Population genomic insights into the evolution of the SARS-CoV-2 Omicron variant
Garg, K. M.; Lamba, V.; Chattopadhyay, B.
Show abstract
A thorough understanding of the patterns of population subdivision of a pathogen can prevent disease spread. For SARS-CoV-2, the availability of millions of genomes makes this task analytically challenging. Our study used population genomic methods and identified subtle subdivisions within the Omicron variant, in addition to that captured by the Pango lineage. Further, some of the identified clusters of the Omicron variant revealed statistically significant signatures of selection or expansion revealing the role of microevolutionary processes in the spread of the virus. These are crucial information for policy makers as preventive measures can be designed to mitigate further spread based on a holistic understanding of the variability of the virus and evolutionary processes aiding its spread.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Intrahost SARS-CoV-2 k-mer identification method (iSKIM) for rapid detection of mutations of concern reveals emergence of global mutation patterns 95%
- Early emergence phase of SARS-CoV-2 Delta variant in Florida 94%
- Emergence and spread of a B.1.1.28-derived P.6 lineage with Q675H and Q677H Spike mutations in Uruguay 94%
Similar papers in this journal
- Phylogenomics and population genomics of SARS-CoV-2 in Mexico reveals variants of interest (VOI) and a mutation in the Nucleocapsid protein associated with symptomatic versus asymptomatic carriers 94%
- Comparison of gene-by-gene and genome-wide short nucleotide sequence based approaches to define the global population structure of Streptococcus pneumoniae 93%
- Unusual SARS-CoV-2 intra-host diversity reveals lineages superinfection 93%
Similar papers in this journal
- Pango lineage designation and assignment using SARS-CoV-2 spike gene nucleotide sequences 94%
- Cov2clusters: genomic clustering of SARS-CoV-2 sequences 93%
- SARS-CoV-2 surveillance in Italy through phylogenomic inferences based on Hamming distances derived from functional annotations of SNPs, MNPs and InDels 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.