VirusRecom

VirusRecom detects recombination events in viral genomes using an information-theoretic framework to distinguish recombination from in situ mutation in low-divergence viruses such as SARS-CoV-2.

Key Features:

• Information theory-based methodology: Conceptualizes recombination as a transmission process of "information" to enable detection in highly similar sequences.
• Weighted Information Content (WIC): Quantifies the contribution of recombination within specific genomic regions by comparing WIC values across segments.
• Benchmark performance: Demonstrated, on simulated data, a superior balance of precision and recall relative to RDP5 and 3SEQ.
• Application to SARS-CoV-2 recombinants: Applied to detect and more precisely localize recombination regions in SARS-CoV-2 recombinant forms XE, XD, and XF.

Scientific Applications:

• Recombination detection in low-divergence viruses: Distinguishes recombination from in situ mutation in highly similar viral genomes such as SARS-CoV-2.
• Breakpoint localization: Identifies genomic regions with elevated WIC to localize putative recombination breakpoints.
• Tracking recombinant lineages: Supports identification and regional localization of recombinant SARS-CoV-2 forms including XE, XD, and XF.
• Method benchmarking: Provides a comparative framework for assessing recombination detection performance against tools like RDP5 and 3SEQ using simulated datasets.

Methodology:

Implements an information-theoretic framework that models recombination as information transmission, computes Weighted Information Content (WIC) per genomic region, benchmarks performance on simulated data against RDP5 and 3SEQ using precision and recall, and applies WIC-based localization to SARS-CoV-2 sequences to detect XE, XD, and XF recombinants.