DNA Storage Research Articles

Design of Constraint Coding Sets for Archive DNA Storage.

With the advent of the era of massive data, the increase of storage demand has far exceeded current storage capacity. DNA molecules provide a reliable solution for big data storage by virtue of their large capacity, high density, and long-term stability. To reduce errors in storing procedures, constructing a sufficient set of constraint encoding is critical for achieving DNA storage. A new version of the Marine Predator algorithm (called QRSS-MPA) is proposed in this paper to increase the lower bound of the coding set while satisfying the specific combination of constraints. In order to demonstrate the effectiveness of the improvement, the classical CEC-05 test function is used to test and compare the mean, variance, scalability, and significance. In terms of storage, the lower bound of construction is compared with previous works, and the result is found to be significantly improved. In order to prevent the emergence of a secondary structure that leads to sequencing failure, we give a more stringent lower bound for the constraint coding set, which is of great significance for reducing the error rate of DNA storage amidst its rapid development.

On the Reverse-Complement String-Duplication System

Motivated by DNA storage in living organisms, and by known biological mutation processes, we study the reverse-complement string-duplication system. We fully classify the conditions under which the system has full expressiveness, for all alphabets and all fixed duplication lengths. We then focus on binary systems with duplication length 2 and prove that they have full capacity, yet surprisingly, have zero entropy-rate. Finally, by using binary single burst-insertion correcting codes, we construct codes that correct a single reverse-complement duplication of odd length, over any alphabet. The redundancy (in bits) of the constructed code does not depend on the alphabet size.

Upper and Lower Bounds on the Capacity of the DNA-Based Storage Channel

The application of DNA as a powerful tool for storing digital information in chemically synthesized molecules has undergone continuous development. To explore its potential and limitations, we model the DNA storage channel as a cascade of a series of parallel and independent DNA noisy synchronization error channels and a shuffling-sampling channel, and derive novel lower and upper capacity bounds through a purely information-theoretic approach. Our results reveal the potential of DNA storage density and can be used to guide the design of error correction codes.

PyDockDNA: A new web server for energy-based protein-DNA docking and scoring.

Proteins and nucleic acids are essential biological macromolecules for cell life. Indeed, interactions between proteins and DNA regulate many biological processes such as protein synthesis, signal transduction, DNA storage, or DNA replication and repair. Despite their importance, less than 4% of total structures deposited in the Protein Data Bank (PDB) correspond to protein-DNA complexes, and very few computational methods are available to model their structure. We present here the pyDockDNA web server, which can successfully model a protein-DNA complex with a reasonable predictive success rate (as benchmarked on a standard dataset of protein-DNA complex structures, where DNA is in B-DNA conformation). The server implements the pyDockDNA program, as a module of pyDock suite, thus including third-party programs, modules, and previously developed tools, as well as new modules and parameters to handle the DNA properly. The user is asked to enter Protein Data Bank files for protein and DNA input structures (or suitable models) and select the chains to be docked. The server calculations are mainly divided into three steps: sampling by FTDOCK, scoring with new energy-based parameters and the possibility of applying external restraints. The user can select different options for these steps. The final output screen shows a 3D representation of the top 10 models and a table sorting the model according to the scoring function selected previously. All these output files can be downloaded, including the top 100 models predicted by pyDockDNA. The server can be freely accessed for academic use (https://model3dbio.csic.es/pydockdna).

Integrated Microfluidic DNA Storage Platform with Automated Sample Handling and Physical Data Partitioning.

Biopolymers are considered a promising alternative for information storage, and the most successful implementation has been using chemically synthesized DNA to represent binary data, which has achieved tremendous progress at multiple fronts bridging biotechnology with digital information. Currently, a majority of these systems are lacking the system integration and process automation expected by users of digital data and overly use tubes/vials for DNA storage. Herein, we present a microfluidic platform for automated storage and retrieval of data-encoding oligonucleotide samples enabled by a microvalve network architecture. Our platform, equipped with individually addressable compartments, offers an orthogonal strategy of data partitioning and file indexing with respect to the molecular-based random access implementation, with each partition amounting to an equivalence of 9.5 TB data within a 4 × 2 mm2 area. We examined the functionality of the presented platform and its compatibility with the DNA storage workflow coupled with nanopore sequencing to fully recover the stored files, demonstrating a significantly enhanced degree of function integration and process automation compared to that of the existing microfluidic approach.

Using Artificial Neural Networks to Model Errors in Biochemical Manipulation of DNA Molecules.

