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Lartillot, N.

Publications and source records attributed to Lartillot, N..

18 recordsLinked to original sources
bioRxiv2025

Structural mutations set an equilibrium non-coding genome fraction

Non-coding genome size evolution is poorly understood. While some fraction of non-coding DNA has arguably a regulatory function, a large part does not seem to have a detectable impact on any phenotypic trait. The abundance of non-functional DNA in genomes, observed across the Tree of Life, challenges a purely adaptationist explanation. Several non-adaptive theories have been proposed to explain its presence and identify its determinants, emphasizing either the mutational processes or the mutational hazard entailed by non-coding and non-functional DNA. However, those theories have not yet been integrated into a single framework, and the exact nature of the mutational hazard is not yet fully understood. In this work, we propose a simple mathematical model of genome size evolution. The model shows how the non-coding fraction of the genome is shaped by two factors: unavoidable biases in the neutrality of the different mutation types (adding base pairs is more likely to be neutral than removing some), and the robustness selection imposed by the mere existence of structural mutations (larger genomes are more prone to double-strand breaks that can initiate structural mutations, imposing a second-order selection on robustness). Together, these two factors ensure the existence of an equilibrium non-coding fraction. We show that this equilibrium depends solely on mutation biases and the product of population size and mutation rate.

evolutionary biology↗
bioRxiv2024

The evolution of GC-biased gene conversion by means of natural selection

GC-biased gene conversion (gBGC) is a recombination-associated evolutionary process that biases the segregation ratio of AT:GC polymorphisms in the gametes of heterozygotes, in favour of GC alleles. This process is the major determinant of variation in base composition across the human genome and can be the cause of a substantial burden of GC deleterious alleles. While the importance of GC-biased gene conversion in molecular evolution is increasingly recognised, the reasons for its existence and its variation between species remain largely unknown. Using simulations and semi-analytical approximations, we investigated the evolution of gBGC as a quantitative trait evolving by mutation, drift and natural selection. We show that in a finite population where most mutations are deleterious, gBGC is under weak stabilising selection around a positive value that mainly depends on the intensity of the mutation bias and on the intensity of selective constraints exerted on the genome. Importantly, the levels of gBGC that evolve by natural selection do not minimize the load in the population, and even increase it substantially in regions of high recombination rate. Therefore, despite reducing the populations fitness, levels of gBGC that are currently observed in humans could in fact have been (weakly) positively selected.

evolutionary biology↗
bioRxiv2024

Genome streamlining: effect of mutation rate and population size on genome size reduction

Genome streamlining, i.e. genome size reduction, is observed in bacteria with very different life traits, including endosymbiotic bacteria and several marine bacteria, raising the question of its evolutionary origin. None of the hypotheses proposed in the literature is firmly established, mainly due to the many confounding factors related to the diverse habitats of species with streamlined genomes. Computational models may help overcome these difficulties and rigorously test hypotheses. In this work, we used Aevol, a platform designed to study the evolution of genome architecture, to test two main hypotheses: that an increase in population size (N) or mutation rate ({micro}) could cause genome reduction. In our experiments, both conditions lead to streamlining, but with very different resulting genome structures. Under increased population sizes, genomes loose a significant fraction of non-coding sequences, but maintain their coding size, resulting in densely packed genomes (akin to streamlined marine bacteria genomes). By contrast, under increased mutation rate, genomes loose both coding and non-coding sequences (akin to endosymbiotic bacteria genomes). Hence, both factors lead to an overall reduction in genome size, but the coding density of the genome appears to be determined by N x {micro}. Thus, a broad range of genome size and density can be achieved by different combinations of N and {micro}. Our results suggest that genome size and coding density are determined by the interplay between selection for phenotypic adaptation and selection for robustness. Significance statementMany bacterial species show reduced genomes. However, the diversity of these species and of their life traits makes it difficult to identify the mechanisms that led to this reduction. Indeed, no unifying hypothesis accounts for the whole diversity of genome size reduction. Here, we used simulations to systematically explore the effect of population size and mutation rate on genome size. We show that the interaction between these two factors tightly determine the size, but also the density of genomes, making it possible to account for the whole diversity of reduced genomes by acting on these two parameters only. Our results suggest a theoretical model in which genome reduction is driven by a robustness/fitness trade off.

evolutionary biology↗
bioRxiv2023

Bridging the gap between the evolutionary dynamics and the molecular mechanisms of meiosis: a model based exploration of the PRDM9 intra-genomic Red Queen

Molecular dissection of meiotic recombination in mammals, combined with population-genetic and comparative studies, have revealed a complex evolutionary dynamic characterized by short-lived recombination hotspots. Hotspots are chromosome positions containing DNA sequences where the protein PRDM9 can bind and cause crossing-over. To explain these fast evolutionary dynamic, a so-called intra-genomic Red Queen model has been proposed, based on the interplay between two antagonistic forces: biased gene conversion, mediated by double-strand breaks, resulting in hotspot extinction (the hotspot conversion paradox), followed by positive selection favoring mutant PRDM9 alleles recognizing new sequence motifs. Although this model predicts many empirical observations, the exact causes of the positive selection acting on new PRDM9 alleles is still not well understood. In this direction, experiment on mouse hybrids have suggested that, in addition to targeting double strand breaks, PRDM9 has another role during meiosis. Specifically, PRDM9 symmetric binding (simultaneous binding at the same site on both homologues) would facilitate homology search and, as a result, the pairing of the homologues. Although discovered in hybrids, this second function of PRDM9 could also be involved in the evolutionary dynamic observed within populations. To address this point, here, we present a theoretical model of the evolutionary dynamic of meiotic recombination integrating current knowledge about the molecular function of PRDM9. Our modeling work gives important insights into the selective forces driving the turnover of recombination hotspots. Specifically, the reduced symmetrical binding of PRDM9 caused by the loss of high affinity binding sites induces a net positive selection eliciting new PRDM9 alleles recognizing new targets. The model also offers new insights about the influence of the gene dosage of PRDM9, which can paradoxically result in negative selection on new PRDM9 alleles entering the population, driving their eviction and thus reducing standing variation at this locus. Author summaryMeiosis is an important step in the eukaryotic life cycle, leading to the formation of gametes and implementing genetic mixing by recombination of paternal and maternal genomes. A key step of meiosis is the pairing of homologous chromosomes, which is required in order to distribute them evenly into the gametes. Chromosome pairing will also determine the exact position at which paternal and maternal chromosomes will exchange material. Research on the molecular basis of meiosis has revealed the role of a key gene, PRDM9. The protein encoded by PRDM9 binds to specific DNA sequences, by which it determines the location of recombination points. Symmetric binding of the protein (at the same position on the homologous chromosomes) also facilitates chromosome pairing. This molecular mechanism, however, has paradoxical consequences, among which the local destruction of the DNA sequences recognized by PRDM9, leading to their rapid loss at the level of the population over a short evolutionary time. In order to better understand why recombination is maintained over time despite this process, we have developed a simulation program implementing a model taking into account these molecular mechanisms. Our model makes realistic predictions about recombination evolution and confirms the important role played by PRDM9 during meiosis.

evolutionary biology↗
bioRxiv2022

Genes and sites under adaptation at the phylogenetic scale also exhibit adaptation at the population-genetic scale

