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• This study proposes a model to consider quality degradation in glass recycling. • The model is based on technical quality substitutability and market applicability. • The model was applied to a case study on glass waste treatment. • Accounting for quality degradation, only 83% of cullet can replaces raw materials. • The decrease of recycling benefits was of 13–23% for the examined impact categories. The sustainability assessment of waste management systems requires tools capable of evaluating material quality degradation during recycling. Existing research has predominantly focused on the development of substitutability models for plastics, leaving a gap in addressing other materials like glass. Glass is commonly regarded as endlessly recyclable, even though its actual recyclability depends on several crucial factors, such as colour and pollutant contamination. Many Life Cycle Assessment (LCA) studies in this field assume a one-to-one substitution coefficient, neglecting material deterioration and inaccurately representing real-world scenarios. This study proposes and assesses a substitutability model for glass, aiming to measure the replacement extent between virgin materials and recycled crushed glass (cullet). The methodology is based on two key factors: technical quality substitutability, considering impurities and colour contaminations in cullet, and market applicability, accounting for market demand. Once formulated, the model was applied to a European case study on glass waste treatment. Two scenarios were considered: one assuming complete substitution between cullet and raw materials, the other incorporating quality degradation. Findings indicate that, accounting for quality degradation, only 83% of cullet effectively replaces raw materials, resulting in a decrease of the benefit associated with recycling of 13–23% for the different examined impact categories, compared to complete replacement assumption. This underscores the importance of considering quality deterioration in glass recycling impact assessments. [ABSTRACT FROM AUTHOR]

Substitution models of evolution are necessary for diverse evolutionary analyses including phylogenetic tree and ancestral sequence reconstructions. At the protein level, empirical substitution models are traditionally used due to their simplicity, but they ignore the variability of substitution patterns among protein sites. Next, in order to improve the realism of the modeling of protein evolution, a series of structurally constrained substitution models were presented, but still they usually ignore constraints on the protein activity. Here, we present a substitution model of protein evolution with selection on both protein structure and enzymatic activity, and that can be applied to phylogenetics. In particular, the model considers the binding affinity of the enzyme–substrate complex as well as structural constraints that include the flexibility of structural flaps, hydrogen bonds, amino acids backbone radius of gyration, and solvent-accessible surface area that are quantified through molecular dynamics simulations. We applied the model to the HIV-1 protease and evaluated it by phylogenetic likelihood in comparison with the best-fitting empirical substitution model and a structurally constrained substitution model that ignores the enzymatic activity. We found that accounting for selection on the protein activity improves the fitting of the modeled functional regions with the real observations, especially in data with high molecular identity, which recommends considering constraints on the protein activity in the development of substitution models of evolution. [ABSTRACT FROM AUTHOR]

Model selection aims to choose the most adequate model for the statistical analysis at hand. The model must be complex enough to capture the complexity of the data but should be simple enough not to overfit. In phylogenetics, the most common model selection scenario concerns selecting an adequate substitution and partition model for sequence evolution to infer a phylogenetic tree. Previously, several studies showed that substitution model under-parameterization can bias phylogenetic studies. Here, we explored the impact of substitution model over-parameterization in a Bayesian statistical framework. We performed simulations under the simplest substitution model, the Jukes–Cantor model, and compare posterior estimates of phylogenetic tree topologies and tree length under the true model to the most complex model, the |$\text{GTR}+\Gamma+\text{I}$| substitution model, including over-splitting the data into additional subsets (i.e. applying partitioned models). We explored 4 choices of prior distributions: the default substitution model priors of MrBayes, BEAST2, and RevBayes and a newly devised prior choice (Tame). Our results show that Bayesian inference of phylogeny is robust to substitution model over-parameterization and over-partitioning but only under our new prior settings. All 3 current default priors introduced biases for the estimated tree length. We conclude that substitution and partition model selection are superfluous steps in Bayesian phylogenetic inference pipelines if well-behaved prior distributions are applied and more effort should focus on more complex and biologically realistic substitution models. [ABSTRACT FROM AUTHOR]

