CodonTransformer: a multispecies codon optimizer using context-aware neural networks

Degeneracy in the genetic code allows many possible DNA sequences to encode the same protein. Optimizing codon usage within a sequence to meet organism-specific preferences faces combinatorial explosion. Nevertheless, natural sequences optimized through evolution provide a rich source of data for machine learning algorithms to explore the underlying rules. Here, we introduce CodonTransformer, a multispecies deep learning model trained on over 1 million DNA-protein pairs from 164 organisms spanning all domains of life. The model demonstrates context-awareness thanks to its Transformers architecture and to our sequence representation strategy that combines organism, amino acid, and codon encodings.…

• IN
  Institut National de la Santé et de la Recherche Médicale
• UO
  University of Toronto
• CN
  Centre National de la Recherche Scientifique
• FB
  Fondation Bettencourt Schueller
• CI
  Canadian Institutes of Health Research
• NS
  Natural Sciences and Engineering Research Council of Canada

  Awards: RGPIN-2020, RGPIN-2020-06377
• UO
  University of Toronto Scarborough