Journal articles: 'Microarrays'

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Aparna, G. M., and Kishore K. R. Tetala. "Recent Progress in Development and Application of DNA, Protein, Peptide, Glycan, Antibody, and Aptamer Microarrays." Biomolecules 13, no. 4 (March 27, 2023): 602. http://dx.doi.org/10.3390/biom13040602.

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Microarrays are one of the trailblazing technologies of the last two decades and have displayed their importance in all the associated fields of biology. They are widely explored to screen, identify, and gain insights on the characteristics traits of biomolecules (individually or in complex solutions). A wide variety of biomolecule-based microarrays (DNA microarrays, protein microarrays, glycan microarrays, antibody microarrays, peptide microarrays, and aptamer microarrays) are either commercially available or fabricated in-house by researchers to explore diverse substrates, surface coating, immobilization techniques, and detection strategies. The aim of this review is to explore the development of biomolecule-based microarray applications since 2018 onwards. Here, we have covered a different array of printing strategies, substrate surface modification, biomolecule immobilization strategies, detection techniques, and biomolecule-based microarray applications. The period of 2018–2022 focused on using biomolecule-based microarrays for the identification of biomarkers, detection of viruses, differentiation of multiple pathogens, etc. A few potential future applications of microarrays could be for personalized medicine, vaccine candidate screening, toxin screening, pathogen identification, and posttranslational modifications.

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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 (May 25, 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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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 (March 26, 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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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 (January 6, 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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Fesseha, Haben, and Hiwot Tilahun. "Principles and Applications of Deoxyribonucleic Acid Microarray: A Review." Pathology and Laboratory Medicine – Open Journal 3, no. 1 (March 30, 2021): 1–9. http://dx.doi.org/10.17140/plmoj-3-109.

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Deoxyribonucleic acid (DNA) microarrays are collections of DNA probes arranged on a base pair and the latest commercialized molecular diagnostic technologies that offer high throughput results, more sensitive and require less time. It is the most reliable and widely accepted tool facilitating the simultaneous identification of thousands of genetic elements even a single gene. Microarrays are powerful new tools for the investigation of global changes in gene expression profiles in cells and tissues. The different types of DNA microarray or DNA chip devices and systems are described along with their methods of fabrication and their use. The DNA microarrays assembly process is automatized and further miniaturized. DNA microarrays are used in the search of various specific genes or in gene polymorphism and expression analysis. They will be widely used to investigate the expression of various genes connected with various diseases in order to find the causes of these diseases and to enable their accurate treatment. Generally, microarray analysis is not only applied for gene expression studies, but also used in immunology, genotyping, diagnostics and sequence analysis. Additionally, microarray technology being developed and applied to new areas of proteomics, cancer research, and cellular analysis.

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

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

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Korbelik, J., M. Cardeno, J. P. Matisic, A. C. Carraro, and C. MacAulay. "Cytology Microarrays." Analytical Cellular Pathology 29, no. 5 (January 1, 2007): 435–42. http://dx.doi.org/10.1155/2007/258297.

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The use of high throughput genetic and expression platforms are generating many candidate diagnostic markers and therapeutic targets for a wide variety of clinical conditions. Tissue microarrays can be used for the evaluation of the utility of many of these markers. However, tissue microarrays can suffer from the limitations associated with sampling and sectioning tissues. We introduce a novel microarray technique based on cell suspensions. Multiple slides can be made, all of which are equally representative of the initial sample. A robotic device was designed that can deposit 60 distinct spots of cytological material on a glass slide. Each spot of cells deposited in this manner may correspond to a unique source. Controlling the number of cells per spot, their distribution within the spot and the size of the spot can be achieved by modifying the viscosity of the cell solution or regulating the amount of fluid deposited. A fully automated analysis of quantitatively stained microarray samples has been performed to quantify the number of cells per spot, the size of spots and the DNA amount per cell in each spot. The reproducibility of these parameters was found to be high.

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Wilson, K. J., and E. de la Vega. "The potential of microarrays to assist shrimp breeding and production: a review." Australian Journal of Experimental Agriculture 45, no. 8 (2005): 901. http://dx.doi.org/10.1071/ea05060.