Introduction. This article, based on the material of the Khakass language, describes models of complicated sentences with comparative constructions. Notably, many issues related to the description of complicated sentences in Khakass have not received proper coverage yet, which determines the relevance of this study. The article aims to identify and analyze the structural-semantic types, as well as ways of expressing complicated sentences with comparative constructions in the Khakass language. The research is based on a solid sample of examples from the texts of fiction of various genres, folklore, and journalistic texts, as well as recordings of oral speech. Results. The research shows that there are three types of complicated sentences in Khakass, including a comparative model based on the equality of compared features, a comparative-gradation model, and a substitution model. Each model is described in terms of its basic semantics and ways of formalizing the relationship between dependent and main parts, with specific examples illustrating their use. Each model varies in semantic and structural terms. The comparative model of equal features and the comparative-gradation model have two variants, the substitution model has five. In a comparative model based on equality of features, equal relations between two given events are expressed, while the comparison-gradation model compares the degree of significance of given events, with the action in the main part becoming significant. The substitution model may be of two types: substitutive per se and substitutive-preferential. In substitutive models proper, the actions of the main part do not meet the speaker’s expectation, while in the other model, preference is given to the main event.

The selection of the optimal substitution model of molecular evolution imposes a high computational burden for long sequence alignments in phylogenomics. We discovered that the analysis of multiple tiny subsamples of site patterns from a full sequence alignment recovers the correct optimal substitution model when sites in the subsample are upsampled to match the total number of sites in the full alignment. The computational costs of maximum-likelihood analyses are reduced by orders of magnitude in the subsample–upsample (SU) approach because the upsampled alignment contains only a small fraction of all site patterns. We present an adaptive protocol, ModelTamer, that implements the new SU approach and automatically selects subsamples to estimate optimal models reliably. ModelTamer selects models hundreds to thousands of times faster than the full data analysis while needing megabytes rather than gigabytes of computer memory. [ABSTRACT FROM AUTHOR]

Substitutions between closely related noncoding chloroplast DNA sequences are studied with respect to the composition of the 3 bases on each side of the substitution, that is the hexanucleotide context. There is about 100-fold variation in rate, among the contexts, particularly on substitutions of A and T. Rate heterogeneity of transitions differs from that of transversions, resulting in a more than 200-fold variation in the transitions: transversion bias. The data are consistent with a CpG effect, and it is shown that both the AþT content and the arrangement of purines/pyrimidines along the same DNA strand are correlated with rate variation. Expected equilibrium AþT content ranges from 36.4% to 82.8% across contexts, while G-C skew ranges from -77.4 to 72.2 and A-T skew ranges from -63.9 to 68.2. The predicted equilibria are associated with specific features of the content of the hexanucleotide context, and also show close agreement with the observed context-dependent compositions. Finally, by controlling for the content of nucleotides closer to the substitution site, it is shown that both the third and fourth nucleotide removed on each side of the substitution directly influence substitution dynamics at that site. Overall, the results demonstrate that noncoding sites in different contexts are evolving along very different evolutionary trajectories and that substitution dynamics are far more complex than typically assumed. This has important implications for a number of types of sequence analysis, particularly analyses of natural selection, and the context-dependent substitution matrices developed here can be applied in future analyses. [ABSTRACT FROM AUTHOR]

