• Genomics & Informatics
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• v.5 no.1
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• pp.10-18
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• 2007

Numerous studies have reported that genes with similar expression patterns are co-regulated. From gene expression data, we have assumed that genes having similar expression pattern would share similar transcription factor binding sites (TFBSs). These function as the binding regions for transcription factors (TFs) and thereby regulate gene expression. In this context, various analysis tools have been developed. However, they have shortcomings in the combined analysis of expression patterns and significant TFBSs and in the functional analysis of target genes of significantly overrepresented putative regulators. In this study, we present a web-based A Functional Clustering Analysis Tool for Predicted Transcription Regulatory Elements and Gene Ontology Terms (FCAnalyzer). This system integrates microarray clustering data with similar expression patterns, and TFBS data in each cluster. FCAnalyzer is designed to perform two independent clustering procedures. The first process clusters gene expression profiles using the K-means clustering method, and the second process clusters predicted TFBSs in the upstream region of previously clustered genes using the hierarchical biclustering method for simultaneous grouping of genes and samples. This system offers retrieved information for predicted TFBSs in each cluster using $Match^{TM}$ in the TRANSFAC database. We used gene ontology term analysis for functional annotation of genes in the same cluster. We also provide the user with a combinatorial TFBS analysis of TFBS pairs. The enrichment of TFBS analysis and GO term analysis is statistically by the calculation of P values based on Fisher’s exact test, hypergeometric distribution and Bonferroni correction. FCAnalyzer is a web-based, user-friendly functional clustering analysis system that facilitates the transcriptional regulatory analysis of co-expressed genes. This system presents the analyses of clustered genes, significant TFBSs, significantly enriched TFBS combinations, their target genes and TFBS-TF pairs.

• BMB Reports
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• v.47 no.6
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• pp.348-353
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• 2014

The speed of sound (SOS) value is an indicator of bone mineral density (BMD). Previous genome-wide association (GWA) studies have identified a number of genes, whose variations may affect BMD levels. However, their biological implications have been elusive. We re-analyzed the GWA study dataset for the SOS values in skeletal sites of 4,659 Korean women, using a gene-set analysis software, GSA-SNP. We identified 10 common representative GO terms, and 17 candidate genes between these two traits (PGS < 0.05). Implication of these GO terms and genes in the bone mechanism is well supported by the literature survey. Interestingly, the significance levels of some member genes were inversely related, in several gene-sets that were shared between two skeletal sites. This implies that biological process, rather than SNP or gene, is the substantial unit of genetic association for SOS in bone. In conclusion, our findings may provide new insights into the biological mechanisms for BMD.

• Journal of the Korea Society of Computer and Information
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• v.15 no.12
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• pp.197-207
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• 2010

Using a variety of data-mining methods on high-throughput cDNA microarray data, the level of gene expression in two different tissues can be compared, and DEG(Differentially Expressed Gene) genes in between normal cell and tumor cell can be detected. Diagnosis can be made with these genes, and also treatment strategy can be determined according to the cancer stages. Existing cancer classification methods using machine learning select the marker genes which are differential expressed in normal and tumor samples, and build a classifier using those marker genes. However, in addition to the differences in gene expression levels, the difference in gene-gene correlations between two conditions could be a good marker in disease diagnosis. In this study, we identify gene pairs with a big correlation difference in two sets of samples, build gene classification modules using these gene pairs. This cancer classification method using gene modules achieves higher accuracy than current methods. The implementing clinical kit can be considered since the number of genes in classification module is small. For future study, Authors plan to identify novel cancer-related genes with functionality analysis on the genes in a classification module through GO(Gene Ontology) enrichment validation, and to extend the classification module into gene regulatory networks.

• Molecular & Cellular Toxicology
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• v.4 no.3
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• pp.183-191
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• 2008

