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Open AccessArticle

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

Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA
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Author to whom correspondence should be addressed.
Academic Editor: Philippe Colson
Viruses 2021, 13(2), 150; https://doi.org/10.3390/v13020150
Received: 14 December 2020 / Revised: 7 January 2021 / Accepted: 18 January 2021 / Published: 20 January 2021
(This article belongs to the Special Issue Genomics of Giant Viruses)
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 (NCLDVs), these eukaryotic viruses harbor the largest and most complex viral genomes known. Studies have shown that NCLDVs 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 NCLDVs 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 NCLDVs. 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. View Full-Text
Keywords: giant viruses; nucleo-cytoplasmic large DNA viruses; metagenomics; endogenous viral elements; viral diversity giant viruses; nucleo-cytoplasmic large DNA viruses; metagenomics; endogenous viral elements; viral diversity
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MDPI and ACS Style

Aylward, F.O.; Moniruzzaman, M. ViralRecall—A Flexible Command-Line Tool for the Detection of Giant Virus Signatures in ‘Omic Data. Viruses 2021, 13, 150. https://doi.org/10.3390/v13020150

AMA Style

Aylward FO, Moniruzzaman M. ViralRecall—A Flexible Command-Line Tool for the Detection of Giant Virus Signatures in ‘Omic Data. Viruses. 2021; 13(2):150. https://doi.org/10.3390/v13020150

Chicago/Turabian Style

Aylward, Frank O.; Moniruzzaman, Mohammad. 2021. "ViralRecall—A Flexible Command-Line Tool for the Detection of Giant Virus Signatures in ‘Omic Data" Viruses 13, no. 2: 150. https://doi.org/10.3390/v13020150

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