Visualizing Co-Phylogenetic Reconciliations

We introduce a hybrid metaphor for the visualization of the reconciliations of co-phylogenetic trees, that are mappings among the nodes of two trees. The typical application is the visualization of the co-evolution of hosts and parasites in biology. Our strategy combines a space-filling and a node-link approach. Differently from traditional methods, it guarantees an unambiguous and `downward' representation whenever the reconciliation is time-consistent (i.e., meaningful). We address the problem of the minimization of the number of crossings in the representation, by giving a characterization of planar instances and by establishing the complexity of the problem. Finally, we propose heuristics for computing representations with few crossings.Comment: This paper appears in the Proceedings of the 25th International Symposium on Graph Drawing and Network Visualization (GD 2017

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

Malagasy bats shelter a considerable genetic diversity of pathogenic Leptospira suggesting notable host-specificity patterns

Pathogenic Leptospira are the causative agents of leptospirosis, a disease of global concern with major impact in tropical regions. Despite the importance of this zoonosis for human health, the evolutionary and ecological drivers shaping bacterial communities in host reservoirs remain poorly investigated. Here, we describe Leptospira communities hosted by Malagasy bats, composed of mostly endemic species, in order to characterize host-pathogen associations and investigate their evolutionary histories. We screened 947 individual bats (representing 31 species, 18 genera and seven families) for Leptospira infection and subsequently genotyped positive samples using three different bacterial loci. Molecular identification showed that these Leptospira are notably diverse and include several distinct lineages mostly belonging to Leptospira borgpetersenii and L. kirschneri. The exploration of the most probable host-pathogen evolutionary scenarios suggests that bacterial genetic diversity results from a combination of events related to the ecology and the evolutionary history of their hosts. Importantly, based on the data set presented herein, the notable host-specificity we have uncovered, together with a lack of geographical structuration of bacterial genetic diversity, indicates that the Leptospira community at a given site depends on the co-occurring bat species assemblage. The implications of such tight host-specificity on the epidemiology of leptospirosis are discussed.http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1574-69412017-06-30hb2016Microbiology and Plant Patholog

Gene Family Histories: Theory and Algorithms

Detailed gene family histories and reconciliations with species trees are a prerequisite for studying associations between genetic and phenotypic innovations. Even though the true evolutionary scenarios are usually unknown, they impose certain constraints on the mathematical structure of data obtained from simple yes/no questions in pairwise comparisons of gene sequences. Recent advances in this field have led to the development of methods for reconstructing (aspects of) the scenarios on the basis of such relation data, which can most naturally be represented by graphs on the set of considered genes. We provide here novel characterizations of best match graphs (BMGs) which capture the notion of (reciprocal) best hits based on sequence similarities. BMGs provide the basis for the detection of orthologous genes (genes that diverged after a speciation event). There are two main sources of error in pipelines for orthology inference based on BMGs. Firstly, measurement errors in the estimation of best matches from sequence similarity in general lead to violations of the characteristic properties of BMGs. The second issue concerns the reconstruction of the orthology relation from a BMG. We show how to correct estimated BMG to mathematically valid ones and how much information about orthologs is contained in BMGs. We then discuss implicit methods for horizontal gene transfer (HGT) inference that focus on pairs of genes that have diverged only after the divergence of the two species in which the genes reside. This situation defines the edge set of an undirected graph, the later-divergence-time (LDT) graph. We explore the mathematical structure of LDT graphs and show how much information about all HGT events is contained in such LDT graphs

Towards Accurate Reconstruction of Phylogenetic Networks

Since Darwin proposed that all species on the earth have evolved from a common ancestor, evolution has played an important role in understanding biology. While the evolutionary relationships/histories of genes are represented using trees, the genomic evolutionary history may not be adequately captured by a tree, as some evolutionary events, such as horizontal gene transfer (HGT), do not fit within the branches of a tree. In this case, phylogenetic networks are more appropriate for modeling evolutionary histories. In this dissertation, we present computational algorithms to reconstruct phylogenetic networks from different types of data. Under the assumption that species have single copies of genes, and HGT and speciation are the only events through the course of evolution, gene sequences can be sampled one copy per species for HGT detection. Given the alignments of the sequences, we propose systematic methods that estimate the significance of detected HGT events under maximum parsimony (MP) and maximum likelihood (ML). The estimated significance aims at addressing the issue of overestimation of both optimization criteria in the search for phylogenetic networks and helps the search identify networks with the ``right" number of HGT edges. We study their performance on both synthetic and biological data sets. While the studies show very promising results in identifying HGT edges, they also highlight the issues that are challenging for each criterion. We also develop algorithms that estimate the amount of HGT events and reconstruct phylogenetic networks by utilizing the pairwise Subtree-Prune-Regraft (SPR) operation from a collection of trees. The methods produce good results in general in terms of quickly estimating the minimum number of HGT events required to reconcile a set of trees. Further, we identify conditions under which the methods do not work well in order to help in the development of new methods in this area. Finally, we extend the assumption for the genetic evolutionary process and allow for duplication and loss. Under this assumption, we analyze gene family trees of proteobacterial strains using a parsimony-based approach to detect evolutionary events. Also we discuss the current issues of parsimony-based approaches in the biological data analysis and propose a way to retrieve significant estimates. The evolutionary history of species is complex with various evolutionary events. As HGT contributes largely to this complexity, accurately identifying HGT will help untangle evolutionary histories and solve important questions. As our algorithms identify significant HGT events in the data and reconstruct accurate phylogenetic networks from them, they can be used to address questions arising in large-scale biological data analyses