Scaling up DNA digital data storage by efficiently predicting DNA hybridisation using deep learning.
Authors
Buterez, David
Publication Date
2021-10-15Journal Title
Sci Rep
ISSN
2045-2322
Publisher
Springer Science and Business Media LLC
Volume
11
Issue
1
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Buterez, D. (2021). Scaling up DNA digital data storage by efficiently predicting DNA hybridisation using deep learning.. Sci Rep, 11 (1) https://doi.org/10.1038/s41598-021-97238-y
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.
Keywords
Article, /631/114/1314, /631/114/1305, /631/114/2405, /631/114/2398, article
Identifiers
s41598-021-97238-y, 97238
External DOI: https://doi.org/10.1038/s41598-021-97238-y
This record's URL: https://www.repository.cam.ac.uk/handle/1810/329502
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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