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The increasing demand for image data storage exceeds the capabilities of current technology and DNA, as an emerging storage medium, is expected to resolve the challenge of storing massive image data. Recent DNA image storage methods adopted fixed code tables and error-correcting codes without leveraging image characteristics, leading to low coding density, low security, and poor reconstruction. In this paper, according to the characteristics of images and DNA storage, we propose a method called DNA Lossy Storage Image Encryption and Denoising (DNA-LSIED), which focuses on image security and high-quality image reconstruction. Firstly, DNA-LSIED converts the pixel matrix into DNA sequences using the chaotic encryption algorithm to balance the GC content. Then, it employs the maximum-probability insertion and deletion strategy to convert base insertion and deletion errors into substitution errors which are dispersed and converted into noise on the image through data interleaving. Finally, median filtering is applied to remove the noise caused by base substitution errors to reconstruct high-quality images. Compared to other methods, DNA-LSIED has significant advantages in terms of reconstructed image quality, coding density and synthesized cost, which contributes to cost savings and large-scale applications. DNA-LSIED provides a new insight into the storage of images in DNA and combine biotechnology with computer technology, which facilitates interdisciplinary applications. [ABSTRACT FROM AUTHOR]

Food anti-counterfeiting and traceability are important ways to ensure food safety. In response to the upgrading of counterfeiting means, a variety of identification technologies have been gradually developed, which play a crucial role in the field of food quality and safety. In recent years, relying on the advantages of DNA molecules, more advanced and precise identification and tracking technologies have been developed, which provide new ideas for the establishment of an efficient anti-counterfeiting and accurate traceability system for food safety monitoring. This paper outlines the development process of DNA storage technology and DNA traceability code, describes the current status of and latest progress in the application of DNA traceability code in food anti-counterfeiting and traceability, and discusses the necessity of interdisciplinary cooperation in promoting the development of DNA traceability technology, as well as sustainability and safety issues that may be encountered in the implementation process. Finally, future trends in the development of DNA traceability technology are envisioned, and possible solutions are proposed for the current challenges, aiming to provide more scientific and efficient means for food authenticity identification and origin tracing.

Abstract This paper presents a low-cost, portable sensing platform for rapid DNA mass measurement, addressing a critical need in life science research. The platform features a novel interdigital open-gate junction field-effect transistor (ID-OGJFET) with a large sensing area that converts negatively charged DNA mass into an electrical current. The system enables DNA mass detection in under ten seconds with a resolution of less than 1 µA, demonstrating sensitivity across a range from 0.48 ng to 29.5 ng, achieving a Limit of Detection as low as 1.18–1.25 ng/µL. A custom-designed electronic reader and fluidic sample holder facilitate efficient operation. Simulation studies using molecular dynamics and finite element methods provide further insights into the sensor’s DNA detection mechanism. This highly sensitive system is significantly more cost-effective than commercially available semiconductor characterization alternatives. The device’s high performance and affordability make it a valuable tool for molecular biology applications, and it holds potential for advancing FET-based sensing instrumentation and measurement research.

Abstract Background Efficient DNA-based storage systems offer substantial capacity and longevity at reduced costs, addressing anticipated data growth. However, encoding data into DNA sequences is limited by two key constraints: 1) a maximum of h consecutive identical bases (homopolymer constraint h), and 2) a GC ratio between $$ [0.5 - c_{{GC}}, 0.5 + c_{{GC}} ] $$ [ 0.5 - c GC , 0.5 + c GC ] (GC content constraint $$c_{GC}$$ c GC ). Sequencing or synthesis errors tend to increase when these constraints are violated. Results In this research, we address a pure source coding problem in the context of DNA storage, considering both homopolymer and GC content constraints. We introduce a novel coding technique that adheres to these constraints while maintaining linear complexity for increased block lengths and achieving near-optimal rates. We demonstrate the effectiveness of the proposed method through experiments on both randomly generated data and existing files. For example, when $$h = 4$$ h = 4 and $$c_{GC} = 0.05$$ c GC = 0.05 , the rate reached 1.988, close to the theoretical limit of 1.990. The associated code can be accessed at GitHub. Conclusion We propose a variable-to-variable-length encoding method that does not rely on concatenating short predefined sequences, which achieves near-optimal rates.

With the rapid increase in global data and rapid development of information technology, DNA sequences have been collected and manipulated on computers. This has yielded a new and attractive field of bioinformatics, DNA storage, where DNA has been considered as a great potential storage medium. It is known that one gram of DNA can store 215 GB of data, and the data stored in the DNA can be preserved for tens of thousands of years. In this study, a lossless and reversible DNA data storage method was proposed. The proposed approach employs a vector representation of each DNA base in a two-dimensional (2D) spatial domain for both encoding and decoding. The structure of the proposed method is reversible, rendering the decompression procedure possible. Experiments were performed to investigate the capacity, compression ratio, stability, and reliability. The obtained results show that the proposed method is much more efficient in terms of capacity than other known algorithms in the literature. [ABSTRACT FROM AUTHOR]