PurposeWe aim to compare the severity of infections between omicron and delta variants in 609,352 SARS-CoV-2 positive cases using local hospitalization, vaccination, and variants data from the Catalan Health Care System (which covers around 7. 8 million people).MethodsWe performed a substitution model to establish the increase in transmissibility of omicron using variant screening data from primary care practices (PCP) and hospital admissions. In addition, we used this data from PCP to establish the two periods when delta and omicron were, respectively, dominant (above 95% of cases). After that, we performed a population-based cohort analysis to calculate the rates of hospital and intensive care unit (ICU) admissions for both periods and to estimate reduction in severity. Rate ratios (RR) and 95% confidence intervals (95% CI) were calculated and stratified by age and vaccination status. In a second analysis, the differential substitution model in primary care vs. hospitals allowed us to obtain a population-level average change in severity.ResultsWe have included 48,874 cases during the delta period and 560,658 during the omicron period. During the delta period, on average, 3.8% of the detected cases required hospitalization for COVID-19. This percentage dropped to 0.9% with omicron [RR of 0.46 (95% CI: 0.43 to 0.49)]. For ICU admissions, it dropped from 0.8 to 0.1% [RR 0.25 (95% CI: 0.21 to 0.28)]. The proportion of cases hospitalized or admitted to ICU was lower in the vaccinated groups, independently of the variant. Omicron was associated with a reduction in risk of admission to hospital and ICU in all age and vaccination status strata. The differential substitution models showed an average RR between 0.19 and 0.50.ConclusionBoth independent methods consistently show an important decrease in severity for omicron relative to delta. The systematic reduction happens regardless of age. The severity is also reduced for non-vaccinated and vaccinated groups, but it remains always higher in the non-vaccinated population. This suggests an overall reduction in severity, which could be intrinsic to the omicron variant. The fact is that the RR in ICU admission is systematically smaller than in hospitalization points in the same direction.

Recently developed MinHash-based techniques were proven successful in quickly estimating the level of similarity between large nucleotide sequences. This article discusses their usage and limitations in practice to approximating uncorrected distances between genomes, and transforming these pairwise dissimilarities into proper evolutionary distances. It is notably shown that complex distance measures can be easily approximated using simple transformation formulae based on few parameters. MinHash-based techniques can therefore be very useful for implementing fast yet accurate alignment-free phylogenetic reconstruction procedures from large sets of genomes. This last point of view is assessed with a simulation study using a dedicated bioinformatics tool.

Background Bayesian analyses offer many benefits for phylogenetic, and have been popular for analysis of amino acid alignments. It is necessary to specify a substitution and site model for such analyses, and often an ad hoc, or likelihood based method is employed for choosing these models that are typically of no interest to the analysis overall. Methods We present a method called OBAMA that averages over substitution models and site models, thus letting the data inform model choices and taking model uncertainty into account. It uses trans-dimensional Markov Chain Monte Carlo (MCMC) proposals to switch between various empirical substitution models for amino acids such as Dayhoff, WAG, and JTT. Furthermore, it switches base frequencies from these substitution models or use base frequencies estimated based on the alignment. Finally, it switches between using gamma rate heterogeneity or not, and between using a proportion of invariable sites or not. Results We show that the model performs well in a simulation study. By using appropriate priors, we demonstrate both proportion of invariable sites and the shape parameter for gamma rate heterogeneity can be estimated. The OBAMA method allows taking in account model uncertainty, thus reducing bias in phylogenetic estimates. The method is implemented in the OBAMA package in BEAST 2, which is open source licensed under LGPL and allows joint tree inference under a wide range of models.

Maximum likelihood (ML) analysis of nucleotide or amino-acid alignments is widely used to infer evolutionary relationships among species. Computing the likelihood of a phylogenetic tree from such alignments is a complicated task because the evolutionary processes typically vary across sites. A number of studies have shown that partitioning alignments into sub-alignments of sites, where each sub-alignment is analyzed using a different model of evolution (e.g., GTR + I + G), is a sensible strategy. Current partitioning methods group sites into subsets based on the inferred rates of evolution at the sites. However, these do not provide sufficient information to adequately reflect the substitution processes of characters at the sites. Moreover, the site rate-based methods group all invariant sites into one subset, potentially resulting in wrong phylogenetic trees. In this study, we propose a partitioning method, called mPartition, that combines not only the evolutionary rates but also substitution models at sites to partition alignments. Analyses of different partitioning methods on both real and simulated datasets showed that mPartition was better than the other partitioning methods tested. Notably, mPartition overcame the pitfall of grouping all invariant sites into one subset. Using mPartition may lead to increased accuracy of ML-based phylogenetic inference, especially for multiple loci or whole genome datasets. [ABSTRACT FROM AUTHOR]

