• 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.

• Communications for Statistical Applications and Methods
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• 제13권1호
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• pp.113-123
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• 2006

Normal mixture model(NMM) frequently used to cluster genes on microarray gene expression data. In this paper some of component means of NMM are modelled by a linear regression model so that its design matrix presents the pattern between sample classes in microarray matrix. This modelling for the component means by given design matrices certainly has an advantage that we can lead the clusters that are previously designed. However, it suffers from 'overfitting' problem because in practice genes often are highly dimensional. This problem also arises when the NMM restricted by the linear model for component-means is fitted. To cope with this problem, in this paper, the use of the factor analyzer NMM restricted by linear model is proposed to cluster genes. Also several design matrices which are useful for clustering genes are provided.

• Interdisciplinary Bio Central
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• 제2권2호
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• pp.4.1-4.6
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• 2010

The most common type of microarray experiment has a simple design using microarray data obtained from two different groups or conditions. A typical method to identify differentially expressed genes (DEGs) between two conditions is the conventional Student's t-test. The t-test is based on the simple estimation of the population variance for a gene using the sample variance of its expression levels. Although empirical Bayes approach improves on the t-statistic by not giving a high rank to genes only because they have a small sample variance, the basic assumption for this is same as the ordinary t-test which is the equality of variances across experimental groups. The t-test and empirical Bayes approach suffer from low statistical power because of the assumption of normal and unimodal distributions for the microarray data analysis. We propose a method to address these problems that is robust to outliers or skewed data, while maintaining the advantages of the classical t-test or modified t-statistics. The resulting data transformation to fit the normality assumption increases the statistical power for identifying DEGs using these statistics.

• 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.

• Biotechnology and Bioprocess Engineering:BBE
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• 제10권5호
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• pp.432-443
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• 2005

Microarray technology has contributed Significantly to the understanding of bacterial genetics and transcriptional regulation. One neglected aspect of this technology has been optimization of microarray-generated signals and quality of generated information. Full genome microarrays were developed for Clostridium acetobutylicum through spotting of PCR products that were designed with minimal homology with all other genes within the genome. Using statistical analyses it is demonstrated that Signal quality is significantly improved by increasing the hybridization volume. possibly increasing the effective number of transcripts available to bind to a given spot, while changes in labeled probe amounts were found to be less sensitive to improving signal quality. In addition to Q-RT-PCR, array validation was tested by examining the transcriptional program of a mutant (M5) strain lacking the pSOL1 178-gene megaplasmid relative to the wildtype (WT) strain. Under optimal conditions, it is demonstrated that the fraction of false positive genes is 1% when considering differentially expressed genes and 7% when considering all genes with signal above background. To enhance genomic-scale understanding of organismal physiology, using data from these microarrays we estimated that $40{\sim}55%$ of the C. acetobutylicum genome is expressed at any time during batch culture, similar to estimates made for Bacillus subtilis.

• Journal of Microbiology and Biotechnology
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• 제15권6호
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• pp.1377-1383
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• 2005

In a cDNA microarray experiment using Cy3 and Cy5 as labeling agents, particularly for the direct design, cDNAs from some genes incorporate one dye more efficiently than the other, which is referred to as the gene-specific dye bias. Dye-swaps, in which two dyes are switched on replicate arrays, are commonly used to control the gene-specific dye bias. We developed a simple procedure to extract the gene-specific dye bias information from a partial dye swap experiment. We detected gene-specific dye bias by identifying outliers in an X-Y plane, where the X axis represents the average log-ratio from two sets of dye swap pairs and the Y axis exhibits the average log ratio of four forward labeled arrays. We used this information for detecting differentially expressed genes, of which the additionally detected genes were validated by real-time RT-PCR.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 추계학술대회
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• pp.204-204
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• 2005

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 추계학술대회
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• pp.95-102
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• 2005

• 한국작물학회:학술대회논문집
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• 한국작물학회 2007년도 6th OSONG INTERNATIONAL BIO-SYMPOSIUM
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• pp.217.1-217.1
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• 2007

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2006년도 추계 학술발표회 논문집
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• pp.143-143
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• 2006