Bio-Constrained Codes with Neural Network for Density-Based DNA Data Storage

Design of DNA Storage Coding Scheme With LDPC Codes and Interleaving

In this paper, we propose a new coding scheme for DNA storage using low-density parity-check (LDPC) codes and interleaving techniques. While conventional coding schemes generally employ error correcting codes in both inter and intra-oligo directions, we show that inter-oligo LDPC codes, optimized by differential evolution, are sufficient in ensuring the reliability of DNA storage due to the powerful soft decoding of LDPC codes. In addition, we apply interleaving techniques for handling non-uniform error characteristics of DNA storage to enhance the decoding performance. Consequently, the proposed coding scheme reduces the required number of oligo reads for perfect recovery by 26.25% ~ 38.5% compared to existing state-of-the-art coding schemes. Moreover, we develop an analytical DNA channel model in terms of non-uniform binary symmetric channels. This mathematical model allows us to demonstrate the superiority of the proposed coding scheme while isolating the experimental variation, as well as confirm the independent effects of LDPC codes and interleaving techniques.

An Effective DNA-Based File Storage System for Practical Archiving and Retrieval of Medical MRI Data

DNA-based data storage is a new technology in computational and synthetic biology, that offers a solution for long-term, high-density data archiving. Given the critical importance of medical data in advancing human health, there is a growing interest in developing an effective medical data storage system based on DNA. Data integrity, accuracy, reliability, and efficient retrieval are all significant concerns. Therefore, this study proposes an Effective DNA Storage (EDS) approach for archiving medical MRI data. The EDS approach incorporates three key components (i) a novel fraction strategy to address the critical issue of rotating encoding, which often leads to data loss due to single base error propagation; (ii) a novel rule-based quaternary transcoding method that satisfies bio-constraints and ensure reliable mapping; and (iii) an indexing technique designed to simplify random search and access. The effectiveness of this approach is validated through computer simulations and biological experiments, confirming its practicality. The EDS approach outperforms existing methods, providing superior control over bio-constraints and reducing computational time. The results and code provided in this study open new avenues for practical DNA storage of medical MRI data, offering promising prospects for the future of medical data archiving and retrieval.

Turbo autoencoders for the DNA data storage channel with Autoturbo-DNA

DNA, with its high storage density and long-term stability, is a potential candidate for a next-generation storage device. The DNA data storage channel, composed of synthesis, amplification, storage, and sequencing, exhibits error probabilities and error profiles specific to the components of the channel. Here, we present Autoturbo-DNA, a PyTorch framework for training error-correcting, overcomplete autoencoders specifically tailored for the DNA data storage channel. It allows training different architecture combinations and using a wide variety of channel component models for noise generation during training. It further supports training the encoder to generate DNA sequences that adhere to user-defined constraints. Autoturbo-DNA exhibits error-correction capabilities close to non-neural-network state-of-the-art error correction and constrained codes for DNA data storage. Our results indicate that neural-network-based codes can be a viable alternative to traditionally designed codes for the DNA data storage channel.

Efficient data reconstruction: The bottleneck of large-scale application of DNA storage

Over the past decade, the rapid development of DNA synthesis and sequencing technologies has enabled preliminary use of DNA molecules for digital data storage, overcoming the capacity and persistence bottlenecks of silicon-based storage media. DNA storage has now been fully accomplished in the laboratory through existing biotechnology, which again demonstrates the viability of carbon-based storage media. However, the high cost and latency of data reconstruction pose challenges that hinder the practical implementation of DNA storage beyond the laboratory. In this article, we review existing advanced DNA storage methods, analyze the characteristics and performance of biotechnological approaches at various stages of data writing and reading, and discuss potential factors influencing DNA storage from the perspective of data reconstruction.

Storing Images in DNA via base128 Encoding

Current DNA storage schemes lack flexibility and consistency in processing highly redundant and correlated image data, resulting in low sequence stability and image reconstruction rates. Therefore, according to the characteristics of image storage, this paper proposes storing images in DNA via base128 encoding (DNA-base128). In the data writing stage, data segmentation and probability statistics are carried out, and then, the data block frequency and constraint encoding set are associated with achieving encoding. When the image needs to be recovered, DNA-base128 completes internal error correction by threshold setting and drift comparison. Compared with representative work, the DNA-base128 encoding results show that the undesired motifs were reduced by 71.2-90.7% and that the local guanine-cytosine content variance was reduced by 3 times, indicating that DNA-base128 can store images more stably. In addition, the structural similarity index (SSIM) and multiscale structural similarity (MS-SSIM) of image reconstruction using DNA-base128 were improved by 19-102 and 6.6-20.3%, respectively. In summary, DNA-base128 provides image encoding with internal error correction and provides a potential solution for DNA image storage. The data and code are available at the GitHub repository: https://github.com/123456wk/DNA_base128.

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.

RBS: A Rotational Coding Based on Blocking Strategy for DNA Storage

The data volume of global information has grown exponentially in recent years, but the development of silicon-based memory has entered a bottleneck period. Deoxyribonucleic acid (DNA) storage is drawing attention owing to its advantages of high storage density, long storage time, and easy maintenance. However, the base utilization and information density of existing DNA storage methods are insufficient. Therefore, this study proposes a rotational coding based on blocking strategy (RBS) for encoding digital information such as text and images in DNA data storage. This strategy satisfies multiple constraints and produces low error rates in synthesis and sequencing. To illustrate the superiority of the proposed strategy, it was compared and analyzed with existing strategies in terms of entropy value change, free energy size, and Hamming distance. The experimental results show that the proposed strategy has higher information storage density and better coding quality in DNA storage, so it will improve the efficiency, practicality, and stability of DNA storage.

DBTRG: De Bruijn Trim rotation graph encoding for reliable DNA storage

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.

DUHI: Dynamically updated hash index clustering method for DNA storage

The exponential growth of global data leads to the problem of insufficient data storage capacity. DNA storage can be an ideal storage method due to its high storage density and long storage time. However, the DNA storage process is subject to unavoidable errors that can lead to increased cluster redundancy during data reading, which in turn affects the accuracy of the data reads. This paper proposes a dynamically updated hash index (DUHI) clustering method for DNA storage, which clusters sequences by constructing a dynamic core index set and using hash lookup. The proposed clustering method is analyzed in terms of overall reliability evaluation and visualization evaluation. The results show that the DUHI clustering method can reduce the redundancy of more than 10% of the sequences within the cluster and increase the reconstruction rate of the sequences to more than 99%. Therefore, our method solves the high redundancy problem after DNA sequence clustering, improves the accuracy of data reading, and promotes the development of DNA storage.

In-vitro validated methods for encoding digital data in deoxyribonucleic acid (DNA)

Deoxyribonucleic acid (DNA) is emerging as an alternative archival memory technology. Recent advancements in DNA synthesis and sequencing have both increased the capacity and decreased the cost of storing information in de novo synthesized DNA pools. In this survey, we review methods for translating digital data to and/or from DNA molecules. An emphasis is placed on methods which have been validated by storing and retrieving real-world data via in-vitro experiments.

GCNSA: DNA storage encoding with a graph convolutional network and self-attention

DNA Encoding, as a key step in DNA storage, plays an important role in reading and writing accuracy and the storage error rate. However, currently, the encoding efficiency is not high enough and the encoding speed is not fast enough, which limits the performance of DNA storage systems. In this work, a DNA storage encoding system with a graph convolutional network and self-attention (GCNSA) is proposed. The experimental results show that DNA storage code constructed by GCNSA increases by 14.4% on average under the basic constraints, and by 5%-40% under other constraints. The increase of DNA storage codes effectively improves the storage density of 0.7-2.2% in the DNA storage system. The GCNSA predicted more DNA storage codes in less time while ensuring the quality of codes, which lays a foundation for higher read and write efficiency in DNA storage.

