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Abstract Soil microorganisms are pivotal in the global carbon cycle, but the viruses that affect them and their impact on ecosystems are less understood. In this study, we explored the diversity, dynamics, and ecology of soil viruses through 379 metagenomes collected annually from 2010 to 2017. These samples spanned the seasonally thawed active layer of a permafrost thaw gradient, which included palsa, bog, and fen habitats. We identified 5051 virus operational taxonomic units (vOTUs), doubling the known viruses for this site. These vOTUs were largely ephemeral within habitats, suggesting a turnover at the vOTU level from year to year. While the diversity varied by thaw stage and depth‐related patterns were specific to each habitat, the virus communities did not significantly change over time. The abundance ratios of virus to host at the phylum level did not show consistent trends across the thaw gradient, depth, or time. To assess potential ecosystem impacts, we predicted hostsin silicoand found viruses linked to microbial lineages involved in the carbon cycle, such as methanotrophy and methanogenesis. This included the identification of viruses ofCandidatusMethanoflorens, a significant global methane contributor. We also detected a variety of potential auxiliary metabolic genes, including 24 carbon‐degrading glycoside hydrolases, six of which are uniquely terrestrial. In conclusion, these long‐term observations enhance our understanding of soil viruses in the context of climate‐relevant processes and provide opportunities to explore their role in terrestrial carbon cycling. 

Abstract The marine picoeukaryote Bathycoccus prasinos has been considered a cosmopolitan alga, although recent studies indicate two ecotypes exist, Clade BI ( B. prasinos ) and Clade BII. Viruses that infect Bathycoccus Clade BI are known (BpVs), but not that infect BII. We isolated three dsDNA prasinoviruses from the Sargasso Sea against Clade BII isolate RCC716. The BII-Vs do not infect BI, and two (BII-V2 and BII-V3) have larger genomes (~210 kb) than BI-Viruses and BII-V1. BII-Vs share ~90% of their proteins, and between 65% to 83% of their proteins with sequenced BpVs. Phylogenomic reconstructions and PolB analyses establish close-relatedness of BII-V2 and BII-V3, yet BII-V2 has 10-fold higher infectivity and induces greater mortality on host isolate RCC716. BII-V1 is more distant, has a shorter latent period, and infects both available BII isolates, RCC716 and RCC715, while BII-V2 and BII-V3 do not exhibit productive infection of the latter in our experiments. Global metagenome analyses show Clade BI and BII algal relative abundances correlate positively with their respective viruses. The distributions delineate BI/BpVs as occupying lower temperature mesotrophic and coastal systems, whereas BII/BII-Vs occupy warmer temperature, higher salinity ecosystems. Accordingly, with molecular diagnostic support, we name Clade BII Bathycoccus calidus sp. nov. and propose that molecular diversity within this new species likely connects to the differentiated host-virus dynamics observed in our time course experiments. Overall, the tightly linked biogeography of Bathycoccus host and virus clades observed herein supports species-level host specificity, with strain-level variations in infection parameters. 

Background Viruses influence global patterns of microbial diversity and nutrient cycles. Though viral metagenomics (viromics), specifically targeting dsDNA viruses, has been critical for revealing viral roles across diverse ecosystems, its analyses differ in many ways from those used for microbes. To date, viromics benchmarking has covered read pre-processing, assembly, relative abundance, read mapping thresholds and diversity estimation, but other steps would benefit from benchmarking and standardization. Here we use in silico-generated datasets and an extensive literature survey to evaluate and highlight how dataset composition (i.e., viromes vs bulk metagenomes) and assembly fragmentation impact (i) viral contig identification tool, (ii) virus taxonomic classification, and (iii) identification and curation of auxiliary metabolic genes (AMGs). Results The in silico benchmarking of five commonly used virus identification tools show that gene-content-based tools consistently performed well for long (≥3 kbp) contigs, while k -mer- and blast-based tools were uniquely able to detect viruses from short (≤3 kbp) contigs. Notably, however, the performance increase of k -mer- and blast-based tools for short contigs was obtained at the cost of increased false positives (sometimes up to ∼5% for virome and ∼75% bulk samples), particularly when eukaryotic or mobile genetic element sequences were included in the test datasets. For viral classification, variously sized genome fragments were assessed using gene-sharing network analytics to quantify drop-offs in taxonomic assignments, which revealed correct assignations ranging from ∼95% (whole genomes) down to ∼80% (3 kbp sized genome fragments). A similar trend was also observed for other viral classification tools such as VPF-class, ViPTree and VIRIDIC, suggesting that caution is warranted when classifying short genome fragments and not full genomes. Finally, we highlight how fragmented assemblies can lead to erroneous identification of AMGs and outline a best-practices workflow to curate candidate AMGs in viral genomes assembled from metagenomes. Conclusion Together, these benchmarking experiments and annotation guidelines should aid researchers seeking to best detect, classify, and characterize the myriad viruses ‘hidden’ in diverse sequence datasets.