Huai-Chun Wang

Rapid divergence of codon usage patterns within the rice genome

BackgroundSynonymous codon usage varies widely between genomes, and also between genes within genomes. Although there is now a large body of data on variations in codon usage, it is still not clear if the observed patterns reflect the effects of positive Darwinian selection acting at the level of translational efficiency or whether these patterns are due simply to the effects of mutational bias. In this study, we have included both intra-genomic and inter-genomic comparisons of codon usage. This allows us to distinguish more efficiently between the effects of nucleotide bias and translational selection.ResultsWe show that there is an extreme degree of heterogeneity in codon usage patterns within the rice genome, and that this heterogeneity is highly correlated with differences in nucleotide content (particularly GC content) between the genes. In contrast to the situation observed within the rice genome, Arabidopsis genes show relatively little variation in both codon usage and nucleotide content. By exploiting a combination of intra-genomic and inter-genomic comparisons, we provide evidence that the differences in codon usage among the rice genes reflect a relatively rapid evolutionary increase in the GC content of some rice genes. We also noted that the degree of codon bias was negatively correlated with gene length.ConclusionOur results show that mutational bias can cause a dramatic evolutionary divergence in codon usage patterns within a period of approximately two hundred million years.The heterogeneity of codon usage patterns within the rice genome can be explained by a balance between genome-wide mutational biases and negative selection against these biased mutations. The strength of the negative selection is proportional to the length of the coding sequences. Our results indicate that the large variations in synonymous codon usage are not related to selection acting on the translational efficiency of synonymous codons.

A class frequency mixture model that adjusts for site-specific amino acid frequencies and improves inference of protein phylogeny

BackgroundWidely used substitution models for proteins, such as the Jones-Taylor-Thornton (JTT) or Whelan and Goldman (WAG) models, are based on empirical amino acid interchange matrices estimated from databases of protein alignments that incorporate the average amino acid frequencies of the data set under examination (e.g JTT + F). Variation in the evolutionary process between sites is typically modelled by a rates-across-sites distribution such as the gamma (Γ) distribution. However, sites in proteins also vary in the kinds of amino acid interchanges that are favoured, a feature that is ignored by standard empirical substitution matrices. Here we examine the degree to which the pattern of evolution at sites differs from that expected based on empirical amino acid substitution models and evaluate the impact of these deviations on phylogenetic estimation.ResultsWe analyzed 21 large protein alignments with two statistical tests designed to detect deviation of site-specific amino acid distributions from data simulated under the standard empirical substitution model: JTT+ F + Γ. We found that the number of states at a given site is, on average, smaller and the frequencies of these states are less uniform than expected based on a JTT + F + Γ substitution model. With a four-taxon example, we show that phylogenetic estimation under the JTT + F + Γ model is seriously biased by a long-branch attraction artefact if the data are simulated under a model utilizing the observed site-specific amino acid frequencies from an alignment. Principal components analyses indicate the existence of at least four major site-specific frequency classes in these 21 protein alignments. Using a mixture model with these four separate classes of site-specific state frequencies plus a fifth class of global frequencies (the JTT + cF + Γ model), significant improvements in model fit for real data sets can be achieved. This simple mixture model also reduces the long-branch attraction problem, as shown by simulations and analyses of a real phylogenomic data set.ConclusionProtein families display site-specific evolutionary dynamics that are ignored by standard protein phylogenetic models. Accurate estimation of protein phylogenies requires models that accommodate the heterogeneity in the evolutionary process across sites. To this end, we have implemented a class frequency mixture model (cF) in a freely available program called QmmRAxML for phylogenetic estimation.