Purpose: Previous studies in older Australians have reported higher alcohol intake in those with low added sugar intake, yet the relationship between energy in liquid form [alcoholic beverages vs. sugar-sweetened beverages (SSB)] and measures of obesity has not been evaluated. We aimed to assess the association between the energy derived from SSB and alcoholic beverages, and to model the association between the substitution of SSB with alcoholic beverages and waist circumference. Methods: In this cross-sectional analysis, dietary data from the Australian Health Survey 2011–12 were analyzed. Participants with implausible dietary intake were excluded by applying the Goldberg cut-off. Usual SSB intake of adults ≥ 19 years old was estimated using the Multiple Source Method and participants were classified into zero-, low- or high-SSB consumers according to their usual SSB intake. Energy from alcoholic beverages in the three SSB consumption groups was compared using multivariable general linear models. A substitution model was used to assess the association between the replacement of SSB with alcoholic beverages and waist circumference. Results: Zero-SSB consumers made up 33% of the included participants. In all age groups, zero-SSB consumers had significantly higher energy intakes from alcoholic beverages than low- and high-SSB consumers. Low- and high-SSB consumers had similar consumption of alcoholic beverages. Substituting SSB intake with alcoholic beverage intake was not associated with significant differences in waist circumference in most age groups. Conclusions: Australian adults who avoid SSB are common but consume substantially more energy in the form of alcoholic beverages. An increase in alcoholic beverage intake could be an 'unintended consequence' of strictly discouraging SSB consumption. [ABSTRACT FROM AUTHOR]

Background. Bayesian analyses offer many benefits for phylogenetic, and have been popular for analysis of amino acid alignments. It is necessary to specify a substitution and site model for such analyses, and often an ad hoc, or likelihood based method is employed for choosing these models that are typically of no interest to the analysis overall. Methods. We present a method called OBAMA that averages over substitution models and site models, thus letting the data inform model choices and taking model uncertainty into account. It uses trans-dimensional Markov Chain Monte Carlo (MCMC) proposals to switch between various empirical substitution models for amino acids such as Dayhoff, WAG, and JTT. Furthermore, it switches base frequencies from these substitution models or use base frequencies estimated based on the alignment. Finally, it switches between using gamma rate heterogeneity or not, and between using a proportion of invariable sites or not. Results. We show that the model performs well in a simulation study. By using appropriate priors, we demonstrate both proportion of invariable sites and the shape parameter for gamma rate heterogeneity can be estimated. The OBAMA method allows taking in account model uncertainty, thus reducing bias in phylogenetic estimates. The method is implemented in the OBAMA package in BEAST 2, which is open source licensed under LGPL and allows joint tree inference under a wide range of models. [ABSTRACT FROM AUTHOR]

The conventional wisdom in molecular evolution is to apply parameter-rich models of nucleotide and amino acid substitutions for estimating divergence times. However, the actual extent of the difference between time estimates produced by highly complex models compared with those from simple models is yet to be quantified for contemporary data sets that frequently contain sequences from many species and genes. In a reanalysis of many large multispecies alignments from diverse groups of taxa, we found that the use of the simplest models can produce divergence time estimates and credibility intervals similar to those obtained from the complex models applied in the original studies. This result is surprising because the use of simple models underestimates sequence divergence for all the data sets analyzed. We found three fundamental reasons for the observed robustness of time estimates to model complexity in many practical data sets. First, the estimates of branch lengths and node-to-tip distances under the simplest model show an approximately linear relationship with those produced by using the most complex models applied on data sets with many sequences. Second, relaxed clock methods automatically adjust rates on branches that experience considerable underestimation of sequence divergences, resulting in time estimates that are similar to those from complex models. And, third, the inclusion of even a few good calibrations in an analysis can reduce the difference in time estimates from simple and complex models. The robustness of time estimates to model complexity in these empirical data analyses is encouraging, because all phylogenomics studies use statistical models that are oversimplified descriptions of actual evolutionary substitution processes. [ABSTRACT FROM AUTHOR]

