• Journal of Acupuncture Research
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• 제37권2호
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• pp.128-135
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• 2020

Background: Classical acupuncture is being used in the treatment of rheumatoid arthritis (RA). To explore the biological response to acupuncture, a network-based analysis was performed on gene expression data collected from an animal model of RA treated with acupuncture. Methods: Gene expression data were obtained from published microarray studies on blood samples from rats with collagen induced arthritis (CIA) and non-CIA rats, both treated with manual acupuncture. The weighted gene co-expression network analysis was performed to identify gene clusters expressed in association with acupuncture treatment time and RA status. Gene ontology and pathway analyses were applied for functional annotation and network visualization. Results: A cluster of 347 genes were identified that differentially downregulated expression in association with acupuncture treatment over time; specifically in rats with CIA with module-RA correlation at 1 hour after acupuncture (-0.27; p < 0.001) and at 34 days after acupuncture (-0.33; p < 0.001). Functional annotation showed highly significant enrichment of porphyrin-containing compound biosynthetic processes (p < 0.001). The network-based analysis also identified a module of 140 genes differentially expressed between CIA and non-CIA in rats (p < 0.001). This cluster of genes was enriched for antigen processing and presentation of exogenous peptide antigen (p < 0.001). Other functional gene clusters previously reported in earlier studies were also observed. Conclusion: The identified gene expression networks and their hub-genes could help with the understanding of mechanisms involved in the pathogenesis of RA, as well understanding the effects of acupuncture treatment of RA.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2000년도 International Symposium on Bioinformatics
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• pp.56-58
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• 2000

Post-genomics may be defined in different ways depending on how one views the challenges after the genome. A popular view is to follow the concept of the central dogma in molecular biology, namely from genome to transcriptome to proteome. Projects are going on to analyze gene expression profiles both at the mRNA and protein levels and to catalog protein 3D structure families, which will no doubt help the understanding of information in the genome. However complete, such catalogs of genes, RNAs, and proteins only tell us about the building blocks of life. They do not tell us much about the wiring (interaction) of building blocks, which is essential for uncovering systemic functional behaviors of the cell or the organism. Thus, an alternative view of post-genomics is to go up from the molecular level to the cellular level, and to understand, what I call, the "interactome"or a complete picture of molecular interactions in the cell. KEGG (http://www.genome.ad.jp/kegg/) is our attempt to computerize current knowledge on various cellular processes as a collection of "generalized"protein-protein interaction networks, to develop new graph-based algorithms for predicting such networks from the genome information, and to actually reconstruct the interactomes for all the completely sequenced genomes and some partial genomes. During the reconstruction process, it becomes readily apparent that certain pathways and molecular complexes are present or absent in each organism, indicating modular structures of the interactome. In addition, the reconstruction uncovers missing components in an otherwise complete pathway or complex, which may result from misannotation of the genome or misrepresentation of the KEGG pathway. When combined with additional experimental data on protein-protein interactions, such as by yeast two-hybrid systems, the reconstruction possibly uncovers unknown partners for a particular pathway or complex. Thus, the reconstruction is tightly coupled with the annotation of individual genes, which is maintained in the GENES database in KEGG. We are also trying to expand our literature surrey to include in the GENES database most up-to-date information about gene functions.

• Journal of Microbiology and Biotechnology
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• 제19권2호
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• pp.140-146
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• 2009

MAPSI(Management and Analysis for Polyketide Synthase Type I) has been developed to offer computational analysis methods to detect type I PKS(polyketide synthase) gene clusters in genome sequences. MAPSI provides a genome analysis component, which detects PKS gene clusters by identifying domains in proteins of a genome. MAPSI also contains databases on polyketides and genome annotation data, as well as analytic components such as new PKS assembly and domain analysis. The polyketide data and analysis component are accessible through Web interfaces and are displayed with diverse information. MAPSI, which was developed to aid researchers studying type I polyketides, provides diverse components to access and analyze polyketide information and should become a very powerful computational tool for polyketide research. The system can be extended through further studies of factors related to the biological activities of polyketides.

• Journal of Information Processing Systems
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• 제12권1호
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• pp.1-34
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• 2016

Gene identification is at the center of genomic studies. Although the first phase of the Encyclopedia of DNA Elements (ENCODE) project has been claimed to be complete, the annotation of the functional elements is far from being so. Computational methods in gene identification continue to play important roles in this area and other relevant issues. So far, a lot of work has been performed on this area, and a plethora of computational methods and avenues have been developed. Many review papers have summarized these methods and other related work. However, most of them focus on the methodologies from a particular aspect or perspective. Different from these existing bodies of research, this paper aims to comprehensively summarize the mainstream computational methods in gene identification and tries to provide a short but concise technical reference for future studies. Moreover, this review sheds light on the emerging trends and cutting-edge techniques that are believed to be capable of leading the research on this field in the future.

