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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.

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.

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.

As part a body donation program, blood samples were collected and stored on untreated (non-FTA) blood cards. The blood cards were evaluated in terms of DNA preservation and STR typing success with resulting profiles assessed with special consideration given to profile matching for positive identification and biogeographic ancestry estimation. While STR profiles were successfully generated for all samples, results indicate that the time interval between date of death and sample collection have an impact on DNA quantity and quality. There is a statistically significant decrease in relative fluorescent unit (RFU) values with increasing time interval between date of death and sample collection, indicating degradation in the blood card samples related to the post-mortem interval prior to sample collection. The STR profiles were used to estimate ancestry and admixture using the program STRUCTURE, demonstrating utility of these markers beyond individual identification purposes, with caveats for application based on population history.

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 (R 2 ). It achieved MRE = 0.109 and R 2  = 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

DNA storage systems have begun to attract considerable attention as next-generation storage technologies due to their high densities and longevity. However, efficient primer design for random-access in synthesized DNA strands is still an issue that needs to be solved. Although previous studies have explored various constraints for primer design in DNA storage systems, there is no attention paid to the combination of weakly mutually uncorrelated codes with the maximum run length constraint. In this paper, we first propose a code design by combining weakly mutually uncorrelated codes with the maximum run length constraint. Moreover, we also explore the weakly mutually uncorrelated codes to satisfy combinations of maximum run length constraint with more constraints such as being almost-balanced and having large Hamming distance, which are also efficient constraints for random-access in DNA storage systems. To guarantee that the proposed codes can be adapted to primer design with variable length, we present modified code construction methods to achieve different lengths of the code. Then, we provide an analysis of the size of the proposed codes, which indicates the capacity to support primer design. Finally, we compare the codes with those of previous works to show that the proposed codes can always guarantee the maximum run length constraint, which is helpful for random-access for DNA storage., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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., Competing Interests: Declaration of competing interest None., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare no competing financial and nonfinancial interests., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Long-term storage of DNA is a routine practice in biomedical research, diagnostics and drug discovery. Periodic monitoring is important for early detection of changes in DNA quality and quantity. Existing methods include agarose gel, ultraviolet (UV) absorbance, fluorometric reading and qPCR. However, these methods are either limited by sensitivity or depend on DNA standards, which face the same storage challenges. In this paper, we tested the state-of-the-art droplet digital PCR (ddPCR) technology that can quantify the absolute DNA copy number with no need of a standard curve. We found that ddPCR was very accurate in determining the level of a plasmid DNA standard and was sensitive to DNA loss due to degradation or adsorption. With the ddPCR technology, we found a gradual process of DNA adsorption to several types of low binding tubes, which was unnoticed before. Although modest, adsorption significantly affected recovery of highly diluted DNA (<0.2 μg/mL), which could be rescued by addition of carrier DNA. In conclusion, this paper not only demonstrated that ddPCR is an ideal method for monitoring DNA storage, but also provided an effective approach to improving recovery of highly diluted DNA, which may have broad implications in assay development, diagnostics and forensic sciences., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Introduction: The goal of this study was to examine patterns in the likelihood of consent to genetic research among participants in a prospective kidney disease cohort and biobank, and to determine demographic, clinical, and socioeconomic factors linked to consent for ongoing and future genetic research., Methods: The Clinical Phenotyping Resource and Biobank Core (C-PROBE) enrolled 1628 adult and pediatric patients with chronic kidney disease from 2009 to 2017 across 7 sites in the United States. Participants were asked at annual study visits for consent to provide DNA samples for future genetic studies. We compared characteristics of participants by initial consent outcome and consent status at their most recent study visit., Results: Of the C-PROBE participants, 96% consented to genetic studies at their initial study visit. Although African Americans were slightly less likely to consent at baseline (93% vs. 97%, odds ratio = 0.3, P  < 0.02), there were no significant racial or ethnic differences with longitudinal participation. Also, pediatric and adult genetic consent rates were equivalent. The major persistent differences in the likelihood of consent were based on enrollment site, which ranged from 85% to 100% ( P  < 0.0001)., Conclusion: Overall, genetic consent rates for kidney research within the C-PROBE cohort were high. However, differences in consent rates over time and by recruitment site highlight the complexity of genetic consent for biobanking, and potential limitations for generalizability of observations.