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Journal articles on the topic 'CDNA microarray'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Handley, Daniel, Nicoleta Serban, David G. Peters, and Clark Glymour. "Concerns About Unreliable Data from Spotted cDNA Microarrays Due to Cross-Hybridization and Sequence Errors." Statistical Applications in Genetics and Molecular Biology 3, no. 1 (2004): 1–2. http://dx.doi.org/10.2202/1544-6115.1091.

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We discuss our concerns regarding the reliability of data generated by spotted cDNA microarrays. Two types of error we highlight are cross-hybridization artifact due to sequence homologies and sequence errors in the cDNA used for spotting on microarrays. We feel that statisticians who analyze microarray data should be aware of these sources of unreliability intrinsic to cDNA microarray design and use.

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2

Pérez-Enciso, Miguel, Miguel A. Toro, Michel Tenenhaus, and Daniel Gianola. "Combining Gene Expression and Molecular Marker Information for Mapping Complex Trait Genes: A Simulation Study." Genetics 164, no. 4 (2003): 1597–606. http://dx.doi.org/10.1093/genetics/164.4.1597.

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Abstract A method for mapping complex trait genes using cDNA microarray and molecular marker data jointly is presented and illustrated via simulation. We introduce a novel approach for simulating phenotypes and genotypes conditionally on real, publicly available, microarray data. The model assumes an underlying continuous latent variable (liability) related to some measured cDNA expression levels. Partial least-squares logistic regression is used to estimate the liability under several scenarios where the level of gene interaction, the gene effect, and the number of cDNA levels affecting liabi

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3

Wang, Zidong, Bachar Zineddin, Jinling Liang, et al. "cDNA microarray adaptive segmentation." Neurocomputing 142 (October 2014): 408–18. http://dx.doi.org/10.1016/j.neucom.2014.03.052.

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4

Liang, Mingyu, Amy G. Briggs, Elizabeth Rute, Andrew S. Greene, and Allen W. Cowley. "Quantitative assessment of the importance of dye switching and biological replication in cDNA microarray studies." Physiological Genomics 14, no. 3 (2003): 199–207. http://dx.doi.org/10.1152/physiolgenomics.00143.2002.

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Dye switching and biological replication substantially increase the cost and the complexity of cDNA microarray studies. The objective of the present analysis was to quantitatively assess the importance of these procedures to provide a quantitative basis for decision-making in the design of microarray experiments. Taking advantage of the unique characteristics of a published data set, the impact of these procedures on the reliability of microarray results was calculated. Adding a second microarray with dye switching substantially increased the correlation coefficient between observed and predic

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5

Yang, Y. H. "Analysis of cDNA microarray images." Briefings in Bioinformatics 2, no. 4 (2001): 341–49. http://dx.doi.org/10.1093/bib/2.4.341.

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6

Emi, Mitsuru. "cDNA Microarray and SNP Analysis." Journal of Nippon Medical School 68, no. 5 (2001): 411–12. http://dx.doi.org/10.1272/jnms.68.411.

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7

Smyth, Gordon K., and Terry Speed. "Normalization of cDNA microarray data." Methods 31, no. 4 (2003): 265–73. http://dx.doi.org/10.1016/s1046-2023(03)00155-5.

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8

Mizukami, Satomi, Yoshiteru Suzuki, Emiko Kitagawa, and Hitoshi Iwahashi. "Standardization of cDNA microarray technology for toxicogenomics; essential data for initiating cDNA microarray studies." Chem-Bio Informatics Journal 4, no. 2 (2004): 38–55. http://dx.doi.org/10.1273/cbij.4.38.

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9

LEE, PHILIP R., JONATHAN E. COHEN, ELISABETTA A. TENDI, et al. "Transcriptional profiling in an MPNST-derived cell line and normal human Schwann cells." Neuron Glia Biology 1, no. 2 (2004): 135–47. http://dx.doi.org/10.1017/s1740925x04000274.

