Abstract: It aimed to design a technology for the faster and more accurate identification of SARS-CoV-2 through RNA barcode segments. Sequences of Beta-CoV genera and 7 HCoVs were screened to construct the sequences database based on the NCBI online database. The genetic diversity analysis, genetic distance analysis and phylogenetic tree construction of the sequences database were performed on bioinformatics methods, so as to examine the accuracy and stability of barcode segments in identifying SARS-CoV-2 in different cases. Finally, the fragments clipped from the SNP sites on sequences were scored by using the Blast on NCBI to obtain the barcode segments and visual barcodes with the optimal identification effect. Above results showed that the RNA barcode segments could precisely identify SARS-CoV-2 from all strains within the sequences database, and the two barcode segments (located in the coding regions of ORF1ab gene and S gene respectively) screened with Blast Total scores and P value tests also had good stability for identifying SARS-CoV-2. RNA barcoding contributed to explore the relationship between the whole genome sequence polymorphism of SARS-CoV-2 and the species-specific genetic markers and to provide new idea for identification technologies of SARS-CoV-2.

Key words: SARS-CoV-2, barcoding, species identification, RNA segment