Pareto-optimal phylogenetic tree reconciliation

Abstract

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.Availability and implementation: Our Python tools are freely available at www.cs.hmc.edu/∼hadas/xscape.Contact: mukul@engr.uconn.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Genomes in turmoil: Quantification of genome dynamics in prokaryote supergenomes

“…In principle, more precise reconstructions taking into account this additional information are possible through the use of tree reconciliation approaches that compare gene trees to species trees [76][77][78]. Most of the available tree reconciliation algorithms are computationally prohibitive but recently, efficient, fast methods have been reported [79,80].…”

Integrating Pareto Optimization into Dynamic Programming

“…By the same arguments as before, applying a function of the evaluation algebra to an intermediate list that constitutes a Pareto front, the result will again be a Pareto front. Since the choice function is already applied at individual steps in Equations (15), (16) and (18), # in Equation (14) can become the identity. A particular Pareto front operator becomes superfluous when all other operations are Pareto-aware (The Pareto-eager implementation presents many difficulties in the full generality of Bellman's GAP, because GAP-L supports the use of Pareto products in combination with other products.…”

“…The outcome is of interest for programmers who consider Pareto optimization, but not necessarily in a dynamic programming context. In an algebraic dynamic programming application, the case of simultaneously merging two or more sorted sublists can occur in Equations (15) and (16). Intermediate results are generated from a search space with particular properties and will be far-from-random datasets.…”

“…Examples include the shortest path problem [12], the allocation problem [13], as well as some biological problems [14][15][16]. Although independent objectives arise frequently in bioinformatics, say balancing sequence conservation versus structural similarity when aligning RNA molecules, researchers tend to shy away from using Pareto optimization and rather amalgamate the 1.…”

On the Complexity of Duplication-Transfer-Loss Reconciliation with Non-binary Gene Trees

“…Specifically, given a gene tree and a species tree, DTL reconciliation shows the evolution of the gene tree inside the species tree, and explicitly infers duplication, transfer, and loss events. Accurate knowledge of gene family evolution has many uses in biology, including inference of orthologs, paralogs and xenologs for functional genomic studies, e.g., [1,2], reconstruction of ancestral gene content, e.g., [3,4], and accurate gene tree and species tree construction, e.g., [2,[5][6][7], and the DTL reconciliation problem has therefore been widely studied, e.g., [4,[8][9][10][11][12][13][14][15].…”