Computational approaches to predict bacteriophage–host relationships

Edwards, Robert A.; McNair, Katelyn; Faust, Karoline; Raes, Jeroen; Dutilh, Bas E.

doi:10.1093/femsre/fuv048

Title: Computational approaches to predict bacteriophage–host relationships

Journal Article · Wed Dec 09 00:00:00 EST 2015 · FEMS Microbiology Reviews

DOI:https://doi.org/10.1093/femsre/fuv048· OSTI ID:1625320

Edwards, Robert A. ^[1]; McNair, Katelyn ^[2]; Faust, Karoline ^[3]; Raes, Jeroen ^[4]; Dutilh, Bas E. ^[5]

San Diego State Univ., CA (United States). Dept. of Computer Science; Federal Univ. of Rio de Janeiro (Brazil). Dept. of Marine Biology, Institute of Biology; Argonne National Lab. (ANL), Argonne, IL (United States). Division of Mathematics and Computer Science
San Diego State Univ., CA (United States). Dept. of Computer Science
Rega Institute KU Leuven, Leuven (Belgium). Dept. of Microbiology and Immunology; VIB Center for the Biology of Disease, Leuven (Belgium); rije Universiteit Brussel, Pleinlaan, Brussels (Belgium). Laboratory of Microbiology
Rega Institute KU Leuven, Leuven (Belgium). Dept. of Microbiology and Immunology; VIB Center for the Biology of Disease, Leuven (Belgium); rije Universiteit Brussel, Pleinlaan, Brussels (Belgium). Laboratory of Microbiology
Federal Univ. of Rio de Janeiro (Brazil). Dept. of Marine Biology, Institute of Biology; Univ. of Utrecht (Netherlands). Theoretical Biology and Bioinformatics; Radboud Univ., Medical Center, Nijmegen (Netherlands). Centre for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences

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.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE; National Science Foundation (NSF); Netherlands Organization for Scientific Research (NWO)

Grant/Contract Number:: AC02-06CH11357; CNS-1305112; MCB-1330800; 864.14.004

OSTI ID:: 1625320

Journal Information:: FEMS Microbiology Reviews, Vol. 40, Issue 2; ISSN 1574-6976

Publisher:: Federation of European Microbiological SocietiesCopyright Statement

Country of Publication:: United States

Language:: English

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