title: 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. 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.
authors.affiliation: INRIA Grenoble Rhône-Alpes, 38330 Montbonnot Saint-Martin, France; Université de Lyon, F-69000 Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Évolutive, F-69622 Villeurbanne, France; Università di Firenze, Dipartimento di Sistemi e Informatica, I-50134 Firenze, Italy; and Laboratoire Statistique et Génome, UMR CNRS 8071 & USC INRA, Université d'Évry INRIA Grenoble Rhône-Alpes, 38330 Montbonnot Saint-Martin, France; Université de Lyon, F-69000 Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Évolutive, F-69622 Villeurbanne, France; Università di Firenze, Dipartimento di Sistemi e Informatica, I-50134 Firenze, Italy; and Laboratoire Statistique et Génome, UMR CNRS 8071 & USC INRA, Université d'Évry.
authors.forename: C
authors.initials: C
authors.lastname: Baudet
authors.affiliation: INRIA Grenoble Rhône-Alpes, 38330 Montbonnot Saint-Martin, France; Université de Lyon, F-69000 Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Évolutive, F-69622 Villeurbanne, France; Università di Firenze, Dipartimento di Sistemi e Informatica, I-50134 Firenze, Italy; and Laboratoire Statistique et Génome, UMR CNRS 8071 & USC INRA, Université d'Évry INRIA Grenoble Rhône-Alpes, 38330 Montbonnot Saint-Martin, France; Université de Lyon, F-69000 Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Évolutive, F-69622 Villeurbanne, France; Università di Firenze, Dipartimento di Sistemi e Informatica, I-50134 Firenze, Italy; and Laboratoire Statistique et Génome, UMR CNRS 8071 & USC INRA, Université d'Évry INRIA Grenoble Rhône-Alpes, 38330 Montbonnot Saint-Martin, France; Université de Lyon, F-69000 Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Évolutive, F-69622 Villeurbanne, France; Università di Firenze, Dipartimento di Sistemi e Informatica, I-50134 Firenze, Italy; and Laboratoire Statistique et Génome, UMR CNRS 8071 & USC INRA, Université d'Évry.
authors.forename: B
authors.initials: B
authors.lastname: Donati
authors.affiliation: INRIA Grenoble Rhône-Alpes, 38330 Montbonnot Saint-Martin, France; Université de Lyon, F-69000 Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Évolutive, F-69622 Villeurbanne, France; Università di Firenze, Dipartimento di Sistemi e Informatica, I-50134 Firenze, Italy; and Laboratoire Statistique et Génome, UMR CNRS 8071 & USC INRA, Université d'Évry INRIA Grenoble Rhône-Alpes, 38330 Montbonnot Saint-Martin, France; Université de Lyon, F-69000 Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Évolutive, F-69622 Villeurbanne, France; Università di Firenze, Dipartimento di Sistemi e Informatica, I-50134 Firenze, Italy; and Laboratoire Statistique et Génome, UMR CNRS 8071 & USC INRA, Université d'Évry.
authors.forename: B
authors.initials: B
authors.lastname: Sinaimeri
authors.affiliation: INRIA Grenoble Rhône-Alpes, 38330 Montbonnot Saint-Martin, France; Université de Lyon, F-69000 Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Évolutive, F-69622 Villeurbanne, France; Università di Firenze, Dipartimento di Sistemi e Informatica, I-50134 Firenze, Italy; and Laboratoire Statistique et Génome, UMR CNRS 8071 & USC INRA, Université d'Évry.
authors.forename: P
authors.initials: P
authors.lastname: Crescenzi
authors.affiliation: INRIA Grenoble Rhône-Alpes, 38330 Montbonnot Saint-Martin, France; Université de Lyon, F-69000 Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Évolutive, F-69622 Villeurbanne, France; Università di Firenze, Dipartimento di Sistemi e Informatica, I-50134 Firenze, Italy; and Laboratoire Statistique et Génome, UMR CNRS 8071 & USC INRA, Université d'Évry INRIA Grenoble Rhône-Alpes, 38330 Montbonnot Saint-Martin, France; Université de Lyon, F-69000 Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Évolutive, F-69622 Villeurbanne, France; Università di Firenze, Dipartimento di Sistemi e Informatica, I-50134 Firenze, Italy; and Laboratoire Statistique et Génome, UMR CNRS 8071 & USC INRA, Université d'Évry.
authors.forename: C
authors.initials: C
authors.lastname: Gautier
authors.affiliation: INRIA Grenoble Rhône-Alpes, 38330 Montbonnot Saint-Martin, France; Université de Lyon, F-69000 Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Évolutive, F-69622 Villeurbanne, France; Università di Firenze, Dipartimento di Sistemi e Informatica, I-50134 Firenze, Italy; and Laboratoire Statistique et Génome, UMR CNRS 8071 & USC INRA, Université d'Évry.
authors.forename: C
authors.initials: C
authors.lastname: Matias
authors.affiliation: INRIA Grenoble Rhône-Alpes, 38330 Montbonnot Saint-Martin, France; Université de Lyon, F-69000 Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Évolutive, F-69622 Villeurbanne, France; Università di Firenze, Dipartimento di Sistemi e Informatica, I-50134 Firenze, Italy; and Laboratoire Statistique et Génome, UMR CNRS 8071 & USC INRA, Université d'Évry INRIA Grenoble Rhône-Alpes, 38330 Montbonnot Saint-Martin, France; Université de Lyon, F-69000 Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Évolutive, F-69622 Villeurbanne, France; Università di Firenze, Dipartimento di Sistemi e Informatica, I-50134 Firenze, Italy; and Laboratoire Statistique et Génome, UMR CNRS 8071 & USC INRA, Université d'Évry marie-france.sagot@inria.fr.
authors.forename: M-F
authors.initials: MF
authors.lastname: Sagot
mesh_terms: D000465:Algorithms; D000818:Animals; D001181:Arthropods / Q000145:classification / Q000382:microbiology; D001499:Bayes Theorem; D002965:Classification / Q000379:methods*; D006790:Host-Parasite Interactions; D010802:Phylogeny; D020577:Wolbachia / Q000145:classification / Q000502:physiology
keywords: approximate Bayesian computation; cophylogeny; host/parasite systems; likelihood-free inference