• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.13-13
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• 2017

By the progress of genome sequencing, infrastructures for marker-assisted breeding (MAB) of rice came to be established. Fine mapping and gene isolation have been conducted using the breeding materials derived from natural variations and artificial mutants. Such genetic analysis by the genome-wide dense markers provided us the knowledge about the many genes controlling important traits. We identified several genes or quantitative trait loci (QTL) for heading date, blast resistance, eating quality, high-temperature stress tolerance, and so on. NILs of each gene controlling heading date contribute to elongate the rice harvest period. Determination of precise gene location of blast resistance gene pi21, allowed us to overcome linkage drag, co-introduction of undesirable eating quality. We could also breed the first practical rice cultivar in Japan with a brown planthopper resistance gene bph11 in the genetic back-ground of an elite cultivar. Discovery of major and minor QTLs for good eating quality allowed us to fine-tune of eating quality according to the rice planting area or usage of rice grain. Many rice cultivars have bred efficiently by MAB for several traits, or by marker-assisted backcross breeding through chromosome segment substitution lines (CSSLs) using genetically diverse accessions. We are also systematically supporting the crop breeding of other sectors by MAB or by providing resources such as CSSLs. It is possible to pyramid many genes for important traits by using MAB, but is still difficult to improve the yielding ability. We are performing a Genomic Selection (GS) for improvement of rice biomass and grain yield. We are also trying to apply the genome editing technology for high yield rice breeding.

• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.269-277
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• 2012

In microarray data analysis, recent efforts have focused on the discovery of gene sets from a pathway or functional categories such as Gene Ontology terms(GO terms) rather than on individual gene function for its direct interpretation of genome-wide expression data. We introduce a meta-analysis method that combines $p$-values for changes of each gene in the group. The method measures the significance of overall treatment-induced change in a gene group. An application of the method to a real data demonstrates that it has benefits over other statistical methods such as Fisher's exact test and permutation methods. The method is implemented in a SAS program and it is available on the author's homepage(http://cafe.daum.net/go.analysis).

• Journal of KIISE
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• v.43 no.1
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• pp.54-60
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• 2016

Brain gene expression information is closely related to the structural and functional characteristics of the brain. Thus, extensive research has been carried out on the relationship between gene expression patterns and the brain's structural organization. In this study, Principal Component Analysis was used to extract features of gene expression patterns, and genes were automatically classified by spatial distribution. Voxels were then clustered with classified specific region expressed genes. Finally, we visualized the clustering results for mouse hippocampal region gene expression with the Allen Brain Atlas. This experiment allowed us to classify the region-specific gene expression of the mouse hippocampal region and provided visualization of clustering results and a brain atlas in an integrated manner. This study has the potential to allow neuroscientists to search for experimental groups of genes more quickly and design an effective test according to the new form of data. It is also expected that it will enable the discovery of a more specific sub-region beyond the current known anatomical regions of the brain.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.97-100
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• 2004

The identification of the DNA structure as a double-stranded helix consting of two nucleotide chain molecules was a milestone in modern molecular biology. The DNA chip technology is based on reverse hybridization that follows the principle of complementary binding of double-stranded DNA. DNA chip can be described as the deposition of defined nucleic acid sequences, probes, on a solid substrate to form a regular array of elements that are available for hybridization to complementary nucleic acids, targets. DNA chips based on cDNA clons, oligonucleotides and genomic clons have been developed for gene expression studies, genetic variation analysis and genomic changes associated with disease including cancers and genetic diseases. DNA chips for gene expression profiling can be used for functional analysis in human eel Is and animal models, disease-related gene studies, assessment of gene therapy, assessment of genetically modified food, and research for drug discovery. DNA chips for genetic variation detection can be used for the detection of mutations or chromosomal abnormalities in cnacers, drug resistances in cancer cells or pathogenic microbes, histocompatibility analysis for transplantation, individual identification for forensic medicine, and detection and discrimination of pathogenic microbes. The DNA chip will be generalized as a useful tool in clinical diagnostics in near future. Lab-on-a chip and informatics will facilitate the development of a variety of DNA chips for diagnostic purpose.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.59-60
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• 2000

The past few years have seen a dramatic increase in gene expression data on the basis of DNA microarrays or DNA chips. Going beyond a generic view on the genome, microarray data are able to distinguish between gene populations in different tissues of the same organism and in different states of cells belonging to the same tissue. This affords a cell-wide view of the metabolic and regulatory processes under different conditions, building an effective basis for new diagnoses and therapies of diseases. In this talk we present machine learning techniques for effective mining of DNA microarray data. A brief introduction to the research field of machine learning from the computer science and artificial intelligence point of view is followed by a review of recently-developed learning algorithms applied to the analysis of DNA chip gene expression data. Emphasis is put on graphical models, such as Bayesian networks, latent variable models, and generative topographic mapping. Finally, we report on our own results of applying these learning methods to two important problems: the identification of cell cycle-regulated genes and the discovery of cancer classes by gene expression monitoring. The data sets are provided by the competition CAMDA-2000, the Critical Assessment of Techniques for Microarray Data Mining.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.627-640
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• 2017

A common interest in gene expression data analysis is to identify genes that present significant changes in expression levels among biological experimental conditions. In this paper, we develop a Bayesian approach to make a gene-by-gene comparison in the case with a control and more than one treatment experimental condition. The proposed approach is within a Bayesian framework with a Dirichlet process prior. The comparison procedure is based on a model selection procedure developed using the discreteness of the Dirichlet process and its representation via Polya urn scheme. The posterior probabilities for models considered are calculated using a Gibbs sampling algorithm. A numerical simulation study is conducted to understand and compare the performance of the proposed method in relation to usual methods based on analysis of variance (ANOVA) followed by a Tukey test. The comparison among methods is made in terms of a true positive rate and false discovery rate. We find that proposed method outperforms the other methods based on ANOVA followed by a Tukey test. We also apply the methodologies to a publicly available data set on Plasmodium falciparum protein.

