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Handley, Daniel, Nicoleta Serban, David G. Peters e 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, n. 1 (6 gennaio 2004): 1–2. http://dx.doi.org/10.2202/1544-6115.1091.
Testo completoAbstract (sommario):
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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Chiodi, Elisa, Allison M. Marn, Matthew T. Geib e M. Selim Ünlü. "The Role of Surface Chemistry in the Efficacy of Protein and DNA Microarrays for Label-Free Detection: An Overview". Polymers 13, n. 7 (26 marzo 2021): 1026. http://dx.doi.org/10.3390/polym13071026.
Testo completoAbstract (sommario):
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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BS, Shreenidhi, e Saravanakumar Ramachandran. "Microarray image enhancement techniques by denoising: Current status and future directions". International Journal of Natural Sciences Research 11, n. 1 (12 giugno 2023): 44–51. http://dx.doi.org/10.18488/63.v11i1.3393.
Testo completoAbstract (sommario):
Microarray imaging is a technique for simultaneously detecting the expression of numerous genes. Microarrays are simply a slide with unique Deoxyribonucleic Acid (DNA) probes, often known as gene chips. This paper aims to provide an overview of important contributions to the field of image denoising in the context of microarray imaging. The methodologies discussed in the article include various techniques of transform domain and spatial filtering methods for denoising microarray images that can be used to improve the quality of microarray images. We have identified the strengths and limitations of these techniques and highlights their potential impact. The paper also explores future developments in this area and discusses their potential impact, on microarray imaging.
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Raczynski, Lech, Krzysztof Wozniak, Tymon Rubel e Krzysztof Zaremba. "Application of Density Based Clustering to Microarray Data Analysis". International Journal of Electronics and Telecommunications 56, n. 3 (1 settembre 2010): 281–86. http://dx.doi.org/10.2478/v10177-010-0037-9.
Testo completoAbstract (sommario):
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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Liang, Mingyu, Amy G. Briggs, Elizabeth Rute, Andrew S. Greene e Allen W. Cowley. "Quantitative assessment of the importance of dye switching and biological replication in cDNA microarray studies". Physiological Genomics 14, n. 3 (15 agosto 2003): 199–207. http://dx.doi.org/10.1152/physiolgenomics.00143.2002.
Testo completoAbstract (sommario):
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 predicted ln(ratio) values from 0.38 ± 0.06 to 0.62 ± 0.04 ( n = 12) and the outlier concordance from 21 ± 3% to 43 ± 4%. It also increased the correlation with the entire set of microarrays from 0.60 ± 0.04 to 0.79 ± 0.04 and the outlier concordance from 31 ± 6% to 58 ± 5% and tended to improve the correlation with Northern blot results. Adding a second microarray to include biological replication also improved the performance of these indices but often to a lesser degree. Inclusion of both procedures in the second microarray substantially improved the consistency with the entire set of microarrays but had minimal effect on the consistency with predicted results. Analysis of another data set generated using a different cDNA labeling method also supported a significant impact of dye switching. In conclusion, both dye switching and biological replication substantially increased the reliability of microarray results, with dye switching likely having even greater benefits. Recommendations regarding the use of these procedures were proposed.
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Whipple, Mark Eliot, e Winston Patrick Kuo. "DNA Microarrays in Otolaryngology-Head and Neck Surgery". Otolaryngology–Head and Neck Surgery 127, n. 3 (settembre 2002): 196–204. http://dx.doi.org/10.1067/mhn.2002.127383.
Testo completoAbstract (sommario):
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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García-Albert, L., F. Martín-Sánchez, A. García-Sáiz e G. H. López-Campos. "Analysis and Management of HIV Peptide Microarray Experiments". Methods of Information in Medicine 45, n. 02 (2006): 158–62. http://dx.doi.org/10.1055/s-0038-1634060.
Testo completoAbstract (sommario):
Summary Objectives: To develop a tool for then easy and user-friendly management of peptide microarray experiments and for the use of the results of these experiments for the study the immune response against HIV virus infection in clinical samples. Methods: Applying bioinformatics and statistics for the analysis of data coming from microarray experiments as well as implementing a MIAME (Minimum Information About a Microarray Experiment) compliant database for managing and annotating experiments, results and samples. Results: We present a new tool for managing not only nucleic acid microarray experiments but also protein microarray experiments. From the analysis of experimental data, we can detect different profiles in the reactivity of the sera with different genotypes. Conclusions: We have developed a new tool for managing microarray data including clinical annotations for the samples as well as the capability of annotating other microarray formats different to those based on nucleic acids. The use of peptide microarrays and bioinformatics analysis opens a new scope for the characterization of the immune response, and analyzing and identifying the humoral response of viruses with different genotypes.
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White, Christine A., e Lois A. Salamonsen. "A guide to issues in microarray analysis: application to endometrial biology". Reproduction 130, n. 1 (luglio 2005): 1–13. http://dx.doi.org/10.1530/rep.1.00685.
Testo completoAbstract (sommario):
Within the last decade, the development of DNA microarray technology has enabled the simultaneous measurement of thousands of gene transcripts in a biological sample. Conducting a microarray study is a multi-step process; starting with a well-defined biological question, moving through experimental design, target RNA preparation, microarray hybridisation, image acquisition and data analysis – finishing with a biological interpretation requiring further study. Advances continue to be made in microarray quality and methods of statistical analysis, improving the reliability and therefore appeal of microarray analysis for a wide range of biological questions. The purpose of this review is to provide both an introduction to microarray methodology, as well as a practical guide to the use of microarrays for gene expression analysis, using endometrial biology as an example of the applications of this technology. While recommendations are based on previous experience in our laboratory, this review also summarises the methods currently considered to be best practice in the field.
