High‐Fidelity Data Retrieval from Synthetic DNA Pools via Machine Learning Model

ABSTRACT Synthetic DNA offers significant benefits, remarkable information density and long‐term stability for data storage. The practical success of DNA data storage, however, depends on solving the challenge of selective retrieval‐accessing specific data with high fidelity from complex DNA mixtures. Here, we present a machine learning method to achieve high‐fidelity, isothermal selective data retrieval from synthetic DNA pools. We designed a toehold‐triggered isothermal DNA storage, wherein each data sequence is indexed by a unique stem‐loop “lock” sequence. Complementary “key” oligos are designed to unlock these lock‐sequences. A machine learning model was trained on a comprehensive dataset generated from a synthetic DNA pool comprising 12,000 diverse 8‐nt lock sequences. This library was designed to encompass nearly complete theoretical sequence diversity of 8‐nt sequences, enabling the model to learn nucleotide sequence recognition specificity beyond conventional thermodynamic hybridization principles. As a result, the machine learning model generated high specific keys achieving a maximum improvement of 292‐fold in the signal‐to‐noise ratio during the amplification of selected data sequences from a complex synthetic DNA pool. Thus, this isothermal selective retrieval method enables practical, low‐energy DNA data storage. Concurrently, the machine learning model provides insight into DNA sequence specificity, extending its utility beyond data storage applications.