RaFAH: Host prediction for viruses of Bacteria and Archaea based on protein content

Coutinho, Felipe H.; Zaragoza-Solas, Asier; López‐Pérez, Mario; Barylski, Jakub; Zieleziński, Andrzej; Dutilh, Bas E.; Edwards, Robert A.; Rodrı́guez-Valera, Francisco

doi:10.1016/j.patter.2021.100274

Cited by 62 publications

(80 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Phage-encoded tRNAs also enable phages to optimize protein synthesis in a host with different codon usage and thus serve as both a marker of increased host range and evolution through different hosts ( 95 ). Indeed, tRNAs are often used for computational host prediction of phages due to high sequence conservation of the gene between host and viral forms ( 96 , 97 ). We postulated that the host range of Melnitz would be evident in the four tRNAs found within its genome and reflect potential hosts in the OM43 clade.…”

Section: Resultsmentioning

confidence: 99%

A Novel and Ubiquitous Marine Methylophage Provides Insights into Viral-Host Coevolution and Possible Host-Range Expansion in Streamlined Marine Heterotrophic Bacteria

Buchholz

Bolaños

Bell

et al. 2022

Appl Environ Microbiol

View full text Add to dashboard Cite

show abstract

Section: Resultsmentioning

confidence: 99%

A Novel and Ubiquitous Marine Methylophage Provides Insights into Viral-Host Coevolution and Possible Host-Range Expansion in Streamlined Marine Heterotrophic Bacteria

Buchholz

Bolaños

Bell

et al. 2022

Appl Environ Microbiol

View full text Add to dashboard Cite

show abstract

“…The potential hosts of HMO-2011-MVGs were predicted using RaFAH tool with default settings [ 55 ]. The training and validating random forest model for RaFAH was built with 4269 host-known phages, including 11 HMO-2011-type phages and 4258 bacteriophage genomes downloaded from the NCBI RefSeq (v208).…”

Section: Methodsmentioning

confidence: 99%

Newly identified HMO-2011-type phages reveal genomic diversity and biogeographic distributions of this marine viral group

Qin

Zhang

et al. 2022

The ISME Journal

View full text Add to dashboard Cite

Viruses play critical roles in influencing biogeochemical cycles and adjusting host mortality, population structure, physiology, and evolution in the ocean. Marine viral communities are composed of numerous genetically distinct subfamily/genus-level viral groups. Among currently identified viral groups, the HMO-2011-type group is known to be dominant and broadly distributed. However, only four HMO-2011-type cultivated representatives that infect marine SAR116 and Roseobacter strains have been reported to date, and the genetic diversity, potential hosts, and ecology of this group remain poorly elucidated. Here, we present the genomes of seven HMO-2011-type phages that were isolated using four Roseobacter strains and one SAR11 strain, as well as additional 207 HMO-2011-type metagenomic viral genomes (MVGs) identified from various marine viromes. Phylogenomic and shared-gene analyses revealed that the HMO-2011-type group is a subfamily-level group comprising at least 10 discernible genus-level subgroups. Moreover, >2000 HMO-2011-type DNA polymerase sequences were identified, and the DNA polymerase phylogeny also revealed that the HMO-2011-type group contains diverse subgroups and is globally distributed. Metagenomic read-mapping results further showed that most HMO-2011-type phages are prevalent in global oceans and display distinct geographic distributions, with the distribution of most HMO-2011-type phages being associated with temperature. Lastly, we found that members in subgroup IX, represented by pelagiphage HTVC033P, were among the most abundant HMO-2011-type phages, which implies that SAR11 bacteria are crucial hosts for this viral group. In summary, our findings substantially expand current knowledge regarding the phylogenetic diversity, evolution, and distribution of HMO-2011-type phages, highlighting HMO-2011-type phages as major ecological agents that can infect certain key bacterial groups.

show abstract

“…We compared our tools with several state-of-the-art tools: WIsH [ 22 ], PHP [ 12 ], HoPhage [ 24 ], VPF-Class [ 21 ], VHM-net [ 14 ], vHULK [ 25 ], and RaFAH [ 23 ]. We also recorded the output of BLASTN to show the performance of the alignment-based tool.…”

Section: Resultsmentioning

confidence: 99%

“…This model integrates CRISPR, score of WIsH, and BLASTN results and applies Random Markov field to generate predictions. A more recently published model, RaFAH [ 23 ] uses alignment features to construct a random forest for host prediction.…”

Section: Introductionmentioning

confidence: 99%

Predicting the hosts of prokaryotic viruses using GCN-based semi-supervised learning

Shang

Sun

2021

BMC Biol

View full text Add to dashboard Cite

Background Prokaryotic viruses, which infect bacteria and archaea, are the most abundant and diverse biological entities in the biosphere. To understand their regulatory roles in various ecosystems and to harness the potential of bacteriophages for use in therapy, more knowledge of viral-host relationships is required. High-throughput sequencing and its application to the microbiome have offered new opportunities for computational approaches for predicting which hosts particular viruses can infect. However, there are two main challenges for computational host prediction. First, the empirically known virus-host relationships are very limited. Second, although sequence similarity between viruses and their prokaryote hosts have been used as a major feature for host prediction, the alignment is either missing or ambiguous in many cases. Thus, there is still a need to improve the accuracy of host prediction. Results In this work, we present a semi-supervised learning model, named HostG, to conduct host prediction for novel viruses. We construct a knowledge graph by utilizing both virus-virus protein similarity and virus-host DNA sequence similarity. Then graph convolutional network (GCN) is adopted to exploit viruses with or without known hosts in training to enhance the learning ability. During the GCN training, we minimize the expected calibrated error (ECE) to ensure the confidence of the predictions. We tested HostG on both simulated and real sequencing data and compared its performance with other state-of-the-art methods specifically designed for virus host classification (VHM-net, WIsH, PHP, HoPhage, RaFAH, vHULK, and VPF-Class). Conclusion HostG outperforms other popular methods, demonstrating the efficacy of using a GCN-based semi-supervised learning approach. A particular advantage of HostG is its ability to predict hosts from new taxa.

show abstract

RaFAH: Host prediction for viruses of Bacteria and Archaea based on protein content

Cited by 62 publications

References 62 publications

A Novel and Ubiquitous Marine Methylophage Provides Insights into Viral-Host Coevolution and Possible Host-Range Expansion in Streamlined Marine Heterotrophic Bacteria

A Novel and Ubiquitous Marine Methylophage Provides Insights into Viral-Host Coevolution and Possible Host-Range Expansion in Streamlined Marine Heterotrophic Bacteria

Newly identified HMO-2011-type phages reveal genomic diversity and biogeographic distributions of this marine viral group

Predicting the hosts of prokaryotic viruses using GCN-based semi-supervised learning

Contact Info

Product

Resources

About