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FEMS Microbiol Rev. 2016 Mar;40(2):258-72. doi: 10.1093/femsre/fuv048. Epub 2015 Dec 9.

Computational approaches to predict bacteriophage-host relationships.

Author information

1
Department of Computer Science, San Diego State University, 5500 Campanile Dr., San Diego, CA 92182, USA Department of Marine Biology, Institute of Biology, Federal University of Rio de Janeiro, CEP 21941-902, Brazil Division of Mathematics and Computer Science, Argonne National Laboratory, 9700 S. Cass Ave, Argonne, IL 60439, USA.
2
Department of Computer Science, San Diego State University, 5500 Campanile Dr., San Diego, CA 92182, USA.
3
Department of Microbiology and Immunology, Rega Institute KU Leuven, Herestraat 49, 3000 Leuven, Belgium VIB Center for the Biology of Disease, VIB, Herestraat 49, 3000 Leuven, Belgium Laboratory of Microbiology, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium.
4
Department of Marine Biology, Institute of Biology, Federal University of Rio de Janeiro, CEP 21941-902, Brazil Theoretical Biology and Bioinformatics, Utrecht University, Padualaan 8, 3584 CH, Utrecht, the Netherlands Centre for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, Geert Grooteplein 28, 6525 GA, Nijmegen, the Netherlands bedutilh@gmail.com.

Abstract

Metagenomics has changed the face of virus discovery by enabling the accurate identification of viral genome sequences without requiring isolation of the viruses. As a result, metagenomic virus discovery leaves the first and most fundamental question about any novel virus unanswered: What host does the virus infect? The diversity of the global virosphere and the volumes of data obtained in metagenomic sequencing projects demand computational tools for virus-host prediction. We focus on bacteriophages (phages, viruses that infect bacteria), the most abundant and diverse group of viruses found in environmental metagenomes. By analyzing 820 phages with annotated hosts, we review and assess the predictive power of in silico phage-host signals. Sequence homology approaches are the most effective at identifying known phage-host pairs. Compositional and abundance-based methods contain significant signal for phage-host classification, providing opportunities for analyzing the unknowns in viral metagenomes. Together, these computational approaches further our knowledge of the interactions between phages and their hosts. Importantly, we find that all reviewed signals significantly link phages to their hosts, illustrating how current knowledge and insights about the interaction mechanisms and ecology of coevolving phages and bacteria can be exploited to predict phage-host relationships, with potential relevance for medical and industrial applications.

KEYWORDS:

CRISPR; co-occurrence; metagenomics; oligonucleotide usage; phages; viruses of microbes

PMID:
26657537
PMCID:
PMC5831537
DOI:
10.1093/femsre/fuv048
[Indexed for MEDLINE]
Free PMC Article

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