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cDNA microarrays were utilized to identify abnormally expressed genes in a malignant peripheral nerve sheath tumor (MPNST)-derived cell line, T265, by comparing the mRNA abundance profiles with that of normal human Schwann cells (nhSCs). The findings characterize the molecular phenotype of this important cell-line model of MPNSTs, and elucidate the contribution of Schwann cells in MPNSTs. In total, 4608 cDNA sequences were screened and hybridizations replicated on custom cDNA microarrays. In order to verify the microarray data, a large selection of differentially expressed mRNA transcripts wer

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10

Nobis, William, Xiaoning Ren, Steven P. Suchyta, Thomas R. Suchyta, Adroaldo J. Zanella, and Paul M. Coussens. "Development of a porcine brain cDNA library, EST database, and microarray resource." Physiological Genomics 16, no. 1 (2003): 153–59. http://dx.doi.org/10.1152/physiolgenomics.00099.2003.

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Recent developments in expressed sequence tag (EST) and cDNA microarray technology have had a dramatic impact on the ability of scientists to study responses of thousands of genes to internal and external stimuli. In neurobiology, studies of the human brain have been expanding rapidly by use of functional genomics techniques. To enhance these studies and allow use of a porcine brain model, a normalized porcine brain cDNA library (PBL) has been generated and used as a base for EST discovery and microarray generation. In this report, we discuss initial sequence analysis of 965 clones from this r

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11

Leimena, Milkha M., Michiel Wels, Roger S. Bongers, Eddy J. Smid, Erwin G. Zoetendal, and Michiel Kleerebezem. "Comparative Analysis of Lactobacillus plantarum WCFS1 Transcriptomes by Using DNA Microarray and Next-Generation Sequencing Technologies." Applied and Environmental Microbiology 78, no. 12 (2012): 4141–48. http://dx.doi.org/10.1128/aem.00470-12.

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ABSTRACTRNA sequencing is starting to compete with the use of DNA microarrays for transcription analysis in eukaryotes as well as in prokaryotes. The application of RNA sequencing in prokaryotes requires additional steps in the RNA preparation procedure to increase the relative abundance of mRNA and cannot employ the poly(T)-primed approach in cDNA synthesis. In this study, we aimed to validate the use of RNA sequencing (direct cDNA sequencing and 3′-untranslated region [UTR] sequencing) usingLactobacillus plantarumWCFS1 as a model organism, employing its established microarray platform as a r

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12

Thomson, S. A. M., E. Kennerly, N. Olby, et al. "Microarray Analysis of Differentially Expressed Genes of Primary Tumors in the Canine Central Nervous System." Veterinary Pathology 42, no. 5 (2005): 550–58. http://dx.doi.org/10.1354/vp.42-5-550.

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The pathophysiologic similarities of many human and canine cancers support the role of the domestic dog as a model for brain tumor research. Here we report the construction of a custom canine brain-specific cDNA microarray and the analysis of gene expression patterns of several different types of canine brain tumor The microarray contained 4000 clones from a canine brain specific cDNA library including 2161 clones that matched known genes or expressed sequence tags (ESTs) and 25 cancer-related genes. Our study included 16 brain tumors (seven meningiomas, five glial tumors, two ependymomas, and

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13

Kuzhali, S. Elavaar, and Suresh D. S. "Collaborative Priors with SVD for Denoising of cDNA Microarray Images." Indian Journal of Science and Technology 12, no. 37 (2019): 1–15. http://dx.doi.org/10.17485/ijst/2019/v12i37/147036.

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14

Pang, Jin-Song, Meng-Yuan He, and Bao Liu. "Construction of the Seed-Coat cDNA Microarray and Screening of Differentially Expressed Genes in Barley." Acta Biochimica et Biophysica Sinica 36, no. 10 (2004): 695–700. http://dx.doi.org/10.1093/abbs/36.10.695.

