Kenshirou Kunii (22216611), Shigeru Sugiki (22216614), Chiharuko Ushimoto (22216617) et al. · Figshare · 2025
Scatter plots were used to evaluate the mRNA expression variation among the three groups of rats and the control group. (a) Scatter plots of the cortex in the control group rats and in those of each of the three other groups. (b) Scatter plots of the medullae in the control group rats and in those in each of the other three groups. The values on the x- and y-axes of the scatter plots are the normalized (log2 transformed) values for each sample. The green line represents the fold-change (the default fold-change value is 2.0). The mRNAs above the upper green line and below the lower green line have a 2.0-fold-change in mRNA expression among the three groups. The results of the microarray analysis of the crystal formation group (EG + VitD group) are posted as supplementary data. (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0328721#pone.0328721.s004" target="_blank">S4 Fig</a>.).
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Qingbin Guo · Current Opinion in Oncology · 2003
Since the discovery of the first oncogene 26 years ago, a large body of research has convincingly demonstrated that the initiation and progression of cancers involve the accumulation of genetic aberrations in the cell. Many techniques have been developed to identify these genetic abnormalities. The recent completion of human genome sequencing and advances in DNA microarray technology allow rapid genetic analysis to take place on a genome-wide scale and have revolutionized the way cancers are studied. This ground-breaking approach of studying cancer promises to provide a better understanding of the underlying mechanism for tumorigenesis, more accurate diagnosis, more comprehensive prognosis, and more effective therapeutic interventions.
Cited by 62
Sampsa Hautaniemi, Antti Lehmussola, Olli Yli‐Harja · 2004
Microarray technology is an essential part of modern biomedical research. At the moment, the most widely utilized microarray platform in academic biomedical research is DNA microarray due to its flexibility and low costs. One of the biggest drawback of the DNA microarray technology is high variation in data quality. Thus, in order to obtain reliable results, sophisticated image analysis and extensive preprocessing steps should be applied before actual microarray data analysis can take place. In this study we highlight some essential microarray preprocessing steps.
Cited by 4
RCIN (Digital Repository of the Scientifics Institutes) (Institute of Archaeology and Ethnology of the Polish Academy of Sciences) · 2008
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Mahsa Gharibi Marzancola, Abootaleb Sedighi, Paul C. H. Li · Methods in molecular biology · 2015
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Kevin K. Dobbin, Richard Simon · 2006
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LA SALA, Dario, EMANUELE, Sonia, CARLISI, Daniela et al. · Nova Science Publishers (Nova Science Publishers, Inc.) · 2008
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RCIN (Digital Repository of the Scientifics Institutes) (Institute of Archaeology and Ethnology of the Polish Academy of Sciences) · 2008
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Martin Bilban, Lukas K. Buehler, Steven R. Head et al. · Current Issues in Molecular Biology · 2002
DNA microarrays are a powerful tool to investigate differential gene expression for thousands of genes simultaneously. Although DNA microarrays have been widely used to understand the critical events underlying growth, development, homeostasis, behavior and the onset of disease, the management of the resulting data has received little attention. Presently, the fluorescent dyes Cy3 and Cy5 are most often used to prepare labeled cDNA for microarray hybridizations. Raw microarray data are image files that have to be transformed into gene expression formats--a process that requires data manipulation due to systematic variations which may be attributed to differences in the physical and chemical dye applications is to identify differences in transcript levels calculated from fluorescence ratios it is necessary to normalize fluorescence signals to compensate for systematic variations. Here, we will review current normalization strategies applied to cDNA microarrays and discuss their limits. We will show that experimental design determines normalization success.
Cited by 100Open access
Microarray Technology, Yoshikazu Koizumi, John J. Kelly et al. · 2002
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Arjang Hassibi, Haris Vikalo, José Luis Riechmann et al. · Nucleic Acids Research · 2009
We present a quantification method for affinity-based DNA microarrays which is based on the real-time measurements of hybridization kinetics. This method, i.e. real-time DNA microarrays, enhances the detection dynamic range of conventional systems by being impervious to probe saturation in the capturing spots, washing artifacts, microarray spot-to-spot variations, and other signal amplitude-affecting non-idealities. We demonstrate in both theory and practice that the time-constant of target capturing in microarrays, similar to all affinity-based biosensors, is inversely proportional to the concentration of the target analyte, which we subsequently use as the fundamental parameter to estimate the concentration of the analytes. Furthermore, to empirically validate the capabilities of this method in practical applications, we present a FRET-based assay which enables the real-time detection in gene expression DNA microarrays.