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The shrimp aquaculture industry is a relatively new livestock industry, having developed over the past 30 years. Thus, it is poised to take advantage of new technologies from the outset of selective breeding programs. This contrasts with long established livestock industries, where there are already highly specialised breeds. This review focuses specifically on the potential application of microarrays to shrimp breeding. Potential applications of microarrays in selective breeding programs are summarised. Microarrays can be used as a rapid means to generate molecular markers for genetic linkage mapping, and genetic maps have been constructed for yeast, Arabidopsis and barley using microarray technology. Microarrays can also be used in the hunt for candidate genes affecting particular traits, leading to development of perfect markers for these traits (i.e. causative mutations). However, this requires that microarray analysis be combined with genetic linkage mapping, and that substantial genomic information is available for the species in question. A novel application of microarrays is to treat gene expression as a quantitative trait in itself and to combine this with linkage mapping to identify quantitative trait loci controlling the levels of gene expression; this approach may identify higher level regulatory genes in specific pathways. Finally, patterns of gene expression observed using microarrays may themselves be treated as phenotypic traits in selection programs (e.g. a particular pattern of gene expression might be indicative of a disease tolerant individual). Microarrays are now being developed for a number of shrimp species in laboratories around the world, primarily with a focus on identifying genes involved in the immune response. However, at present, there is no central repository of shrimp genomic information, which limits the rate at which shrimp genomic research can be progressed. The application of microarrays to shrimp breeding will be extremely limited until there is a shared repository of genomic information for shrimp, and the collective will and resources to develop comprehensive genomic tools for shrimp.

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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 (September 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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Raczynski, Lech, Krzysztof Wozniak, Tymon Rubel, and Krzysztof Zaremba. "Application of Density Based Clustering to Microarray Data Analysis." International Journal of Electronics and Telecommunications 56, no. 3 (September 1, 2010): 281–86. http://dx.doi.org/10.2478/v10177-010-0037-9.

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Application of Density Based Clustering to Microarray Data AnalysisIn just a few years, gene expression microarrays have rapidly become a standard experimental tool in the biological and medical research. Microarray experiments are being increasingly carried out to address the wide range of problems, including the cluster analysis. The estimation of the number of clusters in datasets is one of the main problems of clustering microarrays. As a supplement to the existing methods we suggest the use of a density based clustering technique DBSCAN that automatically defines the number of clusters. The DBSCAN and other existing methods were compared using the microarray data from two datasets used for diagnosis of leukemia and lung cancer.

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Berthuy, Ophélie I., Sinan K. Muldur, François Rossi, Pascal Colpo, Loïc J. Blum, and Christophe A. Marquette. "Multiplex cell microarrays for high-throughput screening." Lab on a Chip 16, no. 22 (2016): 4248–62. http://dx.doi.org/10.1039/c6lc00831c.

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Coughlan, Sean J., Vikas Agrawal, and Blake Meyers. "A Comparison of Global Gene Expression Measurement Technologies inArabidopsis thaliana." Comparative and Functional Genomics 5, no. 3 (2004): 245–52. http://dx.doi.org/10.1002/cfg.397.

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Microarrays and tag-based transcriptional profiling technologies represent diverse but complementary data types. We are currently conducting a comparison of high-densityin situsynthesized microarrays and massively-parallel signature sequencing (MPSS) data in the model plant,Arabidopsis thaliana. The MPSS data (available at http://mpss.udel.edu/at) and the microarray data have been compiled using the same RNA source material. In this review, we outline the experimental strategy that we are using, and present preliminary data and interpretations from the transcriptional profiles ofArabidopsisleaves and roots. The preliminary data indicate that the log ratio differences of transcripts between leaves and roots measured by microarray data are in better agreement with the MPSS data than the absolute intensities measured for individual microarrays hybridized to only one of the cRNA populations. The correlation was substantially improved by focusing on a subset of genes excluding those with very low expression levels; this selection may have removed noisy data. Future reports will incorporate more than 10 tissues that have been sampled by MPSS.

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KATHLEEN KERR, M., and GARY A. CHURCHILL. "Statistical design and the analysis of gene expression microarray data." Genetical Research 77, no. 2 (February 2001): 123–28. http://dx.doi.org/10.1017/s0016672301005055.

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Gene expression microarrays are an innovative technology with enormous promise to help geneticists explore and understand the genome. Although the potential of this technology has been clearly demonstrated, many important and interesting statistical questions persist. We relate certain features of microarrays to other kinds of experimental data and argue that classical statistical techniques are appropriate and useful. We advocate greater attention to experimental design issues and a more prominent role for the ideas of statistical inference in microarray studies.

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Marjani, Sadie L., Daniel Le Bourhis, Xavier Vignon, Yvan Heyman, Robin E. Everts, Sandra L. Rodriguez-Zas, Harris A. Lewin, Jean-Paul Renard, Xiangzhong Yang, and X. Cindy Tian. "Embryonic gene expression profiling using microarray analysis." Reproduction, Fertility and Development 21, no. 1 (2009): 22. http://dx.doi.org/10.1071/rd08217.

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Microarray technology enables the interrogation of thousands of genes at one time and therefore a systems level of analysis. Recent advances in the amplification of RNA, genome sequencing and annotation, and the lower cost of developing microarrays or purchasing them commercially, have facilitated the analysis of single preimplantation embryos. The present review discusses the components of embryonic expression profiling and examines current research that has used microarrays to study the effects of in vitro production and nuclear transfer.

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Smith, David F., Richard D. Cummings, and Xuezheng Song. "History and future of shotgun glycomics." Biochemical Society Transactions 47, no. 1 (January 9, 2019): 1–11. http://dx.doi.org/10.1042/bst20170487.