• 전자공학회논문지CI
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• 제45권4호
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• pp.19-26
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• 2008

Gene set enrichment analysis (GSEA)는 두 개의 클래스를 가지는 마이크로어레이 실험 데이터 분석을 위해 생물학적 특징을 기반으로 구성된 다양한 유전자-집합 중에서 두 클래스의 발현값들이 통계적으로 중요한 차이를 나타내는 유의한 유전자-집합을 추출하기 위한 분석 방법이다. 특히, 유전자에 대한 다양한 생물학적인 정보를 지닌 유전자 주석 데이터베이스(Cytogenetic Band, KEGG pathway, Gene Ontology 등)를 이용하여 마이크로어레이 실험에 사용된 전체 유전자 중 특정 기능을 가지는 유전자들을 그룹화하여 다양한 유전자-집합을 발굴하고, 각 유전자-집합 내에서 두 클래스간에 발현값의 차이를 참조하여 유의한 유전자들을 결정하여, 이를 기반으로 통계적으로 유의한 유전자-집합들을 최종 검출하는 방법이다. 본 논문에서는 GSEA 분석 과정에서 현재 주로 사용되고 있는 signal-to-noise ratio 기반 유전자 서열화(gene ranking) 방법 대신에, Fisher criterion을 이용한 유전자 서열화 방법을 적용함으로써 기존의 GSEA 방법에서 추출하지 못한 생물학적으로 의미 있는 새로운 유의 유전자-집합을 추출하는 방법을 제안하고자 한다. 또한, 제안한 방법의 성능을 고찰하기 위하여 공개된 Leukemia 관련 마이크로어레이 실험 데이터 분석에 적용하였으며, 기존의 알려진 결과와 비교 분석함으로써 제안한 방법의 유용성을 검증하고자 하였다.

• Genomics & Informatics
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• 제19권3호
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• pp.27.1-27.4
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• 2021

Due to the rapid evolution of high-throughput technologies, a tremendous amount of data is being produced in the biological domain, which poses a challenging task for information extraction and natural language understanding. Biological named entity recognition (NER) and named entity normalisation (NEN) are two common tasks aiming at identifying and linking biologically important entities such as genes or gene products mentioned in the literature to biological databases. In this paper, we present an updated version of OryzaGP, a gene and protein dataset for rice species created to help natural language processing (NLP) tools in processing NER and NEN tasks. To create the dataset, we selected more than 15,000 abstracts associated with articles previously curated for rice genes. We developed four dictionaries of gene and protein names associated with database identifiers. We used these dictionaries to annotate the dataset. We also annotated the dataset using pretrained NLP models. Finally, we analysed the annotation results and discussed how to improve OryzaGP.

• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2003년도 추계학술발표대회(상)
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• pp.355-358
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• 2003

최근 바이오인포매틱스 분야에서는 유전자 주석정보 데이터들의 통합 방안에 대한 논의가 활발하게 진행 중에 있다. 본 논문에서는 BioDAS의 웹 서비스 개념을 이용, 분산된 주석 데이터서버들간의 통합검색 시스템을 구축함으로써 메타검색 시스템을 구현하였다. 본 시스템은 사용자에게 메타검색 기능 및 결과 저장기능을 제공해 주며 외부 사용자에게 웹 서비스를 제공한다.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2019년도 추계학술대회
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• pp.157-157
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• 2019

• 한국미생물·생명공학회지
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• 제46권3호
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• pp.300-312
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• 2018

Whole-genome sequencing of the wood-rotting fungus, Flammulina fennae, was carried out to identify carbohydrate-active enzymes (CAZymes). De novo genome assembly (31 kmer) of short reads by next-generation sequencing revealed a total genome length of 32,423,623 base pairs (39% GC). A total of 11,591 gene models in the assembled genome sequence of F. fennae were predicted by ab initio gene prediction using the AUGUSTUS tool. In a genome-wide comparison, 6,715 orthologous groups shared at least one gene with F. fennae and 10,667 (92%) of 11,591 genes for F. fennae proteins had orthologs among the Dikarya. Additionally, F. fennae contained 23 species-specific genes, of which 16 were paralogous. CAZyme identification and annotation revealed 513 CAZymes, including 82 auxiliary activities, 220 glycoside hydrolases, 85 glycosyltransferases, 20 polysaccharide lyases, 57 carbohydrate esterases, and 45 carbohydrate binding-modules in the F. fennae genome. The genome information of F. fennae increases the understanding of this basidiomycete fungus. CAZyme gene information will be useful for detailed studies of lignocellulosic biomass degradation for biotechnological and industrial applications.

• 생명과학회지
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• 제16권3호
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• pp.472-476
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• 2006

공개된 데이터베이스들에서 말에 대한 생물학적인 데이터와 지놈 데이터를 분석하여 말 데이터베이스를 구축하였다. 말 데이터베이스는 말의 생물학적인 데이터와 지놈 데이터를 생물정보학적인 분석방법으로 분석하고 이들 데이터를 통합하여 제공하는데 목적을 두고 있다. 본 데이터베이스는 말의 생물학적 데이터와 지놈 분석 데이터 그리고 생물정보학적인 분석프로그램을 제공하는 인터페이스로 구성하였다. 또한 사용자의 편의를 돕기 위해서 쉽게 이용할 수 있도록 웹 메뉴를 구성 하였으며 말에 대한 다양한 정보를 제공할 수 있게 하였다. 말 데이터베이스를 이용할 수 있는 웹 주소는 http://www.primate.or.kr/horse이다.