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

Abstract

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.

CHERRY: a Computational metHod for accuratE pRediction of virus-pRokarYotic interactions using a graph encoder-decoder model

“…In this section, we will show our experimental results on different datasets and compare CHERRY against the stateof-the-art tools: WIsH [10], PHP [24], VHM-Net [12], VPF-Class [13], vHULK [19], RaFAH [21], and HostG [27].…”

“…They also use a deep learning model and finally integrate the results of deep learning model with the Markov model for host prediction. On the other hand, PHP [24] utilizes the k-mer frequency, which can reflect the codon usage patterns shared by the viruses and the hosts [25,26]. HostG [27] utilizes the shared protein clusters between viruses and prokaryotes to create a knowledge graph and trains a graph convolutional network for prediction.…”

“…Dataset: we followed [24] and used the same virus-host relationship benchmark dataset for training (the VHM dataset) and testing (the TEST dataset). The detailed information is shown in Fig.…”

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

“…Several attempts have been made to predict hosts for phages based on the genomic sequences. They can be roughly divided into two groups: alignment-based [15,16] and learning-based [10,17,18] models. Alignment-based methods utilize sequence similarity search between query contigs and reference genomes of candidate hosts (bacteria).…”

Accurate identification of bacteriophages from metagenomic data using Transformer

“…First, both lytic and temperate phages can integrate the host genetic materials into their genomes, leading to local sequence similarities between the genomes of phages and bacteria [13]. For example, ~76% phages with known hosts in the RefSeq database have detectable alignments (E-value <1e-5) with their host genomes [14]. These common regions will pose challenges for distinguishing phages from their bacterial hosts.…”