• Journal of Microbiology and Biotechnology
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• v.20 no.12
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• pp.1637-1646
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• 2010

The bacterium Deinococcus radiodurans is one of the most resistant organisms to ionizing radiation and other DNA-damaging agents. Although, at present, 30 Deinococcus species have been identified, the whole-genome sequences of most species remain unknown, with the exception of D. radiodurans (DRD), D. geothermalis, and D. deserti. In this study, comparative genomic hybridization (CGH) microarray analysis of three Deinococcus species, D. radiopugnans (DRP), D. proteolyticus (DPL), and D. radiophilus (DRPH), was performed using oligonucleotide arrays based on DRD. Approximately 28%, 14%, and 15% of 3,128 open reading frames (ORFs) of DRD were absent in the genomes of DRP, DPL, and DRPH, respectively. In addition, 162 DRD ORFs were absent in all three species. The absence of 17 randomly selected ORFs was confirmed by a Southern blot. Functional classification showed that the absent genes spanned a variety of functional categories: some genes involved in amino acid biosynthesis, cell envelope, cellular processes, central intermediary metabolism, and DNA metabolism were not present in any of the three deinococcal species tested. Finally, comparative genomic data showed that 120 genes were Deinococcus-specific, not the 230 reported previously. Specifically, ddrD, ddrO, and ddrH genes, previously identified as Deinococcus-specific, were not present in DRP, DPL, or DRPH, suggesting that only a portion of ddr genes are shared by all members of the genus Deinococcus.

• Clinical and Experimental Pediatrics
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• v.64 no.7
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• pp.355-363
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• 2021

Background: Nephrotic syndrome (NS) is a common renal disorder in children attributed to podocyte injury. However, children with the same diagnosis have markedly variable treatment responses, clinical courses, and outcomes, suggesting molecular heterogeneity. Purpose: This study aimed to explore the molecular responses of podocytes to nephrotic plasma to identify specific genes and signaling pathways differentiating various clinical NS groups as well as biological processes that drive injury in normal podocytes. Methods: Transcriptome profiles from immortalized human podocyte cell line exposed to the plasma of 8 subjects (steroid-sensitive nephrotic syndrome [SSNS], n=4; steroid-resistant nephrotic syndrome [SRNS], n=2; and healthy adult individuals [control], n=2) were generated using microarray analysis. Results: Unsupervised hierarchical clustering of global gene expression data was broadly correlated with the clinical classification of NS. Differential gene expression (DGE) analysis of diseased groups (SSNS or SRNS) versus healthy controls identified 105 genes (58 up-regulated, 47 down-regulated) in SSNS and 139 genes (78 up-regulated, 61 down-regulated) in SRNS with 55 common to SSNS and SRNS, while the rest were unique (50 in SSNS, 84 genes in SRNS). Pathway analysis of the significant (P≤0.05, -1≤ log2 FC ≥1) differentially expressed genes identified the transforming growth factor-β and Janus kinase-signal transducer and activator of transcription pathways to be involved in both SSNS and SRNS. DGE analysis of SSNS versus SRNS identified 2,350 genes with values of P≤0.05, and a heatmap of corresponding expression values of these genes in each subject showed clear differences in SSNS and SRNS. Conclusion: Our study observations indicate that, although podocyte injury follows similar pathways in different clinical subgroups, the pathways are modulated differently as evidenced by the heatmap. Such transcriptome profiling with a larger cohort can stratify patients into intrinsic subtypes and provide insight into the molecular mechanisms of podocyte injury.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2259-2264
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• 2008

Significant genes are defined as genes in which the expression level characterizes a specific experimental condition. Such genes in which the expression levels differ significantly between different groups are highly informative relevant to the studied phenomenon. In this paper, first the system can detect informative genes by similarity scale combination method being proposed in this paper after normalizing data with methods that are the most widely used among several normalization methods proposed the while. And it compare and analyze a performance of each of normalization methods with multi-perceptron neural network layer. The Result classifying in Multi-Perceptron neural network classifier for selected 200 genes using combination of PC(Pearson correlation coefficient) and ED(Euclidean distance coefficient) after Lowess normalization represented the improved classification performance of 98.84%.

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.9-20
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• 2007

Groups of genes control the functioning of a cell by complex interactions. Such interactions of gene groups are tailed Gene Regulatory Networks(GRNs). Two previous data mining approaches, clustering and classification, have been used to analyze gene expression data. Though these mining tools are useful for determining membership of genes by homology, they don't identify the regulatory relationships among genes found in the same class of molecular actions. Furthermore, we need to understand the mechanism of how genes relate and how they regulate one another. In order to detect regulatory relationships among genes from time-series Microarray data, we propose a novel approach using frequent pattern mining and chain rules. In this approach, we propose a method for transforming gene expression data to make suitable for frequent pattern mining, and gene expression patterns we detected by applying the FP-growth algorithm. Next, we construct a gene regulatory network from frequent gene patterns using chain rules. Finally, we validate our proposed method through our experimental results, which are consistent with published results.

