Automated classification of giant virus genomes using a random forest model built on trademark protein families.

ABSTRACT: Viruses of the phylum Nucleocytoviricota, often referred to as "giant viruses," are prevalent in various environments around the globe and play significant roles in shaping eukaryotic diversity and activities in global ecosystems. Given the extensive phylogenetic diversity within this viral group and the highly complex composition of their genomes, taxonomic classification of giant viruses, particularly incomplete metagenome-assembled genomes (MAGs) can present a considerable challenge. Here we developed TIGTOG (Taxonomic Information of Giant viruses using Trademark Orthologous Groups), a machine learning-based approach to predict the taxonomic classification of novel giant virus MAGs based on profiles of protein family content. We applied a random forest algorithm to a training set

SUBMITTER: Ha AD 

PROVIDER: S-EPMC10680617 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature