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Review
. 2019 Jun 1;8(6):giz075.
doi: 10.1093/gigascience/giz075.

Carbon-based archiving: current progress and future prospects of DNA-based data storage

Affiliations
Review

Carbon-based archiving: current progress and future prospects of DNA-based data storage

Zhi Ping et al. Gigascience. .

Abstract

The information explosion has led to a rapid increase in the amount of data requiring physical storage. However, in the near future, existing storage methods (i.e., magnetic and optical media) will be insufficient to store these exponentially growing data. Therefore, data scientists are continually looking for better, more stable, and space-efficient alternatives to store these huge datasets. Because of its unique biological properties, highly condensed DNA has great potential to become a storage material for the future. Indeed, DNA-based data storage has recently emerged as a promising approach for long-term digital information storage. This review summarizes state-of-the-art methods, including digital-to-DNA coding schemes and the media types used in DNA-based data storage, and provides an overview of recent progress achieved in this field and its exciting future.

Keywords: in vitro DNA digital storage; in vivo DNA digital storage; DNA digital storage; binary-DNA encoding scheme.

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Figures

Figure 1:
Figure 1:
Binary transcoding methods used in DNA-based data storage schemes. (A) One binary bit is mapped to 2 optional bases [9]. Two binary bits are mapped to 1 fixed base [10]. (B) Eight binary bits are transcoded through Huffman coding and then transcoded to 5 or 6 bases [11]. (C) Two bytes (16 binary bits) are mapped to 9 bases [12]. (D) Eight binary bits are mapped to 5 bases [13].
Figure 2:
Figure 2:
Redundancy types used in DNA-based data storage schemes. (A) Increasing redundancy by repetition. (B) Increasing redundancy by an exclusive-or (XOR) calculation. (C) Increasing redundancy using Reed-Solomon (RS) code for 2 rounds. (D) Increasing redundancy using fountain code.
Figure 3:
Figure 3:
Two categories of DNA-based data storage application. (A) and (B) demonstrate 2 methods of in vivo DNA-based data storage; (C) and (D) demonstrate 2 methods of in vitro DNA-based data storage. (A) Array-based high-throughput DNA oligo analysis. DNA oligos carrying digital information are stored in the form of oligo pool. (B) DNA fragments synthesized by polymerase cycling assembly will carry the information to be stored. (C) Digital information inserted into a plasmid; plasmids are then transferred into bacterial cells. (D) DNA fragments carrying digital information are inserted into the bacterial genome using the CRISPR system using Cas1-Cas2 integrase.
Figure 4:
Figure 4:
Key events in DNA synthesis and DNA sequencing, and their key applications in DNA-based data storage. PacBio: Pacific Biosciences.
Figure 5:
Figure 5:
Interrelationship between DNA oligo length, optimal index length, and net coding efficiency in a model of 1-GB digital file transcoding.

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