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Insertions and deletions (indels) are common molecular evolutionary events. However, probabilistic models for indel evolution are under-developed due to their computational complexity. Here, we introduce several improvements to indel modeling: 1) While previous models for indel evolution assumed that the rates and length distributions of insertions and deletions are equal, here we propose a richer model that explicitly distinguishes between the two; 2) we introduce numerous summary statistics that allow approximate Bayesian computation-based parameter estimation; 3) we develop a method to correct for biases introduced by alignment programs, when inferring indel parameters from empirical data sets; and 4) using a model-selection scheme, we test whether the richer model better fits biological data compared with the simpler model. Our analyses suggest that both our inference scheme and the model-selection procedure achieve high accuracy on simulated data. We further demonstrate that our proposed richer model better fits a large number of empirical data sets and that, for the majority of these data sets, the deletion rate is higher than the insertion rate.

Preventing and controlling epidemics caused by vector-borne viruses are particularly challenging due to their diverse pool of hosts and highly adaptive nature. Many vector-borne viruses belong to the Flavivirus genus, whose members vary greatly in host range and specificity. Members of the Flavivirus genus can be categorized to four main groups: insect-specific viruses that are maintained solely in arthropod populations, mosquito-borne viruses and tick-borne viruses that are transmitted to vertebrate hosts by mosquitoes or ticks via blood feeding, and those with no-known vector. The mosquito-borne group encompasses the yellow fever, dengue, and West Nile viruses, all of which are globally spread and cause severe morbidity in humans. The Flavivirus genus is genetically diverse, and its members are subject to different host-specific and vector-specific selective constraints, which do not always align. Thus, understanding the underlying genetic differences that led to the diversity in host range within this genus is an important aspect in deciphering the mechanisms that drive host compatibility and can aid in the constant arms-race against viral threats. Here, we review the phylogenetic relationships between members of the genus, their infection bottlenecks, and phenotypic and genomic differences. We further discuss methods that utilize these differences for prediction of host shifts in flaviviruses and can contribute to viral surveillance efforts.

Statistical criteria have long been the standard for selecting the best model for phylogenetic reconstruction and downstream statistical inference. Although model selection is regarded as a fundamental step in phylogenetics, existing methods for this task consume computational resources for long processing time, they are not always feasible, and sometimes depend on preliminary assumptions which do not hold for sequence data. Moreover, although these methods are dedicated to revealing the processes that underlie the sequence data, they do not always produce the most accurate trees. Notably, phylogeny reconstruction consists of two related tasks, topology reconstruction and branch-length estimation. It was previously shown that in many cases the most complex model, GTR+I+G, leads to topologies that are as accurate as using existing model selection criteria, but overestimates branch lengths. Here, we present ModelTeller, a computational methodology for phylogenetic model selection, devised within the machine-learning framework, optimized to predict the most accurate nucleotide substitution model for branch-length estimation. We demonstrate that ModelTeller leads to more accurate branch-length inference than current model selection criteria on data sets simulated under realistic processes. ModelTeller relies on a readily implemented machine-learning model and thus the prediction according to features extracted from the sequence data results in a substantial decrease in running time compared with existing strategies. By harnessing the machine-learning framework, we distinguish between features that mostly contribute to branch-length optimization, concerning the extent of sequence divergence, and features that are related to estimates of the model parameters that are important for the selection made by current criteria.

Detecting the signature of selection in coding sequences and associating it with shifts in phenotypic states can unveil genes underlying complex traits. Of the various signatures of selection exhibited at the molecular level, changes in the pattern of selection at protein-coding genes have been of main interest. To this end, phylogenetic branch-site codon models are routinely applied to detect changes in selective patterns along specific branches of the phylogeny. Many of these methods rely on a prespecified partition of the phylogeny to branch categories, thus treating the course of trait evolution as fully resolved and assuming that phenotypic transitions have occurred only at speciation events. Here, we present TraitRELAX, a new phylogenetic model that alleviates these strong assumptions by explicitly accounting for the uncertainty in the evolution of both trait and coding sequences. This joint statistical framework enables the detection of changes in selection intensity upon repeated trait transitions. We evaluated the performance of TraitRELAX using simulations and then applied it to two case studies. Using TraitRELAX, we found an intensification of selection in the primate SEMG2 gene in polygynandrous species compared to species of other mating forms, as well as changes in the intensity of purifying selection operating on sixteen bacterial genes upon transitioning from a free-living to an endosymbiotic lifestyle.[Evolutionary selection; intensification; $\gamma $-proteobacteria; genotype–phenotype; relaxation; SEMG2.]

