• Molecules and Cells
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• v.25 no.2
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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.

• Molecular & Cellular Toxicology
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• v.1 no.2
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• pp.92-98
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• 2005

DNA microarray analysis of gene expression in toxicogenomics typically requires relatively large amounts of total RNA. This limits the use of DNA microarray when the sample available is small. To confront this limitation, different methods of linear RNA amplification that generate antisense RNA (aRNA) have been optimized for microarray use. The target preparation strategy using amplified RNA in DNA microarray protocol can be divided into direct-incorporation labeling which resulted in cDNA targets (Cy-dye labeled cDNA from aRNA) and indirect-labeling which resulted in cRNA targets (i.e. Cy-dye labeled aRNA), respectively. However, despite the common use of amplified targets (cDNA or cRNA) from aRNAs, no systemic assessment for the use of amplified targets and bias in terms of hybridization performance has been reported. In this investigation, we have compared the hybridization performance of cRNA targets with cDNA targets from aRNA on a 10 K cDNA microarrays. Under optimized hybridization conditions, we found that 43% of outliers from cDNA technique and 86% from the outlier genes were reproducibly detected by both targets hybridization onto cDNA microarray. This suggests that the cRNA labeling method may have a reduced capacity for detecting the differential gene expression when compared to the cDNA target preparation. However, further validation of this discordant result should be pursued to determine which techniques possesses better accuracy in identifying truly differential genes.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.59-60
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• 2000

The past few years have seen a dramatic increase in gene expression data on the basis of DNA microarrays or DNA chips. Going beyond a generic view on the genome, microarray data are able to distinguish between gene populations in different tissues of the same organism and in different states of cells belonging to the same tissue. This affords a cell-wide view of the metabolic and regulatory processes under different conditions, building an effective basis for new diagnoses and therapies of diseases. In this talk we present machine learning techniques for effective mining of DNA microarray data. A brief introduction to the research field of machine learning from the computer science and artificial intelligence point of view is followed by a review of recently-developed learning algorithms applied to the analysis of DNA chip gene expression data. Emphasis is put on graphical models, such as Bayesian networks, latent variable models, and generative topographic mapping. Finally, we report on our own results of applying these learning methods to two important problems: the identification of cell cycle-regulated genes and the discovery of cancer classes by gene expression monitoring. The data sets are provided by the competition CAMDA-2000, the Critical Assessment of Techniques for Microarray Data Mining.

• YAKHAK HOEJI
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• v.51 no.3
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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.

• Molecules and Cells
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• v.22 no.3
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• pp.254-261
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• 2006

DNA microarray is a powerful tool for high-throughput analysis of biological systems. Various computational tools have been created to facilitate the analysis of the large volume of data produced in DNA microarray experiments. Normalization is a critical step for obtaining data that are reliable and usable for subsequent analysis such as identification of differentially expressed genes and clustering. A variety of normalization methods have been proposed over the past few years, but no methods are still perfect. Various assumptions are often taken in the process of normalization. Therefore, the knowledge of underlying assumption and principle of normalization would be helpful for the correct analysis of microarray data. We present a review of normalization techniques from single-labeled platforms such as the Affymetrix GeneChip array to dual-labeled platforms like spotted array focusing on their principles and assumptions.

• Preventive Nutrition and Food Science
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• v.8 no.4
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• pp.390-394
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• 2003

In the post-genome period, the technique for identifying gene expression has been progressed to high throughput screening. In the field of molecular nutrition, the use of screening techniques to clarify molecular function of specific nutrients would be very advantageous. In this study, we have evaluated Zn-regulated gene expression in Zn-deficient, homocystein-treated EA.hy926 cells, using cDNA microarray, which can be used to screen the expression of many genes simultaneously. The information obtained can be used for preliminary assessment of molecular and signaling events modulated by Zn under pro-atherogenic conditions. EA.hy926 cells derived from human umbilical vein endothelial cells were cultured in Zn-adequate (control, 15 $\mu$M Zn) or Zn-deficient (experimental, 0 $\mu$M Zn) Dulbecco's MEM media under high homocysteine level (100 $\mu$M) for 3 days of post-confluency. Cells were harvested and RNA was extracted. Total RNA was reverse-transcribed and the synthesized cDNA was labeled with Cy3 or Cy5. Fluorescent labeled cDNA probe was applied to microarray slides for hybridization, and the slide was then scanned using a fluorescence scanner. The expression of seven genes was found to be significantly decreased, and one significantly increased, in response to treatment of EA.hy926 cells with Zn-deficient medium, compared with Zn-supplemented medium. The upregulated genes were oncogenes and tumor suppressor genes, cell cycle-related genes and transporter genes. The down-regulated gene was RelB, a component of the NF-kappaB complex of transcription factors. The results of this study imply the effectiveness of cDNA microarray for expression profiling of a singly nutrient deficiency, namely Zn. Furthur study, using tailored-cDNA array and vascular endothelial cell lines, would be beneficial to clarify the molecular function of Zn in atherosclerosis, more in detail.

