The Minimum-Evolution Distance-Based Approach to Phylogeny Inference

Richard Desper 1 Olivier Gascuel 2
2 MAB - Méthodes et Algorithmes pour la Bioinformatique
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Distance algorithms remain among the most popular for reconstructing phylogenies, especially for researchers faced with data sets with large num- bers of taxa. Distance algorithms are much faster in practice than character or likelihood algorithms, and least-squares algorithms produce trees that have several desirable statistical properties. The fast Neighbor Joining heuristic has proven to be quite popular with researchers, but suffers some- what from a lack of a statistical foundation. We show here that the balanced minimum evolution approach provides a robust statistical justification and is amenable to fast heuristics that provide topologies superior among the class of distance algorithms. The aim of this chapter is to present a compre- hensive survey of the minimum evolution principle, detailing its variants, algorithms, and statistical and combinatorial properties. The focus is on the balanced version of this principle, as it appears quite well suited for phylogenetic inference, from a theoretical perspective as well as through computer simulations.
Document type :
Book sections
Complete list of metadatas

Cited literature [51 references]  Display  Hide  Download

https://hal-lirmm.ccsd.cnrs.fr/lirmm-00106579
Contributor : Isabelle Gouat <>
Submitted on : Wednesday, May 13, 2015 - 4:14:14 PM
Last modification on : Thursday, May 24, 2018 - 3:59:22 PM
Long-term archiving on: Tuesday, September 15, 2015 - 12:05:46 AM

File

DesperGascuel_MEP05.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : lirmm-00106579, version 1

Collections

Citation

Richard Desper, Olivier Gascuel. The Minimum-Evolution Distance-Based Approach to Phylogeny Inference. O. Gascuel. Mathematics of Evolution and Phylogeny, Oxford University Press, pp.1-32, 2005, 0-19-856610-7. ⟨lirmm-00106579⟩

Share

Metrics

Record views

579

Files downloads

331