9,959 research outputs found
The inference of gene trees with species trees
Molecular phylogeny has focused mainly on improving models for the
reconstruction of gene trees based on sequence alignments. Yet, most
phylogeneticists seek to reveal the history of species. Although the histories
of genes and species are tightly linked, they are seldom identical, because
genes duplicate, are lost or horizontally transferred, and because alleles can
co-exist in populations for periods that may span several speciation events.
Building models describing the relationship between gene and species trees can
thus improve the reconstruction of gene trees when a species tree is known, and
vice-versa. Several approaches have been proposed to solve the problem in one
direction or the other, but in general neither gene trees nor species trees are
known. Only a few studies have attempted to jointly infer gene trees and
species trees. In this article we review the various models that have been used
to describe the relationship between gene trees and species trees. These models
account for gene duplication and loss, transfer or incomplete lineage sorting.
Some of them consider several types of events together, but none exists
currently that considers the full repertoire of processes that generate gene
trees along the species tree. Simulations as well as empirical studies on
genomic data show that combining gene tree-species tree models with models of
sequence evolution improves gene tree reconstruction. In turn, these better
gene trees provide a better basis for studying genome evolution or
reconstructing ancestral chromosomes and ancestral gene sequences. We predict
that gene tree-species tree methods that can deal with genomic data sets will
be instrumental to advancing our understanding of genomic evolution.Comment: Review article in relation to the "Mathematical and Computational
Evolutionary Biology" conference, Montpellier, 201
RevBayes: Bayesian Phylogenetic Inference Using Graphical Models and an Interactive Model-Specification Language.
Programs for Bayesian inference of phylogeny currently implement a unique and fixed suite of models. Consequently, users of these software packages are simultaneously forced to use a number of programs for a given study, while also lacking the freedom to explore models that have not been implemented by the developers of those programs. We developed a new open-source software package, RevBayes, to address these problems. RevBayes is entirely based on probabilistic graphical models, a powerful generic framework for specifying and analyzing statistical models. Phylogenetic-graphical models can be specified interactively in RevBayes, piece by piece, using a new succinct and intuitive language called Rev. Rev is similar to the R language and the BUGS model-specification language, and should be easy to learn for most users. The strength of RevBayes is the simplicity with which one can design, specify, and implement new and complex models. Fortunately, this tremendous flexibility does not come at the cost of slower computation; as we demonstrate, RevBayes outperforms competing software for several standard analyses. Compared with other programs, RevBayes has fewer black-box elements. Users need to explicitly specify each part of the model and analysis. Although this explicitness may initially be unfamiliar, we are convinced that this transparency will improve understanding of phylogenetic models in our field. Moreover, it will motivate the search for improvements to existing methods by brazenly exposing the model choices that we make to critical scrutiny. RevBayes is freely available at http://www.RevBayes.com [Bayesian inference; Graphical models; MCMC; statistical phylogenetics.]
Uncertainty in phylogenetic tree estimates
Estimating phylogenetic trees is an important problem in evolutionary
biology, environmental policy and medicine. Although trees are estimated, their
uncertainties are discarded by mathematicians working in tree space. Here we
explicitly model the multivariate uncertainty of tree estimates. We consider
both the cases where uncertainty information arises extrinsically (through
covariate information) and intrinsically (through the tree estimates
themselves). The importance of accounting for tree uncertainty in tree space is
demonstrated in two case studies. In the first instance, differences between
gene trees are small relative to their uncertainties, while in the second, the
differences are relatively large. Our main goal is visualization of tree
uncertainty, and we demonstrate advantages of our method with respect to
reproducibility, speed and preservation of topological differences compared to
visualization based on multidimensional scaling. The proposal highlights that
phylogenetic trees are estimated in an extremely high-dimensional space,
resulting in uncertainty information that cannot be discarded. Most
importantly, it is a method that allows biologists to diagnose whether
differences between gene trees are biologically meaningful, or due to
uncertainty in estimation.Comment: Final version accepted to Journal of Computational and Graphical
Statistic
Genome-scale phylogenetic analysis finds extensive gene transfer among Fungi
Although the role of lateral gene transfer is well recognized in the
evolution of bacteria, it is generally assumed that it has had less influence
among eukaryotes. To explore this hypothesis we compare the dynamics of genome
evolution in two groups of organisms: Cyanobacteria and Fungi. Ancestral
genomes are inferred in both clades using two types of methods. First, Count, a
gene tree unaware method that models gene duplications, gains and losses to
explain the observed numbers of genes present in a genome. Second, ALE, a more
recent gene tree-aware method that reconciles gene trees with a species tree
using a model of gene duplication, loss, and transfer. We compare their merits
and their ability to quantify the role of transfers, and assess the impact of
taxonomic sampling on their inferences. We present what we believe is
compelling evidence that gene transfer plays a significant role in the
evolution of Fungi
Efficient Exploration of the Space of Reconciled Gene Trees
Gene trees record the combination of gene level events, such as duplication,
transfer and loss, and species level events, such as speciation and extinction.
