Optimal Pair DNA Sequence Alignment based on Matching Regions and Multi-Zone Genetic Algorithm

  • Authors

    • Sara Q. Abedulridha
    • Eman S. Al-Shamery
    2018-11-27
    https://doi.org/10.14419/ijet.v7i4.19.27993
  • Bioinformatics, DNA Sequence Alignment, Matching regions, Multi-Zone Genetic Algorithm, Parallel processing.

  • Abstract

    DNA sequence alignment is an important and challenging task in Bioinformatics, which is used for finding the optimal arrangement between two sequences. In this paper, two methods are proposed in two stages to solve the pairwise sequence alignment problem. The first method is Matching Regions(MR) concerns on splitting the DNA into regions with adaptive interleaving windows to isolate the DNA tape into matched and non-matched regions. Additionally, a Multi-Zone Genetic Algorithm (MZGA) is proposed as an improved method in the second stage. It consists of segmenting a large non-matched region into smaller search space. Then, the MZGA is implemented in parallel to save time. Genetic Algorithm can be applied as an optimization toolto produce multiple solutions. Furthermore, the improvement focuses on the enhancement of Simple GA operators. In the selection, the population is divided into three Zones according to the fitness score. A new crossover approach is proposed depending on cut-points and location of gaps. The proposed method guarantees that the value of fitness tends to improvement or convergence in each successive generation. Thus, the offspring of populations will have better fitness value. The system has been applied to the real-world dataset of DNA with variable lengths which are ranged from 66 bases up to 26037 bases. As a result, the proposed technique satisfied the best alignment score of the DNA sequences. Finally, it is worth mentioning that the proposed system proved to be generalizable.

     

     

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  • How to Cite

    Q. Abedulridha, S., & S. Al-Shamery, E. (2018). Optimal Pair DNA Sequence Alignment based on Matching Regions and Multi-Zone Genetic Algorithm. International Journal of Engineering & Technology, 7(4.19), 751-756. https://doi.org/10.14419/ijet.v7i4.19.27993

    Received date: 2019-02-26

    Accepted date: 2019-02-26

    Published date: 2018-11-27