Thermal adaptation of the small subunit ribosomal RNA gene : A comparative study

We carried out a comprehensive survey of small subunit ribosomal RNA sequences from archaeal, bacterial, and eukaryotic lineages in order to understand the general patterns of thermal adaptation in the rRNA genes. Within each lineage, we compared sequences from mesophilic, moderately thermophilic, and hyperthermophilic species. We carried out a more detailed study of the archaea, because of the wide range of growth temperatures within this group. Our results confirmed that there is a clear correlation between the GC content of the paired stem regions of the 16S rRNA genes and the optimal growth temperature, and we show that this correlation cannot be explained simply by phylogenetic relatedness among the thermophilic archaeal species. In addition, we found a significant, positive relationship between rRNA stem length and growth temperature. These correlations are found in both bacterial and archaeal rRNA genes. Finally, we compared rRNA sequences from warm-blooded and cold-blooded vertebrates. We found that, while rRNA sequences from the warm-blooded vertebrates have a higher overall GC content than those from the cold-blooded vertebrates, this difference is not concentrated in the paired regions of the molecule, suggesting that thermal adaptation is not the cause of the nucleotide differences between the vertebrate lineages.

An Amino Acid Substitution-Selection Model Adjusts Residue Fitness to Improve Phylogenetic Estimation

Standard protein phylogenetic models use fixed rate matrices of amino acid interchange derived from analyses of large databases. Differences between the stationary amino acid frequencies of these rate matrices from those of a data set of interest are typically adjusted for by matrix multiplication that converts the empirical rate matrix to an exchangeability matrix which is then postmultiplied by the amino acid frequencies in the alignment. The result is a time-reversible rate matrix with stationary amino acid frequencies equal to the data set frequencies. On the basis of population genetics principles, we develop an amino acid substitution-selection model that parameterizes the fitness of an amino acid as the logarithm of the ratio of the frequency of the amino acid to the frequency of the same amino acid under no selection. The model gives rise to a different sequence of matrix multiplications to convert an empirical rate matrix to one that has stationary amino acid frequencies equal to the data set frequencies. We incorporated the substitution-selection model with an improved amino acid class frequency mixture (cF) model to partially take into account site-specific amino acid frequencies in the phylogenetic models. We show that 1) the selection models fit data significantly better than corresponding models without selection for most of the 21 test data sets; 2) both cF and cF selection models favored the phylogenetic trees that were inferred under current sophisticated models and methods for three difficult phylogenetic problems (the positions of microsporidia and breviates in eukaryote phylogeny and the position of the root of the angiosperm tree); and 3) for data simulated under site-specific residue frequencies, the cF selection models estimated trees closer to the generating trees than a standard Г model or cF without selection. We also explored several ways of estimating amino acid frequencies under neutral evolution that are required for these selection models. By better modeling the amino acid substitution process, the cF selection models will be valuable for phylogenetic inference and evolutionary studies.

Modeling Site Heterogeneity with Posterior Mean Site Frequency Profiles Accelerates Accurate Phylogenomic Estimation

&NA; Proteins have distinct structural and functional constraints at different sites that lead to site‐specific preferences for particular amino acid residues as the sequences evolve. Heterogeneity in the amino acid substitution process between sites is not modeled by commonly used empirical amino acid exchange matrices. Such model misspecification can lead to artefacts in phylogenetic estimation such as long‐branch attraction. Although sophisticated site‐heterogeneous mixture models have been developed to address this problem in both Bayesian and maximum likelihood (ML) frameworks, their formidable computational time and memory usage severely limits their use in large phylogenomic analyses. Here we propose a posterior mean site frequency (PMSF) method as a rapid and efficient approximation to full empirical profile mixture models for ML analysis. The PMSF approach assigns a conditional mean amino acid frequency profile to each site calculated based on a mixture model fitted to the data using a preliminary guide tree. These PMSF profiles can then be used for in‐depth tree‐searching in place of the full mixture model. Compared with widely used empirical mixture models with k classes, our implementation of PMSF in IQ‐TREE (http://www.iqtree.org) speeds up the computation by approximately k/1.5‐fold and requires a small fraction of the RAM. Furthermore, this speedup allows, for the first time, full nonparametric bootstrap analyses to be conducted under complex site‐heterogeneous models on large concatenated data matrices. Our simulations and empirical data analyses demonstrate that PMSF can effectively ameliorate long‐branch attraction artefacts. In some empirical and simulation settings PMSF provided more accurate estimates of phylogenies than the mixture models from which they derive.

