• Genomics & Informatics
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• 제3권1호
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• pp.39-42
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• 2005

DNA microarray is a high-throughput biomedical technology that monitors gene expression for thousands of genes in parallel. The abundance and complexity of the gene expression data have given rise to a requirement for their systematic management and analysis to support many laboratories performing microarray research. On these demands, we developed Xperanto for integrated data management and analysis using user-friendly web-based interface. Xperanto provides an integrated environment for management and analysis by linking the computational tools and rich sources of biological annotation. With the growing needs of data sharing, it is designed to be compliant to MGED (Microarray Gene Expression Data) standards for microarray data annotation and exchange. Xperanto enables a fast and efficient management of vast amounts of data, and serves as a communication channel among multiple researchers within an emerging interdisciplinary field.

• Molecules and Cells
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• 제25권2호
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• pp.279-288
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• 2008

The analysis of microarray data is essential for large amounts of gene expression data. In this review we focus on clustering techniques. The biological rationale for this approach is the fact that many co-expressed genes are co-regulated, and identifying co-expressed genes could aid in functional annotation of novel genes, de novo identification of transcription factor binding sites and elucidation of complex biological pathways. Co-expressed genes are usually identified in microarray experiments by clustering techniques. There are many such methods, and the results obtained even for the same datasets may vary considerably depending on the algorithms and metrics for dissimilarity measures used, as well as on user-selectable parameters such as desired number of clusters and initial values. Therefore, biologists who want to interpret microarray data should be aware of the weakness and strengths of the clustering methods used. In this review, we survey the basic principles of clustering of DNA microarray data from crisp clustering algorithms such as hierarchical clustering, K-means and self-organizing maps, to complex clustering algorithms like fuzzy clustering.

• 대한화학회지
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• 제56권4호
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• pp.530-534
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• 2012

• 생물정신의학
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• 제7권2호
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• pp.123-130
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• 2000

The development of inexpensive high throughput methods to identify individual DNA sequences is important to the future growth of medical genetics. This has become increasingly apparent as psychiatric geneticists focus more attention on the molecular basis of complex multifactorial diseases at which most of psychiatric disease is estimated. Furthermore, candidate gene approaches used in identifying disease associated genes necessitate screening large sequence blocks for changes tracking with the disease state. Even after such genes are isolated, large scale mutational analysis will often be needed for risk assessment studies to define the likely medical consequences of carrying a mutated gene. This review provide basic knowledge of up-to-date technology, cDNA microarray which enables above mentioned various research themes.

• 한국전기전자재료학회논문지
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• 제17권4호
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• pp.410-414
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• 2004

High throughput analysis using a DNA chip microarray is powerful tool in the post genome era. Less labor-intensive and lower cost-performance is required. Thus, this paper aims to develop the multi-channel type label-free DNA chip and detect SNP (Single nucleotide polymorphisms). At first, we fabricated a high integrated type DNA chip array by lithography technology. Various probe DNAs were immobilized on the microelectrode array. We succeeded to discriminate of DNA hybridization between target DNA and mismatched DNA on microarray after immobilization of a various probe DNA and hybridization of label-free target DNA on the electrodes simultaneously. This method is based on redox of an electrochemical ligand.

• 약학회지
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• 제51권3호
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• pp.179-185
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• 2007

Gastric cancer is one of the most common malignancies in Korea, but the predominant molecular event underlying gastric carcinogenesis remain unknown. Recently, DNA microarray technology has enabled the comprehensive analysis of gene expression level, and as such has yielded great insight into the molecular nature of cancer, However, despite the powerful approach of this techniques, the technical artifacts and/or bias in applied array platform limited the liability of resultant tens of thousand data points from microarray experiments. Therefore, we applied two different any platforms, such as olignucleotide microarray and cDNA microarray, to identify gastric cancer related large-scale molecular signature of the same human specimens. When thirty sets of matched human gastric cancer and normal tissues subjected to oligonucleotide microarray, total 623 genes were resulted as differently expressed genes in gastric cancer compared to normal tissues, and 252 genes for cDNA microarray analysis. In addition, forty three outlier genes which reflect the characteristic expression signature of gastric cancer beyond array platform and analytical protocol was recapitulated from two different expression profile. In conclusion, we were able to identify robust large-scale molecular changes in gastric cancer by applying two different platform of DNA microarray, this may facilitate to understand molecular carcinogenesis of gastric cancer.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2004년도 하계학술대회 논문집 Vol.5 No.2
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• pp.845-848
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• 2004

