• Asian-Australasian Journal of Animal Sciences
• /
• v.23 no.11
• /
• pp.1399-1404
• /
• 2010

We have created a Bovine Genome Database, an integrated genomic resource for Bos taurus, by merging bovine data from various databases and our own data. We produced 55,213 Korean cattle (Hanwoo) ESTs from cDNA libraries from three tissues. We concentrated on genomic information based on Hanwoo transcripts and provided user-friendly search interfaces within the Bovine Genome Database. The genome browser supported alignment results for the various types of data: Hanwoo EST, consensus sequence, human gene, and predicted bovine genes. The database also provides transcript data information, gene annotation, genomic location, sequence and tissue distribution. Users can also explore bovine disease genes based on comparative mapping of homologous genes and can conduct searches centered on genes within user-selected quantitative trait loci (QTL) regions. The Bovine Genome Database can be accessed at http://bgd.nabc.go.kr.

• Journal of Institute of Control, Robotics and Systems
• /
• v.11 no.10
• /
• pp.851-856
• /
• 2005

The DNA and protein data of diverse species have been daily discovered and deposited in the public archives according to each established format. Database systems in the public archives provide not only an easy-to-use, flexible interface to the public, but also in silico analysis tools of unidentified sequence data. Of such in silico analysis tools, multiple sequence alignment [1] methods relying on pairwise alignment and Smith-Waterman algorithm [2] enable us to identify unknown DNA, protein sequences or phylogenetic relation among several species. However, in the existing multiple alignment method as the number of sequences increases, the runtime increases exponentially. In order to remedy this problem, we adopted a parallel processing suffix tree algorithm that is able to search for common subsequences at one time without pairwise alignment. Also, the cross-matching subsequences triggering inexact-matching among the searched common subsequences might be produced. So, the cross-matching masking process was suggested in this paper. To identify the function of the clusters generated by suffix tree clustering, BLAST was combined with a clustering tool. Our clustering and annotating tool is summarized as the following steps: (1) construction of suffix tree; (2) masking of cross-matching pairs; (3) clustering of gene sequences and (4) annotating gene clusters by BLAST search. The system was successfully evaluated with 22 gene sequences in the pyrubate pathway of bacteria, clustering 7 clusters and finding out representative common subsequences of each cluster

• The Korean Journal of Mycology
• /
• v.41 no.3
• /
• pp.200-204
• /
• 2013

Dihydroxynaphthalene (DHN) melanin is known to be present in some ascomycete fungi. To verify the type of melanin in Venturia nashicola that cause scab on pear, we investigated scytalone dehydratase (SD) gene, one of DHN melanin genes, from 11 isolates of V. nashicola from different provinces in Korea and Japan. Through PCR approach, 429 bp amplicon was produced from the 11 isolates and sequenced. All of the PCR-amplified sequences were determined as SD gene through GenBank database search. All the determined sequences were composed of an intron and two exons coding for 122 amino acids of SD. The homology of SD gene was 100% among the 11 isolates. Sequence identity of the predicted SD protein of 122 amino acids ranged 69 to 73% with other fungi. Our results proved that V. nashicola operates DHN melanin pathway.

• Korean Journal of Breeding Science
• /
• v.41 no.3
• /
• pp.205-212
• /
• 2009

Wheat (Triticum aestivum L.) grain texture is an important determinant of milling properties and end product use. Two linked genes, puroindoline a (PINA) and puroindoline b (PINB), control most of the genetic variation in wheat grain texture. Wheat seed proteins were examined to identify PINA and PINB gene using two pre-harvest sprouting wheat cultivars; Jinpum (resistant) and Keumgang (susceptible).Wheat seed proteins were separated by two-dimensional electrophoresis with IEF gels over pH ranges: pH 3-10. A total of 73 spots were digested with trypsin resulting peptide fragmentation were analyzed by matrix assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF/MS). Mass spectra were automatically processed and searched through NCBInr, SWISS-PORT and MSDB database with mono isotopic masses and complete gene sequence were found by UniProt database. Puroindoline a and puroindoline b that is responsible for grain texture related with baking performance and roughness. Two spots were found Pin b (16.7 kDa) and Pin a (16.3 kDa) in Jinpum compare to seven spots were identified Pin a (16.1 kDa, 16.3 kDa) and Pin b (16.7 kDa, 9.5 kDa and 14.4 kDa) in Keumgang. Some selected spots were identified puroindoline like grain softness protein (16.9 kDa, 17 kDa and 18.1 kDa) in Keumgang. Moreover, to gain a better inferring the identification of puroindoline related proteins using proteomics, we accomplished a complete gene sequence of PINA and PINB gene in pre-harvesting sprouting wheat seeds between resistant (Jinpum) and susceptible (Keumgang).

• Asian Pacific Journal of Cancer Prevention
• /
• v.14 no.9
• /
• pp.5281-5286
• /
• 2013

Purpose: Prostate cancer caused by the abnormal disorderly growth of prostatic acinar cells is the most prevalent cancer of men in western countries. We aimed to screen out differentially expressed genes (DEGs) and explore small molecule drugs for prostate cancer. Materials and Methods: The GSE3824 gene expression profile of prostate cancer was downloaded from Gene Expression Omnibus database which including 21 normal samples and 18 prostate cancer cells. The DEGs were identified by Limma package in R language and gene ontology and pathway enrichment analyses were performed. In addition, potential regulatory microRNAs and the target sites of the transcription factors were screened out based on the molecular signature database. In addition, the DEGs were mapped to the connectivity map database to identify potential small molecule drugs. Results: A total of 6,588 genes were filtered as DEGs between normal and prostate cancer samples. Examples such as ITGB6, ITGB3, ITGAV and ITGA2 may induce prostate cancer through actions on the focal adhesion pathway. Furthermore, the transcription factor, SP1, and its target genes ARHGAP26 and USF1 were identified. The most significant microRNA, MIR-506, was screened and found to regulate genes including ITGB1 and ITGB3. Additionally, small molecules MS-275, 8-azaguanine and pyrvinium were discovered to have the potential to repair the disordered metabolic pathways, abd furthermore to remedy prostate cancer. Conclusions: The results of our analysis bear on the mechanism of prostate cancer and allow screening for small molecular drugs for this cancer. The findings have the potential for future use in the clinic for treatment of prostate cancer.