The exponential growth in data volume has necessitated the adoption of alternative storage solutions, and DNA storage stands out as the most promising solution. However, the exorbitant costs associated with synthesis and sequencing impeded its development. Pre-compressing the data is recognized as one of the most effective approaches for reducing storage costs. However, different compression methods yield varying compression ratios for the same file, and compressing a large number of files with a single method may not achieve the maximum compression ratio. This study proposes a multi-file dynamic compression method based on machine learning classification algorithms that selects the appropriate compression method for each file to minimize the amount of data stored into DNA as much as possible. Firstly, four different compression methods are applied to the collected files. Subsequently, the optimal compression method is selected as a label, as well as the file type and size are used as features, which are put into seven machine learning classification algorithms for training. The results demonstrate that k-nearest neighbor outperforms other machine learning algorithms on the validation set and test set most of the time, achieving an accuracy rate of over 85% and showing less volatility. Additionally, the compression rate of 30.85% can be achieved according to k-nearest neighbor model, more than 4.5% compared to the traditional single compression method, resulting in significant cost savings for DNA storage in the range of $0.48 to 3 billion/TB. In comparison to the traditional compression method, the multi-file dynamic compression method demonstrates a more significant compression effect when compressing multiple files. Therefore, it can considerably decrease the cost of DNA storage and facilitate the widespread implementation of DNA storage technology. File compression is an important step in DNA storage, and different types of files may have varying degrees of compression under different compression tools. In this article, we propose a multi file dynamic compression (MDC) method based on a classification algorithm in machine learning, with the aim of maximizing the compression rate of each compressed file as much as possible, thereby reducing the amount of data in DNA storage. [ABSTRACT FROM AUTHOR]

Recent advancements in synthesis and sequencing techniques have made deoxyribonucleic acid (DNA) a promising alternative for next-generation digital storage. As it approaches practical application, ensuring the security of DNA-stored information has become a critical problem. Deniable encryption allows the decryption of different information from the same ciphertext, ensuring that the "plausible" fake information can be provided when users are coerced to reveal the real information. In this paper, we propose a deniable encryption method that uniquely leverages DNA noise channels. Specifically, true and fake messages are encrypted by two similar modulation carriers and subsequently obfuscated by inherent errors. Experiment results demonstrate that our method not only can conceal true information among fake ones indistinguishably, but also allow both the coercive adversary and the legitimate receiver to decrypt the intended information accurately. Further security analysis validates the resistance of our method against various typical attacks. Compared with conventional DNA cryptography methods based on complex biological operations, our method offers superior practicality and reliability, positioning it as an ideal solution for data encryption in future large-scale DNA storage applications. [ABSTRACT FROM AUTHOR]

Background: Efficient DNA-based storage systems offer substantial capacity and longevity at reduced costs, addressing anticipated data growth. However, encoding data into DNA sequences is limited by two key constraints: 1) a maximum of h consecutive identical bases (homopolymer constraint h), and 2) a GC ratio between [ 0.5 - c GC , 0.5 + c GC ] (GC content constraint c GC ). Sequencing or synthesis errors tend to increase when these constraints are violated. Results: In this research, we address a pure source coding problem in the context of DNA storage, considering both homopolymer and GC content constraints. We introduce a novel coding technique that adheres to these constraints while maintaining linear complexity for increased block lengths and achieving near-optimal rates. We demonstrate the effectiveness of the proposed method through experiments on both randomly generated data and existing files. For example, when h = 4 and c GC = 0.05 , the rate reached 1.988, close to the theoretical limit of 1.990. The associated code can be accessed at GitHub. Conclusion: We propose a variable-to-variable-length encoding method that does not rely on concatenating short predefined sequences, which achieves near-optimal rates. [ABSTRACT FROM AUTHOR]

ABSTRACT Advances in technology have facilitated extensive sample collection to explore microbiomes across diverse systems, leading to a growing reliance on ultracold freezers for storing both samples and extracted DNA. However, freezer malfunctions can jeopardize data integrity. To evaluate the impact of an unexpected −80°C freezer failure and the recoverability of thawed soil samples, we extracted DNA and compared it to long-term DNA stored at −20°C and original 16S and ITS2 sequencing data collected before the malfunction. Using Illumina MiSeq, we assessed how the freezer failure and long-term storage influenced the resilience of bacterial or fungal richness or community composition and our ability to accurately determine experimental treatment effects. Our results reveal substantial resilience in fungal richness and both bacterial and fungal beta-diversity to soil sample thawing and extended frozen DNA storage. This resilience facilitated biological inferences that closely mirrored those observed in the original study. Notably, fungi exhibited greater resilience to short-term thawing compared to bacteria, which showed sensitivity to both thawing and long-term freezing. Moreover, taxonomic composition analysis revealed the persistence of dominant microbial taxa under thawing and prolonged freezing, suggesting that dominant microbes remain viable for tracking across temporal studies. In conclusion, our study highlights that beta-diversity is more robust than alpha-diversity and fungi are more resilient to freezer failure than bacteria. Furthermore, our findings underscore the effectiveness of soil storage at −80°C compared to storage of extracted DNA at −20°C, despite potential freezer failures, as the most robust method for long-term storage in microbiome studies.IMPORTANCEMicrobiome studies heavily rely on ultracold freezers for sample storage. Unfortunately, these freezers are prone to frequent malfunctions, resulting in the loss of invaluable samples at laboratories worldwide. Such losses can halt research progress due to potential issues with sample reliability. Our research demonstrates that not all is lost when an unforeseen freezer failure occurs. Samples can still be reliably used to assess treatment effects, which is particularly important for long-term temporal studies where samples cannot be readily obtained again.

