Anti-consensus: detecting trees that have an evolutionary signal that is lost in consensus

Abstract

AO_SCPLOWBSTRACTC_SCPLOWIn phylogenetics, a set of gene trees is often summarized by a consensus tree, such as the majority consensus, which is based on the set of all splits that are present in more than 50% of the input trees. A \"consensus network\" is obtained by lowering the threshold and considering all splits that are contained in 10% of the trees, say, and then computing the corresponding splits network. By construction and in practice, a consensus network usually shows the majority tree, extended by a number of rectangles that represent local rearrangements around internal nodes of the consensus tree. This may lead to the false conclusion that the input trees do not differ in a significant way because \"even a phylogenetic network\" does not display any large discrepancies. To harness the full potential of a phylogenetic network, we introduce the new concept of an anti-consensus network that aims at representing the largest interesting discrepancies found in a set of gene trees. We provide an efficient algorithm for computing an anti-consensus and illustrate its application using a set of gene trees from species and strains in the genus Kosakonia.