Abstract
The inference of the phylogenetic tree that best express the evolutionary relationships concerning data is one of the central problem of bioinformatics. Several single optimality criterion have been proposed for the phylogenetic reconstruction problem. However, different criteria may lead to conflicting phylogenies. In this scenario, a multi-objective approach can be useful since it could produce a set of optimal trees according to multiple criteria. PhyloMOEA is a multi objective evolutionary approach applied to phylogenetic inference using maximum parsimony and maximum likelihood criteria. On the other hand, the computational power required for phylogenetic inference of large alignments easily surpasses the capabilities of single machines. In this context, the parallelization of the heuristic reconstruction methods can not only help to reduce the inference execution time but also improve the results quality and search robustness. On the other hand, The PhyloMOEA parallelization represents the next development step in order to reduce the execution time. In this paper, we present the PhyloMOEA parallel version developed using the ParadisEO framework. The experiments conducted show significant speedup in the execution time for the employed datasets.
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References
Felsenstein, J.: Inferring Phylogenies. Sinauer, Sunderland (2004)
Stamatakis, A.: Raxml-vi-hpc: Maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22(21), 2688–2690 (2006)
Lewis, P.O.: A Genetic Algorithm for Maximum-Likelihood Phylogeny Inference Using Nucleotide Sequence Data. Molecular Biology and Evolution 15(3), 277–283 (1998)
Zwickl, D.: Genetic Algorithm Approaches for the Phylogenetic Analysis of Large Biological Sequence Datasets under the Maximum Likelihood Criterion. PhD thesis, Faculty of the Graduate School. University of Texas (2006)
Huelsenbeck, J.: Performance of Phylogenetic Methods in Simulation. Systematic Biology 44, 17–48 (1995)
Tateno, Y., Takezaki, N., Nei, M.: Relative Efficiences of the Maximum-Likelihood, Neighbor-Joining, and Maximum Parsimony Methods when Substitution Rate Varies with Site. Molecular Biology and Evolution 11, 261–267 (1994)
Cancino, W., Delbem, A.: Multi-criterion phylogenetic inference using evolutionary algorithms. In: IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, CIBCB 2007, pp. 351–358 (2007)
Fitch, W.: Toward Defining the Course of Evolution: Minimum Change for a Specific Tree Topology. Systematic Zoology 20(4), 406–416 (1972)
Felsenstein, J.: Evolutionary Trees from DNA Sequences: A Maximum Likelihood Approach. Journal of Molecular Evolution 17, 368–376 (1981)
Talbi, E.: Metaheuristics: from design to implementation. Wiley, Chichester (2009)
Bader, D., Roshan, U., Stamatakis, A.: Computational Grand Challenges in Assembling the Tree of Life: Problems and Solutions. Advances in Computers 68, 128 (2006)
Cahon, S., Melab, N., Talbi, E.: Paradiseo: a framework for the flexible design of parallel and distributed hybrid metaheuristics. Journal of Heuristics 10, 357–380 (2004)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, New York (2001)
Rokas, A., Wiliams, B., King, N., Carroll, S.: Genome-Scale Approaches to Resolving Incongrounce in Molecular Phylogenies. Nature 425(23), 798–804 (2003)
Poladian, L., Jermiin, L.: Multi-Objective Evolutionary Algorithms and Phylogenetic Inference with Multiple Data Sets. Soft. Computing 10(4), 359–368 (2006)
Jayaswal, V., Poladian, L., Jermiin, L.: Single- and multi-objective phylogenetic analysis of primate evolution using a genetic algorithm. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 4146–4153 (2007)
Vinh, L., von Haeseler, A.: Iqpnni: Moving fast through tree space and stopping in time. Molecular Biology and Evolution 21(8), 1565–1571 (2004)
Minh, B., Vinh, L., von Haeseler, A., Schmidt, H.: pIQPNNI: parallel reconstruction of large maximum likelihood phylogenies. Bioinformatics 21(19), 3794–3796 (2005)
Brauer, M.J., Holder, M.T., Dries, L.A., Zwickl, D.J., Lewis, P.O., Hillis, D.M.: Genetic algorithms and parallel processing in maximum-likelihood phylogeny inference. Molecular Biology and Evolution 19(10), 1717–1726 (2002)
Stamatakis, A., Ott, M.: Exploiting Fine-Grained Parallelism in the Phylogenetic Likelihood Function with MPI, Pthreads, and OpenMP: A Performance Study. In: Chetty, M., Ngom, A., Ahmad, S. (eds.) PRIB 2008. LNCS (LNBI), vol. 5265, pp. 424–435. Springer, Heidelberg (2008)
Pratas, F., Trancoso, P., Stamatakis, A., Sousa, L.: Fine-grain Parallelism using Multi-core, Cell/BE, and GPU Systems: Accelerating the Phylogenetic Likelihood Function. In: 38th International Conference on Parallel Processing (2009) (accepted for publication)
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Cancino, W., Jourdan, L., Talbi, EG., Delbem, A.C.B. (2010). Parallel Multi-Objective Approaches for Inferring Phylogenies. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2010. Lecture Notes in Computer Science, vol 6023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12211-8_3
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DOI: https://doi.org/10.1007/978-3-642-12211-8_3
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