• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.437-448
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• 2016

Gene set analysis utilizing biologic information is expected to produce more interpretable results because the occurrence of tumors (or diseases) is believed to be associated with the regulation of related genes. Many methods have been developed to identify statistically significant gene sets across different phenotypes; however, most focus exclusively on either the differential gene expression or the differential correlation structure in the gene set. This research provides a new method that simultaneously considers the differential expression of genes and differential coexpression with multiple genes in the gene set. Application of this NEW method is illustrated with real microarray data example, p53; subsequently, a simulation study compares its type I error rate and power with GSEA, SAMGS, GSCA and GSNCA.

• Biomolecules & Therapeutics
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• v.22 no.2
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• pp.143-148
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• 2014

Marfan syndrome (MFS) is a dominantly inherited connective tissue disorder caused by mutations in the gene encoding fibrillin-1 (FBN1) and is characterized by aortic dilatation and dissection, which is the primary cause of death in untreated MFS patients. However, disease progression-associated changes in gene expression in the aortic lesions of MFS patients remained unknown. Using a mouse model of MFS, FBN1 hypomorphic mouse (mgR/mgR), we characterized the aortic gene expression profiles during the progression of the MFS. Homozygous mgR mice exhibited MFS-like phenotypic features, such as fragmentation of elastic fibers throughout the vessel wall and were graded into mgR1-4 based on the pathological severity in aortic walls. Comparative gene expression profiling of WT and four mgR mice using microarrays revealed that the changes in the transcriptome were a direct reflection of the severity of aortic pathological features. Gene ontology analysis showed that genes related to oxidation/reduction, myofibril assembly, cytoskeleton organization, and cell adhesion were differentially expressed in the mgR mice. Further analysis of differentially expressed genes identified several candidate genes whose known roles were suggestive of their involvement in the progressive destruction of aorta during MFS. This study is the first genome-wide analysis of the aortic gene expression profiles associated with the progression of MFS. Our findings provide valuable information regarding the molecular pathogenesis during MFS progression and contribute to the development of new biomarkers as well as improved therapeutic strategies.

• 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.

• BMB Reports
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• v.39 no.4
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• pp.457-463
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• 2006

Insulin resistance is commonly observed in patients prior to the development of type 2 diabetes and may predict the onset of the disease. We tested the hypothesis that impairment in insulin stimulated glucose-disposal in insulin resistant patients would be reflected in the gene expression profile of skeletal muscle. We performed gene expression profiling on skeletal muscle of insulin resistant and insulin sensitive subjects using microarrays. Microarray analysis of 19,000 genes in skeletal muscle did not display a significant difference between insulin resistant and insulin sensitive muscle. This was confirmed with real-time PCR. Our results suggest that insulin resistance is not reflected by changes in the gene expression profile in skeletal muscle.

• Korean Journal of Microbiology
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• v.31 no.2
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• pp.136-140
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• 1993

The A. nidulans expression vector which contained trpC marker gene from A. nidulans was constructed to produce glucoamy]ase. The recombinant plasmid was introduced into auxotrophic mutant A. nidulans B17. Southern blot analysis of the genomic DNA from transformant showed that pKHG2 DNA had integrated into the A. nidulans chromosomes. Northern analysis of the total RNA from transform ant showed that mRNA of glucoamylase gene was synthesized in induction condition. Specific activity of glucoamylase was increased in transform ants. G]ucoamylase was shown to be active in non-denaturing acrylamide gel.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.4
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• pp.467-472
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• 2018

Objective: Molecular cloning and bioinformatics analysis of annexin A2 (ANXA2) gene in sika deer antler tip were conducted. The role of ANXA2 gene in the growth and development of the antler were analyzed initially. Methods: The reverse transcriptase polymerase chain reaction (RT-PCR) was used to clone the cDNA sequence of the ANXA2 gene from antler tip of sika deer (Cervus Nippon hortulorum) and the bioinformatics methods were applied to analyze the amino acid sequence of Anxa2 protein. The mRNA expression levels of the ANXA2 gene in different growth stages were examined by real time reverse transcriptase polymerase chain reaction (real time RT-PCR). Results: The nucleotide sequence analysis revealed an open reading frame of 1,020 bp encoding 339 amino acids long protein of calculated molecular weight 38.6 kDa and isoelectric point 6.09. Homologous sequence alignment and phylogenetic analysis indicated that the Anxa2 mature protein of sika deer had the closest genetic distance with Cervus elaphus and Bos mutus. Real time RT-PCR results showed that the gene had differential expression levels in different growth stages, and the expression level of the ANXA2 gene was the highest at metaphase (rapid growing period). Conclusion: ANXA2 gene may promote the cell proliferation, and the finding suggested Anxa2 as an important candidate for regulating the growth and development of deer antler.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.161-164
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• 2005