DNA molecules, as a storage medium, possess unique advantages. Not only does DNA storage exhibit significantly higher storage density compared to electromagnetic storage media, but it also features low energy consumption and extremely long storage times. However, the integration of DNA storage into daily life remains distant due to challenges such as low storage density, high latency, and inevitable errors during the storage process. Therefore, this paper proposes constructing a DNA storage coding set based on the Levy Sooty Tern Optimization Algorithm (LSTOA) to achieve an efficient random-access DNA storage system. Firstly, addressing the slow iteration speed and susceptibility to local optima of the Sooty Tern Optimization Algorithm (STOA), this paper introduces Levy flight operations and propose the LSTOA. Secondly, utilizing the LSTOA, this paper constructs a DNA storage encoding set to facilitate random access while meeting combinatorial constraints. To demonstrate the coding performance of the LSTOA, this paper consists of analyses on 13 benchmark test functions, showcasing its superior performance. Furthermore, under the same combinatorial constraints, the LSTOA constructs larger DNA storage coding sets, effectively reducing the read–write latency and error rate of DNA storage. [ABSTRACT FROM AUTHOR]

Prior research has introduced the Single-Instruction-Multiple-Data paradigm for DNA computing (SIMD DNA). It offers the potential for storing information and performing in-memory computations on DNA, with massive parallelism. This paper introduces three new SIMD DNA operations: sorting, shifting, and searching. Each is a fundamental operation in computer science. Our implementations demonstrate the effectiveness of parallel pairwise operations with this new paradigm. [ABSTRACT FROM AUTHOR]

DNA molecules as storage media are characterized by high encoding density and low energy consumption, making DNA storage a highly promising storage method. However, DNA storage has shortcomings, especially when storing multimedia data, wherein image reconstruction fails when address errors occur, resulting in complete data loss. Therefore, we propose a parity encoding and local mean iteration (PELMI) scheme to achieve robust DNA storage of images. The proposed parity encoding scheme satisfies the common biochemical constraints of DNA sequences and the undesired motif content. It addresses varying pixel weights at different positions for binary data, thus optimizing the utilization of Reed–Solomon error correction. Then, through lost and erroneous sequences, data supplementation and local mean iteration are employed to enhance the robustness. The encoding results show that the undesired motif content is reduced by 23%–50% compared with the representative schemes, which improves the sequence stability. PELMI achieves image reconstruction under general errors (insertion, deletion, substitution) and enhances the DNA sequences quality. Especially under 1% error, compared with other advanced encoding schemes, the peak signal-to-noise ratio and the multiscale structure similarity address metric were increased by 10%–13% and 46.8%–122%, respectively, and the mean squared error decreased by 113%–127%. This demonstrates that the reconstructed images had better clarity, fidelity, and similarity in structure, texture, and detail. In summary, PELMI ensures robustness and stability of image storage in DNA and achieves relatively high-quality image reconstruction under general errors. [ABSTRACT FROM AUTHOR]

With the exponential growth of digital data, there is a pressing need for innovative storage media and techniques. DNA molecules, due to their stability, storage capacity, and density, offer a promising solution for information storage. However, DNA storage also faces numerous challenges, such as complex biochemical constraints and encoding efficiency. This paper presents Explorer , a high-efficiency DNA coding algorithm based on the De Bruijn graph, which leverages its capability to characterize local sequences. Explorer enables coding under various biochemical constraints, such as homopolymers, GC content, and undesired motifs. This paper also introduces Codeformer , a fast decoding algorithm based on the transformer architecture, to further enhance decoding efficiency. Numerical experiments indicate that, compared with other advanced algorithms, Explorer not only achieves stable encoding and decoding under various biochemical constraints but also increases the encoding efficiency and bit rate by ¿10%. Additionally, Codeformer demonstrates the ability to efficiently decode large quantities of DNA sequences. Under different parameter settings, its decoding efficiency exceeds that of traditional algorithms by more than two-fold. When Codeformer is combined with Reed–Solomon code, its decoding accuracy exceeds 99%, making it a good choice for high-speed decoding applications. These advancements are expected to contribute to the development of DNA-based data storage systems and the broader exploration of DNA as a novel information storage medium. [ABSTRACT FROM AUTHOR]

Here, a new‐rising technology (DNA storage) in which the binary digital information/data is converted into a DNA sequence composed of distinct nucleotides, providing a dense, stable, energy‐efficient, and sustainable data storage solution is studied. In principle, this technology offers substantial data density. However, the theoretical limit of DNA storage has not been achieved yet. In this study, a new DNA storage system is proposed based on hydrogels with SiO2 layer (their pore size can be regulated). The highest data density of the hydrogels is obtained to be 6.3 × 109 GB g−1. Further, by coupling them with digital polymerase chain reaction (dPCR), the recovery limit reaches five copies of DNA. Subsequently, three‐dimensional (3D) printing is used to explore the effect of macrostructures on the DNA storage capacity. Finally, bionic‐structure thermally‐responsive robust (Best) hydrogels with four different structures are developed, which achieve a data density of 1.04 × 1010 GB g−1. [ABSTRACT FROM AUTHOR]

