Journal articles: 'Microarray analysis'

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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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García-Albert, L., F. Martín-Sánchez, A. García-Sáiz, and G. H. López-Campos. "Analysis and Management of HIV Peptide Microarray Experiments." Methods of Information in Medicine 45, no. 02 (2006): 158–62. http://dx.doi.org/10.1055/s-0038-1634060.

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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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Gibson, Greg. "Microarray Analysis." PLoS Biology 1, no. 1 (October 13, 2003): e15. http://dx.doi.org/10.1371/journal.pbio.0000015.

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Wenstrom, Katharine D. "Microarray Analysis." Obstetrics & Gynecology 124, no. 2, PART 1 (August 2014): 199–201. http://dx.doi.org/10.1097/aog.0000000000000407.

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Shangkuan, Wei-Chuan, Hung-Che Lin, Yu-Tien Chang, Chen-En Jian, Hueng-Chuen Fan, Kang-Hua Chen, Ya-Fang Liu, et al. "Risk analysis of colorectal cancer incidence by gene expression analysis." PeerJ 5 (February 15, 2017): e3003. http://dx.doi.org/10.7717/peerj.3003.

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Background Colorectal cancer (CRC) is one of the leading cancers worldwide. Several studies have performed microarray data analyses for cancer classification and prognostic analyses. Microarray assays also enable the identification of gene signatures for molecular characterization and treatment prediction. Objective Microarray gene expression data from the online Gene Expression Omnibus (GEO) database were used to to distinguish colorectal cancer from normal colon tissue samples. Methods We collected microarray data from the GEO database to establish colorectal cancer microarray gene expression datasets for a combined analysis. Using the Prediction Analysis for Microarrays (PAM) method and the GSEA MSigDB resource, we analyzed the 14,698 genes that were identified through an examination of their expression values between normal and tumor tissues. Results Ten genes (ABCG2, AQP8, SPIB, CA7, CLDN8, SCNN1B, SLC30A10, CD177, PADI2, and TGFBI) were found to be good indicators of the candidate genes that correlate with CRC. From these selected genes, an average of six significant genes were obtained using the PAM method, with an accuracy rate of 95%. The results demonstrate the potential of utilizing a model with the PAM method for data mining. After a detailed review of the published reports, the results confirmed that the screened candidate genes are good indicators for cancer risk analysis using the PAM method. Conclusions Six genes were selected with 95% accuracy to effectively classify normal and colorectal cancer tissues. We hope that these results will provide the basis for new research projects in clinical practice that aim to rapidly assess colorectal cancer risk using microarray gene expression analysis.

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Cao, Yiwei, Sang-Jun Park, Akul Y. Mehta, Richard D. Cummings, and Wonpil Im. "GlyMDB: Glycan Microarray Database and analysis toolset." Bioinformatics 36, no. 8 (December 16, 2019): 2438–42. http://dx.doi.org/10.1093/bioinformatics/btz934.

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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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White, Christine A., and Lois A. Salamonsen. "A guide to issues in microarray analysis: application to endometrial biology." Reproduction 130, no. 1 (July 2005): 1–13. http://dx.doi.org/10.1530/rep.1.00685.

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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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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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Huang, Joe Xi, Dorothy Mehrens, Rick Wiese, Sandy Lee, Sun W. Tam, Steve Daniel, James Gilmore, Michael Shi, and Deval Lashkari. "High-Throughput Genomic and Proteomic Analysis Using Microarray Technology." Clinical Chemistry 47, no. 10 (October 1, 2001): 1912–16. http://dx.doi.org/10.1093/clinchem/47.10.1912.

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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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Wang, Zhiyou, Xiaoqing Huang, and Zhiqiang Cheng. "Automatic Spot Identification Method for High Throughput Surface Plasmon Resonance Imaging Analysis." Biosensors 8, no. 3 (September 13, 2018): 85. http://dx.doi.org/10.3390/bios8030085.

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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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Jacobsen, M., D. Repsilber, A. Gutschmidt, A. Neher, K. Feldmann, H. J. Mollenkopf, S. H. E. Kaufmann, and A. Ziegler. "Deconfounding Microarray Analysis." Methods of Information in Medicine 45, no. 05 (2006): 557–63. http://dx.doi.org/10.1055/s-0038-1634118.

