• Proceedings of the Korean Information Science Society Conference
• /
• 2007.10a
• /
• pp.38-39
• /
• 2007

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

• Journal of Institute of Control, Robotics and Systems
• /
• v.11 no.10
• /
• pp.830-836
• /
• 2005

Application of microarray technologies which monitor simultaneously the expression pattern of thousands of individual genes in different biological systems results in a tremendous increase of the amount of available gene expression data and have provided new insights into gene expression during drug development, within disease processes, and across species. There is a great need of data mining methods allowing straightforward interpretation, visualization and analysis of the relevant information contained in gene expression profiles. Specially, classifying biological samples into known classes or phenotypes is an important practical application for microarray gene expression profiles. Gene expression profiles obtained from tissue samples of patients thus allowcancer classification. In this research, molecular classification of microarray gene expression data is applied for multi-class cancer using computational biology such gene selection, principal component analysis and fuzzy clustering. The proposed method was applied to microarray data from leukemia patients; specifically, it was used to interpret the gene expression pattern and analyze the leukemia subtype whose expression profiles correlated with four cases of acute leukemia gene expression. A basic understanding of the microarray data analysis is also introduced.

• Journal of Information Processing Systems
• /
• v.12 no.1
• /
• pp.1-34
• /
• 2016

Gene identification is at the center of genomic studies. Although the first phase of the Encyclopedia of DNA Elements (ENCODE) project has been claimed to be complete, the annotation of the functional elements is far from being so. Computational methods in gene identification continue to play important roles in this area and other relevant issues. So far, a lot of work has been performed on this area, and a plethora of computational methods and avenues have been developed. Many review papers have summarized these methods and other related work. However, most of them focus on the methodologies from a particular aspect or perspective. Different from these existing bodies of research, this paper aims to comprehensively summarize the mainstream computational methods in gene identification and tries to provide a short but concise technical reference for future studies. Moreover, this review sheds light on the emerging trends and cutting-edge techniques that are believed to be capable of leading the research on this field in the future.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.4
• /
• pp.1000-1006
• /
• 2011

We examined the effects of various phenolic compounds, Ti plasmids(octopine, nopaline) and A. tumefaciens on the vir gene expression. Nine phenolic compounds were tested using 3 types of Ti plasmid and 3 strains of A. tumefaciens on the vir gene expression. It was also investigated how the levels of vir gene expression could be related to specific phenolic compounds. Six phenolic compounds as 4-hydroxyacetophenone, phenol, catechol, resorcinol, acetosyringone and vanillin had exhibited a strong effect on the vir gene expression of A. tumefaciens MW107 containing nopaline Ti plasmid. The vir genes of A. tumefaciens MW105 and MW108 containing octopine Ti plasmids were remarkably stimulated only by acetosyringone. Thus, it appeared that the vir gene inducing abilities were differed by the kinds of phenolic compounds, A. tumefaciens and Ti plasmids.

• Journal of Pharmaceutical Investigation
• /
• v.34 no.5
• /
• pp.351-362
• /
• 2004

The basic concept underlying gene therapy is that human diseases may be treated by the transfer of genetics material into specific cells of a patient in order to correct or supplement defective genes responsible for disease development. There are several systems that can be used to transfer foreign genetic material into the human body. Both viral and non-viral vectors are developed and evaluated for delivering therapeutic genes. Viral vectors are biological systems derived from naturally evolved viruses capable of transferring their genetics materials into host cells. However, the limitations associated with viral vectors, in terms of their safety, particularly immunogenecity, and their limited capacity of transgenic materials, have encouraged researchers to increasingly focus on non-viral vectors as an alternative to viral vectors. Although non-viral vectors are less efficient than viral ones, they have the advantages of safety, simplicity of preparation and high gene encapsulation capability. This article reviews the most recent studies highlighting the advantages and the limitation of gene delivery systems focused on non-viral systems compared to viral systems.

• Mycobiology
• /
• v.38 no.4
• /
• pp.331-335
• /
• 2010

In this study, we developed an efficient electroporation-mediated transformation system featuring Flammulina velutipes. The flammutoxin (ftx) gene of F. velutipes was isolated by reverse transcription-PCR. pFTXHg plasmid was constructed using the partial ftx gene (410 bp) along with the hygromycin B phosphotransferase gene (hygB) downstream of the glyceraldehydes-3-phosphate dehydrogenase (gpd) promoter. The plasmid was transformed into protoplasts of monokaryotic strain 4019-20 of F. velutipes by electroporation. High transformation efficiency was obtained with an electric-pulse of 1.25 kV/cm by using 177 transformants/${\mu}g$ of DNA in $1{\times}10^7$ protoplasts. PCR and Southern blot hybridization indicated that a single copy of the plasmid DNA was inserted at different locations in the F. velutipes genome by non-homologous recombination. Therefore, this transformation system could be used as a useful tool for gene function analysis of F. velutipes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.10a
• /
• pp.946-948
• /
• 2013

Several baculovirus vector systems recombined with coding genes of polyhedron promoter, vesicular stomatitis virus G (VSVG), polyA, cytomegalovirus (CMV) promoter, enhanced green fluorescent protein (EGFP), and protein transduction domain (PTD) were constructed. These recombinant baculovirus vector systems were applied into human foreskin fibroblast cells and compared the effects of gene transfer and gene expression of these recombinant baculovirus vector systems with control vector system. From this study, it showed that these novel recombinant baculovirus vector systems were superior efficacy to control vector system in view of gene transfer and gene expression.

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

• Molecular & Cellular Toxicology
• /
• v.2 no.1
• /
• pp.7-10
• /
• 2006

We performed comprehensive SNP validation and linkage disequilibrium (LD) analysis of the c-fos induced growth factor (Figf) gene in Korean population. Out of 32 SNPs, only 9 SNPs were polymorphic in Korean population. Validated SNPs formed a single extended haplotype block with strong LD through the entire length of the gene. Tagging SNP analysis picked only 2 SNPs to represent most of the genetic variation information of the Figf gene. Our results demonstrate the utility of LD block and tagging SNP analysis for an efficient way of performing a candidate gene based association study.