• Journal of Digital Contents Society
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• v.19 no.4
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• pp.749-756
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• 2018

In this study, we developed a prediction model for greenhouse control using machine learning technique. The prediction model was developed using measured data (2016) on greenhouse in the Protected Horticulture Research Institute. In order to improve the predictive performance of model and to ensure the reliability of data, the dimension of the data was reduced by correlation analysis. The dataset were divided into spring, summer, autumn, and winter considering the seasonal characteristics. An artificial neural network, recurrent neural network, and multiple regression model were constructed as a machine leaning based prediction model and evaluated by comparative analysis with real dataset. As a result, ANN showed good performance in selected dataset, while MRM showed good performance in full dataset.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.5
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• pp.425-431
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• 2005

The systems-level analysis of microbes with myriad of heterologous data generated by omics technologies has been applied to improve our understanding of cellular function and physiology and consequently to enhance production of various bioproducts. At the heart of this revolution resides in silico genome-scale metabolic model, In order to fully exploit the power of genome-scale model, a systematic approach employing user-friendly software is required. Metabolic flux analysis of genome-scale metabolic network is becoming widely employed to quantify the flux distribution and validate model-driven hypotheses. Here we describe the development of an upgraded MetaFluxNet which allows (1) construction of metabolic models connected to metabolic databases, (2) calculation of fluxes by metabolic flux analysis, (3) comparative flux analysis with flux-profile visualization, (4) the use of metabolic flux analysis markup language to enable models to be exchanged efficiently, and (5) the exporting of data from constraints-based flux analysis into various formats. MetaFluxNet also allows cellular physiology to be predicted and strategies for strain improvement to be developed from genome-based information on flux distributions. This integrated software environment promises to enhance our understanding on metabolic network at a whole organism level and to establish novel strategies for improving the properties of organisms for various biotechnological applications.

• Journal of Microbiology and Biotechnology
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• v.20 no.1
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• pp.63-70
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• 2010

Novel influenza A (H1N1) is a newly emerged flu virus that was first detected in April 2009. Unlike the avian influenza (H5N1), this virus has been known to be able to spread from human to human directly. Although it is uncertain how severe this novel H1N1 virus will be in terms of human illness, the illness may be more widespread because most people will not have immunity to it. In this study, we compared the codon usage bias between the novel H1N1 influenza A viruses and other viruses such as H1N1 and H5N1 subtypes to investigate the genomic patterns of novel influenza A (H1N1). Totally, 1,675 nucleotide sequences of the hemagglutinin (HA) and neuraminidase (NA) genes of influenza A virus, including H1N1 and H5N1 subtypes occurring from 2004 to 2009, were used. As a result, we found that the novel H1N1 influenza A viruses showed the most close correlations with the swine-origin H1N1 subtypes than other H1N1 viruses, in the result from not only the analysis of nucleotide compositions, but also the phylogenetic analysis. Although the genetic sequences of novel H1N1 subtypes were not exactly the same as the other H1N1 subtypes, the HA and NA genes of novel H1N1s showed very similar codon usage patterns with other H1N1 subtypes, especially with the swine-origin H1N1 influenza A viruses. Our findings strongly suggested that those novel H1N1 viruses seemed to be originated from the swine-host H1N1 viruses in terms of the codon usage patterns.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.2
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• pp.155-160
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• 2008

Several studies have reported quantitative trait loci (QTL) for meat quality on porcine chromosome 2 (http://www.animalgenome.org/QTLdb/pig.html). For application of the molecular genetic information to the pig industry through marker-assisted selection, single nucleotide polymorphism (SNP) markers were analyzed by comparative re-sequencing of polymerase chain reaction (PCR) products of 13 candidate genes with DNA from commercial pig breeds such as Berkshire, Yorkshire, Landrace, Duroc and Korean Native pig. A total of 34 SNPs were identified in 15 PCR products producing an average of one SNP in every 253 bp. PCR restriction fragment length polymorphism (RFLP) assays were developed for 11 SNPs and used to investigate allele frequencies in five commercial pig breeds in Korea. Eight of the SNPs appear to be fixed in at least one of the five pig breeds, which indicates that different selection among pig breeds might be applied to these SNPs. Polymorphisms detected in the PTH, CSF2 and FOLR genes were chosen to genotype a Berkshire-Yorkshire pig breed reference family for linkage and association analyses. Using linkage analysis, PTH and CSF2 loci were mapped to pig chromosome 2, while FOLR was mapped to pig chromosome 9. Association analyses between SNPs in the PTH, CSF2 and FOLR suggested that the CSF2 MboII polymorphism was significantly associated with several pork quality traits in the Berkshire and Yorkshire crossed F2 pigs. Our current findings provide useful SNP marker information to fine map QTL regions on pig chromosome 2 and to clarify the relevance of SNP and quantitative traits in commercial pig populations.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.131-137
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• 2004

