• Molecular & Cellular Toxicology
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• v.1 no.1
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• pp.7-12
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• 2005

Proteomics has recently received intense scientific interest after the completion of the Human Genome Project, because this genome-based high technology allows to search new drug targets or diagnostic markers. Many proteome projects including Human plasma proteome projects (HPPP), Human liver proteome projects (HLPP), Human brain proteome projects (HBPP), and Mouse and Rat Proteome Project (MRPP) have been carried out and proteomic analytical techniques have been developed in second dimensional electrophoresis (2-DE) and LC/MS system. This powerful method has been applied in toxicology producing a new term "Toxicoproteomics". In this review, recent proteome projects, proteomic technologies, and toxicoproteomics will be discussed.

• Genomics & Informatics
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• v.12 no.1
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• pp.2-11
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• 2014

After the initial enthusiasm of the human genome project, it became clear that without additional data pertaining to the epigenome, i.e., how the genome is marked at specific developmental periods, in different tissues, as well as across individuals and species-the promise of the genome sequencing project in understanding biology cannot be fulfilled. This realization prompted several large-scale efforts to map the epigenome, most notably the Encyclopedia of DNA Elements (ENCODE) project. While there is essentially a single genome in an individual, there are hundreds of epigenomes, corresponding to various types of epigenomic marks at different developmental times and in multiple tissue types. Unprecedented advances in next-generation sequencing (NGS) technologies, by virtue of low cost and high speeds that continue to improve at a rate beyond what is anticipated by Moore's law for computer hardware technologies, have revolutionized molecular biology and genetics research, and have in turn prompted innovative ways to reduce the problem of measuring cellular events involving DNA or RNA into a sequencing problem. In this article, we provide a brief overview of the epigenome, the various types of epigenomic data afforded by NGS, and some of the novel discoveries yielded by the epigenomics projects. We also provide ample references for the reader to get in-depth information on these topics.

• Proceedings of the Zoological Society Korea Conference
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• 1999.04a
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• pp.28-28
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• 1999

No Abstract, See Full Text

• Proceedings of the PSK Conference
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• 2000.10a
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• pp.47-49
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• 2000

• Proceedings of the KSAR Conference
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• 2004.06a
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• pp.173-175
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• 2004

단백질체(Proteome)이란 말은 어원적으로 단백질(protein)에 전체란 뜻을 가진 어미(-body, -some)가 연결된 합성어로 주어진 순간에 세포나 조직이 발현하는 모든 단백질의 총체를 의미하고 이를 연구하는 학문은 Proteomics라 일컫는다. 2001년 2월 International Human Genome Project에 의해 human genome sequence가 밝혀짐으로써 유전체 연구는 일단락 완성되었지만, 염기서열만 가지고는 이 유전자 산물의 기능을 알 수 없었고, 이것이 전사되고 최종적으로 완벽한 모양이 갖추어진 단백질을 분석해야만 그 기능을 알 수 있었다. (중략)

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.69-70
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• 2000

The completion of sequencing human genome would motivate us to map millions of human cDNAs onto the unique ruler "genome sequence", in order to identify the exact address of each cDNA together with its exons, its promoter region, and its alternative splicing patterns. The expression patterns of some cDNAs could therefore be associated with these precise gene addresses, which further accelerate studies on mining correlations between motifs of promoters and expressions of genes in tissues. Towards the realization of this goal, we have developed a time-and-space efficient software named SQUALL that is able to map one cDNA sequence of length a few thousand onto a long genome sequence of length thirty million in a couple of minutes on average. Using SQUALL, we have mapped twenty thousand of our Bodymap (http://bodymap.ims.u-tokyo.ac.jp) cDNAs onto the genome sequences of Chr.21st and 22nd. In this talk, I will report the status of this ongoing project.

• Journal of Korean Biological Nursing Science
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• v.7 no.1
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• pp.5-14
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• 2005

Since the Human Genome Project(HGP) has begun in the mid 1980s, the sequencing of the human genome has been finally completed in 2001. The knowledge developed from the HGP has revolutionized how health care professionals think about patient care, mandating a new paradigm of patient care in totally inconceivable ways from the past. For instance, the patients at risk for disease can be identified early enough for intervention; the medicine can be tailored for individual patients based on their own genetic information ; the gene therapy could be a common procedure in the near future. The advancement in genetics, therefore, requires the shift of paradigm not only in nursing education, practice, but also in nursing research. It is attempted, in this article to introduce briefly the basic knowledge of genetics, the pharmacogenomics, and the overview of national genetic research initiated and organized by the Center for Functional Analysis of Human Genome in Korea. The current state of nursing genetic knowledge and its implications on nursing education, practice, and research has examined. Furthermore, the visions and the opportunities for nursing science and practice to participate in this genetic revolution were also explored.

• Journal of Preventive Medicine and Public Health
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• v.40 no.2
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• pp.102-107
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• 2007

Human Genome Project (HGP) could unveil the secrets of human being by a long script of genetic codes, which enabled us to get access to mine the cause of diseases more efficiently. Two wheels for HGP, bioinformatics and high throughput technology are essential techniques for the genomic medicine. While microarray platforms are still evolving, we can screen more than 500,000 genotypes at once. Even we can sequence the whole genome of an organism within a day. Because the future medicne will focus on the genetic susceptibility of individuals, we need to find genetic variations of each person by efficient genotyping methods.

• Genomics & Informatics
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• v.12 no.3
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• pp.98-104
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• 2014

Approximately 45% of the human genome is comprised of transposable elements (TEs). Results from the Human Genome Project have emphasized the biological importance of TEs. Many studies have revealed that TEs are not simply "junk" DNA, but rather, they play various roles in processes, including genome evolution, gene expression regulation, genetic instability, and cancer disposition. The effects of TE insertion in the genome varies from negligible to disease conditions. For the past two decades, many studies have shown that TEs are the causative factors of various genetic disorders and cancer. TEs are a subject of interest worldwide, not only in terms of their clinical aspects but also in basic research, such as evolutionary tracking. Although active TEs contribute to genetic instability and disease states, non-long terminal repeat transposons are well studied, and their roles in these processes have been confirmed. In this review, we will give an overview of the importance of TEs in studying genome evolution and genetic instability, and we suggest that further in-depth studies on the mechanisms related to these phenomena will be useful for both evolutionary tracking and clinical diagnostics.

• Genomics & Informatics
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• v.14 no.4
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• pp.138-148
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• 2016

The success of genome-wide association studies (GWASs) has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the "large p and small n" problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN). We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC) for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.