Pareto-optimal phylogenetic tree reconciliation

Motivation: Phylogenetic tree reconciliation is a widely used method for reconstructing the evolutionary histories of gene families and species, hosts and parasites and other dependent pairs of entities. Reconciliation is typically performed using maximum parsimony, in which each evolutionary event type is assigned a cost and the objective is to find a reconciliation of minimum total cost. It is generally understood that reconciliations are sensitive to event costs, but little is understood about the relationship between event costs and solutions. Moreover, choosing appropriate event costs is a notoriously difficult problem. Results: We address this problem by giving an efficient algorithm for computing Pareto-optimal sets of reconciliations, thus providing the first systematic method for understanding the relationship between event costs and reconciliations. This, in turn, results in new techniques for computing event support values and, for cophylogenetic analyses, performing robust statistical tests. We provide new software tools and demonstrate their use on a number of datasets from evolutionary genomic and cophylogenetic studies.National Science Foundation (U.S.) (CAREER award 0644282)University of Connecticut (Startup funds)Harvey Mudd College (R. Michael Shanahan Endowment

Assessing the robustness of parsimonious predictions for gene neighborhoods from reconciled phylogenies

The availability of a large number of assembled genomes opens the way to study the evolution of syntenic character within a phylogenetic context. The DeCo algorithm, recently introduced by B{\'e}rard et al. allows the computation of parsimonious evolutionary scenarios for gene adjacencies, from pairs of reconciled gene trees. Following the approach pioneered by Sturmfels and Pachter, we describe how to modify the DeCo dynamic programming algorithm to identify classes of cost schemes that generates similar parsimonious evolutionary scenarios for gene adjacencies, as well as the robustness to changes to the cost scheme of evolutionary events of the presence or absence of specific ancestral gene adjacencies. We apply our method to six thousands mammalian gene families, and show that computing the robustness to changes to cost schemes provides new and interesting insights on the evolution of gene adjacencies and the DeCo model.Comment: Accepted, to appear in ISBRA - 11th International Symposium on Bioinformatics Research and Applications - 2015, Jun 2015, Norfolk, Virginia, United State

Cophylogenetic analysis of dated trees

Parasites and the associations they form with their hosts is an important area of research due to the associated health risks which parasites pose to the human population. The associations parasites form with their hosts are responsible for a number of the worst emerging diseases impacting global health today, including Ebola, HIV, and malaria. Macro-scale coevolutionary research aims to analyse these associations to provide further insights into these deadly diseases. This approach, first considered by Fahrenholz in 1913, has been applied to hundreds of coevolutionary systems and remains the most robust means to infer the underlying relationships which form between coevolving species. While reconciling the coevolutionary relationships between a pair of evolutionary systems is NP-Hard, it has been shown that if dating information exists there is a polynomial solution. These solutions however are computationally expensive, and are quickly becoming infeasible due to the rapid growth of phylogenetic data. If the rate of growth continues in line with the last three decades, the current means for analysing dated systems will become computationally infeasible. Within this thesis a collection of algorithms are introduced which aim to address this problem. This includes the introduction of the most efficient solution for analysing dated coevolutionary systems optimally, along with two linear time heuristics which may be applied where traditional algorithms are no longer feasible, while still offering a high degree of accuracy 91%. Finally, this work integrates these incremental results into a single model which is able to handle widespread parasitism, the case where parasites infect multiple hosts. This proposed model reconciles two competing theories of widespread parasitism, while also providing an accuracy improvement of 21%, one of the largest single improvements provided in this field to date. As such, the set of algorithms introduced within this thesis offers another step toward a unified coevolutionary analysis framework, consistent with Fahrenholz original coevolutionary analysis model

Phylogenetic framework for coevolutionary studies: A compass for exploring jungles of tangled trees

Phylogenetics is used to detect past evolutionary events, from how species originated to how their ecological interactions with other species arose, which can mirror cophylogenetic patterns. Cophylogenetic reconstructions uncover past ecological relationships between taxa through inferred coevolutionary events on trees, for example, codivergence, duplication, host-switching, and loss. These events can be detected by cophylogenetic analyses based on nodes and the length and branching pattern of the phylogenetic trees of symbiotic associations, for example, host-parasite. In the past 2 decades, algorithms have been developed for cophylogetenic analyses and implemented in different software, for example, statistical congruence index and event-based methods. Based on the combination of these approaches, it is possible to integrate temporal information into cophylogenetical inference, such as estimates of lineage divergence times between 2 taxa, for example, hosts and parasites. Additionally, the advances in phylogenetic biogeography applying methods based on parametric process models and combined Bayesian approaches, can be useful for interpreting coevolutionary histories in a scenario of biogeographical area connectivity through time. This article briefly reviews the basics of parasitology and provides an overview of software packages in cophylogenetic methods. Thus, the objective here is to present a phylogenetic framework for coevolutionary studies, with special emphasis on groups of parasitic organisms. Researchers wishing to undertake phylogeny-based coevolutionary studies can use this review as a "compass" when "walking" through jungles of tangled phylogenetic trees.Facultad de Ciencias Naturales y Muse

Phylogenetic framework for coevolutionary studies: A compass for exploring jungles of tangled trees

