Introducing TreeCollapse: a novel greedy algorithm to solve the cophylogeny reconstruction problem

Abstract

BackgroundCophylogeny mapping is used to uncover deep coevolutionary associations between two or more phylogenetic histories at a macro coevolutionary scale. As cophylogeny mapping is NP-Hard, this technique relies heavily on heuristics to solve all but the most trivial cases. One notable approach utilises a metaheuristic to search only a subset of the exponential number of fixed node orderings possible for the phylogenetic histories in question. This is of particular interest as it is the only known heuristic that guarantees biologically feasible solutions. This has enabled research to focus on larger coevolutionary systems, such as coevolutionary associations between figs and their pollinator wasps, including over 200 taxa. Although able to converge on solutions for problem instances of this size, a reduction from the current cubic running time is required to handle larger systems, such as Wolbachia and their insect hosts.ResultsRather than solving this underlying problem optimally this work presents a greedy algorithm called TreeCollapse, which uses common topological patterns to recover an approximation of the coevolutionary history where the internal node ordering is fixed. This approach offers a significant speed-up compared to previous methods, running in linear time. This algorithm has been applied to over 100 well-known coevolutionary systems converging on Pareto optimal solutions in over 68% of test cases, even where in some cases the Pareto optimal solution has not previously been recoverable. Further, while TreeCollapse applies a local search technique, it can guarantee solutions are biologically feasible, making this the fastest method that can provide such a guarantee.ConclusionAs a result, we argue that the newly proposed algorithm is a valuable addition to the field of coevolutionary research. Not only does it offer a significantly faster method to estimate the cost of cophylogeny mappings but by using this approach, in conjunction with existing heuristics, it can assist in recovering a larger subset of the Pareto front than has previously been possible.

Life history and eco‐evolutionary dynamics in light of the gut microbiota

“…This could lead to interesting observations regarding the hypotheses that recent changes in diet could be associated with microbes that are still harmful to the host and/or that such recent changes in diet actually occur because of a shift in microbiota -through host manipulation or changes in ontogeny for instance. Adapting models of trait evolution on phylogenies (Garland et al 1993, Paradis and Claude 2002, Ives and Godfray 2006, Ives and Garland 2010 to models of gut microbiota associations on host phylogenies, or extending the framework of co-phylogenetic studies (Banks and Paterson 2005, Charleston and Perkins 2006, Conow et al 2010, Drinkwater and Charleston 2014 to include whole gut microbiota would help understand the link between host-microbe evolutionary contact duration and the effect of the microbe on the host.…”

A time and space complexity reduction for coevolutionary analysis of trees generated under both a Yule and Uniform model

“…Of the 1000 data sets, 47 contain trees with only a single node. These data sets were excluded, consistent with previous analysis (Drinkwater and Charleston, 2014b).…”

“…This analysis focuses on demonstrating the running time reduction offered by the newly proposed data structure, as this is the limiting factor in the analysis of larger coevolutionary systems (Drinkwater and Charleston, 2014b). The simulation was therefore constructed where both algorithms were implemented using a common code base for cophylogenetic reconstruction, where the data structure used to store mapping sites was the only code not shared by each approach.…”

“…The second data set contains 102 previously published coevolutionary instances (Drinkwater and Charleston, 2014b). This set includes evolutionary data from a number of fields including but not limited to, pathogens and their hosts (Charleston and Robertson, 2002), plant-insect relationships (McLeish et al, 2007), the evolutionary dependencies between plants and fungi (Refrégier et al, 2008), parasitic (Page et al, 2004) and mutualistic (Jackson, 2004) coevolution, and biogeography (Badets et al, 2011).…”

Tanglegrams: A Reduction Tool for Mathematical Phylogenetics

“…Tanglegrams have been an object of study since before DNA sequences were widely available for the reconstruction of phylogenetic trees [38]. So far they have been studied before in the context of co-evolutionary analyses, classically that between a host and a parasite, a subject of continuing interest [39,40]. As such, there has been extensive work on the case in which two rooted trees are distinguished between one another, as when one tree represents hosts and one parasites, which we call the ordered rooted case.…”