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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, n. 5 (24 settembre 2010): 1361–67. http://dx.doi.org/10.1042/bst0381361.
Testo completoAbstract (sommario):
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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Paredes, Carlos J., Ryan S. Senger, Iwona S. Spath, Jacob R. Borden, Ryan Sillers e 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, n. 14 (25 maggio 2007): 4631–38. http://dx.doi.org/10.1128/aem.00144-07.
Testo completoAbstract (sommario):
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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Fesseha, Haben, e Hiwot Tilahun. "Principles and Applications of Deoxyribonucleic Acid Microarray: A Review". Pathology and Laboratory Medicine – Open Journal 3, n. 1 (30 marzo 2021): 1–9. http://dx.doi.org/10.17140/plmoj-3-109.
Testo completoAbstract (sommario):
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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Kyselková, M., J. Kopecký, M. Ságová-Marečková, G. L. Grundmann e Y. Moënne-Loccoz. "Oligonucleotide microarray methodology for taxonomic and functional monitoringof microbial community composition". Plant, Soil and Environment 55, No. 9 (14 ottobre 2009): 379–88. http://dx.doi.org/10.17221/140/2009-pse.
Testo completoAbstract (sommario):
Microarray analysis is a cultivation-independent, high-throughput technology that can be used for direct and simultaneous identification of microorganisms in complex environmental samples. This review summarizes current methodologies for oligonucleotide microarrays used in microbial ecology. It deals with probe design, microarray manufacturing, sample preparation and labeling, and data handling, as well as with the key features of microarray analysis such as specificity, sensitivity and quantification potential. Microarray analysis has been validated as an effective approach to describe the composition and dynamics of taxonomic and functional microbial communities, in environments including soil, compost, sediment, air or humans. It is now part of the technical arsenal available to address key issues in microbial community ecology, ranging from biogeography to ecosystem functioning.
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Trost, Brett, Catherine A. Moir, Zoe E. Gillespie, Anthony Kusalik, Jennifer A. Mitchell e 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, n. 9 (settembre 2015): 150402. http://dx.doi.org/10.1098/rsos.150402.
Testo completoAbstract (sommario):
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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Roszkowiak, Lukasz, e Carlos Lopez. "PATMA: parser of archival tissue microarray". PeerJ 4 (1 dicembre 2016): e2741. http://dx.doi.org/10.7717/peerj.2741.
Testo completoAbstract (sommario):
The tissue microarrays are commonly used in modern pathology for cancer tissue evaluation, as it is a very potent technique. Tissue microarray slides are often scanned to perform computer-aided histopathological analysis of the tissue cores. For processing the image, splitting the whole virtual slide into images of individual cores is required. The only way to distinguish cores corresponding to specimens in the tissue microarray is through their arrangement. Unfortunately, distinguishing the correct order of cores is not a trivial task as they are not labelled directly on the slide. The main aim of this study was to create a procedure capable of automatically finding and extracting cores from archival images of the tissue microarrays. This software supports the work of scientists who want to perform further image processing on single cores. The proposed method is an efficient and fast procedure, working in fully automatic or semi-automatic mode. A total of 89% of punches were correctly extracted with automatic selection. With an addition of manual correction, it is possible to fully prepare the whole slide image for extraction in the mean time of 2 min per tissue microarray. The proposed technique requires minimum skill and time to parse big array of cores from tissue microarray whole slide image into individual core images.
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Miller, Melissa B., e Yi-Wei Tang. "Basic Concepts of Microarrays and Potential Applications in Clinical Microbiology". Clinical Microbiology Reviews 22, n. 4 (ottobre 2009): 611–33. http://dx.doi.org/10.1128/cmr.00019-09.
Testo completoAbstract (sommario):
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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Wang, Zhiyou, Xiaoqing Huang e Zhiqiang Cheng. "Automatic Spot Identification Method for High Throughput Surface Plasmon Resonance Imaging Analysis". Biosensors 8, n. 3 (13 settembre 2018): 85. http://dx.doi.org/10.3390/bios8030085.
Testo completoAbstract (sommario):
An automatic spot identification method is developed for high throughput surface plasmon resonance imaging (SPRi) analysis. As a combination of video accessing, image enhancement, image processing and parallel processing techniques, the method can identify the spots in SPRi images of the microarray from SPRi video data. In demonstrations of the method, SPRi video data of different protein microarrays were processed by the method. Results show that our method can locate spots in the microarray accurately regardless of the microarray pattern, spot-background contrast, light nonuniformity and spotting defects, but also can provide address information of the spots.
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Aparna, G. M., e Kishore K. R. Tetala. "Recent Progress in Development and Application of DNA, Protein, Peptide, Glycan, Antibody, and Aptamer Microarrays". Biomolecules 13, n. 4 (27 marzo 2023): 602. http://dx.doi.org/10.3390/biom13040602.
Testo completoAbstract (sommario):
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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Berthuy, Ophélie I., Sinan K. Muldur, François Rossi, Pascal Colpo, Loïc J. Blum e Christophe A. Marquette. "Multiplex cell microarrays for high-throughput screening". Lab on a Chip 16, n. 22 (2016): 4248–62. http://dx.doi.org/10.1039/c6lc00831c.
Testo completo
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Cao, Yiwei, Sang-Jun Park, Akul Y. Mehta, Richard D. Cummings e Wonpil Im. "GlyMDB: Glycan Microarray Database and analysis toolset". Bioinformatics 36, n. 8 (16 dicembre 2019): 2438–42. http://dx.doi.org/10.1093/bioinformatics/btz934.