The study of intraspecific genomic variation in eukaryotic species has been the focus of numerous genome resequencing projects in recent years. One emerging approach for the analysis of intraspecific diversity uses the concept of a pan-genome, which theoretically represents the full set of genomic sequences and coding genes from all individuals of a given species. This approach has many advantages over reference-based methods and has been successfully applied to study both prokaryotic and eukaryotic species. However, the process of pan-genome construction still presents considerable scientific and technical challenges, especially for eukaryotic species with large and complex genomes. Although general approaches for the construction of pan-genomes have been devised, currently available software tools implement only certain modules of the entire computational procedure. Therefore, each pan-genome project requires the development of tailored analysis pipelines, thus complicating and prolonging the process and impairing research reproducibility and comparison across studies. Here, we present Panoramic, a software package for the automatic construction of eukaryotic pan-genomes. Panoramic takes raw sequencing reads as input and applies two alternative approaches for pan-genome construction. Panoramic makes pan-genome construction a considerably easier task by providing simple user interface and efficient data processing algorithms. We demonstrate the use of Panoramic by constructing the pan-genome of the model plant species Arabidopsis thaliana from sequencing data of 20 diverse ecotypes.

Chromosome numbers have been widely used to describe the most fundamental genomic attribute of an organism or a lineage. Although providing strong phylogenetic signal, chromosome numbers vary remarkably among eukaryotes at all levels of taxonomic resolution. Changes in chromosome numbers regularly serve as indication of major genomic events, most notably polyploidy and dysploidy. Here, we review recent advancements in our ability to make inferences regarding historical events that led to alterations in the number of chromosomes of a lineage. We first describe the mechanistic processes underlying changes in chromosome numbers, focusing on structural chromosomal rearrangements. Then, we focus on experimental procedures, encompassing comparative cytogenomics and genomics approaches, and on computational methodologies that are based on explicit models of chromosome-number evolution. Together, these tools offer valuable predictions regarding historical events that have changed chromosome numbers and genome structures, as well as their phylogenetic and temporal placements.

Phylogeny reconstruction is a key instrument in numerous biological analyses, ranging from evolutionary and ecology research, to conservation and systems biology. The increasing accumulation of genomic data makes it possible to reconstruct phylogenies with both high accuracy and at increasingly finer resolution. Yet, taking advantage of the enormous amount of sequence data available requires the use of computational tools for efficient data retrieval and processing, or else the process could quickly become an error-prone endeavour. Here, we present OneTwoTree (http://onetwotree.tau.ac.il/), a Web-based tool for tree reconstruction based on the supermatrix paradigm. Given a list of taxa names of interest as the sole input requirement, OneTwoTree retrieves all available sequence data from NCBI GenBank, clusters these into orthology groups, identifies the most informative set of markers, searches for an appropriate outgroup, and assembles a partitioned sequence matrix that is then used for the final phylogeny reconstruction step. OneTwoTree further allows users to control various steps of the process, such as the merging of sequences from similar clusters, or phylogeny reconstruction based on markers from a specific genome type. By comparing the performance of OneTwoTree to a manually reconstructed phylogeny of the Antirrhineae tribe, we show that the use of OneTwoTree resulted in substantially higher data coverage in terms of both taxon sampling and the number of informative markers assembled. OneTwoTree provides a flexible online tool for species-tree reconstruction, aimed to assist researchers ranging in their level of prior expertise in the task of phylogeny reconstruction.

