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Scalable Phylogenetics through Input Preprocessing

  • Roberto Blanco
  • Elvira Mayordomo
  • Esther Montes
  • Rafael Mayo
  • Angelines Alberto
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 74)

Abstract

Phylogenetic reconstruction is one of the fundamental problems in computational biology. The combinatorial explosion of the state space and the complexity of mathematical models impose practical limits on workable problem sizes. In this article we explore the scalability of popular algorithms under real datasets as problem dimensions grow. We furthermore develop an efficient preclassification and partitioning strategy based on guide trees, which are used to intently define an evolutionary hierarchy of groups of related data, and to determine membership of individual data to their corresponding subproblems. Finally, we apply this method to efficiently calculate exhaustive phylogenies of human mitochondrial DNA according to phylogeographic criteria.

Keywords

Steiner Tree Guide Tree Problem Dimension Supertree Method Bayesian Phylogenetic Inference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Roberto Blanco
    • 1
  • Elvira Mayordomo
    • 1
  • Esther Montes
    • 2
  • Rafael Mayo
    • 2
  • Angelines Alberto
    • 2
  1. 1.Departamento de Informática e Ingeniería de Sistemas (DIIS)/Instituto de Investigación en Ingeniería de Aragón (I3A)Universidad de ZaragozaZaragozaSpain
  2. 2.Centro de Investigaciones EnergéticasMedioambientales y Tecnológicas (CIEMAT)MadridSpain

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