Combinatorial group testing for efficient scaling across biological applications.

Abstract

Combinatorial group testing can reduce experimental costs and turnaround time by strategically pooling samples to minimize the number of measurements needed for a given experiment. Despite broad potential utility, it remains underutilized due to its intrinsic complexity and the lack of implementation tools. Here we present PoolPy, a unified end-to-end framework and web platform to benchmark, automate, and decode combinatorial group testing strategies. PoolPy tailors pooling designs to application-specific constraints, such as time, cost, or signal dilution, across experiment types. By implementing ten different pooling algorithms, which we comprehensively benchmark in silico across >100,000 conditions, we identify key design trade-offs that define pooling applicability to specific use cases. We experimentally validate PoolPy across diverse applications, including protein-ligand interaction screening, RT-qPCR viral testing and genome-wide protein-DNA interaction profiling, achieving a 60 to 93% reduction in number of measurements needed. Overall, PoolPy provides a scalable, user-friendly ecosystem to increase throughput and reduce costs across biological applications. PoolPy is available at https://poolpy.trouillonlab.org  for open use.