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CoViT: Real-time phylogenetics for the SARS-CoV-2 pandemic using Vision Transformers

by   Zuher Jahshan, et al.

Real-time viral genome detection, taxonomic classification and phylogenetic analysis are critical for efficient tracking and control of viral pandemics such as Covid-19. However, the unprecedented and still growing amounts of viral genome data create a computational bottleneck, which effectively prevents the real-time pandemic tracking. We are attempting to alleviate this bottleneck by modifying and applying Vision Transformer, a recently developed neural network model for image recognition, to taxonomic classification and placement of viral genomes, such as SARS-CoV-2. Our solution, CoViT, places newly acquired samples onto the tree of SARS-CoV-2 lineages. One of the two potential placements returned by CoVit is the true one with the probability of 99.0 probability of the correct placement to be found among five potential placements generated by CoViT is 99.8 individual genome running on NVIDIAs GeForce RTX 2080 Ti GPU. We make CoViT available to research community through GitHub:


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