Viromes vs. mixed community metagenomes: choice of method dictates interpretation of viral community ecology

Viruses, the majority of which are uncultivated, are among the most abundant biological entities in microbiomes and ecosystems on Earth. From selfishly altering community-wide microbial physiology to driving microbiome dynamics, viruses (particularly bacteriophage) are fundamental members of microbiomes. While the number of studies leveraging viral metagenomics (viromics) for studying uncultivated viruses is growing, standards for viromics research are lacking. Viromics can leverage computational discovery of viruses from total metagenomes of all community members (hereafter metagenomes) or use physical separation of virus-specific fractions (hereafter viromes). However, differences in the recovery and interpretation of viruses from metagenomes and viromes obtained from the same samples remains understudied. Here, we compare viral communities from paired viromes and metagenomes obtained from 51 diverse samples across human gut, soil, freshwater, and marine ecosystems. Overall, viral communities obtained from viromes were more abundant and species rich than those obtained from metagenomes, although there were some exceptions. Despite this, metagenomes still contained many viral genomes not detected in viromes. We also found notable differences in the predicted lytic state of viruses detected in viromes vs. metagenomes at the time of sequencing. Other forms of variation observed include genome presence/absence, genome quality, and encoded protein content between viromes and metagenomes, but the magnitude of these differences varied by environment. Overall, our results here show that one’s choice of method can lead to differing interpretations of viral community ecology. We suggest that the choice of whether to target a metagenome or virome should be dependent on the environmental context and ecological questions being asked. However, our overall recommendation to researchers investigating viral ecology and evolution is to pair both approaches to maximize their respective benefits.