Computational approaches to predict bacteriophage–host relationships

By Robert A. Edwards, Katelyn McNair, Karoline Faust, Jeroen Raes, Bas E. Dutilh. FEMS Microbiology Reviews, 2015

430 citations recorded by the source corpus.

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.

Subfields: Ecology, Microbiology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8ba1532f29a2ad058772273da0aaebf7946da723

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Citations in the corpus

Lysogeny in nature: mechanisms, impact and ecology of temperate phages (2017, background): “Such sequencebased approaches can even help identify hosts (Waller et al., 2014; Hannigan et al., 2015; Labonte et al., 2015a,b; Edwards et al., 2016) and the relative proportions of temperate phages undergoing lysogenic or lytic infections (Waller et al.”
Molecular and Evolutionary Determinants of Bacteriophage Host Range. (2019, background): “Thus, the process is often host-specific [61], or more precisely it is limited to hosts with the correct integration target site.”
VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data (2017, methodology): “We and others have shown that viruses often share higher similarity in k -mer patterns with its specific host than with random hosts [29, 30], presumably because of evolutionary selection to share similar codon usage.”
iPHoP: An integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria (2023, neutral): “Because they rely on different signals, these host-based tools display varying levels of recall and specificity and are likely to be each relevant for different types of samples and viruses [10].”
Bacteriophage Therapy (2018, background): “One of the issues associated with the study of mature biofilms is their increased proportion of dormant bacteria [13], some of which are viable but nonculturable, leading to an underestimated number of total viable bacteria when a CFU analysis is performed [6, 14].”
Identifying viruses from metagenomic data using deep learning (2020, background): “On the other hand, previous studies showed that virus and their infecting hosts share k - mer similarity and utilized this phenomenon to predict hosts of viruses based on genomic sequences [18,45,46] .”
IMG/VR v3: an integrated ecological and evolutionary framework for interrogating genomes of uncultivated viruses (2020, background): “0+) with options ‘-task blastn-short evalue 1 -dust no -word size 7’, as the alignment provided by SpacePharer did not always extend over the full length of the spacer (30).”