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The Cano-eMLST Program: An Approach for the Calculation of Canonical Extended Multi-Locus Sequence Typing, Making Comparison of Genetic Differences Among Bunches of Bacterial Strains.
Liu, Yen-Yi; Lin, Ji-Wei; Chen, Chih-Chieh.
Afiliación
  • Liu YY; Central Regional Laboratory, Center for Diagnostics and Vaccine Development, Centers for Disease Control, Taichung 40855, Taiwan. current788@gmail.com.
  • Lin JW; Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung 80424, Taiwan. jwlin@imst.nsysu.edu.tw.
  • Chen CC; Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung 80424, Taiwan. chieh@imst.nsysu.edu.tw.
Microorganisms ; 7(4)2019 Apr 03.
Article en En | MEDLINE | ID: mdl-30987190
ABSTRACT
Extended multi-locus sequence typing (eMLST) methods have become popular in the field of genomic epidemiology. Before eMLST methods can be applied in epidemiological investigations, the selection of a suitable scheme is critical. The core genome scheme (cgMLST) has become the most popular eMLST approach for strain typing in the epidemiological domain. In addition to strain typing, many public health researchers and clinical microbiologists wish to investigate which genes cause genetic differences between compared strains. Therefore, a tool that can be used to extract canonical genes with an eMLST scheme would be particularly useful. In this study, we present cano-eMLST, a well-designed program that applies a feature-selection methodology to create a canonical locus combination with discriminatory power by traversing a genetic relatedness tree based on a user-selected scheme. The cano-eMLST program is provided mainly to help infectious disease laboratory researchers identify potential factors related to bacterial pathogenesis. The core program (tree-traversing approach) of cano-eMLST is implemented in Perl and Python. All the necessary dependencies and environmental settings are provided in the encapsulated version (VirtualBox or VMware) and self-installation version (all use source code and libraries).
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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Microorganisms Año: 2019 Tipo del documento: Article País de afiliación: Taiwán

Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Microorganisms Año: 2019 Tipo del documento: Article País de afiliación: Taiwán