Abstract
Genome binning has been essential for characterization of bacteria, archaea, and even eukaryotes from metagenomes. Yet, few approaches exist for viruses. We developed vRhyme, a fast and precise software for construction of viral metagenome-assembled genomes (vMAGs). vRhyme utilizes single- or multi-sample coverage effect size comparisons between scaffolds and employs supervised machine learning to identify nucleotide feature similarities, which are compiled into iterations of weighted networks and refined bins. To refine bins, vRhyme utilizes unique features of viral genomes, namely a protein redundancy scoring mechanism based on the observation that viruses seldom encode redundant genes. Using simulated viromes, we displayed superior performance of vRhyme compared to available binning tools in constructing more complete and uncontaminated vMAGs. When applied to 10,601 viral scaffolds from human skin, vRhyme advanced our understanding of resident viruses, highlighted by identification of a Herelleviridae vMAG comprised of 22 scaffolds, and another vMAG encoding a nitrate reductase metabolic gene, representing near-complete genomes post-binning. vRhyme will enable a convention of binning uncultivated viral genomes and has the potential to transform metagenome-based viral ecology.