DNA Data Storage Research Articles

(Invited) BioFETs and Nanopore FETs: Nanoscale Silicon Field-Effect Transistors for Single-Molecule Sensing

High read throughput single-molecule sensing is a cornerstone of established third generation long-read DNA sequencing technologies and is crucial for emerging protein sequencing technologies. These omics technologies are of great interest for essential understanding and applications in the life sciences. Field Effect Transistor (FET)-based single-molecule sensors promise advances in omics, by further enhancing read throughput with massive parallelization.Here an overview is given of our recent progress on nanoscale bioFETs and nanopore FETs (NPFETs).A bioFET is a FET gated by a liquid electrolyte rather than by a solid gate. The FET is turned into a biosensor by attaching probe biomolecules to the gate dielectric. These probes bind specifically to the target biomolecule of interest. The attachment of the targets to the probes on the gate dielectric surface shifts the FET threshold voltage.A nanopore FET is an electrolytically gated FET as well, in which the gate gauges potential changes caused by a molecule moving, or translocating, through a nanopore, a nanoscale opening connecting two reservoirs. Nanopores have been firmly established as single-molecule sensors as the ionic current running through them is modulated by passing single molecules.Nanoscale silicon bioFET sensors have single-molecule capabilities within their reach [1-4]. To realize single-molecule sensing the single-molecule signal-to-noise ratio (SNR) must be sufficiently high. Downscaling the device area is key to increase SNR (∝1/√area) [5]. Although many reported FET sensors have nanoscale cross-sections, their gate lengths are typically micron-scale. Our FET modeling work shows that a sufficiently large single-molecule Signal-to-Noise Ratio (SNR) can be obtained when nano-scaling all FET channel dimensions [6]. We have shown significant progress [1] toward single-molecule detection by fabricating and characterizing finFET sensors with a gate length of 50nm and have shown that Si FETs have the potential to enable single-molecule sensing.Noise plays a central role in single-molecule sensing [7,8] and drift can also be a challenge in FET sensors [9]. Perhaps the greatest challenge for bioFETs is electrolyte screening which reduces the signal by masking the charge of target molecules with counterions. We have experimentally established the importance of non-linear screening, and the pH interference effect which can aggravate screening [10,11] and propose solutions.The nanopore FET, senses single molecules translocating through the pore due to the so-called resistive divider effect [6]. The nanopore FET has been experimentally demonstrated as a single-molecule sensor in 2012 [12]. The nanopore FET’s device-level advantage is its potential high > 100 MHz bandwidth with sufficient signal-to-noise ratio (SNR). This has been studied theoretically [13] but is yet to be proven experimentally. The second, and most important advantage, is the superior integrability of nanopore-FETs into large-scale arrays.In our recent simulation work making use of a dedicated nanofluidic nanodevice simulator we reveal that sensitivity can be boosted exponentially by increasing cis-trans bias [14].We have fabricated and characterized a Nanopore-FET milestone device [15,16], which has a nanopore running through a nanoscale silicon FET channel, but without bottom reservoir (see Fig. 1). On this milestone device we obtain excellent electrolytically gated FET transfer and output characteristics demonstrating a low subthreshold swing of 67mV/decade, indicating excellent device quality.Applications of the nanopore FET for omics and DNA data storage will be discussed.FET-based sensing promises to strongly enhance the read throughput of single-molecule sensing by means of massive parallelization.<ECSTEM.jpg>Fig. 1. TEM cross-section of the NPFET milestone device.[1] Santermans et al. IEEE International Electron Devices Meeting, 2020[2] Li et al. ACS Nano 2017, 11, 12789−12795[3] He et al. Angewandte Chemie, 55, 1, 2016[4] Li et al. Nanoscale, 2016, 8, 16172[5] Männik et al. Nano Letters, 8, 2008[6] Santermans et al., to be published[7] Martens et al. Int. Conf. Noise Fluctuations (ICNF), 2019[8] Bergfeld Mori et al. Solid-State Electronics 194, p. 108358, 2022[9] Wuytens et al. IEEE Int. Reliability Physics Symposium, 2020[10] Gupta, et al. IEEE Sensors Journal, 21, 4, p. 4143, 2020[11] Santermans et al. ACS Sensors 6, 3, p. 1049, 2021[12] Xie et al. Nature Nanotechnology, 7(2), pp.119-125, 2012[13] Parkin and Drndić, ACS sensors, 3, 2, p. 313, 2018[14] Verhulst et al., IEEE Sensors Journal, 22, 6, pp.5732-5742, 2022[15] Liu et al., MNE, Leuven, Sep., 2022[16] Martens et al. IEEE International Electron Devices Meeting, 2022 Figure 1