Testo completoAbstract (sommario):
Abstract Motivation Glycan microarrays are capable of illuminating the interactions of glycan-binding proteins (GBPs) against hundreds of defined glycan structures, and have revolutionized the investigations of protein–carbohydrate interactions underlying numerous critical biological activities. However, it is difficult to interpret microarray data and identify structural determinants promoting glycan binding to glycan-binding proteins due to the ambiguity in microarray fluorescence intensity and complexity in branched glycan structures. To facilitate analysis of glycan microarray data alongside protein structure, we have built the Glycan Microarray Database (GlyMDB), a web-based resource including a searchable database of glycan microarray samples and a toolset for data/structure analysis. Results The current GlyMDB provides data visualization and glycan-binding motif discovery for 5203 glycan microarray samples collected from the Consortium for Functional Glycomics. The unique feature of GlyMDB is to link microarray data to PDB structures. The GlyMDB provides different options for database query, and allows users to upload their microarray data for analysis. After search or upload is complete, users can choose the criterion for binder versus non-binder classification. They can view the signal intensity graph including the binder/non-binder threshold followed by a list of glycan-binding motifs. One can also compare the fluorescence intensity data from two different microarray samples. A protein sequence-based search is performed using BLAST to match microarray data with all available PDB structures containing glycans. The glycan ligand information is displayed, and links are provided for structural visualization and redirection to other modules in GlycanStructure.ORG for further investigation of glycan-binding sites and glycan structures. Availability and implementation http://www.glycanstructure.org/glymdb. Supplementary information Supplementary data are available at Bioinformatics online.
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Jack, Philippa, e David Boyle. "DNA microarrays for pathogen detection and characterisation". Microbiology Australia 27, n. 2 (2006): 68. http://dx.doi.org/10.1071/ma06068.
Testo completoAbstract (sommario):
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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Rao, Sreevidya Sadananda Sadasiva, Lori A. Shepherd, Andrew E. Bruno, Song Liu e Jeffrey C. Miecznikowski. "Comparing Imputation Procedures for Affymetrix Gene Expression Datasets Using MAQC Datasets". Advances in Bioinformatics 2013 (9 ottobre 2013): 1–10. http://dx.doi.org/10.1155/2013/790567.
Testo completoAbstract (sommario):
Introduction. The microarray datasets from the MicroArray Quality Control (MAQC) project have enabled the assessment of the precision, comparability of microarrays, and other various microarray analysis methods. However, to date no studies that we are aware of have reported the performance of missing value imputation schemes on the MAQC datasets. In this study, we use the MAQC Affymetrix datasets to evaluate several imputation procedures in Affymetrix microarrays. Results. We evaluated several cutting edge imputation procedures and compared them using different error measures. We randomly deleted 5% and 10% of the data and imputed the missing values using imputation tests. We performed 1000 simulations and averaged the results. The results for both 5% and 10% deletion are similar. Among the imputation methods, we observe the local least squares method with is most accurate under the error measures considered. The k-nearest neighbor method with has the highest error rate among imputation methods and error measures. Conclusions. We conclude for imputing missing values in Affymetrix microarray datasets, using the MAS 5.0 preprocessing scheme, the local least squares method with has the best overall performance and k-nearest neighbor method with has the worst overall performance. These results hold true for both 5% and 10% missing values.
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KOSTIĆ, TANJA, BEATRIX STESSL, MARTIN WAGNER e ANGELA SESSITSCH. "Microarray Analysis Reveals the Actual Specificity of Enrichment Media Used for Food Safety Assessment". Journal of Food Protection 74, n. 6 (1 giugno 2011): 1030–34. http://dx.doi.org/10.4315/0362-028x.jfp-10-388.
Testo completoAbstract (sommario):
Microbial diagnostic microarrays are tools for simultaneous detection and identification of microorganisms in food, clinical, and environmental samples. In comparison to classic methods, microarray-based systems have the potential for high throughput, parallelism, and miniaturization. High specificity and high sensitivity of detection have been demonstrated. A microbial diagnostic microarray for the detection of the most relevant bacterial food- and waterborne pathogens and indicator organisms was developed and thoroughly validated. The microarray platform based on sequence-specific end labeling of oligonucleotides and the phylogenetically robust gyrB marker gene allowed a highly specific (resolution on genus and/or species level) and sensitive (0.1% relative and 104 CFU absolute sensitivity) detection of the target pathogens. In initial challenge studies of the applicability of microarray-based food analysis, we obtained results demonstrating the questionable specificity of standardized culture-dependent microbiological detection methods. Taking into consideration the importance of reliable food safety assessment methods, comprehensive performance assessment is essential. Results demonstrate the potential of this new pathogen diagnostic microarray to evaluate culture-based standard methods in microbiological food analysis.
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Weninger, F., S. Merk, A. Kohlmann, T. Haferlach e M. Dugas. "A Generic Concept for Large-scale Microarray Analysis Dedicated to Medical Diagnostics". Methods of Information in Medicine 45, n. 02 (2006): 146–52. http://dx.doi.org/10.1055/s-0038-1634058.