Volatile organic compounds (VOCs) are common constituents of cleaning and degreasing agents, paints, pesticides, personal care products, gasoline and solvents. And VOCs are evaporated at room temperature and most of them exhibit acute and chronic toxicity to human. Benzene is the most widely used prototypical VOC and the toxic mechanisms of them are still unclear. The multi-step process of toxic mechanism can be more fully understood by characterizing gene expression changes induced in cells by toxicants. In this study, DNA microarray was used to monitor the expression levels of genes in HepG2 cells and HL-60 cells exposed to the benzene on IC20 and IC50 dose respectively. In the clustering analysis of gene expression profiles, although clusters of HepG2 and HL-60 cells by benzene were divided differently, expression pattern of many genes observed similarly. We identified 916 up-regulated genes and 1,144 down-regulated genes in HepG2 cells and also 1,002 up-regulated genes and 919 down-regulated genes in HL-60 cells. The gene ontology analysis on genes expressed by benzene in HepG2 and HL-60 cells, respectively, was performed. Thus, we found some principal pathways, such as, focal adhesion, gap junction and signaling pathway in HepG2 cells and toll-like receptor signaling pathway, MAPK signaling pathway, p53 signaling pathway and neuroactive ligand-receptor interaction in HL-60 cells. And we also found 16 up-regulated and 14 down-regulated commonly expressed total 30 genes that belong in the same biological process like inflammatory response, cell cycle arrest, cell migration, transmission of nerve impulse and cell motility in two cell lines. In conclusion, we suggest that this study is meaningful because these genes regarded as strong potential biomarkers of benzene independent of cell type.

• Molecular & Cellular Toxicology
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• v.5 no.2
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• pp.99-107
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• 2009

Naphthalene is bicyclic aromatic compound that is widely used in various domestic and commercial applications including lavatory scent disks, soil fumigants and moth balls. Exposure to naphthalene results in the development of bronchiolar damage, cataracts and hemolytic anemia in humans and laboratory animals. However, little information is available regarding the mechanism of naphthalene toxicity. We investigated gene expression profiles and potential signature genes in human hepatocellular carcinoma HepG2 cells and human promyelocytic leukemia HL-60 cells after 3 h and 48 h incubation with the IC$_{20}$ and IC$_{50}$ of naphthalene by using 44 k agilent whole human genome oligomicroarray and operon human whole 35 k oligomicroarray, respectively. We identified 616 up-regulated genes and 2,088 down-regulated genes changed by more than 2-fold by naphthalene in HepG2 cells. And in HL-60, we identified 138 up-regulated genes and 182 down-regulated genes changed by more than 2-fold. This study identified several interesting targets and functions in relation to naphthalene-induced toxicity through a gene ontology analysis method. Apoptosis and cell cycle related genes are more commonly expressed than other functional genes in both cell lines. In summary, the use of in vitro models with global expression profiling emerges as a relevant approach toward the identification of biomarkers associated with toxicity after exposure to a variety of environmental toxicants.

• Journal of Plant Biotechnology
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• v.45 no.1
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• pp.45-54
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• 2018

This research was conducted to study the gene expression of coffee (Coffea arabica L.) seedlings under salt stress condition. A solution of five percent ($2.3dS\;m^{-1}$) deep sea water was used for the salt treatment, and it was thereby compared to normal irrigation water ($0.2dS\;m^{-1}$) used for the control treatment. The mRNA was extracted from the leaves of the coffee seedlings for a comprehensive analysis. In this study, a total of 19,581 genes were identified and aligned to the reference sequences available in the coffee genome database. The gene ontology analysis was performed to estimate the number of genes associated with the identified biological processes, cellular components and molecular functions. Among the 19,581 genes, 7369 (37.64%) were associated with biological processes, 5909 (30.18%) with cellular components, and 5325 (27.19%) with molecular functions. The remaining 978 (4.99%) genes were therefore grouped as unclassified. A differential gene expression analysis was performed using the DESeq2 package to identify the genes that were differentially expressed between the treatments based on fold changes and p-values. Namely, a total of 611 differentially expressed genes were identified (treatment/control) in that case. Among these, 336 genes were up-regulated while 275 of the genes were down-regulated. Of the differentially expressed genes, 60 genes showed statistically significant (p < 0.05) expression, 44 of which were up-regulated and 16 which were down-regulated. We also identified 11 differentially expressed transcription factor genes, 6 of which were up-regulated and rest 5 genes were down-regulated. The data generated from this study will help in the continued interest and understanding of the responses of coffee seedlings genes associated with salinity stress, in particular. This study will also provide important resources for further functional genomics studies.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.137-147
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• 2015

This study proposes an algorithm for predicting breast cancer prognosis based on genetic network. We identify prognosis-specific network using gene expression data and PPI(protein-protein interaction) data. To acquire the network, we calculate Pearson's correlation coefficient(PCC) between genes in all PPI pairs using gene expression data. We develop a prediction model for breast cancer patients with estrogen-receptor-negative using the network as a classifier. We compare classification performance of our algorithm with existing algorithms on independent data and shows our algorithm is improved. In addition, we make an functionality analysis on the genes in the prognosis-specific network using GO(Gene Ontology) enrichment validation.

