Polymerase Chain Reaction (PCR) amplification is widely used for retrieving information from DNA storage. During the PCR amplification process, nonspecific pairing between the 3\' end of the primer and the DNA sequence can cause cross-talk in the amplification reaction, leading to the generation of interfering sequences and reduced amplification accuracy. To address this issue, we propose an efficient coding algorithm for PCR amplification information retrieval (ECA-PCRAIR). This algorithm employs variable-length scanning and pruning optimization to construct a codebook that maximizes storage density while satisfying traditional biological constraints. Subsequently, a codeword search tree is constructed based on the primer library to optimize the codebook, and a variable-length interleaver is used for constraint detection and correction, thereby minimizing the likelihood of nonspecific pairing. Experimental results demonstrate that ECA-PCRAIR can reduce the probability of nonspecific pairing between the 3\' end of the primer and the DNA sequence to 2-25%, enhancing the robustness of the DNA sequences. Additionally, ECA-PCRAIR achieves a storage density of 2.14-3.67 bits per nucleotide (bits/nt), significantly improving storage capacity.

The exponential growth in data volume has necessitated the adoption of alternative storage solutions, and DNA storage stands out as the most promising solution. However, the exorbitant costs associated with synthesis and sequencing impeded its development. Pre-compressing the data is recognized as one of the most effective approaches for reducing storage costs. However, different compression methods yield varying compression ratios for the same file, and compressing a large number of files with a single method may not achieve the maximum compression ratio. This study proposes a multi-file dynamic compression method based on machine learning classification algorithms that selects the appropriate compression method for each file to minimize the amount of data stored into DNA as much as possible. Firstly, four different compression methods are applied to the collected files. Subsequently, the optimal compression method is selected as a label, as well as the file type and size are used as features, which are put into seven machine learning classification algorithms for training. The results demonstrate that k-nearest neighbor outperforms other machine learning algorithms on the validation set and test set most of the time, achieving an accuracy rate of over 85% and showing less volatility. Additionally, the compression rate of 30.85% can be achieved according to k-nearest neighbor model, more than 4.5% compared to the traditional single compression method, resulting in significant cost savings for DNA storage in the range of $0.48 to 3 billion/TB. In comparison to the traditional compression method, the multi-file dynamic compression method demonstrates a more significant compression effect when compressing multiple files. Therefore, it can considerably decrease the cost of DNA storage and facilitate the widespread implementation of DNA storage technology.

Robust encapsulation and controllable release of biomolecules have wide biomedical applications ranging from biosensing, drug delivery to information storage. However, conventional biomolecule encapsulation strategies have limitations in complicated operations, optical instability, and difficulty in decapsulation. Here, we report a simple, robust, and solvent-free biomolecule encapsulation strategy based on gallium liquid metal featuring low-temperature phase transition, self-healing, high hermetic sealing, and intrinsic resistance to optical damage. We sandwiched the biomolecules with the solid gallium films followed by low-temperature welding of the films for direct sealing. The gallium can not only protect DNA and enzymes from various physical and chemical damages but also allow the on-demand release of biomolecules by applying vibration to break the liquid gallium. We demonstrated that a DNA-coded image file can be recovered with up to 99.9% sequence retention after an accelerated aging test. We also showed the practical applications of the controllable release of bioreagents in a one-pot RPA-CRISPR/Cas12a reaction for SARS-COV-2 screening with a low detection limit of 10 copies within 40 min. This work may facilitate the development of robust and stimuli-responsive biomolecule capsules by using low-melting metals for biotechnology.

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.

Environmental DNA (eDNA) workflows contain many familiar molecular-lab techniques, but also employ several unique methodologies. When working with eDNA, it is essential to avoid contamination from the point of collection through preservation and select a meaningful negative control. As eDNA can be obtained from a variety of samples and habitats (e.g., soil, water, air, or tissue), protocols will vary depending on usage. Samples may require additional steps to dilute, block, or remove inhibitors or physically break up samples or filters. Thereafter, standard DNA isolation techniques (kit-based or phenol:chloroform:isoamyl [PCI]) are employed. Once DNA is extracted, it is typically quantified using a fluorometer. Yields vary greatly, but are important to know prior to amplification of the gene(s) of interest. Long-term storage of both the sampled material and the extracted DNA is encouraged, as it provides a backup for spilled/contaminated samples, lost data, reanalysis, and future studies using newer technology. Storage in a freezer is often ideal; however, some storage buffers (e.g., Longmires) require that filters or swabs are kept at room temperature to prevent precipitation of buffer-related solutes. These baseline methods for eDNA isolation, validation, and preservation are detailed in this protocol chapter. In addition, we outline a cost-effective, homebrew extraction protocol optimized to extract eDNA.

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.

Due to its high information density, DNA is very attractive as a data storage system. However, a major obstacle is the high cost and long turnaround time for retrieving DNA data with next-generation sequencing. Herein, the use of a microfluidic very large-scale integration (mVLSI) platform is described to perform highly parallel and rapid readout of data stored in DNA. Additionally, it is demonstrated that multi-state data encoded in DNA can be deciphered with on-chip melt-curve analysis, thereby further increasing the data content that can be analyzed. The pairing of mVLSI network architecture with exquisitely specific DNA recognition gives rise to a scalable platform for rapid DNA data reading.

DNA, as the storage medium in organisms, can address the shortcomings of existing electromagnetic storage media, such as low information density, high maintenance power consumption, and short storage time. Current research on DNA storage mainly focuses on designing corresponding encoders to convert binary data into DNA base data that meets biological constraints. We have created a new Chinese character code table that enables exceptionally high information storage density for storing Chinese characters (compared to traditional UTF-8 encoding). To meet biological constraints, we have devised a DNA shift coding scheme with low algorithmic complexity, which can encode any strand of DNA even has excessively long homopolymer. The designed DNA sequence will be stored in a double-stranded plasmid of 744bp, ensuring high reliability during storage. Additionally, the plasmid\'s resistance to environmental interference ensuring long-term stable information storage. Moreover, it can be replicated at a lower cost.

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.
METHODS: 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.

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.