Design considerations for advancing data storage with synthetic DNA for long-term archiving

Optimizing fountain codes for DNA data storage

Fountain codes, originally developed for reliable multicasting in communication networks, are effectively applied in various data transmission and storage systems. Their recent use in DNA data storage systems has unique challenges, since the DNA storage channel deviates from the traditional Gaussian white noise erasure model considered in communication networks and has several restrictions as well as special properties. Thus, optimizing fountain codes to address these challenges promises to improve their overall usability in DNA data storage systems. In this article, we present several methods for optimizing fountain codes for DNA data storage. Apart from generally applicable optimizations for fountain codes, we propose optimization algorithms to create tailored distribution functions of fountain codes, which is novel in the context of DNA data storage. We evaluate the proposed methods in terms of various metrics related to the DNA storage channel. Our evaluation shows that optimizing fountain codes for DNA data storage can significantly enhance the reliability and capacity of DNA data storage systems. The developed methods represent a step forward in harnessing the full potential of fountain codes for DNA-based data storage applications. The new coding schemes and all developed methods are available under a free and open-source software license.

Exploring DNA Computers: Advances in Storage, Cryptography and Logic Circuits

Over the last four decades, research on DNA as a functional material has primarily focused on its predictable conformation and programmable interaction. However, its low energy consumption, high responsiveness and sensitivity also make it ideal for designing specific signaling pathways, and enabling the development of molecular computers. This review mainly discusses recent advancements in the utilization of DNA nanotechnology for molecular computer, encompassing applications in storage, cryptography and logic circuits. It elucidates the challenges encountered in the application process and presents solutions exemplified by representative works. Lastly, it delineates the challenges and opportunities within this filed.

"Multi-layer" encryption of medical data in DNA for highly-secure storage

The exponential increasement and the attributes of medical data drive the requirement for secure medical data archiving. DNA data storage shows promise for storing sensitive and important data like medical records due to its high density and endurance. Nevertheless, current DNA data storage working scheme generally does not fully consider the data encryption, posing a risk of data corruption by routine DNA sequencing. Here, we designed a "multi-layer" encryption pipeline for medical data archiving. Initially, digital information was encrypted using Blowfish algorithm at information technology (IT) layer, followed by two-layer data encryption at the biotechnology (BT) layer. The first BT layer exploited the molecular weight of synthetic DNA or nucleoside to encrypt the key, while the second BT layer encrypted digital information within DNA sequences. Consequently, decryption involved layer-by-layer interpretation of data, including mass spectroscopy, sequencing, and Blowfish decryption, significantly enhancing data security. Utilizing mass spectroscopy to retrieve information allows for employment of both natural and unnatural nucleosides, as well as their synthetic oligonucleotides, for data storage, thereby considerably boosting scalability. Our work implies expanded flexibility of DNA-based data storage, highlighting the potential for leveraging various physical and chemical characteristics of DNA molecules to encode and access digital information.

PELMI: Realize robust DNA image storage under general errors via parity encoding and local mean iteration

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.

Fully in vitro iterative construction of a 24 kb-long artificial DNA sequence to store digital information

In the absence of a DNA template, the ab initio production of long double-stranded DNA molecules of predefined sequences is particularly challenging. The DNA synthesis step remains a bottleneck for many applications such as functional assessment of ancestral genes, analysis of alternative splicing or DNA-based data storage. In this report we propose a fully in vitro protocol to generate very long double-stranded DNA molecules starting from commercially available short DNA blocks in less than 3 days using Golden Gate assembly. This innovative application allowed us to streamline the process to produce a 24 kb-long DNA molecule storing part of the Declaration of the Rights of Man and of the Citizen of 1789 . The DNA molecule produced can be readily cloned into a suitable host/vector system for amplification and selection.