Polymerase Chain Reaction (PCR) amplification is widely used for retrieving information from DNA storage. During the PCR amplification process, nonspecific pairing between the 3' end of the primer and the DNA sequence can cause cross-talk in the amplification reaction, leading to the generation of interfering sequences and reduced amplification accuracy. To address this issue, we propose an efficient coding algorithm for PCR amplification information retrieval (ECA-PCRAIR). This algorithm employs variable-length scanning and pruning optimization to construct a codebook that maximizes storage density while satisfying traditional biological constraints. Subsequently, a codeword search tree is constructed based on the primer library to optimize the codebook, and a variable-length interleaver is used for constraint detection and correction, thereby minimizing the likelihood of nonspecific pairing. Experimental results demonstrate that ECA-PCRAIR can reduce the probability of nonspecific pairing between the 3' end of the primer and the DNA sequence to 2–25%, enhancing the robustness of the DNA sequences. Additionally, ECA-PCRAIR achieves a storage density of 2.14–3.67 bits per nucleotide (bits/nt), significantly improving storage capacity. [ABSTRACT FROM AUTHOR]

Abstract Here, a new‐rising technology (DNA storage) in which the binary digital information/data is converted into a DNA sequence composed of distinct nucleotides, providing a dense, stable, energy‐efficient, and sustainable data storage solution is studied. In principle, this technology offers substantial data density. However, the theoretical limit of DNA storage has not been achieved yet. In this study, a new DNA storage system is proposed based on hydrogels with SiO2 layer (their pore size can be regulated). The highest data density of the hydrogels is obtained to be 6.3 × 109 GB g−1. Further, by coupling them with digital polymerase chain reaction (dPCR), the recovery limit reaches five copies of DNA. Subsequently, three‐dimensional (3D) printing is used to explore the effect of macrostructures on the DNA storage capacity. Finally, bionic‐structure thermally‐responsive robust (Best) hydrogels with four different structures are developed, which achieve a data density of 1.04 × 1010 GB g−1.

Many DNA storage codes take into account homopolymer and GC-content constraints. Still, these codes often need to meet additional practical database requirements, such as error correction and data queries, necessitating considerable financial and time investment in their training or design. As DNA storage technologies, including sequencing and synthesis, continue to evolve rapidly, these codes may need to be retrained or redesigned to adapt to new constraints. In this study, we aim to design a method for adapting an existing DNA storage code to satisfy a new constraint, specifically concerning homopolymer variations. We present a simple and effective framework known as Transfer Coding, which directly maps DNA sequences from an original homopolymer constraint $h_{1}$ to a new constraint $h_{2}$ . This approach essentially combines the existing coding scheme with a Transfer encoder. The proposed method uses strategic base replacements to ensure compliance with constraints, achieving results close to the theoretical limit while keeping alterations to the original sequence minimal.

In application scenarios such as DNA storage, the traditional fountain code algorithm must transmit the number K of source file packets to the decoder through an additional channel. In practical applications, although K can be embedded in each coded data packet to transmit this key parameter, this method will seriously waste the channel's bandwidth. Aiming at the above problems, a large and mini fountain code model is proposed, which optimizes the transmission of critical parameters by adding the out-of-band channel of the mini fountain code. The mini fountain code reduces the granularity of the space occupied by the critical information about the parameter K in each coding group to 1 bit, effectively reducing the consumption of bandwidth resources. In addition, the mini fountain code can also adapt to the restriction of the indefinite length of the coding sequence caused by the inhomogeneity of the DNA storage medium. Under certain conditions, it cannot even occupy additional channel bandwidth at all. [ABSTRACT FROM AUTHOR]

Over the past decade, DNA has emerged as a new storage medium with intriguing data volume and durability capabilities. Despite its advantages, DNA storage also has crucial limitations, such as intricate data access interfaces and restricted random accessibility. To overcome these limitations, DNAContainer has been introduced with a novel storage interface for DNA that spans a very large virtual address space on objects and allows random access to DNA at scale. In this paper, we substantially improve the first version of DNAContainer, focusing on the update capabilities of its data structures and optimizing its memory footprint. In addition, we extend the previous set of experiments on DNAContainer with new ones whose results reveal the impact of essential parameters on the performance and memory footprint. [ABSTRACT FROM AUTHOR]

