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Machine learning–directed massively parallel programmable nucleic acid amplification

delete2026-03-25
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OA
AI
Z
Zhi Weng
W
Wenle Huang
Y
Yi Wu
X
Xuehao Xiu
H
Hui Lv
王飞 cover
王飞 (Fei Wang)
左
左小磊 (Xiaolei Zuo)
樊春海 cover
樊春海 (Chunhai Fan) *
宋萍 cover
宋萍 (Ping Song) *
DOI:10.1126/sciadv.aec9175delete
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Abstract

Abstract

En 中文
Dynamic regulation of amplification efficiency is pivotal yet challenging in molecular diagnostics and DNA data storage. Here, we develop a thermodynamics-based approach to achieve continuous and precise modulation of nucleic acid amplification efficiency. By decoupling sequence specificity from hybridization energy regulation via a primer-tag compensation strategy, we demonstrate programmed amplification with high resolution (33 versus 81%). Leveraging 2483 experimental data, we constructed a machine learning model that improved prediction accuracy from R2 = 0.62 to = 0.86. In DNA data storage, this amplification strategy increases the density for information preview by nearly one order of magnitude and robust file steganography via differential amplification. In clinical validation, our method outperformed uniform amplification in cervical cancer RNA variant analysis, detecting rare RNA fusions and improving detection sensitivity by 100-fold under 104 simulated sequencing depth. This programmable technique is anticipated to extend to single-cell sequencing and spatial transcriptomics, offering a powerful tool for molecular diagnostics and synthetic biology.
Keywords:
Nucleic acid amplification
Machine learning
DNA data storage
Molecular diagnostics
Programmable amplification

Journal

Science Advances cover
Science Advances
IF:
12.5
Papers:
2.1W
Citations:
18.1W

Organization

S
shanghai jiao tong university
Scholars:
15.7W
Papers: 11.7W
Citations: 159
S
Shanghai Jiao Tong University
Scholars:
7.8K
Papers: 2.4K
Citations: 14.8W
S
shanghai university
Scholars:
3.9W
Papers: 2.7W
Citations: 52
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