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. 2021 Jul 27;37(13):1805-1813.
doi: 10.1093/bioinformatics/btab026.

VPF-Class: taxonomic assignment and host prediction of uncultivated viruses based on viral protein families

Joan Carles Pons  1 , David Paez-Espino  2 , Gabriel Riera  1 , Natalia Ivanova  2 , Nikos C Kyrpides  2 , Mercè Llabrés  1
Affiliations

VPF-Class: taxonomic assignment and host prediction of uncultivated viruses based on viral protein families

Joan Carles Pons et al. Bioinformatics. .

Abstract

Motivation: Two key steps in the analysis of uncultured viruses recovered from metagenomes are the taxonomic classification of the viral sequences and the identification of putative host(s). Both steps rely mainly on the assignment of viral proteins to orthologs in cultivated viruses. Viral Protein Families (VPFs) can be used for the robust identification of new viral sequences in large metagenomics datasets. Despite the importance of VPF information for viral discovery, VPFs have not yet been explored for determining viral taxonomy and host targets.

Results: In this work, we classified the set of VPFs from the IMG/VR database and developed VPF-Class. VPF-Class is a tool that automates the taxonomic classification and host prediction of viral contigs based on the assignment of their proteins to a set of classified VPFs. Applying VPF-Class on 731K uncultivated virus contigs from the IMG/VR database, we were able to classify 363K contigs at the genus level and predict the host of over 461K contigs. In the RefSeq database, VPF-class reported an accuracy of nearly 100% to classify dsDNA, ssDNA and retroviruses, at the genus level, considering a membership ratio and a confidence score of 0.2. The accuracy in host prediction was 86.4%, also at the genus level, considering a membership ratio of 0.3 and a confidence score of 0.5. And, in the prophages dataset, the accuracy in host prediction was 86% considering a membership ratio of 0.6 and a confidence score of 0.8. Moreover, from the Global Ocean Virome dataset, over 817K viral contigs out of 1 million were classified.

Availability and implementation: The implementation of VPF-Class can be downloaded from https://github.com/biocom-uib/vpf-tools.

Supplementary information: Supplementary data are available at Bioinformatics online.

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Figures

Fig. 1.
Fig. 1.
Graphical representation of the pipeline for viral genome classification (steps from 1 to 3). The first step is the initial classification of the VPFs regarding taxonomy and host infection at three different levels based on the information of the isolate reference viruses retrieved from IMG/M and ViralZone databases. The second step is to classify the uncultured viral genomes (UViGs) based on hits to homogeneous classified VPFs. The third step is the final VPFs classification adding the information from the UViGs classification
Fig. 2.
Fig. 2.
Score distributions of the UViGs regarding taxonomic classification (left panel) and host prediction (right panel). Scores values are displayed in the x-axis while the y-axis represents the number of UViGs with the corresponding score
Fig. 3.
Fig. 3.
Heatmaps depicting results obtained with the NCBI test. The x-axes show the confidence score thresholds while the y-axes show the membership ratio thresholds. The colors represent the ratio of classified viral sequences above a confidence score (x value) and a membership ratio (y value) with respect to the total number of sequences
Fig. 4.
Fig. 4.
Coverage and accuracy values obtained by VPF-Class (left panel) and VirHostMatcher (right panel) in host prediction of the prophages test. On the right, we show the coverage and accuracy values (y-axis) obtained by VirHostMatcher with respect to the values of d2* ONF dissimilarity measure (x-axis)
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