Coding, Decoding and Retrieving a Message Using DNA: An Experience from a Brazilian Center Research on DNA Data Storage

DNA data storage based on synthetic oligonucleotides is a major attraction due to the possibility of storage over long periods. Nowadays, the quantity of data generated has been growing exponentially, and the storage capacity needs to keep pace with the growth caused by new technologies and globalization. Since DNA can hold a large amount of information with a high density and remains stable for hundreds of years, this technology offers a solution for current long-term data centers by reducing energy consumption and physical storage space. Currently, research institutes, technology companies, and universities are making significant efforts to meet the growing need for data storage. DNA data storage is a promising field, especially with the advancement of sequencing techniques and equipment, which now make it possible to read genomes (i.e., to retrieve the information) and process this data easily. To overcome the challenges associated with developing new technologies for DNA data storage, a message encoding and decoding exercise was conducted at a Brazilian research center. The exercise performed consisted of synthesizing oligonucleotides by the phosphoramidite route. An encoded message, using a coding scheme that adheres to DNA sequence constraints, was synthesized. After synthesis, the oligonucleotide was sequenced and decoded, and the information was fully recovered.

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.

Limit and screen sequences with high degree of secondary structures in DNA storage by deep learning method

BACKGROUND

In single-stranded DNAs/RNAs, secondary structures are very common especially in long sequences. It has been recognized that the high degree of secondary structures in DNA sequences could interfere with the correct writing and reading of information in DNA storage. However, how to circumvent its side-effect is seldom studied.

METHOD

As the degree of secondary structures of DNA sequences is closely related to the magnitude of the free energy released in the complicated folding process, we first investigate the free-energy distribution at different encoding lengths based on randomly generated DNA sequences. Then, we construct a bidirectional long short-term (BiLSTM)-attention deep learning model to predict the free energy of sequences.

RESULTS

Our simulation results indicate that the free energy of DNA sequences at a specific length follows a right skewed distribution and the mean increases as the length increases. Given a tolerable free energy threshold of 20 kcal/mol, we could control the ratio of serious secondary structures in the encoding sequences to within 1% of the significant level through selecting a feasible encoding length of 100 nt. Compared with traditional deep learning models, the proposed model could achieve a better prediction performance both in the mean relative error (MRE) and the coefficient of determination (R2). It achieved MRE = 0.109 and R2 = 0.918 respectively in the simulation experiment. The combination of the BiLSTM and attention module can handle the long-term dependencies and capture the feature of base pairing. Further, the prediction has a linear time complexity which is suitable for detecting sequences with severe secondary structures in future large-scale applications. Finally, 70 of 94 predicted free energy can be screened out on a real dataset. It demonstrates that the proposed model could screen out some highly suspicious sequences which are prone to produce more errors and low sequencing copies.

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.

A possible mechanism of neural read-out from a molecular engram

What is the physical basis of declarative memory? The predominant view holds that stored information is embedded in the structure of a neural net, that is, in the signs and weights of its synaptic connections. An alternative possibility is that storage and processing are separated, and that the engram is encoded chemically, most probably in the sequence of a nucleic acid. One deterrent to adoption of the latter hypothesis has been the difficulty of envisaging how neural actively could be converted to and from a molecular code. Our purpose here is limited to suggesting how a molecular sequence could be read out from nucleic acid to neural activity by means of nanopores.

DNAsmart: Multiple attribute ranking tool for DNA data storage systems

In an ever-growing need for data storage capacity, the Deoxyribonucleic Acid (DNA) molecule gains traction as a new storage medium with a larger capacity, higher density, and a longer lifespan over conventional storage media. To effectively use DNA for data storage, it is important to understand the different methods of encoding information in DNA and compare their effectiveness. This requires evaluating which decoded DNA sequences carry the most encoded information based on various attributes. However, navigating the field of coding theory requires years of experience and domain expertise. For instance, domain experts rely on various mathematical functions and attributes to score and evaluate their encodings. To enable such analytical tasks, we provide an interactive and visual analytical framework for multi-attribute ranking in DNA storage systems. Our framework follows a three-step view with user-settable parameters. It enables users to find the optimal en-/de-coding approaches by setting different weights and combining multiple attributes. We assess the validity of our work through a task-specific user study on domain experts by relying on three tasks. Results indicate that all participants completed their tasks successfully under two minutes, then rated the framework for design choices, perceived usefulness, and intuitiveness. In addition, two real-world use cases are shared and analyzed as direct applications of the proposed tool. DNAsmart enables the ranking of decoded sequences based on multiple attributes. In sum, this work unveils the evaluation of en-/de-coding approaches accessible and tractable through visualization and interactivity to solve comparison and ranking tasks.