Engineering DNA Materials for Sustainable Data Storage Using a DNA Movable-Type System

DNA molecules are green materials with great potential for high-density and long-term data storage. However, the current data-writing process of DNA data storage via DNA synthesis suffers from high costs and the production of hazards, limiting its practical applications. Here, we developed a DNA movable-type storage system that can utilize DNA fragments pre-produced by cell factories for data writing. In this system, these pre-generated DNA fragments, referred to herein as “DNA movable types,” are used as basic writing units in a repetitive way. The process of data writing is achieved by the rapid assembly of these DNA movable types, thereby avoiding the costly and environmentally hazardous process of de novo DNA synthesis. With this system, we successfully encoded 24 bytes of digital information in DNA and read it back accurately by means of high-throughput sequencing and decoding, thereby demonstrating the feasibility of this system. Through its repetitive usage and biological assembly of DNA movable-type fragments, this system exhibits excellent potential for writing cost reduction, opening up a novel route toward an economical and sustainable digital data-storage technology.

A Low-Complexity Deep Learning Model for Predicting Targeted Sequencing Depth from Probe Sequence

Targeted sequencing has been widely utilized for genomic molecular diagnostics and the emerging DNA data storage paradigm. However, the probe sequences used to enrich regions of interest have different hybridization kinetic properties, resulting in poor sequencing uniformity and setting limitations for the large-scale application of the technology. Here, a low-complexity deep learning model is proposed for prediction of sequencing depth from probe sequences. To capture the representation of probe and target sequences, we utilized a sequence-encoding model that incorporates k-mer and word embedding techniques, providing a streamlined alternative to the intricate computations involved in biochemical feature analysis. We employed bidirectional long short-term memory (Bi-LSTM) to effectively capture both long-range and short-range interactions within the representation. Furthermore, the attention mechanism was adopted to identify pivotal regions in the sequences that significantly influence sequencing depth. The ratio of the predicted sequencing depth to the actual sequencing depth was in the interval of 1/3—3 as the evaluation metric of model accuracy. The prediction accuracy was 94.3% in the human single-nucleotide polymorphism (SNP) panel and 99.7% in the synthetic DNA information storage sequence (SynDNA) panel. Our model substantially reduced data processing time (from 334 min to 4 min of CPU time in the SNP panel) and model parameters (from 300 k to 70 k) compared with the baseline model.

Sustainable DNA Data Storage on Cellulose Paper.

DNA is a promising material for high density and long-term archival data storage. In addition to algorithms for encoding digital information into DNA sequences, the DNA writing (chemical synthesis) and reading (DNA sequencing), the preservation of DNA mixtures with high sequence diversity is another critical issue for sustainable, long-term, and large-scale DNA data storage. Here, this work demonstrates a method for low-cost, convenient and sustainable DNA data storage on cellulose paper. A DNA pool comprising thousands of sequences, in which archival data are encoded, is conveniently stored on a cellulose paper with a calculated density as high as 15TB per mm3 through electrostatic adsorption. This work demonstrates that these digitally encoded DNA pools can be stable for years on the cellulose paper after drying even when directly exposed to air. Furthermore, the reversible electrostatic adsorption enables repeated loading/retrieval of DNA on/off cellulose paper. Therefore, this sustainable DNA preservation on cellulose paper through the convenient electrostatic adsorption exhibits a great advantage in terms of storage capacity and cost that is crucial for practical systems to achieve large-scale and long-time data storage.

Spatially Selective Electrochemical Cleavage of a Polymerase-Nucleotide Conjugate.

Novel enzymatic methods are poised to become the dominant processes for de novo synthesis of DNA, promising functional, economic, and environmental advantages over the longstanding approach of phosphoramidite synthesis. Before this can occur, however, enzymatic synthesis methods must be parallelized to enable production of multiple DNA sequences simultaneously. As a means to this parallelization, we report a polymerase-nucleotide conjugate that is cleaved using electrochemical oxidation on a microelectrode array. The developed conjugate maintains polymerase activity toward surface-bound substrates with single-base control and detaches from the surface at mild oxidative voltages, leaving an extendable oligonucleotide behind. Our approach readies the way for enzymatic DNA synthesis on the scale necessary for DNA-intensive applications such as DNA data storage or gene synthesis.