Hybridization and genome doubling (allopolyploidy) have led to evolutionary novelties as well as to the origin of new clades and species. Despite the importance of allopolyploidization, the dynamics of post-polyploid diploidization (PPD) at the genome level has been only sparsely studied. The Microlepidieae (MICR) is a crucifer tribe of 17 genera and c. 56 species endemic to Australia and New Zealand. Our phylogenetic and cytogenomic analyses revealed that MICR originated via an inter-tribal hybridization between ancestors of Crucihimalayeae (n = 8; maternal genome) and Smelowskieae (n = 7; paternal genome), both native to the Northern Hemisphere. The reconstructed ancestral allopolyploid genome (n = 15) originated probably in north-eastern Asia or western North America during the Late Miocene (c. 10.6 - 7 million years ago) and reached the Australian mainland via long-distance dispersal. In Australia, the allotetraploid genome diverged into at least three main subclades exhibiting different levels of PPD and diversity: 1.25-fold descending dysploidy (DD) of n = 15 → n = 12 (autopolyploidy → 24) in perennial Arabidella (3 species), 1.5-fold DD of n = 15 → n = 10 in the perennial Pachycladon (11 spp.), and 2.1 to 3.75-fold DD of n = 15 → n = 7 - 4 in the largely annual crown group genera (42 spp. in 15 genera). These results are among the first to demonstrate multi-speed genome evolution in taxa descending from a common allopolyploid ancestor. It is suggested that clade-specific PPD can operate at different rates and efficacies, and can be tentatively linked to life histories and the extent of taxonomic diversity. This article is protected by copyright. All rights reserved.

Understanding species adaptation at the molecular level has been a central goal of evolutionary biology and genomics research. This important task becomes increasingly relevant with the constant rise in both genotypic and phenotypic data availabilities. The TraitRateProp web server offers a unique perspective into this task by allowing the detection of associations between sequence evolution rate and whole-organism phenotypes. By analyzing sequences and phenotypes of extant species in the context of their phylogeny, it identifies sequence sites in a gene/protein whose evolutionary rate is associated with shifts in the phenotype. To this end, it considers alternative histories of whole-organism phenotypic changes, which result in the extant phenotypic states. Its joint likelihood framework that combines models of sequence and phenotype evolution allows testing whether an association between these processes exists. In addition to predicting sequence sites most likely to be associated with the phenotypic trait, the server can optionally integrate structural 3D information. This integration allows a visual detection of trait-associated sequence sites that are juxtapose in 3D space, thereby suggesting a common functional role. We used TraitRateProp to study the shifts in sequence evolution rate of the RPS8 protein upon transitions into heterotrophy in Orchidaceae. TraitRateProp is available at http://traitrate.tau.ac.il/prop.

Sexual system is a key determinant of genetic variation and reproductive success, affecting evolution within populations and within clades. Much research in plants has focused on evolutionary transitions away from the most common state of hermaphroditism and toward the rare state of dioecy (separate sexes). Rather than transitions predominantly toward greater sexual differentiation, however, evolution may proceed in the direction of lesser sexual differentiation. We analyzed the macroevolutionary dynamics of sexual system in angiosperm genera that contain both dioecious and nondioecious species. Our phylogenetic analyses encompass a total of 2145 species from 40 genera. Overall, we found little evidence that rates of sexual system transitions are greater in any direction. Counting the number of inferred state changes revealed a mild prevalence of transitions away from hermaphroditism and away from dioecy, toward states of intermediate sexual differentiation. We identify genera in which future studies of sexual system evolution might be especially productive, and we discuss how integrating genetic or population-level studies of sexual system could improve the power of phylogenetic comparative analyses. Our work adds to the evidence that different selective pressures and constraints act in different groups, helping maintain the variety of sexual systems observed among plants.

As species richness varies along the tree of life, there is a great interest in identifying factors that affect the rates by which lineages speciate or go extinct. To this end, theoretical biologists have developed a suite of phylogenetic comparative methods that aim to identify where shifts in diversification rates had occurred along a phylogeny and whether they are associated with some traits. Using these methods, numerous studies have predicted that speciation and extinction rates vary across the tree of life. In this study, we show that asymmetric rates of sequence evolution lead to systematic biases in the inferred phylogeny, which in turn lead to erroneous inferences regarding lineage diversification patterns. The results demonstrate that as the asymmetry in sequence evolution rates increases, so does the tendency to select more complicated models that include the possibility of diversification rate shifts. These results thus suggest that any inference regarding shifts in diversification pattern should be treated with great caution, at least until any biases regarding the molecular substitution rate have been ruled out.

PREMISE OF THE STUDY Flowering plants display a variety of sexual systems, ranging from complete cosexuality (hermaphroditism) to separate-sexed individuals (dioecy). While dioecy is relatively rare, it has evolved many times and is present in many plant families. Transitions in sexual systems are hypothesized to be affected by large genomic events such as whole-genome duplication, or polyploidy, and several models have been proposed to explain the observed patterns of association. METHODS In this study, we assessed the association between ploidy and sexual system (separate or combined sexes). To this end, we assembled a database of ploidy levels and sexual systems for ∼1000 species, spanning 18 genera and 15 families. We applied several phylogenetic comparative approaches, including Pagel's coevolutionary framework and sister clade analyses, for detecting correlations between ploidy level and sexual system. KEY RESULTS Our results indicate a broad association between polyploidy and sexual system dimorphism, with low evolutionary stability of the diploid-dioecious condition observed in several clades. A detailed examination of the clades exhibiting this correlation reveals that it is underlain by various patterns of transition rate asymmetry. CONCLUSIONS We conclude that the long-hypothesized connection between ploidy and sexual system holds in some clades, although it may well be affected by factors that differ from clade to clade. Our results further demonstrate that to better understand the evolutionary processes involved, more sophisticated methods and extensive and detailed data sets are required for both broad and focused inquiry.

The degree of evolutionary conservation of an amino acid in a protein or a nucleic acid in DNA/RNA reflects a balance between its natural tendency to mutate and the overall need to retain the structural integrity and function of the macromolecule. The ConSurf web server (http://consurf.tau.ac.il), established over 15 years ago, analyses the evolutionary pattern of the amino/nucleic acids of the macromolecule to reveal regions that are important for structure and/or function. Starting from a query sequence or structure, the server automatically collects homologues, infers their multiple sequence alignment and reconstructs a phylogenetic tree that reflects their evolutionary relations. These data are then used, within a probabilistic framework, to estimate the evolutionary rates of each sequence position. Here we introduce several new features into ConSurf, including automatic selection of the best evolutionary model used to infer the rates, the ability to homology-model query proteins, prediction of the secondary structure of query RNA molecules from sequence, the ability to view the biological assembly of a query (in addition to the single chain), mapping of the conservation grades onto 2D RNA models and an advanced view of the phylogenetic tree that enables interactively rerunning ConSurf with the taxa of a sub-tree.

Chromosome number is perhaps the most basic characteristic of a genome, yet generalizations that can explain the evolution of this trait across large clades have remained elusive. Using karyotype data from over 1000 mammals, we developed and applied a phylogenetic model of chromosome evolution that links chromosome number changes with karyotype morphology. Using our model, we infer that rates of chromosome number evolution are significantly lower in species with karyotypes that consist of either all bibrachial or all monobrachial chromosomes than in species with a mix of both types of morphologies. We suggest that species with homogeneous karyotypes may represent cases where meiotic drive acts to stabilize the karyotype, favoring the chromosome morphologies already present in the genome. In contrast, rapid bouts of chromosome number evolution in taxa with mixed karyotypes may indicate that a switch in the polarity of female meiotic drive favors changes in chromosome number. We do not find any evidence that karyotype morphology affects rates of speciation or extinction. Furthermore, we document that switches in meiotic drive polarity are likely common and have occurred in most major clades of mammals, and that rapid remodeling of karyotypes may be more common than once thought.

The identification of the genetic structure of populations from multilocus genotype data has become a central component of modern population-genetic data analysis. Application of model-based clustering programs often entails a number of steps, in which the user considers different modeling assumptions, compares results across different pre-determined values of the number of assumed clusters (a parameter typically denoted K), examines multiple independent runs for each fixed value of K, and distinguishes among runs belonging to substantially distinct clustering solutions. Here, we present Clumpak (Cluster Markov Packager Across K), a method that automates the post-processing of results of model-based population structure analyses. For analyzing multiple independent runs at a single K value, Clumpak identifies sets of highly similar runs, separating distinct groups of runs that represent distinct modes in the space of possible solutions. This procedure, which generates a consensus solution for each distinct mode, is performed by the use of a Markov clustering algorithm that relies on a similarity matrix between replicate runs, as computed by the software Clumpp. Next, Clumpak identifies an optimal alignment of inferred clusters across different values of K, extending a similar approach implemented for a fixed K in Clumpp, and simplifying the comparison of clustering results across different K values. Clumpak incorporates additional features, such as implementations of methods for choosing K and comparing solutions obtained by different programs, models, or data subsets. Clumpak, available at http://clumpak.tau.ac.il, simplifies the use of model-based analyses of population structure in population genetics and molecular ecology.

