Ball, C. L., Daniel, S. G., Besselsen, D. G., Hurwitz, B. L., & Doetschman, T. C. (2017). Functional changes in the gut microbiome contribute to Transforming Growth Factor β-deficient colon cancer. mSystems, 2(5), 1-17.
David G Besselsen, Thomas C Doetschman, Bonnie L Hurwitz
Watts, G. S., Hurwitz, B. L., Youens-Clark, K., Wolk, D. M., Oshiro, M. M., Metzger, G. S., Dhingra, D., & Slepian, M. J. (2016). Clinical 16S rRNA next-generation sequencing: Factors affecting real world sensitivity and threshold of detection. ASM Clinical Microbiology.
Hurwitz, B. L., Brum, J. R., & Sullivan, M. B. (2015). Depth-stratified functional and taxonomic niche specialization in the 'core' and 'flexible' Pacific Ocean Virome. The ISME journal, 9(2), 472-84.
Microbes drive myriad ecosystem processes, and their viruses modulate microbial-driven processes through mortality, horizontal gene transfer, and metabolic reprogramming by viral-encoded auxiliary metabolic genes (AMGs). However, our knowledge of viral roles in the oceans is primarily limited to surface waters. Here we assess the depth distribution of protein clusters (PCs) in the first large-scale quantitative viral metagenomic data set that spans much of the pelagic depth continuum (the Pacific Ocean Virome; POV). This established 'core' (180 PCs; one-third new to science) and 'flexible' (423K PCs) community gene sets, including niche-defining genes in the latter (385 and 170 PCs are exclusive and core to the photic and aphotic zones, respectively). Taxonomic annotation suggested that tailed phages are ubiquitous, but not abundant (
Teytelman, L., Stoliartchouk, A., Kindler, L., & Hurwitz, B. L. (2016). Protocols.io: Virtual Communities for Protocol Development and Discussion. PLoS biology, 14(8), e1002538.
The detailed know-how to implement research protocols frequently remains restricted to the research group that developed the method or technology. This knowledge often exists at a level that is too detailed for inclusion in the methods section of scientific articles. Consequently, methods are not easily reproduced, leading to a loss of time and effort by other researchers. The challenge is to develop a method-centered collaborative platform to connect with fellow researchers and discover state-of-the-art knowledge. Protocols.io is an open-access platform for detailing, sharing, and discussing molecular and computational protocols that can be useful before, during, and after publication of research results.
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.