• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.34-34
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• 2022

Capacity building for bioinformatics could be achieved with the systematic training of research staff and higher degree students in the current best practices for analysis of data from 'omic-type experiments. It is anticipated that the KOICA-University of the Philippines Los Baños - International Rice Research Insitute Agricultural Genomics Research Center activities will focus on the use of next generation sequencing technology for genome sequencing and annotation, genome variant discovery for use in GWAS and QTL mapping, and transcriptome analysis of organisms important to agriculture and food security. Such activities require that researchers have high levels of knowledge and skills in bioinformatics in order to gain insights from the results of the experiments performed. In this talk the bioinformatic tools/solutions and online training materials already available will be presented, as well the upcoming resources under development in support of the project.

• Journal of KIISE:Databases
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• v.30 no.6
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• pp.573-584
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• 2003

Recent massive data generation by genomics and proteomics requires bioinformatic tools to extract the biological meaning from the massive results. Here we introduce ROSPath, a database system to deal with information on reactive oxygen species (ROS)-mediated cell signaling pathways. It provides a structured repository for handling pathway related data and tools for querying, displaying, and analyzing pathways. ROSPath data model provides the extensibility for representing incomplete knowledge and the accessibility for linking the existing biochemical databases via the Internet. For flexibility and efficient retrieval, hierarchically structured data model is defined by using the object-oriented model. There are two major data types in ROSPath data model: ‘bio entity’ and ‘interaction’. Bio entity represents a single biochemical entity: a protein or protein state involved in ROS cell-signaling pathways. Interaction, characterized by a list of inputs and outputs, describes various types of relationship among bio entities. Typical interactions are protein state transitions, chemical reactions, and protein-protein interactions. A complex network can be constructed from ROSPath data model and thus provides a foundation for describing and analyzing various biochemical processes.

• Biotechnology and Bioprocess Engineering:BBE
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• v.9 no.2
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• pp.93-99
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• 2004

Pathogens pose a significant threat to humans, animals, and plants. Consequently, a considerable effort has been devoted to developing rapid, convenient, and accurate assays for the detection of these unfavorable organisms. Recently, DNA-microarray based technology is receiving much attention as a powerful tool for pathogen detection. After the target gene is first selected for the unique identification of microorganisms, species-specific probes are designed through bioinformatic analysis of the sequences, which uses the info rmation present in the databases. DNA samples, which were obtained from reference and/or clinical isolates, are properly processed and hybridized with species-specific probes that are immobilized on the surface of the microarray for fluorescent detection. In this study, we review the methods and strategies for the development of DNA microarray for pathogen detection, with the focus on probe design.

• Asian-Australasian Journal of Animal Sciences
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• v.26 no.8
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• pp.1144-1151
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• 2013

In this study, protein domains with cellulase activity in goat rumen microbes were investigated using metagenomic and bioinformatic analyses. After the complete genome of goat rumen microbes was obtained using a shotgun sequencing method, 217,892,109 pair reads were filtered, including only those with 70% identity, 100-bp matches, and thresholds below $E^{-10}$ using METAIDBA. These filtered contigs were assembled and annotated using blastN against the NCBI nucleotide database. As a result, a microbial community structure with 1431 species was analyzed, among which Prevotella ruminicola 23 bacteria and Butyrivibrio proteoclasticus B316 were the dominant groups. In parallel, 201 sequences related with cellulase activities (EC.3.2.1.4) were obtained through blast searches using the enzyme.dat file provided by the NCBI database. After translating the nucleotide sequence into a protein sequence using Interproscan, 28 protein domains with cellulase activity were identified using the HMMER package with threshold E values below $10^{-5}$. Cellulase activity protein domain profiling showed that the major protein domains such as lipase GDSL, cellulase, and Glyco hydro 10 were present in bacterial species with strong cellulase activities. Furthermore, correlation plots clearly displayed the strong positive correlation between some protein domain groups, which was indicative of microbial adaption in the goat rumen based on feeding habits. This is the first metagenomic analysis of cellulase activity protein domains using bioinformatics from the goat rumen.

• BMB Reports
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• v.47 no.8
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• pp.469-474
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• 2014

Cell therapies utilizing mesenchymal stem cells (MSCs) have a great potential in many research and clinical settings. The mechanisms underlying the therapeutic effects of MSCs have been studied previously and the paracrine effects elicited by their production of various growth factors and cytokines were recognized as being crucial. However, the molecular controls that govern these paracrine effects remain poorly understood. To elucidate the molecular regulators of this process, we performed a global knockdown of microRNAs (miRNAs) in human adipose-derived mesenchymal stem cells (hADSCs) by inhibiting DGCR8, a key protein in miRNA biogenesis. Global disruption of miRNA biogenesis in hADSCs caused dramatic changes in the expression of subsets of growth factors and cytokines. By performing an extensive bioinformatic analysis, we were able to associate numerous putative miRNAs with these genes. Taken together, our results strongly suggest that miRNAs are essential for the production of growth factors and cytokines in hADSCs.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.6
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• pp.812-819
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• 2018

Objective: An experiment was conducted to identify and characterize the circular RNA expression and metabolic characteristics in the liver of Jinhua pigs and Landrace pigs. Methods: Three Jinhua pigs and three Landrace pigs respectively at 70-day were slaughtered to collect the liver tissue samples. Immediately after slaughter, blood samples were taken to detect serum biochemical indicators. Total RNA extracted from liver tissue samples were used to prepare the library and then sequence on HiSeq 2500. Bioinformatic methods were employed to analyze sequence data to identify the circRNAs and predict the potential roles of differentially expressed circRNAs between the two breeds. Results: Significant differences in physiological and biochemical traits were observed between growing Jinhua and Landrace pigs. We identified 84,864 circRNA candidates in two breeds and 366 circRNAs were detected as significantly differentially expressed. Their host genes are involved in lipid biosynthetic and metabolic processes according to the gene ontology analysis and associated with metabolic pathways. Conclusion: Our research represents the first description of circRNA profiles in the porcine liver from two divergent phenotype pigs. The predicted miRNA-circRNA interaction provides important basis for miRNA-circRNA relationships in the porcine liver. These data expand the repertories of porcine circRNA and are conducive to understanding the possible molecular mechanisms involved in miRNA and circRNA. Our study provides basic data for further research of the biological functions of circRNAs in the porcine liver.