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Summary Objectives: Microarray analysis requires standardized specimens and evaluation procedures to achieve acceptable results. A major limitation of this method is caused by heterogeneity in the cellular composition of tissue specimens, which frequently confounds data analysis. We introduce a linear model to deconfound gene expression data from tissue heterogeneity for genes exclusively expressed by a single cell type. Methods: Gene expression data are deconfounded from tissue heterogeneity effects by analyzing them using an appropriate linear regression model. In our illustrating data set tissue heterogeneity is being measured using flow cytometry. Gene expression data are determined in parallel by real time quantitative polymerase chain reaction (qPCR) and microarray analyses. Verification of deconfounding is enabled using protein quantification for the respective marker genes. Results: For our illustrating dataset, quantification of cell type proportions for peripheral blood mononuclear cells (PBMC) from tuberculosis patients and controls revealed differences in B cell and monocyte proportions between both study groups, and thus heterogeneity for the tissue under investigation. Gene expression analyses reflected these differences in celltype distribution. Fitting an appropriate linear model allowed us to deconfound measured transcriptome levels from tissue heterogeneity effects. In the case of monocytes, additional differential expression on the single cell level could be proposed. Protein quantification verified these deconfounded results. Conclusions: Deconfounding of transcriptome analyses for cellular heterogeneity greatly improves interpretability, and hence the validity of transcriptome profiling results.

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Menezes, Ren??e X. de, Judith M. Boer, and Hans C. van Houwelingen. "Microarray Data Analysis." Applied Bioinformatics 3, no. 4 (2004): 229–35. http://dx.doi.org/10.2165/00822942-200403040-00004.

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Larsson, Ola, Kristian Wennmalm, and Rickard Sandberg. "Comparative Microarray Analysis." OMICS: A Journal of Integrative Biology 10, no. 3 (September 2006): 381–97. http://dx.doi.org/10.1089/omi.2006.10.381.

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Wurmbach, Elisa, Tony Yuen, and Stuart C. Sealfon. "Focused microarray analysis." Methods 31, no. 4 (December 2003): 306–16. http://dx.doi.org/10.1016/s1046-2023(03)00161-0.

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Weninger, F., S. Merk, A. Kohlmann, T. Haferlach, and M. Dugas. "A Generic Concept for Large-scale Microarray Analysis Dedicated to Medical Diagnostics." Methods of Information in Medicine 45, no. 02 (2006): 146–52. http://dx.doi.org/10.1055/s-0038-1634058.

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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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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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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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KOSTIĆ, TANJA, BEATRIX STESSL, MARTIN WAGNER, and ANGELA SESSITSCH. "Microarray Analysis Reveals the Actual Specificity of Enrichment Media Used for Food Safety Assessment." Journal of Food Protection 74, no. 6 (June 1, 2011): 1030–34. http://dx.doi.org/10.4315/0362-028x.jfp-10-388.

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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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Yuvaraj, K., and D. Manjula. "A performance analysis of clustering based algorithms for the microarray gene expression data." International Journal of Engineering & Technology 7, no. 2.21 (April 20, 2018): 201. http://dx.doi.org/10.14419/ijet.v7i2.21.12172.

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

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

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Verducci, Joseph S., Vincent F. Melfi, Shili Lin, Zailong Wang, Sashwati Roy, and Chandan K. Sen. "Microarray analysis of gene expression: considerations in data mining and statistical treatment." Physiological Genomics 25, no. 3 (May 16, 2006): 355–63. http://dx.doi.org/10.1152/physiolgenomics.00314.2004.

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DNA microarray represents a powerful tool in biomedical discoveries. Harnessing the potential of this technology depends on the development and appropriate use of data mining and statistical tools. Significant current advances have made microarray data mining more versatile. Researchers are no longer limited to default choices that generate suboptimal results. Conflicting results in repeated experiments can be resolved through attention to the statistical details. In the current dynamic environment, there are many choices and potential pitfalls for researchers who intend to incorporate microarrays as a research tool. This review is intended to provide a simple framework to understand the choices and identify the pitfalls. Specifically, this review article discusses the choice of microarray platform, preprocessing raw data, differential expression and validation, clustering, annotation and functional characterization of genes, and pathway construction in light of emergent concepts and tools.

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Witney, Adam A., and Jason Hinds. "BμG@Sbase—a Microarray Database and Analysis Tool." Comparative and Functional Genomics 3, no. 4 (2002): 369–71. http://dx.doi.org/10.1002/cfg.197.

