Cophylogeny Reconstruction via an Approximate Bayesian Computation

Abstract : 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.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.
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Systematic Biology, Oxford University Press (OUP), 2015, 64 (3), pp.416-431. 〈10.1093/sysbio/syu129〉
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Christian Baudet, Béatrice Donati, Blerina Sinaimeri, Pierluigi Crescenzi, Christian Gautier, et al.. Cophylogeny Reconstruction via an Approximate Bayesian Computation. Systematic Biology, Oxford University Press (OUP), 2015, 64 (3), pp.416-431. 〈10.1093/sysbio/syu129〉. 〈hal-01092972〉

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