Hybrid Genetic Algorithm and Lasso Test Approach for Inferring Well Supported Phylogenetic Trees Based on Subsets of Chloroplastic Core Genes
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.
KeywordsChloroplasts Phylogeny Genetic algorithms Lasso test
Computations have been performed on the supercomputer facilities of the Mésocentre de calcul de Franche-Comté.
- 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.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
- 6.Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1993)Google Scholar
- 8.Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
- 9.Holland, J.H.: Adaptation in Natural and Artificial Systems, 2nd edn. MIT Press, Cambridge (1992)Google Scholar
- 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
- 12.Prebys, E.K.: The genetic algorithm in computer science. MIT Undergrad. J. Math 2007, 165–170 (2007)Google Scholar
- 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