DNA 단계에서의 유전자의 증폭과 소실은 종양의 발생과 진행에 중요한 역할을 한다. 유전자의 변화를 관찰하기 위해서 Comparative Genomic Hybridization(CGH) 기술이 많이 이용되어져 왔다. 최근에는 이러한 CGH 기술을 응용하여 cDNA microarray 를 이용한 고밀도 CGH(Microarray-CGH) 기술이 보고 되고 있다. Microarray-CGH 에서 유전자별 변화 정도를 유전자의 log-비의 값의 변화 정도와 염색체 위치 정보를 이용하여 DNA 단계에서의 유전자의 변화 정도를 확인 할 수 있다. 또한 동일한 유전자의 칩을 사용하여 RNA단계에서의 발현 양상과 직접 비교할 수 있는 장점이 있다. 현재 microarray 분석법은 많이 개발되고 실용화 되고 있으나 Microarray-CGH 분석을 위한 프로그램들은 아직 초보 단계며, 생물학자들이 사용하기 힘들고, 프로그램에 분석 자료를 적용하기 어려운 경향이 있다. 위와 같은 단점을 보완하기 위해서 개발된 CAMVS(V1.0) 프로그램은 S-plus(2000)을 기반으로 개발하였고, 복잡한 분석보다는 모든 결과들을 이미지화 할 수 있으며 파일로 결과를 쉽게 확인할 수 있도록 디자인하였다. CAMVS(V1.0)는 전체 염색체를 각 실험별로 비교 분석하는 부분, 특정 염색체를 특정 실험별로 비교 분석하는 부분과 실험간의 차이를 통계적으로 비교 분석하는 3 가지 카테고리로 구성되어 있다. 쉬운 알고리즘과 사용의 편리함, 분석결과의 다양한 그래픽, 새로운 알고리즘 추가의 용이성 등이 CAMVS(V1.0)가 가지고 있는 장점이며, Microarray-CGH를 분석하는데 아주 유용한 분석 도구이다.

• Journal of Microbiology
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• v.41 no.2
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• pp.129-136
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• 2003

The Epstein-Barr virus (EBV)-encoded latent membrane protein 1 (LMPI) exhibits considerable sequence heterogeneity among EBV isolates. Seven distinct EBV strains have been defined based on sequence polymorphisms in the LMPI gene, which are designated China 1, China 2, China 3, Alaskan, Mediterranean, NC, and the B95-8 strains. In this study, we analyzed a 30-bp deletion and sequence variations in the carboxy-terminal region of the LMPl gene in 12 EBV isolates from spontaneous lym-phoblastoid cell lines derived from individuals with non-EBV associated cancers in Korea. Eleven of the 12 isolates showed a 30-bp deletion spanning LMPI amino acids 342 to 353, suggesting a high prevalence of the LMPI 30-bp deletion variant among EBV isolates in Korea. In addition, all 12 isolates had a 15-bp common deletion in the 33-bp repeat region and multiple base-pair changes relative to the prototype B95-8 EBV strain along with variations in the number of the 33-bp repeats. The bp changes at positions 168746, 168694, 168687, 168395, 168357, 168355, 168631, 168320, 168308, 168295, and 168225 were highly conserved among the isolates. Comparative analysis of sequence change patterns in the LMPI carboxy-terminal coding region identified nine 30-bp deletion variants as China 1, two deletion variants as a possible interstrain between the Alaskan and China 1 strains, and a single undeleted variant as a possible variant of the Alaskan strain. These results suggest the predominance of the China 1 EBV strain in the Korean population.

