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DNA Fragment Assembly Using Quantum-Inspired Genetic Algorithm

DNA Fragment Assembly Using Quantum-Inspired Genetic Algorithm

Manisha Rathee, Kumar Dilip, Ritu Rathee
Copyright: © 2021 |Pages: 18
ISBN13: 9781799880486|ISBN10: 1799880486|EISBN13: 9781799880998
DOI: 10.4018/978-1-7998-8048-6.ch041
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MLA

Rathee, Manisha, et al. "DNA Fragment Assembly Using Quantum-Inspired Genetic Algorithm." Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, edited by Information Resources Management Association, IGI Global, 2021, pp. 811-828. https://doi.org/10.4018/978-1-7998-8048-6.ch041

APA

Rathee, M., Dilip, K., & Rathee, R. (2021). DNA Fragment Assembly Using Quantum-Inspired Genetic Algorithm. In I. Management Association (Ed.), Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms (pp. 811-828). IGI Global. https://doi.org/10.4018/978-1-7998-8048-6.ch041

Chicago

Rathee, Manisha, Kumar Dilip, and Ritu Rathee. "DNA Fragment Assembly Using Quantum-Inspired Genetic Algorithm." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, edited by Information Resources Management Association, 811-828. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-8048-6.ch041

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Abstract

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.

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