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Identification of fish species through tRNA-based primer design

Authors :
Ting-Hui Wu
Cing-Han Yang
Tun-Wen Pai
Li-Ping Ho
Jen-Leih Wu
Hsin-Yiu Chou
Source :
BMC Bioinformatics, Vol 22, Iss S10, Pp 1-16 (2022)
Publication Year :
2022
Publisher :
BMC, 2022.

Abstract

Abstract 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.

Details

Language :
English
ISSN :
14712105 and 40073289
Volume :
22
Issue :
S10
Database :
Directory of Open Access Journals
Journal :
BMC Bioinformatics
Publication Type :
Academic Journal
Accession number :
edsdoj.5f9fb3be7142ff8ec4007328913e49
Document Type :
article
Full Text :
https://doi.org/10.1186/s12859-022-04717-8