Convolutional Codes Based Index-Free Coding Strategy for High-Density DNA Storage

DNA data storage has become a promising solution for large-scale data storage as the cost of high-throughput DNA synthesis and DNA sequencing drops rapidly. In previous DNA data storage methods, the overall storage density is compromised as a specific portion of DNA bases are used as index segments to facilitate data recovery. In this work, a novel DNA storage strategy termed as Index-Free High-Density Convolutional Codes (IFHDCC) is introduced. Due to the serial coding nature of the convolutional code in the IFHDCC strategy, the correlation of data segment information between groups is fully exploited, the same organisational efficiency as index segment method is achived without sacrificing storage density. IFHDCC strategy generates DNA sequences compatible with current synthesis and sequencing technologies by superimposing pseudo-random sequences and employing row and column interleaving methods. To evaluate IFHDCC, we have constructed a dataset containing videos, images, audios, and documents. The computer simulation experiments conducted on the dataset show that the information density of our strategy is 1.75 bits/nt, which is a great improvement over the DNA Fountain strategy (1.57 bits/nt). Furthermore, the convolution matrix used in IFHDCC adds an additional layer of security, DNA sequences can only be decoded accurately with the correct convolution matrix, enhancing the security of information transmission.