Variant evolution graph: Can we infer how SARS-CoV-2 variants are evolving?
Badhan Das, Lenwood S. Heath
TL;DR
This paper introduces a new framework to model how SARS-CoV-2 variants evolve, using a graph-based approach that captures complex evolutionary patterns and transmission pathways.
Contribution
The novel Variant Evolution Graph (VEG) framework models viral evolution with multiple ancestral pathways and identifies key evolutionary patterns like recombination and hotspots.
Findings
The VEG framework reveals evolutionary patterns such as recombination events and mutation hotspots.
The Disease Transmission Network (DTN) derived from VEG helps infer transmission pathways and super-spreaders.
VEG is computationally efficient compared to traditional phylogenetic methods like Maximum Likelihood.
Abstract
The SARS-CoV-2 virus has undergone extensive mutations over time, resulting in considerable genetic diversity among circulating strains. This diversity directly affects important viral characteristics, such as transmissibility and disease severity. During a viral outbreak, the rapid mutation rate produces a large cloud of variants, referred to as a viral quasispecies. However, many variants are lost due to the bottleneck of transmission and survival. Advances in next-generation sequencing have enabled continuous and cost-effective monitoring of viral genomes, but constructing reliable phylogenetic trees from the vast collection of sequences in GISAID (the Global Initiative on Sharing All Influenza Data) presents significant challenges. We introduce a novel graph-based framework inspired by quasispecies theory, the Variant Evolution Graph (VEG), to model viral evolution. Unlike…
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Taxonomy
TopicsGenomics and Phylogenetic Studies · Evolution and Genetic Dynamics · Machine Learning in Bioinformatics