The effect of non-reversibility on inferring rooted phylogenies

The effect of non-reversibility on inferring rooted phylogenies

S. Cherlin, T. M. W. Nye, R. J. Boys, S. E. Heaps, T. A. Williams, T. M. Embley
(Submitted on 29 May 2015)

Most phylogenetic models assume that the evolutionary process is stationary and reversible. As a result, the root of the tree cannot be inferred as part of the analysis because the likelihood of the data does not depend on the position of the root. Yet defining the root of a phylogenetic tree is a key component of phylogenetic inference because it provides a point of reference for polarising ancestor/descendant relationships and therefore interpreting the tree. In this paper we investigate the effect of relaxing the reversibility assumption and allowing the position of the root to be another unknown quantity in the model. We propose two hierarchical models which are centred on a reversible model but perturbed to allow non-reversibility. The models differ in the degree of structure imposed on the perturbations. The analysis is performed in the Bayesian framework using Markov chain Monte Carlo methods. We illustrate the performance of the two non-reversible models in analyses of simulated datasets using two types of topological priors. We then apply the models to a real biological dataset, the radiation of polyploid yeasts, for which there is a robust biological opinion about the root position. Finally we apply the models to a second biological dataset for which the rooted tree is controversial: the ribosomal tree of life. We compare the two non-reversible models and conclude that both are useful in inferring the position of the root from real biological datasets.


A Bayesian Approach for Detecting Mass-Extinction Events When Rates of Lineage Diversification Vary

A Bayesian Approach for Detecting Mass-Extinction Events When Rates of Lineage Diversification Vary

Michael R. May, Sebastian Höhna, Brian R. Moore

The paleontological record chronicles numerous episodes of mass extinction that severely culled the Tree of Life. Biologists have long sought to assess the extent to which these events may have impacted particular groups. We present a novel method for detecting mass-extinction events from phylogenies estimated from molecular sequence data. We develop our approach in a Bayesian statistical framework, which enables us to harness prior information on the frequency and magnitude of mass-extinction events. The approach is based on an episodic stochastic-branching process model in which rates of speciation and extinction are constant between rate-shift events. We model three types of events: (1) instantaneous tree-wide shifts in speciation rate; (2) instantaneous tree-wide shifts in extinction rate, and; (3) instantaneous tree-wide mass-extinction events. Each of the events is described by a separate compound Poisson process (CPP) model, where the waiting times between each event are exponentially distributed with event-specific rate parameters. The magnitude of each event is drawn from an event-type specific prior distribution. Parameters of the model are then estimated using a reversible-jump Markov chain Monte Carlo (rjMCMC) algorithm. We demonstrate via simulation that this method has substantial power to detect the number of mass-extinction events, provides unbiased estimates of the timing of mass-extinction events, while exhibiting an appropriate (i.e., below 5%) false discovery rate even in the case of background diversification rate variation. Finally, we provide an empirical application of this approach to conifers, which reveals that this group has experienced two major episodes of mass extinction. This new approach—the CPP on Mass Extinction Times (CoMET) model—provides an effective tool for identifying mass-extinction events from molecular phylogenies, even when the history of those groups includes more prosaic temporal variation in diversification rate.

On the equivalence of Maximum Parsimony and Maximum Likelihood on phylogenetic networks

On the equivalence of Maximum Parsimony and Maximum Likelihood on phylogenetic networks

Mareike Fischer, Parisa Bazargani
(Submitted on 26 May 2015)

Phylogenetic inference aims at reconstructing the evolutionary relationships of different species given some data (e.g. DNA, RNA or proteins). Traditionally, the relationships between species were assumed to be treelike, so the most frequently used phylogenetic inference methods like e.g. Maximum Parsimony or Maximum Likelihood were originally introduced to reconstruct phylogenetic trees. However, it has been well-known that some evolutionary events like hybridization or horizontal gene transfer cannot be represented by a tree but rather require a phylogenetic network. Therefore, current research seeks to adapt tree inference methods to networks. In the present paper, we analyze Maximum Parsimony and Maximum Likelihood on networks for various network definitions which have recently been introduced, and we investigate the well-known Tuffley and Steel equivalence result concerning these methods under the setting of a phylogenetic network.