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Abstract Glycans in polysaccharides and glycoconjugates of the hydrophilic exterior of all animal cells participate in signal transduction, cellular adhesion, intercellular signaling, and sites for binding of pathogens largely through protein–glycan interactions. Microarrays of defined glycans have been used to study the binding specificities of biologically relevant glycan-binding proteins (GBP), but such arrays are limited by their lack of diversity or relevance to the GBP being investigated. Shotgun glycan microarrays are made up of structurally undefined glycans that were released from natural sources, labeled with bifunctional reagents so that they can be monitored during their purification using multidimensional chromatographic procedures, stored as a tagged glycan library (TGL) and subsequently printed onto microarrays at equal molar concentrations. The shotgun glycan microarray is then interrogated with a biologically relevant GBP and the corresponding glycan ligands can be retrieved from the TGL for detailed structural analysis and further functional analysis. Shotgun glycomics extended the defined glycan microarray to a discovery platform that supports functional glycomic analyses and may provide a useful process for ultimately defining the human glycome.

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ZHANG, YONG. "INTEGRATION OF NANOPARTICLES WITH PROTEIN MICROARRAYS." International Journal of Nanoscience 05, no. 02n03 (April 2006): 189–94. http://dx.doi.org/10.1142/s0219581x0600422x.

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A variety of DNA, protein or cell microarray devices and systems have been developed and commercialized. In addition to the biomolecule related analysis, they are also being used for pharmacogenomic research, infectious and genetic disease and cancer diagnostics, and proteomic and cellular analysis.1 Currently, microarray is fabricated on a planar surface; this limits the amount of biomolecules that can be bounded on the surface. In this work, a planar protein microarray chip with nonplanar spot surface was fabricated to enhance the chip performance. A nonplanar spot surface was created by first coating the silica nanoparticles with albumin and depositing them into the patterned microwells. The curve surfaces of the nanoparticles increase the surface area for immobilization of proteins, which helps to enhance the detection sensitivity of the chip. Using this technique, proteins are immobilized onto the nanoparticles before they are deposited onto the chip, and therefore the method of protein immobilization can be customized at each spot. Furthermore, a nonplanar surface promotes the retention of native protein structure better than planar surface.2 The technique developed can be used to produce different types of microarrays, such as DNA, protein and antibody microarrays.

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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 (January 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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Zhao, Jianmei, Xuecang Li, Jincheng Guo, Meng Li, Jian Zhang, Jiyu Ding, Shang Li, et al. "ReCirc: prediction of circRNA expression and function through probe reannotation of non-circRNA microarrays." Molecular Omics 15, no. 2 (2019): 150–63. http://dx.doi.org/10.1039/c8mo00252e.

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Lederman, Lynne. "Microarrays." BioTechniques 44, no. 6 (May 2008): 729–33. http://dx.doi.org/10.2144/000112852.

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Lederman, Lynne. "Microarrays." BioTechniques 47, no. 2 (August 2009): 659–61. http://dx.doi.org/10.2144/000113213.

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Tuma, Rabiya S. "Microarrays." Oncology Times 25, no. 14 (July 2003): 48–51. http://dx.doi.org/10.1097/01.cot.0000289312.60141.d6.

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Plomin, Robert, and Leonard C. Schalkwyk. "Microarrays." Developmental Science 10, no. 1 (January 2007): 19–23. http://dx.doi.org/10.1111/j.1467-7687.2007.00558.x.

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Christie, Jason D. "Microarrays." Critical Care Medicine 33, Suppl (December 2005): S449—S452. http://dx.doi.org/10.1097/01.ccm.0000186078.26361.96.

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Speed, Terry, and Hongyu Zhao. "Microarrays." Statistical Methods in Medical Research 18, no. 6 (December 2009): 531–32. http://dx.doi.org/10.1177/0962280209352042.

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McKay, David. "Microarrays." Trends in Biotechnology 19, no. 6 (June 2001): 203. http://dx.doi.org/10.1016/s0167-7799(01)01675-4.

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Rao, J. Sunil, and Meredith Bond. "Microarrays." Circulation Research 88, no. 12 (June 22, 2001): 1226–27. http://dx.doi.org/10.1161/hh1201.093165.

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Fathallah-Shaykh, Hassan M. "Microarrays." Archives of Neurology 62, no. 11 (November 1, 2005): 1669. http://dx.doi.org/10.1001/archneur.62.11.1669.

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Wullschleger, Stan D., and Stephen P. Difazio. "Emerging Use of Gene Expression Microarrays in Plant Physiology." Comparative and Functional Genomics 4, no. 2 (2003): 216–24. http://dx.doi.org/10.1002/cfg.277.

