ReLume: Enhancing DNA storage data reconstruction with flow network and graph partitioning

Abstract

DNA storage is an ideal alternative to silicon-based storage, but focusing on data writing alone cannot address the inevitable errors and durability issues. Therefore, we propose ReLume, a DNA storage data reconstruction method based on flow networks and graph partitioning technology, which can accomplish the data reconstruction task of millions of reads on a laptop with 24 GB RAM. The results show that ReLume copes well with many types of errors, more than doubles sequence recovery rates, and reduces memory usage by about 60 %. ReLume is 10 times more durable than other representative methods, meaning that data can be read without loss after 100 years. Results from the wet lab DNA storage dataset show that ReLume's sequence recovery rates of 73 % and 93.2 %, respectively, significantly outperform existing methods. In summary, ReLume effectively overcomes the accuracy and hardware limitations and provides a feasible idea for the portability of DNA storage.

Keywords: DNA storage; DNA storage data reconstruction; De novo assembly; Hash conflict detection.