Combating future pandemics through data analysis of recombinant virus genomes. The study Data-driven recombination detection in viral genomes, published in the journal Nature Communications, presents the promising results of RecombinHunt, a new data-driven method developed by Stefano Ceri, Anna Bernasconi and Tommaso Alfonsi from the Department of Electronics, Information and Bioengineering of the Politecnico di Milano and Matteo Chiara from the University of Milan, which can identify, with high accuracy and computational efficiency, recombinant SARS-CoV-2 genomes with one or two breakpoints.
Recombination, that is, the composition of two or more viral genomes to form a new genome, is an efficient molecular mechanism for virus evolution and adaptation. Exploiting the incentive of the COVID-19 pandemic, several methods have been proposed to detect recombinant genomes of SARS-CoV-2 virus; however, so far, none has been able to faithfully confirm the manual analyses of experts in the field. ReconbinHunt shows high specificity and sensitivity, is more effective than all other methods already developed, and faithfully confirms manual expert analyses.
The method, developed under the PRIN PNRR 2022, SENSIBLE project (Small-data Early warNing System for viral pathogens In puBLic hEalth), also identifies recombinant viral genomes from the recent monkeypox epidemic with high concordance with analyses manually curated by experts, suggesting that the approach is robust and can be applied to any epidemic or pandemic virus, representing an important tool to combat future pandemics.