With the informationization of social processes, the amount of related data has greatly increased, making traditional storage media unable to meet the current requirements for data storage. Due to its advantages of a high storage capacity and persistence, deoxyribonucleic acid (DNA) has been considered the most prospective storage media to solve the data storage problem. Synthesis is an important process for DNA storage, and low-quality DNA coding can increase errors during sequencing, which can affect the storage efficiency. To reduce errors caused by the poor stability of DNA sequences during storage, this paper proposes a method that uses the double-matching and error-pairing constraints to improve the quality of the DNA coding set. First, the double-matching and error-pairing constraints are defined to solve problems of sequences with self-complementary reactions in the solution that are prone to mismatch at the 3′ end. In addition, two strategies are introduced in the arithmetic optimization algorithm, including a random perturbation of the elementary function and a double adaptive weighting strategy. An improved arithmetic optimization algorithm (IAOA) is proposed to construct DNA coding sets. The experimental results of the IAOA on 13 benchmark functions show a significant improvement in its exploration and development capabilities over the existing algorithms. Moreover, the IAOA is used in the DNA encoding design under both traditional and new constraints. The DNA coding sets are tested to estimate their quality regarding the number of hairpins and melting temperature. The DNA storage coding sets constructed in this study are improved by 77.7% at the lower boundary compared to existing algorithms. The DNA sequences in the storage sets show a reduction of 9.7–84.1% in the melting temperature variance, and the hairpin structure ratio is reduced by 2.1–80%. The results indicate that the stability of the DNA coding sets is improved under the two proposed constraints compared to traditional constraints.

Abstract Synchronization (insertions–deletions) errors are still a major challenge for reliable information retrieval in DNA storage. Unlike traditional error correction codes (ECC) that add redundancy in the stored information, multiple sequence alignment (MSA) solves this problem by searching the conserved subsequences. In this paper, we conduct a comprehensive simulation study on the error correction capability of a typical MSA algorithm, MAFFT. Our results reveal that its capability exhibits a phase transition when there are around 20% errors. Below this critical value, increasing sequencing depth can eventually allow it to approach complete recovery. Otherwise, its performance plateaus at some poor levels. Given a reasonable sequencing depth (≤ 70), MSA could achieve complete recovery in the low error regime, and effectively correct 90% of the errors in the medium error regime. In addition, MSA is robust to imperfect clustering. It could also be combined with other means such as ECC, repeated markers, or any other code constraints. Furthermore, by selecting an appropriate sequencing depth, this strategy could achieve an optimal trade-off between cost and reading speed. MSA could be a competitive alternative for future DNA storage.

DNA data storage systems have rapidly developed with novel error-correcting techniques, random access algorithms, and query systems. However, designing an algorithm for DNA storage systems is challenging, mainly due to the unpredictable nature of errors and the extremely high price of experiments. Thus, a simulator is of interest that can imitate the error statistics of a DNA storage system and replace the experiments in developing processes. We introduce novel generative adversarial networks that learn DNA storage channel statistics. Our simulator takes oligos (DNA sequences to write) as an input and generates a FASTQ file that includes output DNA reads and quality scores as if the oligos are synthesized and sequenced. We trained the proposed simulator with data from a single experiment consisting of 14,400 input oligo strands and 12,108,573 output reads. The error statistics between the input and the output of the trained generator match the actual error statistics, including the error rate at each position, the number of errors for each nucleotide, and high-order statistics. The code is available at https://github.com/gyfbianhuanyun/DNA_storage_simulator_GAN.

Future data analytics will require enormous storage space for data-driven decisions, necessitating alternative storage sources for massive data archives. Storage solutions have always been in demand due to the limitations of existing media. Deoxyribonucleic Acid (DNA) is an emergent storage medium suitable for archival storage of rapidly increasing digital volumes. Due to its longevity, DNA storage technology has led to numerous applications to store and retrieve entire data. In this way, DNA synthesis and sequencing costs can be reduced by compressing data in full before it is stored. However, prior works have not used DNA storage to retrieve partial data from complex graphs, while taking advantage of cost-effective advanced analytics. In this paper, we present an efficient DNA-based query processing system to retrieve partial information using RDF graph data. Moreover, using binary search, we fetch and decode significantly fewer DNA strands to obtain partial information about RDF graph data based on SPARQL queries. Specifically, the experimental analysis shows that the average data retrieval per query as output is found less than 1% for RDF graphs with more than 1MB (Megabytes) in size, which consequently reduces a significant amount of sequencing costs.

DNA is a high-density, long-term stable, and scalable storage medium that can meet the increased demands on storage media resulting from the exponential growth of data. The existing DNA storage encoding schemes tend to achieve high-density storage but do not fully consider the local and global stability of DNA sequences and the read and write accuracy of the stored information. To address these problems, this article presents a graph-based De Bruijn Trim Rotation Graph (DBTRG) encoding scheme. Through XOR between the proposed dynamic binary sequence and the original binary sequence, k-mers can be divided into the De Bruijn Trim graph, and the stored information can be compressed according to the overlapping relationship. The simulated experimental results show that DBTRG ensures base balance and diversity, reduces the likelihood of undesired motifs, and improves the stability of DNA storage and data recovery. Furthermore, the maintenance of an encoding rate of 1.92 while storing 510 KB images and the introduction of novel approaches and concepts for DNA storage encoding methods are achieved.

