• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.361-365
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• 2005

Over the past few years, the complex and subtle roles of microRNA (miRNA) in gene regulation have been increasingly appreciated. Computational approaches have played one of important roles in identifying miRNAs from plant and animals, as well as in predicting their putative gene target. We present a new approach of comprehensive analysis of the evolutionarily conserved element scores and applied data compression technique to detect putative miRNA genes. We used the evolutionarily conserved elements [19] (see more detail on method and material) to calculate for base-by-base along the candidate pre-miRNA gene region by detecting common conserved pattern from target sequence. We applied the data compression technique [20] to detect unknown miRNA genes. This zipping method devises, without loss of generality with respect to the nature of the character strings, a method to measure the similarity between the strings under consideration [20]. Our experience to using our new computational method for detecting miRNA gene identification (or miRNA gene prediction) has been stratified and we were able to find 28 putative miRNA genes.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.164-169
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• 2003

In this paper, we propose the integrated Bayesian network framework to reconstruct genetic regulatory networks from genome expression data. The proposed model overcomes the dimensionality problem of multivariate analysis by building coherent sub-networks from confined gene clusters and combining these networks via intermediary points. Gene Shaving algorithm is used to cluster genes that share a common function or co-regulation. Retrieved clusters incorporate prior biological knowledge such as Gene Ontology, pathway, and protein protein interaction information for extracting other related genes. With these extended gene list, system builds genetic sub-networks using Bayesian network with MDL score and Sparse Candidate algorithm. Identifying functional modules of genes is done by not only microarray data itself but also well-proved biological knowledge. This integrated approach can improve there liability of a network in that false relations due to the lack of data can be reduced. Another advantage is the decreased computational complexity by constrained gene sets. To evaluate the proposed system, S. Cerevisiae cell cycle data [1] is applied. The result analysis presents new hypotheses about novel genetic interactions as well as typical relationships known by previous researches [2].

• Genomics & Informatics
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• v.20 no.1
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• pp.8.1-8.14
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• 2022

Despite the success of recent genome-wide association studies investigating longitudinal traits, a large fraction of overall heritability remains unexplained. This suggests that some of the missing heritability may be accounted for by gene-gene and gene-time/environment interactions. In this paper, we develop a Bayesian variable selection method for longitudinal genetic data based on mixed models. The method jointly models the main effects and interactions of all candidate genetic variants and non-genetic factors and has higher statistical power than previous approaches. To account for the within-subject dependence structure, we propose a grid-based approach that models only one fixed-dimensional covariance matrix, which is thus applicable to data where subjects have different numbers of time points. We provide the theoretical basis of our Bayesian method and then illustrate its performance using data from the 1000 Genome Project with various simulation settings. Several simulation studies show that our multivariate method increases the statistical power compared to the corresponding univariate method and can detect gene-time/ environment interactions well. We further evaluate our method with different numbers of individuals, variants, and causal variants, as well as different trait-heritability, and conclude that our method performs reasonably well with various simulation settings.

• Journal of Crop Science and Biotechnology
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• v.10 no.4
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• pp.201-210
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• 2007

Soybean [Glycine max(L.) Merr.] oil is versatile and used in many products. Modifying the fatty acid profile would make soy oil more functional in food and other products. The ideal oil with the most end uses would have saturates(palmitic + stearic acids) reduced from 15 to < 7%, oleic acid increased from 23 to > 55%, and linolenic acid reduced from 8 to < 3%. Reduced palmitic acid(16:0) is conditioned by three or more recessive alleles at the Fap locus. QTLs for reduced palmitic acid have mapped to linkage groups(LGs) A1, A2, B2, H, J, and L. Genes at the Fad locus control oleic acid content(18:1). Six QTLs($R^2$=4-25%) for increased 18:1 in N00-3350(50 to 60% 18:1) explained four to 25% of the phenotypic variation. M23, a Japanese mutant line with 40 to 50% 18:1 is controlled by a single recessive gene, ol. A candidate gene for FAD2-1A can be used in marker-assisted breeding for high 18:1 from M23. Low linolenic acid(18:3) is desirable in soy oil to reduce hydrogenation and trans-fat accumulation. Three independent recessive genes affecting omega-3 fatty acid desaturase enzyme activity are responsible for the lower 18:3 content in soybeans. Linolenic acid can be reduced from 8 to about 4, 2, and 1% from copies of one, two, or three genes, respectively. Using a candidate gene approach perfect markers for three microsomal omega-3 desaturase genes have been characterized and can readily be used in for marker assisted selection in breeding for low 18:3.

• 대한임상약리학회:학술대회논문집
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• 2008.05a
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• pp.3-6
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• 2008

See Full Text

• 한국인삼전략화협의회:학술대회논문집
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• v.2003 no.09
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• pp.54-63
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• 2003

"Korea ginseng (Panax ginseng C.A Meyer) is an important medicinal plant. Its root has been used as an herbal medicine that provides resistance to stress and disease, and prevents exhaustion since the ancient time. Ginsenosides, glycosylated triterpene (saponin), are considered to be the main active compounds of the ginseng root. Despite of considerable commercial interests of ginsenosides, very little is known about the genes and their biochemical pathways for ginsenoside biosynthesis. This work will focus on the identification of genes involved in ginsenoside biosynthesis and the dissection of ginsenoside biosynthetic pathway using a functional genomics tool. Expression sequence tags (ESTs) provide a valuable tool to discovery the genes in secondary metabolite biosynthesis. We generated over 21,155 ginseng ESTs that is now sufficient to facilitate discovering the genes involved in ginsenoside biosynthesis such as oxidosqualene cyclase(OSC), cytochrome P450 and glycosyltransferase. With ESTs information, microarray technology will be used for the analysis of gene expression, and the identification of genes including transcription factors expressed in tissues under given experimental condition. Heterogous system such as yeast and plants will allow us to do the functional analysis. And selected ginseng hairy root which show variation in ginsenoside production will be used as a material for functional analysis of candidate gene. Functional genomics approach will successfully accelerate gene discovery, and also provide promises of metabolic engineering for the ginsenoside production."

