DNA Data Storage Research Articles

DNA Information Storage and Cryptography System

With the development of information technology, the global data volume is growing exponentially. In order to alleviate the contradiction between massive data and traditional storage technology, people begin to seek for a new generation of storage media. As a carrier of genetic information, DNA has the characteristics of high information density, long storage life and low maintenance cost, which can effectively overcome the deficiency of traditional storage media. With the development of DNA synthesis and DNA sequencing technology, DNA data storage technology has attracted more and more attention, and a series of major breakthroughs have been made. In this paper, with the workflow of DNA data storage as the main line, expounds the basic theory of DNA data storage and related technology, mainly introduced the research progress of DNA storage method and strategy, briefly summarizes the latest research results of DNA data cryptography, and finally discussed the major challenges that DNA data storage technology is facing , especially, DNA synthesis efficiency, DNA sequencing time cost and DNA data cryptography will be an important research direction of DNA storage technology in the future. It is believed that with the deepening of the data storage research on DNA, DNA data storage will become the most potential new storage method in the future, and can be a practical application in the future.

CRISPR-powered quantitative keyword search engine in DNA data storage

Despite the growing interest of archiving information in synthetic DNA to confront data explosion, quantitatively querying the data stored in DNA is still a challenge. Herein, we present Search Enabled by Enzymatic Keyword Recognition (SEEKER), which utilizes CRISPR-Cas12a to rapidly generate visible fluorescence when a DNA target corresponding to the keyword of interest is present. SEEKER achieves quantitative text searching since the growth rate of fluorescence intensity is proportional to keyword frequency. Compatible with SEEKER, we develop non-collision grouping coding, which reduces the size of dictionary and enables lossless compression without disrupting the original order of texts. Using four queries, we correctly identify keywords in 40 files with a background of ~8000 irrelevant terms. Parallel searching with SEEKER can be performed on a 3D-printed microfluidic chip. Overall, SEEKER provides a quantitative approach to conducting parallel searching over the complete content stored in DNA with simple implementation and rapid result generation.

Programming crystallization kinetics of self-assembled DNA crystals with 5-methylcytosine modification

Self-assembled DNA crystals offer a precise chemical platform at the ångström-scale for DNA nanotechnology, holding enormous potential in material separation, catalysis, and DNA data storage. However, accurately controlling the crystallization kinetics of such DNA crystals remains challenging. Herein, we found that atomic-level 5-methylcytosine (5mC) modification can regulate the crystallization kinetics of DNA crystal by tuning the hybridization rates of DNA motifs. We discovered that by manipulating the axial and combination of 5mC modification on the sticky ends of DNA tensegrity triangle motifs, we can obtain a series of DNA crystals with controllable morphological features. Through DNA-PAINT and FRET-labeled DNA strand displacement experiments, we elucidate that atomic-level 5mC modification enhances the affinity constant of DNA hybridization at both the single-molecule and macroscopic scales. This enhancement can be harnessed for kinetic-driven control of the preferential growth direction of DNA crystals. The 5mC modification strategy can overcome the limitations of DNA sequence design imposed by limited nucleobase numbers in various DNA hybridization reactions. This strategy provides a new avenue for the manipulation of DNA crystal structure, valuable for the advancement of DNA and biomacromolecular crystallography.

Exploring data storage innovations: From DNA to holography

This paper delves into the evolution of data storage technologies, focusing on the solid-state drive (SSD) and hard disk drive (HDD) and their working principles. It explores the advancements in DNA as a storage medium, highlighting its exceptional data density and potential as a long-term storage solution. The paper discusses the challenges and limitations of DNA data storage, emphasizing the need for further research to overcome these hurdles. Additionally, the paper introduces holographic data storage as a novel optical storage technology and discusses its potential applications. It also touches on the decline of the optical disc industry due to the emergence of alternative storage technologies. The use of metal nanoparticles (MNPs) in optical disks is explored, emphasizing their role in high-contrast imaging and data storage. The paper concludes by highlighting the diverse methods of data storage, each with its unique advantages and drawbacks, and anticipates future improvements in electronic information processing technology, leading to faster data transmission and increased data storage capacity within the same volume of storage media.

DNA as a universal chemical substrate for computing and data storage.

DNA computing and DNA data storage are emerging fields that are unlocking new possibilities in information technology and diagnostics. These approaches use DNA molecules as a computing substrate or a storage medium, offering nanoscale compactness and operation in unconventional media (including aqueous solutions, water-in-oil microemulsions and self-assembled membranized compartments) for applications beyond traditional silicon-based computing systems. To build a functional DNA computer that can process and store molecular information necessitates the continued development of strategies for computing and data storage, as well as bridging the gap between these fields. In this Review, we explore how DNA can be leveraged in the context of DNA computing with a focus on neural networks and compartmentalized DNA circuits. We also discuss emerging approaches to the storage of data in DNA and associated topics such as the writing, reading, retrieval and post-synthesis editing of DNA-encoded data. Finally, we provide insights into how DNA computing can be integrated with DNA data storage and explore the use of DNA for near-memory computing for future information technology and health analysis applications.