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The manufacture and use of a whole-genome microarray is a complex process and it is essential that all data surrounding the process is stored, is accessible and can be easily associated with the data generated following hybridization and scanning. As part of a program funded by the Wellcome Trust, the Bacterial Microarray Group at St. George's Hospital Medical School (BμG@S) will generate whole-genome microarrays for 12 bacterial pathogens for use in collaboration with specialist research groups. BμG@S will collaborate with these groups at all levels, including the experimental design, methodology and analysis. In addition, we will provide informatic support in the form of a database system (BμG@Sbase). BμG@Sbase will provide access through a web interface to the microarray design data and will allow individual users to store their data in a searchable, secure manner. Tools developed by BμG@S in collaboration with specific research groups investigating analysis methodology will also be made available to those groups using the arrays and submitting data to BμG@Sbase.

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

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

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Hoffmann, Julia, Jochen Wilhelm, Andrea Olschewski, and Grazyna Kwapiszewska. "Microarray analysis in pulmonary hypertension." European Respiratory Journal 48, no. 1 (April 13, 2016): 229–41. http://dx.doi.org/10.1183/13993003.02030-2015.

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Microarrays are a powerful and effective tool that allows the detection of genome-wide gene expression differences between controls and disease conditions. They have been broadly applied to investigate the pathobiology of diverse forms of pulmonary hypertension, namely group 1, including patients with idiopathic pulmonary arterial hypertension, and group 3, including pulmonary hypertension associated with chronic lung diseases such as chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis. To date, numerous human microarray studies have been conducted to analyse global (lung homogenate samples), compartment-specific (laser capture microdissection), cell type-specific (isolated primary cells) and circulating cell (peripheral blood) expression profiles. Combined, they provide important information on development, progression and the end-stage disease. In the future, system biology approaches, expression of noncoding RNAs that regulate coding RNAs, and direct comparison between animal models and human disease might be of importance.

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Liang, Mingyu, Amy G. Briggs, Elizabeth Rute, Andrew S. Greene, and Allen W. Cowley. "Quantitative assessment of the importance of dye switching and biological replication in cDNA microarray studies." Physiological Genomics 14, no. 3 (August 15, 2003): 199–207. http://dx.doi.org/10.1152/physiolgenomics.00143.2002.

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Dye switching and biological replication substantially increase the cost and the complexity of cDNA microarray studies. The objective of the present analysis was to quantitatively assess the importance of these procedures to provide a quantitative basis for decision-making in the design of microarray experiments. Taking advantage of the unique characteristics of a published data set, the impact of these procedures on the reliability of microarray results was calculated. Adding a second microarray with dye switching substantially increased the correlation coefficient between observed and 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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Moussati, Omar, and Mohamed Benyettou. "Analysis of Microarray Data." Circulation in Computer Science 2, no. 1 (January 24, 2017): 5–8. http://dx.doi.org/10.22632/ccs-2017-251-42.

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The computerized interpretation of biological information has taken a great interest in the scientific community, since it opens up very rich perspectives for the understanding of biological phenomena. These phenomena require collaboration between biologists, doctors, computer scientists, mathematicians and physicists. In this article we studied one of the most important subjects of bioinformatics, it is the biochip.We presented the various steps involved in the analysis of microarray data, Then we applied the KPPV method to the biochip data.

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Stears, Robin L., Todd Martinsky, and Mark Schena. "Trends in microarray analysis." Nature Medicine 9, no. 1 (January 2003): 140–45. http://dx.doi.org/10.1038/nm0103-140.

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Levy, Shawn E., and James A. S. Muldowney. "Microarray Analysis of Neointima." Arteriosclerosis, Thrombosis, and Vascular Biology 22, no. 12 (December 2002): 1946–47. http://dx.doi.org/10.1161/01.atv.0000042201.18694.0d.

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Loring, Jeanne F. "Evolution of microarray analysis." Neurobiology of Aging 27, no. 8 (August 2006): 1084–86. http://dx.doi.org/10.1016/j.neurobiolaging.2005.06.014.

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Bruno, Giulia, and Alessandro Fiori. "MicroClAn: Microarray clustering analysis." Journal of Parallel and Distributed Computing 73, no. 3 (March 2013): 360–70. http://dx.doi.org/10.1016/j.jpdc.2012.09.008.

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Samuel Lattimore, B., Stijn van Dongen, and M. James C. Crabbe. "GeneMCL in microarray analysis." Computational Biology and Chemistry 29, no. 5 (October 2005): 354–59. http://dx.doi.org/10.1016/j.compbiolchem.2005.07.002.