• Proceedings of the KSRS Conference
• /
• 2007.10a
• /
• pp.438-441
• /
• 2007

Recently, due to molecular biology and engineering technology, DNA microarray makes people watch thousands of genes and the state of variation from the tissue samples of living body. With DNA Microarray, it is possible to construct a genetic group that has similar expression patterns and grasp the progress and variation of gene. This paper practices Cluster Analysis which purposes the discovery of biological subgroup or class by using gene expression information. Hence, the purpose of this paper is to predict a new class which is unknown, open leukaemia data are used for the experiment, and MCL (Markov CLustering) algorithm is applied as an analysis method. The MCL algorithm is based on probability and graph flow theory. MCL simulates random walks on a graph using Markov matrices to determine the transition probabilities among nodes of the graph. If you look at closely to the method, first, MCL algorithm should be applied after getting the distance by using Euclidean distance, then inflation and diagonal factors which are tuning modulus should be tuned, and finally the threshold using the average of each column should be gotten to distinguish one class from another class. Our method has improved the accuracy through using the threshold, namely the average of each column. Our experimental result shows about 70% of accuracy in average compared to the class that is known before. Also, for the comparison evaluation to other algorithm, the proposed method compared to and analyzed SOM (Self-Organizing Map) clustering algorithm which is divided into neural network and hierarchical clustering. The method shows the better result when compared to hierarchical clustering. In further study, it should be studied whether there will be a similar result when the parameter of inflation gotten from our experiment is applied to other gene expression data. We are also trying to make a systematic method to improve the accuracy by regulating the factors mentioned above.

• Genomics & Informatics
• /
• v.3 no.3
• /
• pp.80-85
• /
• 2005

HExDB is a database for analyzing exon and splicing pattern information in Homo sapiens. HExDB is useful for specific purposes: 1) to design primers for exon amplification from cDNA and 2) to understand the change of ORFs by alternative splicing. HExDB was constructed by integrating data from AltExtron which is the computationally predicted exon database, Ensemble cDNA annotation, and Affymetrix genome tile published recently. Although it may contain false positive data, HExDB is good starting point due to its sensitivity. At present, there areas many as 2,046,519 exons stored in the HExDB. We found that $16.8\%$ of the exons in the database was constitutive exons and $83.1\%$ were novel gene exons.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2013.11a
• /
• pp.87-89
• /
• 2013

이 논문에서는 비침윤성 방광암 환자의 재발 예측을 위해 마이크로어레이 데이터에서 최적의 속성 부분 집합을 찾고 이를 비교 평가한다. 정보 이득(information gain)을 통해 구한 상위 40개, 80개, 100개의 속성 집합과 FCBF(fast correlation based filter) 알고리즘을 적용하여 구한 최적의 속성 부분집합을 SVM 분류 모델에 적용하여 정확도를 비교 평가한 결과 정보 이득을 적용한 상위 100개 속성 부분집합의 분류 정확도가 가장 높게 나왔으며, FCBF 알고리즘을 적용한 속성 집합은 비교적 적은 속성을 사용하면서 이와 비슷한 분류 정확도를 보임을 확인할 수 있었다.

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

• Clinical and Experimental Reproductive Medicine
• /
• v.33 no.2
• /
• pp.125-132
• /
• 2006

Objective: In the previous study, we complied the differentially expressed genes during early folliculogenesis. Objective of the present study was to identify downstream target genes of transcription factors (TFs) using bioinformatics for selecting the target TFs among the gene lists for further functional analysis. Materials & Methods: By using bioinformatics tools, constituent domains were identified from database searches using Gene Ontology, MGI, and Entrez Gene. Downstream target proteins/genes of each TF were identified from database searches using TF database ($TRANSFAC^{(R)}$ 6.0) and eukaryotic promoter database (EPD). Results: DNA binding and trans-activation domains of all TFs listed previously were identified, and the list of downstream target proteins/genes was obtained from searches of TF database and promoter database. Based on the known function of identified downstream genes and the domains, 3 (HNF4, PPARg, and TBX2) out of 26 TFs were selected for further functional analysis. The genes of wee1-like protein kinase and p21WAF1 (cdk inhibitor) were identified as potential downstream target genes of HNF4 and TBX2, respectively. PPARg, through protein-protein interaction with other protein partners, acts as a transcription regulator of genes of EGFR, p21WAF1, cycD1, p53, and VEGF. Among the selected 3 TFs, further study is in progress for HNF4 and TBX2, since wee1-like protein kinase and cdk inhibitor may involved in regulating maturation promoting factor (MPF) activity during early folliculogenesis. Conclusions: Approach used in the present study, in silico analysis of downstream target genes, was useful for analyzing list of TFs obtained from high-throughput cDNA microarray study. To verify its binding and functions of the selected TFs in early folliculogenesis, EMSA and further relevant characterizations are under investigation.