Efficient primer design for broad-range virus detection: A semi-automated workflow using sequence clustering and varVAMP

Abstract

Broad-range RT-qPCR assays are essential for detecting highly diverse viruses within virus families, but designing suitable primers remains challenging because of extensive sequence diversity and biases in public sequence databases. Here, we present a semi-automated workflow that combines sequence clustering and varVAMP to streamline pan-RT-qPCR primer design. The workflow reduces sequence redundancy, improves the representation of viral diversity, and facilitates the identification of broadly reactive primer candidates. We demonstrate the workflow using the arboviral genera Orthoflavivirus and Alphavirus as proof-of-concept datasets, showing its applicability to large and genetically diverse viral sequence collections. The workflow provides a practical, scalable, and reproducible framework for developing broad-range primer candidates and may support surveillance, diagnostics, and preparedness for emerging or divergent viruses.