• Journal of Life Science
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• v.28 no.2
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• pp.176-186
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• 2018

In this study, the total polyphenol contents, antioxidant activities and anti-proliferation effects of HepG2 cell lines in organic slovent fractions obtained from the main methanolic extract of P. yezoensis were analyzed. The polyphenol content of the $CHCl_3$ fraction was $10.3{\mu}g/mg$, slightly less than $13.08{\mu}g/mg$ of the water fraction, but $ED_{50}$ estimated by measuring DPPH free radical scavenging activity exhibited the highest $16.96{\mu}g/ml$ in the $CHCl_3$ fraction. The proliferation effects of $CHCl_3$ and EtOAc fraction toward HepG2 cells inhibited in a dose-dependent manner, showed 90% inhibition when treated for 24 hr at $900{\mu}g/ml$ of $CHCl_3$ fraction. Meanwhile gene expression patterns in HepG2 cells treated $50{\mu}g/ml$ of $CHCl_3$ fraction were identified with microarray analysis. Concerning the efficacy of P. yezoensis, gene ontology analysis explored the genes associated with response to molecule of bacterial origin, vitamin D metabolic process, and response to nutrient. Thus IL6R, CYP1A1 were selected as significant genes based on expression patterns of HepG2 cells, and pathway analysis indicates that ARNT might be considered as a upstream regulator. Also, expression analysis of IL6R and CYP1A1, activity of upstream regulator ARNT in HepG2 cells was confirmed based on Western blotting analysis at the protein level after being treated with 50 and $100{\mu}g/ml$ of $CHCl_3$ fraction.

• Journal of Plant Biotechnology
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• v.43 no.2
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• pp.204-212
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• 2016

To understand the responses of grapevines in response to cold stress causing the limited growth and development, differentially expressed genes (DEGs) were screened through transcriptome analysis of shoots from 2 grapevine cultivars ('Campbell Early' and 'Muscat Baily A') kept at -$2^{\circ}C$ for 4 days. In gene ontology analysis of DEGs from 'Campbell Early', there were 17,424 clones related with biological process, 28,954 with cellular component, and 6,972 with molecular function genes in response to freezing temperature. The major induced genes included dehydrin xero 1, K-box region and MADS-box transcription factor family protein, and MYB domain protein 36, and inhibited genes included light-harvesting chlorophyll B-binding protein 3, FASCICLIN-like arabinoogalactan 9, and pectin methylesterase 61 in 'Campbell Early' grapevines. In gene ontology analysis of DEGs from 'Muscat Baily A', there were 1,157 clones related with biological process, 1,350 with cellular component, and 431 with molecular function gene. The major induced genes of 'Muscat Baily A' included NB-ARC domain-containing disease resistance protein, fatty acid hydrozylase superfamily, and isopentenyltransferase 3, and inhibited genes included binding, IAP-like protein 1, and pentatricopeptide repeat superfamily protein. All major DEGs were shown to be expressed differentially by freezing temperature in real time-PCR analysis. Protein domain analysis using InterPro Scan revealed that ubiquitin-protein ligase was redundant in both tested grapevines. Transcriptome profile of shoots exposed to cold can provide new insights into the molecular basis of tolerance to low-temperature in grapevines, and can be used as resources for development new grapevines tolerant to coldness.

• Journal of Microbiology
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• v.45 no.6
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• pp.541-546
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• 2007

Red algae are distributed globally, and the group contains several commercially important species. Griffithsia okiensis is one of the most extensively studied red algal species. In this study, we conducted expressed sequence tag (ESTs) analysis and synonymous codon usage analysis using cultured G. okiensis samples. A total of 1,104 cDNA clones were sequenced using a cDNA library made from samples collected from Dolsan Island, on the southern coast of Korea. The clustering analysis of these sequences allowed for the identification of 1,048 unigene clusters consisting of 36 consensus and 1,012 singleton sequences. BLASTX searches generated 532 significant hits (E-value <$10^{-4}$) and via further Gene Ontology analysis, we constructed a functional classification of 434 unigenes. Our codon usage analysis showed that unigene clusters with more than three ESTs had higher GC contents (76.5%) at the third position of the codons than the singletons. Also, the majority of the optimal codons of G. okiensis and Chondrus crispus belonging to Bangiophycidae were G-ending, whereas those of Porphyra yezoensis belonging to Florideophycidae were G-ending. An orthologous gene search for the P. yezoensis EST database resulted in the identification of 39 unigenes commonly expressed in two rhodophytes, which have putative functions for structural proteins, protein degradation, signal transduction, stress response, and physiological processes. Although experiments have been conducted on a limited scale, this study provides a material basis for the development of microarrays useful for gene expression studies, as well as useful information for the comparative genomic analysis of red algae.