Influence of Parameters in Multiple Sequence Alignment Methods for Protein Sequences

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 710)

Abstract

Protein sequence alignment is necessary to specify functions to unknown proteins. The alignment of protein sequences is used to determine the relatedness of organisms. Constructing a perfect multiple sequence alignment (MSA) for protein/DNA sequences without having similarity is a difficult task in computational biology. Nucleotide and amino acid sequence are ordered with feasible alignment, and minimal quantity of gap values is treated by multiple sequence alignment that expresses to the evolutionary, structural, and functional relationships between the protein/DNA sequences. This research work compares the various multiple sequence alignments such as Artificial Bee Colony (ABC), Bacterial Foraging Optimization (BFO), and online MSA tools, namely T-Coffee, Clustal Omega, and Muscle to predict the best method for aligning the sequences. The parameters such as single and double shift for ABC algorithm and swim length for BFO algorithm have been analyzed using 19% gap penalty values. The experiments were examined on different protein sequences, and the final result proves that the BFO algorithm obtains better significant results as compared with the other methods.

Keywords

Multiple sequence alignment Artificial bee colony Bacterial foraging optimization T-Coffee Clustal Omega Muscle 

Notes

Acknowledgements

The authors are thankful to the DST, New Delhi, India (Grant Number: DST/INSPIRE Fellowship/2015/IF150093) for the financial grant under INSPIRE Fellowship scheme for this work.

References

  1. 1.
    Keehyoung Joo, et al. Multiple Sequence Alignment by Conformational Space Annealing. Biophys J. 2008 Nov 15;95(10):4813–9.Google Scholar
  2. 2.
    Robert C. Edgar. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004 Mar 19;32(5):1792–7.Google Scholar
  3. 3.
    J D Thompson, D G Higgins, and T J Gibson. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994 Nov 11;22(22): 4673–4680.Google Scholar
  4. 4.
    Julie D. Thompson, Toby J. Gibson, Frédéric Plewniak, François Jeanmougin, Desmond G. Higgins. The CLUSTAL_X windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools. Nucleic Acids Res. 1997 Dec 15;25(24):4876–82.Google Scholar
  5. 5.
    Depiereux E, Baudoux G, Briffeuil P, Reginster I, De Bolle X, Vinals C, Feytmans E. Match-Box_server: a multiple sequence alignment tool placing emphasis on reliability. Comput Appl Biosci. 1997 Jun;13(3):249–56.Google Scholar
  6. 6.
    B. Morgenstern, K. Frech, A. Dress, T. Werner. DIALIGN: finding local similarities by multiple sequence alignment. Bioinformatics, (Apr. 1998), 14 (3), pp. 290–294.Google Scholar
  7. 7.
    Cedric Notredame, Desmond G Higgins, Jaap Heringa. T-coffee: a novel method for fast and accurate multiple sequence alignment. J Mol Biol. 2000 Sep 8; 302(1):205–17.Google Scholar
  8. 8.
    Sievers F, Higgins DG. Clustal Omega, accurate alignment of very large numbers of sequences. Methods Mol Biol. 2014; 1079:105–16.Google Scholar
  9. 9.
    Lawrence, et al., Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment. Science. 1993 Oct 8;262(5131):208–14.Google Scholar
  10. 10.
    Marco Botta, Guido Negro. Multiple Sequence Alignment with Genetic Algorithms. Computational Intelligence Methods for Bioinformatics and Biostatistics: 6th International Meeting, CIBB 2009, Genoa, Italy, October 15–17, Springer chapter, Volume 6160, pp 206–214.Google Scholar
  11. 11.
    F. Naznin, R. Sarker, D. Essam. Progressive alignment method using genetic algorithm for multiple sequence alignment. IEEE Trans. Evolutionary. Computation. 2012 Oct 16 (5): 615–631.Google Scholar
  12. 12.
    Ruchi Gupta, Dr. Pankaj Agarwal, Dr. A. K. Soni. MSA-GA: Multiple Sequence Alignment Tool Based On Genetic Approach. International Journal Of Soft Computing And Software Engineering, 2013 Aug 3(8): 1–11.Google Scholar
  13. 13.
    Cédric Notredame, Desmond G. Higgins. SAGA: sequence alignment by genetic algorithm. Nucleic Acids Res. 1996 Apr 15;24(8): 1515–1524.Google Scholar
  14. 14.
    Simeon Tsvetanov, Desislava Ivanova, Boris Zografov. Ant Colony Optimization Applied for Multiple Sequence Alignment. Biomath communications, 2015 Jun 2(1):599–1.Google Scholar
  15. 15.
    Fasheng Xu, Yuehui Chen. A Method for Multiple Sequence Alignment Based on Particle Swarm Optimization. Springer Chapter, Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence, 2009. Volume 5755: 965–973.Google Scholar
  16. 16.
    Long HX, Xu WB, Sun J. Binary Particle Swarm Optimization algorithm with mutation for multiple sequence alignment. Rivista di Biologia. 2009 Jan–Apr;102(1):75–94.Google Scholar
  17. 17.
    Xiujuan Lei, Jingjing Sun, Xiaojun Xu, Ling Guo. Artificial bee colony algorithm for solving multiple sequence alignment. IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2010,  https://doi.org/10.1109/bicta.2010.5645304.
  18. 18.
    Zne-Jung Lee, Shun-Feng Su, Chen-Chia Chuang, Kuan-Hung Liu. Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment. Applied Soft Computing 8 (2008) 55–78.Google Scholar
  19. 19.
    D. Karaboga. An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005).Google Scholar
  20. 20.
    Álvaro Rubio-Largo, Miguel A.Vega-Rodríguez, David L.González-Álvarez. Hybrid multiobjective artificial bee colony for multiple sequence alignment, Applied Soft Computing, 2016 Apr 41:157–168.Google Scholar
  21. 21.
    R. Ranjani Rani & D. Ramyachitra. Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm. Biosystems. 2016 Dec;150:177–189.Google Scholar
  22. 22.
    Passino,K.M., 2010. Bacterial foraging optimization. Int. J. Swarm Intell. Res. 2010. 1 (1), 1–16.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceBharathiar UniversityCoimbatoreIndia

Personalised recommendations