either culture or polymerase chain reaction methods. The use of ancillary in vitro tests alongside the DNA microarray enhanced the detection of cattle infected with M. tuberculosis and/or M. bovis and reduced the number of false-positive animals that would be culled. More species may be easily added to this system, and supplementary probes can be added to increase the simultaneous detection power.

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7

Kostrzynska, M., and A. Bachand. "Application of DNA microarray technology for detection, identification, and characterization of food-borne pathogens." Canadian Journal of Microbiology 52, no. 1 (2006): 1–8. http://dx.doi.org/10.1139/w05-105.

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DNA microarrays represent the latest advance in molecular technology. In combination with bioinformatics, they provide unparalleled opportunities for simultaneous detection of thousands of genes or target DNA sequences and offer tremendous potential for studying food-borne microorganisms. This review provides an up-to-date look at the application of DNA microarray technology to detect food-borne pathogenic bacteria, viruses, and parasites. In addition, it covers the advantages of using microarray technology to further characterize microorganisms by providing information for specific identification of isolates, to understand the pathogenesis based on the presence of virulence genes, and to indicate how new pathogenic strains evolved epidemiologically and phylogenetically.Key words: DNA microarrays, food-borne pathogens, detection.

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8

Xu, Lizhe, Grace A. Maresh, Jason Giardina, and Seth H. Pincus. "Comparison of Different Microarray Data Analysis Programs and Description of a Database for Microarray Data Management." DNA and Cell Biology 23, no. 10 (2004): 643–51. http://dx.doi.org/10.1089/dna.2004.23.643.

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9

Valente, Eduardo, and Miguel Rocha. "Integrating data from heterogeneous DNA microarray platforms." Journal of Integrative Bioinformatics 12, no. 4 (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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10

Chiodi, Elisa, Allison M. Marn, Matthew T. Geib, and M. Selim Ünlü. "The Role of Surface Chemistry in the Efficacy of Protein and DNA Microarrays for Label-Free Detection: An Overview." Polymers 13, no. 7 (2021): 1026. http://dx.doi.org/10.3390/polym13071026.

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The importance of microarrays in diagnostics and medicine has drastically increased in the last few years. Nevertheless, the efficiency of a microarray-based assay intrinsically depends on the density and functionality of the biorecognition elements immobilized onto each sensor spot. Recently, researchers have put effort into developing new functionalization strategies and technologies which provide efficient immobilization and stability of any sort of molecule. Here, we present an overview of the most widely used methods of surface functionalization of microarray substrates, as well as the most recent advances in the field, and compare their performance in terms of optimal immobilization of the bioreceptor molecules. We focus on label-free microarrays and, in particular, we aim to describe the impact of surface chemistry on two types of microarray-based sensors: microarrays for single particle imaging and for label-free measurements of binding kinetics. Both protein and DNA microarrays are taken into consideration, and the effect of different polymeric coatings on the molecules’ functionalities is critically analyzed.

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11

Lacroix, M., N. Zammatteo, J. Remacle, and G. Leclercq. "A Low-Density DNA Microarray for Analysis of Markers in Breast Cancer." International Journal of Biological Markers 17, no. 1 (2002): 5–23. http://dx.doi.org/10.1177/172460080201700102.

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Breast cancer remains a major cause of death in women from Western countries. In the near future, advances in both nucleic acids technology and tumor biology should be widely exploited to improve the diagnosis, prognosis, and outcome prediction of this disease. The DNA microarray, also called biochip, is a promising tool for performing massive, simultaneous, fast, and standardized analyses of multiple molecular markers in tumor samples. However, most currently available microarrays are expensive, which is mainly due to the amount (several thousands) of different DNA capture sequences that they carry. While these high-density microarrays are best suited for basic studies, their introduction into the clinical routine remains hypothetical. We describe here the principles of a low-density microarray, carrying only a few hundreds of capture sequences specific to markers whose importance in breast cancer is generally recognized or suggested by the current medical literature. We provide a list of about 250 of these markers. We also examine some potential difficulties (homologies between marker and/or variant sequences, size of sequences, etc.) associated with the production of such a low-cost microarray.

