Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 111, Issue 29, Pages 10714-10719Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1319778111
Keywords
virus; microbial ecology; Bayesian network
Categories
Funding
- Office of Science of the US Department of Energy [DE-AC02-05CH11231]
- Gordon and Betty Moore Foundation Marine Microbial Initiative
- National Science Foundation [DBI-0850105, OCE-0961947]
- Biosphere 2 [BIO5]
- Gordon and Betty Moore Foundation [GBMF3790]
- Integrative Graduate Education Research Traineeship
- National Science Foundation
- Division Of Ocean Sciences
- Directorate For Geosciences [0961947] Funding Source: National Science Foundation
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Long-standing questions in marine viral ecology are centered on understanding how viral assemblages change along gradients in space and time. However, investigating these fundamental ecological questions has been challenging due to incomplete representation of naturally occurring viral diversity in single gene-or morphology-based studies and an inability to identify up to 90% of reads in viral metagenomes (viromes). Although protein clustering techniques provide a significant advance by helping organize this unknown metagenomic sequence space, they typically use only similar to 75% of the data and rely on assembly methods not yet tuned for naturally occurring sequence variation. Here, we introduce an annotation- and assembly-free strategy for comparative metagenomics that combines shared k-mer and social network analyses (regression modeling). This robust statistical framework enables visualization of complex sample networks and determination of ecological factors driving community structure. Application to 32 viromes from the Pacific Ocean Virome dataset identified clusters of samples broadly delineated by photic zone and revealed that geographic region, depth, and proximity to shore were significant predictors of community structure. Within subsets of this dataset, depth, season, and oxygen concentration were significant drivers of viral community structure at a single open ocean station, whereas variability along onshore-offshore transects was driven by oxygen concentration in an area with an oxygen minimum zone and not depth or proximity to shore, as might be expected. Together these results demonstrate that this highly scalable approach using complete metagenomic network-based comparisons can both test and generate hypotheses for ecological investigation of viral and microbial communities in nature.
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