Monte Carlo Strategies for Selecting Parameter Values in Simulation Experiments.

Simulation experiments are used widely throughout evolutionary biology and bioinformatics to compare models, promote methods, and test hypotheses. The biggest practical constraint on simulation experiments is the computational demand, particularly as the number of parameters increases. Given the extraordinary success of Monte Carlo methods for conducting inference in phylogenetics, and indeed throughout the sciences, we investigate ways in which Monte Carlo framework can be used to carry out simulation experiments more efficiently. The key idea is to sample parameter values for the experiments, rather than iterate through them exhaustively. Exhaustive analyses become completely infeasible when the number of parameters gets too large, whereas sampled approaches can fare better in higher dimensions. We illustrate the framework with applications to phylogenetics and genetic archaeology.

Evaluating phylogenetic congruence in the post-genomic era.

Congruence is a broadly applied notion in evolutionary biology used to justify multigene phylogeny or phylogenomics, as well as in studies of coevolution, lateral gene transfer, and as evidence for common descent. Existing methods for identifying incongruence or heterogeneity using character data were designed for data sets that are both small and expected to be rarely incongruent. At the same time, methods that assess incongruence using comparison of trees test a null hypothesis of uncorrelated tree structures, which may be inappropriate for phylogenomic studies. As such, they are ill-suited for the growing number of available genome sequences, most of which are from prokaryotes and viruses, either for phylogenomic analysis or for studies of the evolutionary forces and events that have shaped these genomes. Specifically, many existing methods scale poorly with large numbers of genes, cannot accommodate high levels of incongruence, and do not adequately model patterns of missing taxa for different markers. We propose the development of novel incongruence assessment methods suitable for the analysis of the molecular evolution of the vast majority of life and support the investigation of homogeneity of evolutionary process in cases where markers do not share identical tree structures.

Let them fall where they may: congruence analysis in massive phylogenetically messy data sets.

Interest in congruence in phylogenetic data has largely focused on issues affecting multicellular organisms, and animals in particular, in which the level of incongruence is expected to be relatively low. In addition, assessment methods developed in the past have been designed for reasonably small numbers of loci and scale poorly for larger data sets. However, there are currently over a thousand complete genome sequences available and of interest to evolutionary biologists, and these sequences are predominantly from microbial organisms, whose molecular evolution is much less frequently tree-like than that of multicellular life forms. As such, the level of incongruence in these data is expected to be high. We present a congruence method that accommodates both very large numbers of genes and high degrees of incongruence. Our method uses clustering algorithms to identify subsets of genes based on similarity of phylogenetic signal. It involves only a single phylogenetic analysis per gene, and therefore, computation time scales nearly linearly with the number of genes in the data set. We show that our method performs very well with sets of sequence alignments simulated under a wide variety of conditions. In addition, we present an analysis of core genes of prokaryotes, often assumed to have been largely vertically inherited, in which we identify two highly incongruent classes of genes. This result is consistent with the complexity hypothesis.

A multi-locus time-calibrated phylogeny of the brown algae (Heterokonta, Ochrophyta, Phaeophyceae): Investigating the evolutionary nature of the "brown algal crown radiation".

The most conspicuous feature in previous phaeophycean phylogenies is a large polytomy known as the brown algal crown radiation (BACR). The BACR encompasses 10 out of the 17 currently recognized brown algal orders. A recent study has been able to resolve a few nodes of the BACR, suggesting that it may be a soft polytomy caused by a lack of signal in molecular markers. The present work aims to refine relationships within the BACR and investigate the nature and timeframe of the diversification in question using a dual approach. A multi-marker phylogeny of the brown algae was built from 10 mitochondrial, plastid and nuclear loci (>10,000 nt) of 72 phaeophycean taxa, resulting in trees with well-resolved inter-ordinal relationships within the BACR. Using Bayesian relaxed molecular clock analysis, it is shown that the BACR is likely to represent a gradual diversification spanning most of the Lower Cretaceous rather than a sudden radiation. Non-molecular characters classically used in ordinal delimitation were mapped on the molecular topology to study their evolutionary history.

Network analyses structure genetic diversity in independent genetic worlds.

DNA flows between chromosomes and mobile elements, following rules that are poorly understood. This limited knowledge is partly explained by the limits of current approaches to study the structure and evolution of genetic diversity. Network analyses of 119,381 homologous DNA families, sampled from 111 cellular genomes and from 165,529 phage, plasmid, and environmental virome sequences, offer challenging insights. Our results support a disconnected yet highly structured network of genetic diversity, revealing the existence of multiple "genetic worlds." These divides define multiple isolated groups of DNA vehicles drawing on distinct gene pools. Mathematical studies of the centralities of these worlds' subnetworks demonstrate that plasmids, not viruses, were key vectors of genetic exchange between bacterial chromosomes, both recently and in the past. Furthermore, network methodology introduces new ways of quantifying current sampling of genetic diversity.

