  SARS-CoV-2 is an upper respiratory system RNA virus that has caused over 3
million deaths and infecting over 150 million worldwide as of May 2021. With
thousands of strains sequenced to date, SARS-CoV-2 mutations pose significant
challenges to scientists on keeping pace with vaccine development and public
health measures. Therefore, an efficient method of identifying the divergence
of lab samples from patients would greatly aid the documentation of SARS-CoV-2
genomics. In this study, we propose a neural network model that leverages
recurrent and convolutional units to directly take in amino acid sequences of
spike proteins and classify corresponding clades. We also compared our model's
performance with Bidirectional Encoder Representations from Transformers (BERT)
pre-trained on protein database. Our approach has the potential of providing a
more computationally efficient alternative to current homology based
intra-species differentiation.