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Abstract Some barley mutants can synthesize neither anthocyanins nor proanthocyanidins in the seed coat, which is related to several genes in locus Ant13, but the exact model of action remains unknown. We used the cDNA microarray technology with barley transcription-deficient mutant (ant13-152) that does not synthesize proanthocyanidins as the tester, and its wild type genotype (Triumph) as the driver, to study this question. Six-thousand and forty-eight clones from the wild type Morex testa+pericarp cDNA library were amplified using PCR, and the DNA fragments were spotted on commercial amino-

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15

Zhao, Baiteng, Robert A. Bowden, Salomon A. Stavchansky, and Phillip D. Bowman. "Human endothelial cell response to gram-negative lipopolysaccharide assessed with cDNA microarrays." American Journal of Physiology-Cell Physiology 281, no. 5 (2001): C1587—C1595. http://dx.doi.org/10.1152/ajpcell.2001.281.5.c1587.

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To assess the feasibility of using cDNA microarrays to understand the response of endothelial cells to lipopolysaccharide (LPS) and to evaluate potentially beneficial agents in treatment of septic shock, human umbilical vein endothelial cells were exposed to Escherichia coli LPS for 1, 4, 7, 12, or 24 h. Total RNA was isolated and reverse-transcribed into33P-labeled cDNA probes that were hybridized to human GeneFilter microarrays containing ∼4,000 genes. The mRNA levels of several genes known to respond to LPS changed after stimulation. In addition, a number of genes not previously implicated

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16

Becker, Kevin G. "The sharing of cDNA microarray data." Nature Reviews Neuroscience 2, no. 6 (2001): 438–40. http://dx.doi.org/10.1038/35077580.

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17

Yue Wang, Jianping Lu, R. Lee, Zhiping Gu, and R. Clarke. "Iterative normalization of cDNA microarray data." IEEE Transactions on Information Technology in Biomedicine 6, no. 1 (2002): 29–37. http://dx.doi.org/10.1109/4233.992159.

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18

Welling, D. Bradley, John M. Lasak, Elena Akhmametyeva, Bobak Ghaheri, and Long-Sheng Chang. "cDNA Microarray Analysis of Vestibular Schwannomas." Otology & Neurotology 23, no. 5 (2002): 736–48. http://dx.doi.org/10.1097/00129492-200209000-00022.

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19

Roy, Sashwati, and Chandan K. Sen. "cDNA microarray screening in food safety." Toxicology 221, no. 1 (2006): 128–33. http://dx.doi.org/10.1016/j.tox.2005.12.025.

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20

Xiang, Charlie C., and Yidong Chen. "cDNA microarray technology and its applications." Biotechnology Advances 18, no. 1 (2000): 35–46. http://dx.doi.org/10.1016/s0734-9750(99)00035-x.

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21

Sahar, David E., George P. Yang, Michael T. Longaker, and Alden H. Harken. "Surgical application of cDNA microarray technique." Surgery 138, no. 3 (2005): 399–403. http://dx.doi.org/10.1016/j.surg.2005.01.011.

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22

Yang, Yee Hwa, and Terry Speed. "Design issues for cDNA microarray experiments." Nature Reviews Genetics 3, no. 8 (2002): 579–88. http://dx.doi.org/10.1038/nrg863.

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23

Burgueño, Juan, Jose Crossa, Daniel Grimanelli, Olivier Leblanc, and Daphne Autran. "Spatial Analysis of cDNA Microarray Experiments." Crop Science 45, no. 2 (2005): 748–57. http://dx.doi.org/10.2135/cropsci2005.0748.

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24

Folpe, Andrew L. "cDNA microarray study of synovial sarcomas." Advances in Anatomic Pathology 10, no. 4 (2003): 237. http://dx.doi.org/10.1097/00125480-200307000-00010.

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25

Washington, Kay. "cDNA Microarray Study of Biliary Atresia." Advances in Anatomic Pathology 11, no. 1 (2004): 65. http://dx.doi.org/10.1097/00125480-200401000-00007.