• Korean Journal of Plant Resources
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• v.20 no.6
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• pp.491-498
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• 2007

It has been hypothesized that efficient exclusion of methylated retrotransposons and repeated DNA region is one of the rapid and cost-effective approaches for comprehensive gene discovery in large genome size of maize. Three kinds of methylation-sensitive restriction enzymes, HapII, MspI and McrBC, were used to identify the restriction frequency of cytosine methylation sites in maize genome. Roughly 60% of total maize genomic DNA was restricted less than 500bp by McrBC, and the most of restricted small size fraction was composed retrotransposon. In order to validate the efficient construction of gene-rich shotgun library, we compare two gene-rich methyl-filtration shotgun libraries using in vivo and in vitro methyl-filtration system. The size selected DNA fraction by Sau3A-McrBC enzyme treated was very stable and has not appeared modification in E. coli, but most insert DNA size of partially digested with Sau3A were decrease less than 500bp by bacterial methylation-modification system. In compare of retroelements portion, A 44.6% of the sequences were retroelement in unmethyl-filtered library, and the most of them was Copia type, such as Prem, Opie and Ji. The portion of retroelement was drastically decreased to 25% and 20% by in vivo and in vitro filtration system, respectively.

• Journal of Microbiology and Biotechnology
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• v.29 no.5
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• pp.667-686
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• 2019

Streptomyces are attractive microbial cell factories that have industrial capability to produce a wide array of bioactive secondary metabolites. However, the genetic potential of the Streptomyces species has not been fully utilized because most of their secondary metabolite biosynthetic gene clusters (SM-BGCs) are silent under laboratory culture conditions. In an effort to activate SM-BGCs encoded in Streptomyces genomes, synthetic biology has emerged as a robust strategy to understand, design, and engineer the biosynthetic capability of Streptomyces secondary metabolites. In this regard, diverse synthetic biology tools have been developed for Streptomyces species with technical advances in DNA synthesis, sequencing, and editing. Here, we review recent progress in the development of synthetic biology tools for the production of novel secondary metabolites in Streptomyces, including genomic elements and genome engineering tools for Streptomyces, the heterologous gene expression strategy of designed biosynthetic gene clusters in the Streptomyces chassis strain, and future directions to expand diversity of novel secondary metabolites.

• Korean Journal of Biological Psychiatry
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• v.7 no.2
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• pp.115-122
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• 2000

The development of molecular biology has brought many changes in psychiatry. Molecular biology makes us possible to know the cause of mental disorders that provide the way to prevent the disorders, and to develop various accurate diagnostic and treatment methods for mental disorders. The author discusses the concept, cause, and treatment of mental disorders in the aspect of molecular biology. Importing the methods of molecular biology into psychiatry, we can anticipate to get a number of the goals of psychiatric genetics, including identification of specific susceptibility genes, clarification of the pathophysiological processes whereby these genes lead to symptoms, establishment of epigenetic factors that interact with these genes to produce disease, validation of nosological boundaries that more closely reflect the actions of these genes, and development of effective preventive and therapeutic interventions based on genetic counseling, gene therapy, and modification of permissive or protective environmental influences. In addition to their capacity to accelerate the discovery of new molecules participating in the nervous system's response to disease or to self-administered drugs, molecular biological strategies can also be used to determine how critical a particular gene product may be in mediating a cellular event with behavioral importance. Molecular biology probably enables us discover the environmental factors of mental disorders and allow rational drug design and gene therapies for mental disorders, by isolation of gene products that facilitate a basic understanding of the pathogenesis of these disorders. A specific genetic linkage may suggest a novel class of drugs that has not yet been tried. With respect to gene therapy, the hypothetical method would use a gene delivery system, most likely a modified virus, to insert a functional copy of a mutant gene into those brain cells that require the gene for normal function.

• Asian-Australasian Journal of Animal Sciences
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• v.23 no.12
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• pp.1543-1551
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• 2010

A major aim of cattle genome research is to identify candidate genes associated with meat quantity and quality through QTL analysis for application in the livestock industry. Therefore, this study focused on discovery of useful SNPs within the LOC534614 gene, containing 12273_165 SNP which is located on the same site as the QTL on chromosome 6, and evaluation of the association between SNP and body weight and cold carcass weight in Hanwoo (Korean cattle) As a result of a BLAST search of the NCBI web site, we discovered that the mRNA sequence of the LOC534614 gene was similar to that of the coiled-coil domain containing 158 (CCDC158) for dog and human. According to the direct DNA sequence from the CCDC158 gene, we identified 19 polymorphic SNPs within exons and their flanking regions. Among them, 17 polymorphic SNPs were selected for genotyping in Hanwoo (n = 476) and seventeen marker haplotypes containing 12273_165 SNP (frequency >0.1) were identified. As a result of the association between 17 polymorphic SNPs and Hanwoo (n = 476), g.8778G>A SNP in exon 6 was found to be a non-synonymous SNP, and was significantly associated with body weight and cold carcass weight (p<0.05). We discovered 19 polymorphic SNPs in the CCDC158 gene on the QTL region of BTA 6 in Hanwoo and identified that the g.8778G>A SNP was significantly associated with body weight and cold carcass weight (p<0.05), which causes an amino acid variation from valine to methionine. Furthermore, statistical analysis demonstrated that the CCDC158 gene is strongly associated with body weight and cold carcass weight in Hanwoo. In this regard, the g.8778G>A SNP in the CCDC158 gene can be useful as a positional candidate for body weight and cold carcass weight for marker-assisted selection in Hanwoo.