DNA Storage Research Articles

Data recovery methods for DNA storage based on fountain codes

Today's digital data storage systems typically offer advanced data recovery solutions to address the problem of catastrophic data loss, such as software-based disk sector analysis or physical-level data retrieval methods for conventional hard disk drives. However, DNA-based data storage currently relies solely on the inherent error correction properties of the methods used to encode digital data into strands of DNA. Any error that cannot be corrected utilizing the redundancy added by DNA encoding methods results in permanent data loss. To provide data recovery for DNA storage systems, we present a method to automatically reconstruct corrupted or missing data stored in DNA using fountain codes. Our method exploits the relationships between packets encoded with fountain codes to identify and rectify corrupted or lost data. Furthermore, we present file type-specific and content-based data recovery methods for three file types, illustrating how a fusion of fountain encoding-specific redundancy and knowledge about the data can effectively recover information in a corrupted DNA storage system, both in an automatic and in a guided manual manner. To demonstrate our approach, we introduce DR4DNA, a software toolkit that contains all methods presented. We evaluate DR4DNA using both in-silico and in-vitro experiments.

GradHC: highly reliable gradual hash-based clustering for DNA storage systems.

As data storage challenges grow and existing technologies approach their limits, synthetic DNA emerges as a promising storage solution due to its remarkable density and durability advantages. While cost remains a concern, emerging sequencing and synthetic technologies aim to mitigate it, yet introduce challenges such as errors in the storage and retrieval process. One crucial task in a DNA storage system is clustering numerous DNA reads into groups that represent the original input strands. In this paper, we review different methods for evaluating clustering algorithms and introduce a novel clustering algorithm for DNA storage systems, named Gradual Hash-based clustering (GradHC). The primary strength of GradHC lies in its capability to cluster with excellent accuracy various types of designs, including varying strand lengths, cluster sizes (including extremely small clusters), and different error ranges. Benchmark analysis demonstrates that GradHC is significantly more stable and robust than other clustering algorithms previously proposed for DNA storage, while also producing highly reliable clustering results. https://github.com/bensdvir/GradHC.

VSD: A Novel Method for Video Segmentation and Storage in DNA Using RS Code

As data continue to grow in complexity and size, there is an imperative need for more efficient and robust storage solutions. DNA storage has emerged as a promising avenue to solve this problem, but existing approaches do not perform efficiently enough on video data, particularly for information density and time efficiency. This paper introduces VSD, a pioneering encoding method for video segmentation and storage in DNA, leveraging the Reed–Solomon (RS) error correction code. This method addresses these limitations through an innovative combination of segmentation and encoding, accompanied by RS coding to bolster error resilience. Additionally, the method ensures that the GC-content of the resultant DNA sequences remains around 50%, which further enhances the storage robustness. The experimental results demonstrate the method has commendable encoding efficiency and offers a solution to the prevailing issue of time inefficiency and error correction rates in DNA storage. This groundbreaking approach paves the way for the practical and reliable storage of large-scale video data in DNA, heralding a new era in the domain of information storage.

An Encoding Table Corresponding to ASCII Codes for DNA Data Storage and a New Error Correction Method HMSA.

DNA storage stands out from other storage media due to its high capacity, eco-friendliness, long lifespan, high stability, low energy consumption, and low data maintenance costs. To standardize the DNA encoding system, maintain consistency in character representation and transmission, and link binary, base, and character together, this paper combines the encoding method with ASCII code to construct an ASCII-DNA encoding table. The encoding method can encode not only pure text information but also audio and video information and satisfies the GC content constraint and the homopolymer constraint, with the encoding density reaching 1.4 bits/nt. In particular, when encoding textual information, it directly skips the binary conversion process, which reduces the complexity of encoding, and increasing the encoding density to 1.6 bits/nt. In order to solve the problem of errors in sequences, under the influence of heuristic algorithms, this paper proposes a new error correction method (HMSA) by combining minimum Hamming distance, multiple sequence alignment, and encoding scheme. It can correct not only substitution, insertion, and deletion errors in Reads but also consecutive errors in Reads. It greatly improves the utilization of the Reads and avoids the waste of resources. Simulation results show that the recovery rate of Reads increases with the increasing number of sequencing times. When the number of erroneous bases in a 150nt sequence reaches 5nt, the error correction rate can exceed 96% by sequencing the base sequence only 10 times regardless of whether the errors are consecutive or not. Additionally, the HMSA error correction method is applicable to all coding schemes for lookup code table types.