Evolution leaves heterogeneous patterns of nucleotide variation across the genome, with different loci subject to varying degrees of mutation, selection, and drift. In phylogenetics, the potential impacts of partitioning sequence data for the assignment of substitution models are well appreciated. In contrast, the treatment of branch lengths has received far less attention. In this study, we examined the effects of linking and unlinking branch-length parameters across loci or subsets of loci. By analyzing a range of empirical data sets, we find consistent support for a model in which branch lengths are proportionate between subsets of loci: gene trees share the same pattern of branch lengths, but form subsets that vary in their overall tree lengths. These models had substantially better statistical support than models that assume identical branch lengths across gene trees, or those in which genes form subsets with distinct branch-length patterns. We show using simulations and empirical data that the complexity of the branch-length model with the highest support depends on the length of the sequence alignment and on the numbers of taxa and loci in the data set. Our findings suggest that models in which branch lengths are proportionate between subsets have the highest statistical support under the conditions that are most commonly seen in practice. The results of our study have implications for model selection, computational efficiency, and experimental design in phylogenomics. [ABSTRACT FROM AUTHOR]

Model selection aims to choose the most adequate model for the statistical analysis at hand. The model must be complex enough to capture the complexity of the data but should be simple enough not to overfit. In phylogenetics, the most common model selection scenario concerns selecting an adequate substitution and partition model for sequence evolution to infer a phylogenetic tree. Previously, several studies showed that substitution model under-parameterization can bias phylogenetic studies. Here, we explored the impact of substitution model over-parameterization in a Bayesian statistical framework. We performed simulations under the simplest substitution model, the Jukes-Cantor model, and compare posterior estimates of phylogenetic tree topologies and tree length under the true model to the most complex model, the $\text{GTR}+\Gamma+\text{I}$ substitution model, including over-splitting the data into additional subsets (i.e., applying partitioned models). We explored 4 choices of prior distributions: the default substitution model priors of MrBayes, BEAST2, and RevBayes and a newly devised prior choice (Tame). Our results show that Bayesian inference of phylogeny is robust to substitution model over-parameterization and over-partitioning but only under our new prior settings. All 3 current default priors introduced biases for the estimated tree length. We conclude that substitution and partition model selection are superfluous steps in Bayesian phylogenetic inference pipelines if well-behaved prior distributions are applied and more effort should focus on more complex and biologically realistic substitution models., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Society of Systematic Biologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Few models of sequence evolution incorporate parameters describing protein structure, despite its high conservation, essential functional role and increasing availability. We present a structurally aware empirical substitution model for amino acid sequence evolution in which proteins are expressed using an expanded alphabet that relays both amino acid identity and structural information. Each character specifies an amino acid as well as information about the rotamer configuration of its side-chain: the discrete geometric pattern of permitted side-chain atomic positions, as defined by the dihedral angles between covalently linked atoms. By assigning rotamer states in 251,194 protein structures and identifying 4,508,390 substitutions between closely related sequences, we generate a 55-state "Dayhoff-like" model that shows that the evolutionary properties of amino acids depend strongly upon side-chain geometry. The model performs as well as or better than traditional 20-state models for divergence time estimation, tree inference, and ancestral state reconstruction. We conclude that not only is rotamer configuration a valuable source of information for phylogenetic studies, but that modeling the concomitant evolution of sequence and structure may have important implications for understanding protein folding and function. [ABSTRACT FROM AUTHOR]

We aimed to investigate associations between dietary macronutrient proportions and prospective visceral adiposity index changes (ΔVAI). The study included 1254 adults (18–74 years), from the Tehran Lipid and Glucose Study (TLGS), who were followed for three years. Dietary intakes were assessed twice using food frequency questionnaires. Associations of dietary macronutrient with ΔVAI and risk of visceral adiposity dysfunction (VAD) after three years were investigated. The percentage of energy intake from protein in the total population, and from fat in women, were associated with higher increases in VAI. A 5% higher energy intake from protein substituted for carbohydrate, monounsaturated fatty acids (MUFAs), and polyunsaturated fatty acids (PUFAs) was associated with higher ΔVAI. Higher energy intake from animal protein substituted for PUFAs was positively associated with ΔVAI. Substituting protein and PUFAs with MUFAs were related to higher ΔVAI. The associations were similar in men and women, but reached significance mostly among women. Risk of VAD was increased when 1% of energy from protein was replaced with MUFAs. Substituting protein for carbohydrate and fat, and fat for carbohydrate, resulted in increased risk of VAD in women. Higher dietary proportions of protein and animal-derived MUFA may be positively associated with ΔVAI and risk of VAD.

