A Review on Viral Metagenomics in Extreme Environments

Dávila-Ramos, Sonia; Castelán-Sánchez, Hugo G; Martínez-Ávila, Liliana; Sánchez-Carbente, María del Rayo; Peralta, Raúl; Hernández-Mendoza, Armando; Dobson, Alan D. W.; González, Rafael; Pastor, Nina; Batista-García, Ramón Alberto

doi:10.3389/fmicb.2019.02403

Cited by 65 publications

(50 citation statements)

References 136 publications

(230 reference statements)

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“…The virus-host interactions in our datasets are biased towards some common viruses and host taxa, which reflects the taxonomic distribution of viruses and prokaryote in nature. For example, the three most commonly observed families (Siphoviridae, Myoviridae, and Podoviridae) account for 77% of all viruses in the VHM dataset (Additional file 1 : Figure S5) and are indeed the most commonly observed phage taxa in nature [ 4 , 33 ]. Another limitation of the study is that the GM was inferior to WIsH in predicting virus hosts on the viral contigs less than 10 kb.…”

Section: Discussionmentioning

confidence: 99%

Prokaryotic virus host predictor: a Gaussian model for host prediction of prokaryotic viruses in metagenomics

Zhang

Cai

et al. 2021

BMC Biol

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Background Viruses are ubiquitous biological entities, estimated to be the largest reservoirs of unexplored genetic diversity on Earth. Full functional characterization and annotation of newly discovered viruses requires tools to enable taxonomic assignment, the range of hosts, and biological properties of the virus. Here we focus on prokaryotic viruses, which include phages and archaeal viruses, and for which identifying the viral host is an essential step in characterizing the virus, as the virus relies on the host for survival. Currently, the method for determining the viral host is either to culture the virus, which is low-throughput, time-consuming, and expensive, or to computationally predict the viral hosts, which needs improvements at both accuracy and usability. Here we develop a Gaussian model to predict hosts for prokaryotic viruses with better performances than previous computational methods. Results We present here Prokaryotic virus Host Predictor (PHP), a software tool using a Gaussian model, to predict hosts for prokaryotic viruses using the differences of k-mer frequencies between viral and host genomic sequences as features. PHP gave a host prediction accuracy of 34% (genus level) on the VirHostMatcher benchmark dataset and a host prediction accuracy of 35% (genus level) on a new dataset containing 671 viruses and 60,105 prokaryotic genomes. The prediction accuracy exceeded that of two alignment-free methods (VirHostMatcher and WIsH, 28–34%, genus level). PHP also outperformed these two alignment-free methods much (24–38% vs 18–20%, genus level) when predicting hosts for prokaryotic viruses which cannot be predicted by the BLAST-based or the CRISPR-spacer-based methods alone. Requiring a minimal score for making predictions (thresholding) and taking the consensus of the top 30 predictions further improved the host prediction accuracy of PHP. Conclusions The Prokaryotic virus Host Predictor software tool provides an intuitive and user-friendly API for the Gaussian model described herein. This work will facilitate the rapid identification of hosts for newly identified prokaryotic viruses in metagenomic studies.

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Section: Discussionmentioning

confidence: 99%

Prokaryotic virus host predictor: a Gaussian model for host prediction of prokaryotic viruses in metagenomics

Zhang

Cai

et al. 2021

BMC Biol

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“…Recent investigations suggest that viruses in Antarctic soils and hypoliths are highly diverse, mainly dominated by Mycobacterium phages [ 68 , 69 ]. Studies in other systems have hinted at the importance of viruses in metabolic control [ 70 , 71 ]. It is tempting to speculate that the extreme environmental conditions may promote a lysogenic rather than lytic phage lifestyle, and there is circumstantial evidence of this from Antarctic “metaviromic” studies [ 69 ].…”

