• Molecules and Cells
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• v.40 no.10
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• pp.714-730
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• 2017

Pre-mRNA splicing further increases protein diversity acquired through evolution. The underlying driving forces for this phenomenon are unknown, especially in terms of gene expression. A rice alternatively spliced transcript detection microarray (ASDM) and RNA sequencing (RNA-Seq) were applied to differentiate the transcriptome of 4 representative organs of Oryza sativa L. cv. Ilmi: leaves, roots, 1-cm-stage panicles and young seeds at 21 days after pollination. Comparison of data obtained by microarray and RNA-Seq showed a bell-shaped distribution and a co-lineation for highly expressed genes. Transcripts were classified according to the degree of organ enrichment using a coefficient value (CV, the ratio of the standard deviation to the mean values): highly variable (CVI), variable (CVII), and constitutive (CVIII) groups. A higher index of the portion of loci with alternatively spliced transcripts in a group (IAST) value was observed for the constitutive group. Genes of the highly variable group showed the characteristics of the examined organs, and alternatively spliced transcripts tended to exhibit the same organ specificity or less organ preferences, with avoidance of 'organ distinctness'. In addition, within a locus, a tendency of higher expression was found for transcripts with a longer coding sequence (CDS), and a spliced intron was the most commonly found type of alternative splicing for an extended CDS. Thus, pre-mRNA splicing might have evolved to retain maximum functionality in terms of organ preference and multiplicity.

• Horticultural Science & Technology
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• v.32 no.5
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• pp.684-693
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• 2014

Abiotic stress conditions such as cold, drought, and salinity trigger physiological and morphological changes and yield loss in plants. Hence, plants adapt to adverse environments by developing tolerance through complex regulation of genes related to various metabolic processes. This study was conducted to construct a coexpression network for multidirectional analysis of salt-stress response genes in Brassica rapa (Chinese cabbage). To construct the coexpression network, we collected KBGP-24K microarray data from the B. rapa EST and microarray database (BrEMD) and performed time-based expression analyses of B. rapa plants. The constructed coexpression network model showed 1,853 nodes, 5,740 edges, and 142 connected components (correlation coefficient > 0.85). On the basis of the significantly expressed genes in the network, we concluded that the development of salt tolerance is closely related to the activation of $Na^+$ transport by reactive oxygen species signaling and the accumulation of proline in Chinese cabbage.

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.999-1009
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• 2014

Hierarchical clustering analysis helps easily exploring massive microarray data and understanding biological phenomena with dendrogram. But, because hierarchical clustering algorithms only consider the absolute similarity, it is difficult to illustrate a relative dissimilarity, which consider not only the distance between a pair of clusters, but also how distant are they from the rest of the clusters. In this study, we introduced the relative hierarchical clustering method proposed by Mollineda and Vidal (2000) and compared hierarchical clustering method and relative hierarchical method using the simulated data and the real data in the various situations. The evaluation of the quality of two hierarchical methods was performed using percentage of incorrectly grouped points (PIGP), homogeneity and separation.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.44-55
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• 2009

Diagnosis of diseases using gene expression data obtained from microarray chip is an active research area recently. It has been done by general machine learning algorithms, because it is difficult to analyze directly. However, recent research results about the analysis based on the interaction between genes is essential for the gene expression analysis, which means the analysis using the traditional machine learning algorithms has limitations. In this paper, we classify the gene expression data using the hyper-network model that considers the higher-order correlations between the features, and then compares the classification accuracies. And also, we present the new hypo-network model that improve the disadvantage of existing model, and compare the processing performances of the existing hypo-network model based on general sequential processor and the improved hypo-network model implemented on parallel processors. In the experimental results, we show that the performance of our model shows improved and competitive classification performance than traditional machine learning methods, as well as, the existing hypo-network model. We show that the performance is maximized when the hypernetwork model is implemented on our parallel processors.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.3681-3685
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• 2012

Background: In breast cancer, estrogen receptors have been demonstrated to interact with transcription factors to regulate target gene expression. However, high-throughput identification of the transcription regulation relationship between transcription factors and their target genes in response to estradiol is still in its infancy. Purpose: Thus, the objective of our study was to interpret the transcription regulation network of MCF7 breast cancer cells exposed to estradiol. Methods: In this work, GSE11352 microarray data were used to identify differentially expressed genes (DEGs). Results: Our results showed that the MYB (v-myb myeloblastosis viral oncogene homolog [avian]), PGR (progesterone receptor), and MYC (v-myc myelocytomatosis viral oncogene homolog [avian]) were hub nodes in our transcriptome network, which may interact with ER and, in turn, regulate target gene expression. MYB can up-regulate MCM3 (minichromosome maintenance 3) and MCM7 expression; PGR can suppress BCL2 (B-cell lymphoma 2) expression; MYC can inhibit TGFB2 (transforming growth factor, beta 2) expression. These genes are associated with breast cancer progression via cell cycling and the $TGF{\beta}$ signaling pathway. Conclusion: Analysis of transcriptional regulation may provide a better understanding of molecular mechanisms and clues to potential therapeutic targets in the treatment of breast cancer.