Adaptation in protein-coding sequences can be detected from multiple sequence alignments across species, or alternatively by leveraging polymorphism data inside a population. Across species, quantification of the adaptive rate relies on phylogenetic codon models, classically formulated in terms of the ratio of non-synonymous over synonymous substitution rates. Evidence of an accelerated non-synonymous substitution rate is considered a signature of pervasive adaptation. However, because of the background of purifying selection, these models are potentially limited in their sensitivity. Recent developments have led to more sophisticated mutation-selection codon models aimed at making a more detailed quantitative assessment of the interplay between mutation, purifying and positive selection. In this study, we conducted a large-scale exome-wide analysis of placental mammals with mutation-selection models, assessing their performance at detecting proteins and sites under adaptation. Importantly, mutation-selection codon models are based on a population-genetic formalism and thus are directly comparable to McDonald & Kreitman tests at the population level to quantify adaptation. Taking advantage of this relationship between phylogenetic and population genetics, we integrated divergence and polymorphism data across the entire exome for 29 populations across 7 genera, and showed that proteins and sites detected to be under adaptation at the phylogenetic scale are also under adaptation at the population-genetic scale. Altogether, our exome-wide analysis shows that phylogenetic mutation-selection codon models and population-genetic test of adaptation can be reconciled and are congruent, paving the way for integrative models and analyses across individuals and populations. Significance StatementDetecting genes under adaptation represents a key step in the decoding of genomes. Several methods have been proposed, focussing either on the short time scale (population genetics, e.g. human populations), or on the long time scale (phylogenetics, e.g. across mammals). However, the accuracy of these methods is still under debate, and it is still unclear whether the signatures of adaptation are congruent across evolutionary scales. In this study, using novel phylogenetic methods and gathering genome data across and within species, we show that the signatures of adaptation at the phylogenetic and population-genetic scales can be reconciled. While providing a mutual confirmation of the two approaches, our work paves the way for further methodological integration between micro- and macro-evolutionary genomics.

evolutionary biology↗
bioRxiv2022

The birth-death diffusion leading to present-day Mammal diversity

Dramatic spatial, temporal and taxonomic variation in biodiversity is ultimately explained by differences in speciation and extinction rates. Mammals represent a [~]200 My old radiation that resulted in over 6500 extant species, with stark temporal, spatial and taxonomic heterogeneity in biodiversity. Throughout their history, every mammal lineage is expected to have undergone diversification rates that vary instantaneously in time resulting from the complex interplay of context-specific extrinsic factors (e.g., K-Pg mass extinction event, rise of angiosperms) with their evolving ecologies (e.g., body size, diet). When studying the diversification history of a clade, however, mathematical and computational limitations have hindered inference of such a flexible birth-death model where speciation and extinction rates evolve continuously along a phylogenetic tree. Here we overcome these challenges by implementing a series of phylogenetic models in which speciation and extinction rates are inherited and diffuse following a latent Geometric Brownian motion process. We enable full Bayesian inference using data augmentation techniques to sample from the posterior distribution of model parameters, including augmented phylogenetic trees and validate using simulations. Using a genome-informed time-calibrated tree for over 4000 Mammals species, we are able to estimate a complete and fine-grained picture of the variation in diversification rates that captures both global and lineage specific effects. We find that, contrary to the idea of a suppressed mammalian diversification before the K-Pg mass extinction event (i.e., explosive- or delayed-rise), mammal speciation rates dramatically increased around 10-20 My before the K-Pg. Our new model opens exciting possibilities in disentangling the drivers behind variation in diversification and assaying how small-scale processes scale-up to macroevolutionary dynamics.

evolutionary biology↗
bioRxiv2022

Phylogenomic Analysis of Protein-Coding Genes Resolves Complex Gall Wasp Relationships

The phylogeny of gall wasps (Cynipidae) and their parasitic relatives has attracted considerable attention in recent years. The family is now widely recognized to fall into thirteen natural lineages, designated tribes, but the relationships among them have remained elusive. This has stymied any progress in understanding how cynipid gall inducers evolved from insect parasitoids, and what role inquilinism (development as a herbivore inside galls induced by other cynipids) might have played in this transition. A recent analysis of ultraconserved elements (UCEs) represents the first attempt at resolving these questions using phylogenomics. Here, we present the first analysis based on protein-coding sequences from genome and transcriptome assemblies. To address potential problems due to model misfit, we focus on models that accommodate site-specific amino-acid profiles and that are less sensitive than standard models to long-branch attraction. Our results show that the Cynipidae as previously circumscribed are not monophyletic. Specifically, the Paraulacini and a clade formed by Diplolepidini + Pediaspidini both fall outside a core clade (Cynipidae s. str.), which is more closely related to Figitidae. This result is robust to the exclusion of long-branch taxa that could potentially mislead the analysis, and it is consistent with the UCE analysis. Given this, we propose that the Cynipidae be divided into three families: the Paraulacidae, Diplolepididae and Cynipidae (s. str.). Our results suggest that the Eschatocerini are the sister group of the remaining Cynipidae (s. str.). Within the latter, our results are consistent with the UCE analysis but place two additional tribes: (1) the Aylacini (s. str.), more closely related to the oak gall wasps (Cynipini) and some of their inquilines (Ceroptresini) than to other herb gallers (Aulacideini and Phanacidini); and (2) the Qwaqwaiini, likely the sister group to Synergini (s. str.) + Rhoophilini. Several alternative scenarios for the evolution of cynipid life histories are compatible with the relationships suggested by our analysis, but all are complex and require multiple shifts between parasitoids, inquilines and gall inducers. Linking the different types of life-history transitions to specific genomic signatures may be one of the best ways of differentiating among these alternative scenarios. Our study represents the first step towards enabling such analyses.

