• Journal of Life Science
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• v.25 no.4
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• pp.376-384
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• 2015

To investigate the molecular events of controlling intramuscular fat (or marbling), which is an important factor in the evaluation of beef quality, we performed cDNA microarray analyses using the longissimus dorsi muscle and back fat tissues. For this study, we constructed normalized cDNA libraries: fat tissues in native Korean cattle (displaying 1,211 specific genes), and muscle tissues in native Korean cattle (displaying 1,346 specific genes). A bovine cDNA chip was constructed with 1,680 specific genes, consisting of 760 genes from muscle tissues and 920 genes from fat tissues. The microarray analysis in this experiment showed a number of differentially expressed genes, which compared the longissimus dorsi muscle (Cy5) with back fat tissue (Cy3). Among many specific differentially expressed genes, 12-lipoxygenase (oxidizing esterified fatty acids) and prostaglandin D synthase (differentiation of fibroblasts to adipocytes) are the key candidate enzymes that should be involved in controlling the accumulation of intramuscular fat. In this study, differentially and commonly expressed genes in the muscle and fat tissues of native Korean cattle were found in large numbers, using the hybridization assay. The expression levels of the selected genes were confirmed by semi-quantitative RT-PCR, and the results were similar to those of the cDNA microarray.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.29-34
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• 2005

DNA microarray becomes a major tool for the investigation of global gene expression in all aspects of cancer and biomedical research. DNA microarray experiment generates enormous amounts of data and they are meaningful only in the context of a detailed description of microarrays, biomaterials, and conditions under which they were generated. MicroArray Gene Expression Data (MGED) society has established microarray standard for structured management of these diverse and large amount data. MGED MAGE-OM (MicroArray Gene Expression Object Model) is an object oriented data model, which attempts to define standard objects for gene expression. To assess the relevance of DNA microarray analysis of cancer research it is required to combine clinical and genomics data. MAGE-OM, however, does not have an appropriate structure to describe clinical information of cancer. For systematic integration of gene expression and clinical data, we create a new model, Cancer Genomics Object Model.

• Genomics & Informatics
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• v.1 no.2
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• pp.87-93
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• 2003

DNA microarray is currently the most prominent tool for investigating large-scale gene expression data. Different algorithms for measuring gene expression levels from scanned images of microarray experiments may significantly impact the following steps of functional genomic analyses. $Affymetrix^{(R)}$ recently introduced high-density microarrays and new statistical algorithms in Microarray Suit (MAS) version 5.0$^{(R)}$. Very high correlations (0.92 - 0.97) between the new algorithms and the old algorithms (MAS 4.0) across several species and conditions were reported. We found that the column-wise array correlations had a tendency to be much higher than the row-wise gene correlations, which may be much more meaningful in the following higher-order data analyses including clustering and pattern analyses. In this paper, not only the detailed comparison of the two sets of algorithms is illustrated, but the impact of the introducing new algorithms on the further clustering analysis of microarray data and of possible pitfalls in mixing the old and the new algorithms were also described.

• Korean Journal of Microbiology
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• v.41 no.2
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• pp.105-111
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• 2005

Streptomyces produces many kinds of secondary-metabolites including antibiotics. Screening of a new compound and elucidation of a biosynthetic pathway for the secondary metabolites are very important fields of biology, however, there is a main problem that most of the identified compounds are already researched compounds. To solve these problems, a microarray system that is based on the data related to the biosynthetic genes for secondary-metabolites was designed. For the main contents of DNA microarray, the important genes for the bio-synthesis of aminoglycosides, polyenes group, enediyne group, alpha-glucosidase inhibitors, glycopeptide group, and orthosomycin group were chosen. A DNA microarray with 69 genes that were involved in the bio-synthesis for the antibiotics mentioned above was prepared. The usability of the DNA microarray was confirmed with the chromosomal DNA and total RNA extracted from S. coelicolor whose genomic sequence had already been reported.

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.12
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• pp.1837-1841
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• 2003

In an attempt to isolate novel molecules that may play a regulatory role in adipocyte differentiation, we devised an experimental strategy to identify adipose tissue-specific genes by modifying cDNA microarray technique. We used genefilter membranes containing approximately 15,000 rat non-redundant EST clones of which 4,000 EST were representative clones of known genes and 11,000 ESTs were uncharacterized clones. A series of hybridization of genefilter membranes with cDNA probes prepared from various rat tissues and nucleic acids sequence analysis allowed us to identify two adipose-tissue specific genes, adipocyte-specific secretory factor (ADSF) and H-rev107. Verification of tissue-specific expression patterns of these two genes by Northern blot analysis showed that ADSF mRNA is exclusive expressed in adipose tissue and the H-rev107 mRNA is predominantly expressed in adipose tissue. Further analysis of gene expression of ADSF and H-rev107 during 3T3-L1 adipocyte differentiation revealed that the ADSF and H-rev107 gene expression patterns are closely associated with the adipocyte differentiation program, indicating their possible role in the regulation of adipose tissue development. Overall, we demonstrated an application of modified cDNA microarray technique in molecular cloning, resulting in identification of two novel adipose tissue-specific genes. This technique will also be used as a useful tool in identifying novel genes expressed in a tissue-specific manner.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.617-626
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• 2009

