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首页> 外文期刊>International journal of applied evolutionary computation >DNA Fragment Assembly Using Multi-Objective Genetic Algorithms
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DNA Fragment Assembly Using Multi-Objective Genetic Algorithms

机译：使用多目标遗传算法的DNA片段组装

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摘要

DNA Fragment Assembly Problem (FAP) is concerned with the reconstruction of the target DNA, using the several hundreds (or thousands) of sequencedfragments, by identifying the right order and orientation of each fragment in the layout. Several algorithms have been proposed for solving FAP. Most of these have solely dwelt on the single objective of maximizing the sum of the overlaps between adjacent fragments in order to optimize the fragment layout. This paper aims to formulate this FAP as a bi-objective optimization problem, with the two objectives being the maximization of the overlap between the adjacent fragments and the minimization of the overlap between the distant fragments. Moreover, since there is greater desirability for having lesser number of contigs, FAP becomes a tri-objective optimization problem where the minimization of the number of contigs becomes the additional objective. These problems were solved using the multi-objective genetic algorithm NSGA-Ⅱ. The experimental results show that the NSGA-Ⅱ-based Bi-Objective Fragment Assembly Algorithm (BOFAA) and the Tri-Objective Fragment Assembly Algorithm (TOFAA) are able to produce better quality layouts than those generated by the GA-based Single Objective Fragment Assembly Algorithm (SOFAA). Further, the layouts produced by TOFAA are also comparatively better than those produced using BOFAA.
机译：DNA片段组装问题（FAP）通过确定布局中每个片段的正确顺序和方向，使用数百个（或数千个）测序片段来重建目标DNA。已经提出了几种用于解决FAP的算法。这些中的大多数仅出于最大化相邻片段之间的重叠之和以优化片段布局的单一目标。本文旨在将该FAP公式化为一个双目标优化问题，其两个目标是使相邻片段之间的重叠最大化和使远片段之间的重叠最小化。此外，由于具有较少的重叠群数目的需求更大，因此FAP成为三目标优化问题，其中重叠群数目的最小化成为附加目标。使用多目标遗传算法NSGA-Ⅱ解决了这些问题。实验结果表明，基于NSGA-Ⅱ的双目标片段组装算法（BOFAA）和三目标片段组装算法（TOFAA）能够产生比基于GA的单目标片段组装更好的布局质量。算法（SOFAA）。此外，TOFAA生产的布局也比BOFAA生产的布局要好。

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