Scaling Variant-Aware Multiplex Primer Design

Abstract

MotivationRobust primer design is essential for reliable multiplex PCR in diverse and evolving pathogen, microbial, and host genomes. Traditional methods optimized for a single reference often fail on emerging variants, leading to reduced efficiency. Variant-aware design seeks primers that remain effective across diverse targets, but this introduces two key challenges: identifying robust candidates and selecting an optimal subset of primers. Although there are methods for the first challenge, namely the Primer Design Region (PDR) optimization problem, existing approaches lack optimality guarantees. ResultsWe introduce a near-linear algorithm with provable guarantees for efficient PDR optimization. Complementing this, we introduce a reference-free risk model based on Gini impurity that provides a stable, biologically interpretable measure of site-specific variation and yields PDRs that are robust to sequence diversity across datasets without ad hoc smoothing. For the second challenge related to thermodynamic stability, we optimize predicted {Delta}G and cast subset selection as a k-partite maximum-weight clique problem (NP-hard). We then design an efficient local-search heuristic with linear time updates. Together, these advances yield a principled, scalable framework for variant-aware primer design. Across Foot-and-Mouth Disease virus and Zika virus datasets, {Delta}-PRO produces more compact and robust PDR sets and multiplex panels with reduced predicted dimerization compared to existing tools, demonstrating the practical gains of principled and scalable variant-aware primer design for high-throughput multiplex PCR assays. AvailabilityThe proposed methods are implemented in a software package. Our implementation and results are publicly available at https://github.com/yhhan19/variant-aware-primer-design. Supplementary informationSupplementary materials are available online.