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Open Access Research

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

Heather K Allen1*, John Bunge2, James A Foster3, Darrell O Bayles4 and Thaddeus B Stanton1

Author Affiliations

1 Food Safety and Enteric Pathogens Research Unit, National Animal Disease Center, Agricultural Research Service, Ames, IA, 50010, USA

2 Department of Statistical Science, Cornell University, Ithaca, NY, 14583, USA

3 Department of Biological Sciences, Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID, 83844, USA

4 Infectious Bacterial Diseases Research Unit, National Animal Disease Center, Agricultural Research Service, Ames, IA, 50010, USA

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Microbiome 2013, 1:5  doi:10.1186/2049-2618-1-5

Published: 4 February 2013

Abstract

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/ webcite) 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.

Keywords:
Phage; Metagenomics; Virome; Ecology; Richness; CatchAll; Singleton