Testo completoAbstract (sommario):
Summary Background: The development of diagnostic procedures based on microarray analysis confronts the bioinformatician and the biomedical researcher with a variety of challenges. Microarrays generate a huge amount of data. There are many, not yet clearly defined, data processing steps and many clinical response variables which may not match gene expression patterns. Objectives: To design a generic concept for large-scale microarray experiments dedicated to medical diagnostics; to create a system capable of handling several 1000 microarrays per analysis and more than 100 clinical response variables; to design a standardized workflow for quality control, data calibration, identification of differentially expressed genes and estimation of classification accuracy; and to provide a user-friendly interface for clinical researchers with respect to biomedical interpretation. Methods: We designed a database structure suitable for the storage of microarray data and analysis results. We applied statistical procedures to identify differential genes and developed a technique to estimate classification accuracy of gene patterns with confidence intervals. Results: We implemented a Gene Analysis Management System (GAMS) based on this concept, using MySQL for data storage, R/Bioconductor for analysis and PHP for a web-based front-end for the exploration of microarray data and analysis results. This system was utilized with large data sets from several medical disciplines, mainly from oncology (~ 2000 micro-arrays). Conclusions: A systematic approach is necessary for the analysis of microarray experiments in a medical diagnostics setting to get comprehensible results. Due to the complexity of the analysis, data processing (by bioinformaticians) and interactive exploration of results (by biomedical experts) should be separated.
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Huang, Joe Xi, Dorothy Mehrens, Rick Wiese, Sandy Lee, Sun W. Tam, Steve Daniel, James Gilmore, Michael Shi e Deval Lashkari. "High-Throughput Genomic and Proteomic Analysis Using Microarray Technology". Clinical Chemistry 47, n. 10 (1 ottobre 2001): 1912–16. http://dx.doi.org/10.1093/clinchem/47.10.1912.
Testo completoAbstract (sommario):
Abstract Background: High-density microarrays are ideally suited for analyzing thousands of genes against a small number of samples. The next step in the discovery process is to take the resulting genes of interest and rapidly screen them against thousands of patient samples, tissues, or cell lines to further investigate their involvement in disease risk or the response to medication. Methods: We used a microarray technology platform for both single-nucleotide polymorphisms (SNPs) and protein expression. Each microarray contains up to 250 elements that can be customized for each application. Slides contained either a 16- or 96-microarray format (4000–24 000 elements per slide), allowing the corresponding number of samples to be rapidly processed in parallel. Results: Results for SNP genotyping and protein profiling agreed with results of restriction fragment length polymorphism (RFLP) analysis or ELISA, respectively. Genotyping analyses, using the microarray technology, on large sample sets over multiple polymorphisms in the NAT2 gene were in full agreement with traditional methodologies, such as sequencing and RFLP analysis. The multiplexed protein microarray had correlation coefficients of 0.82–0.99 (depending on analyte) compared with ELISAs. Conclusions: The integrated microarray technology platform is adaptable and versatile, while offering the high-throughput capabilities needed for drug development and discovery applications.
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Aittokallio, Tero, Markus Kurki, Olli Nevalainen, Tuomas Nikula, Anne West e Riitta Lahesmaa. "Computational Strategies for Analyzing Data in Gene Expression Microarray Experiments". Journal of Bioinformatics and Computational Biology 01, n. 03 (ottobre 2003): 541–86. http://dx.doi.org/10.1142/s0219720003000319.
Testo completoAbstract (sommario):
Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.
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Kostrzynska, M., e A. Bachand. "Application of DNA microarray technology for detection, identification, and characterization of food-borne pathogens". Canadian Journal of Microbiology 52, n. 1 (1 gennaio 2006): 1–8. http://dx.doi.org/10.1139/w05-105.
Testo completoAbstract (sommario):
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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Lacroix, M., N. Zammatteo, J. Remacle e G. Leclercq. "A Low-Density DNA Microarray for Analysis of Markers in Breast Cancer". International Journal of Biological Markers 17, n. 1 (gennaio 2002): 5–23. http://dx.doi.org/10.1177/172460080201700102.
Testo completoAbstract (sommario):
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 e Ulrich Mansmann. "affyPara—a Bioconductor Package for Parallelized Preprocessing Algorithms of Affymetrix Microarray Data". Bioinformatics and Biology Insights 3 (gennaio 2009): BBI.S3060. http://dx.doi.org/10.4137/bbi.s3060.
Testo completoAbstract (sommario):
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.
29
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 e X. Cindy Tian. "Embryonic gene expression profiling using microarray analysis". Reproduction, Fertility and Development 21, n. 1 (2009): 22. http://dx.doi.org/10.1071/rd08217.
Testo completoAbstract (sommario):
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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Chandler, Wells M., Leslie R. Rowe, Scott R. Florell, Mona S. Jahromi, Joshua D. Schiffman e Sarah T. South. "Differentiation of Malignant Melanoma From Benign Nevus Using a Novel Genomic Microarray With Low Specimen Requirements". Archives of Pathology & Laboratory Medicine 136, n. 8 (1 agosto 2012): 947–55. http://dx.doi.org/10.5858/arpa.2011-0330-oa.
Testo completoAbstract (sommario):
Context.—Histologic examination of clinically suspicious melanocytic lesions is very sensitive and specific for the detection of malignant melanoma. Yet, the malignant potential of a small percentage of melanocytic lesions remains histologically uncertain. Molecular testing offers the potential to detect the genetic alterations that lead to malignant behavior without overt histologic evidence of malignancy. Objective.—To differentiate benign melanocytic nevi from malignant melanoma and to predict the clinical course of melanocytic lesions with ambiguous histology using a novel genomic microarray. Design.—We applied a newly developed single-nucleotide polymorphism genomic microarray to formalin-fixed, paraffin-embedded melanocytic lesions to differentiate benign nevi (n = 23) from malignant melanoma (n = 30) and to predict the clinical course of a set of histologically ambiguous melanocytic lesions (n = 11). Results.—For cases with unambiguous histology, there was excellent sensitivity and specificity for identifying malignant melanoma with this genomic microarray (89% sensitivity, 100% specificity). For cases with ambiguous histology, the performance of this genomic microarray was less impressive. Conclusions.—Without microdissection and with quantities of DNA one-tenth what is required for more commonly used microarrays, this microarray can differentiate between malignant melanoma and benign melanocytic nevi. For histologically ambiguous lesions, longer clinical follow-up is needed to confidently determine the sensitivity and specificity of this microarray. Some of the previous technical hurdles to the clinical application of genomic microarray technology are being overcome, and the advantages over targeted fluorescence in situ hybridization assays currently in clinical use are becoming apparent.
