• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.45-52
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• 2000

Genomic approach produces massive amount of data within a short time period, New high-throughput automatic sequencers can generate over a million nucleotide sequence information overnight. A typical DNA chip experiment produces tens of thousands expression information, not to mention the tens of megabyte image files, These data must be handled automatically by computer and stored in electronic database, Thus there is a need for systematic approach of data collection, processing, and analysis. DNA sequence information is translated into amino acid sequence and is analyzed for key motif related to its biological and/or biochemical function. Functional genomics will play a significant role in identifying novel drug targets and diagnostic markers for serious diseases. As an enabling technology for functional genomics, bioinformatics is in great need worldwide, In Korea, a new functional genomics project has been recently launched and it focuses on identi☞ing genes associated with cancers prevalent in Korea, namely gastric and hepatic cancers, This involves gene discovery by high throughput sequencing of cancer cDNA libraries, gene expression profiling by DNA microarray and proteomics, and SNP profiling in Korea patient population, Our bioinformatics team will support all these activities by collecting, processing and analyzing these data.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.109-109
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• 2017

The core components of ABA-dependent gene expression signaling have been identified in Arabidopsis and rice. This signaling pathway consists of four major components; group A OsbZIPs, SAPKs, subclass A OsPP2Cs and OsPYL/RCARs in rice. These might be able to make thousands of combinations through interaction networks resulting in diverse signaling responses. We tried to characterize those gene functions using transient gene expression for rice protoplasts (TGERP) because it is instantaneous and convenient system. Firstly, in order to monitor the ABA signaling output, we developed reporter system named pRab16A-fLUC which consists of Rab16A promoter of rice and luciferase gene. It responses more rapidly and sensitively to ABA than pABRC3-fLUC that consists of ABRC3 of HVA1 promoter in TGERP. We screened the reporter responses for over-expression of each signaling components from group A OsbZIPs to OsPYL/RCARs with or without ABA in TGERP. OsbZIP46 induced reporter most strongly among OsbZIPs tested in the presence of ABA. SAPKs could activate the OsbZIP46 even in the ABA independence. Subclass A OsPP2C6 and -8 almost completely inhibited the OsbZIP46 activity in the different degree through the SAPK9. Lastly, OsPYL/RCAR2 and -5 rescued the OsbZIP46 activity in the presence of SAPK9 and OsPP2C6 dependent on ABA concentration and expression level. By using TGERP, we could characterize successfully the effects of ABA dependent gene expression signaling components in rice. In conclusion, TGERP represents very useful technology to study systemic functional genomics in rice or other monocots.

• KOGO NEWS
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• v.6 no.1
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• pp.29-33
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• 2006

Gene set analysis is a new concept and method. to analyze and interpret microarray gene expression data and tries to extract biological meaning from gene expression data at gene set level rather than at gene level. Compared with methods which select a few tens or hundreds of genes before gene ontology and pathway analysis, gene set analysis identifies important gene ontology terms and pathways more consistently and performs well even in gene expression data sets with minimal or moderate gene expression changes. Moreover, gene set analysis is useful for comparing multiple gene expression data sets dealing with similar biological questions. This review briefly summarizes the rationale behind the gene set analysis and introduces several algorithms and tools now available for gene set analysis.

• Journal of Microbiology and Biotechnology
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• v.26 no.3
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• pp.521-529
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• 2016

Extensive use of antibiotics over recent decades has led to bacterial resistance against antibiotics, including gentamicin, one of the most effective aminoglycosides. The emergence of resistance is problematic for hospitals, since gentamicin is an important broad-spectrum antibiotic for the control of bacterial pathogens in the clinic. Previous study to identify gentamicin resistance genes from environmental samples have been conducted using culture-dependent screening methods. To overcome these limitations, we employed a metagenome-based culture-independent protocol to identify gentamicin resistance genes. Through functional screening of metagenome libraries derived from soil samples, a fosmid clone was selected as it conferred strong gentamicin resistance. To identify a specific functioning gene conferring gentamicin resistance from a selected fosmid clone (35-40 kb), a shot-gun library was constructed and four shot-gun clones (2-3 kb) were selected. Further characterization of these clones revealed that they contained sequences similar to that of the RNA ligase, T4 rnlA that is known as a toxin gene. The overexpression of the rnlA-like gene in Escherichia coli increased gentamicin resistance, indicating that this toxin gene modulates this trait. The results of our metagenome library analysis suggest that the rnlA-like gene may represent a new class of gentamicin resistance genes in pathogenic bacteria. In addition, we demonstrate that the soil metagenome can provide an important resource for the identification of antibiotic resistance genes, which are valuable molecular targets in efforts to overcome antibiotic resistance.