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Microarrays have become an important technology for the global analysis of gene expression in humans, animals, plants, and microbes. Implemented in the context of a well-designed experiment, cDNA and oligonucleotide arrays can provide highthroughput, simultaneous analysis of transcript abundance for hundreds, if not thousands, of genes. However, despite widespread acceptance, the use of microarrays as a tool to better understand processes of interest to the plant physiologist is still being explored. To help illustrate current uses of microarrays in the plant sciences, several case studies that we believe demonstrate the emerging application of gene expression arrays in plant physiology were selected from among the many posters and presentations at the 2003 Plant and Animal Genome XI Conference. Based on this survey, microarrays are being used to assess gene expression in plants exposed to the experimental manipulation of air temperature, soil water content and aluminium concentration in the root zone. Analysis often includes characterizing transcript profiles for multiple post-treatment sampling periods and categorizing genes with common patterns of response using hierarchical clustering techniques. In addition, microarrays are also providing insights into developmental changes in gene expression associated with fibre and root elongation in cotton and maize, respectively. Technical and analytical limitations of microarrays are discussed and projects attempting to advance areas of microarray design and data analysis are highlighted. Finally, although much work remains, we conclude that microarrays are a valuable tool for the plant physiologist interested in the characterization and identification of individual genes and gene families with potential application in the fields of agriculture, horticulture and forestry.

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Kusi-Appiah, A. E., T. W. Lowry, E. M. Darrow, K. A. Wilson, B. P. Chadwick, M. W. Davidson, and S. Lenhert. "Quantitative dose–response curves from subcellular lipid multilayer microarrays." Lab on a Chip 15, no. 16 (2015): 3397–404. http://dx.doi.org/10.1039/c5lc00478k.

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Campanero-Rhodes, María Asunción, Enrique Llobet, José Antonio Bengoechea, and Dolores Solís. "Bacteria microarrays as sensitive tools for exploring pathogen surface epitopes and recognition by host receptors." RSC Advances 5, no. 10 (2015): 7173–81. http://dx.doi.org/10.1039/c4ra14570d.

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We have developed a readily adaptable microarray technology for high-throughput screening of pathogen-binding biomolecules and inhibitors of pathogen–counter-receptor interactions, based on the generation of bacteria microarrays.

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Campbell, A. Malcolm, Mary Lee S. Ledbetter, Laura L. M. Hoopes, Todd T. Eckdahl, Laurie J. Heyer, Anne Rosenwald, Edison Fowlks, Scott Tonidandel, Brooke Bucholtz, and Gail Gottfried. "Genome Consortium for Active Teaching: Meeting the Goals of BIO2010." CBE—Life Sciences Education 6, no. 2 (June 2007): 109–18. http://dx.doi.org/10.1187/cbe.06-10-0196.

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The Genome Consortium for Active Teaching (GCAT) facilitates the use of modern genomics methods in undergraduate education. Initially focused on microarray technology, but with an eye toward diversification, GCAT is a community working to improve the education of tomorrow's life science professionals. GCAT participants have access to affordable microarrays, microarray scanners, free software for data analysis, and faculty workshops. Microarrays provided by GCAT have been used by 141 faculty on 134 campuses, including 21 faculty that serve large numbers of underrepresented minority students. An estimated 9480 undergraduates a year will have access to microarrays by 2009 as a direct result of GCAT faculty workshops. Gains for students include significantly improved comprehension of topics in functional genomics and increased interest in research. Faculty reported improved access to new technology and gains in understanding thanks to their involvement with GCAT. GCAT's network of supportive colleagues encourages faculty to explore genomics through student research and to learn a new and complex method with their undergraduates. GCAT is meeting important goals of BIO2010 by making research methods accessible to undergraduates, training faculty in genomics and bioinformatics, integrating mathematics into the biology curriculum, and increasing participation by underrepresented minority students.

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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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Wellhausen, Robert, and Harald Seitz. "Facing Current Quantification Challenges in Protein Microarrays." Journal of Biomedicine and Biotechnology 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/831347.

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The proteome is highly variable and differs from cell to cell. The reasons are posttranslational modifications, splice variants, and polymorphisms. Techniques like next-generation sequencing can only give an inadequate picture of the protein status of a cell. Protein microarrays are able to track these changes on the level they occur: the proteomic level. Therefore, protein microarrays are powerful tools for relative protein quantification, to unveil new interaction partners and to track posttranslational modifications. This papers gives an overview on current protein microarray techniques and discusses recent advances in relative protein quantification.

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Khalid, Sabah, Daniela Grazio, Ping Wang, Xiaohui Liu, and Su-Ling Li. "DESIGNING CUSTOMISED GENE EXPRESSION ARRAYS BASED ON SPECIFIC BIOLOGICAL QUESTIONS OR FUNCTIONS." Journal of Integrated Design and Process Science: Transactions of the SDPS, Official Journal of the Society for Design and Process Science 11, no. 3 (January 2007): 69–83. http://dx.doi.org/10.3233/jid-2007-11306.