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Yuvaraj, K., e D. Manjula. "A performance analysis of clustering based algorithms for the microarray gene expression data". International Journal of Engineering & Technology 7, n. 2.21 (20 aprile 2018): 201. http://dx.doi.org/10.14419/ijet.v7i2.21.12172.
Testo completoAbstract (sommario):
Current advancements in microarray technology permit simultaneous observing of the expression levels of huge number of genes over various time points. Microarrays have obtained amazing implication in the field of bioinformatics. It includes an ordered set of huge different Deoxyribonucleic Acid (DNA) sequences that can be used to measure both DNA as well as Ribonucleic Acid (RNA) dissimilarities. The Gene Expression (GE) summary aids in understanding the basic cause of gene activities, the growth of genes, determining recent disorders like cancer and as well analysing their molecular pharmacology. Clustering is a significant tool applied for analyzing such microarray gene expression data. It has developed into a greatest part of gene expression analysis. Grouping the genes having identical expression patterns is known as gene clustering. A number of clustering algorithms have been applied for the analysis of microarray gene expression data. The aim of this paper is to analyze the precision level of the microarray data by using various clustering algorithms.
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Santoro, Stephanie L., Sayaka Hashimoto, Aimee McKinney, Theresa Mihalic Mosher, Robert Pyatt, Shalini C. Reshmi, Caroline Astbury e Scott E. Hickey. "Assessing the Clinical Utility of SNP Microarray for Prader-Willi Syndrome due to Uniparental Disomy". Cytogenetic and Genome Research 152, n. 2 (2017): 105–9. http://dx.doi.org/10.1159/000478921.
Testo completoAbstract (sommario):
Maternal uniparental disomy (UPD) 15 is one of the molecular causes of Prader-Willi syndrome (PWS), a multisystem disorder which presents with neonatal hypotonia and feeding difficulty. Current diagnostic algorithms differ regarding the use of SNP microarray to detect PWS. We retrospectively examined the frequency with which SNP microarray could identify regions of homozygosity (ROH) in patients with PWS. We determined that 7/12 (58%) patients with previously confirmed PWS by methylation analysis and microsatellite-positive UPD studies had ROH (>10 Mb) by SNP microarray. Additional assessment of 5,000 clinical microarrays, performed from 2013 to present, determined that only a single case of ROH for chromosome 15 was not caused by an imprinting disorder or identity by descent. We observed that ROH for chromosome 15 is rarely incidental and strongly associated with hypotonic infants having features of PWS. Although UPD microsatellite studies remain essential to definitively establish the presence of UPD, SNP microarray has important utility in the timely diagnostic algorithm for PWS.
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Murphy, David. "GENE EXPRESSION STUDIES USING MICROARRAYS: PRINCIPLES, PROBLEMS, AND PROSPECTS". Advances in Physiology Education 26, n. 4 (dicembre 2002): 256–70. http://dx.doi.org/10.1152/advan.00043.2002.
Testo completoAbstract (sommario):
Anumber of mammalian genomes having been sequenced, an important next step is to catalog the expression patterns of all transcription units in health and disease by use of microarrays. Such discovery programs are crucial to our understanding of the gene networks that control developmental, physiological, and pathological processes. However, despite the excitement, the full promise of microarray technology has yet to be realized, as the superficial simplicity of the concept belies considerable problems. Microarray technology is very new; methodologies are still evolving, common standards have yet to be established, and many problems with experimental design and variability have still to be fully understood and overcome. This review will describe the time course of a microarray experiment—RNA isolation from sample, target preparation, hybridization to the microarray probe, data capture, and bioinformatic analysis. For each stage, the advantages and disadvantages of competing techniques are compared, and inherent sources of error are identified and discussed.
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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, n. 2 (2019): 150–63. http://dx.doi.org/10.1039/c8mo00252e.
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Call, Douglas R., Marlene K. Bakko, Melissa J. Krug e Marilyn C. Roberts. "Identifying Antimicrobial Resistance Genes with DNA Microarrays". Antimicrobial Agents and Chemotherapy 47, n. 10 (ottobre 2003): 3290–95. http://dx.doi.org/10.1128/aac.47.10.3290-3295.2003.
Testo completoAbstract (sommario):
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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Croner, Roland S., Berthold Lausen, Vera Schellerer, Isabel Zeittraeger, Axel Wein, Claus Schildberg, Thomas Papadopoulos et al. "Comparability of Microarray Data between Amplified and Non Amplified RNA in Colorectal Carcinoma". Journal of Biomedicine and Biotechnology 2009 (2009): 1–9. http://dx.doi.org/10.1155/2009/837170.
Testo completoAbstract (sommario):
Microarray analysis reaches increasing popularity during the investigation of prognostic gene clusters in oncology. The standardisation of technical procedures will be essential to compare various datasets produced by different research groups. In several projects the amount of available tissue is limited. In such cases the preamplification of RNA might be necessary prior to microarray hybridisation. To evaluate the comparability of microarray results generated either by amplified or non amplified RNA we isolated RNA from colorectal cancer samples (stage UICC IV) following tumour tissue enrichment by macroscopic manual dissection (CMD). One part of the RNA was directly labelled and hybridised to GeneChips (HG-U133A, Affymetrix), the other part of the RNA was amplified according to the “Eberwine” protocol and was then hybridised to the microarrays. During unsupervised hierarchical clustering the samples were divided in groups regarding the RNA pre-treatment and 5.726 differentially expressed genes were identified. Using independent microarray data of 31 amplified vs. 24 non amplified RNA samples from colon carcinomas (stage UICC III) in a set of 50 predictive genes we validated the amplification bias. In conclusion microarray data resulting from different pre-processing regarding RNA pre-amplification can not be compared within one analysis.
