L.U.St: a tool for approximated maximum likelihood supertree reconstruction

Wasiu A. Akanni, Christopher J. Creevey, Mark Wilkinson, Davide Pisani*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
133 Downloads (Pure)


Background: Supertrees combine disparate, partially overlapping trees to generate a synthesis that provides a high level perspective that cannot be attained from the inspection of individual phylogenies. Supertrees can be seen as meta-analytical tools that can be used to make inferences based on results of previous scientific studies. Their meta-analytical application has increased in popularity since it was realised that the power of statistical tests for the study of evolutionary trends critically depends on the use of taxon-dense phylogenies. Further to that, supertrees have found applications in phylogenomics where they are used to combine gene trees and recover species phylogenies based on genome-scale data sets.Results: Here, we present the L.U.St package, a python tool for approximate maximum likelihood supertree inference and illustrate its application using a genomic data set for the placental mammals. L.U.St allows the calculation of the approximate likelihood of a supertree, given a set of input trees, performs heuristic searches to look for the supertree of highest likelihood, and performs statistical tests of two or more supertrees. To this end, L.U.St implements a winning sites test allowing ranking of a collection of a-priori selected hypotheses, given as a collection of input supertree topologies. It also outputs a file of input-tree-wise likelihood scores that can be used as input to CONSEL for calculation of standard tests of two trees (e.g. Kishino-Hasegawa, Shimidoara-Hasegawa and Approximately Unbiased tests).Conclusion: This is the first fully parametric implementation of a supertree method, it has clearly understood properties, and provides several advantages over currently available supertree approaches. It is easy to implement and works on any platform that has python installed.Availability: bitBucket page - https://afro-juju@bitbucket.org/afro-juju/l.u.st.git.Contact: Davide.Pisani@bristol.ac.uk.

Original languageEnglish
Article number183
Pages (from-to)1-6
JournalBMC Bioinformatics
Issue number1
Publication statusPublished - 12 Jun 2014


  • Maximum likelihood
  • Phylogenomics
  • Supertrees
  • Tests of two trees

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics
  • Structural Biology


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