iVirus: facilitating new insights in viral ecology with software and community datasets imbedded in a cyberinfrastructure

Abstract

Microbes impact nutrient and energy transformations throughout the world’s ecosystems, yet they do so under viral constraints. In complex communities, viral metagenome (virome) sequencing is transforming our ability to quantify viral diversity and impacts. While some bottlenecks, e.g., few reference genomes and non-quantitative viromics, have been overcome, the void of centralized datasets and specialized tools now prevents viromics from being broadly applied to answer fundamental ecological questions. Here we present iVirus, a community resource that leverages the CyVerse cyberinfrastructure to provide access to viromic tools and datasets. The iVirus Data Commons contains both raw and processed data from 1866 samples and 73 projects derived from global ocean expeditions, as well as existing and legacy public repositories. Through the CyVerse Discovery Environment, users can interrogate these datasets using existing analytical tools (software applications known as “Apps”) for assembly, ORF prediction, and annotation, as well as several new Apps specifically developed for analyzing viromes. Because Apps are web-based and powered by CyVerse super-computing resources, they enable scalable analyses for a broad user base. Finally, a use-case scenario documents how to apply these advances towards new data. This growing iVirus resource should help researchers utilize viromics as yet another tool to elucidate viral roles in nature.

Glacier ice archives nearly 15,000-year-old microbes and phages

“…All metagenomic analyses were supported by the Ohio Supercomputer Center. Viromic sequence data was processed using iVirus pipeline with default parameters described previously [35,137]. Briefly, raw reads of three viromes, including two glacier ice samples (D25 and D49) and the River water control (RiverV), were filtered for quality using Trimmomatic v0.36 [138], followed by the assembly using metaSPAdes v3.11.1 (k-mer values include 21, 33, and 55) [139], and the prediction of viral contigs using VirSorter v1.0.3 in virome decontamination mode on CyVerse [65].…”

“…The longest contig within each vOTU was selected as the seed sequence to represent that vOTU. A coverage table of each vOTU was generated using iVirus BowtieBatch and Read2RefMapper tools by mapping quality-controlled reads to vOTUs, and the resulting coverage depths were normalized by library size to "coverage per gigabase of virome" [137]. Rarefaction curves of the two glacier ice viromes were produced by estimating vOTU (length ≥10 kb) numbers along sequencing depth (i.e., read number), which was obtained by subsampling quality-controlled reads (Additional file 2: Fig.…”

“…To explore the geographic distribution of glacier viruses, the genome fragments of 33 vOTUs were used as baits to recruit reads from 225 previously published viromes from a wide range of environments including global oceans (145 viromes of GOV 2.0) [66], Arctic sea ice and ancient permafrost brine (cryopeg) [42], soils [72,73], lakes [74,75], deserts [76][77][78][79], air [80,81], cryoconite [40], and Greenland ice sheet [40]. The coverage of all vOTUs in each environmental virome was calculated as described above using iVirus BowtieBatch and Read2-RefMapper tools [137]. None of the 33 vOTUs were detected from any of these viromes.…”

Viral ecology comes of age

“…Engagement with models and modelers will help disentangle the ecologically relevant signals to better understand how microbial viruses impact ecosystems. The blueprint for the former is already playing out in the democratization of virus ecology specific community tools and database development (Wommack et al ., ; Roux et al ., ; Bolduc et al ., ; Kindler et al ., ). Similarly, collective efforts to quantitatively model virus–microbe interactions at scales from molecules to ecosystems have provided the foundation for tighter integration between empiricists and modelers (Weitz, ).…”