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12

Paredes, Carlos J., Ryan S. Senger, Iwona S. Spath, Jacob R. Borden, Ryan Sillers, and Eleftherios T. Papoutsakis. "A General Framework for Designing and Validating Oligomer-Based DNA Microarrays and Its Application to Clostridium acetobutylicum." Applied and Environmental Microbiology 73, no. 14 (2007): 4631–38. http://dx.doi.org/10.1128/aem.00144-07.

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ABSTRACT While DNA microarray analysis is widely accepted as an essential tool for modern biology, its use still eludes many researchers for several reasons, especially when microarrays are not commercially available. In that case, the design, construction, and use of microarrays for a sequenced organism constitute substantial, time-consuming, and expensive tasks. Recently, it has become possible to construct custom microarrays using industrial manufacturing processes, which offer several advantages, including speed of manufacturing, quality control, no up-front setup costs, and need-based microarray ordering. Here, we describe a strategy for designing and validating DNA microarrays manufactured using a commercial process. The 22K microarrays for the solvent producer Clostridium acetobutylicum ATCC 824 are based on in situ-synthesized 60-mers employing the Agilent technology. The strategy involves designing a large library of possible oligomer probes for each target (i.e., gene or DNA sequence) and experimentally testing and selecting the best probes for each target. The degenerate C. acetobutylicum strain M5 lacking the pSOL1 megaplasmid (with 178 annotated open reading frames [genes]) was used to estimate the level of probe cross-hybridization in the new microarrays and to establish the minimum intensity for a gene to be considered expressed. Results obtained using this microarray design were consistent with previously reported results from spotted cDNA-based microarrays. The proposed strategy is applicable to any sequenced organism.

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13

Hackl, Hubert, Fatima Cabo, Alexander Sturn, Olaf Wolkenhauer, and Zlatko Trajanoski. "Analysis of DNA Microarray Data." Current Topics in Medicinal Chemistry 4, no. 13 (2004): 1355–68. http://dx.doi.org/10.2174/1568026043387773.

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14

Kunz, Manfred. "DNA Microarray Technology in Dermatology." Seminars in Cutaneous Medicine and Surgery 27, no. 1 (2008): 16–24. http://dx.doi.org/10.1016/j.sder.2007.12.004.

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15

Hassibi, Arjang, Haris Vikalo, José Luis Riechmann, and Babak Hassibi. "Real-time DNA microarray analysis." Nucleic Acids Research 37, no. 20 (2009): e132-e132. http://dx.doi.org/10.1093/nar/gkp675.

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16

Chow, Kwok-Fan, François Mavré, and Richard M. Crooks. "Wireless Electrochemical DNA Microarray Sensor." Journal of the American Chemical Society 130, no. 24 (2008): 7544–45. http://dx.doi.org/10.1021/ja802013q.

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17

Argraves, G. L., J. L. Barth, and W. S. Argraves. "The MUSC DNA Microarray Database." Bioinformatics 19, no. 18 (2003): 2473–74. http://dx.doi.org/10.1093/bioinformatics/btg325.

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18

Lin, Haohao, Li Sun, and Richard M. Crooks. "Replication of a DNA Microarray." Journal of the American Chemical Society 127, no. 32 (2005): 11210–11. http://dx.doi.org/10.1021/ja051914u.

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19

Br�ndle, Norbert, Horst Bischof, and Hilmar Lapp. "Robust DNA microarray image analysis." Machine Vision and Applications 15, no. 1 (2003): 11–28. http://dx.doi.org/10.1007/s00138-002-0114-x.

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20

Philippakis, Anthony A., Aaron M. Qureshi, Michael F. Berger, and Martha L. Bulyk. "Design of Compact, Universal DNA Microarrays for Protein Binding Microarray Experiments." Journal of Computational Biology 15, no. 7 (2008): 655–65. http://dx.doi.org/10.1089/cmb.2007.0114.

