Third International conference on advances in communication, network and computing

Research Article

A Genetic Algorithm for Alignment of Multiple DNA Sequences

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  • @INPROCEEDINGS{10.1007/978-3-642-35615-5_71,
        author={Pankaj Agarwal and Ruchi Gupta and Taru Maheswari and Princy Agarwal and Shubhanjali Yadav and Vishnu Bali},
        title={A Genetic Algorithm for Alignment of Multiple DNA Sequences},
        proceedings={Third International conference on advances in communication, network and computing},
        proceedings_a={CNC},
        year={2012},
        month={12},
        keywords={Multiple Sequence Alignment Genetic Algorithms NP-Complete Computational Biology etc},
        doi={10.1007/978-3-642-35615-5_71}
    }
    
  • Pankaj Agarwal
    Ruchi Gupta
    Taru Maheswari
    Princy Agarwal
    Shubhanjali Yadav
    Vishnu Bali
    Year: 2012
    A Genetic Algorithm for Alignment of Multiple DNA Sequences
    CNC
    Springer
    DOI: 10.1007/978-3-642-35615-5_71
Pankaj Agarwal1,*, Ruchi Gupta2,*, Taru Maheswari2, Princy Agarwal1, Shubhanjali Yadav1, Vishnu Bali1
  • 1: IMS Engineering College
  • 2: AKGEC
*Contact email: pankaj7877@gmail.com, 80ruchi@gmail.com

Abstract

This paper presents a new genetic algorithm based solution to obtain alignment of multiple DNA molecular sequences. Multiple Sequence alignment is one of the most active ongoing research problems in the field of computational molecular biology. Sequence alignment is important because it allows scientists to analyze protein strands (such as DNA and RNA) and determine where there are overlaps. These overlaps can show commonalities in evolution and they also allow scientists to better prepare vaccines against viruses, which are made of protein strands. We have proposed new genetic operations for crossover, mutation, fitness calculation, population initialization. Proposed scheme generates new populations with better fitness value. We have also reviewed the some of the popular works by different researchers towards solving the MSA problem w.r.t various phases involved in general GA procedure. A working example is presented to validate the proposed scheme. Improvement in the overall population fitness is also calculated.