Assembly of Reusable DNA Blocks for Data Storage Using the Principle of Movable Type Printing

Due to its high coding density and longevity, DNA is a compelling data storage alternative. However, current DNA data storage systems rely on the de novo synthesis of enormous DNA molecules, resulting in low data editability, high synthesis costs, and restrictions on further applications. Here, we demonstrate the programmable assembly of reusable DNA blocks for versatile data storage using the ancient movable type printing principle. Digital data are first encoded into nucleotide sequences in DNA hairpins, which are then synthesized and immobilized on solid beads as modular DNA blocks. Using DNA polymerase-catalyzed primer exchange reaction, data can be continuously replicated from hairpins on DNA blocks and attached to a primer in tandem to produce new information. The assembly of DNA blocks is highly programmable, producing various data by reusing a finite number of DNA blocks and reducing synthesis costs (∼1718 versus 3000 to 30,000 US$ per megabyte using conventional methods). We demonstrate the flexible assembly of texts, images, and random numbers using DNA blocks and the integration with DNA logic circuits to manipulate data synthesis. This work suggests a flexible paradigm by recombining already synthesized DNA to build cost-effective and intelligent DNA data storage systems.

Modelling, Characterization of Data-Dependent and Process-Dependent Errors in DNA Data Storage.

Using DNA as the medium to store information has recently been recognized as a promising solution for long-term data storage. While several system prototypes have been demonstrated, the error characteristics in DNA data storage are discussed with limited content. Due to the data and process variations from experiment to experiment, the error variation and its effect on data recovery remain to be uncovered. To close the gap, we systematically investigate the storage channel, i.e., error characteristics in the storage process. In this work, we first propose a new concept named sequence corruption to unify the error characteristics into the sequence level, easing the channel analysis. Then we derived the formulations of the data imperfection at the decoder including both sequence loss and sequence corruption, revealing the decoding demand and monitoring the data recovery. Furthermore, we extensively explored several data-dependent unevenness observed in the base error patterns and studied a few potential factors and their impacts on the data imperfection at the decoder both theoretically and experimentally. The results presented here introduce a more comprehensive channel model and offer a new angle towards the data recovery issue in DNA data storage by further elucidating the error characteristics of the storage process.

Content-based filter queries on DNA data storage systems

Recent developments in DNA data storage systems have revealed the great potential to store large amounts of data at a very high density with extremely long persistence and low cost. However, despite recent contributions to robust data encoding, current DNA storage systems offer limited support for random access on DNA storage devices due to restrictive biochemical constraints. Moreover, state-of-the-art approaches do not support content-based filter queries on DNA storage. This paper introduces the first encoding for DNA that enables content-based searches on structured data like relational database tables. We provide the details of the methods for coding and decoding millions of directly accessible data objects on DNA. We evaluate the derived codes on real data sets and verify their robustness.

DNA Data Storage: The Fusion of Digital and Biological Information

With the development of Internet technology, human information data grows by a huge amount. Traditional data storage media are no longer suitable for large amounts of data storage due to their inherent shortcomings, such as high power consumption, large physical size, and short storage life. DNA information storage, on the other hand, can overcome these shortcomings to a certain extent. This paper introduces the process and mechanism of DNA storage, such as the DNA synthesis method, DNA coding, DNA preservation and sequencing, the history of DNA storage, its defects, and shortcomings. This article introduces the principles and mechanisms of DNA storage, including DNA synthesis method, data storage, and DNA preservation, as well as the history of DNA storage and its shortcomings and prospects for improvement, using the comparison of traditional storage methods and DNA storage methods as an import cut.

The bottom of the memory hierarchy: Semiconductor and DNA data storage

The bottom of the memory hierarchy: Semiconductor and DNA data storage

The Long and Winding Road: On-Demand DNA Synthesis in High Demand

The Long and Winding Road: On-Demand DNA Synthesis in High Demand

DNA Computing-Based Multi-Source Data Storage Model in Digital Twins

The work aims to study the application of Deoxyribonucleic Acid (DNA) multi-source data storage in Digital Twins (DT) . Through the investigation of the research status of DT and DNA computing, the work puts forward the concept of DNA multi-source data storage for DT. Raptor code is improved from the design direction of degree distribution function, and six degree function distribution schemes are proposed in turn in the process of describing the research method. Additionally, a quaternary dynamic Huffman coding method is applied in DNA data storage, combined with the improved concatenated code as the error correction code. Considering the content of cytosine deoxynucleotide (C) and guanine deoxynucleotide Guanine (G) and the distribution of homopolymer in DNA storage, the work proposes and verifies an improved concatenated code algorithm Deoxyribonucleic Acid-Improved Concatenated code (DNA-ICC) . The results show that while the Signal-to-Noise Ratio (SNR) increases, the Bit Error Rate (BER) decreases gradually and the trend is similar. But the anti-interference ability of the degree distribution function optimized by the probability transfer method is better. The BER of DNA-ICC scheme decreases with the decrease of error probability, which is stronger than other error correction codes. Compared with the original concatenated code, it saves at least 1.65 s, and has a good control effect on homopolymer. When the size of homopolymer exceeds 4 nt, the probability of homopolymer is only 0.44%. The proposed Quaternary dynamic Huffman code and concatenated error correction code have excellent performance.