evolutionary biology↗
bioRxiv2022

Identifying the best approximating model in Bayesian phylogenetics: Bayes factors, cross-validation or wAIC?

AO_SCPLOWBSTRACTC_SCPLOWThere is still no consensus as to how to select models in Bayesian phylogenetics, and more generally in applied Bayesian statistics. Bayes factors are often presented as the method of choice, yet other approaches have been proposed, such as cross-validation or information criteria. Each of these paradigms raises specific computational challenges, but they also differ in their statistical meaning, being motivated by different objectives: either testing hypotheses or finding the best-approximating model. These alternative goals entail different compromises, and as a result, Bayes factors, cross-validation and information criteria may be valid for addressing different questions. Here, the question of Bayesian model selection is revisited, with a focus on the problem of finding the best-approximating model. Several model selection approaches were re-implemented, numerically assessed and compared: Bayes factors, cross-validation (CV), in its different forms (k-fold or leave-one-out), and the widely applicable information criterion (wAIC), which is asymptotically equivalent to leave-one-out cross validation (LOO-CV). Using a combination of analytical results and empirical and simulation analyses, it is shown that Bayes factors are unduly conservative. In contrast, cross-validation represents a more adequate formalism for selecting the model returning the best approximation of the data-generating process and the most accurate estimates of the parameters of interest. Among alternative CV schemes, LOO-CV and its asymptotic equivalent represented by the wAIC, stand out as the best choices, conceptually and computationally, given that both can be simultaneously computed based on standard MCMC runs under the posterior distribution.

bioinformatics↗
bioRxiv2022

Compositionally constrained sites drive long branch attraction

AO_SCPLOWBSTRACTC_SCPLOWAccurate phylogenies are fundamental to our understanding of the pattern and process of evolution. Yet, phylogenies at deep evolutionary timescales, with correspondingly long branches, have been fraught with controversy resulting from conflicting estimates from models with varying complexity and goodness of fit. Analyses of historical as well as current empirical datasets, such as alignments including Microsporidia, Nematoda or Platyhelminthes, have demonstrated that inadequate modeling of across-site compositional heterogeneity, which is the result of biochemical constraints that lead to varying patterns of accepted amino acids along sequences, can lead to erroneous topologies that are strongly supported. Unfortunately, models that adequately account for across-site compositional heterogeneity remain computationally challenging or intractable for an increasing fraction of contemporary datasets. Here, we introduce "compositional constraint analysis", a method to investigate the effect of site-specific constraints on amino acid composition on phylogenetic inference. We show that more constrained sites with lower diversity and less constrained sites with higher diversity exhibit ostensibly conflicting signal under models ignoring across-site compositional heterogeneity that lead to long branch attraction artifacts and demonstrate that more complex models accounting for across-site compositional heterogeneity can ameliorate this bias. We present CAT-PMSF, a pipeline for diagnosing and resolving phylogenetic bias resulting from inadequate modeling of across-site compositional heterogeneity based on the CAT model. CAT-PMSF is robust against long branch attraction in all alignments we have examined. We suggest using CAT-PMSF when convergence of the CAT model cannot be assured. We find evidence that compositionally constrained sites are driving long branch attraction in two metazoan datasets and recover evidence for Porifera as the sister group to all other animals.

