Estimation of viral richness from shotgun metagenomes using a frequency count approach

BACKGROUND: Viruses are important drivers of ecosystem functions, yet little is known about the vast majority of viruses. Viral shotgun metagenomics enables the investigation of broad ecological questions in phage communities. One ecological characteristic is species richness, which is the number of different species in a community. Viruses do not have a phylogenetic marker analogous to the bacterial 16S rRNA gene with which to estimate richness, and so contig spectra are employed to measure the number of virus taxa in a given community. A contig spectrum is generated from a viral shotgun metagenome by assembling the random sequence reads into groups of sequences that overlap (contigs) and counting the number of sequences that group within each contig. Current tools available to analyze contig spectra to estimate phage richness are limited by relying on rank-abundance data. RESULTS: We present statistical estimates of virus richness from contig spectra. The program CatchAll (http://www.northeastern.edu/catchall/) was used to analyze contig spectra in terms of frequency count data rather than rank-abundance, thus enabling formal statistical analyses. Also, the influence of potentially spurious low-frequency counts on richness estimates was minimized by two methods, empirical and statistical. The results show greater estimates of viral richness than previous calculations in nearly all environments analyzed, including swine feces and reclaimed fresh water. CONCLUSIONS: CatchAll yielded consistent estimates of richness across viral metagenomes from the same or similar environments. Additionally, analysis of pooled viral metagenomes from different environments via mixed contig spectra resulted in greater richness estimates than those of the component metagenomes. Using CatchAll to analyze contig spectra will improve estimations of richness from viral shotgun metagenomes, particularly from large datasets, by providing statistical measures of richness

Assessing Species Diversity Using Metavirome Data: Methods and Challenges

Assessing biodiversity is an important step in the study of microbial ecology associated with a given environment. Multiple indices have been used to quantify species diversity, which is a key biodiversity measure. Measuring species diversity of viruses in different environments remains a challenge relative to measuring the diversity of other microbial communities. Metagenomics has played an important role in elucidating viral diversity by conducting metavirome studies; however, metavirome data are of high complexity requiring robust data preprocessing and analysis methods. In this review, existing bioinformatics methods for measuring species diversity using metavirome data are categorised broadly as either sequence similarity-dependent methods or sequence similarity-independent methods. The former includes a comparison of DNA fragments or assemblies generated in the experiment against reference databases for quantifying species diversity, whereas estimates from the latter are independent of the knowledge of existing sequence data. Current methods and tools are discussed in detail, including their applications and limitations. Drawbacks of the state-of-the-art method are demonstrated through results from a simulation. In addition, alternative approaches are proposed to overcome the challenges in estimating species diversity measures using metavirome data.DH is fully supported by the PhD scholarships of The University of Melbourne. This work is also supported by Australian Research Council grant LP140100670 and the industry partner YourGeneBioScience

ENVirT: inference of ecological characteristics of viruses from metagenomic data

Background Estimating the parameters that describe the ecology of viruses,particularly those that are novel, can be made possible using metagenomic approaches. However, the best-performing existing methods require databases to first estimate an average genome length of a viral community before being able to estimate other parameters, such as viral richness. Although this approach has been widely used, it can adversely skew results since the majority of viruses are yet to be catalogued in databases. Results In this paper, we present ENVirT, a method for estimating the richness of novel viral mixtures, and for the first time we also show that it is possible to simultaneously estimate the average genome length without a priori information. This is shown to be a significant improvement over database-dependent methods, since we can now robustly analyze samples that may include novel viral types under-represented in current databases. We demonstrate that the viral richness estimates produced by ENVirT are several orders of magnitude higher in accuracy than the estimates produced by existing methods named PHACCS and CatchAll when benchmarked against simulated data. We repeated the analysis of 20 metavirome samples using ENVirT, which produced results in close agreement with complementary in virto analyses. Conclusions These insights were previously not captured by existing computational methods. As such, ENVirT is shown to be an essential tool for enhancing our understanding of novel viral populations.This work was supported partially by Australia Research Council [grant numbers LP140100670 and DP150103512] and the Biodiversity Research Center, Academia Sinica, Taiwan. DJ, DH, DS and YS were funded by the MIFRS and MIRS scholarships of The University of Melbourne. Publication costs were funded by The Australian National University

Restricted streptomycin use in apple orchards did not adversely alter the soil bacteria communities

Streptomycin has been authorized for restricted use in the prevention of the fire blight disease of pome fruit orchards in the EU and Switzerland. This study addresses the important topic of the influence of the use of streptomycin in agriculture on the total bacteria community within the soil ecosystem. Soil samples were taken from soils under apple trees, prior to streptomycin application and 2 weeks post streptomycin application or water application (untreated control). High throughput 16S rRNA gene amplicon sequencing was used to generate datasets from the soils under apple trees in apple orchards from three different locations in Switzerland. We hypothesized that the use of streptomycin would reduce the bacterial diversity within the soil samples and enhance a reduction in the variety of taxa present. Bacterial species such as Pseudomonas, Burkholderia, and Stenotrophomonas are intrinsically resistant to many antibiotics and as such it is of interest to investigate if the use of streptomycin provided a selective advantage for these bacteria in the soil ecosystem. The application of streptomycin did not influence the abundance and diversities of major bacteria taxa of the soils or the Pseudomonas, Burkholderia, and Stenotrophomonas species. We also discovered that apple orchards under the same management practices, did not harbor the same bacterial communities. The restricted application of streptomycin in the protection of apple orchards from the fire blight pathogen Erwinia amylovora under the guidelines in Switzerland did not alter either the bacterial diversity or abundance within these soil ecosystems

ILR Faculty Research in Progress, 2014-2015

The production of scholarly research continues to be one of the primary missions of the ILR School. During a typical academic year, ILR faculty members published or had accepted for publication over 25 books, edited volumes, and monographs, 170 articles and chapters in edited volumes, numerous book reviews. In addition, a large number of manuscripts were submitted for publication, presented at professional association meetings, or circulated in working paper form. Our faculty's research continues to find its way into the very best industrial relations, social science and statistics journals.ResearchinProgress_2014_15.pdf: 17 downloads, before Oct. 1, 2020