Can comprehensive background knowledge be incorporated into substitution models to improve phylogenetic analyses? A case study on major arthropod relationships.

BACKGROUND: Whenever different data sets arrive at conflicting phylogenetic hypotheses, only testable causal explanations of sources of errors in at least one of the data sets allow us to critically choose among the conflicting hypotheses of relationships. The large (28S) and small (18S) subunit rRNAs are among the most popular markers for studies of deep phylogenies. However, some nodes supported by this data are suspected of being artifacts caused by peculiarities of the evolution of these molecules. Arthropod phylogeny is an especially controversial subject dotted with conflicting hypotheses which are dependent on data set and method of reconstruction. We assume that phylogenetic analyses based on these genes can be improved further i) by enlarging the taxon sample and ii) employing more realistic models of sequence evolution incorporating non-stationary substitution processes and iii) considering covariation and pairing of sites in rRNA-genes. RESULTS: We analyzed a large set of arthropod sequences, applied new tools for quality control of data prior to tree reconstruction, and increased the biological realism of substitution models. Although the split-decomposition network indicated a high noise content in the data set, our measures were able to both improve the analyses and give causal explanations for some incongruities mentioned from analyses of rRNA sequences. However, misleading effects did not completely disappear. CONCLUSION: Analyses of data sets that result in ambiguous phylogenetic hypotheses demand for methods, which do not only filter stochastic noise, but likewise allow to differentiate phylogenetic signal from systematic biases. Such methods can only rely on our findings regarding the evolution of the analyzed data. Analyses on independent data sets then are crucial to test the plausibility of the results. Our approach can easily be extended to genomic data, as well, whereby layers of quality assessment are set up applicable to phylogenetic reconstructions in general.

A reversible jump method for Bayesian phylogenetic inference with a nonhomogeneous substitution model.

Nonhomogeneous substitution models have been introduced for phylogenetic inference when the substitution process is nonstationary, for example, when sequence composition differs between lineages. Existing models can have many parameters, and it is then difficult and computationally expensive to learn the parameters and to select the optimal model complexity. We extend an existing nonhomogeneous substitution model by introducing a reversible jump Markov chain Monte Carlo method for efficient Bayesian inference of the model order along with other phylogenetic parameters of interest. We also introduce a new hierarchical prior which leads to more reasonable results when only a small number of lineages share a particular substitution process. The method is implemented in the PHASE software, which includes specialized substitution models for RNA genes with conserved secondary structure. We apply an RNA-specific nonhomogeneous model to a structure-based alignment of rRNA sequences spanning the entire tree of life. A previous study of the same genes from a similar set of species found robust evidence for a mesophilic last universal common ancestor (LUCA) by inference of the G+C composition at the root of the tree. In the present study, we find that the helical GC composition at the root is strongly dependent on the root position. With a bacterial rooting, we find that there is no longer strong support for either a mesophile or a thermophile LUCA, although a hyperthermophile LUCA remains unlikely. We discuss reasons why results using only RNA helices may differ from results using all aligned sites when applying nonhomogeneous models to RNA genes.

On the correlation between composition and site-specific evolutionary rate: implications for phylogenetic inference.

Model-based phylogenetic reconstruction methods traditionally assume homogeneity of nucleotide frequencies among sequence sites and lineages. Yet, heterogeneity in base composition is a characteristic shared by most biological sequences. Compositional variation in time, reflected in the compositional biases among contemporary sequences, has already been extensively studied, and its detrimental effects on phylogenetic estimates are known. However, fewer studies have focused on the effects of spatial compositional heterogeneity within genes. We show here that different sites in an alignment do not always share a unique compositional pattern, and we provide examples where nucleotide frequency trends are correlated with the site-specific rate of evolution in RNA genes. Spatial compositional heterogeneity is shown to affect the estimation of evolutionary parameters. With standard phylogenetic methods, estimates of equilibrium frequencies are found to be biased towards the composition observed at fast-evolving sites. Conversely, the ancestral composition estimates of some time-heterogeneous but spatially homogeneous methods are found to be biased towards frequencies observed at invariant and slow-evolving sites. The latter finding challenges the result of a previous study arguing against a hyperthermophilic last universal ancestor from the low apparent G + C content of its rRNA sequences. We propose a new model to account for compositional variation across sites. A Gaussian process prior is used to allow for a smooth change in composition with evolutionary rate. The model has been implemented in the phylogenetic inference software PHASE, and Bayesian methods can be used to obtain the model parameters. The results suggest that this model can accurately capture the observed trends in present-day RNA sequences.

