RecombinHunt: predicting new pandemics through data analysis
Combating future pandemics through data analysis of recombinant virus genomes. A study published in the journal Nature Communications presents the promising results of RecombinHunt, a new data-driven method developed by the Department of Electronics, Information and Bioengineering of the Politecnico di Milano and the University of Milan, which can identify, with high accuracy and computational efficiency, recombinant SARS-CoV-2 genomes with one or two breakpoints.
The research was possible thanks to the extraordinary contribution of laboratories from all over the world, which made more than 15 million viral sequences available to the international community.
Stefano Ceri, Professor of Information Processing Systems
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.
Our goal is to build warning tools to anticipate and combat new viral epidemics and pandemics.
Anna Bernasconi, SENSIBLE project leader
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.
A main contribution to the study was given by Dr. Tommaso Alfonsi, who recently earned a doctorate "cum laude" in Information Engineering, presenting this and other timely research.
The study demonstrates how the development of innovative and efficient computational methods allows us to more accurately and rigorously appreciate the evolution of pathogens, and any implications for human health.
Matteo Chiara, Professor of Molecular Biology at Università degli Studi di Milano and co-leader of the SENSIBLE project