Variability Of Substitution Rates Research Articles

Equiprobable discrete models of site-specific substitution rates underestimate the extent of rate variability.

It is standard practice to model site-to-site variability of substitution rates by discretizing a continuous distribution into a small number, K, of equiprobable rate categories. We demonstrate that the variance of this discretized distribution has an upper bound determined solely by the choice of K and the mean of the distribution. This bound can introduce biases into statistical inference, especially when estimating parameters governing site-to-site variability of substitution rates. Applications to two large collections of sequence alignments demonstrate that this upper bound is often reached in analyses of real data. When parameter estimation is of primary interest, additional rate categories or more flexible modeling methods should be considered.

Non-Markovian effects on protein sequence evolution due to site dependent substitution rates.

BackgroundMany models of protein sequence evolution, in particular those based on Point Accepted Mutation (PAM) matrices, assume that its dynamics is Markovian. Nevertheless, it has been observed that evolution seems to proceed differently at different time scales, questioning this assumption. In 2011 Kosiol and Goldman proved that, if evolution is Markovian at the codon level, it can not be Markovian at the amino acid level. However, it remains unclear up to which point the Markov assumption is verified at the codon level.ResultsHere we show how also the among-site variability of substitution rates makes the process of full protein sequence evolution effectively not Markovian even at the codon level. This may be the theoretical explanation behind the well known systematic underestimation of evolutionary distances observed when omitting rate variability. If the substitution rate variability is neglected the average amino acid and codon replacement probabilities are affected by systematic errors and those with the largest mismatches are the substitutions involving more than one nucleotide at a time. On the other hand, the instantaneous substitution matrices estimated from alignments with the Markov assumption tend to overestimate double and triple substitutions, even when learned from alignments at high sequence identity.ConclusionsThese results discourage the use of simple Markov models to describe full protein sequence evolution and encourage to employ, whenever possible, models that account for rate variability by construction (such as hidden Markov models or mixture models) or substitution models of the type of Le and Gascuel (2008) that account for it explicitly.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1135-1) contains supplementary material, which is available to authorized users.

Variability in the substitution rates between mitochondrial cytochrome c oxidase subunit II domains

We investigated the variability in amino acid sequences between mitochondrial cytochrome c oxidase subunit II (COII) domains, as well as that of gene sequences encoding the corresponding domains. According to the secondary structure, COII consisted of five domains of N- and C-terminal regions posited in the intermembrane space, two transmembrane helices (TM1 and TM2) in the lipid bilayer, and a matrix-embedded loop (ML) that intervened between the two helices. Our analysis, using dictyopteran insects as model species, revealed that amino acid and nucleotide substitution rates were heterogeneous between the COII domains. The amino acid substitution rates were higher in the TM1 (0.380±0.123) and ML domains (0.416±0.184), whereas they were relatively lower in the N-terminal (0.204±0.123) and TM2 domains (0.184±0.088). As expected by the variability in the amino acid substitution rates, the average nucleotide substitution rates were also relatively higher in the TM1 (0.312±0.081) and ML domains (0.302±0.093), whereas the lowest substitution rate was observed in the N domain (0.191±0.073). These results indicate that the heterogeneous substitution rates between COII domains, as well as genes encoding the domains, might be closely related to the inner membrane environment where each region of the amino acid sequence is laid.

