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김동혁

Kim, Donghyuk
Systems Biology and Machine Learning Lab.
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Developing the PIP-eco: An integrated genomic pipeline for identification and characterization of Escherichia coli pathotypes encompassing hybrid forms

Author(s)
Ko, SeyoungNguyen, Huynh Minh TrietLee, WoojungKim, Donghyuk
Issued Date
2024-12
DOI
10.1016/j.csbj.2024.07.017
URI
https://scholarworks.unist.ac.kr/handle/201301/83537
Citation
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, v.23, pp.3040 - 3049
Abstract
Pathogenic Escherichia coli (E. coli) strains are distinguished by their diverse virulence factors, which contribute to a wide spectrum of diseases. These pathogens evolve through the horizontal transfer of virulence factors, resulting in the emergence of hybrid pathotypes with complex and heterogeneous characteristics. Recognizing their profound impact on public health, this study introduces the PIP-eco pipeline, a comprehensive analytical tool designed for the precise identification and characterization of E. coli pathotypes. This PIP-eco pipeline advances beyond traditional molecular techniques by facilitating detailed analysis of both single and hybrid pathotypes. It integrates targeted marker gene analysis, virulence factor-based phylogenetic analysis, and pathogenicity islands (PAIs) profiling to elucidate the genetic diversity of E. coli pathotypes and support their accurate classification. This integrative approach enables PIP-eco to uncover connections among various E. coli pathotypes, highlight shared virulence factors, and provide insights into their evolutionary trajectories. By utilizing experimentally validated marker genes, the pipeline ensures robust identification of pathotypes, particularly those of hybrid pathotypes. Additionally, PAI analysis offers comprehensive genetic investigations, revealing strain-specific variations and potential virulence mechanisms. As a result, the PIP-eco pipeline emerges as a useful tool for dissecting the evolutionary dynamics of E. coli and characterizing complex pathotypes, addressing the critical need for accurate detection and understanding of hybrid pathotypes.
Publisher
ELSEVIER
ISSN
2001-0370
Keyword (Author)
Escherichia coliHybrid pathotypeClassification toolComprehensive analysis
Keyword
MULTIPLE SEQUENCE ALIGNMENTPATHOGENICITY ISLANDSVIRULENCEEVOLUTIONGENESRESISTANCESTRAINMODELPANELIBEA

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