Predicting viral host codon fitness and path shifting through tree-based learning on codon usage biases and genomic characteristics.

ABSTRACT: Viral codon fitness (VCF) of the host and the VCF shifting has seldom been studied under quantitative measurements, although they could be concepts vital to understand pathogen epidemiology. This study demonstrates that the relative synonymous codon usage (RSCU) of virus genomes together with other genomic properties are predictive of virus host codon fitness through tree-based machine learning. Statistical analysis on the RSCU data matrix also revealed that the wobble position of the virus codons is critically important for the host codon fitness distinction. As the trained models can well characterise the host codon fitness of the viruses, the frequency and other details stored at the leaf nodes of these models can be reliably translated into human virus codon fitness score (HVCF score)

SUBMITTER: Su S 

PROVIDER: S-EPMC11986112 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature