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

Citation

Drinkwater, B and Charleston, MA, Introducing TreeCollapse: a novel greedy algorithm to solve the cophylogeny reconstruction problem, BMC Bioinformatics, 15, (Suppl 16: S14) pp. 1-15. ISSN 1471-2105 (2014) [Refereed Article]


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Copyright Statement

Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) http://creativecommons.org/licenses/by/4.0/

Official URL: http://www.biomedcentral.com/1471-2105/15/S16/S14

DOI: doi:10.1186/1471-2105-15-S16-S14

Abstract

BACKGROUND:

Cophylogeny 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.

RESULTS:

Rather 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.

CONCLUSION:

As 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.

Item Details

Item Type:Refereed Article
Keywords:cophylogeny, bioinformatics, heuristic
Research Division:Information and Computing Sciences
Research Group:Computation Theory and Mathematics
Research Field:Analysis of Algorithms and Complexity
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Biological Sciences
UTAS Author:Charleston, MA (Associate Professor Michael Charleston)
ID Code:100229
Year Published:2014
Web of Science® Times Cited:7
Deposited By:Mathematics and Physics
Deposited On:2015-05-07
Last Modified:2018-03-18
Downloads:365 View Download Statistics

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