DNA Bloom Filter enables anti-contamination and file version control for DNA-based data storage.

DNA storage is one of the most promising ways for future information storage due to its high data storage density, durable storage time and low maintenance cost. However, errors are inevitable during synthesizing, storing and sequencing. Currently, many error correction algorithms have been developed to ensure accurate information retrieval, but they will decrease storage density or increase computing complexity. Here, we apply the Bloom Filter, a space-efficient probabilistic data structure, to DNA storage to achieve the anti-error, or anti-contamination function. This method only needs the original correct DNA sequences (referred to as target sequences) to produce a corresponding data structure, which will filter out almost all the incorrect sequences (referred to as non-target sequences) during sequencing data analysis. Experimental results demonstrate the universal and efficient filtering capabilities of our method. Furthermore, we employ the Counting Bloom Filter to achieve the file version control function, which significantly reduces synthesis costs when modifying DNA-form files. To achieve cost-efficient file version control function, a modified system based on yin-yang codec is developed.

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.

Levenshtein Distance Embedding with Poisson Regression for DNA Storage

Efficient computation or approximation of Levenshtein distance, a widely-used metric for evaluating sequence similarity, has attracted significant attention with the emergence of DNA storage and other biological applications. Sequence embedding, which maps Levenshtein distance to a conventional distance between embedding vectors, has emerged as a promising solution. In this paper, a novel neural network-based sequence embedding technique using Poisson regression is proposed. We first provide a theoretical analysis of the impact of embedding dimension on model performance and present a criterion for selecting an appropriate embedding dimension. Under this embedding dimension, the Poisson regression is introduced by assuming the Levenshtein distance between sequences of fixed length following a Poisson distribution, which naturally aligns with the definition of Levenshtein distance. Moreover, from the perspective of the distribution of embedding distances, Poisson regression approximates the negative log likelihood of the chi-squared distribution and offers advancements in removing the skewness. Through comprehensive experiments on real DNA storage data, we demonstrate the superior performance of the proposed method compared to state-of-the-art approaches.

DNA-QLC: an efficient and reliable image encoding scheme for DNA storage

BackgroundDNA storage has the advantages of large capacity, long-term stability, and low power consumption relative to other storage mediums, making it a promising new storage medium for multimedia information such as images. However, DNA storage has a low coding density and weak error correction ability.ResultsTo achieve more efficient DNA storage image reconstruction, we propose DNA-QLC (QRes-VAE and Levenshtein code (LC)), which uses the quantized ResNet VAE (QRes-VAE) model and LC for image compression and DNA sequence error correction, thus improving both the coding density and error correction ability. Experimental results show that the DNA-QLC encoding method can not only obtain DNA sequences that meet the combinatorial constraints, but also have a net information density that is 2.4 times higher than DNA Fountain. Furthermore, at a higher error rate (2%), DNA-QLC achieved image reconstruction with an SSIM value of 0.917.ConclusionsThe results indicate that the DNA-QLC encoding scheme guarantees the efficiency and reliability of the DNA storage system and improves the application potential of DNA storage for multimedia information such as images.

Robust multi-read reconstruction from noisy clusters using deep neural network for DNA storage

DNA holds immense potential as an emerging data storage medium. However, the recovery of information in DNA storage systems faces challenges posed by various errors, including IDS errors, strand breaks, and rearrangements, inevitably introduced during synthesis, amplification, sequencing, and storage processes. Sequence reconstruction, crucial for decoding, involves inferring the DNA reference from a cluster of erroneous copies. While most methods assume equal contributions from all reads within a cluster as noisy copies of the same reference, they often overlook the existence of contaminated sequences caused by DNA breaks, rearrangements, or mis-clustering reads. To address this issue, we propose RobuSeqNet, a robust multi-read reconstruction neural network specifically designed to robustly reconstruct multiple reads, accommodating noisy clusters with strand breakage, rearrangements, and mis-clustered strands. Leveraging the attention mechanism and an elaborate network design, RobuSeqNet exhibits resilience to highly-noisy clusters and effectively deals with in-strand IDS errors. The effectiveness and robustness of the proposed method are validated on three representative next-generation sequencing datasets. Results demonstrate that RobuSeqNet maintains high sequence reconstruction success rates of 99.74%, 99.58%, and 96.44% across three datasets, even in the presence of noisy clusters containing up to 20% contaminated sequences, outperforming known sequence reconstruction models. Additionally, in scenarios without contaminated sequences, it exhibits comparable performance to existing models, achieving success rates of 99.88%, 99.82%, and 97.68% across the three datasets.

A dual-rule encoding DNA storage system using chaotic mapping to control GC content.

DNA as a novel storage medium is considered an effective solution to the world's growing demand for information due to its high density and long-lasting reliability. However, early coding schemes ignored the biologically constrained nature of DNA sequences in pursuit of high density, leading to DNA synthesis and sequencing difficulties. This article proposes a novel DNA storage coding scheme. The system encodes half of the binary data using each of the two GC-content complementary encoding rules to obtain a DNA sequence. After simulating the encoding of representative document and image file formats, a DNA sequence strictly conforming to biological constraints was obtained, reaching a coding potential of 1.66 bit/nt. In the decoding process, a mechanism to prevent error propagation was introduced. The simulation results demonstrate that by adding Reed-Solomon code, 90% of the data can still be recovered after introducing a 2% error, proving that the proposed DNA storage scheme has high robustness and reliability. Availability and implementation: The source code for the codec scheme of this paper is available at https://github.com/Mooreniah/DNA-dual-rule-rotary-encoding-storage-system-DRRC.

Buccal swabs for long-term DNA storage in conservation genetics of fish: One-and-a-half-year analysis timeframe

Conservation genetic research is essential for the management and recovery of endangered taxa. However, the invasive collection of biological material for DNA analysis is controversial. From an ethical perspective, non-destructive sampling methods leave the aquatic specimen alive and less invasive procedures minimize stress on the animals. DNA can be obtained from fish using minimally invasive techniques such as buccal swabs. Here we evaluated the performance of buccal swabs for long-term storage of DNA obtained from brown trout (Salmo trutta). The buccal swabs were stored at room temperature and cut into pieces, one part of which was used for extraction of an aliquot and the others were stored as a “biobank” of biological material. The elapsed time from sampling to molecular analysis was one and half year. The amplification of three different DNA targets was tested to assess the effectiveness of the extraction: mitochondrial DNA (the D-LOOP region), nuclear DNA (the LDH gene) and microsatellite DNA at multiple loci. The results showed high quantification (mean value: 281.84±72.4 ng/µL), indicating that DNA could be effectively extracted from the buccal swabs. Our study results suggest that buccal swabs for long-term storage of DNA at room temperature are promising for use in field conservation studies.

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.

An open-source, 3D printed inkjet DNA synthesizer

Synthetic oligonucleotides have become a fundamental tool in a wide range of biological fields, including synthetic biology, biosensing, and DNA storage. Reliable access to equipment for synthesizing high-density oligonucleotides in the laboratory ensures research security and the freedom of research expansion. In this study, we introduced the Open-Source Inkjet DNA Synthesizer (OpenIDS), an open-source inkjet-based microarray synthesizer that offers ease of construction, rapid deployment, and flexible scalability. Utilizing 3D printing, Arduino, and Raspberry Pi, this newly designed synthesizer achieved robust stability with an industrial inkjet printhead. OpenIDS maintains low production costs and is therefore suitable for self-fabrication and optimization in academic laboratories. Moreover, even non-experts can create and control the synthesizer with a high degree of freedom for structural modifications. Users can easily add printheads or alter the design of the microarray substrate according to their research needs. To validate its performance, we synthesized oligonucleotides on 144 spots on a 15 × 25-mm silicon wafer filled with controlled pore glass. The synthesized oligonucleotides were analyzed using urea polyacrylamide gel electrophoresis.

