Cao Ben, Wang Bin, Zhang Qiang
School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.
Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, School of Software Engineering, Dalian University, Dalian 116622, China.
iScience. 2023 Feb 19;26(3):106231. doi: 10.1016/j.isci.2023.106231. eCollection 2023 Mar 17.
DNA Encoding, as a key step in DNA storage, plays an important role in reading and writing accuracy and the storage error rate. However, currently, the encoding efficiency is not high enough and the encoding speed is not fast enough, which limits the performance of DNA storage systems. In this work, a DNA storage encoding system with a graph convolutional network and self-attention (GCNSA) is proposed. The experimental results show that DNA storage code constructed by GCNSA increases by 14.4% on average under the basic constraints, and by 5%-40% under other constraints. The increase of DNA storage codes effectively improves the storage density of 0.7-2.2% in the DNA storage system. The GCNSA predicted more DNA storage codes in less time while ensuring the quality of codes, which lays a foundation for higher read and write efficiency in DNA storage.
DNA编码作为DNA存储中的关键步骤，在读写准确性和存储错误率方面发挥着重要作用。然而，目前编码效率不够高且编码速度不够快，这限制了DNA存储系统的性能。在这项工作中，提出了一种具有图卷积网络和自注意力机制的DNA存储编码系统（GCNSA）。实验结果表明，由GCNSA构建的DNA存储码在基本约束下平均增加了14.4%，在其他约束下增加了5%-40%。DNA存储码的增加有效地将DNA存储系统中的存储密度提高了0.7-2.2%。GCNSA在保证编码质量的同时，在更短的时间内预测出更多的DNA存储码，为提高DNA存储中的读写效率奠定了基础。
