Encoding scheme for data storage and retrieval on DNA computers

DNA encoding schemes herald a new age in cybersecurity for safeguarding digital assets

With the urge to secure and protect digital assets, there is a need to emphasize the immediacy of taking measures to ensure robust security due to the enhancement of cyber security. Different advanced methods, like encryption schemes, are vulnerable to putting constraints on attacks. To encode the digital data and utilize the unique properties of DNA, like stability and durability, synthetic DNA sequences are offered as a promising alternative by DNA encoding schemes. This study enlightens the exploration of DNA's potential for encoding in evolving cyber security. Based on the systematic literature review, this paper provides a discussion on the challenges, pros, and directions for future work. We analyzed the current trends and new innovations in methodology, security attacks, the implementation of tools, and different metrics to measure. Various tools, such as Mathematica, MATLAB, NIST test suite, and Coludsim, were employed to evaluate the performance of the proposed method and obtain results. By identifying the strengths and limitations of proposed methods, the study highlights research challenges and offers future scope for investigation.

Composition, physicochemical property and base periodicity for discriminating lncRNA and mRNA

Annotation of genome data with biological features is a challenging problem. One such problem deals with distinguishing lncRNA from mRNA. In this study, three groups of classification features, namely base periodicity, physicochemical property and nucleotide compositions were considered. We are attempting to propose a simple neural network model to obtain better results using judicious combination of the above said sequence features. Our approach uses balanced dataset, simple prediction model and use of limited features in distinguishing lncRNA and mRNA. Accordingly (a) two properties of base periodicity: peak power spectrum of the signal and noise-to-signal ratio (SNR) of this peak signal (b) three physicochemical properties: solvation, stacking and hydrogen-bonding energy and (c) all dinucleotides and trinucleotides compositions were used. Classification was performed by considering features independently followed by combining these properties for improvement. Classification metric was used to compare the result for seven eukaryotic organisms for various combinations of features. Nucleotide compositions combined with physicochemical property or base periodicity group of features becomes a strong classifier with more than 99 percentage accuracy. Base periodicity analysis with SNR can be used as discriminating feature of lncRNA from mRNA.

DNA-Based Concatenated Encoding System for High-Reliability and High-Density Data Storage

Information storage based on DNA molecules provides a promising solution with advantages of low-energy consumption, high storage efficiency, and long lifespan. However, there are only four natural nucleotides and DNA storage is thus limited by 2 bits per nucleotide. Here, artificial nucleotides into DNA data storage to achieve higher coding efficiency than 2 bits per nucleotide is introduced. To accommodate the characteristics of DNA synthesis and sequencing, two high-reliability encoding systems suitable for four, six, and eight nucleotides, i.e., the RaptorQ-Arithmetic-LZW-RS (RALR) and RaptorQ-Arithmetic-Base64-RS (RABR) systems, are developed. The two concatenated encoding systems realize the advantages of correcting DNA sequence losses, correcting errors within DNA sequences, reducing homopolymers, and controlling specific nucleotide contents. The average coding efficiencies with error correction and without arithmetic compression by the RALR system using four, six, and eight nucleotides reach 1.27, 1.61, and 1.85 bits per nucleotide, respectively. While the average coding efficiencies by the RABR system are up to 1.50, 2.00, and 2.35 bits per nucleotide, respectively. The coding efficiency, versatility, and tunability of the developed artificial DNA systems might provide significant guidance for high-reliability and high-density data storage.