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The declaration "DNA microarrays serve as powerful tools for the global characterization of gene expression" is widely accepted by the scientific community exploiting microarray technology. With the inherent ability to distinguish between the expression levels of genes, microarrays have the power to provide the researcher with a direction for further exploration. However, the immediate results from microarray experiments require further mining to determine the genes, which have been differentially expressed. This narrows down the dataset to those genes that are of most interest to the researcher. In turn, hypotheses regarding the significance of these genes within the molecular mechanism being investigated can be generated. This would result in a more in-depth investigation within the researcher's area of expertise and further microarray experiments. The purpose of these experiments would be to answer more detailed biological questions arising from the analysis of the initial microarray. As a result of the enhanced focus within the investigative area, it would be ideal to use more specialised microarrays facilitating the gain of further knowledge into the biological aspect under scrutiny. Although commercially available, microarrays may be of limited use if they fail to represent the targeted pathways and biological processes to the extent required. Hence, customised microarrays become extremely advantageous, benefiting researchers with particular requirements. Furthermore, due to the sheer cost of DNA gene chips it is impractical to purchase microarrays for every biological need, and thus, it becomes imperative for biologists to maximise the use of existing gene sets. In order to achieve this, bioinformatics tools are necessary to extract meaningful data from the gene set according to a biologist's requirements. To this end, the authors have developed the first software, called Gene Chip Design, which can extract meaningful data from a given gene set according to a biologist's needs. This unique information retrieval software is a powerful, flexible and user-friendly tool aimed at researchers needing to generate personalised custom in-house arrays that represent the genes from specialised biological pathways of interest. With the inbuilt functionality to generate a novel gene chip specific to a researcher's interest from any given generalised gene set, this software can be applied within any biological field. An ontology to allow the global functional characterization of any given gene set, has also been created, together with the unique ability to categorize genes in a more structured and informative manner within numerous functional groups. Furthermore, this software identifies biological pathways pertaining to genes of interest. These aspects also provide researchers with the control to identify further genes with biological functions of interest to extract from their gene set and incorporate into their unique gene chip. Using their NIA 15K Mouse cDNA Clone Gene Set, the authors have designed several gene chips including an oncology gene chip. Furthermore, combined with their previous microarray experimental results, the researchers have developed a novel immuno-tolerance gene chip that can be used for further studies investigating the molecular mechanisms underlying autoimmune disease.

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Shao, Weiping, Zhimin Zhou, Isabelle Laroche, Hong Lu, Qiuling Zong, Dhavalkumar D. Patel, Stephen Kingsmore, and Steven P. Piccoli. "Optimization of Rolling-Circle Amplified Protein Microarrays for Multiplexed Protein Profiling." Journal of Biomedicine and Biotechnology 2003, no. 5 (2003): 299–307. http://dx.doi.org/10.1155/s1110724303209268.

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Protein microarray-based approaches are increasingly being used in research and clinical applications to either profile the expression of proteins or screen molecular interactions. The development of high-throughput, sensitive, convenient, and cost-effective formats for detecting proteins is a necessity for the effective advancement of understanding disease processes. In this paper, we describe the generation of highly multiplexed, antibody-based, specific, and sensitive protein microarrays coupled with rolling-circle signal amplification (RCA) technology. A total of 150 cytokines were simultaneously detected in an RCA sandwich immunoassay format. Greater than half of these proteins have detection sensitivities in the pg/ml range. The validation of antibody microarray with human serum indicated that RCA-based protein microarrays are a powerful tool for high-throughput analysis of protein expression and molecular diagnostics.

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Yu, Xiaobo, Nicole Schneiderhan-Marra, and Thomas O. Joos. "Protein Microarrays for Personalized Medicine." Clinical Chemistry 56, no. 3 (March 1, 2010): 376–87. http://dx.doi.org/10.1373/clinchem.2009.137158.

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Abstract Background: Over the last 10 years, DNA microarrays have achieved a robust analytical performance, enabling their use for analyzing the whole transcriptome or for screening thousands of single-nucleotide polymorphisms in a single experiment. DNA microarrays allow scientists to correlate gene expression signatures with disease progression, to screen for disease-specific mutations, and to treat patients according to their individual genetic profiles; however, the real key is proteins and their manifold functions. It is necessary to achieve a greater understanding of not only protein function and abundance but also their role in the development of diseases. Protein concentrations have been shown to reflect the physiological and pathologic state of an organ, tissue, or cells far more directly than DNA, and proteins can be profiled effectively with protein microarrays, which require only a small amount of sample material. Content: Protein microarrays have become well-established tools in basic and applied research, and the first products have already entered the in vitro diagnostics market. This review focuses on protein microarray applications for biomarker discovery and validation, disease diagnosis, and use within the area of personalized medicine. Summary: Protein microarrays have proved to be reliable research tools in screening for a multitude of parameters with only a minimal quantity of sample and have enormous potential in applications for diagnostic and personalized medicine.

