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Identification of fish species through tRNA-based primer design.
Wu, Ting-Hui; Yang, Cing-Han; Pai, Tun-Wen; Ho, Li-Ping; Wu, Jen-Leih; Chou, Hsin-Yiu.
Afiliación
  • Wu TH; Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan.
  • Yang CH; Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan.
  • Pai TW; Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan. twp@ntut.edu.tw.
  • Ho LP; Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan. twp@ntut.edu.tw.
  • Wu JL; Department of Aquaculture, National Penghu University of Science and Technology, Penghu, Taiwan.
  • Chou HY; Department of Bioscience and Biotechnology, National Taiwan Ocean University, Keelung, Taiwan.
BMC Bioinformatics ; 22(Suppl 10): 633, 2022 Dec 06.
Article en En | MEDLINE | ID: mdl-36474163
BACKGROUND: The correct establishment of the barcode classification system for fish can facilitate biotaxonomists to distinguish fish species, and it can help the government to verify the authenticity of the ingredients of fish products or identify unknown fish related samples. The Cytochrome c oxidation I (COI) gene sequence in the mitochondria of each species possesses unique characteristics, which has been widely used as barcodes in identifying species in recent years. Instead of using COI gene sequences for primer design, flanking tRNA segments of COI genes from 2618 complete fish mitochondrial genomes were analyzed to discover suitable primers for fish classification at taxonomic family level. The minimal number of primer sets is designed to effectively distinguish various clustered groups of fish species for identification applications. Sequence alignment analysis and cross tRNA segment comparisons were applied to check and ensure the primers for each cluster group are exclusive. RESULTS: Two approaches were applied to improve primer design and re-cluster fish species. The results have shown that exclusive primers for 2618 fish species were successfully discovered through in silico analysis. In addition, we applied sequence alignment analysis to confirm that each pair of primers can successfully identify all collected fish species at the taxonomic family levels. CONCLUSIONS: This study provided a practical strategy to discover unique primers for each fishery species and a comprehensive list of exclusive primers for extracting COI barcode sequences of all known fishery species. Various applications of verification of fish products or identification of unknown fish species could be effectively achieved.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: ARN de Transferencia Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: Taiwán

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: ARN de Transferencia Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: Taiwán