Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning.

ABSTRACT: The COVID-19 pandemic has demonstrated the serious potential for novel zoonotic coronaviruses to emerge and cause major outbreaks. The immediate animal origin of the causative virus, SARS-CoV-2, remains unknown, a notoriously challenging task for emerging disease investigations. Coevolution with hosts leads to specific evolutionary signatures within viral genomes that can inform likely animal origins. We obtained a set of 650 spike protein and 511 whole genome nucleotide sequences from 222 and 185 viruses belonging to the family Coronaviridae, respectively. We then trained random forest models independently on genome composition biases of spike protein and whole genome sequences, including dinucleotide and codon usage biases in order to predict animal host (of nine possible categories, inc

SUBMITTER: Brierley L 

PROVIDER: S-EPMC8087038 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature