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Nontargeted virus sequence discovery pipeline and virus clustering for metagenomic data

Nature Protocols volume 12, pages 1673–1682 (2017)Cite this article

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Abstract

The analysis of large microbiome data sets holds great promise for the delineation of the biological and metabolic functioning of living organisms and their role in the environment. In the midst of this genomic puzzle, viruses, especially those that infect microbial communities, represent a major reservoir of genetic diversity with great impact on biogeochemical cycles and organismal health. Overcoming the limitations associated with virus detection directly from microbiomes can provide key insights into how ecosystem dynamics are modulated. Here, we present a computational protocol for accurate detection and grouping of viral sequences from microbiome samples. Our approach relies on an expanded and curated set of viral protein families used as bait to identify viral sequences directly from metagenomic assemblies. This protocol describes how to use the viral protein families catalog (∼7 h) and recommended filters for the detection of viral contigs in metagenomic samples (∼6 h), and it describes the specific parameters for a nucleotide-sequence-identity-based method of organizing the viral sequences into quasi-species taxonomic-level groups (∼10 min).

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Figure 1: Overview of the computational workflow.
Figure 2: Metagenome sample used as an example.

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Acknowledgements

This work was supported by the US Department of Energy Joint Genome Institute, a DOE Office of Science User Facility, under contract no. DE-AC02-05CH11231, and used resources of the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy.

Author information

Authors and Affiliations

  1. Joint Genome Institute,

    David Paez-Espino, Georgios A Pavlopoulos, Natalia N Ivanova & Nikos C Kyrpides

  2. Department of Energy, Walnut Creek, California, USA

    David Paez-Espino, Georgios A Pavlopoulos, Natalia N Ivanova & Nikos C Kyrpides

Authors
  1. David Paez-Espino
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  2. Georgios A Pavlopoulos
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  3. Natalia N Ivanova
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  4. Nikos C Kyrpides
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Contributions

D.P.-E., N.N.I., and N.C.K. conceived and led the protocol. G.A.P. provided computational and scripting support. All authors wrote and edited the manuscript.

Corresponding author

Correspondence to David Paez-Espino.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Details of the protocol for the given example.

Pipeline of the workflow including the name of all the files generated during the virus detection for the sampleidentified as 3300001348 in IMG/M (in blue), as well as the approximate time necessary for each of the stepsof the protocol (in red), and required scripts (bold black). The three yellow boxes indicate the three final outputsof this exercise: (i) 640 unique metagenomic viral contigs (mVCs) detected; (ii) 246 viral groups that include268 mVCs (out of the 640) from the given example as well as 457 metagenomic viral contigs from 32 otherdifferent metagenomes, and (iii) a list of 12,963 viral sequences of low abundance (from 8,436 unique viralgroups) with at least 10% of their length covered by unassembled reads (>90% sequence identity) from the targeted metagenome.

Supplementary information

Supplementary Text and Figures

Supplementary Figure 1 and Supplementary Tables 1 and 2. (PDF 13452 kb)

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Paez-Espino, D., Pavlopoulos, G., Ivanova, N. et al. Nontargeted virus sequence discovery pipeline and virus clustering for metagenomic data. Nat Protoc 12, 1673–1682 (2017). https://doi.org/10.1038/nprot.2017.063

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