In recent years, the non-biological applications of DNA molecules have made considerable progress; most of these applications were performed in vitro, involving biochemical operations such as synthesis, amplification and sequencing. Because errors may occur with specific sequence patterns or experimental instruments, these biochemical operations are not completely reliable. Modeling errors in these biochemical procedures is an interesting research topic. For example, researchers have proposed several methods to avoid the known vulnerable sequence patterns in the study of storing binary information in DNA molecules. However, there are few end-to-end methods to evaluate these biochemical errors with regard to the DNA sequences. In this article, based on the data generated by a DNA storage research, we use artificial neural networks to predict whether a DNA sequence tends to cause errors in biochemical operations. Through comparative experiments and hyperparameter optimization, we analyze the known and potential problems in the research process. As a result, an end-to-end method to model the biochemical errors of DNA molecules in vitro through a computer system is proposed.

Design of DNA Storage Coding with Enhanced Constraints.

Traditional storage media have been gradually unable to meet the needs of data storage around the world, and one solution to this problem is DNA storage. However, it is easy to make errors in the subsequent sequencing reading process of DNA storage coding. To reduces error rates, a method to enhance the robustness of the DNA storage coding set is proposed. Firstly, to reduce the likelihood of secondary structure in DNA coding sets, a repeat tandem sequence constraint is proposed. An improved DTW distance constraint is proposed to address the issue that the traditional distance constraint cannot accurately evaluate non-specific hybridization between DNA sequences. Secondly, an algorithm that combines random opposition-based learning and eddy jump strategy with Aquila Optimizer (AO) is proposed in this paper, which is called ROEAO. Finally, the ROEAO algorithm is used to construct the coding sets with traditional constraints and enhanced constraints, respectively. The quality of the two coding sets is evaluated by the test of the number of issuing card structures and the temperature stability of melting; the data show that the coding set constructed with ROEAO under enhanced constraints can obtain a larger lower bound while improving the coding quality.

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.

Nanocellulose Composites as Smart Devices With Chassis, Light-Directed DNA Storage, Engineered Electronic Properties, and Chip Integration.

The rapid development of green and sustainable materials opens up new possibilities in the field of applied research. Such materials include nanocellulose composites that can integrate many components into composites and provide a good chassis for smart devices. In our study, we evaluate four approaches for turning a nanocellulose composite into an information storage or processing device: 1) nanocellulose can be a suitable carrier material and protect information stored in DNA. 2) Nucleotide-processing enzymes (polymerase and exonuclease) can be controlled by light after fusing them with light-gating domains; nucleotide substrate specificity can be changed by mutation or pH change (read-in and read-out of the information). 3) Semiconductors and electronic capabilities can be achieved: we show that nanocellulose is rendered electronic by iodine treatment replacing silicon including microstructures. Nanocellulose semiconductor properties are measured, and the resulting potential including single-electron transistors (SET) and their properties are modeled. Electric current can also be transported by DNA through G-quadruplex DNA molecules; these as well as classical silicon semiconductors can easily be integrated into the nanocellulose composite. 4) To elaborate upon miniaturization and integration for a smart nanocellulose chip device, we demonstrate pH-sensitive dyes in nanocellulose, nanopore creation, and kinase micropatterning on bacterial membranes as well as digital PCR micro-wells. Future application potential includes nano-3D printing and fast molecular processors (e.g., SETs) integrated with DNA storage and conventional electronics. This would also lead to environment-friendly nanocellulose chips for information processing as well as smart nanocellulose composites for biomedical applications and nano-factories.

Preservation and Encryption in DNA Digital Data Storage.

The exponential growth of the total amount of global data presents a huge challenge to mainstream storage media. The emergence of molecular digital storage inspires the development of the new-generation higher-density digital data storage. In particular, DNA with high storage density, reproducibility, and long recoverable lifetime behaves the ideal representative of molecular digital storage media. With the development of DNA synthesis and sequencing technologies and the reduction of cost, DNA digital storage has attracted more and more attention and achieved significant breakthroughs. Herein, this Review briefly describes the workflow of DNA storage, and highlights the storage step of DNA digital data storage. Then, according to different information storage forms, the current DNA information encryption methods are emphatically expounded. Finally, the brief perspectives on the current challenges and optimizing proposals in DNA information preservation and encryption are presented.

Hidden Addressing Encoding for DNA Storage.

DNA is a natural storage medium with the advantages of high storage density and long service life compared with traditional media. DNA storage can meet the current storage requirements for massive data. Owing to the limitations of the DNA storage technology, the data need to be converted into short DNA sequences for storage. However, in the process, a large amount of physical redundancy will be generated to index short DNA sequences. To reduce redundancy, this study proposes a DNA storage encoding scheme with hidden addressing. Using the improved fountain encoding scheme, the index replaces part of the data to realize hidden addresses, and then, a 10.1 MB file is encoded with the hidden addressing. First, the Dottup dot plot generator and the Jaccard similarity coefficient analyze the overall self-similarity of the encoding sequence index, and then the sequence fragments of GC content are used to verify the performance of this scheme. The final results show that the encoding scheme indexes with overall lower self-similarity, and the local thermodynamic properties of the sequence are better. The hidden addressing encoding scheme proposed can not only improve the utilization of bases but also ensure the correct rate of DNA storage during the sequencing and decoding processes.