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Gaasterland, Douglas J., and Terry Gaasterland. "Microarray analysis made simple." Nature Genetics 33, no. 3 (March 2003): 335. http://dx.doi.org/10.1038/ng0303-335.

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Nielsen, Torsten O. "Microarray Analysis of Sarcomas." Advances in Anatomic Pathology 13, no. 4 (July 2006): 166–73. http://dx.doi.org/10.1097/00125480-200607000-00003.

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Belleville, Erik, Martin Dufva, Jens Aamand, Leif Bruun, Liselotte Clausen, and Claus B. V. Christensen. "Quantitative microarray pesticide analysis." Journal of Immunological Methods 286, no. 1-2 (March 2004): 219–29. http://dx.doi.org/10.1016/j.jim.2004.01.004.

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Kuehn, Bridget M. "Microarray Analysis in Stillbirth." JAMA 309, no. 4 (January 23, 2013): 333. http://dx.doi.org/10.1001/jama.2013.21.

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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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Aittokallio, Tero, Markus Kurki, Olli Nevalainen, Tuomas Nikula, Anne West, and Riitta Lahesmaa. "Computational Strategies for Analyzing Data in Gene Expression Microarray Experiments." Journal of Bioinformatics and Computational Biology 01, no. 03 (October 2003): 541–86. http://dx.doi.org/10.1142/s0219720003000319.

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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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Weidenhammer, Elaine M., Brenda F. Kahl, Ling Wang, Larry Wang, Melanie Duhon, Jo Ann Jackson, Matthew Slater, and Xiao Xu. "Multiplexed, Targeted Gene Expression Profiling and Genetic Analysis on Electronic Microarrays." Clinical Chemistry 48, no. 11 (November 1, 2002): 1873–82. http://dx.doi.org/10.1093/clinchem/48.11.1873.

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Abstract Background: Electronic microarrays comprise independent microelectrode test sites that can be electronically biased positive or negative, or left neutral, to move and concentrate charged molecules such as DNA and RNA to one or more test sites. We developed a protocol for multiplexed gene expression profiling of mRNA targets that uses electronic field-facilitated hybridization on electronic microarrays. Methods: A multiplexed, T7 RNA polymerase-mediated amplification method was used for expression profiling of target mRNAs from total cellular RNA; targets were detected by hybridization to sequence-specific capture oligonucleotides on electronic microarrays. Activation of individual test sites on the electronic microarray was used to target hybridization to designated subsets of sites and allow comparisons of target concentrations in different samples. We used multiplexed amplification and electronic field-facilitated hybridization to analyze expression of a model set of 10 target genes in the U937 cell line during lipopolysaccharide-mediated differentiation. Performance of multiple genetic analyses (single-nucleotide polymorphism detection, gene expression profiling, and splicing isoform detection) on a single electronic microarray was demonstrated using the ApoE and ApoER2 genes as a model system. Results: Targets were detected after a 2-min hybridization reaction. With noncomplementary capture probes, no signal was detectable. Twofold changes in target concentration were detectable throughout the (∼64-fold) range of concentrations tested. Levels of 10 targets were analyzed side by side across seven time points. By confining electronic activation to subsets of test sites, polymorphism detection, expression profiling, and splicing isoform analysis were performed on a single electronic microarray. Conclusions: Microelectronic array technology provides specific target detection and quantification with advantages over currently available methodologies for targeted gene expression profiling and combinatorial genomics testing.

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Kundalia, Paras H., Lucia Pažitná, Kristína Kianičková, Eduard Jáné, Lenka Lorencová, and Jaroslav Katrlík. "A Holistic 4D Approach to Optimize Intrinsic and Extrinsic Factors Contributing to Variability in Microarray Biosensing in Glycomics." Sensors 23, no. 12 (June 6, 2023): 5362. http://dx.doi.org/10.3390/s23125362.

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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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Martinez, Ricardo, Nicolas Pasquier, Martine Collard, Claude Pasquier, and Lucero Lopez-Perez. "Co-expressed gene groups analysis (CGGA): An automatic tool for the interpretation of microarray experiments." Journal of Integrative Bioinformatics 3, no. 2 (December 1, 2006): 188–98. http://dx.doi.org/10.1515/jib-2006-37.