evolutionary biology↗
bioRxiv2021

An improved codon modeling approach for accurate estimation of the mutation bias

Nucleotide composition in protein-coding sequences is the result of the equilibrium between mutation and selection. In particular, the nucleotide composition differs between the three coding positions, with the third position showing more extreme composition than the first and the second positions. Yet, phylogenetic codon models do not correctly capture this phenomenon and instead predict that the nucleotide composition should be the same for all 3 positions of the codons. Alternatively, some models allow for different nucleotide rates at the three positions, a problematic approach since the mutation process should in principle be blind to the coding structure and homogeneous across coding positions. Practically, this misconception could have important consequences in modelling the impact of GC-biased gene conversion (gBGC) on the evolution of protein-coding sequences, a factor which requires mutation and fixation biases to be carefully disentangled. Conceptually, the problem comes from the fact that phylogenetic codon models cannot correctly capture the fixation bias acting against the mutational pressure at the mutation-selection equilibrium. To address this problem, we present an improved codon modeling approach where the fixation rate is not seen as a scalar anymore, but as a tensor unfolding along multiple directions, which gives an accurate representation of how mutation and selection oppose each other at equilibrium. Thanks to this, this modelling approach yields a reliable estimate of the mutational process, while disentangling fixation probabilities in different directions.

evolutionary biology↗
bioRxiv2021

Inferring long-term effective population size with Mutation-Selection models

Mutation-selection phylogenetic codon models are grounded on population genetics first principles and represent a principled approach for investigating the intricate interplay between mutation, selection and drift. In their current form, mutation-selection codon models are entirely characterized by the collection of site-specific amino-acid fitness profiles. However, thus far, they have relied on the assumption of a constant genetic drift, translating into a unique effective population size (Ne) across the phylogeny, clearly an unreasonable hypothesis. This assumption can be alleviated by introducing variation in Ne between lineages. In addition to Ne, the mutation rate () is susceptible to vary between lineages, and both should co-vary with life-history traits (LHTs). This suggests that the model should more globally account for the joint evolutionary process followed by all of these lineage-specific variables (Ne, , and LHTs). In this direction, we introduce an extended mutation-selection model jointly reconstructing in a Bayesian Monte Carlo framework the fitness landscape across sites and long-term trends in Ne, and LHTs along the phylogeny, from an alignment of DNA coding sequences and a matrix of observed LHTs in extant species. The model was tested against simulated data and applied to empirical data in mammals, isopods and primates. The reconstructed history of Ne in these groups appears to correlate with LHTs or ecological variables in a way that suggests that the reconstruction is reasonable, at least in its global trends. On the other hand, the range of variation in Ne inferred across species is surprisingly narrow. This last point suggests that some of the assumptions of the model, in particular concerning the assumed absence of epistatic interactions between sites, are potentially problematic.

evolutionary biology↗
bioRxiv2021

A theoretical approach for quantifying the impact of changes in effective population size and expression level on the rate of coding sequence evolution

Molecular sequences are shaped by selection, where the strength of selection relative to drift is determined by effective population size (Ne). Populations with high Ne are expected to undergo stronger purifying selection, and consequently to show a lower substitution rate for selected mutations relative to the substitution rate for neutral mutations ({omega}). However, computational models based on biophysics of protein stability have suggested that{omega} can also be independent of Ne, a result proven under general conditions. Together, the response of{omega} to changes in Ne depends on the specific mapping from sequence to fitness. Importantly, an increase in protein expression level has been found empirically to result in decrease of{omega} , an observation predicted by theoretical models assuming selection for protein stability. Here, we derive a theoretical approximation for the response of{omega} to changes in Ne and expression level, under an explicit genotype-phenotype-fitness map. The method is generally valid for additive traits and log-concave fitness functions. We applied these results to protein undergoing selection for their conformational stability and corroborate out findings with simulations under more complex models. We predict a weak response of{omega} to changes in either Ne or expression level, which are interchangeable. Based on empirical data, we propose that fitness based on the conformational stability may not be a sufficient mechanism to explain the empirically observed variation in{omega} across species. Other aspects of protein biophysics might be explored, such as protein-protein interactions, which can lead to a stronger response of{omega} to changes in Ne.

