• Bioinformatics and Biosystems
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• v.1 no.1
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• pp.67-72
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• 2006

The aim of this paper is to discuss the effect of missing values in detecting differentially expressed genes in a cDNA microarray experiment in the context of a one sample problem. We conducted a cDNA micro array experiment to detect differentially expressed genes for the metastasis of colorectal cancer based on twenty patients who underwent liver resection due to liver metastasis from colorectal cancer. Total RNAs from metastatic liver tumor and adjacent normal liver tissue from a single patient were labeled with cy5 and cy3, respectively, and competitively hybridized to a cDNA microarray with 7775 human genes. We used $M=log_2(R/G)$ for the signal evaluation, where Rand G denoted the fluorescent intensities of Cy5 and Cy3 dyes, respectively. The statistical problem comprises a one sample test of testing E(M)=0 for each gene and involves multiple tests. The twenty cDNA microarray data would comprise a matrix of dimension 7775 by 20, if there were no missing values. However, missing values occur for various reasons. For each gene, the no missing proportion (NMP) was defined to be the proportion of non-missing values out of twenty. In detecting differentially expressed (DE) genes, we used the genes whose NMP is greater than or equal to 0.4 and then sequentially increased NMP by 0.1 for investigating its effect on the detection of DE genes. For each fixed NMP, we imputed the missing values with K-nearest neighbor method (K=10) and applied the nonparametric t-test of Dudoit et al. (2002), SAM by Tusher et al. (2001) and empirical Bayes procedure by $L\ddot{o}nnstedt$ and Speed (2002) to find out the effect of missing values in the final outcome. These three procedures yielded substantially agreeable result in detecting DE genes. Of these three procedures we used SAM for exploring the acceptable NMP level. The result showed that the optimum no missing proportion (NMP) found in this data set turned out to be 80%. It is more desirable to find the optimum level of NMP for each data set by applying the method described in this note, when the plot of (NMP, Number of overlapping genes) shows a turning point.

• Journal of Microbiology and Biotechnology
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• v.22 no.8
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• pp.1141-1147
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• 2012

Polyhydroxyalkanoate (PHA) is a class of biodegradable plastics that have great potential applications in the near future. In this study, the micro-biodiversity and productivity of PHA-accumulating bacteria in activated sludge from a domestic wastewater treatment plant were investigated. A previously reported primer set and a self-designed primer set (phaCF1BO/phaCR2BO) were both used to amplify the PHA synthase (phaC) gene of isolated colonies. The new primers demonstrated higher sensitivity for phaC, and combining the PCR results of the two primer sets was able to widen the range of detected genera and raise the sensitivity to nearly 90%. Results showed that 85.3% of the identified bacteria were Gram-negative, with Ralstonia as the dominant genus, and 14.7% were Gram-positive. In addition, Zoogloea and Rhizobium contained the highest amounts of intracellular PHA. It is apparent that glucose was a better carbon source than pentone or tryptone for promoting PHA production in Micrococcus. Two different classes, class I and class II, of phaC were detected from alphaproteobacteria, betaproteobacteria, and gammaproteobacteria, indicating the wide diversity of PHA-accumulating bacteria in this particular sampling site. Simultaneous wastewater treatment and PHA production is promising by adopting the high PHA-accumulating bacteria isolated from activated sludge.

• Journal of Microbiology and Biotechnology
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• v.25 no.9
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• pp.1401-1409
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• 2015

The aim of this study was to develop a SYBR Green-based real-time PCR assay for the rapid, specific, and sensitive detection of Acidovorax avenae subsp. citrulli, which causes bacterial fruit blotch (BFB), a serious disease of cucurbit plants. The molecular and serological methods currently available for the detection of this pathogen are insufficiently sensitive and specific. Thus, a novel SYBR Green-based real-time PCR assay targeting the YD-repeat protein gene of A. avenae subsp. citrulli was developed. The specificity of the primer set was evaluated using DNA purified from 6 isolates of A. avenae subsp. citrulli, 7 other Acidovorax species, and 22 of non-targeted strains, including pathogens and non-pathogens. The AC158F/R primer set amplified a single band of the expected size from genomic DNA obtained from the A. avenae subsp. citrulli strains but not from the genomic DNA of other Acidovorax species, including that of other bacterial genera. Using this assay, it was possible to detect at least one genomeequivalents of the cloned amplified target DNA using 5 × 10⁰ fg/µl of purified genomic DNA per reaction or using a calibrated cell suspension, with 6.5 colony-forming units per reaction being employed. In addition, this assay is a highly sensitive and reliable method for identifying and quantifying the target pathogen in infected samples that does not require DNA extraction. Therefore, we suggest that this approach is suitable for the rapid and efficient diagnosis of A. avenae subsp. citrulli contaminations of seed lots and plants.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.775-784
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• 2002