Phylogenetics is used to detect past evolutionary events, from how species originated to how their ecological interactions with other species arose, which can mirror cophylogenetic patterns. Cophylogenetic reconstructions uncover past ecological relationships between taxa through inferred coevolutionary events on trees, for example, codivergence, duplication, host-switching, and loss. These events can be detected by cophylogenetic analyses based on nodes and the length and branching pattern of the phylogenetic trees of symbiotic associations, for example, host-parasite. In the past 2 decades, algorithms have been developed for cophylogetenic analyses and implemented in different software, for example, statistical congruence index and event-based methods. Based on the combination of these approaches, it is possible to integrate temporal information into cophylogenetical inference, such as estimates of lineage divergence times between 2 taxa, for example, hosts and parasites. Additionally, the advances in phylogenetic biogeography applying methods based on parametric process models and combined Bayesian approaches, can be useful for interpreting coevolutionary histories in a scenario of biogeographical area connectivity through time. This article briefly reviews the basics of parasitology and provides an overview of software packages in cophylogenetic methods. Thus, the objective here is to present a phylogenetic framework for coevolutionary studies, with special emphasis on groups of parasitic organisms. Researchers wishing to undertake phylogeny-based coevolutionary studies can use this review as a "compass" when "walking" through jungles of tangled phylogenetic trees.Facultad de Ciencias Naturales y Muse

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

Stochastic Tree Models for Macroevolution: Development, Validation and Application

Phylogenetic trees capture the relationships between species and can be investigated by morphological and/or molecular data. When focusing on macroevolution, one considers the large-scale history of life with evolutionary changes affecting a single species of the entire clade leading to the enormous diversity of species obtained today. One major problem of biology is the explanation of this biodiversity. Therefore, one may ask which kind of macroevolutionary processes have given rise to observable tree shapes or patterns of species distribution which refers to the appearance of branching orders and time periods. Thus, with an increasing number of known species in the context of phylogenetic studies, testing hypotheses about evolution by analyzing the tree shape of the resulting phylogenetic trees became matter of particular interest. The attention of using those reconstructed phylogenies for studying evolutionary processes increased during the last decades. Many paleontologists (Raup et al., 1973; Gould et al., 1977; Gilinsky and Good, 1989; Nee, 2004) tried to describe such patterns of macroevolution by using models for growing trees. Those models describe stochastic processes to generate phylogenetic trees. Yule (1925) was the first who introduced such a model, the Equal Rate Markov (ERM) model, in the context of biological branching based on a continuous-time, uneven branching process. In the last decades, further dynamical models were proposed (Yule, 1925; Aldous, 1996; Nee, 2006; Rosen, 1978; Ford, 2005; Hernández-García et al., 2010) to address the investigation of tree shapes and hence, capture the rules of macroevolutionary forces. A common model, is the Aldous\\\'' Branching (AB) model, which is known for generating trees with a similar structure of \\\"real\\\" trees. To infer those macroevolutionary forces structures, estimated trees are analyzed and compared to simulated trees generated by models. There are a few drawbacks on recent models such as a missing biological motivation or the generated tree shape does not fit well to one observed in empirical trees. The central aim of this thesis is the development and study of new biologically motivated approaches which might help to better understand or even discover biological forces which lead to the huge diversity of organisms. The first approach, called age model, can be defined as a stochastic procedure which describes the growth of binary trees by an iterative stochastic attachment of leaves, similar to the ERM model. At difference with the latter, the branching rate at each clade is no longer constant, but decreasing in time, i.e., with the age. Thus, species involved in recent speciation events have a tendency to speciate again. The second introduced model, is a branching process which mimics the evolution of species driven by innovations. The process involves a separation of time scales. Rare innovation events trigger rapid cascades of diversification where a feature combines with previously existing features. The model is called innovation model. Three data sets of estimated phylogenetic trees are used to analyze and compare the produced tree shape of the new growth models. A tree shape statistic considering a variety of imbalance measurements is performed. Results show that simulated trees of both growth models fit well to the tree shape observed in real trees. In a further study, a likelihood analysis is performed in order to rank models with respect to their ability to explain observed tree shapes. Results show that the likelihoods of the age model and the AB model are clearly correlated under the trees in the databases when considering small and medium-sized trees with up to 19 leaves. For a data set, representing of phylogenetic trees of protein families, the age model outperforms the AB model. But for another data set, representing phylogenetic trees of species, the AB model performs slightly better. To support this observation a further analysis using larger trees is necessary. But an exact computation of likelihoods for large trees implies a huge computational effort. Therefore, an efficient method for likelihood estimation is proposed and compared to the estimation using a naive sampling strategy. Nevertheless, both models describe the tree generation process in a way which is easy to interpret biologically. Another interesting field of research in biology is the coevolution between species. This is the interaction of species across groups such that the evolution of a species from one group can be triggered by a species from another group. Most prominent examples are systems of host species and their associated parasites. One problem is the reconciliation of the common history of both groups of species and to predict the associations between ancestral hosts and their parasites. To solve this problem some algorithmic methods have been developed in recent years. But only a few host parasite systems have been analyzed in sufficient detail which makes an evaluation of these methods complex. Within the scope of coevolution, the proposed age model is applied to the generation of cophylogenies to evaluate such host parasite reconciliation methods. The presented age model as well as the innovation model produce tree shapes which are similar to obtained tree structures of estimated trees. Both models describe an evolutionary dynamics and might provide a further opportunity to infer macroevolutionary processes which lead to the biodiversity which can be obtained today. Furthermore with the application of the age model in the context of coevolution by generating a useful benchmark set of cophylogenies is a first step towards systematic studies on evaluating reconciliation methods

On the Computational Complexity of the Reticulate Cophylogeny Reconstruction Problem

The cophylogeny reconstruction problem is that of finding minimal cost explanations of differences between evolutionary histories of ecologically linked groups of biological organisms. We present a proof that shows that the general problem of reconciling evolutionary histories is NP-complete and provide a sharp boundary where this intractability begins. We also show that a related problem, that of finding Pareto optimal solutions, is NP-hard. As a byproduct of our results, we give a framework by which meta-heuristics can be applied to find good solutions to this problem