IDbSV: An Open-Access Repository for Monitoring SARS-CoV-2 Variations and Evolution

Genetic diversity and evolutionary dynamics of the Omicron variant of SARS-CoV-2 in Morocco

Among the numerous variants of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) that have been reported worldwide, the emergence of the Omicron variant has drastically changed the landscape of the coronavirus disease (COVID-19) pandemic. Here, we analyzed the genetic diversity of Moroccan SARS-CoV-2 genomes with a focus on Omicron variant after one year of its detection in Morocco in order to understand its genomic dynamics, features and its potential introduction sources. From 937 Omicron genomes, we identified a total of 999 non-unique mutations distributed across 92 Omicron lineages, of which 13 were specific to the country. Our findings suggest multiple introductory sources of the Omicron variant to Morocco. In addition, we found that four Omicron clades are more infectious in comparison to other Omicron clades. Remarkably, a clade of Omicron is particularly more transmissible and has become the dominant variant worldwide. Moreover, our assessment of Receptor-Binding Domain (RBD) mutations showed that the Spike K444T and N460K mutations enabled a clade higher ability of immune vaccine escape. In conclusion, our analysis highlights the unique genetic diversity of the Omicron variant in Moroccan SARS-CoV-2 genomes, with multiple introductory sources and the emergence of highly transmissible clades. The distinctiveness of the Moroccan strains compared to global ones underscores the importance of ongoing surveillance and understanding of local genomic dynamics for effective response strategies in the evolving COVID-19 pandemic.

Temporal Dynamics and Genomic Landscape of SARS-CoV-2 After Four Years of Evolution

Introduction Since its emergence, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has undergone extensive genomic evolution, impacting public health policies, diagnosis, medication, and vaccine development. This study leverages advanced bioinformatics to assess the virus's temporal and regional genomic evolution from December 2019 to October 2023. Methods Our analysis incorporates 16,575 complete SARS-CoV-2 sequences collected from 214 countries. These samples were comparatively analyzed, with a detailed characterization of nucleic mutations, lineages, distribution, and evolutionary patterns during each year, using the Wuhan-Hu-1 strain as the reference. Results Our analysis has identified a total of 21,580 mutations that we classified into transient mutations, which diminished over time, and persistent mutations with steadily increasing frequencies. This mutation landscape led to a notable surge in the evolutionary rate, rising from 13 mutations per sample in 2020 to 96 by 2023, with minor geographic variations. The phylogenetic analysis unveiled three distinct evolutionary branches, each representing unique viral evolution pathways. These lineages exhibited a tendency for a reduced duration of dominance with a shortening prevalence period over time, as dominant strains were consistently replaced by more fit variants. Notably, the emergence of the Alpha and Delta variants in 2021 was followed by the subsequent dominance of Omicron clade variants that have branched into several recombinant variants in 2022, marking a significant shift in the viral landscape. Conclusion This study sheds light on the dynamic nature of SARS-CoV-2 evolution, emphasizing the importance of continuous and vigilant genomic surveillance. The dominance of recombinant lineages, coupled with the disappearance of local variants, underscores the virus's adaptability.

Applying the digital data and the bioinformatics tools in SARS-CoV-2 research

Bioinformatics has been playing a crucial role in the scientific progress to fight against the pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The advances in novel algorithms, mega data technology, artificial intelligence and deep learning assisted the development of novel bioinformatics tools to analyze daily increasing SARS-CoV-2 data in the past years. These tools were applied in genomic analyses, evolutionary tracking, epidemiological analyses, protein structure interpretation, studies in virus-host interaction and clinical performance. To promote the in-silico analysis in the future, we conducted a review which summarized the databases, web services and software applied in SARS-CoV-2 research. Those digital resources applied in SARS-CoV-2 research may also potentially contribute to the research in other coronavirus and non-coronavirus viruses.