Principal component analysis and the locus of the Fréchet mean in the space of phylogenetic trees.

Evolutionary relationships are represented by phylogenetic trees, and a phylogenetic analysis of gene sequences typically produces a collection of these trees, one for each gene in the analysis. Analysis of samples of trees is difficult due to the multi-dimensionality of the space of possible trees. In Euclidean spaces, principal component analysis is a popular method of reducing high-dimensional data to a low-dimensional representation that preserves much of the sample's structure.

The Effect of Nonreversibility on Inferring Rooted Phylogenies.

Most phylogenetic models assume that the evolutionary process is stationary and reversible. In addition to being biologically improbable, these assumptions also impair inference by generating models under which the likelihood does not depend on the position of the root. Consequently, the root of the tree cannot be inferred as part of the analysis.

Probabilistic Distances Between Trees.

Most existing measures of distance between phylogenetic trees are based on the geometry or topology of the trees. Instead, we consider distance measures which are based on the underlying probability distributions on genetic sequence data induced by trees. Monte Carlo schemes are necessary to calculate these distances approximately, and we describe efficient sampling procedures.

New substitution models for rooting phylogenetic trees.

The root of a phylogenetic tree is fundamental to its biological interpretation, but standard substitution models do not provide any information on its position. Here, we describe two recently developed models that relax the usual assumptions of stationarity and reversibility, thereby facilitating root inference without the need for an outgroup. We compare the performance of these models on a classic test case for phylogenetic methods, before considering two highly topical questions in evolutionary biology: the deep structure of the tree of life and the root of the archaeal radiation.

Bayesian modelling of compositional heterogeneity in molecular phylogenetics.

In molecular phylogenetics, standard models of sequence evolution generally assume that sequence composition remains constant over evolutionary time. However, this assumption is violated in many datasets which show substantial heterogeneity in sequence composition across taxa. We propose a model which allows compositional heterogeneity across branches, and formulate the model in a Bayesian framework.

An Algorithm for Constructing Principal Geodesics in Phylogenetic Treespace.

Most phylogenetic analyses result in a sample of trees, but summarizing and visualizing these samples can be challenging. Consensus trees often provide limited information about a sample, and so methods such as consensus networks, clustering and multidimensional scaling have been developed and applied to tree samples. This paper describes a stochastic algorithm for constructing a principal geodesic or line through treespace which is analogous to the first principal component in standard principal components analysis.

A congruent phylogenomic signal places eukaryotes within the Archaea.

Determining the relationships among the major groups of cellular life is important for understanding the evolution of biological diversity, but is difficult given the enormous time spans involved. In the textbook 'three domains' tree based on informational genes, eukaryotes and Archaea share a common ancestor to the exclusion of Bacteria. However, some phylogenetic analyses of the same data have placed eukaryotes within the Archaea, as the nearest relatives of different archaeal lineages.

Modelling the evolution of multi-gene families.

A number of biological processes can lead to genes being copied within the genome of some given species. Duplicate genes of this form are called paralogs and such genes share a high degree sequence similarity as well as often having closely related functions. Some genes have become widely duplicated to form multigene families in which the copies are distributed both within the genomes of individual species and across different species.

Trees of trees: an approach to comparing multiple alternative phylogenies.

Phylogenetic analysis very commonly produces several alternative trees for a given fixed set of taxa. For example, different sets of orthologous genes may be analyzed, or the analysis may sample from a distribution of probable trees. This article describes an approach to comparing and visualizing multiple alternative phylogenies via the idea of a "tree of trees" or "meta-tree.

Predicting the strongest domain-domain contact in interacting protein pairs.

Experiments to determine the complete 3-dimensional structures of protein complexes are difficult to perform and only a limited range of such structures are available. In contrast, large-scale screening experiments have identified thousands of pairwise interactions between proteins, but such experiments do not produce explicit structural information. In addition, the data produced by these high through-put experiments contain large numbers of false positive results, and can be biased against detection of certain types of interaction.

A novel algorithm and web-based tool for comparing two alternative phylogenetic trees.

Summary: We describe an algorithm and software tool for comparing alternative phylogenetic trees. The main application of the software is to compare phylogenies obtained using different phylogenetic methods for some fixed set of species or obtained using different gene sequences from those species. The algorithm pairs up each branch in one phylogeny with a matching branch in the second phylogeny and finds the optimum 1-to-1 map between branches in the two trees in terms of a topological score.

Statistical analysis of domains in interacting protein pairs.

Motivation: Several methods have recently been developed to analyse large-scale sets of physical interactions between proteins in terms of physical contacts between the constituent domains, often with a view to predicting new pairwise interactions. Our aim is to combine genomic interaction data, in which domain-domain contacts are not explicitly reported, with the domain-level structure of individual proteins, in order to learn about the structure of interacting protein pairs. Our approach is driven by the need to assess the evidence for physical contacts between domains in a statistically rigorous way.