High throughput analysis using a DNA chip microarray is powerful tool in the post genome era. Less labor-intensive and lower cost-performance is required. Thus, this paper aims to develop the multi-channel type label-free DNA chip and detect SNP (Single nucleotide polymorphisms). At first, we fabricated a high integrated type DNA chip array by lithography technology. Various probe DNAs were immobilized on the microelectrode array. We succeeded to discriminate of DNA hybridization between target DNA and mismatched DNA on microarray after immobilization of a various probe DNA and hybridization of label-free target DNA on the electrodes simultaneously. This method is based on redox of an electrochemical ligand.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2002년도 춘계 학술발표회 논문집
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• pp.79-83
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• 2002

Since its introduction in 1995 by Schena et al. cDNA microarrays have been established as a potential tool for high-throughput analysis which allows the global monitoring of expression levels for thousands of genes simultaneously. One of the characteristics of the cDNA microarray data is that there is inherent noise even after the removal of systematic effects in the experiment. Therefore, replication is crucial to the microarray experiment. The assessment of reproducibility among replicates, however, has drawn little attention. Reproducibility may be assessed with several different endpoints along the process of data reduction of the microarray data. We define the reproducibility to be the degree with which replicate arrays duplicate each other. The aim of this note is to develop a novel measure of reproducibility among replicates in the cDNA microarray experiment based on the unprocessed data. Suppose we have p genes and n replicates in a microarray experiment. We first develop a measure of reproducibility between two replicates and generalize this concept for a measure of reproducibility of one replicate against the remaining n-1 replicates. We used the rank of the outcome variable and employed the concept of a measure of tracking in the blood pressure literature. We applied the reproducibility measure to two sets of microarray experiments in which one experiment was performed in a more homogeneous environment, resulting in validation of this novel method. The operational interpretation of this measure is clearer than Pearson's correlation coefficient which might be used as a crude measure of reproducibility of two replicates.

• Journal of Animal Science and Technology
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• 제48권2호
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• pp.169-190
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• 2006

최근 가축에 있어서 DNA marker를 이용하여 경제적으로 유용한 유전자를 찾아내는 연구가 활발히 진행되고 있다. 소의 경우 생산성을 향상시키기 위하여 경제형질관련 양적 형질좌위에 존재하는 후보 유전자를 선발한 후 형질변이의 원인이 되는 염기서열을 찾아 표지인자로 이용을 하고 있다. 본 연구는 소의 중요 경제형질인 육질 및 육량과 관련된 분자 유전학적 연구와 더불어 경제형질관련 후보유전자를 찾아내기 위하여 최근에 많이 이용되고 있는 microarray에 대하여 고찰하였다. 특히 microarray의 경우 cDNA microarray에서 oligoarray를 제작하여 이용함으로서 실험의 오차를 최대한 줄이는 방향으로 연구가 진행되고 있다. 소의 형질 관련 유전자에 관한 연구는 bovine genome sequencing이 끝난 현 시점에서 연구의 속도가 가속화될 것으로 생각되며 경제형질 원인 유전자의 분석 뿐 아니라 질병 저항성과 환경에 영향을 많이 받는 유전자를 확인하여 선발에 이용하기 위한 연구가 계속될 것으로 생각된다.

• 한국정보과학회논문지:시스템및이론
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• 제30권10호
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• pp.544-555
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• 2003

DNA 마이크로어레이 실험으로 나오는 데이타는 아주 많은 양의 유전자 발현 정보를 담고 있기 때문에 적절한 분석 방법이 필요하다. 대표적인 분석 방법은 계층적 클러스터링(hierarchical clustering) 방법이다. 본 논문에서는 계층적 클러스터링의 결과로 나오게 되는 덴드로그램(dendrogram)에 대해서 후처리(post-Processing)를 시행함으로써 DNA 마이크로어레이 데이타 분석을 더 용이하게 해주는 리프오더링(leaf-ordering)에 대해서 연구하였다. 먼저, 기존의 리프오더링 알고리즘들을 분석하였고, 리프오더링 알고리즘의 새로운 접근 방식을 제안하였다. 또한 이에 대한 성능을 실험하고 분석하기 위해서 계층적 클러스터링과 몇 가지 리프오더링 알고리즘들, 그리고 제안된 접근 방식을 직접 구현한 HCLO (Hierarchical Clustering & Leaf-Ordering Tool)에 대해서 소개하였다.