A series of recent papers reported that the inter-gene correlations in Affymetrix microarray data sets were strong and long-ranged, and the assumption of independence or weak dependence among gene expression signals which was often employed without justification was in conflict with actual data. Qui et al. (2005) indicated that applying the nonparametric empirical Bayes method in which test statistics were pooled across genes for performing the statistical inference resulted in the large variance of the number of differentially expressed genes. Qui et al. (2005) attributed this effect to strong and long-ranged inter-gene correlations. Klebanov and Yakovlev (2007) demonstrated that the inter-gene correlations provided a rich source of information rather than being a nuisance in the statistical analysis and they developed, by transforming the original gene expression sequence, a sequence of independent random variables which they referred to as a ${\delta}$-sequence. We note in this report using two cDNA microarray data sets experimented in this country that the strong and long-ranged inter-gene correlations were still valid in cDNA microarray data and also the ${\delta}$-sequence of independence could be derived from the cDNA microarray data. This note suggests that the inter-gene correlations be considered in the future analysis of the cDNA microarray data sets.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.79-83
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• 2002

Since its introduction in 1995 by Schena et al. cDNA microarrays have been established as a potential tool for high-throughput analysis which allows the global monitoring of expression levels for thousands of genes simultaneously. One of the characteristics of the cDNA microarray data is that there is inherent noise even after the removal of systematic effects in the experiment. Therefore, replication is crucial to the microarray experiment. The assessment of reproducibility among replicates, however, has drawn little attention. Reproducibility may be assessed with several different endpoints along the process of data reduction of the microarray data. We define the reproducibility to be the degree with which replicate arrays duplicate each other. The aim of this note is to develop a novel measure of reproducibility among replicates in the cDNA microarray experiment based on the unprocessed data. Suppose we have p genes and n replicates in a microarray experiment. We first develop a measure of reproducibility between two replicates and generalize this concept for a measure of reproducibility of one replicate against the remaining n-1 replicates. We used the rank of the outcome variable and employed the concept of a measure of tracking in the blood pressure literature. We applied the reproducibility measure to two sets of microarray experiments in which one experiment was performed in a more homogeneous environment, resulting in validation of this novel method. The operational interpretation of this measure is clearer than Pearson's correlation coefficient which might be used as a crude measure of reproducibility of two replicates.

• Molecules and Cells
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• v.22 no.3
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• pp.254-261
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• 2006

DNA microarray is a powerful tool for high-throughput analysis of biological systems. Various computational tools have been created to facilitate the analysis of the large volume of data produced in DNA microarray experiments. Normalization is a critical step for obtaining data that are reliable and usable for subsequent analysis such as identification of differentially expressed genes and clustering. A variety of normalization methods have been proposed over the past few years, but no methods are still perfect. Various assumptions are often taken in the process of normalization. Therefore, the knowledge of underlying assumption and principle of normalization would be helpful for the correct analysis of microarray data. We present a review of normalization techniques from single-labeled platforms such as the Affymetrix GeneChip array to dual-labeled platforms like spotted array focusing on their principles and assumptions.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.951-958
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• 2005

As a foundation study of stem cell applied research, it is necessary to identify the large gene expression through cDNA microarray to understand principles of the level of molecular about cell function. In this paper, we investigated the gene expression through the K-means clustering method and path analysis with genes related to pluripoteny and differentiation in an mouse early stage embryonic development process and embryonic stem cell differentiation. We find a few biological phenomenon through this study. Also, we realize that this process provides functional relationship of unknown genes.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2001.10a
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• pp.29-44
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• 2001

DNA damage by physical insult including UV and g-radiation might provoke genetic alterations in cells, which is followed by either acute cell death or tumorigenesis. The responsiveness to g-radiation depends on cellular context of target cells. To understand the mechanisms of checkpoint control, repair and cell death following genotoxic stimu]i, cDNA microarray can provide the gene expression profile. To make a profile of gene expression in irradiated Jurkat T cells, we hybridized the cDNA microarray using cDNA from g-irradiated Jurkat T cells. Jurkat T cells were exposed to 4Gy to 16Gy, and total RNA were extracted at 4 to 24 hrs after irradiation. The hybridization of the microarray to fluorescence-labeled cDNA from treated and untreated cells was analyzed by bioinformatic analysis to address relative changes in expression levels of the genes present in the array. Responses varied widely in different time points, suggesting acute stress response and chronic restoration or cell death. From these results we could select 384 genes related to radiation response in Tcells, and radiation response might be different in various types of cells. Using Radchip, we could separate "the exposed" from control PBMCs. We propose that Radchip might be useful to check the radiation research as well as radiation carcinogenesis.