StepBrothers: inferring partially shared ancestries among recombinant viral sequences

Computing the joint distribution of tree shape and tree distance for gene tree inference and recombination detection

Ancestral recombination events can cause the underlying genealogy of a site to vary along the genome. We consider Bayesian models to simultaneously detect recombination breakpoints in very long sequence alignments and estimate the phylogenetic tree of each block between breakpoints. The models we consider use a dissimilarity measure between trees in their prior distribution to favor similar trees at neighboring loci. We show empirical evidence in Enterobacteria that neighboring genomic regions have similar trees. The main hurdle in using such models is the need to properly calculate the normalizing function for the prior probabilities on trees. In this work, we quantify the impact of approximating this normalizing function as done in biomc2, a hierarchical Bayesian method to detect recombination based on distance between tree topologies. We then derive an algorithm to calculate the normalizing function exactly, for a Gibbs distribution based on the Robinson-Foulds (RF) distance between gene trees at neighboring loci. At the core is the calculation of the joint distribution of the shape of a random tree and its RF distance to a fixed tree. We also propose fast approximations to the normalizing function, which are shown to be very accurate with little impact on the Bayesian inference.

Analysing recombination in nucleotide sequences

Throughout the living world, genetic recombination and nucleotide substitution are the primary processes that create the genetic variation upon which natural selection acts. Just as analyses of substitution patterns can reveal a great deal about evolution, so too can analyses of recombination. Evidence of genetic recombination within the genomes of apparently asexual species can equate with evidence of cryptic sexuality. In sexually reproducing species, nonrandom patterns of sequence exchange can provide direct evidence of population subdivisions that prevent certain individuals from mating. Although an interesting topic in its own right, an important reason for analysing recombination is to account for its potentially disruptive influences on various phylogenetic-based molecular evolution analyses. Specifically, the evolutionary histories of recombinant sequences cannot be accurately described by standard bifurcating phylogenetic trees. Taking recombination into account can therefore be pivotal to the success of selection, molecular clock and various other analyses that require adequate modelling of shared ancestry and draw increased power from accurately inferred phylogenetic trees. Here, we review various computational approaches to studying recombination and provide guidelines both on how to gain insights into this important evolutionary process and on how it can be properly accounted for during molecular evolution studies.

Detecting phylogenetic breakpoints and discordance from genome-wide alignments for species tree reconstruction

With the easy acquisition of sequence data, it is now possible to obtain and align whole genomes across multiple related species or populations. In this work, I assess the performance of a statistical method to reconstruct the whole distribution of phylogenetic trees along the genome, estimate the proportion of the genome for which a given clade is true, and infer a concordance tree that summarizes the dominant vertical inheritance pattern. There are two main issues when dealing with whole-genome alignments, as opposed to multiple genes: the size of the data and the detection of recombination breakpoints. These breakpoints partition the genomic alignment into phylogenetically homogeneous loci, where sites within a given locus all share the same phylogenetic tree topology. To delimitate these loci, I describe here a method based on the minimum description length (MDL) principle, implemented with dynamic programming for computational efficiency. Simulations show that combining MDL partitioning with Bayesian concordance analysis provides an efficient and robust way to estimate both the vertical inheritance signal and the horizontal phylogenetic signal. The method performed well both in the presence of incomplete lineage sorting and in the presence of horizontal gene transfer. A high level of systematic bias was found here, highlighting the need for good individual tree building methods, which form the basis for more elaborate gene tree/species tree reconciliation methods.

Unifying vertical and nonvertical evolution: a stochastic ARG-based framework

Evolutionary biologists have introduced numerous statistical approaches to explore nonvertical evolution, such as horizontal gene transfer, recombination, and genomic reassortment, through collections of Markov-dependent gene trees. These tree collections allow for inference of nonvertical evolution, but only indirectly, making findings difficult to interpret and models difficult to generalize. An alternative approach to explore nonvertical evolution relies on phylogenetic networks. These networks provide a framework to model nonvertical evolution but leave unanswered questions such as the statistical significance of specific nonvertical events. In this paper, we begin to correct the shortcomings of both approaches by introducing the "stochastic model for reassortment and transfer events" (SMARTIE) drawing upon ancestral recombination graphs (ARGs). ARGs are directed graphs that allow for formal probabilistic inference on vertical speciation events and nonvertical evolutionary events. We apply SMARTIE to phylogenetic data. Because of this, we can typically infer a single most probable ARG, avoiding coarse population dynamic summary statistics. In addition, a focus on phylogenetic data suggests novel probability distributions on ARGs. To make inference with our model, we develop a reversible jump Markov chain Monte Carlo sampler to approximate the posterior distribution of SMARTIE. Using the BEAST phylogenetic software as a foundation, the sampler employs a parallel computing approach that allows for inference on large-scale data sets. To demonstrate SMARTIE, we explore 2 separate phylogenetic applications, one involving pathogenic Leptospirochete and the other Saccharomyces.