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International Conference on Artificial Evolution (Evolution Artificielle)

EA 2017: Artificial Evolution pp 88–99Cite as

MEMSA: A Robust Parisian EA for Multidimensional Multiple Sequence Alignment

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10764))

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.

Author information

Authors and Affiliations

  1. ICube laboratory, UMR CNRS 7357, Strasbourg University, Strasbourg, France

    Julie D. Thompson, Renaud Vanhoutrève & Pierre Collet

  2. Fédération de Médecine Translationnelle de Strasbourg, CS-DC UNESCO UniTwin, Strasbourg, France

    Julie D. Thompson, Renaud Vanhoutrève & Pierre Collet

Authors
  1. Julie D. Thompson
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  2. Renaud Vanhoutrève
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  3. Pierre Collet
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Corresponding author

Correspondence to Pierre Collet .

Editor information

Editors and Affiliations

  1. Inria, Thiverval-Grignon, France

    Evelyne Lutton

  2. Inria Bordeaux, University of Bordeaux, Talence, France

    Pierrick Legrand

  3. ECAM Strasbourg-Europe, Schiltigheim, France

    Pierre Parrend

  4. University of Tours, Tours, France

    Nicolas Monmarché

  5. Université Paris-Sud, Orsay, France

    Marc Schoenauer

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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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  • DOI: https://doi.org/10.1007/978-3-319-78133-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78132-7

  • Online ISBN: 978-3-319-78133-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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