Co-divergence and tree topology.

In reconstructing the common evolutionary history of hosts and parasites, the current method of choice is the phylogenetic tree reconciliation. In this model, we are given a host tree H, a parasite tree P, and a function [Formula: see text] mapping the leaves of P to the leaves of H and the goal is to find, under some biologically motivated constraints, a reconciliation, that is a function from the vertices of P to the vertices of H that respects [Formula: see text] and allows the identification of biological events such as co-speciation, duplication and host switch. The maximum co-divergence problem consists in finding the maximum number of co-speciations in a reconciliation. This problem is NP-hard for arbitrary phylogenetic trees and no approximation algorithm is known. In this paper we consider the influence of tree topology on the maximum co-divergence problem. In particular we focus on a particular tree structure, namely caterpillar, and show that-in this case-the heuristics that are mostly used in the literature provide solutions that can be arbitrarily far from the optimal value. Then, we prove that finding the max co-divergence is equivalent to compute the maximum length of a subsequence with certain properties of a given permutation. This equivalence leads to two consequences: (1) it shows that we can compute efficiently in polynomial time the optimal time-feasible reconciliation and (2) it can be used to understand how much the tree topology influences the value of the maximum number of co-speciations.

Cophylogeny reconstruction via an approximate Bayesian computation.

Despite an increasingly vast literature on cophylogenetic reconstructions for studying host-parasite associations, understanding the common evolutionary history of such systems remains a problem that is far from being solved. Most algorithms for host-parasite reconciliation use an event-based model, where the events include in general (a subset of) cospeciation, duplication, loss, and host switch. All known parsimonious event-based methods then assign a cost to each type of event in order to find a reconstruction of minimum cost. The main problem with this approach is that the cost of the events strongly influences the reconciliation obtained. Some earlier approaches attempt to avoid this problem by finding a Pareto set of solutions and hence by considering event costs under some minimization constraints. To deal with this problem, we developed an algorithm, called Coala, for estimating the frequency of the events based on an approximate Bayesian computation approach. The benefits of this method are 2-fold: (i) it provides more confidence in the set of costs to be used in a reconciliation, and (ii) it allows estimation of the frequency of the events in cases where the data set consists of trees with a large number of taxa. We evaluate our method on simulated and on biological data sets. We show that in both cases, for the same pair of host and parasite trees, different sets of frequencies for the events lead to equally probable solutions. Moreover, often these solutions differ greatly in terms of the number of inferred events. It appears crucial to take this into account before attempting any further biological interpretation of such reconciliations. More generally, we also show that the set of frequencies can vary widely depending on the input host and parasite trees. Indiscriminately applying a standard vector of costs may thus not be a good strategy.