RAD sequencing enables unprecedented phylogenetic resolution and objective species delimitation in recalcitrant divergent taxa

RAD sequencing enables unprecedented phylogenetic resolution and objective species delimitation in recalcitrant divergent taxa

Santiago Herrera, Timothy M. Shank

Species delimitation is problematic in many taxa due to the difficulty of evaluating predictions from species delimitation hypotheses, which chiefly relay on subjective interpretations of morphological observations and/or DNA sequence data. This problem is exacerbated in recalcitrant taxa for which genetic resources are scarce and inadequate to resolve questions regarding evolutionary relationships and uniqueness. In this case study we demonstrate the empirical utility of restriction site associated DNA sequencing (RAD-seq) by unambiguously resolving phylogenetic relationships among recalcitrant octocoral taxa with divergences greater than 80 million years. We objectively infer robust species boundaries in the genus Paragorgia, which contains some of the most important ecosystem engineers in the deep-sea, by testing alternative taxonomy-guided or unguided species delimitation hypotheses using the Bayes factors delimitation method (BFD*) with genome-wide single nucleotide polymorphism data. We present conclusive evidence rejecting the current morphological species delimitation model for the genus Paragorgia and indicating the presence of cryptic species boundaries associated with environmental variables. We argue that the suitability limits of RAD-seq for phylogenetic inferences in divergent taxa cannot be assessed in terms of absolute time, but depend on taxon-specific factors such as mutation rate, generation time and effective population size. We show that classic morphological taxonomy can greatly benefit from integrative approaches that provide objective tests to species delimitation hypothesis. Our results pave the way for addressing further questions in biogeography, species ranges, community ecology, population dynamics, conservation, and evolution in octocorals and other marine taxa.

Character trees from transcriptome data: origin and individuation of morphological characters and the so-called “species signal”

Character trees from transcriptome data: origin and individuation of morphological characters and the so-called “species signal”

Jacob Musser, Gunter Wagner

We elaborate a framework for investigating the evolutionary history of morphological characters. We argue that morphological character trees generated from transcriptomes provide a useful tool for identifying causal gene expression differences underlying the development and evolution of morphological characters. They also enable rigorous testing of different models of morphological character evolution and origination, including the hypothesis that characters originate via divergence of repeated ancestral characters. Finally, morphological character trees provide evidence that character transcriptomes undergo concerted evolution. We argue that concerted evolution of transcriptomes can explain the so-called “species-specific clustering” found in several recent comparative transcriptome studies. The species signal is the phenomenon that transcriptomes cluster by species rather than character type, even though the characters are older than the respective species. We suggest that concerted gene expression evolution results from mutations that alter gene regulatory network interactions shared by the characters under comparison. Thus, character trees generated from transcriptomes allow us to investigate the variational independence, or individuation, of morphological characters at the level of genetic programs.

ReproPhylo: An Environment for Reproducible Phylogenomics

ReproPhylo: An Environment for Reproducible Phylogenomics

Amir Szitenberg, Max John, Mark L Blaxter, David H Lunt

The reproducibility of experiments is key to the scientific process, and particularly necessary for accurate reporting of analyses in data-rich fields such as phylogenomics. We present ReproPhylo, a phylogenomic analysis environment developed to ensure experimental reproducibility, to facilitate the handling of large-scale data, and to assist methodological experimentation. Reproducibility, and instantaneous repeatability, is built in to the ReproPhylo system, and does not require user intervention or configuration because it stores the experimental workflow as a single, serialized Python object containing explicit provenance and environment information. This ‘single file’ approach ensures the persistence of provenance across iterations of the analysis, with changes automatically managed by the version control program Git. ReproPhylo produces an extensive human-readable report, and generates a comprehensive experimental archive file, both of which are suitable for submission with publications. The system facilitates thorough experimental exploration of both parameters and data. ReproPhylo is a platform independent CC0 python module, and is easily installed as a Docker image, with an Jupyter GUI, or as a slimmer version in a Galaxy distribution.

Capturing heterotachy through multi-gamma site models

Capturing heterotachy through multi-gamma site models

Remco Bouckaert , Peter Lockhart

Most methods for performing a phylogenetic analysis based on sequence alignments of gene data assume that the mechanism of evolution is constant through time. It is recognised that some sites do evolve somewhat faster than others, and this can be captured using a (gamma) rate heterogeneity model. Further, some species have shorter replication times than others, and this results in faster rates of substitution in some lineages. This feature of lineage specific rate variation can be captured to some extent, by using relaxed clock models. However, it is also clear that there are additional poorly characterised features of sequence data that can sometimes lead to extreme differences in lineage specific rates. This variation is poorly captured by constant time reversible substitution models. The significance of extreme lineage specific rate differences is that they lead both to errors in reconstructing evolutionary relationships as well as biased estimates for the age of ancestral nodes. We propose a new model that allows gamma rate heterogeneity to change on branches, thus offering a more realistic model of sequence evolution. It adds negligible computational cost to likelihood calculations. We illustrate its effectiveness with an example of green algae and land-plants. For many real world data sets, we find a much better fit with multi-gamma sites models as well as substantial differences in ancestral node date estimates.