"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.

Programmable Primer Switching for Regulating Enzymatic DNA Circuits.

Developing DNA strand displacement reactions (SDRs) offers crucial technical support for regulating artificial nucleic acid circuits and networks. More recently, enzymatic SDR-based DNA circuits have gained significant attention because of their modular design, high orthogonality signaling, and extremely fast reaction rates. Typical enzymatic SDRs are regulated by relatively long primers (20-30 nucleotides) that hybridize to form stable double-stranded structures, facilitating enzyme-initiated events. Implementing more flexible primer-based enzymatic SDR regulations remains challenging due to the lack of convenient and simple primer control mechanism, which consequently limits the development of enzymatic DNA circuits. In this study, we propose an approach, termed primer switching regulation, that implements programmable and flexible regulations of enzymatic circuits by introducing switchable wires into the enzymatic circuits. We applied this method to generate diverse enzymatic DNA circuits, including cascading, fan-in/fan-out, dual-rail, feed-forward, and feedback functions. Through this method, complex circuit functions can be implemented by just introducing additional switching wires without reconstructing the basic circuit frameworks. The method is experimentally demonstrated to provide flexible and programmable regulations to control enzymatic DNA circuits and has future applications in DNA computing, biosensing, and DNA storage.

Reconstruction algorithms for DNA-storage systems

Motivated by DNA storage systems, this work presents the DNA reconstruction problem, in which a length-n string, is passing through the DNA-storage channel, which introduces deletion, insertion and substitution errors. This channel generates multiple noisy copies of the transmitted string which are called traces. A DNA reconstruction algorithm is a mapping which receives t traces as an input and produces an estimation of the original string. The goal in the DNA reconstruction problem is to minimize the edit distance between the original string and the algorithm’s estimation. In this work, we present several new algorithms for this problem. Our algorithms look globally on the entire sequence of the traces and use dynamic programming algorithms, which are used for the shortest common supersequence and the longest common subsequence problems, in order to decode the original string. Our algorithms do not require any limitations on the input and the number of traces, and more than that, they perform well even for error probabilities as high as 0.27. The algorithms have been tested on simulated data, on data from previous DNA storage experiments, and on a new synthesized dataset, and are shown to outperform previous algorithms in reconstruction accuracy.

High-Risk Sequence Prediction Model in DNA Storage: The LQSF Method.

Traditional DNA storage technologies rely on passive filtering methods for error correction during synthesis and sequencing, which result in redundancy and inadequate error correction. Addressing this, the Low Quality Sequence Filter (LQSF) was introduced, an innovative method employing deep learning models to predict high-risk sequences. The LQSF approach leverages a classification model trained on error-prone sequences, enabling efficient pre-sequencing filtration of low-quality sequences and reducing time and resources in subsequent stages. Analysis has demonstrated a clear distinction between high and low-quality sequences, confirming the efficacy of the LQSF method. Extensive training and testing were conducted across various neural networks and test sets. The results showed all models achieving an AUC value above 0.91 on ROC curves and over 0.95 on PR curves across different datasets. Notably, models such as Alexnet, VGG16, and VGG19 achieved a perfect AUC of 1.0 on the Original dataset, highlighting their precision in classification. Further validation using Illumina sequencing data substantiated a strong correlation between model scores and sequence error-proneness, emphasizing the model's applicability. The LQSF method marks a significant advancement in DNA storage technology, introducing active sequence filtering at the encoding stage. This pioneering approach holds substantial promise for future DNA storage research and applications.

DNA Storage Promotes Long-term Storage of Internet of Vehicles Data

DNA Storage Promotes Long-term Storage of Internet of Vehicles Data

Two RRNS-Based Error Correction Schemes for DNA Storage Channels

Two RRNS-Based Error Correction Schemes for DNA Storage Channels

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.