ViRAL: Vision Transformer Based Accelerator for ReAL Time Lineage Assignment of Viral Pathogens

Abstract

Real-time genome detection, classification and lineage assignment are critical for efficient tracking of emerging mutations and variants during viral pandemics such as Covid-19. For genomic surveillance to work effectively, each new viral genome sequence must be quickly and accurately associated with an existing viral family (lineage). ViRAL is a hardware-accelerated platform for real-time viral genome lineage assignment based on minhashing and Vision Transformer. Minhashing is a locality sensitive hashing based technique for finding regions of similarity within sequenced genomes. Vision Transformer is a model for image classification that employs a Transformer-like architecture over patches of images. In ViRAL, such image patches are genome fragments extracted from the regions of high similarity. ViRAL is especially efficient in lineage assignment of extremely low quality (or highly ambiguous) genomic data, i.e. when a large fraction of DNA bases are missing in an assembled genome. We implement ViRAL on CPU, GPU and a custom-designed hardware accelerator denoted ACMI. ViRAL assigns newly sequenced SARS-CoV-2 genomes to existing lineages with the top-1 accuracy of 94.2%. The probability of the correct assignment to be found among the five most likely placements generated by ViRAL (top-5 accuracy) is 99.8%. Accelerated ViRAL outperforms the fastest state-of-the-art assignment tools by $69.4\times $ . It also outperforms ViRAL GPU implementation by $19.5\times $ . ViRAL strongly outperforms the state-of-the-art solutions in assigning highly-ambiguous genomes: while state-of-the-art tools fail to assign lineage to genomes with 50% ambiguity, ViRAL achieves 77.6% assignment accuracy. We make ViRAL available to the research community through GitHub.

Keywords

• Vision transformer
• transformers
• viral pathogens
• SARS-CoV-2
• genome
• accelerator