DNA is an incredibly dense storage medium for digital data. However, computing on the stored information is expensive and slow, requiring rounds of sequencing, in silico computation, and DNA synthesis. Prior work on accessing and modifying data using DNA hybridization or enzymatic reactions had limited computation capabilities. Inspired by the computational power of "DNA strand displacement," we augment DNA storage with "in-memory" molecular computation using strand displacement reactions to algorithmically modify data in a parallel manner. We show programs for binary counting and Turing universal cellular automaton Rule 110, the latter of which is, in principle, capable of implementing any computer algorithm. Information is stored in the nicks of DNA, and a secondary sequence-level encoding allows high-throughput sequencing-based readout. We conducted multiple rounds of computation on 4-bit data registers, as well as random access of data (selective access and erasure). We demonstrate that large strand displacement cascades with 244 distinct strand exchanges (sequential and in parallel) can use naturally occurring DNA sequence from M13 bacteriophage without stringent sequence design, which has the potential to improve the scale of computation and decrease cost. Our work merges DNA storage and DNA computing, setting the foundation of entirely molecular algorithms for parallel manipulation of digital information preserved in DNA. [ABSTRACT FROM AUTHOR]

With the rapid development of DNA (deoxyribonucleic acid) storage technologies, storing digital images in DNA is feasible. Meanwhile, the information security in DNA storage system is still a problem to solve. Therefore, in this paper, we propose a DNA storage-oriented image encryption algorithm utilizing the information processing mechanisms in molecule biology. The basic idea is to perform pixel replacement by gene hybridization, and implement dual diffusion by pixel diffusion and gene mutation. The ciphertext DNA image can be synthesized and stored in DNA storage system after encryption. Experimental results demonstrate it can resist common attacks, and shows a strong robustness against sequence loss and base substitution errors in the DNA storage channel. A DNA storage-oriented image encryption algorithm based on gene hybridization and gene mutation, First, we scramble rows and columns of the plaintext image by dynamic Josephus traversing. Second, we replace the pixels by gene hybridization. Finally, we diffuse the image matrix in binary domain and encode pixels into 8-base strands which are later further diffused by gene mutation. The ciphertext image can be synthesized according to the mutant gene codes and stored in any DNA storage system [ABSTRACT FROM AUTHOR]

Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology, including synthesis and assembly of a genome, DNA storage, gene editing, molecular evolution and de novo design of function proteins, cell and gene circuit engineering, cell-free synthetic biology, artificial intelligence (AI)-aided synthetic biology, as well as biofoundries. We also introduce the concept of quantitative synthetic biology, which is guiding synthetic biology towards increased accuracy and predictability or the real rational design. We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory. [ABSTRACT FROM AUTHOR]

DNA, as the storage medium in organisms, can address the shortcomings of existing electromagnetic storage media, such as low information density, high maintenance power consumption, and short storage time. Current research on DNA storage mainly focuses on designing corresponding encoders to convert binary data into DNA base data that meets biological constraints. We have created a new Chinese character code table that enables exceptionally high information storage density for storing Chinese characters (compared to traditional UTF-8 encoding). To meet biological constraints, we have devised a DNA shift coding scheme with low algorithmic complexity, which can encode any strand of DNA even has excessively long homopolymer. The designed DNA sequence will be stored in a double-stranded plasmid of 744bp, ensuring high reliability during storage. Additionally, the plasmid‘s resistance to environmental interference ensuring long-term stable information storage. Moreover, it can be replicated at a lower cost.

DNA has emerged as a promising medium for digital data storage because of its high density, longevity, as well as energy efficiency. However, the security provided by DNA storage systems remains a concern, particularly as the technology is adopted for sensitive data applications. DNA encryption offers a potential solution to this problem by encoding the stored data in a secure and reversible manner. In this paper, a new DNA encryption for storage applications by editing or creating a new DNA sequence to store big data for archival purposes in an encrypted format to provide security, is proposed. It is concluded that DNA encryption is a promising approach for securing digital data in DNA storage systems, and there is requirement for further research to optimize the performance and reliability of this technology. [ABSTRACT FROM AUTHOR]

This paper introduces a novel portable sensing platform designed to explore the intricate interaction between DNA molecules and silicon dioxide (SiO 2) coated on a field effect transistor (FET), bearing profound implications for a spectrum of life science applications, particularly DNA storage. In-silico molecular dynamic (MD) simulations are employed to gain insights into the effects of DNA on the FET surface's potential under extreme conditions of dehydration of double-stranded DNA (dsDNA), verifying its capacity to generate surface charge when dehydrated due to the evaporation of droplets. The platform is comprised of a backgated interdigital open gate junction FET (ID-OGJFET) fabricated through an innovative electronic sensing platform foundry and accompanied by an electronic interface system incorporated with a fluidic sample holder that reads the surface charge on the sensor induced by negatively charged nucleotides in dsDNA and single-stranded DNA (ssDNA). This novel ID-OGJFET sensor coated with native SiO 2 enables a direct-on-channel sensing area of >16 mm2 without top gate extension for the target application, working at low back gate voltage (<0.4 V) for adsorption analysis in the dry mode. Our study involves designing, synthesizing, and introducing DNA sequences onto the chip surface at various concentrations. This paper presents and discusses experimental results that align with MD simulations, elucidating the impact of DNA's intrinsic charge on the chip's surface under extreme dehydration conditions. These findings pave the way for developing a portable charge-sensitive DNA monitoring system, particularly in the emerging physical storage of DNA. [Display omitted] • A new charge-sensitive sensor based on interdigital open-gate junction FET (ID-OGJFET). • A low-cost portable sensing electronic system is introduced for DNA adsorption analysis. • A dedicated electronic DNA detection system is introduced for portable DNA storage application. • MD simulation is used to understand the sensing mechanism. [ABSTRACT FROM AUTHOR]

