Scaling up DNA digital data storage by efficiently predicting DNA hybridisation using deep learning

TL;DR

This paper demonstrates that deep learning models can efficiently predict DNA hybridisation, significantly improving throughput and scalability for DNA data storage and computing applications.

Contribution

The study introduces a large in silico hybridisation dataset and applies deep learning to predict DNA hybridisation with high accuracy and reduced inference time.

Findings

Achieved 100x faster hybridisation predictions compared to existing tools.

Created a dataset of over 2.5 million DNA hybridisation data points.

Enabled scalable DNA data storage workflows with deep learning.

Abstract

Deoxyribonucleic acid (DNA) has shown great promise in enabling computational applications, most notably in the fields of DNA digital data storage and DNA computing. Information is encoded as DNA strands, which will naturally bind in solution, thus enabling search and pattern-matching capabilities. Being able to control and predict the process of DNA hybridisation is crucial for the ambitious future of Hybrid Molecular-Electronic Computing. Current tools are, however, limited in terms of throughput and applicability to large-scale problems. We present the first comprehensive study of machine learning methods applied to the task of predicting DNA hybridisation. For this purpose, we introduce an in silico-generated hybridisation dataset of over 2.5 million data points, enabling the use of deep learning. Depending on hardware, we achieve a reduction in inference time ranging from one to over two orders of magnitude compared to the state-of-the-art, while retaining high fidelity. We then discuss the integration of our methods in modern, scalable workflows.