Evolutionary Models Accounting for Layers of Selection in Protein-Coding Genes and their Impact on the Inference of Positive Selection

The selective forces acting on a protein-coding gene are commonly inferred using evolutionary codon models by contrasting the rate of nonsynonymous substitutions to the rate of synonymous substitutions. These models usually assume that the synonymous substitution rate, Ks, is homogenous across all sites, which is justified if synonymous sites are free from selection. However, a growing body of evidence indicates that the DNA and RNA levels of protein-coding genes are subject to varying degrees of selective constraints due to various biological functions encoded at these levels. In this paper, we develop evolutionary models that account for these layers of selection by allowing for both among-site variability of substitution rates at the DNA/RNA level (which leads to Ks variability among protein-coding sites) and among-site variability of substitution rates at the protein level (Ka variability). These models are constructed so that positive selection is either allowed or not. This enables statistical testing of positive selection when variability at the DNA/RNA substitution rate is accounted for. Using this methodology, we show that variability of the baseline DNA/RNA substitution rate is a widespread phenomenon in coding sequence data of mammalian genomes, most likely reflecting varying degrees of selection at the DNA and RNA levels. Additionally, we use simulations to examine the impact that accounting for the variability of the baseline DNA/RNA substitution rate has on the inference of positive selection. Our results show that ignoring this variability results in a high rate of erroneous positive-selection inference. Our newly developed model, which accounts for this variability, does not suffer from this problem and hence provides a likelihood framework for the inference of positive selection on a background of variability in the baseline DNA/RNA substitution rate.

Composite Likelihood Modeling of Neighboring Site Correlations of DNA Sequence Substitution Rates

Sequence data from a series of homologous DNA segments from related organisms are typically polymorphic at many sites, and these polymorphisms are the result of evolutionary processes. Such data may be used to estimate the substitution rates as well as the variability of these rates. Careful characterization of the distribution of this variation is essential for accurate estimation of evolutionary distances and phylogeny reconstruction among these sequences. Many researchers have recognized the importance of the variability of substitution rates, which most have modeled using a discrete gamma distribution. Some have extended these methods to explicitly account for the correlation of substitution rates among sites using hidden Markov models; others have proposed context-dependent substitution rate schemes. We accommodate these correlations using a composite likelihood method based on a bivariate gamma distribution, which is more flexible than hidden Markov models in terms of correlation structure and more computationally tractable compared to the context-dependent schemes. We show that the estimates have good theoretical properties. We also use simulations to compare the maximum composite likelihood estimates to those obtained from maximum likelihood based on the independence assumption. We use data from the mitochondrial DNA of ten primates to obtain maximum composite likelihood estimates of the mean substitution rate, overdispersion, and correlation parameters, and use these estimates in a parametric phylogenetic bootstrap to assess the impact of serial correlation on the estimates of substitution rates and branch lengths.

Parameters of proteome evolution from histograms of amino-acid sequence identities of paralogous proteins

BackgroundThe evolution of the full repertoire of proteins encoded in a given genome is mostly driven by gene duplications, deletions, and sequence modifications of existing proteins. Indirect information about relative rates and other intrinsic parameters of these three basic processes is contained in the proteome-wide distribution of sequence identities of pairs of paralogous proteins.ResultsWe introduce a simple mathematical framework based on a stochastic birth-and-death model that allows one to extract some of this information and apply it to the set of all pairs of paralogous proteins in H. pylori, E. coli, S. cerevisiae, C. elegans, D. melanogaster, and H. sapiens. It was found that the histogram of sequence identities p generated by an all-to-all alignment of all protein sequences encoded in a genome is well fitted with a power-law form ~ p-γ with the value of the exponent γ around 4 for the majority of organisms used in this study. This implies that the intra-protein variability of substitution rates is best described by the Gamma-distribution with the exponent α ≈ 0.33. Different features of the shape of such histograms allow us to quantify the ratio between the genome-wide average deletion/duplication rates and the amino-acid substitution rate.ConclusionWe separately measure the short-term ("raw") duplication and deletion rates , which include gene copies that will be removed soon after the duplication event and their dramatically reduced long-term counterparts rdup, rdel. High deletion rate among recently duplicated proteins is consistent with a scenario in which they didn't have enough time to significantly change their functional roles and thus are to a large degree disposable. Systematic trends of each of the four duplication/deletion rates with the total number of genes in the genome were analyzed. All but the deletion rate of recent duplicates were shown to systematically increase with Ngenes. Abnormally flat shapes of sequence identity histograms observed for yeast and human are consistent with lineages leading to these organisms undergoing one or more whole-genome duplications. This interpretation is corroborated by our analysis of the genome of Paramecium tetraurelia where the p-4 profile of the histogram is gradually restored by the successive removal of paralogs generated in its four known whole-genome duplication events.