Abstract Background Using DNA as a storage medium is appealing due to the information density and longevity of DNA, especially in the era of data explosion. A significant challenge in the DNA data storage area is to deal with the noises introduced in the channel and control the trade-off between the redundancy of error correction codes and the information storage density. As running DNA data storage experiments in vitro is still expensive and time-consuming, a simulation model is needed to systematically optimize the redundancy to combat the channel's particular noise structure. Results Here, we present DeSP, a systematic DNA storage error Simulation Pipeline, which simulates the errors generated from all DNA storage stages and systematically guides the optimization of encoding redundancy. It covers both the sequence lost and the within-sequence errors in the particular context of the data storage channel. With this model, we explained how errors are generated and passed through different stages to form final sequencing results, analyzed the influence of error rate and sampling depth to final error rates, and demonstrated how to systemically optimize redundancy design in silico with the simulation model. These error simulation results are consistent with the in vitro experiments. Conclusions DeSP implemented in Python is freely available on Github ( https://github.com/WangLabTHU/DeSP ). It is a flexible framework for systematic error simulation in DNA storage and can be adapted to a wide range of experiment pipelines.

Introduction: Rapid development in synthetic technologies has boosted DNA as a potential medium for large-scale data storage. Meanwhile, how to implement data security in the DNA storage system is still an unsolved problem.Methods: In this article, we propose an image encryption method based on the modulation-based storage architecture. The key idea is to take advantage of the unpredictable modulation signals to encrypt images in highly error-prone DNA storage channels.Results and Discussion: Numerical results have demonstrated that our image encryption method is feasible and effective with excellent security against various attacks (statistical, differential, noise, and data loss). When compared with other methods such as the hybridization reactions of DNA molecules, the proposed method is more reliable and feasible for large-scale applications.

Synchronization (insertions–deletions) errors are still a major challenge for reliable information retrieval in DNA storage. Unlike traditional error correction codes (ECC) that add redundancy in the stored information, multiple sequence alignment (MSA) solves this problem by searching the conserved subsequences. In this paper, we conduct a comprehensive simulation study on the error correction capability of a typical MSA algorithm, MAFFT. Our results reveal that its capability exhibits a phase transition when there are around 20% errors. Below this critical value, increasing sequencing depth can eventually allow it to approach complete recovery. Otherwise, its performance plateaus at some poor levels. Given a reasonable sequencing depth (≤ 70), MSA could achieve complete recovery in the low error regime, and effectively correct 90% of the errors in the medium error regime. In addition, MSA is robust to imperfect clustering. It could also be combined with other means such as ECC, repeated markers, or any other code constraints. Furthermore, by selecting an appropriate sequencing depth, this strategy could achieve an optimal trade-off between cost and reading speed. MSA could be a competitive alternative for future DNA storage. [ABSTRACT FROM AUTHOR]

DNA storage is expected to tackle the dilemma faced by electronic information technology for the effective storage and management of massive amounts of data in the era of big data. Efficient and reliable data retrieval is crucial for DNA storage. However, it is still challenging to actualize DNA storage with fast and accurate readout capabilities, which play a key role in the practicality and reliability of DNA storage. In this study, an integrated system was constructed using homemade microfluidic PCR and DNA magnetic beads for fast and accurate DNA storage and reading with reproducibility. The homemade microfluidic PCR and DNA magnetic beads constructed for the random access of DNA storage have the advantages of short time and low bias named MMBP. The homemade DNA magnetic beads are low cost, stable, and reproducible. The integrated DNA storage and reading system integrated by MMBP can read information not only more accurately and quickly but also at a lower sequencing depth than traditional PCR. Overall, the MMBP-based DNA information storage system (MMBP-DIS) has the advantages of reducing the cost, decreasing the random access time to 10 min, and improving the reading accuracy and sensitivity. In the future, it can be integrated with DNA electrochemical synthesis to develop a fast and accurate portable microfluidic device for DNA synthesis-preservation-reading integration.

DNA storage has become an attractive alternative to long-term, stable digital data storage because of its high storage density and strong stability. Recently, numerous efforts have been made to develop DNA data access methods to improve the efficiency and accuracy of molecular data reading. However, most current data access methods were achieved by well-developed polymerase chain reaction (PCR) and DNA hybridization, which lack the exploration of dynamic and programmable operations for data access. Here, we propose a programmable DNA data access strategy in which the nanoparticle folders are controlled by DNAzyme circuits to achieve specific information manipulation. We experimentally demonstrate three kinds of circuit programs that access specific information in YES, AND, and OR logic manner. In addition, the selective information access was performed by using a DNAzyme circuit to obtain the target information from a DNA data pool. Importantly, we have extended the circuit-controlled framework to multiple manipulation modes, demonstrating four manipulations on two AuNP folders to access different information on demand. The programmable access strategy provides a paradigm for integrated DNA computing and storage systems and has more applications in the fields of molecular computation and DNA data storage.