Characterizing a New Fluorescent Protein for a Low Limit of Detection Sensing in the Cell-Free System.

Cell-free protein synthesis-based biosensors have been developed as highly accurate, low-cost biosensors. However, since most biomarkers exist at low concentrations in various types of biopsies, the biosensor’s dynamic range must be increased in the system to achieve low limits of detection necessary while deciphering from higher background signals. Many attempts to increase the dynamic range have relied on amplifying the input signal from the analyte, which can lead to complications of false positives. In this study, we aimed to increase the protein synthesis capability of the cell-free protein synthesis system and the output signal of the reporter protein to achieve a lower limit of detection. We utilized a new fluorescent protein, mNeonGreen, which produces a higher output than those commonly used in cell-free biosensors. Optimizations of DNA sequence and the subsequent cell-free protein synthesis reaction conditions allowed characterizing protein expression variability by given DNA template types, reaction environment, and storage additives that cause the greatest time constraint on designing the cell-free biosensor. Finally, we characterized the fluorescence kinetics of mNeonGreen compared to the commonly used reporter protein, superfolder green fluorescent protein. We expect that this finely tuned cell-free protein synthesis platform with the new reporter protein can be used with sophisticated synthetic gene circuitry networks to increase the dynamic range of a cell-free biosensor to reach lower detection limits and reduce the false-positive proportion.

Adaptive coding for DNA storage with high storage density and low coverage

The rapid development of information technology has generated substantial data, which urgently requires new storage media and storage methods. DNA, as a storage medium with high density, high durability, and ultra-long storage time characteristics, is promising as a potential solution. However, DNA storage is still in its infancy and suffers from low space utilization of DNA strands, high read coverage, and poor coding coupling. Therefore, in this work, an adaptive coding DNA storage system is proposed to use different coding schemes for different coding region locations, and the method of adaptively generating coding constraint thresholds is used to optimize at the system level to ensure the efficient operation of each link. Images, videos, and PDF files of size 698 KB were stored in DNA using adaptive coding algorithms. The data were sequenced and losslessly decoded into raw data. Compared with previous work, the DNA storage system implemented by adaptive coding proposed in this paper has high storage density and low read coverage, which promotes the development of carbon-based storage systems.

Sequence Reconstruction for Limited-Magnitude Errors

Motivated by applications to DNA storage, we study reconstruction and list-reconstruction schemes for integer vectors that suffer from limited-magnitude errors. We characterize the asymptotic size of the intersection of error balls in relation to the code’s minimum distance. We also devise efficient reconstruction algorithms for various limited-magnitude error parameter ranges. We then extend these algorithms to the list-reconstruction scheme, and show the trade-off between the asymptotic list size and the number of required channel outputs. These results apply to all codes, without any assumptions on the code structure. Finally, we also study linear reconstruction codes with small intersection, as well as show a connection to list-reconstruction codes for the tandem-duplication channel.

Extended XOR Algorithm with Biotechnology Constraints for Data Security in DNA Storage

Background: DNA storage is becoming a global research hotspot in recent years, and today, most research focuses on storage density and big data. The security of DNA storage needs to be observed. Some DNA-based security methods were introduced for traditional information security problems. However, few encryption algorithms considered the limitation of biotechnology and applied it for DNA storage. The difference between DNA cryptography and the traditional one is that the former is based on the limitation of biotechnology, which is unrelated to numeracy. Objective: An extended XOR algorithm (EXA) was introduced for encryption with constraints of biotechnology, which can solve the problems of synthesis and sequencing partly, such as GC content and homopolymer in DNA storage. Methods: The target file was converted by a quaternary DNA storage model to maximize the storage efficiency. The key file could be ‘anything’ converted into a DNA sequence by a binary DNA storage model to make the best utilization for the length of the key file. Results: The input files were encrypted into DNA storage and decrypted to error-free output files. Conclusion: This means error-free encryption DNA storage is feasible, and EXA paves the way for encryption in large-scale DNA storage.

