PoolPy: Automated combinatorial pooling for high-throughput molecular profiling

Fuente: arXiv
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Hauptverfasser: Talamanca, Lorenzo, Trouillon, Julian
Format: Preprint
Veröffentlicht: 2025
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author Talamanca, Lorenzo
Trouillon, Julian
author_facet Talamanca, Lorenzo
Trouillon, Julian
contents Combinatorial group testing reduces screening costs and turnaround time but remains challenging to apply due to design complexity, varying applicability, and 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 tailored to application-specific constraints across assay modalities. We demonstrate PoolPy utility for protein-ligand interaction screening and genome-wide molecular profiling, enabling the scaling up of multi-readout functional assays.
format Preprint
id arxiv_https___arxiv_org_abs_2509_03481
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle PoolPy: Automated combinatorial pooling for high-throughput molecular profiling
Talamanca, Lorenzo
Trouillon, Julian
Information Theory
Combinatorial group testing reduces screening costs and turnaround time but remains challenging to apply due to design complexity, varying applicability, and 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 tailored to application-specific constraints across assay modalities. We demonstrate PoolPy utility for protein-ligand interaction screening and genome-wide molecular profiling, enabling the scaling up of multi-readout functional assays.
title PoolPy: Automated combinatorial pooling for high-throughput molecular profiling
topic Information Theory
url https://arxiv.org/abs/2509.03481