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Liu, Yan. "Neoglycolipid (NGL)-based oligosaccharide microarrays and highlights of their recent applications in studies of the molecular basis of pathogen–host interactions." Biochemical Society Transactions 38, no. 5 (September 24, 2010): 1361–67. http://dx.doi.org/10.1042/bst0381361.

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Carbohydrate microarray technologies are new developments at the frontier of glycomics that are showing great promise as tools for high-throughput analysis of carbohydrate-mediated interactions and the elucidation of carbohydrate ligands involved not only in endogenous receptor systems, but also pathogen–host interactions. The main advantage of microarray analysis is that a broad range of glycan sequences can be immobilized on solid matrices as minute spots and simultaneously interrogated. Different methodologies have emerged for constructing carbohydrate microarrays. The NGL (neoglycolipid)-based oligosaccharide microarray platform is among the relatively few systems that are beyond proof-of-concept and have provided new biological information. In the present article, I dwell, in some detail, on the NGL-based microarray. Highlights are the recent applications of NGL-based microarrays that have contributed to knowledge on the molecular basis of pathogen–host interactions, namely the assignments of the carbohydrate-binding specificities of several key surface-adhesive proteins of Toxoplasma gondii and other apicomplexan parasites, and the elucidation of receptor-binding specificities of the pandemic influenza A (H1N1) 2009 (H1N1pdm) virus compared with seasonal H1N1 virus.

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Анисимов, Д. С., and D. S. Anisimov. "Projection to Latent Structures as a Strategy for Peptides Microarray Data Analysis." Mathematical Biology and Bioinformatics 12, no. 2 (November 29, 2017): 435–45. http://dx.doi.org/10.17537/2017.12.435.

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Currently various microarrays platforms containing nucleotides, proteins, peptides, glycans and other molecules are used in biomedical research. Number and density of immobilized molecules on microarrays are constantly increasing. Microarray data handling requires optimization of methods for their analysis. Peptide microarrays data analysis has certain characteristics that require non-conventional statistical methods. In this paper we present the results of antibody repertoire analysis in breast cancer patients sera utilizing microchips containing 330,000 peptides. We investigated methods for space dimension reduction such as projective methods and methods for selection of informative features. We have shown that method of projection to latent structures can detect an effective data dimension, reduce overfitting of the model and increase the quality of object recognition. Accuracy of the experimental results was assessed with the ROC-curve; the best quality was achieved with three latent structures without normalization and reduction of total numbers of peptides.

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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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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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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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Gryadunov, D. A., B. L. Shaskolskiy, T. V. Nasedkina, A. Yu Rubina, and A. S. Zasedatelev. "The EIMB Hydrogel Microarray Technology: Thirty Years Later." Acta Naturae 10, no. 4 (December 15, 2018): 4–18. http://dx.doi.org/10.32607/20758251-2018-10-4-4-18.

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Biological microarrays (biochips) are analytical tools that can be used to implement complex integrative genomic and proteomic approaches to the solution of problems of personalized medicine (e.g., patient examination in order to reveal the disease long before the manifestation of clinical symptoms, assess the severity of pathological or infectious processes, and choose a rational treatment). The efficiency of biochips is predicated on their ability to perform multiple parallel specific reactions and to allow one to study the interactions of biopolymer molecules, such as DNA, proteins, glycans, etc. One of the pioneers of microarray technology was the Engelhardt Institute of Molecular Biology of the Russian Academy of Sciences (EIMB), with its suggestion to immobilize molecular probes in the three-dimensional structure of a hydrophilic gel. Since the first experiments on sequencing by hybridization on oligonucleotide microarrays conducted some 30 years ago, the hydrogel microarrays designed at the EIMB have come a long and successful way from basic research to clinical laboratory diagnostics. This review discusses the key aspects of hydrogel microarray technology and a number of state-ofthe-art approaches for a multiplex analysis of DNA and the protein biomarkers of socially significant diseases, including the molecular genetic, immunological, and epidemiological aspects of pathogenesis.

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Ulyashova, Mariya M., Galina V. Presnova, Anna A. Filippova, Vitaly G. Grigorenko, Alexey M. Egorov та Maya Yu Rubtsova. "Multiplex Microarrays in 96-Well Plates Photoactivated with 4-Azidotetrafluorobenzaldehyde for the Identification and Quantification of β-Lactamase Genes and Their RNA Transcripts". Current Issues in Molecular Biology 46, № 1 (20 грудня 2023): 53–66. http://dx.doi.org/10.3390/cimb46010005.