• Molecules and Cells
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• v.43 no.11
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• pp.953-963
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• 2020

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an infectious disease with multiple severe symptoms, such as fever over 37.5℃, cough, dyspnea, and pneumonia. In our research, microRNAs (miRNAs) binding to the genome sequences of severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory-related coronavirus (MERS-CoV), and SARS-CoV-2 were identified by bioinformatic tools. Five miRNAs (hsa-miR-15a-5p, hsa-miR-15b-5p, hsa-miR-195-5p, hsa-miR-16-5p, and hsa-miR-196a-1-3p) were found to commonly bind to SARS-CoV, MERS-CoV, and SARS-CoV-2. We also identified miRNAs that bind to receptor proteins, such as ACE2, ADAM17, and TMPRSS2, which are important for understanding the infection mechanism of SARS-CoV-2. The expression patterns of those miRNAs were examined in hamster lung samples infected by SARS-CoV-2. Five miRNAs (hsa-miR-15b-5p, hsa-miR-195-5p, hsa-miR-221-3p, hsa-miR-140-3p, and hsa-miR-422a) showed differential expression patterns in lung tissues before and after infection. Especially, hsa-miR-15b-5p and hsa-miR-195-5p showed a large difference in expression, indicating that they may potentially be diagnostic biomarkers for SARS-CoV-2 infection.

• Journal of Life Science
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• v.16 no.3 s.76
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• pp.472-476
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• 2006

We have built a database server called HorseDB which contains the genome annotation information and biological information for horse from public database entries. The aims of HorseDB are the integration of biological information and horse genome data on genome scale using bioinformatic methods. To facilitate the extraction of useful information among collected horse genome and biological data, we developed a user-friendly interface system, HorseDB; an Integrated Horse Resource and web Service. The database is categorized by the general horse information data, a sequence annotation data, and a world-wide web analysis program interface. The database also provides an easy access for user to find out the useful information within horse genomes and support analyzed information, such as sequence alignment and gene annotation results. HorseDB can be accessed at http://www.primate.or.kr./horse.

• Genomics & Informatics
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• v.10 no.1
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• pp.33-39
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• 2012

High-throughput genomic technologies (HGTs), including next-generation DNA sequencing (NGS), microarray, and serial analysis of gene expression (SAGE), have become effective experimental tools for cancer genomics to identify cancer-associated somatic genomic alterations and genes. The main hurdle in cancer genomics is to identify the real causative mutations or genes out of many candidates from an HGT-based cancer genomic analysis. One useful approach is to refer to known cancer genes and associated information. The list of known cancer genes can be used to determine candidates of cancer driver mutations, while cancer gene-related information, including gene expression, protein-protein interaction, and pathways, can be useful for scoring novel candidates. Some cancer gene or mutation databases exist for this purpose, but few specialized tools exist for an automated analysis of a long gene list from an HGT-based cancer genomic analysis. This report presents a new web-accessible bioinformatic tool, called CaGe, a cancer genome annotation system for the assessment of candidates of cancer genes from HGT-based cancer genomics. The tool provides users with information on cancer-related genes, mutations, pathways, and associated annotations through annotation and browsing functions. With this tool, researchers can classify their candidate genes from cancer genome studies into either previously reported or novel categories of cancer genes and gain insight into underlying carcinogenic mechanisms through a pathway analysis. We show the usefulness of CaGe by assessing its performance in annotating somatic mutations from a published small cell lung cancer study.

• ALGAE
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• v.35 no.3
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• pp.293-301
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• 2020

The applications of DNA barcoding have a wide range of uses, such as in taxonomic studies to help elucidate cryptic species and phylogenetic relationships and analyzing environmental samples for biodiversity monitoring and conservation assessments of species. After obtaining the DNA barcode sequences, sequence similarity-based homology analysis is commonly used. This means that the obtained barcode sequences are compared to the DNA barcode reference databases. This bioinformatic analysis necessarily implies that the overall quantity and quality of the reference databases must be stringently monitored to not have an adverse impact on the accuracy of species identification. With the development of next-generation sequencing techniques, a noticeably large number of DNA barcode sequences have been produced and are stored in online databases, but their degree of validity, accuracy, and reliability have not been extensively investigated. In this study, we investigated the extent to which the amount and types of erroneous barcode sequences were deposited in publicly accessible databases. Over 4.1 million sequences were investigated in three largescale DNA barcode databases (NCBI GenBank, Barcode of Life Data System [BOLD], and Protist Ribosomal Reference database [PR2]) for four major DNA barcodes (cytochrome c oxidase subunit 1 [COI], internal transcribed spacer [ITS], ribulose bisphosphate carboxylase large chain [rbcL], and 18S ribosomal RNA [18S rRNA]); approximately 2% of erroneous barcode sequences were found and their taxonomic distributions were uneven. Consequently, our present findings provide compelling evidence of data quality problems along with insufficient and unreliable annotation of taxonomic data in DNA barcode databases. Therefore, we suggest that if ambiguous taxa are presented during barcoding analysis, further validation with other DNA barcode loci or morphological characters should be mandated.