ArticleBioinformatics (Oxford, England)2025
HI-FEVER: a Nextflow pipeline for the high-throughput discovery and annotation of endogenous viral elements.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- RdRpCATCH: a unified resource for RNA virus discovery using viral RNA-dependent RNA polymerase profile Hidden Markov models.NAR genomics and bioinformatics · 2026Article
- An ancient alpharetrovirus lineage in bats: Evolutionary insights and possible roles in reproduction.PLoS genetics · 2026Article
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Authors and funding
5 authors.
Funding
Abstract
summaryEndogenous viral elements (EVEs) offer valuable insights into virus and host evolution, but their detection remains computationally and biologically challenging. We present HI-FEVER, a user-friendly Nextflow pipeline for the discovery of EVEs in eukaryotic host genomes. HI-FEVER is highly parallelizable and customizable, ensuring computational efficiency while allowing researchers to fine-tune parameters to their specific needs. Its output provides a comprehensive analysis of discovered EVEs, including detailed annotations which can provide evolutionary insights. HI-FEVER scales seamlessly to handle millions of viral protein queries across multiple host genomes on both laptops and high-performance computing nodes. AVAILABILITY AND IMPLEMENTATION: The HI-FEVER source code is available on GitHub at https://github.com/PaleovirologyLab/hi-fever. Minimal reference databases, test datasets and benchmarking results are hosted on the Open Science Framework at https://osf.io/y357r. A detailed wiki is available at https://github.com/PaleovirologyLab/hi-fever/wiki, including usage instructions, parameter descriptions, and guidance on interpreting outputs. The pipeline includes a Pixi environment compatible with Conda and Apptainer containerization, and Docker images. HI-FEVER has been tested on Linux, Windows (via WSL2), and macOS (Intel and ARM64).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.