• Horticultural Science & Technology
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• v.30 no.6
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• pp.734-742
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• 2012

To compare RNA interference-mediated gene silencing technique and T-DNA integration for gene function analysis in Chinese cabbage, BrSAMS-knockout (KO) line and BrSAMS-knockdown (KD) line were used. The KO line had lost the function of a Brassica rapa S-adenosylmethionine synthetase (BrSAMS) gene by T-DNA insertion and the KD line had shown down-regulated BrSAMS genes' expression by dsRNA cleavage. From microarray results of the KO and KD lines, genes linked to SAMS such as sterol, sucrose, homogalacturonan biosynthesis and glutaredoxin-related protein, serine/threonine protein kinase, and gibberellin-responsive protein showed distinct differences in their expression levels. Even though one BrSAMS gene in the KO line was broken by T-DNA insertion, gene expression pattern of that line did not show remarkable differences compared to wild type control. However, the KD line obtained by RNAi technique showed prominent difference in its gene expression. Besides, change of polyamine and ethylene synthesis genes directly associated with BrSAMS was displayed much more in the KD line. In the microarray analysis of the KO line, BrSAMS function could not be clearly defined because of BrSAMS redundancy due to the genome triplication events in Brassicaceae. In conclusion, we supposed that gene knock-down method by RNAi silencing is more effective than knock-out method by T-DNA insertion for gene function analysis of polyploidy crops such as Chinese cabbage.

• Proceedings of the Korean Society of Life Science Conference
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• 2000.06a
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• pp.3-8
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• 2000

The complete genome sequence of a Gram-positive bacterium .Bacillus subtilis has recently been reported and it is now clear that more than 50% of its ORFs have no known function (1). To study the global gene expression in B. subtilis at single gene resolution, we have tested the glass DNA microarrays in a step-wise fashion. As a preliminary experiment, we have created arrays of PCR products for 14 ORF whose transcription patterns have been well established through transcriptional mapping analysis. We measured changes in mRNA transcript levels between early exponential and stationary phase by hybridizing fluorescently labeled cDNA (with Cy3-UTP and Cy5-UTP) onto the array. We then compared the microarray data to confirm that the transcription patterns of these genes are well consistent with the known Northern analysis data. Since the preliminary test has been successful, we scaled up the experiments to ${\sim}$94% of the 4,100 annotated ORFs for the complete genome sequence of B. subtilis. Using this whole genomic microarray, we searched genes that are catabolite-repressive and those that are under the control of ${\sigma}^{Y}$, one of the functionally unknown ECF sigma factors. From these results, we here report that we have established DNA microarray techniques that are applicable for the whole genome of B. subtilis.

• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.643-650
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• 2009

As one of typical techniques for acquiring bio-information, microarray has contributed greatly to development of bioinformatics. Although it is established as a core technology in bioinformatics, it has difficulty in sharing and storing data because data from experiments has huge and complex type. In this paper, we propose a new method which uses the feature that microarray data format in MAGE-ML, a standard format for exchanging data, has frequent structurally similar patterns. This method constructs compact database by simplifying MAGE-ML schema. In this method, Inlining techniques and newly proposed classification techniques using structural similarity of elements are used. The structure of database becomes simpler and number of table-joins is reduced, performance is enhanced using this method.

• Molecular & Cellular Toxicology
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• v.2 no.1
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• pp.11-14
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• 2006

In this study we attempted to develop a novel genomic method to selectively isolate target functional microbial genomes from environmental samples. For this purpose, stable isotope probing (SIP) was applied in selectively isolating organic pollutant-assimilating populations. When soil microbes were fed with $^{13}C-labeled $ biphenyl, biphenyl-utilizing cells were incorporated with the heavy carbon isotope. The heavy DNA portion was successfully separated by CsCl equilibrium density gradient. And the diversity in the heavy DNA was sufficiently reduced, being suitable for the current DNA microarray techniques to detect biphenyl-utilizing populations in the soil. In addition, we proposed a new way to get more genetic information by combining this SIP method with selective metagenomic approach. The increased selective power of these new DNA isolation methods will be expected to provide a good quality of new genetic information, which, in turn, will result in development of a variety of biomarkers that may be used in assessing ecotoxicology issues including the impacts of organic hazards, and antibiotic-resistant pathogens on human and ecological systems.