• Proceedings of the Korean Society of Crop Science Conference
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• 2018.04a
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• pp.114-114
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• 2018

• Proceedings of the Microbiological Society of Korea Conference
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• 2005.05a
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• pp.159-161
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• 2005

Hypovirus infection of the chestnut blight fungus Cryphonectria parasitica is a useful model system to study the hypoviral regulation of fungal gene expression. The hypovirus is known to downregulate the fungal laccase1 (lac 1), the modulation of which is tightly governed by the inositol triphosphate ($IP_3$) and calcium second messenger system in a virus-free strain. We cloned the gene cplc1 encoding a phosphatidyl inositol-specific phospholipase C (PLC), in order to better characterize the fungal gene regulation by hypovirus. Sequence analysis of the cplc1 gene indicated that the protein product contained both the X and Y domains, which are the two conserved regions found in all known PLCs, with a 133 amino acid extension between the 2nd ${\beta}$-strand and the ${\alpha}$-helix in the X domain. In addition, the gene organization appeared to be highly similar to that of a ${\delta}$ type PLC. Disruption of the cplc1 gene resulted in slow growth and produced colonies characterized by little aerial mycelia and deep orange in color. In addition, down regulation of lac1 expression was observed. However, temperature sensitivity, osmosensitivity, virulence, and other hypovirulence-associated characteristics did not differ from the wild-type strain. Functional complementation of the cplc1-null mutant with the PLC1 gene from Saccharomyces cerevisiae restored lac1 expression, which suggests that the cloned gene encodes PLC activity. The present study indicates that the cplc1 gene is required for appropriate mycelial growth, and that it regulates the lac1 expression, which is also modulated by the hypovirus. Although several PLC genes have been identified in various simple eukaryotic organisms, the deletion analysis of the cplc1 gene in this study appears to be the first report on the functional analysis of PLC in filamentous fungi.

• Proceedings of the Microbiological Society of Korea Conference
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• 2002.10a
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• pp.109-115
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• 2002

Gene therapy is to treat and cure diseases by an introduction of therapeutic genes in defective cells or tissues of human body. Gene delivery system, gene expression system, and therapeutic gene are three core elements for gene therapy. The efficient delivery of therapeutic genes and appropriate gene expression are the crucial issues for therapeutic outcome of gene delivery. Because it can be used in common for the treatment and cure of various diseases, gene delivery system is the most important core element for a successful gene therapy. Viruses are naturally evolved to transfer their genomes into host cells efficiently. This ability has made vectorologists exploit viruses as attractive vehicles for the delivery of therapeutic genes. Viral vectors based on adenovirus (Ad) and adeno-associated virus (AAV) have been often used for gene delivery in laboratory. Ad and AAV vectors derived from human DNA viruses differ greatly in their life cycle, expression level and duration of transgenes, immunogenicity, and vector preparation. Both vectors can be used as effective tools for gene therapy and more recently in functional genomics. Here, the characteristics of Ad and AAV vectors are discussed.

• Proceedings of the Korean Society of Crop Science Conference
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• 2018.04a
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• pp.116-116
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• 2018

• Proceedings of the Korean Society of Crop Science Conference
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• 2018.10a
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• pp.240-240
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• 2018

• Genomics & Informatics
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• v.5 no.1
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• pp.10-18
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• 2007

Numerous studies have reported that genes with similar expression patterns are co-regulated. From gene expression data, we have assumed that genes having similar expression pattern would share similar transcription factor binding sites (TFBSs). These function as the binding regions for transcription factors (TFs) and thereby regulate gene expression. In this context, various analysis tools have been developed. However, they have shortcomings in the combined analysis of expression patterns and significant TFBSs and in the functional analysis of target genes of significantly overrepresented putative regulators. In this study, we present a web-based A Functional Clustering Analysis Tool for Predicted Transcription Regulatory Elements and Gene Ontology Terms (FCAnalyzer). This system integrates microarray clustering data with similar expression patterns, and TFBS data in each cluster. FCAnalyzer is designed to perform two independent clustering procedures. The first process clusters gene expression profiles using the K-means clustering method, and the second process clusters predicted TFBSs in the upstream region of previously clustered genes using the hierarchical biclustering method for simultaneous grouping of genes and samples. This system offers retrieved information for predicted TFBSs in each cluster using $Match^{TM}$ in the TRANSFAC database. We used gene ontology term analysis for functional annotation of genes in the same cluster. We also provide the user with a combinatorial TFBS analysis of TFBS pairs. The enrichment of TFBS analysis and GO term analysis is statistically by the calculation of P values based on Fisher’s exact test, hypergeometric distribution and Bonferroni correction. FCAnalyzer is a web-based, user-friendly functional clustering analysis system that facilitates the transcriptional regulatory analysis of co-expressed genes. This system presents the analyses of clustered genes, significant TFBSs, significantly enriched TFBS combinations, their target genes and TFBS-TF pairs.