Section: N-cycling Taxa In Soilsmentioning

confidence: 99%

Microbial Nitrogen Cycling in Antarctic Soils

et al. 2020

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The Antarctic continent is widely considered to be one of the most hostile biological habitats on Earth. Despite extreme environmental conditions, the ice-free areas of the continent, which constitute some 0.44% of the total continental land area, harbour substantial and diverse communities of macro-organisms and especially microorganisms, particularly in the more “hospitable” maritime regions. In the more extreme non-maritime regions, exemplified by the McMurdo Dry Valleys of South Victoria Land, nutrient cycling and ecosystem servicing processes in soils are largely driven by microbial communities. Nitrogen turnover is a cornerstone of ecosystem servicing. In Antarctic continental soils, specifically those lacking macrophytes, cold-active free-living diazotrophic microorganisms, particularly Cyanobacteria, are keystone taxa. The diazotrophs are complemented by heterotrophic bacterial and archaeal taxa which show the genetic capacity to perform elements of the entire N cycle, including nitrification processes such as the anammox reaction. Here, we review the current literature on nitrogen cycling genes, taxa, processes and rates from studies of Antarctic soils. In particular, we highlight the current gaps in our knowledge of the scale and contribution of these processes in south polar soils as critical data to underpin viable predictions of how such processes may alter under the impacts of future climate change.

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“…The virus-host interactions in our datasets are biased towards some common viruses and host taxa, which reflects the taxonomic distribution of viruses and prokaryote in nature. For example, the three most commonly observed families (Siphoviridae, Myoviridae, and Podoviridae) account for 77% of all viruses in the VHM dataset (Additional file 1: Figure S5) and are indeed the most commonly observed phage taxa in nature [4,33]. Another limitation of the study is that the GM was inferior to WIsH in predicting virus hosts on the viral contigs less than 10kb.…”

Section: Discussionmentioning

confidence: 99%

Prokaryotic virus Host Predictor: a Gaussian model for host prediction of prokaryotic viruses in metagenomics

Zhang

Cai

et al. 2020

Preprint

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BackgroundViruses are ubiquitous biological entities, estimated to be the largest reservoirs of unexplored genetic diversity on Earth. Full functional characterization and annotation of newly-discovered viruses requires tools to enable taxonomic assignment, the range of hosts, and biological properties of the virus. Here we focus on prokaryotic viruses, which include phages and archaeal viruses, and for which identifying the viral host is an essential step in characterizing the virus, as the virus relies on the host for survival. Currently, the method for determining the viral host is either to culture the virus, which is low-throughput, time-consuming, and expensive, or to computationally predict the viral hosts, which needs improvements at both accuracy and usability. Here we develop a Gaussian model to predict hosts for prokaryotic viruses with better performances than previous computational methods.ResultsWe present here Prokaryotic virus Host Predictor (PHP), a software tool using a Gaussian model, to predict hosts for prokaryotic viruses using the differences of Æ-mer frequencies between viral and host genomic sequences as features. PHP gave a host prediction accuracy of 34% (genus level) on the VirHostMatcher benchmark dataset and a host prediction accuracy of 35% (genus level) on a new dataset containing 671 viruses and 60,105 prokaryotic genomes. The prediction accuracy exceeded that of two alignment-free methods (VirHostMatcher and WIsH, 28%-34%, genus level). PHP also outperformed these two alignment-free methods much (24%-38%vs 18%-20%, genus level) when predicting hosts for prokaryotic viruses which cannot be predicted by the BLAST-based or the CRISPR-spacer-based methods alone. Requiring a minimal score for making predictions (thresholding) and taking the consensus of the top 30 predictions further improved the host prediction accuracy of PHP.ConclusionsThe Prokaryotic virus Host Predictor software tool provides an intuitive and user-friendly API for the Gaussian model described herein. This work will facilitate the rapid identification of hosts for newly-identified prokaryotic viruses in metagenomic studies.Author SummaryProkaryotic viruses which include phages and archaeal viruses play an important role in balancing the global ecosystem by regulating the composition of bacteria and archaea in water and soil. Identifying the viral host is essential for characterizing the virus, as the virus relies on the host for survival. Currently, the method for determining the viral host is either to culture the virus which is low-throughput, time-consuming, and expensive, or to computationally predict the viral hosts which needs improvements at both accuracy and usability. This study developed a Gaussian model to predict hosts for prokaryotic viruses with better performances than previous computational methods. It will contribute to the rapid identification of hosts for prokaryotic viruses in metagenomic studies, and will extend our knowledge of virus-host interactions.

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A Review on Viral Metagenomics in Extreme Environments

Cited by 65 publications

References 136 publications

Prokaryotic virus host predictor: a Gaussian model for host prediction of prokaryotic viruses in metagenomics

Prokaryotic virus host predictor: a Gaussian model for host prediction of prokaryotic viruses in metagenomics

Microbial Nitrogen Cycling in Antarctic Soils

Prokaryotic virus Host Predictor: a Gaussian model for host prediction of prokaryotic viruses in metagenomics

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