• Molecular & Cellular Toxicology
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• v.4 no.4
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• pp.318-322
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• 2008

Obesity is an increasing worldwide health problem that is strongly related to the imbalance of food intake and energy metabolism. It was well-known that several substances in the hypothalamus regulate food intake and energy metabolism. We planned an integrative study to elucidate the mechanism of the development of obesity. Firstly, to find candidate genes with the marvelous effect, the different expression in the hypothalamus between ob/ob and 48-h fasting mice was investigated by using DNA microarray technology. As a result, we found 3 genes [peroxisome proliferator activated receptor, gamma, coactivator 1 alpha (Ppargc1a), calmodulin 1 (Calm1), and complexin 2 (Cplx2)] showing the different hypothalamic expression between ob/ob and 48-h fasting mice. Secondly, a genetic approach on PPARGC1A gene was performed, because PPARGC1A acts as a transcriptional coactivator and a metabolic regulator. Two hundred forty three obese female patients with body mass index (BMI)${\geq}$25 and 285 control female subjects with BMI 18 to<23 were recruited according to the Classification of Korean Society for the Study of Obesity. Among the coding single nucleotide polymorphisms (cSNPs) of PPARGC1A, 2 missense SNPs (rs8192678, Gly482Ser; rs3736265, Thr612Met) and 1 synonymous SNP (rs3755863, Thr528Thr) were selected, and analyzed by PCR-RFLP and pyrosequencing. For the analysis of genetic data, chi-square ($X^2$) test and EH program were used. The rs8192678 was significantly associated with obese women (P<0.0006; odds ratio, 1.5327; 95% confidence interval, 1.2006-1.9568). Haplotypes also showed significant association with obese women ($X^2$=33.28, P<0.0008). These results suggest that PPARGC1A might be related to the development of obesity.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.409-414
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• 2013

Naive Bayesian classifiers are a powerful and well-known type of classifiers that can be easily induced from a dataset of sample cases. However, the strong conditional independence assumptions can sometimes lead to weak classification performance. Normally, naive Bayesian classifiers use Gaussian distributions to handle continuous attributes and to represent the likelihood of the features conditioned on the classes. The probability density of attributes, however, is not always well fitted by a Gaussian distribution. Another eminent type of classifier is the neuro-fuzzy classifier, which can learn fuzzy rules and fuzzy sets using supervised learning. Since there are specific structural similarities between a neuro-fuzzy classifier and a naive Bayesian classifier, the purpose of this study is to apply learning distribution graphs constructed by a neuro-fuzzy network to naive Bayesian classifiers. We compare the Gaussian distribution graphs with the fuzzy distribution graphs for the naive Bayesian classifier. We applied these two types of distribution graphs to classify leukemia and colon DNA microarray data sets. The results demonstrate that a naive Bayesian classifier with fuzzy distribution graphs is more reliable than that with Gaussian distribution graphs.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2019.10a
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• pp.73-73
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• 2019

Angelica gigas Nakai (Korean danggui), a member of the Umbelliferae family, is a Korean traditional medicinal plant whose roots have been used for treating gynecological diseases. Transcriptomics is the study of the transcriptome, which is the complete set of RNA transcripts that are produced by the genome, using high-throughput methods, such as microarray analysis. In this study, transcriptome analysis of A.gigas Nakai was carried out. Transcriptome sequencing and assembly was carried out by using Illumina Hiseq 2500, Velvet and Oases. A total of 109,591,555 clean reads of A. gigas Nakai was obtained after trimming adaptors. The obtained reads were assembled with an average length of 1,154 bp, a maximum length of 13,166 bp, a minimum length of 200 pb, and N50 of 1,635 bp. Functional annotation and classification was performed using NCBI NR, InterprotScan, KOG, KEGG and GO. Candidate genes for phenylpropanoid biosynthesis were obtanied from A.gigas transcriptome and the genes and its proteins were confirmed through the NCBI homology BLAST searches, revealing high identity with other othologous genes and proteins from various plants pecies. In RNA sequencing analysis using an Illumina Next-Seq2500 sequencer, we identified a total 94,930 transcripts and annotated 71,281 transcripts, which provide basic information for further research in A.gigas Nakai. Our transcriptome data reveal that several differentially expressed genes related to flower color in A.gigas Nakai. The results of this research provide comprehensive information on the A.gigas Nakai genome and enhance our understanding of the flower color related gene pathways in this plant.