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Kundalia, Paras H., Lucia Pažitná, Kristína Kianičková, Eduard Jáné, Lenka Lorencová e Jaroslav Katrlík. "A Holistic 4D Approach to Optimize Intrinsic and Extrinsic Factors Contributing to Variability in Microarray Biosensing in Glycomics". Sensors 23, n. 12 (6 giugno 2023): 5362. http://dx.doi.org/10.3390/s23125362.
Testo completoAbstract (sommario):
Protein–carbohydrate interactions happen to be a crucial facet of biology, discharging a myriad of functions. Microarrays have become a premier choice to discern the selectivity, sensitivity and breadth of these interactions in a high-throughput manner. The precise recognition of target glycan ligands among the plethora of others is central for any glycan-targeting probe being tested by microarray analyses. Ever since the introduction of the microarray as an elemental tool for high-throughput glycoprofiling, numerous distinct array platforms possessing different customizations and assemblies have been developed. Accompanying these customizations are various factors ushering variances across array platforms. In this primer, we investigate the influence of various extrinsic factors, namely printing parameters, incubation procedures, analyses and array storage conditions on the protein–carbohydrate interactions and evaluate these factors for the optimal performance of microarray glycomics analysis. We hereby propose a 4D approach (Design–Dispense–Detect–Deduce) to minimize the effect of these extrinsic factors on glycomics microarray analyses and thereby streamline cross-platform analyses and comparisons. This work will aid in optimizing microarray analyses for glycomics, minimize cross-platform disparities and bolster the further development of this technology.
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Kusi-Appiah, A. E., T. W. Lowry, E. M. Darrow, K. A. Wilson, B. P. Chadwick, M. W. Davidson e S. Lenhert. "Quantitative dose–response curves from subcellular lipid multilayer microarrays". Lab on a Chip 15, n. 16 (2015): 3397–404. http://dx.doi.org/10.1039/c5lc00478k.
Testo completo
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Liu, Quanjun, Yunfei Bai, Qinyu Ge, Shixin Zhou, Tian Wen e Zuhong Lu. "Microarray-in-a-Tube for Detection of Multiple Viruses". Clinical Chemistry 53, n. 2 (1 febbraio 2007): 188–94. http://dx.doi.org/10.1373/clinchem.2006.071720.
Testo completoAbstract (sommario):
Abstract Background: The detection of multiple viruses is important for pathogenic diagnosis and disease control. Microarray detection is a good method, but requires complex procedures for multiple virus detection. Methods: We developed a novel PCR assay, the microarray-in-a-tube system, which integrates multiple PCR processes and DNA microarrays for multiple virus detection. A 5 × 5 oligonucleotide microarray for detecting 4 respiratory tract viruses (severe acute respiratory syndrome–associated coronavirus, influenza A virus, influenza B virus, and enterovirus) with inner controls was arranged on the inner surface of a specially designed Eppendorf cap with a flat, optically transparent window. Results: We were able to perform all detection processes in the encapsulated system without opening the cap. The 4 viruses were successfully amplified by one-step reverse transcription–PCR in the encapsulated tube. After the PCR process, the microarray-in-a-tube was inverted, and the fluorescence-labeled PCR products were directly hybridized on the microarray. Hybridization signals were obtained with an ordinary fluorescent microscope. The sensitivity of the system for virus detection reached 102 copies/μL. With the help of inner controls, the system provided reliable results without false negatives and false positives. Conclusions: The microarray-in-a-tube system is a rapid, labor-saving tool for multiple virus detection with several advantages, such as convenience, prevention of cross-contamination of the PCR products, and potential for multiple-gene detection.
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Jia, Kun, Miao Yu, Gui-Hong Zhang, Jun Zhang, Zhi-Xiong Lin, Chang-Bao Luo, Hai-Qiong Yu e Shou-Jun Li. "Detection and identification of Mycobacterium tuberculosis and Mycobacterium bovis from clinical species using DNA microarrays". Journal of Veterinary Diagnostic Investigation 24, n. 1 (10 ottobre 2011): 156–60. http://dx.doi.org/10.1177/1040638711417141.
Testo completoAbstract (sommario):
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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Nersisyan, Stepan, Maxim Shkurnikov, Andrey Poloznikov, Andrey Turchinovich, Barbara Burwinkel, Nikita Anisimov e Alexander Tonevitsky. "A Post-Processing Algorithm for miRNA Microarray Data". International Journal of Molecular Sciences 21, n. 4 (12 febbraio 2020): 1228. http://dx.doi.org/10.3390/ijms21041228.
Testo completoAbstract (sommario):
One of the main disadvantages of using DNA microarrays for miRNA expression profiling is the inability of adequate comparison of expression values across different miRNAs. This leads to a large amount of miRNAs with high scores which are actually not expressed in examined samples, i.e., false positives. We propose a post-processing algorithm which performs scoring of miRNAs in the results of microarray analysis based on expression values, time of discovery of miRNA, and correlation level between the expressions of miRNA and corresponding pre-miRNA in considered samples. The algorithm was successfully validated by the comparison of the results of its application to miRNA microarray breast tumor samples with publicly available miRNA-seq breast tumor data. Additionally, we obtained possible reasons why miRNA can appear as a false positive in microarray study using paired miRNA sequencing and array data. The use of DNA microarrays for estimating miRNA expression profile is limited by several factors. One of them consists of problems with comparing expression values of different miRNAs. In this work, we show that situation can be significantly improved if some additional information is taken into consideration in a comparison.