"Cell Disk" DNA Storage System Capable of Random Reading and Rewriting.

DNA has emerged as an appealing material for information storage due to its great storage density and durability. Random reading and rewriting are essential tasks for practical large-scale data storage. However, they are currently difficult to implement simultaneously in a single DNA-based storage system, strongly limiting their practicability. Here, a "Cell Disk" storage system is presented, achieving high-density in vivo DNA data storage that enables both random reading and rewriting. In this system, each yeast cell is used as a chamber to store information, similar to a "disk block" but with the ability to self-replicate. Specifically, each genome of yeast cell has a customized CRISPR/Cas9-based "lock-and-key" module inserted, which allows selective retrieval, erasure, or rewriting of the targeted cell "block" from a pool of cells ("disk"). Additionally, a codec algorithm with lossless compression ability is developed to improve the information density of each cell "block". As a proof of concept, target-specific reading and rewriting of the compressed data from a mimic cell "disk" comprising up to 105 "blocks" are demonstrated and achieve high specificity and reliability. The "Cell Disk" system described here concurrently supports random reading and rewriting, and it should have great scalability for practical data storage use.

Nanoporous Dna Field Effect Transistor with Potential for Random‐Access Memory Applications: A Selectivity Performance Evaluation

AbstractMethods to encode digital data items as strands of synthetic DNA followed by selective data retrieval have been demonstrated. However, these initially bio‐oriented processes remain slow and not optimized. DNA field‐effect transistor (DNA‐FET) is studied here as a possible random‐access memory (RAM) device for simple, selective and rapid ssDNA fragment retrieval used as data pool identifier. The DNA‐FET is based on a co‐planar Au‐gated fully organic transistor appended with short single‐stranded DNA (ssDNA) probes bearing a blocking molecule to prevent partial hybridization and achieve near perfect selectivity for short length ssDNA (up to 45 nt). Examination of transconductance of the novel active layer incorporating a DNA nanopore architecture reveals enhanced binding site accessibility. This, in turn, facilitates discriminatory hybridization, particularly in the physical retrieval of short‐length ssDNA from a competitive, concentrated ssDNA background pool consisting of nine different sequences, with at least one nucleotide difference. The DNA‐FET exhibits rapid operation (9 min) in the millivolt range, low detection limit (sub‐femtomolar), high selectivity and reusability. Considering the straightforward concept, near error‐free identification capacity and hypothetically outstanding scalability, the DNA‐FET described here has potential as a foundation for further exploration of advanced RAM technology in the DNA data storage process.

Inkjet-printed quantum dots on paper as concept towards high-density long-term data storage

Handling and storing the immense amounts of data native to the information age is a major challenge in terms of technological sustainability and energy demand. To date, tape storage remains the most widespread method for data archiving, while DNA data storage appears to offer the best data density and long-term stability in the future. However, DNA data storage is still in its infancy primarily due to economic and accessibility challenges. This emphasizes the need for more practical and readily available alternatives. We present a method for data storage utilizing inkjet printable quantum dots on paper with photoluminescence (PL) readout. Our proof of principle study showcases the ability to print and stack multiple bits of data on a single spot by exploiting the unique PL properties of quantum dots. This approach utilizes easily accessible resources, including a consumer-grade printer and paper as the substrate. Additionally, we perform initial stability tests, investigate scalability by controlling emission intensity, and evaluate the potential data density achievable by our approach.

Challenges for error-correction coding in DNA data storage: photolithographic synthesis and DNA decay

Photolithographic synthesis and decay – underexplored challenges to DNA data storage – are characterized in this study, uncovering avenues for workflow and codec optimizations. To aid the latter, scenarios to benchmark codecs are presented.

Unraveling the Promise of Computing DNA Data Storage: An Investigative Analysis of Advancements, Challenges, Future Directions

Unraveling the Promise of Computing DNA Data Storage: An Investigative Analysis of Advancements, Challenges, Future Directions

DNA data storage in electrospun and melt-electrowritten composite nucleic acid-polymer fibers

Incorporating biomolecules as integral parts of computational systems represents a frontier challenge in bio- and nanotechnology. Using DNA to store digital data is an attractive alternative to conventional information technologies due to its high information density and long lifetime. However, developing an adequate DNA storage medium remains a significant challenge in permitting the safe archiving and retrieval of oligonucleotides. This work introduces composite nucleic acid-polymer fibers as matrix materials for digital information-bearing oligonucleotides. We devised a complete workflow for the stable storage of DNA in PEO, PVA, and PCL fibers by employing electrohydrodynamic processes to produce electrospun nanofibers with embedded oligonucleotides. The on-demand retrieval of messages is afforded by non-hazardous chemical treatment and subsequent PCR amplification and DNA sequencing. Finally, we develop a platform for melt-electrowriting of polymer-DNA composites to produce microfiber meshes of programmable patterns and geometries.

