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Open Access Highly Accessed Methodology article

Multiple consensus trees: a method to separate divergent genes

Alain Guénoche

Author Affiliations

Institut de Mathématiques de Luminy, 163 Av. de Luminy, 13009 Marseille, France

BMC Bioinformatics 2013, 14:46  doi:10.1186/1471-2105-14-46

Published: 9 February 2013

Abstract

Background

It is generally admitted that the species tree cannot be inferred from the genetic sequences of a single gene because the evolution of different genes, and thus the gene tree topologies, may vary substantially. Gene trees can differ, for example, because of horizontal transfer events or because some of them correspond to paralogous instead of orthologous sequences. A variety of methods has been proposed to tackle the problem of the reconciliation of gene trees in order to reconstruct a species tree. When the taxa in all the trees are identical, the problem can be stated as a consensus tree problem.

Results

In this paper we define a new method for deciding whether a unique consensus tree or multiple consensus trees can best represent a set of given phylogenetic trees. If the given trees are all congruent, they should be compatible into a single consensus tree. Otherwise, several consensus trees corresponding to divergent genetic patterns can be identified. We introduce a method optimizing the generalized score, over a set of tree partitions in order to decide whether the given set of gene trees is homogeneous or not.

Conclusions

The proposed method has been validated with simulated data (random trees organized in three topological groups) as well as with real data (bootstrap trees, homogeneous set of trees, and a set of non homogeneous gene trees of 30 E. Coli strains; it is worth noting that some of the latter genes underwent horizontal gene transfers). A computer program, MCT - Multiple Consensus Trees, written in C was made freely available for the research community (it can be downloaded from http://bioinformatics.lif.univ-mrs.fr/Consensus/index.html webcite). It handles trees in a standard Newick format, builds three hierarchies corresponding to RF and QS similarities between trees and the greedy ascending algorithm. The generalized score values of all tree partitions are computed.