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Summary Microarray technology produces vast amounts of data by measuring simultaneously the expression levels of thousands of genes under hundreds of biological conditions. Nowadays, one of the principal challenges in bioinformatics is the interpretation of this large amount of data using different sources of information. We have developed a novel data analysis method named CGGA (Co-expressed Gene Groups Analysis) that automatically finds groups of genes that are functionally enriched, i.e. have the same functional annotations, and are co-expressed. CGGA automatically integrates the information of microarrays, i.e. gene expression profiles, with the functional annotations of the genes obtained by the genome-wide information sources such as Gene Ontology. By applying CGGA to wellknown microarray experiments, we have identified the principal functionally enriched and co-expressed gene groups, and we have shown that this approach enhances and accelerates the interpretation of DNA microarray experiments.

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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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Fan, B. J. "Beginning Microarray Data Analysis: A Biologist's Guide to Analysis of DNA Microarray Data." Journal of Cell Science 116, no. 9 (May 1, 2003): 1649–50. http://dx.doi.org/10.1242/jcs.00436.

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Forster, T., D. Roy, and P. Ghazal. "Experiments using microarray technology: limitations and standard operating procedures." Journal of Endocrinology 178, no. 2 (August 1, 2003): 195–204. http://dx.doi.org/10.1677/joe.0.1780195.

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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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Sivalakshmi, Bolem, and N. Naga Malleswara Rao. "Microarray Image Analysis Using Genetic Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 3 (December 1, 2016): 561. http://dx.doi.org/10.11591/ijeecs.v4.i3.pp561-567.

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<p>Microarray technology allows the simultaneous monitoring of thousands of genes. Based on the gene expression measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, segmentation and intensity extraction are the three important steps in microarray image analysis. This paper presents microarray image analysis using Genetic Algorithm. A new algorithm for microarray image contrast enhancement is presented using Genetic Algorithm. Contrast enhancement is crucial step in extracting edge information in image and finally this edge information is used in gridding of microarray image. Mostly segmentation of microarray image is carried out using clustering algorithms. Clustering algorithms have an advantage that they are not restricted to a particular shape and size for the spots. In this paper, segmentation using Genetic Algorithm by optimizing K-means index and Jm measure is presented. The qualitative analysis shows that the proposed method achieves better segmentation results than K-means and FCM algorithms.</p>

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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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Wolf, Maija, Henrik Edgren, Aslaug Muggerud, Sami Kilpinen, Pia Huusko, Therese Sørlie, Spyro Mousses, and Olli Kallioniemi. "NMD Microarray Analysis for Rapid Genome-Wide Screen of Mutated Genes in Cancer." Analytical Cellular Pathology 27, no. 3 (January 1, 2005): 169–73. http://dx.doi.org/10.1155/2005/478316.

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Gene mutations play a critical role in cancer development and progression, and their identification offers possibilities for accurate diagnostics and therapeutic targeting. Finding genes undergoing mutations is challenging and slow, even in the post-genomic era. A new approach was recently developed by Noensie and Dietz to prioritize and focus the search, making use of nonsense-mediated mRNA decay (NMD) inhibition and microarray analysis (NMD microarrays) in the identification of transcripts containing nonsense mutations. We combined NMD microarrays with array-based CGH (comparative genomic hybridization) in order to identify inactivation of tumor suppressor genes in cancer. Such a “mutatomics” screening of prostate cancer cell lines led to the identification of inactivating mutations in the EPHB2 gene. Up to 8% of metastatic uncultured prostate cancers also showed mutations of this gene whose loss of function may confer loss of tissue architecture. NMD microarray analysis could turn out to be a powerful research method to identify novel mutated genes in cancer cell lines, providing targets that could then be further investigated for their clinical relevance and therapeutic potential.

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Mehta, Akul Y., Jamie Heimburg-Molinaro, and Richard D. Cummings. "Tools for generating and analyzing glycan microarray data." Beilstein Journal of Organic Chemistry 16 (September 10, 2020): 2260–71. http://dx.doi.org/10.3762/bjoc.16.187.

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Glycans are one of the major biological polymers found in the mammalian body. They play a vital role in a number of physiologic and pathologic conditions. Glycan microarrays allow a plethora of information to be obtained on protein–glycan binding interactions. In this review, we describe the intricacies of the generation of glycan microarray data and the experimental methods for studying binding. We highlight the importance of this knowledge before moving on to the data analysis. We then highlight a number of tools for the analysis of glycan microarray data such as data repositories, data visualization and manual analysis tools, automated analysis tools and structural informatics tools.

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Sabatti, Chiara. "Statistical Issues in Microarray Analysis." Current Genomics 3, no. 1 (February 1, 2002): 7–12. http://dx.doi.org/10.2174/1389202023350679.

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Journal articles on the topic 'Microarray analysis'

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