Background: Inferring an evolutionary scenario for a gene family is a fundamental problem with applications both in functional and evolutionary genomics. The gene tree/species tree reconciliation approach has been widely used to address this problem, but mostly in a discrete parsimony framework that aims at minimizing the number of gene duplications and/or gene losses. Recently, a probabilistic approach has been developed, based on the classical birth-and-death process, including efficient algorithms for computing posterior probabilities of reconciliations and orthology prediction.
Results: In previous work, we described an algorithm for exploring the whole space of gene tree/species tree reconciliations, that we adapt here to compute efficiently the posterior probability of such reconciliations. These posterior probabilities can be either computed exactly or approximated, depending on the reconciliation space size. We use this algorithm to analyze the probabilistic landscape of the space of reconciliations for a real data set of fungal gene families and several data sets of synthetic gene trees.
Conclusion: The results of our simulations suggest that, with exact gene trees obtained by a simple birth-and-death process and realistic gene duplication/loss rates, a very small subset of all reconciliations needs to be explored in order to approximate very closely the posterior probability of the most likely reconciliations. For cases where the posterior probability mass is more evenly dispersed, our method allows to explore efficiently the required subspace of reconciliations.