Data mining techniques can be applied to identify patterns of interest in the gene expression data. One goal in mining gene expression data is to determine how the expression of any particular gene might affect the expression of other genes. To find relationships between different genes, association rules have been applied to gene expression data set [1]. A notable limitation of association rule mining method is that only the association in a single profile experiment can be detected. It cannot be used to find rules across different condition profiles or different time point profile experiments. However, with the appearance of time-series microarray data, it became possible to analyze the temporal relationship between genes. In this paper, we analyze the time-series microarray gene expression data to extract the sequential patterns which are similar to the association rules between genes among different time points in the yeast cell cycle. The sequential patterns found in our work can catch the associations between different genes which express or repress at diverse time points. We have applied sequential pattern mining method to time-series microarray gene expression data and discovered a number of sequential patterns from two groups of genes (test, control) and more sequential patterns have been discovered from test group (same CO term group) than from the control group (different GO term group). This result can be a support for the potential of sequential patterns which is capable of catching the biologically meaningful association between genes.

• Korean Journal of Biological Psychiatry
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• v.8 no.2
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• pp.203-207
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• 2001

The genome sequencing project has generated and will continue to generate enormous amounts of sequence data including 5 eukaryotic and about 60 prokaryotic genomes. Given this ever-increasing amounts of sequence information, new strategies are necessary to efficiently pursue the next phase of the genome project-the elucidation of gene expression patterns and gene product function on a whole genome scale. In order to assign functional information to the genome sequence, DNA chip(or gene microarray) technology was developed to efficiently identify the differential expression pattern of independent biological samples. DNA chip provides a new tool for genome expression analysis that may revolutionize many aspects of biotechnology including new drug discovery and disease diagnostics.

• Molecular & Cellular Toxicology
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• v.1 no.3
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• pp.164-171
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• 2005

Toxicological studies have an object of detecting adverse effects of a chemical on an organism based on observed toxicity marker (i.e., serum biochemical markers and chemical-specific gene expression) or phenotypic outcome. To date, most toxicogenomic studies concentrated on hepatic toxicity. cDNA microarray analysis enable discrimination of the responses in animals exposed to different classes of hepatotoxicants. In an effort to further characterize the mechanisms of 2, 3, 7, 8,-Tetrachlorodibenzo-p-dioxin (TCDD or dioxin)-mediated toxicity, comprehensive temporal-responsive microarray analyses were performed on hepatic tissue from Sprague-Dawley rats treated with TCDD. Hepatic gene expression profiles were monitored using custom DNA chip containing 490 cDNA clones related with toxicology. Gene expression analysis identified 26 features which exhibited a significant change. In this study, we observed that the genes related with oxidative stress in rats exposed to Dioxin, such as CYPIIA3 and glutathione S-transferase, were up-regulated at 24hr after exposure. In this study, we carried out to discover novel evidence for previously unknown gene expression patterns related to mechanism of hepatic toxicity in rats exposed to dioxin, and to elucidate the effects of dioxin on the gene expression after exposure to dioxin.

• Mycobiology
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• v.50 no.5
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• pp.366-373
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• 2022

Regulation of proper gene expression is important for cellular and organismal survival, maintenance, and growth. Abnormal gene expression, even for a single critical gene, can thwart cellular integrity and normal physiology to cause diseases, aging, and death. Therefore, gene expression profiling serves as a powerful tool to understand the pathology of diseases and to cure them. In this study, the difference in gene expression in Flammulina velutipes was compared between the wild type (WT) mushroom and the mutant one with clogging phenomenon. Differentially expressed transcripts were screened to identify the candidate genes responsible for the mutant phenotype using the DNA microarray analysis. A total of 88 genes including 60 upregulated and 28 downregulated genes were validated using the real-time quantitative PCR analysis. In addition, proteomic differences between the WT and mutant mushroom were analyzed using two-dimensional gel electrophoresis and matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF). Interestingly, the genes identified by these genomic and proteomic analyses were involved in stress response, translation, and energy/sugar metabolism, including HSP70, elongation factor 2, and pyruvate kinase. Together, our data suggest that the aberrant expression of these genes attributes to the mutant clogging phenotype. We propose that these genes can be targeted to foster normal growth in F. velutipes.