This paper presents standards and best practices for reporting genome sequences of uncultivated viruses.Supplementary informationThe online version of this article (doi:10.1038/nbt.4306) contains supplementary material, which is available to authorized users.

Background. Viral metagenomics (viromics) is increasingly used to obtain uncultivated viral genomes, evaluate community diversity, and assess ecological hypotheses. While viromic experimental methods are relatively mature and widely accepted by the research community, robust bioinformatics standards remain to be established. Here we used in silico mock viral communities to evaluate the viromic sequence-to-ecologicalinference pipeline, including (i) read pre-processing and metagenome assembly, (ii) thresholds applied to estimate viral relative abundances based on read mapping to assembled contigs, and (iii) normalization methods applied to the matrix of viral relative abundances for alpha and beta diversity estimates. Results. Tools specifically designed for metagenomes, specifically metaSPAdes, MEGAHIT, and IDBA-UD, were the most effective at assembling viromes. Read pre-processing, such as partitioning, had virtually no impact on assembly output, but may be useful when hardware is limited. Viral populations with 2-5× coverage typically assembled well, whereas lesser coverage led to fragmented assembly. Strain heterogeneity within populations hampered assembly, especially when strains were closely related (average nucleotide identity, or ANI ≥97%) and when the most abundant strain represented <50% of the population. Viral community composition assessments based on read recruitment were generally accurate when the following thresholds for detection were applied: (i) ≥10 kb contig lengths to define populations, (ii) coverage defined from reads mapping at ≥90% identity, and (iii) ≥75% of contig length with

This work is part of a 10-year project to examine thawing permafrost peatlands and is the first virome-particle-based approach to characterize viruses in these systems. This method yielded >2-fold-more viral populations (vOTUs) per gigabase of metagenome than vOTUs derived from bulk-soil metagenomes from the same site (J. B. Emerson, S. Roux, J. R. Brum, B. Bolduc, et al., Nat Microbiol 3:870–880, 2018, https://doi.org/10.1038/s41564-018-0190-y). We compared the ecology of the recovered vOTUs along a permafrost thaw gradient and found (i) habitat specificity, (ii) a shift in viral community identity from soil-like to aquatic-like viruses, (iii) infection of dominant microbial hosts, and (iv) carriage of host metabolic genes. These vOTUs can impact ecosystem carbon processing via top-down (inferred from lysing dominant microbial hosts) and bottom-up (inferred from carriage of auxiliary metabolic genes) controls. This work serves as a foundation which future studies can build upon to increase our understanding of the soil virosphere and how viruses affect soil ecosystem services.

Background. Viral metagenomics (viromics) is increasingly used to obtain uncultivated viral genomes, evaluate community diversity, and assess ecological hypotheses. While viromic experimental methods are relatively mature and widely accepted by the research community, robust bioinformatics standards remain to be established. Here we used in silico mock viral communities to evaluate the viromic sequence-to-ecological-inference pipeline, including (i) read pre-processing and metagenome assembly, (ii) thresholds applied to estimate viral relative abundances based on read mapping to assembled contigs, and (iii) normalization methods applied to the matrix of viral relative abundances for alpha and beta diversity estimates. Results. Tools specifically designed for metagenomes, specifically metaSPAdes and MEGAHIT, were the most effective at assembling viromes. Read pre-processing, such as partitioning, had virtually no impact on assembly output, but may be useful when hardware is limited. Viral populations with 2–5x coverage typically assembled well, whereas lesser coverage led to fragmented assembly. Strain heterogeneity within populations hampered assembly, especially when strains were closely related (average nucleotide identity, or ANI ≥ 97%) and when the most abundant strain represented < 50% of the population. Viral community composition assessments based on read recruitment were generally accurate when the following thresholds for detection were applied: (i) ≥ 10kb contig lengths to define populations, (ii) coverage defined from reads mapping at ≥ 90% identity, and (ii) ≥ 75% of contig length with ≥ 1x coverage. Finally, although data are limited to the most abundant viruses in a community, alpha and beta diversity patterns were robustly estimated (±10%) when comparing samples of similar sequencing depth, but more divergent (up to 80%) when sequencing depth was uneven across the dataset. In the latter cases, the use of normalization methods specifically developed for metagenomes provided the best estimates. Conclusions. These simulations provide benchmarks for selecting analysis cut-offs and establish that an optimized sample-to-ecological-inference viromics pipeline is robust for making ecological inferences from natural viral communities. Continued development to better accessing RNA, rare, and/or diverse viral populations and improved reference viral genome availability will alleviate many of viromics remaining limitations.

As abundant members of microbial communities, viruses impact microbial mortality, carbon and nutrient cycling, and food web dynamics. Although most of our information about viral communities comes from marine systems, evidence is mounting to suggest that viruses are similarly important in soil. Here I outline soil viral metagenomic approaches and the current state of soil viral ecology as a field, and then I highlight existing knowledge gaps that we can begin to fill. We are poised to elucidate soil viral contributions to terrestrial ecosystem processes, considering: the full suite of potential hosts across trophic scales, the ecological impacts of different viral replication strategies, links to economically relevant outcomes like crop productivity, and measurable in situ virus-host population dynamics across spatiotemporal scales and environmental conditions. Soon, we will learn how soil viruses contribute to food webs linked to organic matter decomposition, carbon and nutrient cycling, greenhouse gas emissions, and agricultural productivity.