ViR: a tool to account for intrasample variability in the detection of viral integrations

Abstract

Lateral gene transfer (LT) from viruses to eukaryotic cells is a well-recognized phenomenon. Somatic integrations of viruses have been linked to persistent viral infection and genotoxic effects, including various types of cancer. As a consequence, several bioinformatic tools have been developed to identify viral sequences integrated into the human genome. Viral sequences that integrate into germline cells can be transmitted vertically, be maintained in host genomes and be co-opted for host functions. Endogenous viral elements (EVEs) have long been known, but the extent of their widespread occurrence has only been recently appreciated. Modern genomic sequencing analyses showed that eukaryotic genomes may harbor hundreds of EVEs, which derive not only from DNA viruses and retroviruses, but also from nonretroviral RNA viruses and are mostly enriched in repetitive regions of the genome. Despite being increasingly recognized as important players in different biological processes such as regulation of expression and immunity, the study of EVEs in non-model organisms has rarely gone beyond their characterization from annotated reference genomes because of the lack of computational methods suited to solve signals for EVEs in repetitive DNA. To fill this gap, we developed ViR, a pipeline which ameliorates the detection of integration sites by solving the dispersion of reads in genome assemblies that are rich of repetitive DNA. Using paired-end whole genome sequencing (WGS) data and a user-built database of viral genomes, ViR selects the best candidate couples of reads supporting an integration site by solving the dispersion of reads resulting from intrasample variability. We benchmarked ViR to work with sequencing data from both single and pooled DNA samples and show its applicability using WGS data of a non-model organism, the arboviral vector Aedes albopictus. Viral integrations predicted by ViR were molecularly validated supporting the accuracy of ViR results. Additionally, ViR can be readily adopted to detect any LT event providing ad hoc non-host sequences to interrogate.