Support Measures to Estimate the Reliability of Evolutionary Events Predicted by Reconciliation Methods

Abstract

The genome content of extant species is derived from that of ancestral genomes, distorted by evolutionary events such as gene duplications, transfers and losses. Reconciliation methods aim at recovering such events and at localizing them in the species history, by comparing gene family trees to species trees. These methods play an important role in studying genome evolution as well as in inferring orthology relationships. A major issue with reconciliation methods is that the reliability of predicted evolutionary events may be questioned for various reasons: Firstly, there may be multiple equally optimal reconciliations for a given species tree–gene tree pair. Secondly, reconciliation methods can be misled by inaccurate gene or species trees. Thirdly, predicted events may fluctuate with method parameters such as the cost or rate of elementary events. For all of these reasons, confidence values for predicted evolutionary events are sorely needed. It was recently suggested that the frequency of each event in the set of all optimal reconciliations could be used as a support measure. We put this proposition to the test here and also consider a variant where the support measure is obtained by additionally accounting for suboptimal reconciliations. Experiments on simulated data show the relevance of event supports computed by both methods, while resorting to suboptimal sampling was shown to be more effective. Unfortunately, we also show that, unlike the majority-rule consensus tree for phylogenies, there is no guarantee that a single reconciliation can contain all events having above 50% support. In this paper, we detail how to rely on the reconciliation graph to efficiently identify the median reconciliation. Such median reconciliation can be found in polynomial time within the potentially exponential set of most parsimonious reconciliations.

Gene Duplicability of Core Genes Is Highly Consistent across All Angiosperms

“…For the remaining 39,934 gene trees, prediction of duplication and speciation events is trivial (see below). Since the reconciliation process is error prone (Hahn, 2007;Nguyen et al, 2013;Wu et al, 2013) and depends on the quality of the gene tree, species tree, and the parameter settings of the reconciliation method, we implemented a pipeline to mitigate these problems as much as possible: (1) Since PhyML does not explore the entire search space of possible tree topologies, we investigated whether alternative tree topologies with improved reconciliation duplication/loss costs, obtained by branch rearrangements of the original gene trees in the reconciliation strep (see below), had an increased likelihood under the multiple sequence alignment than the gene tree produced by PhyML. As such, we obtained a reconciled gene tree that is maximally supported by both the reconciliation criterion (in this instance duplication/loss cost) and the multiple sequence alignment as described by Wu et al (2013).…”

“…(2) To deal with the problem of reconciliation solutions being dependent on the parameter settings, we performed the reconciliation with a range of different parameter settings and also considered multiple possible optimal reconciliations under the same parameter settings, if available. Since duplication/speciation events that were predicted for multiple parameter settings are assumed to be more reliable (Nguyen et al, 2013), we built a majority-rule consensus reconciliation in which we only retained duplication/speciation events supported by at least 50% of the reconciliations (see "Gene Tree-Species Tree Reconciliation Pipeline").…”

“…Since running NOTUNG in the rearrange mode essentially modifies the unrooted tree topology, we used the CONSEL program (Shimodaira, 2002) to select the tree topology that has the highest likelihood for the multiple sequence alignment. The motivation behind this whole procedure is to obtain the tree topology that both minimizes duplication-loss cost and has the highest likelihood for the multiple sequence alignment, as was proposed by Nguyen et al (2013). We also performed tree reconciliation for different values for the duplication and loss cost parameters: {(1,1),(1,2), (2,1),(2,2)}.…”

Reconciliation-based detection of co-evolving gene families

“…Since the frequency of an event over most parsimonious reconciliations has been shown to be a good indicator of its reliability [17], we use $w_1{(b,u)}_{\mathrm{double-struckE}}$ as an estimate of the probability that this event has really occurred in G 1 . This provides us with a set of possible events together with their probabilities according to G 1 .…”

Exploring and Visualizing Spaces of Tree Reconciliations

“…These will be based on three different points of view: Considering reconciliations as functions (function based), representing reconciliations as trees (tree based), and defining operations for converting one reconciliation into another (edit based). The edit based approach has been considered in the literature before (see, for example, (Chan et al 2015;Doyon et al 2009)), and approaches that use events to measure the difference between reconciliations have also been considered (Nguyen et al 2013). These approaches use different models for reconciliations to the ones used in this paper.…”