Iterative coding scheme satisfying GC balance and run-length constraints for DNA storage with robustness to error propagation

In this paper, we propose a novel iterative encoding algorithm for DNA storage to satisfy both the GC balance and run-length constraints using a greedy algorithm. DNA strands with run-length more than three and the GC balance ratio far from 50\% are known to be prone to errors. The proposed encoding algorithm stores data at high information density with high flexibility of run-length at most $m$ and GC balance between $0.5\pm\alpha$ for arbitrary $m$ and $\alpha$. More importantly, we propose a novel mapping method to reduce the average bit error compared to the randomly generated mapping method, using a greedy algorithm. The proposed algorithm is implemented through iterative encoding, consisting of three main steps: randomization, M-ary mapping, and verification. The proposed algorithm has an information density of 1.8523 bits/nt in the case of $m=3$ and $\alpha=0.05$. Also, the proposed algorithm is robust to error propagation, since the average bit error caused by the one nt error is 2.3455 bits, which is reduced by $20.5\%$, compared to the randomized mapping.

Improved Rank-Modulation Codes for DNA Storage With Shotgun Sequencing

A common method for reading information stored in DNA molecules is shotgun sequencing. This method outputs a histogram of the frequencies of all the molecules’ substrings of a given length ℓ. To protect against noisy readings, the rank-modulation scheme encodes the information in the relative ranking of the substring frequencies, instead of their absolute values. However, the best rank-modulation codes for shotgun sequencing have low rates which are asymptotically vanishing. In this paper we propose new constructions of rank-modulation codes for shotgun sequencing. The first code construction is systematic, allowing the user to arbitrarily set the frequencies of a large subset of the substrings, which the encoder then completes to a permutation that may be realized by a DNA molecule. The construction is then improved by allowing the user to set the frequencies of additional substrings, at the cost of imposing constraints on the frequencies. The resulting codes have higher, non-vanishing rates, compared with previously known codes. As an example, for histograms of substrings of length ℓ = 2, and an alphabet of size 4 (as in DNA molecules), we are able to construct a code with rate ≈ 0.909, whereas previously, the best construction resulted in a code with rate ≈ 0.654. Additionally, the encoded information in our construction may be written to shorter DNA molecules than possible before. We also prove that the systematic codes constructed in this paper are the largest possible among all systematic codes.

Quality Assessment of Cryopreserved Human Biological Samples from the Biobank of Barretos Cancer Hospital.

Background: Biobanks process, store, and supply biological materials for research. Preanalytical factors, especially storage time and temperature, must be controlled and standardized at all stages when handling biospecimen samples, especially because the literature reports highly contradictory optimal parameters. As large-sample studies are required to better understand the influence of time and temperature on cryopreserved samples' quality for genomic research, this study evaluated the integrity and quality of cryopreserved samples stored for up to 9 years at the biobank of Barretos Cancer Hospital, one of the largest biobanks in Latin America. Methods: We randomly selected 447 samples with tumor tissue paired with buffy coat or peripheral blood mononuclear cells (PBMCs) that were stored from 2008 to 2016. The genetic material quality was evaluated based on RNA integrity (RIN) and DNA integrity (DIN) ≥7, which indicated undegraded samples, and compared with storage time, which means that for DNA storage time, samples <8.1 and ≥8.1 years and for RNA <4.5 and ≥4.5 were used. Results: A total of 190 tumor tissues were eligible for DNA and RNA extraction. Those stored for 8 years had lower DIN (68%) than those stored for a shorter period (92%). A similar pattern, based on storage time (<8.1 and ≥8.1 years), was observed in the buffy coat (74% and 95%, respectively) and PBMCs (54% and 96%, respectively). For RNA extracted from tumor tissues, we observed lower RIN in samples stored for 4.5 years (17%) than in samples stored for a shorter period (45%). Buffy coat and PBMC samples stored at -30°C exhibited greater degradation (26%) than those stored at -80°C (1%). The DIN (p = 0.15) and RNA (p = 0.18) were unrelated to topography type. Conclusion: The temperature, particularly cryopreservation methodology, and storage time were the main factors that affected nucleic acid integrity, especially RNA, during cryopreservation of biospecimens.

DeSP: a systematic DNA storage error simulation pipeline

BackgroundUsing 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.ResultsHere, 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.ConclusionsDeSP 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.

TrnL-trnF copy number is inversely correlated with storage time of Guang Chenpi, the aged sun-dried peels of Citrus reticulata ‘Chachi’

The medicinal value of Guang Chenpi (GCP), the sun-dried peels of Citrus reticulata ‘Chachi’, increases significantly with the extension of its storage time, hence its commercial value, which drives the profitable malpractice of faking the years of storage of GCP in market. In this study, we developed a molecular quantification method for determining the storage time of GCP, which is not based on examining chemical composition changes of GCP with time. Instead, we investigated the correlation between the copy number of 6 DNA fragments (matK, rbcL, psbA-trnH, ITS2, trnL-trnF, and trnL) and storage time of GCP, and found that trnL-trnF copy number in GCP had a significant inverse correlation with the storage time of the drug. We further validated this finding using commercial samples of GCP. This molecular quantification method based on trnL-trnF copy number can provide a useful means for accurate age estimation of GCP.