Abstract
This paper describes a new approach for the multiple alignment of biological sequences (DNA or proteins) using a Parisian Evolution approach called MEMSA, for Multidimensional Evolutionary Multiple Sequence Alignment, coded using the EASEA platform. This approach evolves individual sub-alignments called “patches” that are used to create a new kind of Multiple Sequence Alignment where alternative solutions are computed simultaneously using different fitness functions. Solutions are generated by combining coherent sets of high-scoring individuals that are used to reconstruct multi-dimensional multiple sequence alignments. The alignments of this prototype version show a quality comparable to ClustalW (one of the most widely used existing methods) on the 218 samples of the BAliBASE benchmark in reasonable time.
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Acknowledgement
We would like to thank the members of the BISTRO Bioinformatics Platform in Strasbourg for their support. This work was supported by the Agence Nationale de la Recherche (BIPBIP: ANR-10-BINF-03-02), the Région Alsace and Institute funds from the CNRS, the Université de Strasbourg and the Faculté de Médecine de Strasbourg.
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Thompson, J.D., Vanhoutrève, R., Collet, P. (2018). MEMSA: A Robust Parisian EA for Multidimensional Multiple Sequence Alignment. In: Lutton, E., Legrand, P., Parrend, P., Monmarché, N., Schoenauer, M. (eds) Artificial Evolution. EA 2017. Lecture Notes in Computer Science(), vol 10764. Springer, Cham. https://doi.org/10.1007/978-3-319-78133-4_7
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