Gene tree-species tree reconciliation methods model these processes by drawing
gene trees into the species tree using a series of gene and species level
events. The reconstruction of gene trees based on sequence alone almost always
involves choosing between statistically equivalent or weakly distinguishable
relationships that could be much better resolved based on a putative species
tree. To exploit this potential for accurate reconstruction of gene trees the
space of reconciled gene trees must be explored according to a joint model of
sequence evolution and gene tree-species tree reconciliation.
Here we present amalgamated likelihood estimation (ALE), a probabilistic
approach to exhaustively explore all reconciled gene trees that can be
amalgamated as a combination of clades observed in a sample of trees. We
implement ALE in the context of a reconciliation model, which allows for the
duplication, transfer and loss of genes. We use ALE to efficiently approximate
the sum of the joint likelihood over amalgamations and to find the reconciled
gene tree that maximizes the joint likelihood.
We demonstrate using simulations that gene trees reconstructed using the
joint likelihood are substantially more accurate than those reconstructed using
sequence alone. Using realistic topologies, branch lengths and alignment sizes,
we demonstrate that ALE produces more accurate gene trees even if the model of
sequence evolution is greatly simplified. Finally, examining 1099 gene families
from 36 cyanobacterial genomes we find that joint likelihood-based inference
results in a striking reduction in apparent phylogenetic discord, with 24%, 59%
and 46% percent reductions in the mean numbers of duplications, transfers and
losses.Comment: Manuscript accepted pending revision in Systematic Biolog
Probabilistic Graphical Model Representation in Phylogenetics
Recent years have seen a rapid expansion of the model space explored in
statistical phylogenetics, emphasizing the need for new approaches to
statistical model representation and software development. Clear communication
and representation of the chosen model is crucial for: (1) reproducibility of
an analysis, (2) model development and (3) software design. Moreover, a
unified, clear and understandable framework for model representation lowers the
barrier for beginners and non-specialists to grasp complex phylogenetic models,
including their assumptions and parameter/variable dependencies.
Graphical modeling is a unifying framework that has gained in popularity in
the statistical literature in recent years. The core idea is to break complex
models into conditionally independent distributions. The strength lies in the
comprehensibility, flexibility, and adaptability of this formalism, and the
large body of computational work based on it. Graphical models are well-suited
to teach statistical models, to facilitate communication among phylogeneticists
and in the development of generic software for simulation and statistical
inference.
Here, we provide an introduction to graphical models for phylogeneticists and
extend the standard graphical model representation to the realm of
phylogenetics. We introduce a new graphical model component, tree plates, to
capture the changing structure of the subgraph corresponding to a phylogenetic
tree. We describe a range of phylogenetic models using the graphical model
framework and introduce modules to simplify the representation of standard
components in large and complex models. Phylogenetic model graphs can be
readily used in simulation, maximum likelihood inference, and Bayesian
inference using, for example, Metropolis-Hastings or Gibbs sampling of the
posterior distribution
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