• Genomics & Informatics
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• v.17 no.1
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• pp.6.1-6.9
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• 2019

Understanding the role of the microbiome in human health and how it can be modulated is becoming increasingly relevant for preventive medicine and for the medical management of chronic diseases. The development of high-throughput sequencing technologies has boosted microbiome research through the study of microbial genomes and allowing a more precise quantification of microbiome abundances and function. Microbiome data analysis is challenging because it involves high-dimensional structured multivariate sparse data and because of its compositional nature. In this review we outline some of the procedures that are most commonly used for microbiome analysis and that are implemented in R packages. We place particular emphasis on the compositional structure of microbiome data. We describe the principles of compositional data analysis and distinguish between standard methods and those that fit into compositional data analysis.

• Journal of Microbiology and Biotechnology
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• v.24 no.7
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• pp.979-986
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• 2014

Plant pathogenic RNA viruses are present in a variety of plant-based foods. When ingested by humans, these viruses can survive the passage through the digestive tract, and are frequently detected in human feces. Kimchi is a traditional fermented Korean food made from cabbage or vegetables, with a variety of other plant-based ingredients, including ground red pepper and garlic paste. We analyzed microbial metatranscriptome data from kimchi at five fermentation stages to identify plant RNA virus-derived sequences. We successfully identified a substantial amount of plant RNA virus sequences, especially during the early stages of fermentation: 23.47% and 16.45% of total clean reads on days 7 and 13, respectively. The most abundant plant RNA virus sequences were from pepper mild mottle virus, a major pathogen of red peppers; this constituted 95% of the total RNA virus sequences identified throughout the fermentation period. We observed distinct sequencing read-depth distributions for plant RNA virus genomes, possibly implying intrinsic and/or technical biases during the metatranscriptome generation procedure. We also identified RNA virus sequences in publicly available microbial metatranscriptome data sets. We propose that metatranscriptome data may serve as a valuable resource for RNA virus detection, and a systematic screening of the ingredients may help prevent the use of virus-infected low-quality materials for food production.

• Animal Bioscience
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• v.36 no.9
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• pp.1453-1464
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• 2023

Objective: This study investigated the changes in bacterial communities within decomposing swine microcosms, comparing soil with or without intact microbial communities, and under aerobic and anaerobic conditions. Methods: The experimental microcosms consisted of four conditions: UA, unsterilized soil-aerobic condition; SA, sterilized soil-aerobic condition; UAn, unsterilized soil-anaerobic condition; and San, sterilized soil-anaerobic condition. The microcosms were prepared by mixing 112.5 g of soil and 37.5 g of ground carcass, which were then placed in sterile containers. The carcass-soil mixture was sampled at day 0, 5, 10, 30, and 60 of decomposition, and the bacterial communities that formed during carcass decomposition were assessed using Illumina MiSeq sequencing of the 16S rRNA gene. Results: A total of 1,687 amplicon sequence variants representing 22 phyla and 805 genera were identified in the microcosms. The Chao1 and Shannon diversity indices varied in between microcosms at each period (p<0.05). Metagenomic analysis showed variation in the taxa composition across the burial microcosms during decomposition, with Firmicutes being the dominant phylum, followed by Proteobacteria. At the genus level, Bacillus and Clostridium were the main genera within Firmicutes. Functional prediction revealed that the most abundant Kyoto encyclopedia of genes and genomes metabolic functions were carbohydrate and amino acid metabolisms. Conclusion: This study demonstrated a higher bacteria diversity in UA and UAn microcosms than in SA and SAn microcosms. In addition, the taxonomic composition of the microbial community also exhibited changes, highlighting the impact of soil sterilization and oxygen on carcass decomposition. Furthermore, this study provided insights into the microbial communities associated with decomposing swine carcasses in microcosm.

• Journal of Life Science
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• v.23 no.4
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• pp.595-601
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• 2013

The clusters of orthologous groups of proteins (COG) algorithm was applied to identify essential proteins in eukaryotes and to measure the degree of conservation. Sixty-three orthologous groups, which were conserved in 66 microbial genomes, enlarged to 104 eukaryotic orthologous groups (KOGs) and 71 KOGs were conserved at the nuclear genome of 7 eucaryotes. Fifty-four of 71 translation-related genes were conserved, highlighting the importance of proteins in modern organisms. Translation initiation factors (KOG0343, KOG3271) and prolyl-tRNA synthetase (KOG4163) showed high conservation based on the distance value analysis. The genes of Caenorhabditis elegans appear to harbor high genetic variation because the genome showed the highest variation at 71 conserved proteins among 7 genomes. The 71 conserved genes will be valuable in basic and applied research, for example, targeting for antibiotic development.

• Korean Journal of Microbiology
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• v.39 no.4
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• pp.207-215
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• 2003

Gene structure prediction, which is to predict protein coding regions in a given nucleotide sequence, is the most important process in annotating genes and greatly affects gene analysis and genome annotation. As eukaryotic genes have more complicated stuructures in DNA sequences than those of prokaryotic genes, analysis programs for eukaryotic gene structure prediction have more diverse and more complicated computational models. We have developed EGSP, a eukaryotic gene structure program, using duration hidden markov model. The program consists of two major processes, one of which is a training process to produce parameter values from training data sets and the other of which is to predict protein coding regions based on the parameter values. The program predicts multiple genes rather than a single gene from a DNA sequence. A few computational models were implemented to detect signal pattern and their scanning efficiency was tested. Prediction performance was calculated and was compared with those of a few commonly used programs, GenScan, GeneID and Morgan based on a few criteria. The results show that the program can be practically used as a stand-alone program and a module in a system. For gene prediction of eukaryotic microbial genomes, training and prediction analysis was done with Saccharomyces chromosomes and the result shows the program is currently practically applicable to real eukaryotic microbial genomes.

