Roux, S., Enault, F., Hurwitz, B. L., & Sullivan, M. B. (2015). VirSorter: mining viral signal from microbial genomic data. PeerJ, 3, e985.
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.
Armstrong, D. G., Hurwitz, B. L., & Lipsky, B. A. (2015). Set Phages to Stun: Reducing the Virulence of Staphylococcus aureus in Diabetic Foot Ulcers. Diabetes, 64(8), 2701-3.
Cranston, K. A., Hurwitz, B., Ware, D., Stein, L., & Wing, R. A. (2009). Species trees from highly incongruent gene trees in rice. Systematic biology, 58(5), 489-500.
Several methods have recently been developed to infer multilocus phylogenies by incorporating information from topological incongruence of the individual genes. In this study, we investigate 2 such methods, Bayesian concordance analysis and Bayesian estimation of species trees. Our test data are a collection of genes from cultivated rice (genus Oryza) and the most closely related wild species, generated using a high-throughput sequencing protocol and bioinformatics pipeline. Trees inferred from independent genes display levels of topological incongruence that far exceed that seen in previous data sets analyzed with these species tree methods. We identify differences in phylogenetic results between inference methods that incorporate gene tree incongruence. Finally, we discuss the challenges of scaling these analyses for data sets with thousands of gene trees and extensive levels of missing data.
Bomhoff, M., Youens-Clark, K., Ponsero, A. J., Hurwitz, B. L., Choi, I., & Hartman, J. H. (2018). Libra: Using Hadoop for Comparative Metagenomics. HPDC.
Degnan, P. H., Leonardo, T. E., Cass, B. N., Hurwitz, B., Stern, D., Gibbs, R. A., Richards, S., & Moran, N. A. (2010). Dynamics of genome evolution in facultative symbionts of aphids. Environmental microbiology, 12(8), 2060-9.
Aphids are sap-feeding insects that host a range of bacterial endosymbionts including the obligate, nutritional mutualist Buchnera plus several bacteria that are not required for host survival. Among the latter, 'Candidatus Regiella insecticola' and 'Candidatus Hamiltonella defensa' are found in pea aphids and other hosts and have been shown to protect aphids from natural enemies. We have sequenced almost the entire genome of R. insecticola (2.07 Mbp) and compared it with the recently published genome of H. defensa (2.11 Mbp). Despite being sister species the two genomes are highly rearranged and the genomes only have ∼55% of genes in common. The functions encoded by the shared genes imply that the bacteria have similar metabolic capabilities, including only two essential amino acid biosynthetic pathways and active uptake mechanisms for the remaining eight, and similar capacities for host cell toxicity and invasion (type 3 secretion systems and RTX toxins). These observations, combined with high sequence divergence of orthologues, strongly suggest an ancient divergence after establishment of a symbiotic lifestyle. The divergence in gene sets and in genome architecture implies a history of rampant recombination and gene inactivation and the ongoing integration of mobile DNA (insertion sequence elements, prophage and plasmids).