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21

Sui, Yunxia, Xiaoyue Zhao, Terence P. Speed, and Zhijin Wu. "Background Adjustment for DNA Microarrays Using a Database of Microarray Experiments." Journal of Computational Biology 16, no. 11 (2009): 1501–15. http://dx.doi.org/10.1089/cmb.2009.0063.

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22

Trost, Brett, Catherine A. Moir, Zoe E. Gillespie, Anthony Kusalik, Jennifer A. Mitchell, and Christopher H. Eskiw. "Concordance between RNA-sequencing data and DNA microarray data in transcriptome analysis of proliferative and quiescent fibroblasts." Royal Society Open Science 2, no. 9 (2015): 150402. http://dx.doi.org/10.1098/rsos.150402.

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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.

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23

Bulyk, Martha L. "DNA microarray technologies for measuring protein–DNA interactions." Current Opinion in Biotechnology 17, no. 4 (2006): 422–30. http://dx.doi.org/10.1016/j.copbio.2006.06.015.

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24

Schneeberg, Alexander, Ralf Ehricht, Peter Slickers, et al. "DNA Microarray-Based PCR Ribotyping of Clostridium difficile." Journal of Clinical Microbiology 53, no. 2 (2014): 433–42. http://dx.doi.org/10.1128/jcm.02524-14.

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This study presents a DNA microarray-based assay for fast and simple PCR ribotyping ofClostridium difficilestrains. Hybridization probes were designed to query the modularly structured intergenic spacer region (ISR), which is also the template for conventional and PCR ribotyping with subsequent capillary gel electrophoresis (seq-PCR) ribotyping. The probes were derived from sequences available in GenBank as well as from theoretical ISR module combinations. A database of reference hybridization patterns was set up from a collection of 142 well-characterizedC. difficileisolates representing 48 seq-PCR ribotypes. The reference hybridization patterns calculated by the arithmetic mean were compared using a similarity matrix analysis. The 48 investigated seq-PCR ribotypes revealed 27 array profiles that were clearly distinguishable. The most frequent human-pathogenic ribotypes 001, 014/020, 027, and 078/126 were discriminated by the microarray.C. difficilestrains related to 078/126 (033, 045/FLI01, 078, 126, 126/FLI01, 413, 413/FLI01, 598, 620, 652, and 660) and 014/020 (014, 020, and 449) showed similar hybridization patterns, confirming their genetic relatedness, which was previously reported. A panel of 50C. difficilefield isolates was tested by seq-PCR ribotyping and the DNA microarray-based assay in parallel. Taking into account that the current version of the microarray does not discriminate some closely related seq-PCR ribotypes, all isolates were typed correctly. Moreover, seq-PCR ribotypes without reference profiles available in the database (ribotype 009 and 5 new types) were correctly recognized as new ribotypes, confirming the performance and expansion potential of the microarray.

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25

Tsai, Chen-an, Chun-Houh Chen, Te-Chang Lee, I.-Ching Ho, Ueng-Cheng Yang, and James J. Chen. "Gene Selection for Sample Classifications in Microarray Experiments." DNA and Cell Biology 23, no. 10 (2004): 607–14. http://dx.doi.org/10.1089/dna.2004.23.607.

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26

Ding, Yuanyuan, and Dawn Wilkins. "The Effect of Normalization on Microarray Data Analysis." DNA and Cell Biology 23, no. 10 (2004): 635–42. http://dx.doi.org/10.1089/dna.2004.23.635.

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27

Nahar, Jesmin, Shawkat Ali, and Yi-Ping Phoebe Chen. "Microarray Data Classification Using Automatic SVM Kernel Selection." DNA and Cell Biology 26, no. 10 (2007): 707–12. http://dx.doi.org/10.1089/dna.2007.0590.