Data Storage Using DNA.

The exponential growth of global data has outpaced the storage capacities of current technologies, necessitating innovative storage strategies. DNA, as a natural medium for preserving genetic information, has emerged as a highly promising candidate for next-generation storage medium. Storing data in DNA offers several advantages, including ultrahigh physical density and exceptional durability. Facilitated by significant advancements in various technologies, such as DNA synthesis, DNA sequencing, and DNA nanotechnology, remarkable progress has been made in the field of DNA data storage over the past decade. However, several challenges still need to be addressed to realize practical applications of DNA data storage. In this review, the processes and strategies of in vitro DNA data storage are first introduced, highlighting recent advancements. Next, a brief overview of in vivo DNA data storage is provided, with a focus on the various writing strategies developed to date. At last, the challenges encountered in each step of DNA data storage are summarized and promising techniques are discussed that hold great promise in overcoming these obstacles.

DNA Code Design Based on the Cosets of Codes over Z4

DNA code design is a challenging problem, and it has received great attention in the literature due to its applications in DNA data storage, DNA origami, and DNA computing. The primary focus of this paper is in constructing new DNA codes using the cosets of linear codes over the ring Z4. The Hamming distance constraint, GC-content constraint, and homopolymers constraint are all considered. In this study, we consider the cosets of Simplex alpha code, Kerdock code, Preparata code, and Hadamard code. New DNA codes of lengths four, eight, sixteen, and thirty-two are constructed using a combination of an algebraic coding approach and a variable neighborhood search approach. In addition, good lower bounds for DNA codes that satisfy important constraints have been successfully established using Magma software V2.24-4 and Python 3.10 programming in our comprehensive methodology.

Analysis and review of the possibility of using the generative model as a compression technique in DNA data storage: review and future research agenda

The amount of data in this world is getting higher, and overwriting technology also has severe challenges. Data growth is expected to grow to 175 ZB by 2025. Data storage technology in DNA is an alternative technology with potential in information storage, mainly digital data. One of the stages of storing information on DNA is synthesis. This synthesis process costs very high, so it is necessary to integrate compression techniques for digital data to minimize the costs incurred. One of the models used in compression techniques is the generative model. This paper aims to see if compression using this generative model allows it to be integrated into data storage methods on DNA. To this end, we have conducted a Systematic Literature Review using the PRISMA method in selecting papers. We took the source of the papers from four leading databases and other additional databases. Out of 2440 papers, we finally decided on 34 primary papers for detailed analysis. This systematic literature review (SLR) presents and categorizes based on research questions, namely discussing machine learning methods applied in DNA storage, identifying compression techniques for DNA storage, knowing the role of deep learning in the compression process for DNA storage, knowing how generative models are associated with deep learning, knowing how generative models are applied in the compression process, and knowing latent space can be formed. The study highlights open problems that need to be solved and provides an identified research direction.

DNA-Storage in Future Communication Networks

5G shifted the paradigm from mere communication to in-network computing, so that networks have started applying the compute-and-forward paradigm. With the advent of the current vision of future communication networks (e.g., 6G), in-network intelligence also has become pivotal. Data analytics and their storage for continuous accurate learning and intelligent decisionmaking have become part of the communication. This explosion of data to be stored for unknown duration of time is going to stress the future communication infrastructure, which will be unable to achieve the targeted energy usage together with low-latency and reliability. This article presents a possible solution via data storage based on synthetic DNA. We further propose approaches for the implementation of DNA data storage into future communication networks. The focus is especially on the impact that DNA storage can have for storing the historic data of a network controlled by artificial intelligence for network automation and prediction of future network states (intelligent management). In this sense, DNA data storage can offer a unique opportunity to store efficiently and reliably large amounts of data, mined during network monitoring and traffic engineering.

A digital twin for DNA data storage based on comprehensive quantification of errors and biases

Archiving data in synthetic DNA offers unprecedented storage density and longevity. Handling and storage introduce errors and biases into DNA-based storage systems, necessitating the use of Error Correction Coding (ECC) which comes at the cost of added redundancy. However, insufficient data on these errors and biases, as well as a lack of modeling tools, limit data-driven ECC development and experimental design. In this study, we present a comprehensive characterisation of the error sources and biases present in the most common DNA data storage workflows, including commercial DNA synthesis, PCR, decay by accelerated aging, and sequencing-by-synthesis. Using the data from 40 sequencing experiments, we build a digital twin of the DNA data storage process, capable of simulating state-of-the-art workflows and reproducing their experimental results. We showcase the digital twin’s ability to replace experiments and rationalize the design of redundancy in two case studies, highlighting opportunities for tangible cost savings and data-driven ECC development.

