The Essential Component in DNA-Based Information Storage System: Robust Error-Tolerating Module

Abstract

The size of digital data is ever increasing and is expected to grow to 40,000 EB by 2020, yet the estimated global information storage capacity in 2011 is <300 EB, indicating that most of the data are transient. DNA, as a very stable nano-molecule, is an ideal massive storage device for long-term data archive. The two most notable illustrations are from Church et al. and Goldman et al., whose approaches are well-optimized for most sequencing platforms – short synthesized DNA fragments without homopolymer. Here, we suggested improvements on error handling methodology that could enable the integration of DNA-based computational process, e.g., algorithms based on self-assembly of DNA. As a proof of concept, a picture of size 438 bytes was encoded to DNA with low-density parity-check error-correction code. We salvaged a significant portion of sequencing reads with mutations generated during DNA synthesis and sequencing and successfully reconstructed the entire picture. A modular-based programing framework – DNAcodec with an eXtensible Markup Language-based data format was also introduced. Our experiments demonstrated the practicability of long DNA message recovery with high error tolerance, which opens the field to biocomputing and synthetic biology.

Deoxyribonucleic Acid as a Tool for Digital Information Storage: An Overview

“…As the data increases, the current data storage technology would not be enough to store data in the future as data is growing every day. In 2011, International data corporation (IDC) estimated that the global information storage capacity is 264 EB (Yim et al, 2014). The demand for storing more and more data is increasing day by day, which urges the data scientists to strive for the development of better data storage medium.…”

On optimal family of codes for archival DNA storage

“…But due to redundancy there was an increase in the length of DNA which makes the technique expensive to use for commercial purpose. In 2014, Aldrin Kay-Yuen Yim et al [7] introduced a method using Low Density Parity-Check (LDPC) codes to encode data in the large DNA chunk. The main challenges in the DNA data storage systems are the limitations regarding the size of the data inserted in DNA and the cost associated with DNA synthesis and sequencing technology.…”

Coding Over Sets for DNA Storage

“…One way to address this problem is using block addresses, also called indices, that are stored as part of the strand. Errors in DNA are typically substitutions, insertions, and deletions, where most published studies report that either substitutions or deletions are the most prominent ones, depending upon the specific technology for synthesis and sequencing [2], [3], [4], [5], [6], [7]. For example, in column-based DNA oligo synthesis the dominant errors are deletions that result from either failure to remove the dimethoxytrityl (DMT) or combined inefficiencies in the coupling and capping steps [4].…”