Statistical phylogenetic analyses of genomic data depend on models of nucleotide or amino acid substitution. The adequacy of these substitution models can be assessed using a number of test statistics, allowing the model to be rejected when it is found to provide a poor description of the evolutionary process. A potentially valuable use of model-adequacy test statistics is to identify when data sets are likely to produce unreliable phylogenetic estimates, but their differences in performance are rarely explored. We performed a comprehensive simulation study to identify test statistics that are sensitive to some of the most commonly cited sources of phylogenetic estimation error. Our results show that, for many test statistics, traditional thresholds for assessing model adequacy can fail to reject the model when the phylogenetic inferences are inaccurate and imprecise. This is particularly problematic when analysing loci that have few informative sites. We propose new thresholds for assessing substitution model adequacy and demonstrate their effectiveness in analyses of three phylogenomic data sets. These thresholds lead to frequent rejection of the model for loci that yield topological inferences that are imprecise and are likely to be inaccurate. We also propose the use of a summary statistic that provides a practical assessment of overall model adequacy. Our approach offers a promising means of enhancing model choice in genome-scale data sets, potentially leading to improvements in the reliability of phylogenomic inference. [ABSTRACT FROM AUTHOR]

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. Yet identifying the root position is a key component of phylogenetic inference because it provides a point of reference for polarizing ancestor-descendant relationships and therefore interpreting the tree. In this paper, we investigate the effect of relaxing the unrealistic reversibility assumption and allowing the position of the root to be another unknown. We propose two hierarchical models that are centered on a reversible model but perturbed to allow nonreversibility. The models differ in the degree of structure imposed on the perturbations. The analysis is performed in the Bayesian framework using Markov chain Monte Carlo methods for which software is provided. We illustrate the performance of the two nonreversible models in analyses of simulated data using two types of topological priors. We then apply the models to a real biological data set, the radiation of polyploid yeasts, for which there is robust biological opinion about the root position. Finally, we apply the models to a second biological alignment for which the rooted tree is controversial: the ribosomal tree of life. We compare the two nonreversible models and conclude that both are useful in inferring the position of the root from real biological data. [ABSTRACT FROM AUTHOR]

The use of 18S rDNA sequences for inferring phylogenies, in particular for higher taxonomic level analysis, has a long tradition in phycology. Similar to ITS2, the 18S rDNA displays a conserved secondary structure that could be used simultaneously with the primary sequence to increase the amount of information used when inferring phylogenetic relationships. Sequence-structure phylogenetics is already established for ITS2 research. Secondary structures no longer simply guide alignments and trees but are used simultaneously by encoding the sequence-structure information into a 12-letter alphabet. We used the knowledge gathered from the extensive body of ITS2 research regarding sequence-structure phylogenetics and applied it to 18S rRNA data; we present THESES db, the Algae 18S rDNA Sequence-Structure Database (), which contains sequences and their individual secondary structures for three major groups of algae (Chlorophyta, Bacillariophyta and Rhodophyta). This database was designed to serve as the starting point for future 18S rDNA sequence-structure based phylogenetic analyses that will eventually extend beyond phycology. One hundred phylogenetic trees generated from 18S sequence-only datasets and from parallel 18S sequence-structure datasets were compared for each taxon analyzed in this study (diatoms, green algae and red algae). Half of the comparisons produced trees with different topologies that frequently related to the status of sister genera. Using the lineage information for each species as listed in GenBank, we determined that the sequence-structure approach resolved a genus as monophyletic, while the sequence-only approach failed to do so in comparisons that comprised 3% of the cases examined. The reverse was true for a total of 8.3% of the comparisons that we generated. Future work, both in our labs and among the broader phycological community, will provide additional data to test the accuracy and robustness of a sequence-structure approach at different taxonomic ranks. [ABSTRACT FROM AUTHOR]

