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

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• 作者：Wasiu A Akanni (1) (2)
  Christopher J Creevey (3)
  Mark Wilkinson (2)
  Davide Pisani (1) (4)
  
  1. Department of Biology ; The National University of Ireland ; Maynooth ; Maynooth ; Kildare ; Ireland
  2. Department of Life Sciences ; The Natural History Museum ; London ; SW7 5BD ; UK
  3. Institute of Biological ; Environmental and Rural Sciences (IBERS) ; Aberystwyth University ; Aberystwyth ; Ceredigion ; SY23 3FG ; UK
  4. School of Biological Sciences and School of Earth Sciences ; The University of Bristol ; Woodland Road ; BS8 1UG ; Bristol ; UK
  
• 关键词：Supertrees ; Maximum likelihood ; Phylogenomics ; Tests of two trees
• 刊名：BMC Bioinformatics
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：15
• 期：1
• 全文大小：233 KB
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• 刊物主题：Bioinformatics; Microarrays; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Combinatorial Libraries; Algorithms;
• 出版者：BioMed Central
• ISSN：1471-2105

文摘

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.