ScateClave: software complex for analysis and tracking of evolutionary changes in large amounts of viral genomes

Source abstract

As of June 2026, more than 18.5 million SARS-CoV-2 genomes have been collected and sequenced worldwide, creating a viral dataset of unprecedented scale. Such collections provide exceptional opportunities for studying viral evolution, including the dynamics of nucleotide substitutions, deletions, insertions, recombination-related patterns, and the emergence and spread of new variants across geographic regions. Although several web resources provide access to genome repositories and online analytical tools, many research problems still require local computation with custom algorithms, user-defined workflows, and private or unpublished sequences. In these settings, computational efficiency depends critically on how genomic data are loaded, represented, stored, and processed. Here we present ScateClave, an open-source software complex for local analysis and tracking of evolutionary changes in large viral genome collections. The system was developed and practically validated on SARS-CoV-2. Its computational core is implemented in C++, whereas static and interactive visualization is provided in Python. ScateClave is designed for efficient loading, sparse in-memory representation, and scalable downstream analysis of annotated genomes on a single workstation. The complex produces several coordinated analytical outputs, including a time-distance landscape, position-specific change matrices across the coding region, sliding-window profiles for Pango lineages, graphs of locally plausible transitions, and reconstructed paths between genome states. Although the current implementation and validation are specific to SARS-CoV-2, the underlying algorithms rely on general operations over sparse genomic events, temporal indices, and metadata, making the approach extensible to other viruses and suitable as a foundation for developing new large-scale genomic analysis methods.