• The Plant Pathology Journal
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• v.31 no.3
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• pp.212-218
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• 2015

In this study, we developed a species-specific PCR assay for rapid and accurate detection of three Xanthomonas species, X. axonopodis pv. poinsettiicola (XAP), X. hyacinthi (XH) and X. campestris pv. zantedeschiae (XCZ), based on their draft genome sequences. XAP, XH and XCZ genomes consist of single chromosomes that contain 5,221, 4,395 and 7,986 protein coding genes, respectively. Species-specific primers were designed from variable regions of the draft genome sequence data and assessed by a PCR-based detection method. These primers were also tested for specificity against 17 allied Xanthomonas species as well as against the host DNA and the microbial community of the host surface. Three primer sets were found to be very specific and no amplification product was obtained with the host DNA and the microbial community of the host surface. In addition, a detection limit of $1pg/{\mu}l$ per PCR reaction was detected when these primer sets were used to amplify corresponding bacterial DNAs. Therefore, these primer sets and the developed species-specific PCR assay represent a valuable, sensitive, and rapid diagnostic tool that can be used to detect three specific pathogens at early stages of infection and may help control diseases.

• Genomics & Informatics
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• v.6 no.1
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• pp.44-49
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• 2008

Protein kinase C (PKC) is a family of kinases involved in the transduction of cellular signals that promote lipid hydrolysis. PKC plays a pivotal role in mediating cellular responses to extracellular stimuli involved in proliferation, differentiation and apoptosis. Comparative analysis of the PKC-${\alpha},{\beta},{\varepsilon}$ isozymes of 200 recently sequenced microbial genomes was carried out using variety of bioinformatics tools. Diversity and evolution of PKC was determined by sequence alignment. The ser/thr protein kinases of Streptomyces coelicolor A3 (2), is the only bacteria to show sequence alignment score greater than 30% with all the three PKC isotypes in the sequence alignment. S.coelicolor is the subject of our interest because it is notable for the production of pharmaceutically useful compounds including anti-tumor agents, immunosupressants and over two-thirds of all natural antibiotics currently available. The comparative analysis of three human isotypes of PKC and Serine/threonine protein kinase of S.coelicolor was carried out and possible mechanism of action of PKC was derived. Our analysis indicates that Serine/ threonine protein kinase from S. coelicolor can be a good candidate for potent anti-tumor agent. The presence of three representative isotypes of the PKC super family in this organism helps us to understand the mechanism of PKC from evolutionary perspective.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.17-20
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• 2000

Structural genomics aims to provide a good experimental structure or computational model of every tractable protein in a complete genome. Underlying this goal is the immense value of protein structure, especially in permitting recognition of distant evolutionary relationships for proteins whose sequence analysis has failed to find any significant homolog. A considerable fraction of the genes in all sequenced genomes have no known function, and structure determination provides a direct means of revealing homology that may be used to infer their putative molecular function. The solved structures will be similarly useful for elucidating the biochemical or biophysical role of proteins that have been previously ascribed only phenotypic functions. More generally, knowledge of an increasingly complete repertoire of protein structures will aid structure prediction methods, improve understanding of protein structure, and ultimately lend insight into molecular interactions and pathways. We use computational methods to select families whose structures cannot be predicted and which are likely to be amenable to experimental characterization. Methods to be employed included modern sequence analysis and clustering algorithms. A critical component is consultation of the presage database for structural genomics, which records the community's experimental work underway and computational predictions. The protein families are ranked according to several criteria including taxonomic diversity and known functional information. Individual proteins, often homologs from hyperthermophiles, are selected from these families as targets for structure determination. The solved structures are examined for structural similarity to other proteins of known structure. Homologous proteins in sequence databases are computationally modeled, to provide a resource of protein structure models complementing the experimentally solved protein structures.

• Journal of Microbiology and Biotechnology
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• v.29 no.5
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• pp.667-686
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• 2019

Streptomyces are attractive microbial cell factories that have industrial capability to produce a wide array of bioactive secondary metabolites. However, the genetic potential of the Streptomyces species has not been fully utilized because most of their secondary metabolite biosynthetic gene clusters (SM-BGCs) are silent under laboratory culture conditions. In an effort to activate SM-BGCs encoded in Streptomyces genomes, synthetic biology has emerged as a robust strategy to understand, design, and engineer the biosynthetic capability of Streptomyces secondary metabolites. In this regard, diverse synthetic biology tools have been developed for Streptomyces species with technical advances in DNA synthesis, sequencing, and editing. Here, we review recent progress in the development of synthetic biology tools for the production of novel secondary metabolites in Streptomyces, including genomic elements and genome engineering tools for Streptomyces, the heterologous gene expression strategy of designed biosynthetic gene clusters in the Streptomyces chassis strain, and future directions to expand diversity of novel secondary metabolites.

• Molecules and Cells
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• v.45 no.5
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• pp.294-305
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• 2022

E3 ligase BRUTUS (BTS), a putative iron sensor, is expressed in both root and shoot tissues in seedlings of Arabidopsis thaliana. The role of BTS in root tissues has been well established. However, its role in shoot tissues has been scarcely studied. Comparative transcriptome analysis with shoot and root tissues revealed that BTS is involved in regulating energy metabolism by modulating expression of mitochondrial and chloroplast genes in shoot tissues. Moreover, in shoot tissues of bts-1 plants, levels of ADP and ATP and the ratio of ADP/ATP were greatly increased with a concomitant decrease in levels of soluble sugar and starch. The decreased starch level in bts-1 shoot tissues was restored to the level of shoot tissues of wild-type plants upon vanadate treatment. Through this study, we expand the role of BTS to regulation of energy metabolism in the shoot in addition to its role of iron deficiency response in roots.