A simple hierarchical approach to modeling distributions of substitution rates.

Genetic sequence data typically exhibit variability in substitution rates across sites. In practice, there is often too little variation to fit a different rate for each site in the alignment, but the distribution of rates across sites may not be well modeled using simple parametric families. Mixtures of different distributions can capture more complex patterns of rate variation, but are often parameter-rich and difficult to fit. We present a simple hierarchical model in which a baseline rate distribution, such as a gamma distribution, is discretized into several categories, the quantiles of which are estimated using a discretized beta distribution. Although this approach involves adding only two extra parameters to a standard distribution, a wide range of rate distributions can be captured. Using simulated data, we demonstrate that a "beta-" model can reproduce the moments of the rate distribution more accurately than the distribution used to simulate the data, even when the baseline rate distribution is misspecified. Using hepatitis C virus and mammalian mitochondrial sequences, we show that a beta- model can fit as well or better than a model with multiple discrete rate categories, and compares favorably with a model which fits a separate rate category to each site. We also demonstrate this discretization scheme in the context of codon models specifically aimed at identifying individual sites undergoing adaptive or purifying evolution.

Nonrandom spatial distribution of synonymous substitutions in the GP63 gene from Leishmania.

In this work we analyze the variability in substitution rates in the GP63 gene from Leishmania. By using a sliding window to estimate substitution rates along the gene, we found that the rate of synonymous substitutions along the GP63 gene is highly correlated with both the rate of amino acid substitution and codon bias. Furthermore, we show that comparisons involving genes that represent independent phylogenetic lines yield very similar divergence/conservation patterns, thus suggesting that deterministic forces (i.e., nonstochastic forces such as selection) generated these patterns. We present evidence indicating that the variability in substitution rates is unambiguously related to functionally relevant features. In particular, there is a clear relationship between rates and the tertiary structure of the encoded protein since all divergent segments are located on the surface of the molecule and facing one side (almost parallel to the cell membrane) on the exposed surface of the organism. Remarkably, the protein segments encoded by these variable regions encircle the active site in a funnel-like distribution. These results strongly suggest that the pattern of nucleotide divergence and, notably, of synonymous divergence is affected by functional constraints.

Evolutionary relationships of eukaryotic kingdoms.

The evolutionary relationships of four eukaryotic kingdoms--Animalia, Plantae, Fungi, and Protista--remain unclear. In particular, statistical support for the closeness of animals to fungi rather than to plants is lacking, and a preferred branching order of these and other eukaryotic lineages is still controversial even though molecular sequences from diverse eukaryotic taxa have been analyzed. We report a statistical analysis of 214 sequences of nuclear small-subunit ribosomal RNA (srRNA) gene undertaken to clarify these evolutionary relationships. We have considered the variability of substitution rates and the nonindependence of nucleotide substitution across sites in the srRNA gene in testing alternative hypotheses regarding the branching patterns of eukaryote phylogeny. We find that the rates of evolution among sites in the srRNA sequences vary substantially and are approximately gamma distributed with size and shape parameter equal to 0.76. Our results suggest that (1) the animals and true fungi are indeed closer to each other than to any other "crown" group in the eukaryote tree, (2) red algae are the closest relatives of animals, true fungi, and green plants, and (3) the heterokonts and alveolates probably evolved prior to the divergence of red algae and animal-fungus-green-plant lineages. Furthermore, our analyses indicate that the branching order of the eukaryotic lineages that diverged prior to the evolution of alveolates may be generally difficult to resolve with the srRNA sequence data.