• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.244-246
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• 2004

마이크로어레이 칩 실험을 통하여 대량으로 생산되는 유전자 발현 데이터는 여러 가지 클러스터링 방법을 적용하여 분석할 수 있으며, 생성된 클러스터들 또한 여러 가지 방법으로 해석 할 수 있다. 본 논문에서는 기존의 클러스터링 방법들을 응용한 seed클러스터링 방법을 제안하고 생물학적 온톨로지인 Gene Ontology를 기반으로 클러스터를 해석한다. 본 논문에서는 효과적인 유전자 발현 데이터 클러스터링 방법과 생물학적 지식을 바탕으로 클러스터를 해석, 평가하는 방법을 보여 준다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.292-294
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• 2004

최근 pathway 정보의 중요성에 점점 커지고 있다. 하지만 이런 정보를 이용하기에 많은 문제점이 발생하고 있다. 이런 문제점의 해결 방법으로 웹 서비스가 도입되고 있다. 이 논문에서는 주요 pathway 데이터베이스 중 하나인 BIND와 체계적인 개념으로 유전자 용어를 정리한 Gene Ontology(GO)에 대한 웹 서비스를 개발하였다. 개발자들은 이 웹 서비스를 이용하여 BIND와 GO 데이터를 보다 쉽게 이용할 수 있을 것이다.

• Genomics & Informatics
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• v.10 no.2
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• pp.123-127
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• 2012

Gene set analysis (GSA) is useful in interpreting a genome-wide association study (GWAS) result in terms of biological mechanism. We compared the performance of two different GSA implementations that accept GWAS p-values of single nucleotide polymorphisms (SNPs) or gene-by-gene summaries thereof, GSA-SNP and i-GSEA4GWAS, under the same settings of inputs and parameters. GSA runs were made with two sets of p-values from a Korean type 2 diabetes mellitus GWAS study: 259,188 and 1,152,947 SNPs of the original and imputed genotype datasets, respectively. When Gene Ontology terms were used as gene sets, i-GSEA4GWAS produced 283 and 1,070 hits for the unimputed and imputed datasets, respectively. On the other hand, GSA-SNP reported 94 and 38 hits, respectively, for both datasets. Similar, but to a lesser degree, trends were observed with Kyoto Encyclopedia of Genes and Genomes (KEGG) gene sets as well. The huge number of hits by i-GSEA4GWAS for the imputed dataset was probably an artifact due to the scaling step in the algorithm. The decrease in hits by GSA-SNP for the imputed dataset may be due to the fact that it relies on Z-statistics, which is sensitive to variations in the background level of associations. Judicious evaluation of the GSA outcomes, perhaps based on multiple programs, is recommended.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.11
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• pp.1529-1539
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• 2017

Objective: The objective of this study was to compare the DNA methylation profile in the longissimus dorsi muscle between Small Tailed Han and Dorper${\times}$Small Tailed Han crossbred sheep which were known to exhibit significant difference in meat-production. Methods: Six samples (three in each group) were subjected to the methylated DNA immunoprecipitation sequencing (MeDIP-seq) and subsequent bioinformatics analyses to detect differentially methylated regions (DMRs) between the two groups. Results: 23.08 Gb clean data from six samples were generated and 808 DMRs were identified in gene body or their neighboring up/downstream regions. Compared with Small Tailed Han sheep, we observed a tendency toward a global loss of DNA methylation in these DMRs in the crossbred group. Gene ontology enrichment analysis found several gene sets which were hypomethylated in gene-body region, including nucleoside binding, motor activity, phospholipid binding and cell junction. Numerous genes were found to be differentially methylated between the two groups with several genes significantly differentially methylated, including transforming growth factor beta 3 (TGFB3), acyl-CoA synthetase long chain family member 1 (ACSL1), ryanodine receptor 1 (RYR1), acyl-CoA oxidase 2 (ACOX2), peroxisome proliferator activated receptor-gamma2 (PPARG2), netrin 1 (NTN1), ras and rab interactor 2 (RIN2), microtubule associated protein RP/EB family member 1 (MAPRE1), ADAM metallopeptidase with thrombospondin type 1 motif 2 (ADAMTS2), myomesin 1 (MYOM1), zinc finger, DHHC type containing 13 (ZDHHC13), and SH3 and PX domains 2B (SH3PXD2B). The real-time quantitative polymerase chain reaction validation showed that the 12 genes are differentially expressed between the two groups. Conclusion: In the current study, a tendency to a global loss of DNA methylation in these DMRs in the crossbred group was found. Twelve genes, TGFB3, ACSL1, RYR1, ACOX2, PPARG2, NTN1, RIN2, MAPRE1, ADAMTS2, MYOM1, ZDHHC13, and SH3PXD2B, were found to be differentially methylated between the two groups by gene ontology enrichment analysis. There are differences in the expression of 12 genes, of which ACSL1, RIN2, and ADAMTS2 have a negative correlation with methylation levels and the data suggest that DNA methylation levels in DMRs of the 3 genes may have an influence on the expression. These results will serve as a valuable resource for DNA methylation investigations on screening candidate genes which might be related to meat production in sheep.