We announce the release of chromEvol version 2.0, a software tool for inferring the pattern of chromosome number change along a phylogeny. The software facilitates the inference of the expected number of polyploidy and dysploidy transitions along each branch of a phylogeny and estimates ancestral chromosome numbers at internal nodes. The new version features a novel extension of the model accounting for general multiplication events, other than doubling of the number of chromosomes. This allows the monoploid number (commonly referred to as x, or the base-number) of a group of interest to be inferred in a statistical framework. In addition, we devise an inference scheme, which allows explicit categorization of each terminal taxon as either diploid or polyploid. The new version also supports intraspecific variation in chromosome number and allows hypothesis testing regarding the root chromosome number. The software, alongside a detailed usage manual, is available at http://www.tau.ac.il/∼itaymay/cp/chromEvol/.

Recent years have seen a constant rise in the availability of trait data, including morphological features, ecological preferences, and life history characteristics. These phenotypic data provide means to associate genomic regions with phenotypic attributes, thus allowing the identification of phenotypic traits associated with the rate of genome and sequence evolution. However, inference methodologies that analyze sequence and phenotypic data in a unified statistical framework are still scarce. Here, we present TraitRateProp, a probabilistic method that allows testing whether the rate of sequence evolution is associated with a binary phenotypic character trait. The method further allows the detection of specific sequence sites whose evolutionary rate is most noticeably affected following the character transition, suggesting a shift in functional/structural constraints. TraitRateProp is first evaluated in simulations and then applied to study the evolutionary process of plastid plant genomes upon a transition to a heterotrophic lifestyle. To this end, we analyze 20 plastid genes across 85 orchid species, spanning different lifestyles and representing different genera in this large family of flowering plants. Our results indicate higher evolutionary rates following repeated transitions to a heterotrophic lifestyle in all but four of the loci analyzed. [Evolutionary models; evolutionary rate; genotype-phenotype; orchids; plastome; rate shift.].

Polyploidy is commonly thought to be associated with the domestication process because of its concurrence with agriculturally favourable traits and because it is widespread among the major plant crops(1-4). Furthermore, the genetic consequences of polyploidy(5-7) might have increased the adaptive plasticity of those plants, enabling successful domestication(6-8). Nevertheless, a detailed phylogenetic analysis regarding the association of polyploidy with the domestication process, and the temporal order of these distinct events, has been lacking(3). Here, we have gathered a comprehensive data set including dozens of genera, each containing one or more major crop species and for which sufficient sequence and chromosome number data exist. Using probabilistic inference of ploidy levels conducted within a phylogenetic framework, we have examined the incidence of polyploidization events within each genus. We found that domesticated plants have gone through more polyploidy events than their wild relatives, with monocots exhibiting the most profound difference: 54% of the crops are polyploids versus 40% of the wild species. We then examined whether the preponderance of polyploidy among crop species is the result of two, non-mutually-exclusive hypotheses: (1) polyploidy followed by domestication, and (2) domestication followed by polyploidy. We found support for the first hypothesis, whereby polyploid species were more likely to be domesticated than their wild relatives, suggesting that the genetic consequences of polyploidy have conferred genetic preconditions for successful domestication on many of these plants.

Cultivated plants have been selected by humans for increased yield in a relatively benign environment, where nutrient and water resources are often supplemented, and biotic enemy loads are kept artificially low. Agricultural weeds have adapted to this same benign environment as crops and often have high growth and reproductive rates, even though they have not been specifically selected for yield. Considering the competing demands for resources in any plant, a key question is whether adaptation to agricultural environments has been accompanied by life history trade-offs, in which resistance to (largely absent) stress has been lost in favour of growth and reproduction. The experiments reported here were designed to test for growth-defence trade-offs in agricultural weeds, crops and native varieties of common sunflower (Helianthus annuus L., Asteraceae) by comparing their performance in the presence or absence of abiotic (drought and crowding) or biotic (simulated herbivory, insect herbivory and fungal) stress. We found that growth, as well as viability of crops and weeds, was reduced by abiotic drought stress. The weakened defence in the agricultural genotypes was further evident as increased susceptibility to fungal infection and higher level of insect palatability. To uncover molecular mechanisms underlying these trade-offs, we monitored gene expression kinetics in drought-stressed plants. By correlating phenotypic observations with molecular analyses, we report the identification of several genes, including a protein phosphatase 2C and the HD-Zip transcription factor Athb-8, whose expression is associated with the observed phenotypic variation in common sunflower.

