DNA Fragment Assembly Using Quantum-Inspired Genetic Algorithm

Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms Pub Date : 1900-01-01 DOI:10.4018/978-1-5225-5832-3.CH005

Manisha Rathee, K. Dilip, Ritu Rathee

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引用次数: 5

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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基于量子遗传算法的DNA片段组装

DNA片段组装(DFA)是计算生物学中最重要和最具挑战性的问题之一。DFA问题涉及通过确定片段的正确方向和顺序，从数百(或数千)个已测序片段中重建目标DNA。证明了DFA问题是一个NP-Hard组合优化问题。元启发式技术具有处理大型搜索空间的能力，因此非常适合处理此类问题。在本章中，提出了基于量子启发遗传算法的DNA片段组装(QGFA)方法，使用重叠布局一致性方法执行DNA片段的从头组装。为了评估QGFA的有效性，我们比较了遗传算法、粒子群优化和基于蚁群优化的元启发式方法来解决DFA问题。实验结果表明，与本文考虑的其他方法相比，QGFA(在获得的重叠分数和产生的contigs数量方面)表现相对更好。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms

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