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Title: Viral dark matter and virus–host interactions resolved from publicly available microbial genomes

Abstract

The ecological importance of viruses is now widely recognized, yet our limited knowledge of viral sequence space and virus–host interactions precludes accurate prediction of their roles and impacts. In this study, we mined publicly available bacterial and archaeal genomic data sets to identify 12,498 high-confidence viral genomes linked to their microbial hosts. These data augment public data sets 10-fold, provide first viral sequences for 13 new bacterial phyla including ecologically abundant phyla, and help taxonomically identify 7–38% of ‘unknown’ sequence space in viromes. Genome- and network-based classification was largely consistent with accepted viral taxonomy and suggested that (i) 264 new viral genera were identified (doubling known genera) and (ii) cross-taxon genomic recombination is limited. Further analyses provided empirical data on extrachromosomal prophages and coinfection prevalences, as well as evaluation of in silico virus–host linkage predictions. Together these findings illustrate the value of mining viral signal from microbial genomes.

Authors:
 [1];  [2];  [3];  [1]
  1. Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, United States
  2. Department of Microbiology and Immunology, University of British Columbia, Vancouver, Canada, Graduate Program in Bioinformatics, University of British Columbia, Vancouver, Canada
  3. U.S Department of Energy Joint Genome Institute, Walnut Creek, United States
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1209986
Alternate Identifier(s):
OSTI ID: 1209987; OSTI ID: 1628837
Grant/Contract Number:  
Joint Genome Institute (DE-AC02-05CH11231); AC02-05CH11231
Resource Type:
Published Article
Journal Name:
eLife
Additional Journal Information:
Journal Name: eLife Journal Volume: 4; Journal ID: ISSN 2050-084X
Publisher:
eLife Sciences Publications, Ltd.
Country of Publication:
United States
Language:
English
Subject:
Life Sciences & Biomedicine - Other Topics

Citation Formats

Roux, Simon, Hallam, Steven J., Woyke, Tanja, and Sullivan, Matthew B. Viral dark matter and virus–host interactions resolved from publicly available microbial genomes. United States: N. p., 2015. Web. doi:10.7554/eLife.08490.
Roux, Simon, Hallam, Steven J., Woyke, Tanja, & Sullivan, Matthew B. Viral dark matter and virus–host interactions resolved from publicly available microbial genomes. United States. doi:https://doi.org/10.7554/eLife.08490
Roux, Simon, Hallam, Steven J., Woyke, Tanja, and Sullivan, Matthew B. Wed . "Viral dark matter and virus–host interactions resolved from publicly available microbial genomes". United States. doi:https://doi.org/10.7554/eLife.08490.
@article{osti_1209986,
title = {Viral dark matter and virus–host interactions resolved from publicly available microbial genomes},
author = {Roux, Simon and Hallam, Steven J. and Woyke, Tanja and Sullivan, Matthew B.},
abstractNote = {The ecological importance of viruses is now widely recognized, yet our limited knowledge of viral sequence space and virus–host interactions precludes accurate prediction of their roles and impacts. In this study, we mined publicly available bacterial and archaeal genomic data sets to identify 12,498 high-confidence viral genomes linked to their microbial hosts. These data augment public data sets 10-fold, provide first viral sequences for 13 new bacterial phyla including ecologically abundant phyla, and help taxonomically identify 7–38% of ‘unknown’ sequence space in viromes. Genome- and network-based classification was largely consistent with accepted viral taxonomy and suggested that (i) 264 new viral genera were identified (doubling known genera) and (ii) cross-taxon genomic recombination is limited. Further analyses provided empirical data on extrachromosomal prophages and coinfection prevalences, as well as evaluation of in silico virus–host linkage predictions. Together these findings illustrate the value of mining viral signal from microbial genomes.},
doi = {10.7554/eLife.08490},
journal = {eLife},
number = ,
volume = 4,
place = {United States},
year = {2015},
month = {7}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: https://doi.org/10.7554/eLife.08490

Citation Metrics:
Cited by: 62 works
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