Thymidylate synthases (Thy) are key enzymes in the synthesis of deoxythymidylate, 1 of the 4 building blocks of DNA. As such, they are essential for all DNA-based forms of life and therefore implicated in the hypothesized transition from RNA genomes to DNA genomes. Two evolutionally unrelated Thy enzymes, ThyA and ThyX, are known to catalyze the same biochemical reaction. Both enzymes are sporadically distributed within each of the 3 domains of life in a pattern that suggests multiple nonhomologous lateral gene transfer (LGT) events. We present a phylogenetic analysis of the evolution of the 2 enzymes, aimed at unraveling their entangled evolutionary history and tracing their origin back to early life. A novel probabilistic evolutionary model was developed, which allowed us to compute the posterior probabilities and the posterior expectation of the number of LGT events. Simulation studies were performed to validate the model's ability to accurately detect LGT events, which have occurred throughout a large phylogeny. Applying the model to the Thy data revealed widespread nonhomologous LGT between and within all 3 domains of life. By reconstructing the ThyA and ThyX gene trees, the most likely donor of each LGT event was inferred. The role of viruses in LGT of Thy is finally discussed.

Polyploidy, the genome wide duplication of chromosome number, is a key feature in eukaryote evolution. Poly- ploidy exists in diverse groups including animals, fungi, and invertebrates but is especially prevalent in plants with most, if not all, plant species having descended from a polyploidization event. Polyploids often differ markedly from their diploid progenitors in morphological, physiological, and life history characteristics as well as rates of adaptation. The altered char- acteristics displayed by polyploids may contribute to their success in novel ecological habitats. Clearly, a better understand- ing of the processes underlying changes in the number of chromosomes within genomes is a key goal in our understanding of speciation and adaptation for a wide range of families and genera. Despite the fundamental role of chromosome number change in eukaryotic evolution, probabilistic models describing the evolution of chromosome number along a phylogeny have not yet been formulated. We present a series of likelihood models, each representing a different hypothesis regarding the evolution of chromosome number along a given phylogeny. These models allow us to reconstruct ancestral chromosome numbers and to estimate the expected number of polyploidization events and single chromosome changes (dysploidy) that occurred along a phylogeny. We test, using simulations, the accuracy of this approach and its dependence on the number of taxa and tree length. We then demonstrate the application of the method for the study of chromosome number evolution in 4 plant genera: Aristolochia, Carex, Passiflora , and Helianthus. Considering the depth of the available cytological and phy- logenetic data, formal models of chromosome number evolution are expected to advance significantly our understanding of the importance of polyploidy and dysploidy across different taxonomic groups. (Chromosome evolution; dysploidy; evolutionary models; genome duplication; polyploidy.)

Codon evolutionary models are widely used to infer the selection forces acting on a protein. The non-synonymous to synonymous rate ratio (denoted by Ka/Ks) is used to infer specific positions that are under purifying or positive selection. Current evolutionary models usually assume that only the non-synonymous rates vary among sites while the synonymous substitution rates are constant. This assumption ignores the possibility of selection forces acting at the DNA or mRNA levels. Towards a more realistic description of sequence evolution, we present a model that accounts for among-site-variation of both synonymous and non-synonymous substitution rates. Furthermore, we alleviate the widespread assumption that positions evolve independently of each other. Thus, possible sources of bias caused by random fluctuations in either the synonymous or non-synonymous rate estimations at a single site is removed. Our model is based on two hidden Markov models that operate on the spatial dimension: one describes the dependency between adjacent non-synonymous rates while the other describes the dependency between adjacent synonymous rates. The presented model is applied to study the selection pressure across the HIV-1 genome. The new model better describes the evolution of all HIV-1 genes, as compared to current codon models. Using both simulations and real data analyses, we illustrate that accounting for synonymous rate variability and dependency greatly increases the accuracy of Ka/Ks estimation and in particular of positively selected sites. Finally, we discuss the applicability of the developed model to infer the selection forces in regulatory and overlapping regions of the HIV-1 genome. Contact: talp@post.tau.ac.il