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26

Leung, Yuk Fai, and Duccio Cavalieri. "Fundamentals of cDNA microarray data analysis." Trends in Genetics 19, no. 11 (2003): 649–59. http://dx.doi.org/10.1016/j.tig.2003.09.015.

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27

Kikuchi, Shoshi. "Rice Microarray Project in Japan." Asia-Pacific Biotech News 06, no. 24 (2002): 920–26. http://dx.doi.org/10.1142/s021903030200191x.

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28

STEARS, ROBIN L., ROBERT C. GETTS, and STEVEN R. GULLANS. "A novel, sensitive detection system for high-density microarrays using dendrimer technology." Physiological Genomics 3, no. 2 (2000): 93–99. http://dx.doi.org/10.1152/physiolgenomics.2000.3.2.93.

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Stears, Robin L., Robert C. Getts, and Steven R. Gullans. A novel, sensitive detection system for high-density microarrays using dendrimer technology. Physiol Genomics 3: 93–99, 2000.—To improve signal detection on cDNA microarrays, we adapted a fluorescent oligonucleotide dendrimeric signal amplification system to microarray technology. This signal detection method requires 16-fold less RNA for probe synthesis, does not depend on the incorporation of fluorescent dNTPs into a reverse transcription reaction, generates a high signal-to-background ratio, and can be used to allow for multichannel

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29

Agbemafle, Barbara M., Thomas J. Oesterreicher, Chad A. Shaw, and Susan J. Henning. "Immediate early genes of glucocorticoid action on the developing intestine." American Journal of Physiology-Gastrointestinal and Liver Physiology 288, no. 5 (2005): G897—G906. http://dx.doi.org/10.1152/ajpgi.00454.2004.

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Prior studies have demonstrated that glucocorticoid hormones elicit functional maturation of the small intestine as evidenced by their ability to induce increases in the expression of various digestive hydrolases, such as sucrase-isomaltase and trehalase. However, these increases have a lag time of ∼24 h, suggesting that they are secondary effects of hormone action. To identify candidate primary response genes, we performed microarray analysis on pooled RNA from jejunums of untreated postnatal day 8 mouse pups and from littermates who earlier received dexamethasone 2 h. Fluorescent dye-labeled

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30

Kim, Hi Chul, Jin Yeong Heo, Tae-Kyu Lee, Ssang-Goo Cho, and Yong-Jun Kwon. "Optimization of Cell-Based cDNA Microarray Conditions for Gene Functional Studies in HEK293 Cells." SLAS DISCOVERY: Advancing the Science of Drug Discovery 22, no. 8 (2017): 1053–59. http://dx.doi.org/10.1177/2472555217699823.

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Since the cell-based cDNA microarray (CBCM) technique has been a useful tool for gain-of-function studies, many investigators have used CBCMs to identify interesting genes. However, this method requires better-established conditions to ensure high reverse transfection efficiency without cross-contamination. Therefore, we optimized CBCM techniques through various means. We determined that Lipofectamine 2000 was the most appropriate transfection reagent by evaluating eight commercialized reagents, and we determined that the most effective concentrations for printing solution constituents were 0.

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31

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 individu

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32

Suchyta, Steven P., Sue Sipkovsky, Rachael Kruska, et al. "Development and testing of a high-density cDNA microarray resource for cattle." Physiological Genomics 15, no. 2 (2003): 158–64. http://dx.doi.org/10.1152/physiolgenomics.00094.2003.

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A cDNA microarray resource has been developed with the goal of providing integrated functional genomics resources for cattle. The National Bovine Functional Genomics Consortium’s (NBFGC) expressed sequence tag (EST) collection was established in 2001 to develop resources for functional genomics research. The NBFGC EST collection and microarray contains 18,263 unique transcripts, derived from many different tissue types and various physiologically important states within these tissues. The NBFGC microarray has been tested for false-positive rates using self-self hybridizations and was shown to

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33

Rahnenführer, J. "Image Analysis for cDNA Microarrays." Methods of Information in Medicine 44, no. 03 (2005): 405–7. http://dx.doi.org/10.1055/s-0038-1633984.

