Abstract
Multiple sequence alignment is one of fundamental problems in bioinformatics, and to design a targeted and effective algorithm for multiple DNA, RNA or protein sequences. The research is to find out the maximum similarity matching between them, whether it should be homologous. In this paper, the invasive weed optimization (IWO) algorithm is combined with GA for multiple sequence alignment, in which IWO algorithm is used to improve the ability of global search. Furthermore, the optimal preservation strategy is used into the proposed algorithm. Comparing two test sequence sets, the results show that the proposed algorithm is effective and reliable.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhang, C.T.: Current status and prospects of bioinformatics. World Sci. Technol. Res. Dev. 22(6), 17–20 (2000)
Wu, D.M., Chen, J.: Research on algorithm of pairwise alignment. Comput. Eng. Appl. 44(36), 48–50 (2016)
Zou, Q., Guo, M.Z., Han, Y.P.: Development of multiple sequence alignment algorithms. China J. Bioinform. 04, 311–314 (2010)
Carrillo, H., Lipman, D.J.: The multiple sequence alignment problem in biology. SIAM J. Appl. Math. 48(5), 1073–1082 (1988)
Hogeweg, P., Hesper, B.: The alignment of sets of sequences and the construction of phylogenetic trees: an integrated method. J. Mol. Evol. 20(2), 175–186 (1984)
Taylor, W.R.: A flexible method to align large numbers of biological sequences. J. Mol. Evol. 28(1–2), 161–169 (1988)
Thompson, J.D., Higgins, D.G., Gibson, T.J.: CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucl. Acids Res. 22(22), 4673–4680 (1994)
Notredame, C., Higgins, D.G., Heringa, J.: T-coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. 302(1), 205–217 (2000)
Hu, Y.Q.: Research Foundation and Application. Harbin Institute of Technology Press, Harbin (1987)
Gan, Y.A., Tian, F., Li, W.Z.: Operations Research. Tsinghua University Press, Beijing (1994)
Wang, Y.X.: Planning and Network of Operations Research. Tsinghua University Press, Beijing (1993)
Boyce, K., Sievers, F., Higgins, D.G.: Instability in progressive multiple sequence alignment algorithms. Algorithm Mol. Biol. 10(1), 1–10 (2015)
Orobitg, M., Guirado, F., Cores, F.: High performance computing improvements on bioinformatics consistency-based multiple sequence alignment tools. Parallel Comput. 42, 18–34 (2015)
Katoh, K., Toh, H.: Parallelization of the MAFFT multiple sequence alignment program. Bioinform. Oxf. J. 26(15), 1899–1900 (2010)
DeBlasio, D., Kececioglu, J.: Parameter advising for multiple sequence alignment. BMC Bioinform. 16(2), 516–518 (2015)
Mirarab, S., Nguyen, N., Warnow, T.: PASTA: ultra-large multiple sequence alignment. In: Sharan, R. (ed.) RECOMB 2014. LNCS, vol. 8394, pp. 177–191. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05269-4_15
Mehrabian, A.R., Lucas, C.: A novel numerical optimization algorithm inspired from weed colonization. Ecol. Inform. 1(4), 355–366 (2006)
Li, S.Z., Mo, Z.S., Zhang, X.: Multiple sequence alignment based on immune genetic algorithm. J. Wuhan Univ. 50(5), 537–541 (2004)
Zhang, Y., Achawanantakun, R.: An improved genetic algorithm for multiple sequence alignment. Project report of CSE848, Fall (2010)
Song, X.L.: Research of multiple sequence alignment algorithm based on quantum genetic algorithm and improved immune genetic algorithm. Master thesis, Jilin University, Jilin (2007)
Luo, D.F., Luo, D.J.: The research of DNA coding sequences based on invasive weed optimization. Sci. Technol. Eng. 13, 3545–3551 (2013)
T. J. E.: Timetabling problem research on chaos genetic algorithm. Master thesis, Harbin Engineering University, Harbin (2009)
Yang, J., et al.: Entropy-driven DNA logic circuits regulated by DNAzyme. Nucl. Acids Res. (2018). https://doi.org/10.1093/nar/gky663
Wang, B., et al.: Constructing DNA barcode sets based on particle swarm optimization. IEEE/ACM Trans. Comput. Biol. Bioinform. 15, 999–1002 (2018)
Pan, L., Wang, Z., Li, Y., Xu, F., Zhang, Q., Zhang, C.: Nicking enzyme-controlled toehold regulation for DNA logic circuits. Nanoscale 9(46), 18223–18228 (2017)
Wang, B., Xie, Y., Zhou, S., Zheng, X., Zhou, C.: Correcting errors in image encryption based on DNA coding. Molecules (2018). https://doi.org/10.3390/molecules23081878
Acknowledgment
This work is supported by the National Natural Science Foundation of China (Nos. 61425002, 61751203, 61772100, 61702070, 61672121, 61572093), Program for Changjiang Scholars and Innovative Research Team in University (No. IRT_15R07), the Program for Liaoning Innovative Research Team in University (No. LT2015002).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gao, C., Wang, B., Zhou, C., Zhang, Q., Yin, Z., Fang, X. (2018). Hybrid Invasive Weed Optimization and GA for Multiple Sequence Alignment. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 952. Springer, Singapore. https://doi.org/10.1007/978-981-13-2829-9_8
Download citation
DOI: https://doi.org/10.1007/978-981-13-2829-9_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2828-2
Online ISBN: 978-981-13-2829-9
eBook Packages: Computer ScienceComputer Science (R0)