• Genomics & Informatics
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• v.19 no.3
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• pp.31.1-31.9
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• 2021

Leptospirosis is a zoonotic disease caused by spirochetes from the genus Leptospira. In Thailand, Leptospira interrogans is a major cause of leptospirosis. Leptospirosis patients present with a wide range of clinical manifestations from asymptomatic, mild infections to severe illness involving organ failure. For better understanding the difference between Leptospira isolates causing mild and severe leptospirosis, illumina sequencing was used to sequence genomic DNA in both serotypes. DNA of Leptospira isolated from two patients, one with mild and another with severe symptoms, were included in this study. The paired-end reads were removed adapters and trimmed with Q30 score using Trimmomatic. Trimmed reads were constructed to contigs and scaffolds using SPAdes. Cross-contamination of scaffolds was evaluated by ContEst16s. Prokka tool for bacterial annotation was used to annotate sequences from both Leptospira isolates. Predicted amino acid sequences from Prokka were searched in EggNOG and David gene ontology database to characterize gene ontology. In addition, Leptospira from mild and severe patients, that passed the criteria e-value < 10e^-5 from blastP against virulence factor database, were used to analyze with Venn diagram. From this study, we found 13 and 12 genes that were unique in the isolates from mild and severe patients, respectively. The 12 genes in the severe isolate might be virulence factor genes that affect disease severity. However, these genes should be validated in further study.

• Genomics & Informatics
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• v.20 no.3
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• pp.26.1-26.19
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• 2022

Diabetes and its related complications are associated with long term damage and failure of various organ systems. The microvascular complications of diabetes considered in this study are diabetic retinopathy, diabetic neuropathy, and diabetic nephropathy. The aim is to identify the weighted co-expressed and differentially expressed genes (DEGs), major pathways, and their miRNA, transcription factors (TFs) and drugs interacting in all the three conditions. The primary goal is to identify vital DEGs in all the three conditions. The overlapped five genes (AKT1, NFKB1, MAPK3, PDPK1, and TNF) from the DEGs and the co-expressed genes were defined as key genes, which differentially expressed in all the three cases. Then the protein-protein interaction network and gene set linkage analysis (GSLA) of key genes was performed. GSLA, gene ontology, and pathway enrichment analysis of the key genes elucidates nine major pathways in diabetes. Subsequently, we constructed the miRNA-gene and transcription factor-gene regulatory network of the five gene of interest in the nine major pathways were studied. hsa-mir-34a-5p, a major miRNA that interacted with all the five genes. RELA, FOXO3, PDX1, and SREBF1 were the TFs interacting with the major five gene of interest. Finally, drug-gene interaction network elucidates five potential drugs to treat the genes of interest. This research reveals biomarker genes, miRNA, TFs, and therapeutic drugs in the key signaling pathways, which may help us, understand the processes of all three secondary microvascular problems and aid in disease detection and management.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.135-135
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• 2017

The Gramene database (http://www.gramene.org) is a powerful online resource for agricultural researchers, plant breeders and educators that provides easy access to reference data, visualizations and analytical tools for conducting cross-species comparisons. Learn the benefits of using Gramene to enrich your lectures, accelerate your research goals, and respond to your organismal community needs. Gramene's genomes portal hosts browsers for 44 complete reference genomes, including crops and model organisms, each displaying functional annotations, gene-trees with orthologous and paralogous gene classification, and whole-genome alignments. SNP and structural diversity data, available for 11 species, are displayed in the context of gene annotation, protein domains and functional consequences on transcript structure (e.g., missense variant). Browsers from multiple species can be viewed simultaneously with links to community-driven organismal databases. Thus, while hosting the underlying data for comparative studies, the portal also provides unified access to diverse plant community resources, and the ability for communities to upload and display private data sets in multiple standard formats. Our BioMart data mining interface enable complex queries and bulk download of sequence, annotation, homology and variation data. Gramene's pathway portal, the Plant Reactome, hosts over 240 pathways curated in rice and inferred in 66 additional plant species by orthology projection. Users may compare pathways across species, query and visualize curated expression data from EMBL-EBI's Expression Atlas in the context of pathways, analyze genome-scale expression data, and conduct pathway enrichment analysis. Our integrated search database and modern user interface leverage these diverse annotations to facilitate finding genes through selecting auto-suggested filters with interactive views of the results.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.6
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• pp.27-39
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• 2019

The object of this research is to compare zoology and botany name caused by language and science differences of South and North Korea since division. Biological data are collected North Korea biological information (flora and fauna, an illustrated flora and fauna book of North Korea, Etc.) and compared both side data based on national species list of korea, National Institute of Biological Resources. We could built 3,903 species of flora and 1,487 species flora on biological database. The criteria for comparative method is 5 types (korean name difference, scientific name difference, same species, similar species, North Korea endemic species). As a results, plants were identified korean name difference (911 species), scientific name difference (614 species), same species (880 species), North Korea endemic species (1,037 species) of 3,903 species, and animals were korean name difference (685 species), scientific name difference (104 species), same species (199 species), North Korea endemic species (226 species) of the 1,492 species. This results are expected to be in application with cooperation study for recovering bioinformatics differences of South and North Korea.