Skip to Main content Skip to Navigation
Journal articles

Maximum Likelihood Supertrees

Mike Steel 1 Allen Rodrigo 2, 3
3 MAB - Méthodes et Algorithmes pour la Bioinformatique
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : We analyze a maximum likelihood approach for combining phylogenetic trees into a larger "supertree." This is based on a simple exponential model of phylogenetic error, which ensures that ML supertrees have a simple combinatorial description (as a median tree, minimizing a weighted sum of distances to the input trees). We show that this approach to ML supertree reconstruction is statistically consistent (it converges on the true species supertree as more input trees are combined), in contrast to the widely used MRP method, which we show can be statistically inconsistent under the exponential error model. We also show that this statistical consistency extends to an ML approach for constructing species supertrees from gene trees. In this setting, incomplete lineage sorting (due to coalescence rates of homologous genes being lower than speciation rates) has been shown to lead to gene trees that are frequently different from species trees, and this can confound efforts to reconstruct the species phylogeny correctly.
Complete list of metadata

Cited literature [25 references]  Display  Hide  Download
Contributor : Vincent Lefort Connect in order to contact the contributor
Submitted on : Friday, September 11, 2020 - 9:09:30 AM
Last modification on : Friday, October 22, 2021 - 3:07:28 PM
Long-term archiving on: : Saturday, December 5, 2020 - 2:58:51 AM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License





Mike Steel, Allen Rodrigo. Maximum Likelihood Supertrees. Systematic Biology, Oxford University Press (OUP), 2008, 57, pp.243-250. ⟨10.1080/10635150802033014⟩. ⟨lirmm-00335162⟩