Modeling Global and local Codon Bias with Deep Language Models

Abstract

Codon bias, the usage patterns of synonymous codons for encoding a protein sequence as nucleotides, is a biological phenomenon that is not fully understood. Several methods exist to represent the codon bias of an organism: codon adaptation index (CAI) [1], individual codon usage (ICU), hidden stop codons (HSC) [2] and codon context (CC) [3]. These methods are often employed in the optimization of heterologous gene expression to increase the accuracy and rate of translation. They, however, have many shortcomings as they dont take into account the local and global context of a gene. We present a method for modeling global and local codon bias through deep language models that is more robust than current methods by providing more contextual information and long-range dependencies.