Microarray data, obtained from DNA chip technologies, is the measurement of the expression level of thousands of genes in cells or tissues. It is used for gene function prediction or cancer diagnosis based on gene expression patterns. Among diverse methods for data analysis, the Bayesian network represents the relationships among data attributes in the form of a graph structure. This property enables us to discover various relations among genes and the characteristics of the tissue (e.g., the cancer type) through microarray data analysis. However, most of the present microarray data sets are so sparse that it is difficult to apply general analysis methods, including Bayesian networks, directly. In this paper, we harness an efficient structural learning algorithm and data dimensionality reduction in order to analyze microarray data using Bayesian networks. The proposed method was applied to the analysis of real microarray data, i.e., the NC160 data set. And its usefulness was evaluated based on the accuracy of the teamed Bayesian networks on representing the known biological facts.

• Biomedical Science Letters
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• v.2 no.2
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• pp.241-247
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• 1996

Poor yields of amplified DNAs could be resulted in polymerase chain reaction(PCR) processes with G+C-rich DNA primers because of their high $T_m$ values. To maximize the yields of amplification in PCR processes with G+C-rich primers, we compared the yields of amplified DNA fragments according to the concentrations of specific denaturants added to the reaction mixture of PCR system. With addition of the mixture of 2.5% glycerol and 1.25% formamide, or 2.5% dimethyl sulfoxide to the reaction cocktail, respectively, remarkable increases in the yields of amplified DNA fragments were not observed in the PCR systems with G+C-low primers of Lyl chromosomal gene from Borrelia burgdorferi but observed in the PCR system with G+C- ich primers of Is900 gene from Mycobacterium parahberculosis. Although we were not practically able to discriminate the yields of PCR DNAs according to the concentrations used in this study, addition of the mixture of 5% glycerol and 2.5% formamide, or 5% DMSO tended to increase the production of extra bands.

• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1229-1237
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• 2005

Combining multiple classifiers has been actively exploited to improve classification performance. It is required to construct a pool of accurate and diverse base classifier for obtaining a good ensemble classifier. Conventionally ensemble learning techniques such as bagging and boosting have been used and the diversify of base classifiers for the training set has been estimated, but there are some limitations in classifying gene expression profiles since only a few training samples are available. This paper proposes an ensemble technique that analyzes the diversity of classification rules obtained by genetic programming. Genetic programming generates interpretable rules, and a sample is classified by combining the most diverse set of rules. We have applied the proposed method to cancer classification with gene expression profiles. Experiments on lymphoma cancer dataset, prostate cancer dataset and ovarian cancer dataset have illustrated the usefulness of the proposed method. h higher classification accuracy has been obtained with the proposed method than without considering diversity. It has been also confirmed that the diversity increases classification performance.

• The Korean Journal of Physiology and Pharmacology
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• v.25 no.2
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• pp.119-129
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• 2021

Bladder cancer is one of the most common types of cancer. Most gene mutations related to bladder cancer are dominantly acquired gene mutations and are not inherited. Previous comparative transcriptome analysis of urinary bladder cancer and control samples has revealed a set of genes that may play a role in tumor progression. Here we set out to investigate further the expression of two candidate genes, centromere protein U (CENPU) and mitochondrial ribosomal protein s28 (MRPS28) to better understand their role in bladder cancer pathogenesis. Our results confirmed that CENPU is up-regulated in human bladder cancer tissues at mRNA and protein levels. Gain-of-function and loss-of-function studies in T24 human urinary bladder cancer cell line revealed a hierarchical relationship between CENPU and MRPS28 in the regulation of cell viability, migration and invasion activity. CENPU expression was also up-regulated in in vivo nude mice xenograft model of bladder cancer and mice overexpressing CENPU had significantly higher tumor volume. In summary, our findings identify CENPU and MRPS28 in the molecular pathogenesis of bladder cancer and suggest that CENPU enhances the progression of bladder cancer by promoting MRPS28 expression.