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28

Kappel, Kristina, Joanna Fafińska, Markus Fischer, and Jan Fritsche. "A DNA microarray for the authentication of giant tiger prawn (Penaeus monodon) and whiteleg shrimp (Penaeus (Litopenaeus) vannamei): a proof-of-principle." Analytical and Bioanalytical Chemistry 413, no. 19 (2021): 4837–46. http://dx.doi.org/10.1007/s00216-021-03440-2.

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AbstractThis proof-of-principle study describes the development of a rapid and easy-to-use DNA microarray assay for the authentication of giant tiger prawns and whiteleg shrimp. Following DNA extraction and conventional end-point PCR of a 16S rDNA segment, the PCR products are hybridised to species-specific oligonucleotide probes on DNA microarrays located at the bottom of centrifuge tubes (ArrayTubes) and the resulting signal patterns are compared to those of reference specimens. A total of 21 species-specific probes were designed and signal patterns were recorded for 47 crustacean specimens belonging to 16 species of seven families. A hierarchical clustering of the signal patterns demonstrated the specificity of the DNA microarray for the two target species. The DNA microarray can easily be expanded to other important crustaceans. As the complete assay can be performed within half a day and does not require taxonomic expertise, it represents a rapid and simple alternative to tedious DNA barcoding and could be used by crustacean trading companies as well as food control authorities for authentication of crustacean commodities. Graphical abstract

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29

Chen, Bao-An, Fan Zhang, Yan Wang, et al. "Microarray-Based Method for Quantificationally Detecting Methylation Changes of E-Cadherin Gene in Acute Leukemia." Blood 108, no. 11 (2006): 4406. http://dx.doi.org/10.1182/blood.v108.11.4406.4406.

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Abstract Aberrant DNA methylation of CpG sites is among the earliest and most frequent alterations in cancer. Several studies suggest that aberrant methylation of the CpG sites of the tumor suppressor gene is closely associated with carcinogenesis. So methylation detection is very important. The aim of this study was to describe a microarray-based method for quantificationally detecting changes of E-cadherin methylation in acute leukemia and to simply discuss the effect of microarray on detecting tumor methylation. This method used bisulfite-modified DNA as a template for PCR amplification, resulting in conversion of unmethylated cytosine, but not methylated cytosine, into thymine within CpG islands of interest. Five sets of oligonucleotide probes were designed to fabricate a DNA microarray to detect the methylation changes of E-cadherin gene CpG islands in acute leukemia. Each set contained a pair of methylated and unmethylated oligonucleotides for interrogating 3 or 4 CpG sites in close proximity. By TA cloning, PCR, sequencing, positive and negative DNA targets were obtained. A series of microarray hybridization were performed with mixtures of Cy3 labeled positive and negative DNA targets at different proportions. Next draw a standard curve by fluorescence intensity. Then leukemia samples DNA were abstracted and bisulfite-modified. Sample DNA targets were obtained by PCR amplification and were hybridized with the microarry. Finally the microarry was scanned with ScanArray Lite microarray analysis systems. Five linear relationships(R2=0.9660~0.9963) was established, which said the accuracy and reproducibility of the probes designed for microarray hybridization was good and could be used to eliminate background noise. The experimental results showed that the microarray assay could successfully detect five regions methylation changes of E-cadherin gene in every acute leukemia sample quantificationally. There were different degree of methylation in five acute leukemia samples and the hypermethylation region was the same. The test result was validated by gene sequencing. In tumors E-cadherin expression frequently is reduced, an event that contributes to tumor invasion and metastasis. The methylation of E-cadherin gene promoter is one important reason resulting in the silencing of expression. In normal peripheral blood mononuclear cells and bone marrow, E-cadherin is completely unmethylated. In our results, the methylation of E-cadherin is related to acute leukemia. And the same hypermethylation region may be the critical sites of methylation sites. As shown in this study, the use of a simple control system could test the accuracy and reproducibility of the probes designed for microarray hybridization. This control system can also be used to calibrate the levels of methylation changes detected in the investigated samples by microarray assay. With more and more methylated genes are found, microarray assay could be applied as a useful tool for mapping methylation changes in multiple CpG loci. It’s more time-saving and more labor-saving than gene sequencing and can be readily used to high throughput analysis of DNA methylation. It will contribute significant information to our understanding of CpG island methylation in acute leukemia.