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Summary Objectives: We characterize typical problems encountered in microarray image analysis and present algorithmic approaches dealing with background estimation, spot identification and intensity extraction. Validation of the quality of resulting measurements is discussed. Methods: We describe sources for errors in microarray images and present algorithms that have been specifically developed to deal with such experimental imperfections. Results: For the image analysis of hybridization experiments, discriminating spot regions from a background is the most critical step. Spot shape detection

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34

Eckel, Jeanette E., Antje Hoering, and Irene Ghobrial. "Experimental Design & Analysis of Protein Array Data: Applying Methods from cDNA Arrays." Blood 104, no. 11 (2004): 4280. http://dx.doi.org/10.1182/blood.v104.11.4280.4280.

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Abstract It appears that a number of recent manuscripts using protein microarray technology are using equivalent analysis procedures that the gene-expression microarray community implemented in their infancy. That is, utilizing a classic reference design such that the ratio of the sample of interest to a reference sample is the response of interest and assessing fold change to determine differential expression. For example, recent publications have concluded that proteins with a fold change less than 0.7 or greater than 1.3 demonstrate significant down- or up-regulated differential expression,

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35

Neal, Scott J., Meredith L. Gibson, Anthony K. C. So, and J. Timothy Westwood. "Construction of a cDNA-based microarray for Drosophila melanogaster: a comparison of gene transcription profiles from SL2 and Kc167 cells." Genome 46, no. 5 (2003): 879–92. http://dx.doi.org/10.1139/g03-056.

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We have constructed a DNA microarray that represents approximately 6900 of the estimated 13 598 genes in the Drosophila melanogaster genome. The microarray contains 5756 target cDNAs from the Berkeley Drosophila Genome Project, 1078 cDNAs from the National Institutes of Health Drosophila testis cDNA library, and 546 gene fragments that were amplified from genomic DNA. The methods for DNA amplification and microarray manufacture are presented. Academic researchers can obtain the microarray from the Canadian Drosophila Microarray Centre. To evaluate the utility of these arrays, we compared the g

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36

Sin, Cheol-Kyung, Chae-Woo Lee, Sun-Ae Yoo, et al. "Genes expression by using cDNA Microarray in Whallak-tang." Journal of Korean Institute of Herbal Acupuncture 11, no. 4 (2008): 5–14. http://dx.doi.org/10.3831/kpi.2008.11.4.005.

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37

Mouysset, Sandrine, Ronan Guivarch, Joseph Noailles, and Daniel Ruiz. "Segmentation of cDNA Microarray Images using Parallel Spectral Clustering." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 2, no. 1 (2013): 1–8. http://dx.doi.org/10.14201/adcaij20132418.

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Microarray technology generates large amounts of expression level of genes to be analyzed simultaneously. This analysis implies microarray image segmentation to extract the quantitative information from spots. Spectral clustering is one of the most relevant unsupervised methods able to gather data without a priori information on shapes or locality. We propose and test on microarray images a parallel strategy for the Spectral Clustering method based on domain decomposition with a criterion to determine the number of clusters.

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38

Hegde, P., R. Qi, K. Abernathy, et al. "A Concise Guide to cDNA Microarray Analysis." BioTechniques 29, no. 3 (2000): 548–62. http://dx.doi.org/10.2144/00293bi01.

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39

Wang, J., L. Hu, S. R. Hamilton, K. R. Coombes, and W. Zhang. "RNA Amplification Strategies for cDNA Microarray Experiments." BioTechniques 34, no. 2 (2003): 394–400. http://dx.doi.org/10.2144/03342mt04.

