DNA Cryptography using DNA Fragment Assembly and Fragment Key Expansion for Genomic Data

This research proposes a tweaked scheme based on DNA fragment assembly to improve protection over insecure channel. The proposed procedure utilizes binary coding to change over an underlying plaintext into a reference DNA arrangement to deal with the fragmentation. DNA fragment key expansion is applied over the reference DNA sequence to make the short-chain fragments. The redundancy in the long-chain of reference DNA is removed using DNA fragment assembly. A look-up table is generated to store the binary values of overlapped fragments to be reassembled during the encryption and decryption processes to prevent artefacts. Also, it is used in an overlapped sequence to counteract cipher decomposition. The results and comparisons demonstrate that the proposed scheme can balance the three most important characteristics of any DNA masking scheme: payload, capacity, and BPN. Moreover, the potential for cracking the proposed tweaked method is more complex than the current strategies.

DNA Fragment Assembly Using Hybridized Catfish PSO

DNA fragment assembly aids in uncovering several aspects of the human DNA, and hence in-turn enables scientists in understanding and curing several hereditary problems. Several computational methods have been proposed to solve this problem. However, the huge size and the NP-hard nature of the problem poses several challenges in proposing a time effective system for fragment assembly. This paper proposes a hybridized catfish PSO model for the process of fragment assembly. PSO algorithm is enhanced by incorporating the catfish particles to enable the model to get out of the local optimal solutions. Further, the local search process has been hybridized to incorporate simulated annealing, such that the model performs faster selection of solutions. This has enabled the proposed model to provide effective results with low computational requirements. Experiments were performed with 10 benchmark instances from GenFrag. The results were compared with state-of-the-art models in literature, and it was identified that the proposed model exhibits high performance in comparatively shorter time.

DNA Fragment Assembly Using Quantum-Inspired Genetic Algorithm

DNA fragment assembly (DFA) is one of the most important and challenging problems in computational biology. DFA problem involves reconstruction of target DNA from several hundred (or thousands) of sequenced fragments by identifying the proper orientation and order of fragments. DFA problem is proved to be a NP-Hard combinatorial optimization problem. Metaheuristic techniques have the capability to handle large search spaces and therefore are well suited to deal with such problems. In this chapter, quantum-inspired genetic algorithm-based DNA fragment assembly (QGFA) approach has been proposed to perform the de novo assembly of DNA fragments using overlap-layout-consensus approach. To assess the efficacy of QGFA, it has been compared genetic algorithm, particle swarm optimization, and ant colony optimization-based metaheuristic approaches for solving DFA problem. Experimental results show that QGFA performs comparatively better (in terms of overlap score obtained and number of contigs produced) than other approaches considered herein.

DNA Fragment Assembly Using Quantum-Inspired Genetic Algorithm

DNA fragment assembly (DFA) is one of the most important and challenging problems in computational biology. DFA problem involves reconstruction of target DNA from several hundred (or thousands) of sequenced fragments by identifying the proper orientation and order of fragments. DFA problem is proved to be a NP-Hard combinatorial optimization problem. Metaheuristic techniques have the capability to handle large search spaces and therefore are well suited to deal with such problems. In this chapter, quantum-inspired genetic algorithm-based DNA fragment assembly (QGFA) approach has been proposed to perform the de novo assembly of DNA fragments using overlap-layout-consensus approach. To assess the efficacy of QGFA, it has been compared genetic algorithm, particle swarm optimization, and ant colony optimization-based metaheuristic approaches for solving DFA problem. Experimental results show that QGFA performs comparatively better (in terms of overlap score obtained and number of contigs produced) than other approaches considered herein.

Multiple Sequence Alignment Optimization Using Meta-Heuristic Techniques

Sequence alignment is a vital process in many biological applications such as Phylogenetic trees construction, DNA fragment assembly and structure/function prediction. Two kinds of alignment are pairwise alignment which align two sequences and Multiple Sequence alignment (MSA) that align sequences more than two. The accurate method of alignment is based on Dynamic Programming (DP) approach which suffering from increasing time exponentially with increasing the length and the number of the aligned sequences. Stochastic or meta-heuristics techniques speed up alignment algorithm but with near optimal alignment accuracy not as that of DP. Hence, This chapter aims to review the recent development of MSA using meta-heuristics algorithms. In addition, two recent techniques are focused in more deep: the first is Fragmented protein sequence alignment using two-layer particle swarm optimization (FTLPSO). The second is Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm (MO-BFO).

DNA Fragment Assembly Using Quantum-Inspired Genetic Algorithm

DNA fragment assembly (DFA) is one of the most important and challenging problems in computational biology. DFA problem involves reconstruction of target DNA from several hundred (or thousands) of sequenced fragments by identifying the proper orientation and order of fragments. DFA problem is proved to be a NP-Hard combinatorial optimization problem. Metaheuristic techniques have the capability to handle large search spaces and therefore are well suited to deal with such problems. In this chapter, quantum-inspired genetic algorithm-based DNA fragment assembly (QGFA) approach has been proposed to perform the de novo assembly of DNA fragments using overlap-layout-consensus approach. To assess the efficacy of QGFA, it has been compared genetic algorithm, particle swarm optimization, and ant colony optimization-based metaheuristic approaches for solving DFA problem. Experimental results show that QGFA performs comparatively better (in terms of overlap score obtained and number of contigs produced) than other approaches considered herein.