DNA-Aeon provides flexible arithmetic coding for constraint adherence and error correction in DNA storage

The extensive information capacity of DNA, coupled with decreasing costs for DNA synthesis and sequencing, makes DNA an attractive alternative to traditional data storage. The processes of writing, storing, and reading DNA exhibit specific error profiles and constraints DNA sequences have to adhere to. We present DNA-Aeon, a concatenated coding scheme for DNA data storage. It supports the generation of variable-sized encoded sequences with a user-defined Guanine-Cytosine (GC) content, homopolymer length limitation, and the avoidance of undesired motifs. It further enables users to provide custom codebooks adhering to further constraints. DNA-Aeon can correct substitution errors, insertions, deletions, and the loss of whole DNA strands. Comparisons with other codes show better error-correction capabilities of DNA-Aeon at similar redundancy levels with decreased DNA synthesis costs. In-vitro tests indicate high reliability of DNA-Aeon even in the case of skewed sequencing read distributions and high read-dropout.

Mobile and Self-Sustained Data Storage in an Extremophile Genomic DNA.

DNA has been pursued as a novel biomaterial for digital data storage. While large-scale data storage and random access have been achieved in DNA oligonucleotide pools, repeated data accessing requires constant data replenishment, and these implementations are confined in professional facilities. Here, a mobile data storage system in the genome of the extremophile Halomonas bluephagenesis, which enables dual-mode storage, dynamic data maintenance, rapid readout, and robust recovery. The system relies on two key components: A versatile genetic toolbox for the integration of 10-100kb scale synthetic DNA into H. bluephagenesis genome and an efficient error correction coding scheme targeting noisy nanopore sequencing reads. The storage and repeated retrieval of 5KB data under non-laboratory conditions are demonstrated. The work highlights the potential of DNA data storage in domestic and field scenarios, and expands its application domain from archival data to frequently accessed data.

The emerging landscape of microfluidic applications in DNA data storage.

DNA has been considered a promising alternative to the current solid-state devices for digital information storage. The past decade has witnessed tremendous progress in the field of DNA data storage contributed by researchers from various disciplines. However, the current development status of DNA storage is still far from practical use, mainly due to its high material cost and time consumption for data reading/writing, as well as the lack of a comprehensive, automated, and integrated system. Microfluidics, being capable of handling and processing micro-scale fluid samples in a massively paralleled and highly integrated manner, has gradually been recognized as a promising candidate for addressing the aforementioned issues. In this review, we provide a discussion on recent efforts of applying microfluidics to advance the development of DNA data storage. Moreover, to showcase the tremendous potential that microfluidics can contribute to this field, we will further highlight the recent advancements of applying microfluidics to the key functional modules within the DNA data storage workflow. Finally, we share our perspectives on future directions for how to continue the infusion of microfluidics with DNA data storage and how to advance toward a truly integrated system and reach real-life applications.

Generative Adversarial Networks for DNA Storage Channel Simulator

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 <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/gyfbianhuanyun/DNA_storage_simulator_GAN</uri></monospace> .

Channel Model and Decoder With Memory for DNA Data Storage With Nanopore Sequencing

Channel Model and Decoder With Memory for DNA Data Storage With Nanopore Sequencing

Comment on “Emerging Approaches to DNA Data Storage: Challenges and Prospects”

Comment on “Emerging Approaches to DNA Data Storage: Challenges and Prospects”

RETRACTED: Bionic‐structure thermo‐responsive (best) hydrogels with controllable layer for high‐capacity DNA data storage

AbstractHere, 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.

10 Years of Natural Data Storage

Digital data explosion has been driving a demand for robust and reliable data storage medium. The present day digital storage devices presents a big challenge for data scientists to provide reliable, affordable and dense storage medium. In the last decade, natural storage medium such as DNA, bacteria, and protein has been shown as a promising approach for next generation information storage systems. This article is a review that discusses advancements in the natural data storage, which has potential to replace the current physical storage for archival systems and have high storage per unit weight of medium, and is affordable and reliable. Further, the challenges for natural data storage will also be presented. With the international DNA data storage alliance of more than 30 companies and academic alliance in 2020, one would hope that DNA data storage will soon be available commercially in next 5–10 years. The success of the alliance will also motivate researchers to explore the other natural storage medium such as bacteria and protein which are currently in the lab.