Background: Reconstructing phylogenies through Bayesian methods has many benefits, which include providing a mathematically sound framework, providing realistic estimates of uncertainty and being able to incorporate different sources of information based on formal principles. Bayesian phylogenetic analyses are popular for interpreting nucleotide sequence data, however for such studies one needs to specify a site model and associated substitution model. Often, the parameters of the site model is of no interest and an ad-hoc or additional likelihood based analysis is used to select a single site model. Results: bModelTest allows for a Bayesian approach to inferring and marginalizing site models in a phylogenetic analysis. It is based on trans-dimensional Markov chain Monte Carlo (MCMC) proposals that allow switching between substitution models as well as estimating the posterior probability for gamma-distributed rate heterogeneity, a proportion of invariable sites and unequal base frequencies. The model can be used with the full set of time-reversible models on nucleotides, but we also introduce and demonstrate the use of two subsets of time-reversible substitution models. Conclusion: With the new method the site model can be inferred (and marginalized) during the MCMC analysis and does not need to be pre-determined, as is now often the case in practice, by likelihood-based methods. The method is implemented in the bModelTest package of the popular BEAST 2 software, which is open source, licensed under the GNU Lesser General Public License and allows joint site model and tree inference under a wide range of models. [ABSTRACT FROM AUTHOR]

Background: Sites of positive selection are identified by comparing observed evolutionary patterns to those expected under a null model for evolution in the absence of such selection. For protein-coding genes, the most common null model is that nonsynonymous and synonymous mutations fix at equal rates; this unrealistic model has limited power to detect many interesting forms of selection. Results: I describe a new approach that uses a null model based on experimental measurements of a gene's site-specific amino-acid preferences generated by deep mutational scanning in the lab. This null model makes it possible to identify both diversifying selection for repeated amino-acid change and differential selection for mutations to amino acids that are unexpected given the measurements made in the lab. I show that this approach identifies sites of adaptive substitutions in four genes (lactamase, Gal4, influenza nucleoprotein, and influenza hemagglutinin) far better than a comparable method that simply compares the rates of nonsynonymous and synonymous substitutions. Conclusions: As rapid increases in biological data enable increasingly nuanced descriptions of the constraints on individual protein sites, approaches like the one here can improve our ability to identify many interesting forms of selection in natural sequences. [ABSTRACT FROM AUTHOR]

Substitution models of evolution describe the process of genetic variation through fixed mutations and constitute the basis of the evolutionary analysis at the molecular level. Almost forty years after the development of first substitution models, highly sophisticated and data-specific substitution models continue emerging with the aim of better mimicking real evolutionary processes. Here I describe current trends in substitution models of DNA, codon and amino acid sequence evolution, including advantages and pitfalls of the most popular models. The perspective concludes that despite the large number of currently available substitution models, further research is required for more realistic modeling, especially for DNA coding and amino acid data. Additionally, the development of more accurate complex models should be coupled with new implementations and improvements of methods and frameworks for substitution model selection and downstream evolutionary analysis.

We present a reversible Polymorphism-Aware Phylogenetic Model (revPoMo) for species tree estimation from genome-wide data. revPoMo enables the reconstruction of large scale species trees for many within-species samples. It expands the alphabet of DNA substitution models to include polymorphic states, thereby, naturally accounting for incomplete lineage sorting. We implemented revPoMo in the maximum likelihood software IQ-TREE. A simulation study and an application to great apes data show that the runtimes of our approach and standard substitution models are comparable but that revPoMo has much better accuracy in estimating trees, divergence times and mutation rates. The advantage of revPoMo is that an increase of sample size per species improves estimations but does not increase runtime. Therefore, revPoMo is a valuable tool with several applications, from speciation dating to species tree reconstruction. [ABSTRACT FROM AUTHOR]