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30

Lee, Eun-Ju, Yu-Mi Shin, Hyun-Jeong Lee, et al. "Identification of Cuts-specific Myogenic Marker Genes in Hanwoo by DNA Microarray." Journal of Animal Science and Technology 52, no. 4 (2010): 329–36. http://dx.doi.org/10.5187/jast.2010.52.4.329.

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31

Chagovetz, Alexander, and Steve Blair. "Real-time DNA microarrays: reality check." Biochemical Society Transactions 37, no. 2 (2009): 471–75. http://dx.doi.org/10.1042/bst0370471.

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DNA microarrays are plagued with inconsistent quantifications and false-positive results. Using established mechanisms of surface reactions, we argue that these problems are inherent to the current technology. In particular, the problem of multiplex non-equilibrium reactions cannot be resolved within the framework of the existing paradigm. We discuss the advantages and limitations of changing the paradigm to real-time data acquisition similar to real-time PCR methodology. Our analysis suggests that the fundamental problem of multiplex reactions is not resolved by the real-time approach itself. However, by introducing new detection chemistries and analysis approaches, it is possible to extract target-specific quantitative information from real-time microarray data. The possible scope of applications for real-time microarrays is discussed.

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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 (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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Cooper, Moogega, Myron T. La Duc, Alexander Probst, et al. "Comparison of Innovative Molecular Approaches and Standard Spore Assays for Assessment of Surface Cleanliness." Applied and Environmental Microbiology 77, no. 15 (2011): 5438–44. http://dx.doi.org/10.1128/aem.00192-11.

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ABSTRACTA bacterial spore assay and a molecular DNA microarray method were compared for their ability to assess relative cleanliness in the context of bacterial abundance and diversity on spacecraft surfaces. Colony counts derived from the NASA standard spore assay were extremely low for spacecraft surfaces. However, the PhyloChip generation 3 (G3) DNA microarray resolved the genetic signatures of a highly diverse suite of microorganisms in the very same sample set. Samples completely devoid of cultivable spores were shown to harbor the DNA of more than 100 distinct microbial phylotypes. Furthermore, samples with higher numbers of cultivable spores did not necessarily give rise to a greater microbial diversity upon analysis with the DNA microarray. The findings of this study clearly demonstrated that there is not a statistically significant correlation between the cultivable spore counts obtained from a sample and the degree of bacterial diversity present. Based on these results, it can be stated that validated state-of-the-art molecular techniques, such as DNA microarrays, can be utilized in parallel with classical culture-based methods to further describe the cleanliness of spacecraft surfaces.

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34

Simon, Richard M., and Kevin Dobbin. "Experimental Design of DNA Microarray Experiments." BioTechniques 34, no. 3S (2003): S16—S21. http://dx.doi.org/10.2144/mar03simon.

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35

Yoo, Seung, and Sang Lee. "Diagnosis of Pathogens Using DNA Microarray." Recent Patents on Biotechnology 2, no. 2 (2008): 124–29. http://dx.doi.org/10.2174/187220808784619711.

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36

Tchagang, Alain B., Fazel Famili, and Youlian Pan. "Subspace Clustering of DNA Microarray Data." International Journal of Computational Models and Algorithms in Medicine 4, no. 2 (2014): 1–52. http://dx.doi.org/10.4018/ijcmam.2014070101.

