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STK / Supertree Toolkit
A software tool for collecting, curating, storing and processing data ready for inclusion in supertree analyses. STK does not build supertrees, however, it does include a number of functions to get the data ready for running a supertree analysis. This includes standardizing nomenclature and taxonomy, ensuring adequate taxonomic overlap and creating a matrix. These functions can be used together as a data processing pipeline or independently as stand-alone options for data processing.
PhySIC / PHYlogenetic Signal with Induction and non-Contradiction
Enables the user to quickly summarize consensual information of a set of trees and localize groups of taxa for which the data require consolidation. For k input trees spanning a set of n taxa, this method produces a supertree that satisfies the above-mentioned properties in O(kn3 + n4) computing time. The polytomies of the produced supertree are also tagged by labels indicating areas of conflict as well as those with insufficient overlap.
Constructs supertrees and explore the underlying phylogenomic information from partially overlapping datasets. Clann has been developed to provide implementations of several supertree methods. The methods implemented all allow the investigation of data in a phylogenomic context. There are four supertree methods implemented in Clann: Matrix Representation using Parsimony (MRP); Most Similar Supertree (MSSA); Maximum Quartet Fit (QFIT) and Maximum Splits Fit (SFIT). It is important for the user to know that the software is designed to perform a number of different tasks, however the interpretation of the results is left entirely to the user.
A triplet-based supertree approach to phylogenomics. SuperTriplets infers supertrees with branch support values. The method avoids several practical limitations of the triplet-based binary matrix representation, making it useful to deal with large datasets. When the correct resolution of every triplet appears more often than the incorrect ones in source trees, SuperTriplets warrants to reconstruct the correct phylogeny. Both simulations and case studies on mammalian phylogenomics confirm the advantages of this approach.
FastRFS / Fast Robinson-Foulds Supertrees
A method based on a dynamic programming method developed to find an exact solution to the Robinson-Foulds Supertree problem within a constrained search space. FastRFS has excellent accuracy in terms of criterion scores and topological accuracy of the resultant trees, substantially improving on competing methods on a large collection of biological and simulated data. In addition, FastRFS is extremely fast, finishing in minutes on even very large datasets, and in under an hour on a biological dataset with 2228 species.
L.U.St / Likelihood Utility for Supertrees
Approximates maximum likelihood supertree inference. 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.
Helps for counting and enumerating the trees on a terrace. SUPERB is an algorithm that takes a set of rooted binary trees and then construct, when is it possible, all rooted, binary so-called supertrees that are compatible with all given trees in the input tree set. The algorithm starts with all leaves/taxa. Then, for each leaf, it determines if it belongs to the left or right subtree of the root. In the recursion, the algorithm then again divides the leaves among the children of the next node.
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