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Antibiotic-resistant bacteria represent a global issue that calls for novel approaches to diagnosis and treatment. Given the variety of genetic factors that determine resistance, multiplex methods hold promise in this area. We developed a novel method to covalently attach oligonucleotide probes to the wells of polystyrene plates using photoactivation with 4-azidotetrafluorobenzaldehyde. Then, it was used to develop the technique of microarrays in the wells. It consists of the following steps: activating polystyrene, hybridizing the probes with biotinylated target DNA, and developing the result using a streptavidin–peroxidase conjugate with colorimetric detection. The first microarray was designed to identify 11 different gene types and 16 single-nucleotide polymorphisms (SNPs) of clinically relevant ESBLs and carbapenemases, which confer Gram-negative bacteria resistance to β-lactam antibiotics. The detection of bla genes in 65 clinical isolates of Enterobacteriaceae demonstrated the high sensitivity and reproducibility of the technique. The highly reproducible spot staining of colorimetric microarrays allowed us to design a second microarray that was intended to quantify four different types of bla mRNAs in order to ascertain their expressions. The combination of reliable performance, high throughput in standard 96-well plates, and inexpensive colorimetric detection makes the microarrays suitable for routine clinical application and for the study of multi-drug resistant bacteria.

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Gryadunov, Dmitry, Boris Shaskolskiy, Tatyana Nasedkina, Alla Rubina, and Alexander Zasedatelev. "The EIMB Hydrogel Microarray Technology: Thirty Years Later." Acta Naturae 10, no. 39(4) (December 1, 2018): 4–18. https://doi.org/10.5281/zenodo.2553175.

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Biological microarrays (biochips) are analytical tools that can be used to implement complex integrative genomic and proteomic approaches to the solution of problems of personalized medicine (e.g., patient examination in order to reveal the disease long before the manifestation of clinical symptoms, assess the severity of pathological or infectious processes, and choose a rational treatment). The efficiency of biochips is predicated on their ability to perform multiple parallel specific reactions and to allow one to study the interactions of biopolymer molecules, such as DNA, proteins, glycans, etc. One of the pioneers of microarray technology was the Engelhardt Institute of Molecular Biology of the Russian Academy of Sciences (EIMB), with its suggestion to immobilize molecular probes in the three-dimensional structure of a hydrophilic gel. Since the first experiments on sequencing by hybridization on oligonucleotide microarrays conducted some 30 years ago, the hydrogel microarrays designed at the EIMB have come a long and successful way from basic research to clinical laboratory diagnostics. This review discusses the key aspects of hydrogel microarray technology and a number of state-ofthe- art approaches for a multiplex analysis of DNA and the protein biomarkers of socially significant diseases, including the molecular genetic, immunological, and epidemiological aspects of pathogenesis.

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Vengesai, Arthur, Maritha Kasambala, Hamlet Mutandadzi, Tariro L. Mduluza-Jokonya, Takafira Mduluza, and Thajasvarie Naicker. "Scoping review of the applications of peptide microarrays on the fight against human infections." PLOS ONE 17, no. 1 (January 25, 2022): e0248666. http://dx.doi.org/10.1371/journal.pone.0248666.

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Introduction This scoping review explores the use of peptide microarrays in the fight against infectious diseases. The research domains explored included the use of peptide microarrays in the mapping of linear B-cell and T cell epitopes, antimicrobial peptide discovery, immunosignature characterisation and disease immunodiagnostics. This review also provides a short overview of peptide microarray synthesis. Methods Electronic databases were systematically searched to identify relevant studies. The review was conducted using the Joanna Briggs Institute methodology for scoping reviews and data charting was performed using a predefined form. The results were reported by narrative synthesis in line with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines. Results Ninety-five articles from 103 studies were included in the final data charting process. The majority (92. 0%) of the articles were published during 2010–2020 and were mostly from Europe (44.2%) and North America (34.7%). The findings were from the investigation of viral (45.6%), bacterial (32. 0%), parasitic (23.3%) and fungal (2. 0%) infections. Out of the serological studies, IgG was the most reported antibody type followed by IgM. The largest portion of the studies (77.7%) were related to mapping B-cell linear epitopes, 5.8% were on diagnostics, 5.8% reported on immunosignature characterisation and 8.7% reported on viral and bacterial cell binding assays. Two studies reported on T-cell epitope profiling. Conclusion The most important application of peptide microarrays was found to be B-cell epitope mapping or antibody profiling to identify diagnostic and vaccine targets. Immunosignatures identified by random peptide microarrays were found to be applied in the diagnosis of infections and interrogation of vaccine responses. The analysis of the interactions of random peptide microarrays with bacterial and viral cells using binding assays enabled the identification of antimicrobial peptides. Peptide microarray arrays were also used for T-cell linear epitope mapping which may provide more information for the design of peptide-based vaccines and for the development of diagnostic reagents.

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Chagovetz, Alexander, and Steve Blair. "Real-time DNA microarrays: reality check." Biochemical Society Transactions 37, no. 2 (March 20, 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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A Abdo, M., and P. J Hudson. "Protein microarrays in clinical microbiology." Microbiology Australia 27, no. 2 (2006): 78. http://dx.doi.org/10.1071/ma06078.

