Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the pathogen responsible for coronavirus disease 2019(COVID-19),continues to evolve,giving rise to more variants and global reinfections.Previous research ha...Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the pathogen responsible for coronavirus disease 2019(COVID-19),continues to evolve,giving rise to more variants and global reinfections.Previous research has demonstrated that barcode segments can effectively and cost-efficiently identify specific species within closely related populations.In this study,we designed and tested RNA barcode segments based on genetic evolutionary relationships to facilitate the efficient and accurate identification of SARS-CoV-2 from extensive virus samples,including human coronaviruses(HCoVs)and SARSr-CoV-2 lineages.Nucleotide sequences sourced from NCBI and GISAID were meticulously selected and curated to construct training sets,encompassing 1733 complete genome sequences of HCoVs and SARSr-CoV-2 lineages.Through genetic-level species testing,we validated the accuracy and reliability of the barcode segments for identifying SARS-CoV-2.Subsequently,75 main and subordinate species-specific barcode segments for SARS-CoV-2,located in ORF1ab,S,E,ORF7a,and N coding sequences,were intercepted and screened based on single-nucleotide polymorphism sites and weighted scores.Post-testing,these segments exhibited high recall rates(nearly 100%),specificity(almost 30%at the nucleotide level),and precision(100%)performance on identification.They were eventually visualized using one and two-dimensional combined barcodes and deposited in an online database(http://virusbarcodedatabase.top/).The successful integration of barcoding technology in SARS-CoV-2 identification provides valuable insights for future studies involving complete genome sequence polymorphism analysis.Moreover,this cost-effective and efficient identification approach also provides valuable reference for future research endeavors related to virus surveillance.展开更多
基金supported by grants from Key Research&Development Project of Nanhua Biomedical Co.,Ltd.(No.H202191490139)National Natural Science Foundation of China(No.31872866)+1 种基金China Postdoctoral Science Foundation(Nos.2021M701160 and 2022M721101)Funds of Hunan university(521119400156).
文摘Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the pathogen responsible for coronavirus disease 2019(COVID-19),continues to evolve,giving rise to more variants and global reinfections.Previous research has demonstrated that barcode segments can effectively and cost-efficiently identify specific species within closely related populations.In this study,we designed and tested RNA barcode segments based on genetic evolutionary relationships to facilitate the efficient and accurate identification of SARS-CoV-2 from extensive virus samples,including human coronaviruses(HCoVs)and SARSr-CoV-2 lineages.Nucleotide sequences sourced from NCBI and GISAID were meticulously selected and curated to construct training sets,encompassing 1733 complete genome sequences of HCoVs and SARSr-CoV-2 lineages.Through genetic-level species testing,we validated the accuracy and reliability of the barcode segments for identifying SARS-CoV-2.Subsequently,75 main and subordinate species-specific barcode segments for SARS-CoV-2,located in ORF1ab,S,E,ORF7a,and N coding sequences,were intercepted and screened based on single-nucleotide polymorphism sites and weighted scores.Post-testing,these segments exhibited high recall rates(nearly 100%),specificity(almost 30%at the nucleotide level),and precision(100%)performance on identification.They were eventually visualized using one and two-dimensional combined barcodes and deposited in an online database(http://virusbarcodedatabase.top/).The successful integration of barcoding technology in SARS-CoV-2 identification provides valuable insights for future studies involving complete genome sequence polymorphism analysis.Moreover,this cost-effective and efficient identification approach also provides valuable reference for future research endeavors related to virus surveillance.
