Data from: 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
minimisation 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 twofold: (1) it provides more confidence in the set of costs to
be used in a reconciliation, and (2) it allows estimation of the frequency
of the events in cases where the dataset consists of trees with a large
number of taxa. We evaluate our method on simulated and on biological
datasets. 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.