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Campanero-Rhodes, María Asunción, Enrique Llobet, José Antonio Bengoechea e Dolores Solís. "Bacteria microarrays as sensitive tools for exploring pathogen surface epitopes and recognition by host receptors". RSC Advances 5, n. 10 (2015): 7173–81. http://dx.doi.org/10.1039/c4ra14570d.
Testo completoAbstract (sommario):
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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Rafique, Saima, Farukh Kiyani, Sumbal Jawaid, Rubina Nasir, Mahmoosh Ahmad, Shazia Bashir, Muhammad Idress, Jan Sher Khan e Rizwan Akram. "Reusable, Noninvasive, and Sensitive Fluorescence Enhanced ZnO-Nanorod-Based Microarrays for Quantitative Detection of AFP in Human Serum". BioMed Research International 2021 (15 luglio 2021): 1–11. http://dx.doi.org/10.1155/2021/9916909.
Testo completoAbstract (sommario):
The fabrication of sensitive protein microarrays such as PCR used in DNA microarray is challenging due to lack of signal amplification. The development of microarrays is utilized to improve the sensitivity and limitations of detection towards primal cancer detection. The sensitivity is enhanced by the use of ZnO-nanorods and is investigated as a substrate which enhance the florescent signal to diagnose the hepatocellular carcinoma (HCC) at early stages. The substrate for deposition of ZnO-nanorods is prepared by the conventional chemical bath deposition method. The resultant highly dense ZnO-nanorods enhance the fluorescent signal 7.2 times as compared to the substrate without ZnO-nanorods. The microarray showed sensitivity of 1504.7 ng ml-1 and limit of detection of 0.1 pg ml-1 in wide dynamic range of 0.05 pg-10 μg ml-1 for alpha fetoprotein (AFP) detection in 10% human serum. This immunoassay was successfully applied for human serum samples to detect tumor marker with good recoveries. The ZnO-nanorod substrate is a simple protein microarray which showed a great promise for developing a low-cost, sensitive, and high-throughput protein assay platform for several applications in both fundamental research and clinical diagnosis.
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ZHANG, YONG. "INTEGRATION OF NANOPARTICLES WITH PROTEIN MICROARRAYS". International Journal of Nanoscience 05, n. 02n03 (aprile 2006): 189–94. http://dx.doi.org/10.1142/s0219581x0600422x.
Testo completoAbstract (sommario):
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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Jenkins, Elizabeth S., Caren Broadhead e Robert D. Combes. "The Implications of Microarray Technology for Animal Use in Scientific Research". Alternatives to Laboratory Animals 30, n. 4 (luglio 2002): 459–65. http://dx.doi.org/10.1177/026119290203000408.
Testo completoAbstract (sommario):
Microarray technology has the potential to affect the number of laboratory animals used, the severity of animal experiments, and the development of non-animal alternatives in several areas of scientific research. Microarrays can contain hundreds or thousands of microscopic spots of DNA, immobilised on a solid support, and their use enables global patterns of gene expression to be determined in a single experiment. This technology is being used to improve our understanding of the operation of biological systems during health and disease, and their responses to chemical insults. Although it is impossible to predict with certainty any future trends regarding animal use, microarray technology might not initially reduce animal use, as is often claimed to be the case. The accelerated pace of research as a result of the use of microarrays could increase overall animal use in basic and applied biological research, by increasing the numbers of interesting genes identified for further analysis, and the number of potential targets for drug development. Each new lead will require further evaluation in studies that could involve animals. In toxicity testing, microarray studies could lead to increases in animal studies, if further confirmatory and other studies are performed. However, before such technology can be used more extensively, several technical problems need to be overcome, and the relevance of the data to biological processes needs to be assessed. Were microarray technology to be used in the manner envisaged by its protagonists, there need to be efforts to increase the likelihood that its application will create new opportunities for reducing, refining and replacing animal use. This comment is a critical assessment of the possible implications of the application of microarray technology on animal experimentation in various research areas, and makes some recommendations for maximising the application of the Three Rs.
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Selvarajah, Senthooran, Ola H. Negm, Mohamed R. Hamed, Carolyn Tubby, Ian Todd, Patrick J. Tighe, Tim Harrison e Lucy C. Fairclough. "Development and Validation of Protein Microarray Technology for Simultaneous Inflammatory Mediator Detection in Human Sera". Mediators of Inflammation 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/820304.
Testo completoAbstract (sommario):
Biomarkers, including cytokines, can help in the diagnosis, prognosis, and prediction of treatment response across a wide range of disease settings. Consequently, the recent emergence of protein microarray technology, which is able to quantify a range of inflammatory mediators in a large number of samples simultaneously, has become highly desirable. However, the cost of commercial systems remains somewhat prohibitive. Here we show the development, validation, and implementation of an in-house microarray platform which enables the simultaneous quantitative analysis of multiple protein biomarkers. The accuracy and precision of the in-house microarray system were investigated according to the Food and Drug Administration (FDA) guidelines for pharmacokinetic assay validation. The assay fell within these limits for all but the very low-abundant cytokines, such as interleukin- (IL-) 10. Additionally, there were no significant differences between cytokine detection using our microarray system and the “gold standard” ELISA format. Crucially, future biomarker detection need not be limited to the 16 cytokines shown here but could be expanded as required. In conclusion, we detail a bespoke protein microarray system, utilizing well-validated ELISA reagents, that allows accurate, precise, and reproducible multiplexed biomarker quantification, comparable with commercial ELISA, and allowing customization beyond that of similar commercial microarrays.
