• 대한의생명과학회지
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• 제20권4호
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• pp.185-193
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• 2014

Cancers are based upon an array of orchestrated genetic changes and the identification of changes causally related to the carcinogenic process. To elucidate the mechanism of cancer carcinogenesis, it is necessary to reconstruct these molecular events at each level. Microarray technologies have been extensively used to evaluate genetic alterations associated with cancer onset and progression in clinical oncology. The clinical impact of the genomic alterations identified by microarray technologies are growing rapidly and array analysis has been evolving into a diagnostic tool to better identify high-risk patients and predict patient outcomes from their genomic profiles. Here, we discuss the state-of-the-art microarray technologies and their applications in clinical oncology, and describe the potential benefits of these analysis in the clinical implications and biological insights of cancer biology.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2006년도 Principles and Practice of Microarray for Biomedical Researchers
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• pp.78-78
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• 2006

Gene expression analysis can be performed by one-color (intensity-based) or two-color (ratio-based) microarray platforms depending on the specific applications and needs of the researcher. The traditional two-color approach is well founded from a historical and scientific standpoint, and the one-color approach, when paired with high quality microarrays and a robust workflow, offers additional flexibility in experimental design. Two of the major requirements of any microarray platform are system reproducibility, which provides the means for high confidence experiments and accurate comparison across multiple samples; and high sensitivity, for the detection of significant gene expression changes, including small fold changes across multiple gene sets. Each of these requirements is fulfilled by the Agilent One-color Gene Expression Platform as illustrated by the data included in this study. As a result, researchers have the ability to take advantage of the enhanced performance and sensitivity of Agilent's 60-mer oligonucleotide microarrays, and experience the first commercial microarray platform compatible with both one- and two-color detection.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2006년도 Principles and Practice of Microarray for Biomedical Researchers
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• pp.71-76
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• 2006

Comparative genomic hybridization (CGH) is a technique for studying chromosomal changes in cancer. As cancerous cells multiply, they can undergo dramatic chromosomal changes, including chromosome loss, duplication, and the translocation of DNA from one chromosome to another. Chromosome aberrations have previously been detected using optical imaging of whole chromosomes, a technique with limited sensitivity, resolution, quantification, and throughput. Efforts in recent years to use microarrays to overcome these limitations have been hampered by inadequate sensitivity, specificity and flexibility of the microarray systems. The oligonucleotide CGH microarray system overcomes several scientific hurdles that have impeded comparative genomic studies of cancer. This new system can reliably detect single copy deletions in chromosomes. The system includes a whole human genome microarray, reagents for sample preparation, an optimized microarray processing protocol, and software for data analysis and visualization. In this study, we determined the sensitivity, accuracy and reproducibility of the new system. Using this assay, we find that the performance of the complete system was maintained over a range of input genomic DNA from 5 ug down to 0.15 ug.

• 대한임상검사과학회지
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• 제50권4호
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• pp.375-381
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• 2018

세포 유전학적 분석은 인간에서의 다양한 종류의 질환을 연구하고 진단하는데 매우 유용하게 사용되고 있다. 지난 수년 동안 세포 유전학적 분석을 통해 매우 의미 있는 결과를 얻을 수 있었으며, 현재 임상검사실에서 일반적인 검사로 확대되어 질병을 진단하고 결과를 평가하는데 매우 유용하게 사용 되고 있다. Microarray는 분자 세포 유전학적인 방법과 기존의 세포유전학적 방법이 융합된 검사방법으로 기존 검사 방법의 단점을 보완하여 유전 관련 질환을 진단하는데 매우 유용하게 사용되고 있다. 따라서 본 논문은 유전질환 진단에 있어 기존의 일반적인 세포유전학적 방법에서 마이크로어레이를 통한 분자세포유전학적 방법으로 어떻게 전환되어 왔는지, 유전 진단을 하는데 앞으로 이 검사방법들이 얼마나 의미 있게 사용될 것인지에 관하여 고찰하였다.

• 한국생물공학회:학술대회논문집
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• 한국생물공학회 2005년도 생물공학의 동향(XVI)
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• pp.82-85
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• 2005

Doxorubicin is a highly-valuable anthracycline-family polyketide drug with a very potent anticancer activity, typically produced by a Gram-positive soil bacterium called Streptomyces peucetius. Thanks to the recent development of Streptomyces genomics-based technologies, the random mutagenesis approach for Streptomyces strain improvement has been switched toward the genomics-based technologies including the application of DNA microarray systems. In order to identify and characterize the genomics-driven potential target genes critical for doxorubincin overproduction, three different types of doxorubicin overproducing strains, a dnrI(doxorubicin-specific positive regulatory gene)-overexpressor, a doxA (gene involved in the conversion from daunorubicin to doxorubicin)-overexpressor, and a recursively-mutated industrial strain, were generated and examined their genomic transcription profiles using Streptomyces DNA microarray systems. The DNA microarray results revealed several potential target genes in S. peucetius genome, whose expressions were significantly either up- or down-regulated comparing with the wild-type strain. A systematic understanding of doxorubicin overproduction at the genomic level presented in this research should lead us a rational design of molecular genetic strain improvement strategy.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.366-370
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• 2005

Tissue microarry is one of the high throughput technologies in the post-genomic era. Using tissue microarray, the researchers are able to investigate large amount of gene expressions at the level of DNA, RNA, and protein The important aspect of tissue microarry is its ability to assess a lot of biomarkers which have been used in clinical practice. To manipulate the categorical data of tissue microarray, we applied Bayesian network classifier algorithm. We identified that Bayesian network classifier algorithm could analyze tissue microarray data and integrating prior knowledge about gastric cancer could achieve better performance result. The results showed that relevant integration of prior knowledge promote the prediction accuracy of survival status of the immunohistochemical tissue microarray data of 18 tumor suppressor genes. In conclusion, the application of Bayesian network classifier seemed appropriate for the analysis of the tissue microarray data with clinical information.

• Genomics & Informatics
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• 제5권2호
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• pp.83-87
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• 2007

The analysis of DNA microarray data is a rapidly evolving area of bioinformatics, and various types of microarray are emerging as some of the most exciting technologies for use in biological and clinical research. In recent years, microarray technology has been utilized in various applications such as the profiling of mRNAs, assessment of DNA copy number, genotyping, and detection of methylated sequences. However, the analysis of these heterogeneous microarray platform experiments does not need to be performed separately. Rather, these platforms can be co-analyzed in combination, for cross-validation. There are a number of separate laboratory information management systems (LIMS) that individually address some of the needs for each platform. However, to our knowledge there are no unified LIMS systems capable of organizing all of the information regarding multi-platform microarray experiments, while additionally integrating this information with tools to perform the analysis. In order to address these requirements, we developed a web-based LIMS system that provides an integrated framework for storing and analyzing microarray information generated by the various platforms. This system enables an easy integration of modules that transform, analyze and/or visualize multi-platform microarray data.

• 제어로봇시스템학회논문지
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• 제11권10호
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• pp.830-836
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• 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.

• 한국유전체학회:학술대회논문집
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• 한국유전체학회 2006년도 The 15th Korean Genome Organization Conference KOGO 2006 Annual Meeting
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• pp.50-50
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• 2006

• 한국당과학회:학술대회논문집
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• 한국당과학회 2006년도 제1회 연례학술대회
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• pp.13-14
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• 2006