• KSBB Journal
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• v.20 no.3
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• pp.220-227
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• 2005

Doxorubicin is an anthracycline-family polyketide compound with a very potent anti-cancer activity, typically produced by Streptomyces peucetius. To understand the potential target biosynthetic genes critical for the doxorubicin everproduction, a doxorubicin-specific DNA microarray chip was fabricated and applied to reveal the growth-phase-dependent expression profiles of biosynthetic genes from two doxorubicin-overproducing strains along with the wild-type strain. Two doxorubicin-overproducing 5. peucetius strains were generated via over-expression of a dnrl (a doxorubicin-specific positive regulatory gene) and a doxA (a gene involved in the conversion from daunorubicin to doxorubicin) using a streptomycetes high expression vector containing a strong ermE promoter. Each doxorubicin-overproducing strain was quantitatively compared with the wild-type doxorubicin producer based on the growth-phase-dependent doxorubicin productivity as well as doxorubicin biosynthetic gene expression profiles. The doxorubicin-specific DNA microarray chip data revealed the early-and-steady expressions of the doxorubicin-specific regulatory gene (dnrl), the doxorubicin resistance genes (drrA, drrB, drrC), and the doxorubicin deoxysugar biosynthetic gene (dnmL) are critical for the doxorubicin overproduction in S. peucetius. These results provide that the relationship between the growth-phase-dependent doxorubicin productivity and the doxorubicin biosynthetic gene expression profiles should lead us a rational design of molecular genetic strain improvement strategy.

• Biotechnology and Bioprocess Engineering:BBE
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• v.9 no.2
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• pp.93-99
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• 2004

Pathogens pose a significant threat to humans, animals, and plants. Consequently, a considerable effort has been devoted to developing rapid, convenient, and accurate assays for the detection of these unfavorable organisms. Recently, DNA-microarray based technology is receiving much attention as a powerful tool for pathogen detection. After the target gene is first selected for the unique identification of microorganisms, species-specific probes are designed through bioinformatic analysis of the sequences, which uses the info rmation present in the databases. DNA samples, which were obtained from reference and/or clinical isolates, are properly processed and hybridized with species-specific probes that are immobilized on the surface of the microarray for fluorescent detection. In this study, we review the methods and strategies for the development of DNA microarray for pathogen detection, with the focus on probe design.

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2003.10a
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• pp.173-173
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• 2003

With the introduction of DNA microarrays, a high throughput analysis of gene expression is now possible as a replacement to the traditional time-consuming Southern-blot analysis. This cDNA microarray should be ahighly favored technology in the area of molecular toxicology or analysis of environmental stresses.In this study, therefore, we developed a novel cDNA microarray for analyzing stress-specific responses in japanese Medaka fish. In the design and fabrication of this stress specific functional cDNA microarray, 123 different genes in Medaka fish were selected from eighteen different stress responsive groups and spotted on a 25${\times}$75 mm glass surface. After exposure of the fish to bisphenol A which is the one of the well-known endocrine disrupting chemicals (EDCs), over 1 or 10 days, the responses of the DNA chip were found to show distinct expression patterns according to the mode of toxic actions from environmental toxicants. As a results, they showed specific gene expression pattern to bisphenol A, additionally, the chemical spesific biomarkers could be suggested based on the chip analysis data. Therefore, this chip can be used to monitor stress responses of unknown and/or known toxic chemicals using Medaka fish and may be used for the further development of biomarkers by utilizing the gene expression patterns for known contaminants.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.3
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• pp.747-752
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• 2011

Microarray classification typically possesses two striking attributes: (1) classifier design and error estimation are based on remarkably small samples and (2) cross-validation error estimation is employed in the majority of the papers. A Microarray data of ovarian cancer consists of the expressions of thens of thousands of genes, and there is no systematic procedure to analyze this information instantaneously. In this paper, gene markers are selected by ranking genes according to statistics, popular classification rules - linear discriminant analysis, k-nearest-neighbor and decision trees - has been performed comparing classification accuracy of data selecting gene markers and not selecting gene markers. The Result that apply linear classification analysis at Microarray data set including marker gene that are selected using ANOVA method represent the highest classification accuracy of 97.78% and the lowest prediction error estimate.

• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.643-650
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• 2009

As one of typical techniques for acquiring bio-information, microarray has contributed greatly to development of bioinformatics. Although it is established as a core technology in bioinformatics, it has difficulty in sharing and storing data because data from experiments has huge and complex type. In this paper, we propose a new method which uses the feature that microarray data format in MAGE-ML, a standard format for exchanging data, has frequent structurally similar patterns. This method constructs compact database by simplifying MAGE-ML schema. In this method, Inlining techniques and newly proposed classification techniques using structural similarity of elements are used. The structure of database becomes simpler and number of table-joins is reduced, performance is enhanced using this method.

