ViralRecall: A Flexible Command-Line Tool for the Detection of Giant Virus Signatures in Omic Data

Abstract

Giant viruses are widespread in the biosphere and play important roles in biogeochemical cycling and host genome evolution. Also known as Nucleo-Cytoplasmic Large DNA Viruses (NCLDV), these eukaryotic viruses harbor the largest and most complex viral genomes known. Recent studies have shown that NCLDV are frequently abundant in metagenomic datasets, and that sequences derived from these viruses can also be found endogenized in diverse eukaryotic genomes. The accurate detection of sequences derived from NCLDV is therefore of great importance, but this task is challenging owing to both the high level of sequence divergence between NCLDV families and the extraordinarily high diversity of genes encoded in their genomes, including some encoding for metabolic or translation-related functions that are typically found only in cellular lineages. Here we present ViralRecall, a bioinformatic tool for the identification of NCLDV signatures in omic data. This tool leverages a library of Giant Virus Orthologous Groups (GVOGs) to identify sequences that bear signatures of NCLDV. We demonstrate that this tool can effectively identify NCLDV sequences with high sensitivity and specificity. Moreover, we show that it can be useful both for removing contaminating sequences in metagenome-assembled viral genomes as well as the identification of eukaryotic genomic loci that derived from NCLDV. ViralRecall is written in Python 3.5 and is freely available on GitHub: https://github.com/faylward/viralrecall.