Viruses from many families are important human pathogens, and advances in metagenomic sequencing have enabled broad and unbiased virus detection. At the same time, there is a need for reproducible workflows that support both detection and interpretation of ambiguous and potentially novel viral findings. We developed a scalable and modular viral discovery framework for reproducible metagenomic analysis. The workflow is implemented in Nextflow and integrates read-based analysis, assembly creation, assembly-based classification, assembly statistics, machine learning-based post-classification, and interactive HTML reporting. In addition to automated processing, it includes inspection tools for interpretation of ambiguous and unclassified sequences, including full lineage reporting, metadata, filterable human-virus tagging, built-in support for querying NCBI resources, a built-in ORF viewer for unknown viral hits, and a viral likelihood scoring system for prioritization of uncertain findings. This makes the workflow useful for both virus detection and research-oriented interpretation of unclear results. The workflow was applied to blood plasma, CSF, and fecal samples from Swedish clinical data, together with publicly available oral microbiome data generated for bacterial profiling rather than viral discovery. The analysis identified both RNA and DNA viruses from multiple viral families across diverse sample types, and supported prioritization of uncertain or potentially novel viral sequences. In the oral microbiome dataset, the workflow recovered and supported inspection of a contig matching HIV despite the dataset's original bacterial focus, highlighting utility for exploratory cross-domain metagenomic analysis. The workflow generates portable HTML reports for rapid overview and downstream analysis, and is being applied in ongoing research studies.

Co-authors: Björn Andersson, Tobias Allander