• Genomics & Informatics
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• v.10 no.1
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• pp.33-39
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• 2012

High-throughput genomic technologies (HGTs), including next-generation DNA sequencing (NGS), microarray, and serial analysis of gene expression (SAGE), have become effective experimental tools for cancer genomics to identify cancer-associated somatic genomic alterations and genes. The main hurdle in cancer genomics is to identify the real causative mutations or genes out of many candidates from an HGT-based cancer genomic analysis. One useful approach is to refer to known cancer genes and associated information. The list of known cancer genes can be used to determine candidates of cancer driver mutations, while cancer gene-related information, including gene expression, protein-protein interaction, and pathways, can be useful for scoring novel candidates. Some cancer gene or mutation databases exist for this purpose, but few specialized tools exist for an automated analysis of a long gene list from an HGT-based cancer genomic analysis. This report presents a new web-accessible bioinformatic tool, called CaGe, a cancer genome annotation system for the assessment of candidates of cancer genes from HGT-based cancer genomics. The tool provides users with information on cancer-related genes, mutations, pathways, and associated annotations through annotation and browsing functions. With this tool, researchers can classify their candidate genes from cancer genome studies into either previously reported or novel categories of cancer genes and gain insight into underlying carcinogenic mechanisms through a pathway analysis. We show the usefulness of CaGe by assessing its performance in annotating somatic mutations from a published small cell lung cancer study.

• 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.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.6
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• pp.784-790
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• 2018

Objective: The genetic effects of an individual on the phenotypes of its social partners, such as its pen mates, are known as social genetic effects. This study aims to identify the candidate genes for social (pen-mates') average daily gain (ADG) in pigs by using the genome-wide association approach. Methods: Social ADG (sADG) was the average ADG of unrelated pen-mates (strangers). We used the phenotype data (16,802 records) after correcting for batch (week), sex, pen, number of strangers (1 to 7 pigs) in the pen, full-sib rate (0% to 80%) within pen, and age at the end of the test. A total of 1,041 pigs from Landrace breeds were genotyped using the Illumina PorcineSNP60 v2 BeadChip panel, which comprised 61,565 single nucleotide polymorphism (SNP) markers. After quality control, 909 individuals and 39,837 markers remained for sADG in genome-wide association study. Results: We detected five new SNPs, all on chromosome 6, which have not been associated with social ADG or other growth traits to date. One SNP was inside the prostaglandin $F2{\alpha}$ receptor (PTGFR) gene, another SNP was located 22 kb upstream of gene interferon-induced protein 44 (IFI44), and the last three SNPs were between 161 kb and 191 kb upstream of the EGF latrophilin and seven transmembrane domain-containing protein 1 (ELTD1) gene. PTGFR, IFI44, and ELTD1 were never associated with social interaction and social genetic effects in any of the previous studies. Conclusion: The identification of several genomic regions, and candidate genes associated with social genetic effects reported here, could contribute to a better understanding of the genetic basis of interaction traits for ADG. In conclusion, we suggest that the PTGFR, IFI44, and ELTD1 may be used as a molecular marker for sADG, although their functional effect was not defined yet. Thus, it will be of interest to execute association studies in those genes.

• Journal of Microbiology
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• v.45 no.2
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• pp.105-112
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• 2007

The objective of this study was to analyze the phylogenetic composition of bacterial community in the soil of an earth-cave (Niu Cave) using a culture-independent molecular approach. 16S rRNA genes were amplified directly from soil DNA with universally conserved and Bacteria-specific rRNA gene primers and cloned. The clone library was screened by restriction fragment length polymorphism (RFLP), and representative rRNA gene sequences were determined. A total of 115 bacterial sequence types were found in 190 analyzed clones. Phylogenetic sequence analyses revealed novel 16S rRNA gene sequence types and a high diversity of putative bacterial community. Members of these bacteria included Proteobacteria (42.6%), Acidobacteria (18.6%), Planctomycetes (9.0 %), Chloroflexi (Green nonsulfur bacteria, 7.5%), Bacteroidetes (2.1%), Gemmatimonadetes (2.7%), Nitrospirae (8.0%), Actinobacteria (High G+C Gram-positive bacteria, 6.4%) and candidate divisions (including the OP3, GN08, and SBR1093, 3.2%). Thirty-five clones were affiliated with bacteria that were related to nitrogen, sulfur, iron or manganese cycles. The comparison of the present data with the data obtained previously from caves based on 16S rRNA gene analysis revealed similarities in the bacterial community components, especially in the high abundance of Proteobacteria and Acidobacteria. Furthermore, this study provided the novel evidence for presence of Gemmatimonadetes, Nitrosomonadales, Oceanospirillales, and Rubrobacterales in a karstic hypogean environment.