VirSorter: mining viral signal from microbial genomic data

Abstract

Viruses of microbes impact all ecosystems where microbes drive key energy and substrate transformations including the oceans, humans and industrial fermenters. However, despite this recognized importance, our understanding of viral diversity and impacts remains limited by too few model systems and reference genomes. One way to fill these gaps in our knowledge of viral diversity is through the detection of viral signal in microbial genomic data. While multiple approaches have been developed and applied for the detection of prophages (viral genomes integrated in a microbial genome), new types of microbial genomic data are emerging that are more fragmented and larger scale, such as Single-cell Amplified Genomes (SAGs) of uncultivated organisms or genomic fragments assembled from metagenomic sequencing. Here, we present VirSorter, a tool designed to detect viral signal in these different types of microbial sequence data in both a reference-dependent and reference-independent manner, leveraging probabilistic models and extensive virome data to maximize detection of novel viruses. Performance testing shows that VirSorter’s prophage prediction capability compares to that of available prophage predictors for complete genomes, but is superior in predicting viral sequences outside of a host genome (i.e., from extrachromosomal prophages, lytic infections, or partially assembled prophages). Furthermore, VirSorter outperforms existing tools for fragmented genomic and metagenomic datasets, and can identify viral signal in assembled sequence (contigs) as short as 3kb, while providing near-perfect identification (>95% Recall and 100% Precision) on contigs of at least 10kb. Because VirSorter scales to large datasets, it can also be used in “reverse” to more confidently identify viral sequence in viral metagenomes by sorting away cellular DNA whether derived from gene transfer agents, generalized transduction or contamination. Finally, VirSorter is made available through the iPlant Cyberinfrastructure that provides a web-based user interface interconnected with the required computing resources. VirSorter thus complements existing prophage prediction softwares to better leverage fragmented, SAG and metagenomic datasets in a way that will scale to modern sequencing. Given these features, VirSorter should enable the discovery of new viruses in microbial datasets, and further our understanding of uncultivated viral communities across diverse ecosystems.

Phages rarely encode antibiotic resistance genes: a cautionary tale for virome analyses

“…However, reanalysis of these data suggests that the lung sample conclusions were misled by excessive bacterial DNA content, and the mice virome analyses suffered from inflated false positives due to relaxed thresholds for in silico detection of ARG (E-value o10 − 3 on ARDB+). To guide future work, we suggest that (i) bacterial DNA contamination be quantified in viromes using analyses such as those presented here and/or automated software now available (VirSorter; Roux et al, 2015), (ii) automated analyses use a conservative threshold to quantify bona fide ARG and (iii) discovery-based work proceed with added caution. Specifically, the latter exploratory cutoffs should use a bit-score 470 threshold complemented with manual inspection for removing the kind of false positives identified in this study.…”

Uncovering Earth’s virome

“…Bacterial genomes include representatives of 4 phyla. A total of 132 prophage sequences were identified including 99 prophages identified by CyVerse 54 implementation of VirSorter 55 in the categories 1, 2, 4, and 5, and 33 prophages identified by manual curation based on the presence of hallmark phage genes and analysis of synteny with closely related strains. Coordinates of 35 prophages predicted by VirSorter had to be manually adjusted to eliminate bacterial genes (including ribosomal RNAs and other housekeeping genes) and to separate 2 prophage sequences called as one prophage over an intervening stretch of bacterial genes.…”

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

“…This tool is powerful and highly scalable-its first application was to nearly 15 000 publically available archaeal and bacterial genomes, where VirSorter identified 12 498 new hostassociated viruses and their genomes that augmented publicly available viral genome reference data sets by ∼ 10-fold (Roux et al, 2015b). Furthermore, VirSorter scales to handle contigs derived from metagenomic data sets (Roux et al, 2015a vContact This App assigns contigs to taxonomic groups using the presence or absence of shared PCs along the length of the contig. This is critical as viruses lack a universal gene marker (Edwards and Rohwer, 2005) and o0.1% of viruses in natural environments are represented in public databases (Brum et al, 2015), which necessitates new approaches to taxonomically classify surveyed viral genomes.…”

“…This App identifies viral sequences in microbial genomes and metagenomic data sets (Roux et al, 2015a). This is necessary as viral genomes are underrepresented in databases-for example, 92% of 1659 genome-sequenced phages derive from only 4 of 54 known bacterial phyla (Roux et al, 2015b).…”

“…The iVirus Data Commons leverages these CyVerse resources to make data sets accessible from the Pacific Ocean Virome , Tara Oceans Virome (Brum et al, 2015), Southern Ocean Virome (Brum et al, 2016), Virsorter Curated Dataset (Roux et al, 2015a) and legacy viral data sets from the retired Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis (CAMERA) project (Seshadri et al, 2007). Beyond these, viral data were also mined and hand-curated from Genbank's Sequence Read Archive (SRA; Benson et al, 1998) and MG-RAST (Wilke et al, 2015).…”