Split-based computation of majority-rule supertrees

Anne Kupczok

doi:10.1186/1471-2148-11-205

Abstract

BackgroundSupertree methods combine overlapping input trees into a larger supertree. Here, I consider split-based supertree methods that first extract the split information of the input trees and subsequently combine this split information into a phylogeny. Well known split-based supertree methods are matrix representation with parsimony and matrix representation with compatibility. Combining input trees on the same taxon set, as in the consensus setting, is a well-studied task and it is thus desirable to generalize consensus methods to supertree methods.ResultsHere, three variants of majority-rule (MR) supertrees that generalize majority-rule consensus trees are investigated. I provide simple formulas for computing the respective score for bifurcating input- and supertrees. These score computations, together with a heuristic tree search minmizing the scores, were implemented in the python program PluMiST (Plus- and Minus SuperTrees) available from http://www.cibiv.at/software/plumist. The different MR methods were tested by simulation and on real data sets. The search heuristic was successful in combining compatible input trees. When combining incompatible input trees, especially one variant, MR(-) supertrees, performed well.ConclusionsThe presented framework allows for an efficient score computation of three majority-rule supertree variants and input trees. I combined the score computation with a heuristic search over the supertree space. The implementation was tested by simulation and on real data sets and showed promising results. Especially the MR(-) variant seems to be a reasonable score for supertree reconstruction. Generalizing these computations to multifurcating trees is an open problem, which may be tackled using this framework.

Highlights

Supertree methods combine overlapping input trees into a larger supertree
The aim of this paper is to suggest a general framework for the distance computations underlying the MR supertree methods, to present an implementation evaluating different distance variants, and to compare these by simulation
I present a new framework for the computation of the distances underlying majority-rule supertrees. The basis of this framework is the relationship matrix that stores the possible relationships between an input tree split and a supertree split: subsplit, compatibility, or incompatibility

Summary

Introduction

Supertree methods combine overlapping input trees into a larger supertree. Combining input trees on the same taxon set, as in the consensus setting, is a well-studied task and it is desirable to generalize consensus methods to supertree methods. Supertree methods amalgamate trees containing information from different, but overlapping, relationships into a larger supertree (e.g., [1]). The input trees need not have the same taxon sets, but the supertree contains all of the taxa present in at least one of the input trees With this property, supertrees are applied to combine information present in different gene trees to infer relationships about larger sets of taxa (e.g., [2,3,4,5,6])

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