The degree to which an amino acid site is free to vary is strongly dependent on its structural and functional importance. An amino acid that plays an essential role is unlikely to change over evolutionary time. Hence, the evolutionary rate at an amino acid site is indicative of how conserved this site is and, in turn, allows evaluation of its importance in maintaining the structure/function of the protein. When using probabilistic methods for site-specific rate inference, few alternatives are possible. In this study we use simulations to compare the maximum-likelihood and Bayesian paradigms. We study the dependence of inference accuracy on such parameters as number of sequences, branch lengths, the shape of the rate distribution, and sequence length. We also study the possibility of simultaneously estimating branch lengths and site-specific rates. Our results show that a Bayesian approach is superior to maximum-likelihood under a wide range of conditions, indicating that the prior that is incorporated into the Bayesian computation significantly improves performance. We show that when branch lengths are unknown, it is better first to estimate branch lengths and then to estimate site-specific rates. This procedure was found to be superior to estimating both the branch lengths and site-specific rates simultaneously. Finally, we illustrate the difference between maximum-likelihood and Bayesian methods when analyzing site-conservation for the apoptosis regulator protein Bcl-x(L).

Chromosome evolution has been demonstrated to have profound effects on diversification rates and speciation in angiosperms. While polyploidy has predated some major radiations in plants, it has also been related to decreased diversification rates. There has been comparatively little attention to the evolutionary role of gains and losses of single chromosomes, which may or not entail changes in the DNA content (then called aneuploidy or dysploidy, respectively). In this study we investigate the role of chromosome number transitions and of possible associated genome size changes in angiosperm evolution. We model the tempo and mode of chromosome number evolution and its possible correlation with patterns of cladogenesis in 15 angiosperm clades. Inferred polyploid transitions are distributed more frequently towards recent times than single chromosome gains and losses. This is likely because the latter events do not entail changes in DNA content and are probably due to fission or fusion events (dysploidy), as revealed by an analysis of the relationship between genome size and chromosome number. Our results support the general pattern that recently originated polyploids fail to persist, and suggest that dysploidy may have comparatively longer-term persistence than polyploidy. Changes in chromosome number associated with dysploidy were typically observed across the phylogenies based on a chi-square analysis, consistent with these changes being neutral with respect to diversification, This work was supported by the Spanish Ministry of Economy and Competitivity (Project CGL2009-09972 and CGL2012-38744) to M. Luceño (PI), S. Martín-Bravo, M. Escudero, and P. Jiménez-Mejías; by MEC-Fulbright (FU2009-0946); and Marie Curie (Project HoloChromEvol) fellowships to M. Escudero

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.

Rate heterogeneity within groups of organisms is known to exist even when closely related taxa are examined. A wide variety of phylogenetic and dating methods have been developed that aim either to test for the existence of rate variation or to correct for its bias. However, none of the existing methods track the evolution of features that account for observed rate heterogeneity. Here, we present a likelihood model that assumes that rate variation is caused, in part, by species’ intrinsic characteristics, such as a particular life-history trait, morphological feature, or habitat association. The model combines models of sequence and character state evolution such that rates of sequence change depend on the character state of a lineage at each point in time. We test, using simulations, the power and accuracy of the model to determine whether rates of molecular evolution depend on a particular character state and demonstrate its utility using an empirical example with halophilic and freshwater daphniids.

A phage-display library of random peptides is a combinatorial experimental technique that can be harnessed for studying antibody–antigen interactions. In this technique, a phage peptide library is scanned against an antibody molecule to obtain a set of peptides that are bound by the antibody with high affinity. This set of peptides is regarded as mimicking the genuine epitope of the antibody’s interacting antigen and can be used to define it. Here we present PepSurf, an algorithm for mapping a set of affinity-selected peptides onto the solved structure of the antigen. The problem of epitope mapping is converted into the task of aligning a set of query peptides to a graph representing the surface of the antigen. The best match of each peptide is found by aligning it against virtually all possible paths in the graph. Following a clustering step, which combines the most significant matches, a predicted epitope is inferred. We show that PepSurf accurately predicts the epitope in four cases for which the epitope is known from a solved antibody– antigen co-crystal complex. We further examine the capabilities of PepSurf for predicting other types of protein–protein interfaces. The performance of PepSurf is compared to other available epitope mapping programs.