evolutionary biology↗
bioRxiv2020

Probabilistic programming: a powerful new approach to statistical phylogenetics

Statistical phylogenetic analysis currently relies on complex, dedicated software packages, making it difficult for evolutionary biologists to explore new models and inference strategies. Recent years have seen more generic solutions based on probabilistic graphical models, but this formalism can only partly express phylogenetic problems. Here we show that universal probabilistic programming languages (PPLs) solve the expressivity problem, while still supporting automated generation of efficient inference algorithms. To prove the latter point, we develop automated generation of sequential Monte Carlo (SMC) algorithms for PPL descriptions of arbitrary biological diversification (birth-death) models. SMC is a new inference strategy for these problems, supporting both parameter inference and efficient estimation of Bayes factors that are used in model testing. We take advantage of this in automatically generating SMC algorithms for several recent diversification models that have been difficult or impossible to tackle previously. Finally, applying these algorithms to 40 bird phylogenies, we show that models with slowing diversification, constant turnover and many small shifts generally explain the data best. Our work opens up several related problem domains to PPL approaches, and shows that few hurdles remain before these techniques can be effectively applied to the full range of phylogenetic models.

evolutionary biology↗
bioRxiv2020

Detecting sex-linked genes using genotyped individuals sampled in natural populations

We propose a method, SDpop, able to infer sex-linkage caused by recombination suppression typical of sex chromosomes. The method is based on the modeling of the allele and genotype frequencies of individuals of known sex in natural populations. It is implemented in a hierarchical probabilistic framework, accounting for different sources of error. It allows to statistically test for the presence or absence of sex chromosomes, and to infer sex-linked genes based on the posterior probabilities in the model. Furthermore, for gametologous sequences, the haplotype and level of nucleotide polymorphism of each copy can be inferred, as well as the divergence between both. We test the method using simulated and human sequencing data, and show that, for most cases, robust predictions are obtained with 5 to 10 individuals per sex.

genomics↗
bioRxiv2019

Reconstructing the history of variation in effective population size along phylogenies.

AO_SCPLOWBSTRACTC_SCPLOWThe nearly-neutral theory predicts specific relations between effective population size (Ne) and patterns of divergence and polymorphism, which depend on the shape of the distribution of fitness effects (DFE) of new mutations. However, testing these relations is not straightforward, owing to the difficulty in estimating Ne. Here, we introduce an integrative framework allowing for an explicit reconstruction of the phylogenetic history of Ne, thus leading to a quantitative test of the nearly-neutral theory and an estimation of the allometric scaling of the ratios of non-synonymous over synonymous polymorphism ({pi}N /{pi}S) and divergence (dN/dS) with respect to Ne. As an illustration, we applied our method to primates, for which the nearly-neutral predictions were mostly verified. Under a purely nearly-neutral model with a constant DFE across species, we find that the variation in{pi} N /{pi}S and dN/dS as a function of Ne is too large to be compatible with current estimates of the DFE based on site frequency spectra. The reconstructed history of Ne shows a ten-fold variation across primates. The mutation rate per generation u, also reconstructed over the tree by the method, varies over a three-fold range and is negatively correlated with Ne. As a result of these opposing trends for Ne and u, variation in{pi} S is intermediate, primarily driven by Ne but substantially influenced by u. Altogether, our integrative framework provides a quantitative assessment of the role of Ne and u in modulating patterns of genetic variation, while giving a synthetic picture of their history over the clade. SO_SCPLOWIGNIFICANCEC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWSTATEMENTC_SCPLOWNatural selection tends to increase the frequency of mutants of higher fitness and to eliminate less fit genetic variants. However, chance events over the life of the individuals in the population are susceptible to introduce deviations from these trends, which are expected to have a stronger impact in smaller populations. In the long-term, these fluctuations, called random drift, can lead to the accumulation of mildly deleterious mutations in the genomes of living species, and for that reason, the effective population size (usually denoted Ne, and which captures the relative strength of drift, relative to selection) has been proposed as a major determinant of the evolution of genome architecture and content. A proper quantitative test of this hypothesis, however, is hampered by the fact that Ne is difficult to estimate in practice. Here, we propose a Bayesian integrative approach for reconstructing the broad-scale variation in Ne across an entire phylogeny, which in turns allows for quantifying how Ne correlates with life history traits and with various measures of genetic diversity and selection strength, between and within species. We apply this approach to the phylogeny of primates, and observe that selection is indeed less efficient in primates characterized by smaller effective population sizes.

