• Journal of Life Science
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• v.28 no.1
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• pp.130-139
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• 2018

In ancient times, horse racing was done in ancient European countries in the form of wagon races or mountain races, and wagon racing was adopted as a regular event at the Greek Olympic Games. Thoroughbred horse has been bred since 17th century by intensive selective breeding for its speed, stamina, and racing ability. Then, in the 18th century, horse racing using the Thoroughbred species began to gain popularity among nobles. Since then, horse racing has developed into various forms in various countries and have developed into flat racing, steeplechasing, and harness racing. Thoroughbred racehorse has excellent racing abilities because of powerful selection breeding strategy for 300 years. It is necessary to maintain and maximize horses' ability to race, because horse industries produce enormous economic benefits through breeding, training, and horse racing. Next-generation sequencing (NGS) methods which process large amounts of genomic data have been developed recently. Based on the remarkable development of these genomic analytical techniques, it is now possible to easily carry out animal breeding strategies with superior traits. In order to select breeding racehorse with superior racing traits, the latest genomic analysis techniques have to be introduced. In this paper, we will review the current efforts to improve race performance for racehorses and to examine the research trends of genomic analysis. Finally, we suggest to utilize genomic analysis in Thoroughbred racehorse and Jeju horse, and propose a strategy for selective breeding for Jeju horse, which contributes job creation of Korea.

• Korean Journal of Clinical Laboratory Science
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• v.52 no.3
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• pp.202-213
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• 2020

Although technological advances have allowed the efficient collection of large amounts of microbiome data for microbiological studies, proper analysis tools for such big data are still lacking. Additionally, analyses of microbial communities using poor databases can lead to misleading results. Hence, this study aimed to design an appropriate method for the analysis of big microbial databases. Bacteria were collected from the fingertips and personal belongings (mobile phones and laptop keyboards) of individuals. The genomic DNA was extracted from these bacteria and subjected to next-generation sequencing by targeting the 16S rRNA gene. The accuracy of the bacterial matching percentage between the fingertips and personal belongings was verified using a formula and an environment-related and human-related database. To design appropriate analysis, the bacterial matching accuracy was calculated based on the following three categories: comparison between qualitative and quantitative analysis, comparisons within same-gender participants as well as all participants regardless of gender, and comparison between the use of a human-related bacterial database (hDB) and environment-related bacterial database (eDB). The results showed that qualitative analysis, comparisons within same-gender participants, and the use of hDB provided relatively accurate results. This study provides an analytical method to obtain accurate results when conducting studies involving big microbiological data using human-derived microorganisms.

• Journal of KIISE:Databases
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• v.41 no.4
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• pp.249-255
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• 2014

Copy number variations(CNVs) are a recently recognized class of human structural variations and are associated with a variety of human diseases, including cancer. To find important cancer genes, researchers identify novel CNVs in patients with a particular cancer and analyze large amounts of genomic and clinical data. We present a tool called CNVDAT which is able to detect CNVs from NGS data and systematically analyze the genomic and clinical data associated with variations. CNVDAT consists of two modules, CNV Detection Engine and Sequence Analyser. CNV Detection Engine extracts CNVs by using the multi-resolution system of scale-space filtering, enabling the detection of the types and the exact locations of CNVs of all sizes even when the coverage level of read data is low. Sequence Analyser is a user-friendly program to view and compare variation regions between tumor and matched normal samples. It also provides a complete analysis function of refGene and OMIM data and makes it possible to discover CNV-gene-phenotype relationships. CNVDAT source code is freely available from http://dblab.hallym.ac.kr/CNVDAT/.