ArticleBMC bioinformatics2026
ViralQuest: a user-friendly interactive pipeline for viral-sequences analysis and curation.
Article in BMC bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- From functional annotation to functional meaning in microbiome research.Environmental microbiome · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundHigh-throughput sequencing (HTS) has become an essential, unbiased tool in virology for identifying known and novel viruses. However, analyzing the large and complex datasets generated by HTS presents significant bioinformatics challenges. The process of accurately identifying and characterizing viral sequences from assembled contigs remains a bottleneck, often requiring specialized expertise and involving non-standardized parameters. There is a pressing need for robust, user-friendly, and reproducible pipelines to streamline this post-assembly analysis.
resultsTo address these challenges, we developed ViralQuest, a bioinformatics tool that automates the in-depth characterization of viral sequences from pre-assembled contigs. The pipeline integrates multiple lines of evidence for robust identification, using Diamond BLASTx against the Viral RefSeq database and pyHMMER searches against the RVDB, Vfam, and eggNOG profile HMM databases. For detailed characterization, ViralQuest performs taxonomic classification based on the ICTV nomenclature and functional annotation via Pfam domain analysis. Novel features of ViralQuest include an AI-powered summarization module that uses a Large Language Model (LLM) to generate contextual narratives for key viral findings and a comprehensive confidence score to rank putative viral contigs. All results are consolidated into a single, interactive HTML report that includes dynamic visualizations of contigs, ORFs, and protein domains, alongside detailed data tables that are exportable in TSV and SVG formats.
conclusionViralQuest provides an accessible and comprehensive solution for the post-assembly analysis of viral metagenomic data. By combining rigorous bioinformatics methods with novel AI-driven features and an intuitive reporting interface, it streamlines the complex process of viral identification and characterization. The tool enhances the interpretability and reliability of results, making in-depth virome analysis more accessible to the broader research community. ViralQuest is available on GitHub at https://github.com/gabrielvpina/viralquest/ .
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What OpenQuestion holds
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.