• Korean Journal of Clinical Laboratory Science
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• v.53 no.4
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• pp.353-362
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• 2021

Cancer is a global health problem. There are diverse types of cancers, but there are several common pathways which lead to the development of cancer. Changes in gene expression might be the most common similarity found in almost all cancers. An understanding of the underlying changes in gene expression during cancer progression could lay a valuable foundation for the development of cancer therapeutics and even cancer vaccines. In this study, a well-known carcinogen, N-methyl-N'-nitro-N-nitrosoguanidine (MNNG), was employed to induce changes in gene expression in normal stomach cells. MNNG is known to cause cancer by inducing damage to DNA in MNNG-treated mammalian cells and animals fed with this carcinogen. An analysis was performed by comparing the differentially expressed genes (DEGs) caused by MNNG treatment with DEGs in stomach cancer cell lines. To this end, methods of analysis for functional categorization and protein-protein interaction networks, such as gene ontology (GO), the database for annotation, visualization, and integrated discovery (DAVID), Kyoto encyclopedia of genes and genomics (KEGG) and search tool for the retrieval of interacting genes/proteins (STRING), were used. As a result of these analyses, MNNG-regulated specific genes and interaction networks of their protein products that contributed to stomach cancer were identified.

• Journal of Life Science
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• v.29 no.2
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• pp.181-190
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• 2019

The purpose of this study was to acquire the active compounds of Anisi stellati fructus (ASF) and to analyze the genes and diseases it targets, focusing on its antibacterial effects using a system pharmacological analysis approach. Active compounds of ASF were obtained through the Traditional Chinese Medicine Systems Pharmacology (TCMSP) Database and Analysis Platform. This contains the pharmacokinetic properties of active compounds and related drug-target-disease networks, which is a breakthrough in silico approach possible at the network level. Gene information of targets was gathered from the UnitProt Database, and gene ontology analysis was performed using the David 6.8 Gene Functional Classification Tool. A total of 201 target genes were collected, which corresponded to the nine screened active compounds, and 47 genes were found to act on biological processes related to antimicrobial activity. The representative active compounds involved in antibacterial action were luteolin, kaempferol, and quercetin. Among their targets, Chemokine ligand2, Interleukin-10, Interleukin-6, and Tumor Necrosis Factor were associated with more than three antimicrobial biological processes. This study has provided accurate evidence while saving time and effort to select future laboratory research materials. The data obtained has provided important data for infection prevention and treatment strategies.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.383-387
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• 2005

One of the most important issues in post-genome era is identifying functions of genes and understanding the interaction among them. Such interactions from complex biochemical pathways, which are very useful to understand the organism system. We present an integrated biochemical pathway database system with a set of software tools for reconstruction, visualization, and simulation of the pathways from the database. The novel features of the presented system include: (a) automatic integration of the heterogeneous biochemical pathway databases, (b) gene ontology for high quality of database in the integration and query (c) various biochemical simulations on the pathway database, (d) dynamic pathway reconstruction for the gene list or sequence data, (e) graphical tools which enable users to view the reconstructed pathways in a dynamic form, (f) importing/exporting SBML documents, a data exchange standard for systems biology.

• Genomics & Informatics
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• v.18 no.3
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• pp.26.1-26.6
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• 2020

Single-cell RNA sequencing (scRNA-seq) has been widely applied to provide insights into the cell-by-cell expression difference in a given bulk sample. Accordingly, numerous analysis methods have been developed. As it involves simultaneous analyses of many cell and genes, efficiency of the methods is crucial. The conventional cell type annotation method is laborious and subjective. Here we propose a semi-automatic method that calculates a normalized score for each cell type based on user-supplied cell type-specific marker gene list. The method was applied to a publicly available scRNA-seq data of mouse cardiac non-myocyte cell pool. Annotating the 35 t-stochastic neighbor embedding clusters into 12 cell types was straightforward, and its accuracy was evaluated by constructing co-expression network for each cell type. Gene Ontology analysis was congruent with the annotated cell type and the corollary regulatory network analysis showed upstream transcription factors that have well supported literature evidences. The source code is available as an R script upon request.

• Journal of Life Science
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• v.23 no.11
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• pp.1325-1335
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• 2013

Working dogs, such as rescue dogs, military watch dogs, guide dogs, and search dogs, are selected by in-training examination of desired traits, including concentration, possessiveness, and boldness. In recent years, genetic information has been considered to be an important factor for the outstanding abilities of working dogs. To characterize the molecular features of the canine genes related to phenotypes for working dogs, we investigated the 24 previously reported genes (AR, BDNF, DAT, DBH, DGCR2, DRD4, MAOA, MAOB, SLC6A4, TH, TPH2, IFT88, KCNA3, TBR2, TRKB, ACE, GNB1, MSTN, PLCL1, SLC25A22, WFIKKN2, APOE, GRIN2B, and PIK3CG) that were categorized to personality, olfactory sense, and athletic/learning ability. We analyzed the chromosomal location, gene-gene interactions, Gene Ontology, and expression patterns of these genes using bioinformatic tools. In addition, variable numbers of tandem repeat (VNTR) or microsatellite (MS) polymorphism in the AR, MAOA, MAOB, TH, DAT, DBH, and DRD4 genes were reviewed. Taken together, we suggest that the genetic background of the canine genes associated with various working dog behaviors and skill performance attributes could be used for proper selection of superior working dogs.

• 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.