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Identification of biological significant subspace clusters (biclusters and triclusters) of genes from microarray experimental data is a very daunting task that emerged, especially with the development of high throughput technologies. Several methods and applications of subspace clustering (biclustering and triclustering) in DNA microarray data analysis have been developed in recent years. Various computational and evaluation methods based on diverse principles were introduced to identify new similarities among genes. This review discusses and compares these methods, highlights their mathematical principles, and provides insight into the applications to solve biological problems.

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Patil, Mayura, and Chandrakant Bhong. "Veterinary Diagnostics and DNA Microarray Technology." International Journal of Livestock Research 5, no. 4 (2015): 1. http://dx.doi.org/10.5455/ijlr.20150330032234.

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38

Galbraith, David W. "DNA Microarray Analyses in Higher Plants." OMICS: A Journal of Integrative Biology 10, no. 4 (2006): 455–73. http://dx.doi.org/10.1089/omi.2006.10.455.

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39

Jares, Pedro. "DNA Microarray Applications in Functional Genomics." Ultrastructural Pathology 30, no. 3 (2006): 209–19. http://dx.doi.org/10.1080/01913120500521380.

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Dobrowolski, SF, RA Banas, EW Naylor, T. Powdrill, and D. Thakkar. "DNA microarray technology for neonatal screening." Acta Paediatrica 88 (January 2, 2007): 61–64. http://dx.doi.org/10.1111/j.1651-2227.1999.tb01161.x.

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Bouchie, Aaron. "Shift anticipated in DNA microarray market." Nature Biotechnology 20, no. 1 (2002): 8. http://dx.doi.org/10.1038/nbt0102-8.

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Jung, I. H., S. M. Lee, S. H. Lee, J. H. Park, K. Y. Cha, and K. W. Lee. "DNA microarray analysis in down syndrome." Fertility and Sterility 78 (September 2002): S180. http://dx.doi.org/10.1016/s0015-0282(02)03876-1.

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Letowski, Jaroslaw, Roland Brousseau, and Luke Masson. "DNA Microarray Applications in Environmental Microbiology." Analytical Letters 36, no. 15 (2003): 3165–84. http://dx.doi.org/10.1081/al-120026566.

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Rew, D. A. "DNA microarray technology in cancer research." European Journal of Surgical Oncology (EJSO) 27, no. 5 (2001): 504–8. http://dx.doi.org/10.1053/ejso.2001.1116.

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Vabre, L., A. Dubois, M. C. Potier, J. L. Stehlé, and A. C. Boccara. "DNA microarray inspection by interference microscopy." Review of Scientific Instruments 72, no. 6 (2001): 2834–36. http://dx.doi.org/10.1063/1.1369625.

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Brandt, Regine, Robert Grützmann, Andrea Bauer, et al. "DNA microarray analysis of pancreatic malignancies." Pancreatology 4, no. 6 (2004): 587–97. http://dx.doi.org/10.1159/000082241.

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Liang Xu, Heng Yu, Shu-Jen Han, et al. "Giant Magnetoresistive Sensors for DNA Microarray." IEEE Transactions on Magnetics 44, no. 11 (2008): 3989–91. http://dx.doi.org/10.1109/tmag.2008.2002795.

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Dudda-Subramanya, Raghunandan, Guglielmo Lucchese, Darja Kanduc, and Animesh A. Sinha. "Clinical applications of DNA microarray analysis." Journal of Experimental Therapeutics and Oncology 3, no. 6 (2003): 297–304. http://dx.doi.org/10.1111/j.1533-869x.2003.01104.x.

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Banerjee, Tathagata. "DNA Microarray Data a Major Challenge." Calcutta Statistical Association Bulletin 51, no. 1-2 (2001): 139–56. http://dx.doi.org/10.1177/0008068320010114.

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Schwaenen, C., S. Wessendorf, H. A. Kestler, H. D�hner, P. Lichter, and M. Bentz. "DNA microarray analysis in malignant lymphomas." Annals of Hematology 82, no. 6 (2003): 323–32. http://dx.doi.org/10.1007/s00277-003-0649-6.

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