Insdel codes from subspace and rank-metric codes

Insertion and deletion (insdel in short) codes are designed to deal with synchronization errors in communication channels caused by insertions and deletions of message symbols. These codes have received a lot of attention due to their applications in diverse areas such as computational biology, DNA data storage, race-track memory error corrections, language processing, and synchronous digital communication networks. In the present work, we study constructions and limitations of insdel codes from rank metric and subspace codes. This paper studies and improves the idea of the work [4] by Hao Chen on the connection between insdel codes and subspace codes.We discuss why subspace code is a natural choice for constructing insdel codes and show that the interleaved Gabidulin codes can be used to construct nonlinear insdel codes approaching the Singleton bound. Then we show that the indexing scheme of transforming efficient Hamming metric codes to efficient insdel codes can be adapted for the class of rank metric codes. And that improves the base field size of the construction of insdel codes from lifted rank-metric codes. It is also shown that the size of the insdel code from a subspace code can be improved significantly than in the previously proposed construction. We give an algebraic condition for a linear Gabidulin rank metric code to be optimal insdel code adapting the condition proved for Reed-Solomon codes. Moreover, we give constructions of linear and nonlinear insdel codes from Sidon spaces.

Generalized Unique Reconstruction From Substrings

This paper introduces a new family of reconstruction codes which is motivated by applications in DNA data storage and sequencing. In such applications, DNA strands are sequenced by reading some subset of their substrings. While previous works considered two extreme cases in which all substrings of pre-defined lengths are read or substrings are read with no overlap for the single string case, this work studies two extensions of this paradigm. The first extension considers the setup in which consecutive substrings are read with some given minimum overlap. First, an upper bound is provided on the attainable rates of codes that guarantee unique reconstruction. Then, efficient constructions of codes that asymptotically meet that upper bound are presented. In the second extension, we study the setup where multiple strings are reconstructed together. Given the number of strings and their length, we first derive a lower bound on the read substrings' length $\ell$ that is necessary for the existence of multi-strand reconstruction codes with non-vanishing rates. We then present two constructions of such codes and show that their rates approach 1 for values of $\ell$ that asymptotically behave like the lower bound.

High Net Information Density DNA Data Storage by the MOPE Encoding Algorithm.

DNA has recently been recognized as an attractive storage medium due to its high reliability, capacity, and durability. However, encoding algorithms that simply map binary data to DNA sequences have the disadvantages of low net information density and high synthesis cost. Therefore, this paper proposes an efficient, feasible, and highly robust encoding algorithm called MOPE (Modified Barnacles Mating Optimizer and Payload Encoding). The Modified Barnacles Mating Optimizer (MBMO) algorithm is used to construct the non-payload coding set, and the Payload Encoding (PE) algorithm is used to encode the payload. The results show that the lower bound of the non-payload coding set constructed by the MBMO algorithm is 3%-18% higher than the optimal result of previous work, and theoretical analysis shows that the designed PE algorithm has a net information density of 1.90 bits/nt, which is close to the ideal information capacity of 2 bits per nucleotide. The proposed MOPE encoding algorithm with high net information density and satisfying constraints can not only effectively reduce the cost of DNA synthesis and sequencing but also reduce the occurrence of errors during DNA storage.

BO-DNA: Biologically optimized encoding model for a highly-reliable DNA data storage

DNA data storage is a promising technology that utilizes computer simulation, and synthetic biology, offering high-density and reliable digital information storage. It is challenging to store massive data in a small amount of DNA without losing the original data since nonspecific hybridization errors occur frequently and severely affect the reliability of stored data. This study proposes a novel biologically optimized encoding model for DNA data storage (BO-DNA) to overcome the reliability problem. BO-DNA model is developed by a new rule-based mapping method to avoid data drop during the transcoding of binary data to premier nucleotides. A customized optimization algorithm based on a tent chaotic map is applied to maximize the lower bounds that help to minimize the nonspecific hybridization errors. The robustness of BO-DNA is computed by four bio-constraints to confirm the reliability of newly generated DNA sequences. Experimentally, different medical images are encoded and decoded successfully with 12%–59% improved lower bounds and optimally constrained-based DNA sequences reported with 1.77bit/nt average density. BO-DNA's results demonstrate substantial advantages in constructing reliable DNA data storage.