VirNA: a novel Minimum Spanning Networks algorithm for investigating viral evolution

ABSTRACT Next Generation Sequencing technologies are essential in public health surveillance for tracking pathogen evolution, spread, and the emergence of new variants. However, the extensive sequencing of viral genomes during recent pandemics has highlighted the limitations of traditional molecular phylogenetic algorithms in capturing fine-grained evolutionary details, emphasizing the urge for more effective approaches to manage these large-scale data. VirNA (Viral Network Analyzer) addresses this challenge by reconstructing detailed mutation patterns and tracing pathogen evolutionary routes in specific regions through Minimum Spanning Networks. It analyzes thousands of sequences, generating networks where nodes represent identical genomic sequences linked to their metadata, while edges represent evolutionary pathways. AUTHOR SUMMARY The authors present Viral Network Analyzer (VirNA), a new tool designed to help track the spread of viruses during pandemics. During recent outbreaks, researchers faced challenges due to the sheer volume of viral genome data. Traditional tools struggled to process this massive amount of information effectively. VirNA solves this problem by using a powerful method called Minimum Spanning Networks (MSN) with enhanced features to analyze large datasets quickly and accurately. VirNA allows scientists to map how viruses spread in specific areas, providing critical insights that other tools couldn’t achieve. It complements traditional phylogenetic methods, offering a detailed look at viral transmission routes. This makes it a valuable asset for public health surveillance, helping experts understand and respond to pandemics more effectively. Already tested in previous studies, VirNA represents a significant breakthrough for future pandemic preparedness, offering new ways to handle and interpret big data in viral research.