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Numerical Optimization Techniques in Maximum Likelihood Tree Inference

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Bioinformatics and Phylogenetics

Part of the book series: Computational Biology ((COBO,volume 29))

Abstract

In this chapter, we present recent computational and algorithmic advances for improving the inference of phylogenetic trees from the analysis of homologous genetic sequences under the maximum likelihood criterion. In particular, we detail how the use of matrix algebra at the core of Felsenstein’s pruning algorithm, combined with the architecture of modern day computer processors, leads to efficient techniques for optimizing edge lengths. We also discuss some properties of the likelihood function when considering the optimization of the parameters of mixture models that are used to describe the variation of rates-across sites .

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Acknowledgements

We would like to thank Alexandros Stamatakis for helpful suggestions on how to improve this chapter and Tandy Warnow for inviting us to celebrate Bernard Moret’s contributions to the field of computational evolution.

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Authors and Affiliations

  1. Laboratoire d’Informatique de Robotique et de Microélectronique de Montpellier, CNRS and Université Montpellier (UMR 5506), Montpellier, France

    Stéphane Guindon

  2. Unité Bioinformatique Evolutive, C3BI Institut Pasteur and CNRS (USR 3756), Paris, France

    Olivier Gascuel

Authors
  1. Stéphane Guindon
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  2. Olivier Gascuel
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Correspondence to Olivier Gascuel .

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Editors and Affiliations

  1. University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Tandy Warnow

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Guindon, S., Gascuel, O. (2019). Numerical Optimization Techniques in Maximum Likelihood Tree Inference. In: Warnow, T. (eds) Bioinformatics and Phylogenetics. Computational Biology, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-030-10837-3_2

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  • DOI: https://doi.org/10.1007/978-3-030-10837-3_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-10836-6

  • Online ISBN: 978-3-030-10837-3

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