Hybrid Genetic Algorithm and Lasso Test Approach for Inferring Well Supported Phylogenetic Trees Based on Subsets of Chloroplastic Core Genes

  • Bassam AlKindy
  • Christophe Guyeux
  • Jean-François Couchot
  • Michel Salomon
  • Christian Parisod
  • Jacques M. Bahi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9199)


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 phylogenetic signal. However, a trustworthy phylogenetic tree can still be obtained if the number of problematic genes is low, the problem being 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, we propose an hybrid approach that embeds both genetic algorithms and statistical tests. Given a set of organisms, the result is a pipeline of many stages for the production of well supported phylogenetic trees. The proposal has been applied to different cases of plant families, leading to encouraging results for these families.


Chloroplasts Phylogeny Genetic algorithms Lasso test 



Computations have been performed on the supercomputer facilities of the Mésocentre de calcul de Franche-Comté.


  1. 1.
    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)Google Scholar
  2. 2.
    Alkindy, B., Guyeux, C., Couchot, J.-F., Salomon, M., Bahi, J.M.: Gene similarity-based approaches for determining core-genes of chloroplasts. In: 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 71–74. IEEE (2014)Google Scholar
  3. 3.
    Bhandari, D., Murthy, C., Pal, S.K.: Genetic algorithm with elitist model and its convergence. Int. J. Pattern Recogn. Artif. Intell. 10(06), 731–747 (1996)CrossRefGoogle Scholar
  4. 4.
    Booker, L.B., Goldberg, D.E., Holland, J.H.: Classifier systems and genetic algorithms. Artif. Intell. 40(1), 235–282 (1989)CrossRefGoogle Scholar
  5. 5.
    Edgar, R.C.: Muscle: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32(5), 1792–1797 (2004)CrossRefGoogle Scholar
  6. 6.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1993)Google Scholar
  7. 7.
    Gupta, M., Singh, S.: A novel genetic algorithm based approach for optimization of distance matrix for phylogenetic tree construction. Int. J. Comput. Appl. 52(9), 14–18 (2012)MathSciNetGoogle Scholar
  8. 8.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  9. 9.
    Holland, J.H.: Adaptation in Natural and Artificial Systems, 2nd edn. MIT Press, Cambridge (1992)Google Scholar
  10. 10.
    Matsuda, H.: Construction of phylogenetic trees from amino acid sequences using a genetic algorithm. In: Proceedings of Genome Informatics Workshop, vol. 6, pp. 19–28 (1995)Google Scholar
  11. 11.
    Palmer, J.D.: Plastid chromosomes: structure and evolution. Mol. Biol. Plastids 7, 5–53 (1991)CrossRefGoogle Scholar
  12. 12.
    Prebys, E.K.: The genetic algorithm in computer science. MIT Undergrad. J. Math 2007, 165–170 (2007)Google Scholar
  13. 13.
    Stamatakis, A., Ludwig, T., Meier, H.: Raxml-iii: a fast program for maximum likelihood-based inference of large phylogenetic trees. Bioinformatics 21(4), 456–463 (2005)CrossRefGoogle Scholar
  14. 14.
    Tate, S.I., Yoshihara, I., Yamamori, K., Yasunaga, M.: A parallel hybrid genetic algorithm for multiple protein sequence alignment. In: Proceedings of the World on Congress on Computational Intelligence, vol. 1, pp. 309–314. IEEE (2002)Google Scholar
  15. 15.
    Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc. (Ser. B) 58, 267–288 (1996)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Wyman, S.K., Jansen, R.K., Boore, J.L.: Automatic annotation of organellar genomes with dogma. Bioinformatics 20(17), 3252–3255 (2004). Oxford PressCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Bassam AlKindy
    • 1
    • 3
  • Christophe Guyeux
    • 1
  • Jean-François Couchot
    • 1
  • Michel Salomon
    • 1
  • Christian Parisod
    • 2
  • Jacques M. Bahi
    • 1
  1. 1.FEMTO-ST Institute, UMR 6174 CNRS, DISC Computer Science DepartmentUniversity of Franche-ComtéBesançonFrance
  2. 2.Laboratory of Evolutionary BotanyUniversity of NeuchâtelNeuchâtelSwitzerland
  3. 3.Department of Computer ScienceUniversity of MustansiriyahBaghdadIraq

Personalised recommendations