Phylogenomic analyses support the monophyly of Excavata and resolve relationships among eukaryotic "supergroups".

Nearly all of eukaryotic diversity has been classified into 6 suprakingdom-level groups (supergroups) based on molecular and morphological/cell-biological evidence; these are Opisthokonta, Amoebozoa, Archaeplastida, Rhizaria, Chromalveolata, and Excavata. However, molecular phylogeny has not provided clear evidence that either Chromalveolata or Excavata is monophyletic, nor has it resolved the relationships among the supergroups. To establish the affinities of Excavata, which contains parasites of global importance and organisms regarded previously as primitive eukaryotes, we conducted a phylogenomic analysis of a dataset of 143 proteins and 48 taxa, including 19 excavates. Previous phylogenomic studies have not included all major subgroups of Excavata, and thus have not definitively addressed their interrelationships. The enigmatic flagellate Andalucia is sister to typical jakobids. Jakobids (including Andalucia), Euglenozoa and Heterolobosea form a major clade that we name Discoba. Analyses of the complete dataset group Discoba with the mitochondrion-lacking excavates or "metamonads" (diplomonads, parabasalids, and Preaxostyla), but not with the final excavate group, Malawimonas. This separation likely results from a long-branch attraction artifact. Gradual removal of rapidly-evolving taxa from the dataset leads to moderate bootstrap support (69%) for the monophyly of all Excavata, and 90% support once all metamonads are removed. Most importantly, Excavata robustly emerges between unikonts (Amoebozoa + Opisthokonta) and "megagrouping" of Archaeplastida, Rhizaria, and chromalveolates. Our analyses indicate that Excavata forms a monophyletic suprakingdom-level group that is one of the 3 primary divisions within eukaryotes, along with unikonts and a megagroup of Archaeplastida, Rhizaria, and the chromalveolate lineages.

Phylogenomic analyses support the monophyly of Taphrinomycotina, including Schizosaccharomyces fission yeasts.

Several morphologically dissimilar ascomycete fungi including Schizosaccharomyces, Taphrina, Saitoella, Pneumocystis, and Neolecta have been grouped into the taxon Taphrinomycotina (Archiascomycota or Archiascomycotina), originally based on rRNA phylogeny. These analyses lack statistically significant support for the monophyly of this grouping, and although confirmed by more recent multigene analyses, this topology is contradicted by mitochondrial phylogenies. To resolve this inconsistency, we have assembled phylogenomic mitochondrial and nuclear data sets from four distantly related taphrinomycotina taxa: Schizosaccharomyces pombe, Pneumocystis carinii, Saitoella complicata, and Taphrina deformans. Our phylogenomic analyses based on nuclear data (113 proteins) conclusively support the monophyly of Taphrinomycotina, diverging as a sister group to Saccharomycotina + Pezizomycotina. However, despite the improved taxon sampling, Taphrinomycotina continue to be paraphyletic with the mitochondrial data set (13 proteins): Schizosaccharomyces species associate with budding yeasts (Saccharomycotina) and the other Taphrinomycotina group as a sister group to Saccharomycotina + Pezizomycotina. Yet, as Schizosaccharomyces and Saccharomycotina species are fast evolving, the mitochondrial phylogeny may be influenced by a long-branch attraction (LBA) artifact. After removal of fast-evolving sequence positions from the mitochondrial data set, we recover the monophyly of Taphrinomycotina. Our combined results suggest that Taphrinomycotina is a legitimate taxon, that this group of species diverges as a sister group to Saccharomycotina + Pezizomycotina, and that phylogenetic positioning of yeasts and fission yeasts with mitochondrial data is plagued by a strong LBA artifact.

Testing congruence in phylogenomic analysis.

Phylogenomic analyses of large sets of genes or proteins have the potential to revolutionize our understanding of the tree of life. However, problems arise because estimated phylogenies from individual loci often differ because of different histories, systematic bias, or stochastic error. We have developed Concaterpillar, a hierarchical clustering method based on likelihood-ratio testing that identifies congruent loci for phylogenomic analysis. Concaterpillar also includes a test for shared relative evolutionary rates between genes indicating whether they should be analyzed separately or by concatenation. In simulation studies, the performance of this method is excellent when a multiple comparison correction is applied. We analyzed a phylogenomic data set of 60 translational protein sequences from the major supergroups of eukaryotes and identified three congruent subsets of proteins. Analysis of the largest set indicates improved congruence relative to the full data set and produced a phylogeny with stronger support for five eukaryote supergroups including the Opisthokonts, the Plantae, the stramenopiles + Apicomplexa (chromalveolates), the Amoebozoa, and the Excavata. In contrast, the phylogeny of the second largest set indicates a close relationship between stramenopiles and red algae, to the exclusion of alveolates, suggesting gene transfer from the red algal secondary symbiont to the ancestral stramenopile host nucleus during the origin of their chloroplast. Investigating phylogenomic data sets for conflicting signals has the potential to both improve phylogenetic accuracy and inform our understanding of genome evolution.