Using Avida to test the effects of natural selection on phylogenetic reconstruction methods

Phylogenetic trees group organisms by their ancestral relationships. There are a number of distinct algorithms used to reconstruct these trees from molecular sequence data, but different methods sometimes give conflicting results. Since there are few precisely known phylogenies, simulations are typically used to test the quality of reconstruction algorithms. These simulations randomly evolve strings of symbols to produce a tree, and then the algorithms are run with the tree leaves as inputs. Here we use Avida to test two widely used reconstruction methods, which gives us the chance to observe the effect of natural selection on tree reconstruction. We find that if the organisms undergo natural selection between branch points, the methods will be successful even on very large time scales. However, these algorithms often falter when selection is absent

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

Evolutionary distances in the twilight zone -- a rational kernel approach

Phylogenetic tree reconstruction is traditionally based on multiple sequence alignments (MSAs) and heavily depends on the validity of this information bottleneck. With increasing sequence divergence, the quality of MSAs decays quickly. Alignment-free methods, on the other hand, are based on abstract string comparisons and avoid potential alignment problems. However, in general they are not biologically motivated and ignore our knowledge about the evolution of sequences. Thus, it is still a major open question how to define an evolutionary distance metric between divergent sequences that makes use of indel information and known substitution models without the need for a multiple alignment. Here we propose a new evolutionary distance metric to close this gap. It uses finite-state transducers to create a biologically motivated similarity score which models substitutions and indels, and does not depend on a multiple sequence alignment. The sequence similarity score is defined in analogy to pairwise alignments and additionally has the positive semi-definite property. We describe its derivation and show in simulation studies and real-world examples that it is more accurate in reconstructing phylogenies than competing methods. The result is a new and accurate way of determining evolutionary distances in and beyond the twilight zone of sequence alignments that is suitable for large datasets.Comment: to appear in PLoS ON

Bacterial microevolution and the Pangenome

The comparison of multiple genome sequences sampled from a bacterial population reveals considerable diversity in both the core and the accessory parts of the pangenome. This diversity can be analysed in terms of microevolutionary events that took place since the genomes shared a common ancestor, especially deletion, duplication, and recombination. We review the basic modelling ingredients used implicitly or explicitly when performing such a pangenome analysis. In particular, we describe a basic neutral phylogenetic framework of bacterial pangenome microevolution, which is not incompatible with evaluating the role of natural selection. We survey the different ways in which pangenome data is summarised in order to be included in microevolutionary models, as well as the main methodological approaches that have been proposed to reconstruct pangenome microevolutionary history

The effect of natural selection on the performance of maximum parsimony

<p>Abstract</p> <p>Background</p> <p>Maximum parsimony is one of the most commonly used and extensively studied phylogeny reconstruction methods. While current evaluation methodologies such as computer simulations provide insight into how well maximum parsimony reconstructs phylogenies, they tell us little about how well maximum parsimony performs on taxa drawn from populations of organisms that evolved subject to <it>natural selection </it>in addition to the random factors of drift and mutation. It is clear that natural selection has a significant impact on <it>Among Site Rate Variation </it>(ASRV) and the rate of accepted substitutions; that is, accepted mutations do not occur with uniform probability along the genome and some substitutions are more likely to occur than other substitutions. However, little is know about how ASRV and non-uniform character substitutions impact the performance of reconstruction methods such as maximum parsimony. To gain insight into these issues, we study how well maximum parsimony performs with data generated by Avida, a digital life platform where populations of digital organisms evolve subject to natural selective pressures.</p> <p>Results</p> <p>We first identify conditions where natural selection does affect maximum parsimony's reconstruction accuracy. In general, as we increase the probability that a significant adaptation will occur in an intermediate ancestor, the performance of maximum parsimony improves. In fact, maximum parsimony can correctly reconstruct small 4 taxa trees on data that have received surprisingly many mutations if the intermediate ancestor has received a significant adaptation. We demonstrate that this improved performance of maximum parsimony is attributable more to ASRV than to non-uniform character substitutions.</p> <p>Conclusion</p> <p>Maximum parsimony, as well as most other phylogeny reconstruction methods, may perform significantly better on actual biological data than is currently suggested by computer simulation studies because of natural selection. This is largely due to specific sites becoming fixed in the genome that perform functions associated with an improved fitness.</p