Abstract
BackgroundSupertrees 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.ResultsHere, 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).ConclusionThis 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.
Highlights
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
We present a Python implementation of the maximum likelihood (ML) supertree method of Steel and Rodrigo [17]
L.U.St represent the first implementation of a maximum likelihood supertree method
Summary
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. The first supertree algorithm was introduced by Aho and colleagues [3] as an in genomics where they have been used to combine gene trees and derive species phylogenies [5,6,7,8,9]. Most actual supertrees have been derived using the Matrix Representation with Parsimony (MRP) method of Baum [10] and Ragan [11]. This is due to the availability of excellent parsimony software and the general good understanding of the theory underlying parsimony. The only exceptions seem to be those based on generalisations of well-known consensus methods [16], and the maximum likelihood (ML) method of Steel and Rodrigo [17]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