• The Journal of the Korean Society for Microbiology
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• v.34 no.6
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• pp.519-532
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• 1999

Helicobacter pylori is a causative agent of type B gastritis and plays a central role in the pathogenesis of gastroduodenal ulcer and gastric cancer. To elucidate the host-parasite relationship of the H. pylori infection on the basis of molecular biology, we tried to evaluate the genomic diversity of H. pylori. An ordered overlapping bacterial artificial chromosome (BAC) library of a Korean isolate, H. pylori 51 was constructed to set up a genomic map. A circular physical map was constructed by aligning ApaI, NotI and SfiI-digested chromosomal DNA. When the physical map of H. pylori 51 was compared to that of unrelated strain, H. pylori 26695, completely different restriction patterns were shown. Fifteen known genes were mapped on the chromosome of H. pylori 51 and the genetic map was compared with those of strain 26695 and J99, of which the entire genomic sequences were reported. There were some variability in the gene location as well as gene order among three strains. For further analysis on the genomic diversity of H. pylori, when comparing the genomic structure of 150 H. pylori Korean isolates with one another, genomic macrodiversity of H. pylori was characterized by several features: whether or not susceptible to restriction digestion of the chromsome, variation in chromosomal restriction fingerprint and/or high frequency of gene rearrangement. We also examined the extent of allelic variation in nucleotide or deduced amino acid sequences at the individual gene level. fucT, cagA and vacA were confirmed to carry regions of high variation in nucleotide sequence among strains. The plasticity zone and strain-specific genes of H. pylori 51 were analyzed and compared with the former two genomic sequences. It should be noted that the H. pylori 51-specific sequences were dispersed on the chromosome, not congregated in the plasticity zone unlike J99- or 26695-specific genes, suggesting the high frequency of gene rearrangement in H. pylori genome. The genome of H. pylori 51 shows differences in the overall genomic organization, gene order, and even in the nucleotide sequences among the H. pylori strains, which are far greater than the differences reported on the genomic diversity of H. pylori.

• Journal of Life Science
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• v.17 no.8 s.88
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• pp.1157-1165
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• 2007

VvMSA, a grapevine ASR which is highly inducible by sugar and abscisic acid signals was previously shown to be a transcription factor for a hexose transporter gene VvHT1. We isolated a cDNA clone, VlASR which is regulated temporally during the grape berry development by ACP RT-PCR (annealing control primer reverse transcriptase-polymerase chain reaction) and it proved identical to VvMSA. RT-PCR and real-time PCR analyses revealed that the VlASR gene was expressed in berries at fruit set and that its expression increased as berries aged but decreased at the late ripening stage. In order to understand the regulatory mechanism of the asr gene, a genomic fragment was cloned from grapevine. The genomic DNA was 1375 bp long and a sugar box (sucrose box 3 and sucrose responsive element 1) was identified in the 611 bp upstream region of the open reading frame. Analysis of the VlASR promoter::reporter gene fusion demonstrated that this promoter was expressed in transgenic Arabidopsis even without sucrose treatment. This result suggests that the ASR/VvHT1-mediated sugar/ABA signaling, previously reported in grapevine, may not function in Arabidopsis which has no ASR homologue.

• Asian-Australasian Journal of Animal Sciences
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• v.26 no.12
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• pp.1665-1671
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• 2013

Real-time quantitative PCR (qRT-PCR) is one of the important methods for investigating the changes in mRNA expression levels in cells and tissues. Selection of the proper reference genes is very important when calibrating the results of real-time quantitative PCR. Studies on the selection of reference genes in goat tissues are limited, despite the economic importance of their meat and dairy products. We used real-time quantitative PCR to detect the expression levels of eight reference gene candidates (18S, TBP, HMBS, YWHAZ, ACTB, HPRT1, GAPDH and EEF1A2) in ten tissues types sourced from Boer goats. The optimal reference gene combination was selected according to the results determined by geNorm, NormFinder and Bestkeeper software packages. The analyses showed that tissue is an important variability factor in genes expression stability. When all tissues were considered, 18S, TBP and HMBS is the optimal reference combination for calibrating quantitative PCR analysis of gene expression from goat tissues. Dividing data set by tissues, ACTB was the most stable in stomach, small intestine and ovary, 18S in heart and spleen, HMBS in uterus and lung, TBP in liver, HPRT1 in kidney and GAPDH in muscle. Overall, this study provided valuable information about the goat reference genes that can be used in order to perform a proper normalisation when relative quantification by qRT-PCR studies is undertaken.