The evolutionary rate at an amino acid site is indicative of how conserved this site is and, in turn, allows evaluating the importance of this site in maintaining the structure/function of the protein. When evolutionary rates are estimated, one must reconstruct the phylogenetic tree describing the evolutionary relationship among the sequences under study. However, if the inferred phylogenetic tree is incorrect, it can lead to erroneous site-specific rate estimates. Here we describe a novel Bayesian method that uses Markov chain Monte Carlo methodology to integrate over the space of all possible trees and model parameters. By doing so, the method considers alternative evolutionary scenarios weighted by their posterior probabilities. We show that this comprehensive evolutionary approach is superior over methods that are based on only a single tree. We illustrate the potential of our algorithm by analyzing the conservation pattern of the potassium channel protein family.

Motivation: A number of proteins of known three-dimensional (3D) structure exist, with yet unknown function. In light of the recent progress in structure determination methodology, this number is likely to increase rapidly. A novel method is presented here: ‘Rate4Site’, which maps the rate of evolution among homologous proteins onto the molecular surface of one of the homologues whose 3D-structure is known. Functionally important regions often correspond to surface patches of slowly evolving residues. Results: Rate4Site estimates the rate of evolution of amino acid sites using the maximum likelihood (ML) principle. The ML estimate of the rates considers the topology and branch lengths of the phylogenetic tree, as well as the underlying stochastic process. To demonstrate its potency, we study the Src SH2 domain. Like previously established methods, Rate4Site detected the SH2 peptide-binding groove. Interestingly, it also detected inter-domain interactions between the SH2 domain and the rest of the Src protein that other methods failed to detect. Availability: Rate4Site can be downloaded at: http://ashtoret.tau.ac.il/ It is implemented as a web server at: bioinfo.tau.ac.il/ConSurf Contact: tal@ism.ac.jprebell@ashtoret.tau.ac.ilfabian@ashtoret.tau.ac.ilbental@ashtoret.tau.ac.il Supplementary Information: Multiple sequence alignment of homologous SH2 domains, the corresponding phylogenetic tree and additional examples are available at http://ashtoret.tau.ac.il/~rebell Keywords: rate variation among sites; evolutionary conservation; protein evolution; maximum likelihood; SH2 domains. *To whom correspondence should be addressed. †These authors contributed equally.

Background: Synonymous or silent mutations are usually thought to evolve neutrally. However, accumulating recent evidence has demonstrated that silent mutations may destabilize RNA structures or disrupt cis regulatory motifs superimposed on coding sequences. Such observations suggest the existence of stretches of codon sites that are evolutionary conserved at both DNA-RNA and protein levels. Such stretches may point to functionally important regions within protein coding sequences not necessarily reflecting functional constraints on the amino-acid sequence. The HIV-1 genome is highly compact, and often harbors overlapping functional elements at the protein, RNA, and DNA levels. This superimposition of functions leads to complex selective forces acting on all levels of the genome and proteome. Considering the constraints on HIV-1 to maintain such a highly compact genome, we hypothesized that stretches of synonymous conservation would be common within its genome. Results: We used a combined computational-experimental approach to detect and characterize regions exhibiting strong purifying selection against synonymous substitutions along the HIV-1 genome. Our methodology is based on advanced probabilistic evolutionary models that explicitly account for synonymous rate variation among sites and rate dependencies among adjacent sites. These models are combined with a randomization procedure to automatically identify the most statistically significant regions of conserved synonymous sites along the genome. Using this procedure we identified 21 conserved regions. Twelve of these are mapped to regions within overlapping genes, seven correlate with known functional elements, while the functions of the remaining four are yet unknown. Among these four regions, we chose the one that deviates most from synonymous rate homogeneity for in-depth computational and experimental characterization. In our assays aiming to quantify viral fitness in both early and late stages of the replication cycle, no differences were observed between the mutated and the wild type virus following the introduction of synonymous mutations. Conclusions: The contradiction between the inferred purifying selective forces and the lack of effect of these mutations on viral replication may be explained by the fact that the phenotype was measured in single-cycle infection assays in cell culture. Such a system does not account for the complexity of HIV-1 infections in vivo, which involves multiple infection cycles and interaction with the host immune system.