Large pools of synthetic DNA molecules have been recently used to reliably store significant volumes of digital data. While DNA as a storage medium has enormous potential because of its high storage density, its practical use is currently severely limited because of the high cost and low throughput of available DNA synthesis technologies. We study the role of batch optimization in reducing the cost of large scale DNA synthesis, which translates to the following algorithmic task. Given a large pool $\mathcal {S}$ of random quaternary strings of fixed length, partition $\mathcal {S}$ into batches in a way that minimizes the sum of the lengths of the shortest common supersequences across batches. We introduce two ideas for batch optimization that both improve (in different ways) upon a naive baseline: (1) using both $(ACGT)^{\ast}$ and its reverse $(TGCA)^{\ast}$ as reference strands, and batching appropriately, and (2) batching via the quantiles of an appropriate ordering of the strands. We also prove asymptotically matching lower bounds on the cost of DNA synthesis, showing that one cannot improve upon these two ideas. Our results uncover a surprising separation between two cases that naturally arise in the context of DNA data storage: the asymptotic cost savings of batch optimization are significantly greater in the case where strings in $\mathcal {S}$ do not contain repeats of the same character (homopolymers), as compared to the case where strings in $\mathcal {S}$ are unconstrained. [ABSTRACT FROM AUTHOR]

The DNA storage channel is considered, in which a codeword is comprised of $M$ unordered DNA molecules. At reading time, $N$ molecules are sampled with replacement, and then each molecule is sequenced. A coded-index concatenated-coding scheme is considered, in which the $m$ th molecule of the codeword is restricted to a subset of all possible molecules (an inner code), which is unique for each $m$. The decoder has low-complexity, and is based on first decoding each molecule separately (the inner code), and then decoding the sequence of molecules (an outer code). Only mild assumptions are made on the sequencing channel, in the form of the existence of an inner code and decoder with vanishing error. The error probability of a random code as well as an expurgated code is analyzed and shown to decay exponentially with $N$. This establishes the importance of increasing the coverage depth $N/M$ in order to obtain low error probability. [ABSTRACT FROM AUTHOR]

As an emerging storage medium, DNA is a crucial carrier for future data storage. DNA has a better storage density and a longer storage life than conventional storage media. However, DNA's key limitations as a storage medium are the precision of information reading and storage stability. Herein, we designed and successfully developed a switchable DNA storage nanocomposite structure (MM-DNA/DNA'-SiO2). The composite structure employs magnetic microspheres (MMs) based on Fe3O4 nanoparticles as the DNA carrier for storing information (MM-DNA) and silicon dioxide nanoparticles modified with complementary strands as the protective outer layer (DNA'-SiO2), and its switchability and DNA sequence reading accuracy were both validated in this study. This method provides a novel nanostructure medium and technical proposal for efficient DNA storage with potential application value. [ABSTRACT FROM AUTHOR]

Large scale DNA oligo pools are emerging as a novel material in a variety of advanced applications. However, GC content and length cause significant bias in amplification of oligos. We systematically explored the amplification of one oligo pool comprising of over ten thousand distinct strands with moderate GC content in the range of 35–65%. Uniqual amplification of oligos result to the increased Gini index of the oligo distribution while a few oligos greatly increased their proportion after 60 cycles of PCR. However, the significantly enriched oligos all have relatively high GC content. Further thermodynamic analysis demonstrated that a high value of both GC content and Gibbs free energy could improve the replication of specific oligos during biased amplification. Therefore, this double-G (GC content and Gibbs free energy) driven replication advantage can be used as a guiding principle for the sequence design for a variety of applications, particularly for data storage. [ABSTRACT FROM AUTHOR]

Abstract Background DNA is a promising storage medium for high-density long-term digital data storage. Since DNA synthesis and sequencing are still relatively expensive tasks, the coding methods used to store digital data in DNA should correct errors and avoid unstable or error-prone DNA sequences. Near-optimal rateless erasure codes, also called fountain codes, are particularly interesting codes to realize high-capacity and low-error DNA storage systems, as shown by Erlich and Zielinski in their approach based on the Luby transform (LT) code. Since LT is the most basic fountain code, there is a large untapped potential for improvement in using near-optimal erasure codes for DNA storage. Results We present NOREC4DNA, a software framework to use, test, compare, and improve near-optimal rateless erasure codes (NORECs) for DNA storage systems. These codes can effectively be used to store digital information in DNA and cope with the restrictions of the DNA medium. Additionally, they can adapt to possible variable lengths of DNA strands and have nearly zero overhead. We describe the design and implementation of NOREC4DNA. Furthermore, we present experimental results demonstrating that NOREC4DNA can flexibly be used to evaluate the use of NORECs in DNA storage systems. In particular, we show that NORECs that apparently have not yet been used for DNA storage, such as Raptor and Online codes, can achieve significant improvements over LT codes that were used in previous work. NOREC4DNA is available on https://github.com/umr-ds/NOREC4DNA . Conclusion NOREC4DNA is a flexible and extensible software framework for using, evaluating, and comparing NORECs for DNA storage systems.