TransDNA: A Deep Transfer Learning Network for Sequence Reconstruction in DNA-Based Data Storage

DNA is a promising storage medium, offering advantages in high density, long durability, and low maintenance cost. However, information recovery in DNA storage systems is challenged by errors arising during synthesis, amplification, and sequencing phases. A key challenge in decoding is sequence reconstruction, which involves recovering the original reference sequence from a set of noisy copies. While recent research has explored deep learning-based methods for this task, the high cost of synthesis and sequencing results in a limited availability of training samples. To overcome this challenge, we propose TransDNA, a deep transfer learning network specifically designed for sequence reconstruction in DNA storage. It consists of an encoder, a domain-specific decoder, and a domain-invariant feature extractor, with alternating domain alignment and domain-specific reconstruction mechanisms. By transferring knowledge from a larger source dataset, TransDNA significantly enhances the reconstruction success rate on two target datasets from real DNA storage experiments, outperforming the base model without transfer learning and several comparative methods. Notably, TransDNA surpasses the SDG method in both reconstruction success rate and training efficiency. These results demonstrate the effectiveness of TransDNA as the first transfer learning approach applied to the DNA sequence reconstruction task.