evolutionary biology↗
bioRxiv2019

Scalable empirical mixture models that account for across-site compositional heterogeneity

Biochemical demands constrain the range of amino acids acceptable at specific sites resulting in across-site compositional heterogeneity of the amino acid replacement process. Phylogenetic models that disregard this heterogeneity are prone to systematic errors, which can lead to severe long branch attraction artifacts. State-of-the-art models accounting for across-site compositional heterogeneity include the CAT model, which is computationally expensive, and empirical distribution mixture models estimated via maximum likelihood (C10 to C60 models). Here, we present a new, scalable method EDCluster for finding empirical distribution mixture models involving a simple cluster analysis. The cluster analysis utilizes specific coordinate transformations which allow the detection of specialized amino acid distributions either from curated databases, or from the alignment at hand. We apply EDCluster to the HOGENOM and HSSP databases in order to provide universal distribution mixture (UDM) models comprising up to 4096 components. Detailed analyses of the UDM models demonstrate the removal of various long branch attraction artifacts and improved performance compared to the C10 to C60 models. Ready-to-use implementations of the UDM models are provided for three established software packages (IQ-TREE, Phylobayes, and RevBayes).

bioinformatics↗
bioRxiv2019

Detecting convergent adaptive amino acid evolution

In evolutionary genomics, researchers have taken an interest in identifying in the genomes substitutions that subtend convergent phenotypic adaptations. This is a difficult question to address, because genomes contain billions of sites, many of which have substituted in the lineages where the adaptations took place, and yet are not linked to them. Those extra substitutions may be linked to other adaptations, may be neutral, or may be linked to mutational biases. Furthermore, one can think of various ways of defining substitutions of interest, and various methods that match those definitions have been used, resulting in different sets of candidate substitutions. In this manuscript we first clarify how adaptation to convergent phenotypic evolution can manifest itself in coding sequences. Second, we review methods that have been proposed to detect convergent adaptive evolution in coding sequences and expose the assumptions that underlie them. Finally, we examine their power on simulations of convergent changes, including in the presence of a confounding factor.

bioinformatics↗
bioRxiv2017

Reconstruction of body mass evolution in the Cetartiodactyla and mammals using phylogenomic data.

This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100042).\n\nReconstructing ancestral characters on a phylogeny is an arduous task because the observed states at the tips of the tree correspond to a single realization of the underlying evolutionary process. Recently, it was proposed that ancestral traits can be indirectly estimated with the help of molecular data, based on the fact that life history traits influence substitution rates. Here we challenge these new approaches in the Cetartiodactyla, a clade of large mammals which, according to paleontology, derive from small ancestors. Analysing transcriptome data in 41 species, of which 22 were newly sequenced, we provide a dated phylogeny of the Cetartiodactyla and report a significant effect of body mass on the overall substitution rate, the synonymous vs. non-synonymous substitution rate and the dynamics of GC-content. Our molecular comparative analysis points toward relatively small Cetartiodactyla ancestors, in agreement with the fossil record, even though our data set almost exclusively consists of large species. This analysis demonstrates the potential of phylogenomic methods for ancestral trait reconstruction and gives credit to recent suggestions that the ancestor to placental mammals was a relatively large and long-lived animal.

evolutionary biology↗