R&D: GCNSA, DNA Storage Encoding with Graph Convolutional Network and Self-Attention

iScience has published an article written by Ben Cao, School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China, Bin Wang, Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, School of Software Engineering, Dalian University, Dalian 116622, China, and Qiang Zhang, School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.

Abstract: “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.“