• Molecular & Cellular Toxicology
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• v.1 no.1
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• pp.17-25
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• 2005

There are some critical drawbacks in the use of biomarkers for a global assessment of the toxicological impacts many chemicals and environmental pollutants have, primarily due to an individual biomarker's specificity for an explicit chemical or toxicant. In other words, the biomarker-based assessment methodology used to analyze toxicological effects lacks a high-throughput capability. Therefore, eco-toxicogenomics, or the study of toxicogenomics with organisms present within a given environmental locale, has recently been introduced with the advent of the so-called "-omics" era, which began with the creation of microarray technologies. Fish are comparable with humans in their toxicological responses and thus data from toxicogenomic studies performed with fish could be applied, with appropriate tools and implementation protocols, to the evaluation of environments where human or animal health is of concern. At present, there have been very active research streams for developing expression sequence tag (EST) databases (DBs) for zebra fish and rainbow trout. Even though few reports involve toxicogenomic studies with fish, a few groups have successfully fabricated and used cDNA microarrays or oligo DNA chips when studying the toxicological impacts of hypoxia or some toxicants with fish. Furthermore, it is strongly believed that this technology can also be implemented with non-model fish. With the standardization of DNA microarray technologies and ample progress in bioinformatics and proteomic technologies, data obtained from DNA microarray technologies offer not only multiple biomarker assays or an analysis of gene expression profiles, but also a means of elucidating gene networking, gene-gene relations, chemical-gene interactions, and chemical-chemical relationships. Accordingly, the ultimate target of eco-toxicogenomics should be to predict and map the pathways of stress propagation within an organism and to analyze stress networking.

• Development and Reproduction
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• v.18 no.1
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• pp.1-11
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• 2014

Early growth response 1 (Egr1) is a zinc-finger transcription factor to direct second-wave gene expression leading to cell growth, differentiation and/or apoptosis. While it is well-known that Egr1 controls transcription of an array of targets in various cell types, downstream target gene(s) whose transcription is regulated by Egr1 in the uterus has not been identified yet. Thus, we have tried to identify a list of potential target genes of Egr1 in the uterus by performing multi-step in silico promoter analyses. Analyses of mRNA microarray data provided a cohort of genes (102 genes) which were differentially expressed (DEGs) in the uterus between Egr1(+/+) and Egr1(-/-) mice. In mice, the frequency of putative EGR1 binding sites (EBS) in the promoter of DEGs is significantly higher than that of randomly selected non-DEGs, although it is not correlated with expression levels of DEGs. Furthermore, EBS are considerably enriched within -500 bp of DEG's promoters. Comparative analyses for EBS of DEGs with the promoters of other species provided power to distinguish DEGs with higher probability as EGR1 direct target genes. Eleven EBS in the promoters of 9 genes among analyzed DEGs are conserved between various species including human. In conclusion, this study provides evidence that analyses of mRNA expression profiles followed by two-step in silico analyses could provide a list of putative Egr1 direct target genes in the uterus where any known direct target genes are yet reported for further functional studies.

• Journal of Life Science
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• v.22 no.4
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• pp.492-498
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• 2012

To investigate whether phytochemicals affect cancer cell viability, human colorectal HCT116 cells were treated with four different phytochemicals. Among these phytochemicals, curcumin is the strongest inhibitor of cell proliferation. In addition, it decreased cell viability in a dose-dependent manner. To unveil the molecular mechanisms involved in the inhibition of cell proliferation by curcumin, we carried out oligo DNA microarray analysis. We found that 137 genes were up-regulated more than 2-fold, and 141 genes were down-regulated more than 2-fold by 25 ${\mu}M$ curcumin treatment. Among the up-regulated genes, we selected 3 genes (ATF-3, GADD45A, and NR4A1) to confirm microarray data. The results of RT-PCR strongly agreed with those of the microarray data. Among the phytochemicals used in this study, curcumin is the strongest inducer of ATF3 expression, and increased ATF3 expression in a dose-dependent manner. Interestingly, FACS analysis showed that the inhibition of cell growth by curcumin was recovered by ATF3-siRNA transfection. Finally, we detected the changes of gene expression by ectopic expression of ATF3. The results indicated that many up-regulated genes were related to apoptosis. Overall, these results suggest that ATF3 may play an important role in the anti-proliferative activity of curcumin in human colorectal cancer cells.

• Genomics & Informatics
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• v.1 no.2
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• pp.94-100
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• 2003

Motivation: Many have observed a nonlinear relationship between the signal intensity and the transcript abundance in microarray data. The first step in analyzing the data is to normalize it properly, and this should include a correction for the nonlinearity. The commonly used linear normalization schemes do not address this problem. Results: Nonlinearity is present in both cDNA and oligonucleotide arrays, but we concentrate on the latter in this paper. Across a set of chips, we identify those genes whose within-chip ranks are relatively constant compared to other genes of similar intensity. For each gene, we compute the sum of the squares of the differences in its within-chip ranks between every pair of chips as our statistic and we select a small fraction of the genes with the minimal changes in ranks at each intensity level. These genes are most likely to be non-differentially expressed and are subsequently used in the normalization procedure. This method is a generalization of the rank-invariant normalization (Li and Wong, 2001), using all available chips rather than two at a time to gather more information, while using the chip that is least likely to be affected by nonlinear effects as the reference chip. The assumption in our method is that there are at least a small number of non­differentially expressed genes across the intensity range. The normalized expression values can be substantially different from the unnormalized values and may result in altered down-stream analysis.

• BMB Reports
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• v.42 no.8
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• pp.493-499
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• 2009

MicroRNAs (miRs) are a family of small noncoding RNAs that regulate gene expression by targeting mRNAs in a sequence specific manner, inducing translational repression or mRNA degradation. In this paper we have first analyzed by microarray the miR-profile in erythroid precursor cells from one normal and two thalassemic patients expressing different levels of fetal hemoglobin (one of them displaying HPFH phenotype). The microarray data were confirmed by RT-PCR analysis, and allowed us to identify miR-210 as an highly expressed miR in the erythroid precursor cells from the HPFH patient. When RT-PCR was performed on mithramycin-induced K562 cells and erythroid precursor cells, miR-210 was found to be induced in time-dependent and dose-dependent fashion, together with increased expression of the fetal $\gamma$-globin genes. Altogether, the data suggest that miR-210 might be involved in increased expression of $\gamma$-globin genes in differentiating erythroid cells.