ArticleMolecular ecology resources2025
detectEVE: Fast, Sensitive and Precise Detection of Endogenous Viral Elements in Genomic Data.
Article in Molecular ecology resources, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Endogenous RNA virus-derived elements exhibit functional divergence in modulating development and virus susceptibility inVirulence · 2026Article
- RdRpCATCH: a unified resource for RNA virus discovery using viral RNA-dependent RNA polymerase profile Hidden Markov models.NAR genomics and bioinformatics · 2026Article
- HI-FEVER: a Nextflow pipeline for the high-throughput discovery and annotation of endogenous viral elements.Bioinformatics (Oxford, England) · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Endogenous viral elements (EVEs) are fragments of viral genomic material embedded within the host genome. Retroviruses contribute to the majority of EVEs because of their genomic integration during their life cycle; however, the latter can also arise from non-retroviral RNA or DNA viruses, then collectively known as non-retroviral (nr) EVEs. Detecting nrEVEs poses challenges because of their sequence and genomic structural diversity, contributing to the scarcity of specific tools designed for nrEVEs detection. Here, we introduce detectEVE, a user-friendly and open-source tool designed for the accurate identification of nrEVEs in genomic assemblies. detectEVE deviates from other nrEVE detection pipelines, which usually classify sequences in a more rigid manner as either virus-associated or not. Instead, we implemented a scaling system assigning confidence scores to hits in protein sequence similarity searches, using bit score distributions and search hints related to various viral characteristics, allowing for higher sensitivity and specificity. Our benchmarking shows that detectEVE is computationally efficient and accurate, as well as considerably faster than existing approaches, because of its resource-efficient parallel execution. Our tool can help to fill current gaps in both host-associated fields and virus-related studies. This includes (i) enhancing genome annotations with metadata for EVE loci, (ii) conducting large-scale paleo-virological studies to explore deep viral evolutionary histories, and (iii) aiding in the identification of actively expressed EVEs in transcriptomic data, reducing the risk of misinterpretations between exogenous viruses and EVEs.
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Registered trials
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