The application of deep learning in reads reconstruction for DNA storage
DNA storage technology is a promising new type of data storage technology,it encodes dig-ital information into nucleotide sequences,then writes the sequences into DNA molecules through chemical synthesis,and finally reads the information through DNA sequencing technology.Compared to electronic storage technology,it has great advantages in information density,data security,and li-fespan.However,it outputs a large number of sequencing reads with base errors(substitution,inser-tion and deletion),therefore,to restore the original data from erroneous reads,reads reconstruction is usually performed.Accurate read reconstruction requires high success rate and time efficiency to a-chieve reliable file access and accelerate the reading and writing process of DNA storage technology.This paper introduces the existing deep learning-based reads reconstruction models.By comparing ar-chitecture,basic concepts and error correction abilities,we point out the limitations of these methods and discuss the prospects for future research directions.
DNA storagebase errorreads reconstructiondeep learning
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