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A bi-objective function optimization approach for multiple sequence alignment using genetic algorithm

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Abstract

Multiple sequence alignment (MSA) is characterized as a very high computational complex problem. Therefore, MSA problem cannot be solved by exhaustive methods. Nowadays, MSA is being solved by optimizing more than one objective simultaneously. In this paper, we propose a new genetic algorithm based alignment technique, named bi-objective sequence alignment using genetic algorithm (BSAGA). The novelty of this approach is its selection process. One part of the population is selected based on the Sum of Pair, and rest is selected based on Total Conserve Columns. We applied integer-based chromosomal coding to represent only the gap positions in an alignment. Such representation improves the search technique to reach an optimum even for longer sequences. We tested and compared the alignment score of BSAGA with other relevant alignment techniques on BAliBASE and SABmark. The BSAGA shows better performance than others do, which was further proved by the Wilcoxon sign test.

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Acknowledgements

The authors would like to sincerely thank the reviewers for helpful and constructive suggestions to improve the quality of the paper. The authors would also like to thank Prof. Dr. Ansuman Lahiri of Biophysics, Molecular Biology and Bioinformatics, University of Calcutta, for his helpful suggestions and ideas while doing this work.

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Authors and Affiliations

  1. Department of Biophysics, Molecular Biology and Bioinformatics, University of Calcutta, Kolkata, India

    Biswanath Chowdhury

  2. Computer Section, Saha Institute of Nuclear Physics, Kolkata, India

    Gautam Garai

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  1. Biswanath Chowdhury
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  2. Gautam Garai
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Correspondence to Biswanath Chowdhury.

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Chowdhury, B., Garai, G. A bi-objective function optimization approach for multiple sequence alignment using genetic algorithm. Soft Comput 24, 15871–15888 (2020). https://doi.org/10.1007/s00500-020-04917-5

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