The SARS-CoV-2-induced disease, COVID-19, remains a worldwide public health concern due to its high rate of transmission, even in vaccinated and previously infected people. In the endemic state, it continues to cause significant pathology. To elu- cidate the viral mutational changes and screen the emergence of new variants of concern, we conducted this study in Bangladesh. The viral RNA genomes extracted from 25 ran- domly collected samples of COVID-19-positive patients from March 2021 to February 2022 were sequenced using Illumina COVID Seq protocol and genomic data processing, as well as evaluations performed in DRAGEN COVID Lineage software. In this study, the percentage of Delta, Omicron, and Mauritius variants identified were 88%, 8%, and 4%, respectively. All of the 25 samples had 23,403 A>G (D614G, S gene), 3037 C>T (nsp3), and 14,408 C>T (nsp12) mutations, where 23,403 A>G was responsible for increased transmis- sion. Omicron had the highest number of unique mutations in the spike protein (i.e., sub- stitutions, deletions, and insertions), which may explain its higher transmissibility and immune-evading ability than Delta. A total of 779 mutations were identified, where 691 substitutions, 85 deletions, and 3 insertion mutations were observed. To sum up, our study will enrich the genomic database of SARS-CoV-2, aiding in treatment strategies along with understanding the virus’s preferences in both mutation type and mutation site for predicting newly emerged viruses’ survival strategies and thus for preparing to coun- teract them.

The study aimed to identify the variants of SARS-CoV-2 (Severe Acute Respiratory Syndrome related coronavirus-2) virus isolates within the window of March 2021 to February 2022 in Bangladesh and investigate their comparative mutational profiles, preferences and phylogenetics. After the collection of the sample specimen and RNA extraction, the genome was sequenced using Illumina COVID Seq, and NGS data analysis was performed in DRAGEN COVID Lineage software (version 3.5.9). Among the 96 virus isolates, 24 (25%) were from Delta (clade 21A (n = 21) and 21J (n = 3)) and 72 (75%) were from Omicron (clade 20A (n = 6) and 20B (n = 66)). In Omicron and Delta, substitutions were much higher than deletions and insertions. High-frequency nucleotide change patterns were similar (for C > T, and A > G) in both of the variants, but different in some (i.e., G > T, G > A). Preferences for specific amino acids over the other amino acids in substitutions and deletions were observed to vary in different proteins of these variants. Phylogenetic analysis showed that the most ancestral variants were from clade 21A and clade 20A, and then the other variants emerged. The study demonstrates noteworthy variations of Omicron and Delta in mutational pattern and preferences for amino acids and protein, and further study on their biological functional impact might unveil the reason behind their mutational strategies and behavioral changes.

The SARS-CoV-2 induced disease, COVID-19 is a worldwide public health concern with a rapid transmission, pathogenicity, and mortality rate. To elucidate the viral mutational changes and screening the emergence of new variants of concern, we conducted this study in Bangladesh. The viral RNA genome was sequenced using Illumina COVID Seq protocol and genomic data processing and evaluation was performed in DRAGEN COVID Lineage software. Variant analysis revealed that the number of Delta, Omicron and Mauritius variants identified were 88%, 8%, and 4%, respectively. All of the 25 samples had 23403A>G (D614G, S gene), 3037 C>T (nsp3) and 14408 C>T (nsp12) mutations, where 23403 A>G was responsible for increased transmission. Omicron had the highest number of unique mutations in spike protein (i.e., substitutions, deletions and insertions) which may explain its more transmissibility and immune evading ability than Delta. A total of 779 mutations were identified where 691 substitution, 85 deletion and 3 insertion mutations were observed. To sum up, our study will enrich the genomic database of SARS-CoV-2, aiding treatment strategy along with understanding the virus’s preferences in both mutation type and site for predicting newly emerged virus’s survival strategy and thus for being prepared to counteract them.