VIRify: An integrated detection, annotation and taxonomic classification pipeline using virus-specific protein profile hidden Markov models.

Published version
Repository DOI

Change log
Rangel-Pineros, Guillermo  ORCID logo
Almeida, Alexandre 
Sakharova, Ekaterina 
Marz, Manja 

The study of viral communities has revealed the enormous diversity and impact these biological entities have on various ecosystems. These observations have sparked widespread interest in developing computational strategies that support the comprehensive characterisation of viral communities based on sequencing data. Here we introduce VIRify, a new computational pipeline designed to provide a user-friendly and accurate functional and taxonomic characterisation of viral communities. VIRify identifies viral contigs and prophages from metagenomic assemblies and annotates them using a collection of viral profile hidden Markov models (HMMs). These include our manually-curated profile HMMs, which serve as specific taxonomic markers for a wide range of prokaryotic and eukaryotic viral taxa and are thus used to reliably classify viral contigs. We tested VIRify on assemblies from two microbial mock communities, a large metagenomics study, and a collection of publicly available viral genomic sequences from the human gut. The results showed that VIRify could identify sequences from both prokaryotic and eukaryotic viruses, and provided taxonomic classifications from the genus to the family rank with an average accuracy of 86.6%. In addition, VIRify allowed the detection and taxonomic classification of a range of prokaryotic and eukaryotic viruses present in 243 marine metagenomic assemblies. Finally, the use of VIRify led to a large expansion in the number of taxonomically classified human gut viral sequences and the improvement of outdated and shallow taxonomic classifications. Overall, we demonstrate that VIRify is a novel and powerful resource that offers an enhanced capability to detect a broad range of viral contigs and taxonomically classify them.


Acknowledgements: The authors thank Franziska Hufsky for improving the readability of the illustrations. The authors would also like to acknowledge Lorna Richardson for contributing to the edition of the final manuscript version.

Humans, Eukaryota, Eukaryotic Cells, Genome, Viral, Metagenome, Microbiota
Journal Title
PLoS Comput Biol
Conference Name
Journal ISSN
Volume Title
Public Library of Science (PLoS)
Biotechnology and Biological Sciences Research Council (BB/P027849/1)
Deutsche Forschungsgemeinschaft (CRC 1076 AquaDiva)