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Forster, T., D. Roy e P. Ghazal. "Experiments using microarray technology: limitations and standard operating procedures". Journal of Endocrinology 178, n. 2 (1 agosto 2003): 195–204. http://dx.doi.org/10.1677/joe.0.1780195.
Testo completoAbstract (sommario):
Microarrays are a powerful method for the global analysis of gene or protein content and expression, opening up new horizons in molecular and physiological systems. This review focuses on the critical aspects of acquiring meaningful data for analysis following fluorescence-based target hybridisation to arrays. Although microarray technology is adaptable to the analysis of a range of biomolecules (DNA, RNA, protein, carbohydrates and lipids), the scheme presented here is applicable primarily to customised DNA arrays fabricated using long oligomer or cDNA probes. Rather than provide a comprehensive review of microarray technology and analysis techniques, both of which are large and complex areas, the aim of this paper is to provide a restricted overview, highlighting salient features to provide initial guidance in terms of pitfalls in planning and executing array projects. We outline standard operating procedures, which help streamline the analysis of microarray data resulting from a diversity of array formats and biological systems. We hope that this overview will provide practical initial guidance for those embarking on microarray studies.
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Kennedy, Gavin C., e Iain W. Wilson. "Plant functional genomics: opportunities in microarray databases and data mining". Functional Plant Biology 31, n. 4 (2004): 295. http://dx.doi.org/10.1071/fp03216.
Testo completoAbstract (sommario):
High-throughput gene expression profiling using microarrays has given plant biologists a powerful new technology to discover gene function and understand cellular processes. Bioinformatics has rapidly developed to deliver the tools necessary to interpret this gene expression data, but opportunities to further exploit the mass of data from hundreds of experiments are becoming dependent upon the use of sophisticated database repositories. Data mining of these resources will allow plant biologists to compare and link expression profiles and experimental factors to uncover functions and processes that would not normally be visible from analysing a small set of microarray experiments. This in-silico analysis will become critical when designing new experiments and interpreting new results. Consequently microarray databases and their ongoing development are now as important to plant functional genomics as the initial microarray data capture and analysis tools. In order for plant biologists to grasp these new opportunities, an appreciation of microarray database technology and future developments in biological data integration is required. The challenge for plant functional genomics is to embrace these new technologies lest the opportunities for significant discoveries be lost.
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Fiori, Alessandro, Alberto Grand, Giulia Bruno, Francesco Gavino Brundu, Domenico Schioppa e Andrea Bertotti. "Information Extraction from Microarray Data". Journal of Database Management 25, n. 1 (gennaio 2014): 29–58. http://dx.doi.org/10.4018/jdm.2014010102.
Testo completoAbstract (sommario):
Nowadays, a huge amount of high throughput molecular data are available for analysis and provide novel and useful insights into complex biological systems, through the acquisition of a high-resolution picture of their molecular status in defined experimental conditions. In this context, microarrays are a powerful tool to analyze thousands of gene expression values with a single experiment. A number of approaches have been developed to detecting genes highly correlated to diseases, selecting genes that exhibit a similar behavior under specific conditions, building models to predict disease outcome based on genetic profiles, and inferring regulatory networks. This paper discusses popular and recent data mining techniques (i.e., Feature Selection, Clustering, Classification, and Association Rule Mining) applied to microarray data. The main characteristics of microarray data and preprocessing procedures are presented to understand the critical issues introduced by gene expression values analysis. Each technique is analyzed, and relevant examples of pertinent literature are reported. Moreover, real use cases exploiting analytic pipelines that use these methods are also introduced. Finally, future directions of data mining research on microarray data are envisioned.
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Witney, Adam A., Gemma L. Marsden, Matthew T. G. Holden, Richard A. Stabler, Sarah E. Husain, J. Keith Vass, Philip D. Butcher, Jason Hinds e Jodi A. Lindsay. "Design, Validation, and Application of a Seven-Strain Staphylococcus aureus PCR Product Microarray for Comparative Genomics". Applied and Environmental Microbiology 71, n. 11 (novembre 2005): 7504–14. http://dx.doi.org/10.1128/aem.71.11.7504-7514.2005.
Testo completoAbstract (sommario):
ABSTRACT Bacterial comparative genomics has been revolutionized by microarrays, but the power of any microarray is dependent on the number and diversity of gene reporters it contains. Staphylococcus aureus is an important human pathogen causing a wide range of invasive and toxin-mediated diseases, and more than 20% of the genome of any isolate consists of variable genes. Seven whole-genome sequences of S. aureus are available, and we exploited this rare opportunity to design, build, and validate a comprehensive, nonredundant PCR product microarray carrying reporters that represent every predicted open reading frame (3,623 probes). Such a comprehensive microarray necessitated a novel design strategy. Validation with the seven sequenced strains showed correct identification of 93.9% of genes present or absent/divergent but was dependent on the method of analysis chosen. Microarray data were highly reproducible, reducing the need for many replicate slides. Interpretation of microarray data was enhanced by focusing on the major areas of variation—the presence or absence of mobile genetic elements (MGEs). We compiled “composite genomes” of every individual MGE and visualized their distribution. This allowed the sensitive discrimination of related isolates, including the first clear description of how isolates of the same clone of epidemic methicillin-resistant S. aureus differ substantially in their carriage of MGEs. These MGEs carry virulence and resistance genes, suggesting differences in pathogenic potential. The novel methods of design and interpretation of data generated from this microarray will enable further studies of S. aureus evolution, epidemiology, and pathogenesis.