一种执行多序列比对的进化方法

F. Silva, J. M. Sánchez-Pérez, J. Pulido, M. A. Vega-Rodríguez
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引用次数: 13

摘要

尽管多序列比对是生物信息学中一项非常常见的任务,但它并不是一件小事。排列一组分子序列以揭示它们的相似性和差异性通常会因所涉及的搜索空间的复杂性和大小而变得困难,这破坏了试图彻底探索解决方案的搜索空间的方法。由于遗传算法的性质,它易于在大型和复杂的搜索空间中进行一般组合问题的优化,成为解决多序列比对问题的重要候选者。我们开发了一种进化方法AlineaGA,它使用嵌入了局部搜索优化的遗传算法进行多序列比对。现在,我们通过采用精英策略对其选择方法进行了改进,并开发了一种新的交叉算子。这些转换使AlineaGA能够提高其鲁棒性并获得更好的拟合解决方案。此外,我们还通过分析整个种群的世代表现,研究了突变概率对解进化的影响。我们得出结论,增加突变概率可以在更少的代内得到更好的解决方案,并且突变算子在这一特定领域具有显着作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An evolutionary approach for performing multiple sequence alignment
Despite of being a very common task in bioinformatics, multiple sequence alignment is not a trivial matter. Arranging a set of molecular sequences to reveal their similarities and their differences is often hardened by the complexity and the size of the search space involved, which undermine the approaches that try to explore exhaustively the solution's search space. Due to its nature, Genetic Algorithms, which are prone for general combinatorial problems optimization in large and complex search spaces, emerge as serious candidates to tackle with the multiple sequence alignment problem. We have developed an evolutionary approach, AlineaGA, which uses a Genetic Algorithm with local search optimization embedded on its mutation operators for performing multiple sequence alignment. Now, we have enhanced its selection method by employing an elitist strategy, and we have also developed a new crossover operator. These transformations allow AlineaGA to improve its robustness and to obtain better fit solutions. Also, we have studied the effect of the mutation probability in solutions' evolution by analyzing the performance of the whole population throughout generations. We conclude that increasing the mutation probability leads to better solutions in fewer generations and that the mutation operators have a dramatic effect in this particular domain.
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