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40

Zhou Qifeng, Zhou Qingqing, Yang XiaoQing, and Hong Wencai. "cDNA Microarray Images Gridding Based on Projection." Journal of Convergence Information Technology 6, no. 3 (2011): 188–94. http://dx.doi.org/10.4156/jcit.vol6.issue3.21.

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41

Bao, Zhang, Ma Wenli, Shi Rong, Li Ling, Guo Qiuye, and Zheng Wenling. "Re-use of a stripped cDNA microarray." British Journal of Biomedical Science 59, no. 2 (2002): 112–13. http://dx.doi.org/10.1080/09674845.2002.11783645.

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42

Gupta, Sudhir. "Balanced Factorial Designs for cDNA Microarray Experiments." Communications in Statistics - Theory and Methods 35, no. 8 (2006): 1469–76. http://dx.doi.org/10.1080/03610920600694587.

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43

Statham, Victoria, Michael Bittner, Jeffrey Trent, and Richard A. Morgan. "Applying cDNA microarray technology to gene therapy." Nature Genetics 23, S3 (1999): 75. http://dx.doi.org/10.1038/14407.

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44

Ji-Gang, Zhang, Zhang Qin, and Yin Zong-Jun. "The normalization method for cDNA microarray data." Chinese Journal of Agricultural Biotechnology 3, no. 3 (2006): 195–99. http://dx.doi.org/10.1079/cjb2006113.

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AbstractThe widely used processing method for cDNA microarray data involves background correction, log-ratio transformation and data normalization before the statistical testing can be done. Here we propose a method that avoids the log-transformation step in view of its drawbacks, but goes directly to normalization after background correction. This method could better estimate the ‘noise’ effect by utilizing the information more effectively. Simulation studies were carried out to compare the feasibility and efficiency of this approach for eliminating experimental ‘noise’ with the log-ratio app

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45

Xiang, Daoquan, Raju Datla, Fengling Li, et al. "Development of a Brassica seed cDNA microarray." Genome 51, no. 3 (2008): 236–42. http://dx.doi.org/10.1139/g07-115.

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Brassica species represent several important crops including canola ( Brassica napus ). Understanding of genetic elements that contribute to seed-associated functions will impact future improvements in the canola crop. Brassica species share a very close taxonomic and molecular relationship with Arabidopsis thaliana. However, there are several subtle but distinct seed-associated agronomic characteristics that differ among the oil seed crop species. To address these, we have generated 67 535 ESTs predominately from Brassica seeds, analyzed these sequences, and identified 10 642 unigenes for the

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46

Nagarajan, R., and M. Upreti. "Correlation Statistics for cDNA Microarray Image Analysis." IEEE/ACM Transactions on Computational Biology and Bioinformatics 3, no. 3 (2006): 232–38. http://dx.doi.org/10.1109/tcbb.2006.30.

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47

Bergemann, Tracy L., and Lue Ping Zhao. "Signal Quality Measurements for cDNA Microarray Data." IEEE/ACM Transactions on Computational Biology and Bioinformatics 7, no. 2 (2010): 299–308. http://dx.doi.org/10.1109/tcbb.2008.72.

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48

Li, Z. "DCIS versus IBC: A cDNA microarray study." European Journal of Cancer 38, no. 11 (2002): S117. http://dx.doi.org/10.1016/s0959-8049(02)80380-x.

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49

Beneš, Vladimı́r, and Martina Muckenthaler. "Standardization of protocols in cDNA microarray analysis." Trends in Biochemical Sciences 28, no. 5 (2003): 244–49. http://dx.doi.org/10.1016/s0968-0004(03)00068-9.

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50

Li, Huiyu, Shenghua Jie, Ping Zou, and Guolin Zou. "cDNA Microarray Analysis of Chronic Myeloid Leukemia." International Journal of Hematology 75, no. 4 (2002): 388–93. http://dx.doi.org/10.1007/bf02982130.

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