Predicting sequence-specific amplification efficiency in multi-template PCR with deep learning

Andreas L. Gimpel, Bowen Fan, Dexiong Chen, Laetitia O. D. Wölfle, Max Horn, Laetitia Meng-Papaxanthos, Philipp L. Antkowiak, Wendelin J. Stark, Beat Christen, Karsten Borgwardt () and Robert N. Grass ()
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Andreas L. Gimpel: ETH Zurich
Bowen Fan: ETH Zurich
Dexiong Chen: ETH Zurich
Laetitia O. D. Wölfle: University of Stuttgart
Max Horn: ETH Zurich
Laetitia Meng-Papaxanthos: ETH Zurich
Philipp L. Antkowiak: ETH Zurich
Wendelin J. Stark: ETH Zurich
Beat Christen: University of Stuttgart
Karsten Borgwardt: ETH Zurich
Robert N. Grass: ETH Zurich

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Multi-template polymerase chain reaction (PCR) is a critical technique enabling the parallel amplification of diverse DNA molecules, thereby facilitating applications in fields from quantitative molecular biology to DNA data storage. However, non-homogeneous amplification due to sequence-specific amplification efficiencies often results in skewed abundance data, compromising accuracy and sensitivity. In this study, we address amplification efficiency in complex amplicon libraries by employing one-dimensional convolutional neural networks (1D-CNNs) to predict sequence-specific amplification efficiencies, based on sequence information alone. Trained on reliably annotated datasets derived from synthetic DNA pools, these models achieve a high predictive performance (AUROC: 0.88, AUPRC: 0.44), thereby enabling the design of inherently homogeneous amplicon libraries. We further introduce CluMo, a deep learning interpretation framework that identifies specific motifs adjacent to adapter priming sites as closely associated with poor amplification. This insight leads to the elucidation of adapter-mediated self-priming as the major mechanism causing low amplification efficiency, challenging long-standing PCR design assumptions. By addressing the basis for non-homogeneous amplification in multi-template PCR, our deep-learning approach reduces the required sequencing depth to recover 99% of amplicon sequences fourfold, and opens new avenues to improve the efficiency of DNA amplification in fields such as genomics, diagnostics, and synthetic biology.

Date: 2025
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DOI: 10.1038/s41467-025-64221-4

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