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Clinical microbiology laboratories have, in the past, broadly adopted new molecular biology techniques and automation. In the near future, the adoption of protein microarray technology has the potential to revolutionise the field in a manner similar to that of polymerase chain reaction (PCR). With the advantages of far greater sensitivity, parallel experimentation, reduced sample consumption and cost-per-test, the development of protein microarrays has come about through the realisation that mRNA levels do not necessarily correlate with protein expression.

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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 (January 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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Schmidberger, Markus, Esmeralda Vicedo, and Ulrich Mansmann. "affyPara—a Bioconductor Package for Parallelized Preprocessing Algorithms of Affymetrix Microarray Data." Bioinformatics and Biology Insights 3 (January 2009): BBI.S3060. http://dx.doi.org/10.4137/bbi.s3060.

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Microarray data repositories as well as large clinical applications of gene expression allow to analyse several hundreds of microarrays at one time. The preprocessing of large amounts of microarrays is still a challenge. The algorithms are limited by the available computer hardware. For example, building classification or prognostic rules from large microarray sets will be very time consuming. Here, preprocessing has to be a part of the cross-validation and resampling strategy which is necessary to estimate the rule's prediction quality honestly. This paper proposes the new Bioconductor package affyPara for parallelized preprocessing of Affymetrix microarray data. Partition of data can be applied on arrays and parallelization of algorithms is a straightforward consequence. The partition of data and distribution to several nodes solves the main memory problems and accelerates preprocessing by up to the factor 20 for 200 or more arrays. affyPara is a free and open source package, under GPL license, available form the Bioconductor project at www.bioconductor.org . A user guide and examples are provided with the package.

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Mezzasoma, Letizia, Tito Bacarese-Hamilton, Manlio Di Cristina, Ruggero Rossi, Francesco Bistoni, and Andrea Crisanti. "Antigen Microarrays for Serodiagnosis of Infectious Diseases." Clinical Chemistry 48, no. 1 (January 1, 2002): 121–30. http://dx.doi.org/10.1093/clinchem/48.1.121.

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Abstract Background: Progress in robotic printing technology has allowed the development of high-density nucleic acid and protein arrays that have increased the throughput of a variety of assays. We generated protein microarrays by printing microbial antigens to simultaneously determine in human sera antibodies directed against Toxoplasma gondii, rubella virus, cytomegalovirus (CMV), and herpes simplex virus (HSV) types 1 and 2 (ToRCH antigens). Methods: The antigens were printed on activated glass slides with high-speed robotics. The slides were incubated first with serum samples and subsequently with fluorescently labeled secondary antibodies. Human IgG and IgM bound to the printed antigens were detected by confocal scanning microscopy and quantified with internal calibration curves. Both microarrays and commercial ELISAs were utilized to detect serum antibodies against the ToRCH antigens in a panel of characterized human sera. Results: The detection limit (mean + 2 SD) of the microarray assay was 0.5 pg of IgG or IgM bound to the slides. Within-slide, between-slide, and between-batch precision profiles showed CVs of 1.7–18% for all antigens. Overall, &gt;80% concordance was obtained between microarray assays and ELISAs in the classification of sera; for T. gondii, CMV, and HSV1, concordance exceeded 90%. Conclusions: The microarray is a suitable assay format for the serodiagnosis of infectious diseases and can be easily optimized for clinical use. The ToRCH assay performs equivalently to ELISA and may have potentially important advantages in throughput, convenience, and cost.

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Garosi, Paola, Carlotta De Filippo, Marjan van Erk, Philippe Rocca-Serra, Susanna-Assunta Sansone, and Ruan Elliott. "Defining best practice for microarray analyses in nutrigenomic studies." British Journal of Nutrition 93, no. 4 (April 2005): 425–32. http://dx.doi.org/10.1079/bjn20041385.

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Microarrays represent a powerful tool for studies of diet–gene interactions. Their use is, however, associated with a number of technical challenges and potential pitfalls. The cost of microarrays continues to drop but is still comparatively high. This, coupled with the complex logistical issues associated with performing nutritional microarray studies, often means that compromises have to be made in the number and type of samples analysed. Additionally, technical variations between array platforms and analytical procedures will almost inevitably lead to differences in the transcriptional responses observed. Consequently, conflicting data may be produced, important effects may be missed and/or false leads generated (e.g. apparent patterns of differential gene regulation that ultimately prove to be incorrect or not significant). This is likely to be particularly true in the field of nutrition, in which we expect that many dietary bioactive agents at nutritionally relevant concentrations will elicit subtle changes in gene transcription that may be critically important in biological terms but will be difficult to detect reliably. Thus, great care should always be taken in designing and executing microarray studies. This article seeks to provide an overview of both the main practical and theoretical considerations in microarray use that represent potential sources of technical variation and error. Wherever possible, recommendations are made on what we propose to be the best approach. The overall aims are to provide a basic framework of advice for researchers who are new to the use of microarrays and to promote a discussion of standardisation and best practice in the field.

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Journal articles on the topic 'Microarrays'

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