• Proceedings of the Microbiological Society of Korea Conference
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• 2002.10a
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• pp.22-26
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• 2002

There are a number of ways in which environmental microbiology and microbial ecology will benefit from DNA micro array technology. These include community genome arrays, SSU rDNA arrays, environmental functional gene arrays, population biology arrays, and there are clearly more different applications of microarray technology that can be applied to relevant problems in environmental microbiology. Two types of the applications, bacterial identification chip and functional gene detection chip, will be presented. For the bacterial identification chip, a new approach employing random genome fragments that eliminates the disadvantages of traditional DNA-DNA hybridization is proposed to identify and type bacteria based on genomic DNA-DNA similarity. Bacterial genomes are fragmented randomly, and representative fragments are spotted on a glass slide and then hybridized to test genomes. Resulting hybridization profiles are used in statistical procedures to identify test strains. Second, the direct binding version of microarray with a different array design and hybridization scheme is proposed to quantify target genes in environmental samples. Reference DNA was employed to normalize variations in spot size and hybridization. The approach for designing quantitative microarrays and the inferred equation from this study provide a simple and convenient way to estimate the target gene concentration from the hybridization signal ratio.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.51 no.7
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• pp.328-334
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• 2002

The DNA chips are devices associating the specific recognition properties of two DNA single strands through hybridization process with the performances of the microtechnology. In the literature, the "Gene chip" or "DNA chip" terminology is employed in a wide way and includes macroarrays and microarrays. Standard definitions are not yet clearly exposed. Generally, the difference between macro and microarray concerns the number of active areas and their size, Macroarrays correspond to devices containing some tens spots of 500$\mu$m or larger in diameter. microarrays concern devices containing thousnads spots of size less than 500$\mu$m. The key technical parameters for evaluating microarray-manufacturing technologies include microarray density and design, biochemical composition and versatility, repreducibility, throughput, quality, cost and ease of prototyping. Here we report, a new method in which minute particles are arranged in a random fashion on a chip pattern using random fluidic self-assembly (RFSA) method by hydrophobic interaction. We intend to improve the stability of the particles at the time of arrangement by establishing a wall on the chip pattern, besides distinction of an individual particle is enabled by giving a tag structure. This study demonstrates the fabrication of a chip pattern, immobilization of DNA to the particles and arrangement of the minute particle groups on the chip pattern by hydrophobic interaction.ophobic interaction.

• Bulletin of the Korean Chemical Society
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• v.29 no.6
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• pp.1137-1140
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• 2008

M1 RNA, the catalytic subunit of Escherichia coli RNase P, is an essential ribozyme that processes the 5' leader sequence of tRNA precursors (ptRNAs). Using KS2003, an E. coli strain generating only low levels of M1 RNA, which showed growth defects, we examined whether M1 RNA is involved in polycistronic mRNA processing or degradation. Microarray analysis of total RNA from KS2003 revealed six polycistronic operon mRNAs (acpP-fabF, cysDNC, flgAMN, lepAB, phoPQ, and puuCBE) showing large differences in expression between the adjacent genes in the same mRNA transcript compared with the KS2001 wild type strain. Model substrates spanning an adjacent pair of genes for each polycistronic mRNA were tested for RNase P cleavage in vitro. Five model RNAs (cysNC, flgMN, lepAB, phoPQ, and puuBE) were cleaved by RNase P holoenzyme but not by M1 RNA alone. However, the cleavages occurred at non-ptRNA-like cleavage sites, with much less efficiency than the cleavage of ptRNA. Since cleavage products generated by RNase P from a polycistronic mRNA can have different in vivo stabilities, our results suggest that RNase P cleavage may lead to differential expression of each cistron.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.12
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• pp.2283-2288
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• 2006

As development in technology of bioinformatics recently makes it possible to operate micro-level experiments, we can observe the expression pattern of total genome through on chip and analyze the interactions of thousands of genes at the same time. Thus, DNA microarray technology presents the new directions of understandings for complex organisms. Therefore, it is required how to analyze the enormous gene information obtained through this technology effectively. In this thesis, We used sample data of bioinformatics core group in harvard university. It designed and implemented system that evaluate accuracy after dividing in class of two using Bayesian algorithm, ASA, of feature extraction method through normalization process, reducing or removing of noise that occupy by various factor in microarray experiment. It was represented accuracy of 98.23% after Lowess normalization.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.74-82
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• 2000

Prostate cancer initially responds and regresses in response to androgen depletion therapy, but most human prostate cancers will eventually recur, and re-grow as an androgen independent tumor. Once these tumors become hormone refractory, they usually are incurable leading to death for the patient. Little is known about the molecular details of how prostate cancer cells regress following androgen ablation and which genes are involved in the androgen independent growth following the development of resistance to therapy. Such knowledge would reveal putative drug targets useful in the rational therapeutic design to prevent therapy resistance and control androgen independent growth. The application of genome scale technologies have permitted new insights into the molecular mechanisms associated with these processes. Specifically, we have applied functional genomics using high density cDNA microarray analysis for parallel gene expression analysis of prostate cancer in an experimental xenograft system during androgen withdrawal therapy, and following therapy resistance, The large amount of expression data generated posed a formidable bioinformatics challenge. A novel template based gene clustering algorithm was developed and applied to the data to discover the genes that respond to androgen ablation. The data show restoration of expression of androgen dependent genes in the recurrent tumors and other signaling genes. Together, the discovered genes appear to be involved in prostate cancer cell growth and therapy resistance in this system. We have also developed and applied tissue microarray (TMA) technology for high throughput molecular analysis of hundreds to thousands of clinical specimens simultaneously. TMA analysis was used for rapid clinical translation of candidate genes discovered by cDNA microarray analysis to determine their clinical utility as diagnostic, prognostic, and therapeutic targets. Finally, we have developed a bioinformatic approach to combine pharmacogenomic data on the efficacy and specificity of various drugs to target the discovered prostate cancer growth associated candidate genes in an attempt to improve current therapeutics.