Cited by 42Open access
Hsin‐Chou Yang, Mei-Chu Huang, Ling‐Hui Li et al. · BMC Bioinformatics · 2008
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ANDRIANTERANAGNA, Mamy · Figshare · 2023
Infinium methylationEPIC array data from 54 human ATRT samples were processed using RnBeads (version 1.6.1) Bioconductor package. Probe intensities were normalized with the “illumina” method. Probes outside CpG context, targeting single nucleotide polymorphisms or targeting X and Y chromosomes were all filtered out. Matrix of beta-value were generated and annotated using the RnBeads.hg19 (version 1.26.0) Bioconductor package.
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Takashi Ichinose (742457), Hiroyasu Murasawa (8796533), Tomoko Ishijima (190565) et al. · Figshare · 2020
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Statistics for biology and health · 2006
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Kwok-Fan Chow (2089543), François Mavré (1844383), Richard M. Crooks (1346970) · Figshare · 2016
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Manfred Kunz · Seminars in Cutaneous Medicine and Surgery · 2008
In recent years, DNA microarray technology has been used for the analysis of gene expression patterns in a variety of skin diseases, including malignant melanoma, psoriasis, lupus erythematosus, and systemic sclerosis. Many of the studies described herein confirmed earlier results on individual genes or functional groups of genes. However, a plethora of new candidate genes, gene patterns, and regulatory pathways have been identified. Major progresses were reached by the identification of a prognostic gene pattern in malignant melanoma, an immune signaling cluster in psoriasis, and a so-called interferon signature in systemic lupus erythematosus. In future, interference with genes or regulatory pathways with the use of different RNA interference technologies or targeted therapy may not only underscore the functional significance of microarray data but also may open interesting therapeutic perspectives. Large-scale gene expression analyses may also help to design more individualized treatment approaches of cutaneous diseases.
Cited by 11
Alan Willse, Darrell P. Chandler, A. M. White et al. · Statistical Applications in Genetics and Molecular Biology · 2005
Epidemiologic and forensic investigations often require assays to detect subtle genetic differences between closely related microorganisms. Typically, gel electrophoresis is used to compare randomly amplified DNA fragments between microbial samples, where the patterns of DNA fragment sizes are viewed as genotype 'fingerprints'. The limited genomic sample captured on a gel, however, is not always sufficient to discriminate closely related strains. This paper examines the application of microarray technology to DNA fingerprinting as a high-resolution alternative to gel-based methods. The so-called universal microarray, which uses short oligonucleotide probes that do not target specific genes or species, is intended to be applicable to all microorganisms because it does not require prior knowledge of genomic sequence. In principle, closely related strains can be distinguished if enough independent oligonucleotide probes are used on the microarray, i.e., if the genome is sufficiently sampled. In practice, we confront noisy data, imperfectly matched hybridizations, and a high-dimensional inference problem. We describe the statistical problems of microarray fingerprinting, outline similarities with and differences from more conventional microarray applications, and illustrate a statistical measurement error model to fingerprint 10 closely related strains from three Bacillus species, and 3 strains from non-Bacillus species.
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Haohao Lin (2673760), Li Sun (4624), Richard M. Crooks (1346970) · Figshare · 2016
A mechanical method for efficient replication of DNA microarrays is described. The approach consists of three steps. First, a master DNA microarray consisting of single-stranded DNA elements is exposed to a solution containing the biotin-functionalized complement of each array element. Following hybridization, a replica surface modified with streptavidin is brought into contact with the master. This results in linking of the biotin-functionalized complement with the replica surface. Next, the replica is separated from the master, and the complementary strands are transferred to the replica surface. The resulting complementary DNA microarray contains position-coded sequences that mirror the information contained on the master DNA microarray. Multiple replicas can be prepared from a single master, the replicas efficiently hybridize only their complement, and DNA not labeled with biotin is not transferred to the replica surface.
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Benjamin M. Bolstad · 2007
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