VPF-Class: taxonomic assignment and host prediction of uncultivated viruses based on viral protein families
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.
- Research Organization:
- USDOE Joint Genome Institute (JGI), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC); Ministerio de Ciencia e Innovación (MCI); Agencia Estatal de investigación (AEI); European Regional Development Funds (ERDF)
- Grant/Contract Number:
- AC02–05CH11231; no. DE-AC02–05CH11231; AC02-05CH11231
- OSTI ID:
- 1810588
- Alternate ID(s):
- OSTI ID: 1778483; OSTI ID: 1904109
- Journal Information:
- Bioinformatics, Journal Name: Bioinformatics Vol. 37 Journal Issue: 13; ISSN 1367-4803
- Publisher:
- Oxford University PressCopyright Statement
- Country of Publication:
- United Kingdom
- Language:
- English
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