Topological Estimation Biases with Covarion Evolution

Covarion processes allow changes in evolutionary rates at sites along the branches of a phylogenetic tree. Covarion-like evolution is increasingly recognized as an important mode of protein evolution. Several recent reports suggest that maximum likelihood estimation employing covarion models may support different optimal topologies than estimation using standard rates-across-sites (RAS) models. However, it remains to be demonstrated that ignoring covarion evolution will generally result in topological misestimation. In this study we performed analytical and theoretical studies of limiting distances under the covarion model and four-taxon tree simulations to investigate the extent to which the covarion process impacts on phylogenetic estimation. In particular, we assessed the limits of an RAS model-based maximum likelihood method to recover the phylogenies when the sequence data were simulated under the covarion processes. We find that, when ignored, covarion processes can induce systematic errors in phylogeny reconstruction. Surprisingly, when sequences are evolved under a covarion process but an RAS model is used for estimation, we find that a long branch repel bias occurs.

The Site-Wise Log-Likelihood Score is a Good Predictor of Genes under Positive Selection

The strength and direction of selection on the identity of an amino acid residue in a protein is typically measured by the ratio of the rate of non-synonymous substitutions to the rate of synonymous substitutions. In attempting to predict positively selected sites from amino acid alignments, we made the unexpected observation that the site likelihood of an alignment column for a given tree tends to be negatively correlated with the posterior probability that site is in the positive selection class under widely-used codon models. This is likely because positively selected sites tend to be more variable and display more “radical” amino acid changes; both of these features are expected to result in low site log-likelihoods. We explored the efficacy of using the site log-likelihood (SLL) score as a predictor for positive selection. Through simulation we show that a SLL-based test has a low false positive rate and comparable power as the codon models. In one case where the simulated data violated the assumption that synonymous substitution rates were constant across the sites, the codon models were not able to detect positive selection in the data while the SLL test did. We applied the new method to ten empirical datasets and found that it made similar predictions as the codon models in eight of them. For the tax gene dataset the SLL test seemed to produce more reasonable results. The SLL methods are a valuable complement to codon models, especially for some cases where the assumptions of codon models are likely violated.

Split-specific bootstrap measures for quantifying phylogenetic stability and the influence of taxon selection

Assessing the robustness of an inferred phylogeny is an important element of phylogenetics. This is typically done with measures of stabilities at the internal branches and the variation of the positions of the leaf nodes. The bootstrap support for branches in maximum parsimony, distance and maximum likelihood estimation, or posterior probabilities in Bayesian inference, measure the uncertainty about a branch due to the sampling of the sites from genes or sampling genes from genomes. However, these measures do not reveal how taxon sampling affects branch support and the effects of taxon sampling on the estimated phylogeny. An internal branch in a phylogenetic tree can be viewed as a split that separates the taxa into two nonempty complementary subsets. We develop several split-specific measures of stability determined from bootstrap support for quartets. These include BPtaxon_split (average bootstrap percentage [BP] for all quartets involving a taxon within a split), BPsplit (BPtaxon_split averaged over taxa), BPtaxon (BPtaxon_split averaged over splits) and RBIC-taxon (average BP over all splits after removing a taxon). We also develop a pruned-tree distance metric. Application of our measures to empirical and simulated data illustrate that existing measures of overall stability can fail to detect taxa that are the primary source of a split-specific instability. Moreover, we show that the use of many reduced sets of quartets is important in being able to detect the influence of joint sets of taxa rather than individual taxa. These new measures are valuable diagnostic tools to guide taxon sampling in phylogenetic experimental design.