Tracking and curating putative SARS-CoV-2 recombinants with RIVET

Abstract

AbstractIdentifying and tracking recombinant strains of SARS-CoV-2 is critical to understanding the evolution of the virus and controlling its spread. But confidently identifying SARS-CoV-2 recombinants from thousands of new genome sequences that are being shared online every day is quite challenging, causing many recombinants to be missed or suffer from weeks of delay in being formally identified while undergoing expert curation. We present RIVET – a software pipeline and visual platform that takes advantage of recent algorithmic advances in recombination inference to comprehensively and sensitively search for potential SARS-CoV-2 recombinants, and organizes the relevant information in a web interface that would help greatly accelerate the process identifying and tracking recombinants.Availability and ImplementationRIVET-based web interface displaying the most updated analysis of potential SARS-CoV-2 recombinants is available athttps://rivet.ucsd.edu/. RIVET’s frontend and backend code is freely available under MIT license athttps://github.com/TurakhiaLab/rivet. All inputs necessary for running the RIVET’s backend workflow for SARS-CoV-2 are available through a public database maintained by UCSC (https://hgdownload.soe.ucsc.edu/goldenPath/wuhCor1/UShER_SARS-CoV-2/).Contactyturakhia@ucsd.edu