Consideration of RNA secondary structure significantly improves likelihood-based estimates of phylogeny: examples from the bilateria.

Sequences from ribosomal RNA (rRNA) genes have made a huge contribution to our current understanding of metazoan phylogeny and indeed the phylogeny of all of life. That said, some parts of this rRNA-based phylogeny remain unresolved. One approach to increase the resolution of these trees would be to use more appropriate models of sequence evolution in phylogenetic analysis. RNAs transcribed from rRNA genes have a complex secondary structure mediated by base pairing between sometimes distant regions of the rRNA molecule. The pairing between the stem nucleotides has important consequences for their evolution which differs from that of unpaired loop nucleotides. These differences in evolution should ideally be accounted for when using rRNA sequences for phylogeny estimation. We use a novel permutation approach to demonstrate the significant superiority of models of sequence evolution that allow stem and loop regions to evolve according to separate models and, in common with previous studies, we show that 16-state models that take base pairing of stems into account are significantly better than simpler, 4-state, single-nucleotide models. One of these 16-state models has been applied to the phylogeny of the Bilateria using small subunit rRNA (SSU) sequences. Our optimal tree largely echoes previous results based on SSU in particular supporting the tripartite Bilaterian tree of deuterostomes, lophotrochozoans, and ecdysozoans. There are also a number of differences, however, perhaps most important of which is the observation of a clade consisting of the gastrotrichs plus platyheminthes that is basal to all other lophotrochozoan taxa. Use of 16-state models also appears to reduce the Bayesian support given to certain biologically improbable groups found using standard 4-state models.

A comprehensive analysis of mammalian mitochondrial genome base composition and improved phylogenetic methods.

Phylogenetic analysis of mammalian species using mitochondrial protein genes has proved to be problematic in many previous studies. The high mutation rate of mitochondrial DNA and unusual base composition of several species has prompted us to conduct a detailed study of the composition of 69 mammalian mitochondrial genomes. Most major changes in base composition between lineages can be attributed to shifts between the proportions of C and T on the L-strand. These changes are significant at all codon positions and are shown to affect amino acid composition. Correlated changes in the base composition of the RNA loops and stems are also observed. Following up from previous studies, we investigate changes in the base composition of all 12 H-strand proteins and find that variability in proportions of C and T is correlated with location on the genome. Variation in base composition across genes and species is known to adversely affect the performance of phylogenetic inference methods. We have, therefore, developed a customized three-state general time-reversible DNA substitution model, implemented in the PHASE phylogenetic inference package, which lumps C and T into a composite pyrimidine state. We compare the phylogenetic tree obtained using the new three-state model with that obtained using a standard four-state model. Results using the three-state model are more congruent with recent studies using large sets of nuclear genes and help resolve some of the apparent conflicts between studies using nuclear and mitochondrial proteins.

RNA-based phylogenetic methods: application to mammalian mitochondrial RNA sequences.

The PHASE software package allows phylogenetic tree construction with a number of evolutionary models designed specifically for use with RNA sequences that have conserved secondary structure. Evolution in the paired regions of RNAs occurs via compensatory substitutions, hence changes on either side of a pair are correlated. Accounting for this correlation is important for phylogenetic inference because it affects the likelihood calculation. In the present study we use the complete set of tRNA and rRNA sequences from 69 complete mammalian mitochondrial genomes. The likelihood calculation uses two evolutionary models simultaneously for different parts of the sequence: a paired-site model for the paired sites and a single-site model for the unpaired sites. We use Bayesian phylogenetic methods and a Markov chain Monte Carlo algorithm is used to obtain the most probable trees and posterior probabilities of clades. The results are well resolved for almost all the important branches on the mammalian tree. They support the arrangement of mammalian orders within the four supra-ordinal clades that have been identified by studies of much larger data sets mainly comprising nuclear genes. Groups such as the hedgehogs and the murid rodents, which have been problematic in previous studies with mitochondrial proteins, appear in their expected position with the other members of their order. Our choice of genes and evolutionary model appears to be more reliable and less subject to biases caused by variation in base composition than previous studies with mitochondrial genomes.