Evolutionary approach to construct robust codes for DNA-based data storage

DNA is a practical storage medium with high density, durability, and capacity to accommodate exponentially growing data volumes. A DNA sequence structure is a biocomputing problem that requires satisfying bioconstraints to design robust sequences. Existing evolutionary approaches to DNA sequences result in errors during the encoding process that reduces the lower bounds of DNA coding sets used for molecular hybridization. Additionally, the disordered DNA strand forms a secondary structure, which is susceptible to errors during decoding. This paper proposes a computational evolutionary approach based on a synergistic moth-flame optimizer by Levy flight and opposition-based learning mutation strategies to optimize these problems by constructing reverse-complement constraints. The MFOS aims to attain optimal global solutions with robust convergence and balanced search capabilities to improve DNA code lower bounds and coding rates for DNA storage. The ability of the MFOS to construct DNA coding sets is demonstrated through various experiments that use 19 state-of-the-art functions. Compared with the existing studies, the proposed approach with three different bioconstraints substantially improves the lower bounds of the DNA codes by 12-28% and significantly reduces errors.

FMG: An observable DNA storage coding method based on frequency matrix game graphs

Using complex biomolecules for storage is a new carbon-based storage method. For example, DNA has the potential to be a good method for archival long-term data storage. Reasonable and efficient coding is the first and most important step in DNA storage. However, current coding methods, such as altruism algorithm, have the problem of low coding efficiency and high complexity, and coding constraints and sets make it difficult to see the coding results visually. In this study, a new DNA storage coding method based on frequency matrix game graph (FMG) is proposed to generate DNA storage coding satisfying combinatorial constraints. Compared with the randomness of the heuristic algorithm that satisfies the constraints, the coding method based on the FMG is deterministic and can clearly explain the coding process. In addition, the constraints and coding results have observable characteristics and are better than the previously published results for the size of the coding set. For example, when length of the code n = 10, hamming distance d = 4, the results obtained by proposed approach combining chaos game and graph are 24% better than the previous results. The proposed coding scheme successfully constructs high-quality coding sets with less complexity, which effectively promotes the development of carbon-based storage coding.

TripDesign: A DNA Triplex Design Approach Based on Interaction Forces

A DNA triplex has the advantages of improved nanostructure stability and pH environment responsiveness compared with single-stranded and double-stranded nucleic acids. However, sequence stability and low design efficiency hinder the application of DNA triplexes. Therefore, a DNA triplex design approach (TripDesign) based on interaction forces is proposed. First, we present the stacking force constraint, torsional stress constraint, and G-quadruplex motif constraint and then use an improved memetic algorithm to design triplex sequences under combinatorial constraints. Finally, to quantify the process of triplex formation, we also explore the minimum length of the triplex-forming oligos (TFOs) required to form the triplex and the factors that produce depletion in cyclic pH-jump experiments. The experimental results show that the sequences produced by TripDesign have high stability and reversibility, and the proposed approach achieves efficient and automatic sequence design. In addition, this study characterizes multiple basic parameters of DNA triplex formation and promotes the wider application of DNA triplexes in nanotechnology.

Clover: tree structure-based efficient DNA clustering for DNA-based data storage

Deoxyribonucleic acid (DNA)-based data storage is a promising new storage technology which has the advantage of high storage capacity and long storage time compared with traditional storage media. However, the synthesis and sequencing process of DNA can randomly generate many types of errors, which makes it more difficult to cluster DNA sequences to recover DNA information. Currently, the available DNA clustering algorithms are targeted at DNA sequences in the biological domain, which not only cannot adapt to the characteristics of sequences in DNA storage, but also tend to be unacceptably time-consuming for billions of DNA sequences in DNA storage. In this paper, we propose an efficient DNA clustering method termed Clover for DNA storage with linear computational complexity and low memory. Clover avoids the computation of the Levenshtein distance by using a tree structure for interval-specific retrieval. We argue through theoretical proofs that Clover has standard linear computational complexity, low space complexity, etc. Experiments show that our method can cluster 10 million DNA sequences into 50 000 classes in 10 s and meet an accuracy rate of over 99%. Furthermore, we have successfully completed an unprecedented clustering of 10 billion DNA data on a single home computer and the time consumption still satisfies the linear relationship. Clover is freely available at https://github.com/Guanjinqu/Clover.