Abstract
The amount of completely sequenced chloroplast genomes increases rapidly every day, leading to the possibility to build large-scale phylogenetic trees of plant species. Considering a subset of close plant species defined according to their chloroplasts, the phylogenetic tree that can be inferred by their core genes is not necessarily well supported, due to the possible occurrence of “problematic” genes (i.e., homoplasy, incomplete lineage sorting, horizontal gene transfers, etc.) which may blur the phylogenetic signal. However, a trustworthy phylogenetic tree can still be obtained provided such a number of blurring genes is reduced. The problem is thus to determine the largest subset of core genes that produces the best-supported tree. To discard problematic genes and due to the overwhelming number of possible combinations, this article focuses on how to extract the largest subset of sequences in order to obtain the most supported species tree. Due to computational complexity, a distributed Binary Particle Swarm Optimization (BPSO) is proposed in sequential and distributed fashions. Obtained results from both versions of the BPSO are compared with those computed using an hybrid approach embedding both genetic algorithms and statistical tests. The proposal has been applied to different cases of plant families, leading to encouraging results for these families.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Wyman, S.K., Jansen, R.K., Boore, J.L.: Automatic annotation of organellar genomes with dogma. Bioinformatics 20(172004), 3252–3255 (2004). Oxford Press
Stamatakis, A.: Raxml version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014)
AlKindy, B., Guyeux, C., Couchot, J.-F., Salomon, M., Parisod, C., Bahi, J.M.: Hybrid genetic algorithm and lasso test approach for inferring well supported phylogenetic trees based on subsets of chloroplastic core genes. In: Dediu, A.-H., Hernández-Quiroz, F., Martín-Vide, C., Rosenblueth, D.A. (eds.) AlCoB 2015. LNCS, vol. 9199, pp. 83–96. Springer, Heidelberg (2015)
Alsrraj, R., Alkindy, B., Guyeux, C., Philippe, L., Couchot, J.-F.: Well-supported phylogenies using largest subsets of core-genes by discrete particle swarm optimization. In: CIBB 2015, 12th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Naples, Italy, September 2015
Alkindy, B., Couchot, J.-F., Guyeux, C., Mouly, A., Salomon, M., Bahi, J.M.: Finding the core-genes of chloroplasts. J. Biosci. Biochem. Bioinform. 4(5), 357–364 (2014)
Alkindy, B., Guyeux, C., Couchot, J.-F., Salomon, M., Bahi, J.M.: Gene similarity-based approaches for determining core-genes of chloroplasts. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (to Present) (2014)
Edgar, R.C.: Muscle: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32(5), 1792–1797 (2004)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Khanesar, M.A., Tavakoli, H., Teshnehlab, M., Shoorehdeli, M.A.: Novel binary particle swarm optimization, 11 (2009). www.intechopen.com, ISBN: 978-953-7619-48-0
Premalatha, K., Natarajan, A.M.: Hybrid pso and ga for global maximization. Int. J. Open Probl. Compt. Math 2(4), 597–608 (2009)
Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intell. 1, 33–57 (2007). doi:10.1007/s11721-007-0002-0. (Springer Science + Business Media)
Eberhart, R.C., Shi, Y.: Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 81–86. IEEE (2001)
Sedighizadeh, D., Masehian, E.: Particle swarm optimization methods, taxonomy and applications. Int. J. Comput. Theory Eng. 1(5), 486–502 (2009)
Clerc, M.: The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999, vol. 3. IEEE (1999)
Shimodaira, H., Hasegawa, M.: Consel: for assessing the confidence of phylogenetic tree selection. Bioinformatics 17(12), 1246–1247 (2001)
Acknowledgements
All computations have been performed on the Mésocentre de calculs supercomputer facilities of the University of Franche-Comté.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Alkindy, B. et al. (2016). Binary Particle Swarm Optimization Versus Hybrid Genetic Algorithm for Inferring Well Supported Phylogenetic Trees. In: Angelini, C., Rancoita, P., Rovetta, S. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2015. Lecture Notes in Computer Science(), vol 9874. Springer, Cham. https://doi.org/10.1007/978-3-319-44332-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-44332-4_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44331-7
Online ISBN: 978-3-319-44332-4
eBook Packages: Computer ScienceComputer Science (R0)